Planning Report 07-1

Economic Analysis of the

Technology Infrastructure

Needs of the U.S.

Biopharmaceutical

Industry

Prepared by:

RTI International

for

National Institute of

Standards & Technology

November 2007
November 2007 

Economic Analysis of the Technology 

Infrastructure Needs of the U.S. 

Biopharmaceutical Industry 

Final Report 

Prepared for 

Gregory Tassey 

National Institute for Standards and Technology 

100 Bureau Drive, Stop 1060 

Gaithersburg, MD 20899-1060 

Prepared by 

Michael Gallaher 

Jeffrey Petrusa 

Alan O’Connor 

Stephanie Houghton 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

RTI Project No. 0209843.000.005 
 

RTI Project Number 

 0209843.000.005 

Economic Analysis of the Technology 

Infrastructure Needs of the U.S. 

Biopharmaceutical Industry 

Final Report 

November 2007

Prepared for 

Gregory Tassey 

National Institute for Standards and Technology 

100 Bureau Drive, Stop 1060 

Gaithersburg, MD 20899-1060 

Prepared by 

Michael Gallaher 

Jeffrey Petrusa 

Alan O’Connor 

Stephanie Houghton 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

 

RTI International is a trade name of Research Triangle Institute.
iii 

Contents 

Section Page 

 Executive Summary ES-1 

 1 Introduction 1-1 

1.1 The Nature and Roles of Technology Infrastructure....................1-4 

1.2 The Biopharmaceutical Industry ..................................................1-5 

1.2.1 Finding a Focus within Biotechnology: 

Approaching a Broad Industry Category.........................1-5 

1.2.2 Biopharmaceutical Products ...........................................1-6 

1.2.3 Defining the Biopharmaceutical Industry ......................1-10 

1.2.4 The Biopharmaceutical Product Development 

Cycle .............................................................................1-12 

1.2.5 Biopharmaceutical Technology Infrastructure 

Complexity.....................................................................1-15 

1.3 Project Scope and Goals ...........................................................1-16 

1.4 Report Organization...................................................................1-16 

 2 The Technology Infrastructure Supporting the 

Biopharmaceutical Industry 2-1 

2.1 Defining the Biopharmaceutical Technology Infrastructure .........2-1 

2.1.1 Technology Infrastructure Components..........................2-2 

2.1.2 The Biopharmaceutical Technology Infrastructure 

Taxonomy........................................................................2-3 

2.1.3 The Economic Role of Biopharmaceutical 

Technology Infrastructure ...............................................2-4 

2.2 Technology Focus Areas in This Study .......................................2-6 

2.2.1 Enhanced Bioimaging Techniques .................................2-7 

2.2.2 Gene and Protein Expression Analysis...........................2-7 

2.2.3 Bioinformatics and In Silico Predictive Modeling ..........2-10 

2.2.4 Molecular Biomarkers ...................................................2-11 

2.2.5 Commercial Manufacturing ...........................................2-13 

2.2.6 Postmarket Surveillance ...............................................2-13 
iv 

2.3 Trends in Infrastructure Research and Development................2-14 

2.3.1 Drug Discovery..............................................................2-15 

2.3.2 Preclinical Development and Testing............................2-17 

2.3.3 Clinical Trials.................................................................2-19 

2.3.4 Scale-Up and Commercial Manufacturing ....................2-21 

2.3.5 Postmarket Surveillance ...............................................2-22 

2.3.6 Relevant Federal Technology Infrastructure 

Development Efforts......................................................2-26 

2.4 Technical Barriers to an Improved Infrastructure.......................2-27 

2.4.1 Bioimaging ....................................................................2-27 

2.4.2 Gene and Protein Expression Analysis.........................2-29 

2.4.3 Bioinformatics and In Silico Predictive Modeling ..........2-31 

2.4.4 Molecular Biomarkers ...................................................2-33 

2.4.5 Commercial Manufacturing ...........................................2-33 

2.4.6 Postmarket Surveillance ...............................................2-35 

2.5 Market Barriers to an Improved Infrastructure ...........................2-36 

 3 Economic Analysis Methodology 3-1 

3.1 Existing Biopharmaceutical R&D Cost Studies............................3-2 

3.1.1 Small-Molecule Pharmaceutical Development 

Cost Studies....................................................................3-2 

3.1.2 Large-Molecule Biopharmaceutical Development 

Cost Studies....................................................................3-4 

3.2 Estimating Current Expenditures on the Technology 

Infrastructure................................................................................3-8 

3.2.1 Technology Infrastructure Supporting Drug 

Discovery, Development, and Clinical Trials...................3-9 

3.2.2 Technology Infrastructure Supporting Commercial 

Manufacturing Activities ................................................3-11 

3.2.3 Technology Infrastructure Supporting Postmarket 

Surveillance Activities ...................................................3-11 

3.3 Estimating Potential Reductions in Biopharmaceutical 

Development and Production Costs ..........................................3-12 

3.3.1 Baseline Preclinical Cost per Approved Drug...............3-13 

3.3.2 Baseline Clinical Trials Cost per Approved Drug..........3-13 

3.3.3 Baseline Commercial Manufacturing Costs..................3-15 

3.3.4 Baseline Postmarket Surveillance Costs ......................3-15 

3.3.5 Hypothesized Economic Impact of an Improved 

Technology Infrastructure .............................................3-16 

3.3.6 Model Implementation Using Impact Metrics................3-17 

3.3.7 Hypothetical Improvements by TFA Posed to 

Survey Respondents and Interviewees ........................3-23 

 4 Primary Data Collection 4-1 

4.1 Technical Expert Interviews .........................................................4-3 
v 

4.1.1 Technical Expert Interview Methodology ........................4-3 

4.1.2 Topics Covered in Technical Interviews .........................4-4 

4.2 Internet Survey of Biopharmaceutical Firms................................4-5 

 5 Analysis Results 5-1 

5.1 Annual Biopharmaceutical Industry Expenditures on the 

Technology Infrastructure ............................................................5-1 

5.1.1 Annual Expenditures by Technology Focus Area...........5-2 

5.1.2 Annual Expenditures by Cost Component......................5-3 

5.1.3 Annual Expenditures on R&D-Related Activities ............5-4 

5.1.4 Annual Expenditures on Commercial Activities ..............5-5 

5.2 Potential Efficiency Gains from an Improved Technology 

Infrastructure................................................................................5-7 

5.2.1 Counterfactual Success Rates and Cost and 

Development Time Reductions for 

Biopharmaceutical INDs..................................................5-8 

5.2.2 Potential Gains from Improvements in 

Bioinformatics................................................................5-11 

5.2.3 Potential Gains from Improvements in Biomarkers ......5-15 

5.2.4 Potential Gains from Improvements in Gene and 

Protein Expression........................................................5-16 

5.2.5 Potential Gains from Improvements in Bioimaging.......5-19 

5.2.6 Potential Impacts on Commercial Manufacturing 

Costs .............................................................................5-21 

5.2.7 Potential Impacts on Postmarket Surveillance 

Costs .............................................................................5-23 

5.3 Combined Potential Impact of an Improved Technology 

Infrastructure..............................................................................5-23 

 6 Conclusion 6-1 

6.1 Current State of the Biopharmaceutical Technology 

Infrastructure................................................................................6-1 

6.2 Characterizing Potential Efficiency Gains from an 

Improved Technology Infrastructure ............................................6-4 

6.2.1 Advanced Bioimaging Techniques..................................6-6 

6.2.2 Molecular Biomarkers .....................................................6-8 

6.2.3 Bioinformatics and In Silico Predictive Modeling ............6-8 

6.2.4 Gene and Protein Expression Analysis...........................6-9 

6.2.5 Commercial Manufacturing ...........................................6-10 

6.2.6 Postmarket Surveillance ...............................................6-10 

6.3 Rationale for NIST Involvement.................................................6-11 

 References R-1 

 Appendix A: Survey A-1 
vi 

Figures 

Number Page 

 2-1 DNA Hybridization in a Microarray Experiment .....................................2-9 

 2-2 Summary R&D Framework for Human Therapeutics ..........................2-15

 5-1 Illustration of How Expected Costs per Approved Drug Change 

with an Improved Technology Infrastructure..........................................5-7

 6-1 Annual Biopharmaceutical Industry Technology Infrastructure 

Spending................................................................................................6-3
vii 

Tables 

Number Page 

 1-1 Number of Biopharmaceutical Class Types in 2005..............................1-7

 1-2 10 Largest Biopharmaceutical Companies by Net Sales in 2004........1-11

 2-1 Potential Labor Allocation Involved in Quality Control at One 

Microarray Laboratory with an Improved Technology 

Infrastructure........................................................................................2-31

 3-1 Summary of Cost Studies: Estimated Absolute Costs and 

Relative Share by R&D Stage................................................................3-3

 3-2 Actual and Expected Costs per IND by Stage.......................................3-7

 3-3 Capitalized R&D Costs per IND by Stage..............................................3-8

 3-4 Estimating Current Expenditures on the Technology 

Infrastructure........................................................................................3-10

 3-5 Baseline Parameters for the Biopharmaceutical R&D Cost Model......3-14

 3-6 Estimated Potential Impact of an Improved Technology 

Infrastructure........................................................................................3-19

 3-7 Metrics for Assessing the Impact on R&D Costs.................................3-19

 3-8 Metrics for Assessing the Impact on Success and Failure Rates........3-21

 3-9 Metrics for Assessing the Impact on Manufacturing Costs..................3-22

 3-10 Metrics for Assessing the Impact on Postmarket Costs ......................3-23

 4-1 Impact Metrics Respondents Quantified................................................4-2

 4-2 Number of Responses by Technology Focus Area ...............................4-2

 4-3 Examples of Interviewed Technical Experts’ Job Titles.........................4-4

 5-1 Technology Infrastructure Expenditures by Technology Focus 

Area, 2005..............................................................................................5-3

 5-2 Annual Technology Infrastructure Expenditures by Cost 

Category, 2005 ......................................................................................5-4
viii 

 5-3 Comparison of Estimated Potential Success and Failure Rates 

by TFA....................................................................................................5-9

 5-4 Estimated Reductions in R&D Cost and Stage Length .......................5-12

 5-5 Baseline Expected Cost per IND and per Approved Drug...................5-12

 5-6 Potential Efficiency Gains from an Improved Bioinformatics 

Infrastructure........................................................................................5-13

 5-7 Potential Cost Reductions per IND and per Approved Drug: 

Bioinformatics.......................................................................................5-14

 5-8 Potential Efficiency Gains from an Improved Biomarkers 

Infrastructure........................................................................................5-16

 5-9 Potential Cost Reductions per IND and per Approved Drug: 

Biomarkers...........................................................................................5-17

 5-10 Potential Efficiency Gains from an Improved Gene Expression 

Infrastructure........................................................................................5-18

 5-11 Potential Cost Reductions per IND and per Approved Drug: 

Gene Expression .................................................................................5-19

 5-12 Impact on Commercial Manufacturing Costs.......................................5-22

 5-13 Combined Scenarios’ Potential Improvement in IND Success 

and Failure Rates.................................................................................5-25

 5-14 Combined Scenarios’ Potential Efficiency Gains.................................5-25

 5-15 Combined Scenarios’ Potential Cost Reductions ................................5-26

 6-1 Potential Cost Reductions in Biopharmaceutical Development 

with an Improved Technology Infrastructure..........................................6-6

 6-2 Stakeholders’ Comments on Technology Infrastructure Needs ............6-7
ES-1 

 Executive Summary 

The purpose of this report is to quantify the biopharmaceutical industry’s 

current expenditures on the technology infrastructure; identify key 

infrastructural bottlenecks across the biopharmaceutical development 

cycle; and estimate the drug-development, production, and surveillance 

cost savings an improved technology infrastructure may provide. 

The biopharmaceutical industry’s origins are rooted in research by 

Stanley Cohen and Herbert Boyer that led to a process called 

recombinant DNA, in which pieces of DNA from cultured cells are 

artificially combined in a controlled setting. Recombinant DNA was a 

novel technique that enabled large-scale production of 

biopharmaceutical products like human insulin. From these origins, the 

industry has grown into one that currently spends $21 billion on research 

and development (R&D) annually and has commercialized over 400 

products. 

Traditionally, drug companies have developed new products through the 

trial-and-error investigative processes of medicinal chemistry. They 

created small chemical compounds—referred to as small-molecule 

drugs—developed through the analysis of symptoms that characterize 

certain illnesses and diseases. Although small-molecule drugs still 

account for a significant share of the drug industry’s new product 

pipeline, they no longer dominate the focus of new drug development. 

Over the last two decades, there has been a shift in the drug 

development industry to larger, more complex molecular compounds that 

target biochemical mechanisms instead of symptoms. These “largemolecule” biopharmaceuticals take advantage of how human biological 

systems function. Biotechnology researchers design drugs that capitalize 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-2 

on the specific, precise, and predictable attributes of subcellular 

molecules, like DNA and proteins, across a diverse set of species and 

cell types. 

The dramatic growth in the number of biopharmaceuticals in clinical 

development can be attributed to exponential growth in our scientific 

understanding of the biological systems associated with human health 

and disease. As researchers begin to identify the biological pathways to 

disease, drug development research will continue to shift focus away 

from traditional pharmaceuticals toward complex biopharmaceuticals. 

The industry’s ability to enhance innovation will largely be determined by 

several factors, including the efficiency of its R&D, quality control and 

assurance programs, and postmarket surveillance activities. The 

technology infrastructure supporting biopharmaceutical drug 

development—the core data, methods, and standards—must be 

sufficient to enable effective R&D. Information must be able to be 

communicated among data sets, technology platforms, and 

organizations. This implies that the protocols, descriptors, and assays 

used to acquire these data be transparent and standardized within and 

across organizations. Researchers must have confidence in and 

assurance of the accuracy and precision of the data and processes 

informing drug development decisions. 

The purpose of this study is to investigate current expenditures and 

potential future efficiency gains associated with developing the 

technology infrastructure to inform public policy and support strategic 

planning at the National Institute of Standards and Technology (NIST). 

Study objectives were the following: 

• to estimate the biopharmaceutical industry’s annual spending on 

technology infrastructure–related investments and activities; 

• to assess key areas of technology infrastructure in which 

industry has underinvested because of market and technical 

barriers or because of unattractive risk-reward ratios at the firm 

level; and 

• to characterize the potential efficiency gains an improved 

technology infrastructure holds for the industry, as represented 

by potential drug development cost reductions, shorter time to 

market for new drugs, and greater probabilities of developing 

successful products approved by the Food and Drug 

Administration (FDA). 

This report’s scope is limited to the biopharmaceuticals industry because 

the diversity of and varying definitions of biotechnology make it difficult to 
Executive Summary 

ES-3 

study as a single industry and hence at the level of clarity and specificity 

required by economic analyses. 

 ES.2 THE BIOPHARMACEUTICAL INDUSTRY’S 

TECHNOLOGY INFRASTRUCTURE 

As the term “infrastructure” implies, the technology infrastructure of an 

industry refers to the tools, methods, and data that enable or support 

R&D, products, and services. These tools, methods, and data are 

considered infrastructural because they are not necessarily commercial 

products themselves; rather, they support or embody processes and 

components that make many advanced technology products and 

services possible. 

Many elements of the technology infrastructure are unseen or taken as a 

given because they are deeply embedded in or underlie research 

methodologies and instruments. For example, techniques that control 

process quality or verify the accurate calibration of laboratory 

instruments are part of the technical infrastructure, as are standardized 

reference materials and data that researchers use to increase their 

confidence and assurance of the accuracy and precision of their work. 

More visible components of this infrastructure include analytical 

instruments and advanced software systems and algorithms. 

 ES.2.1 The Economic Role of the Technology Infrastructure 

While laboratory instruments, computational systems, and advanced 

chemistries represent vital technology infrastructure employed in the 

direct application of science to drug discovery, production, and 

postmarket surveillance, a universe of unseen technologies support the 

application of those systems. The technology infrastructure is equally 

composed of the protocols, tests, and other methodologies devised to 

improve R&D efficiency and effectiveness. These infrastructure 

components are often overlooked. However, inadequate technologies 

such as calibrants and standard operating protocols―all of which 

researchers rely on everyday―have significant, cumulative economic 

impacts on overall industry productivity. 

Gaps in the technology infrastructure are most readily apparent to 

researchers when they hamper productivity and collaboration and thus 

manifest obstacles to the development of new drugs. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-4 

Current activities to support the technology infrastructure consist of 

expenses incurred through internal activities; external purchases; and 

participation in consortia, partnerships, or other research activities: 

• the purchase of measurement-related equipment, software, 

reference materials, or services; 

• activities required for setup and validation of analytical 

instruments, reagents, or other research tools; examples of 

these activities may include developing calibration test 

methodologies, standard operating procedures, or process 

standards in measurement and manufacturing practices; 

• in-house customization of technology platforms purchased from 

third-party vendors; 

• efforts to develop interoperability between different software or 

equipment systems; and 

• license fees or any other spending on enhancements to routinely 

used processes or equipment intended to increase productivity, 

reduce redundancy, or improve the confidence in results. 

The methods, techniques, and data discussed above form a complex 

technology infrastructure that enables productivity and efficiency in each 

of the major stages of drug development and provision related to 

biopharmaceuticals (such as basic and applied research, clinical trials, 

commercial manufacturing, and market development). Improvements in 

this infrastructure can have numerous potential economic impacts, 

including the following: 

• Cost reductions 

– Lower labor and materials costs for discovery, development, 

and production of a given therapy 

– Lower transaction costs associated with marketing new 

products and meeting postmarket tracking/assessment 

requirements 

• Accelerated time to market 

– Shorter time between discovery and FDA approval 

• Quality improvements 

– Detection of drug failures in earlier clinical trial phases 

– Investigational New Drugs (INDs) have greater probability of 

receiving FDA approval 

– Reduced uncertainty in drug efficacy and safety in the 

population targeted for prescription, longer shelf lives, and 

less restrictive storage and handling needs 
Executive Summary 

ES-5 

 ES.2.2 The Biopharmaceutical Product Development 

Process 

For the pharmaceutical industry as a whole, only 1 in roughly 10,000 

compounds screened during the drug discovery stage succeeds through 

the clinical trial stage and results in an FDA-approved drug (PhRMA, 

2005). Of drugs entering clinical trials, FDA reports only about 8% ever 

reach the market to recoup R&D expenditures. Furthermore, the failure 

rate in the last and most expensive Phase III stage is nearly 50% (FDA, 

2006). Revenue from successful products must also cover the costs from 

failed efforts as companies seek an overall adequate rate of return on 

investment. 

Recent studies have suggested that biopharmaceutical development 

costs are higher in preclinical stages and the drugs may be more difficult 

to manufacture, but that clinical trials require fewer patients and have 

higher success rates (see Reichert [2004]). Compared with a traditional 

success rate of around 20%, the Tufts University research group reports 

30% to 35% success rates for biopharmaceuticals that entered clinical 

trials between 1990 and 1997 (Tufts, 2005). 

FDA approved the first biopharmaceutical for marketing in 1982. As of 

2006, the biopharmaceutical industry accounted for over 260 

biotechnology-based therapeutics and vaccines approved for over 380 

indications (BIO, 2006). The length of time from initial discovery through 

approval for most biopharmaceuticals is 8 to 15 years, with most costs 

incurred during clinical trials (BIO, 2004). 

The specific R&D and manufacturing processes differ among different 

categories of biopharmaceutical products. However, all these drugs start 

with a foundation in basic research and pass through the following major 

stages:1

• drug discovery, 

• preclinical development and testing, 

• clinical trials, 

• scale-up and commercial manufacturing, and 

• postmarket surveillance. 

In a recent study, DiMasi (2002) explored the effect that shortening 

development times could have on pharmaceutical drug R&D costs. His 

 

1

These five stages are presented in chronological order, with the exception that scale-up to 

commercial manufacturing is undertaken simultaneously with preclinical development 

and clinical trials. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-6 

results suggest that uniform reductions clinical trial stages of 25% would 

lower the future-value total cost per approved drug by 16%. DiMasi found 

that an increase in the overall success rate for compounds entering 

clinical trials from 21.5% to 33.3% would lower the present value cost per 

approved drug by up to 30.2% (DiMasi, 2002). 

 ES.2.3 Technology Focus Areas for this Study 

This study investigated the expenditures and potential benefits from an 

improved technology infrastructure in six specific technology fields, 

termed technology focus areas (TFAs). RTI worked with technical 

experts to identify potential infrastructure improvements that could be 

feasibly achieved within the next 10 years. RTI then estimated potential 

efficiency gains that achieving these improvements would convey to the 

biopharmaceutical industry. 

To assess potential efficiency gains for discovery, preclinical, and clinical 

(Phase I through Phase III) activities, four TFAs were selected: 

1. Enhanced bioimaging techniques. Bioimaging refers to the 

use of imaging technologies (such as CT, MRI, and ultrasound) 

to assess biological structures and phenomena. Imaging studies 

are increasingly required in regulatory and research reviews of 

new drugs. 

2. Standards and metrology in gene and protein expression 

analysis. Gene and protein expression analyses seek to acquire 

information on whether certain genes and proteins are active or 

in-active in biological processes. Expression analysis is essential 

to the screening and analysis of potential drugs and their targets. 

3. Improved bioinformatics and in silico predictive modeling.

Bioinformatics is a synonym for computational biology: the 

application of advanced computing to create and analyze vast 

databases of biological information. In silico modeling refers to 

using complex computer simulations of organisms to predict how 

a given compound may affect the organism. 

4. Identification and validation of molecular biomarkers. The 

term “biomarkers” refers to any biological measurement that 

yields information on disease progression, pharmacology, or 

safety. Biomarkers can be used as the basis for decision making 

in drug development. Cholesterol and blood pressure are two 

traditional biomarkers. Technology advances in areas such as 

bioimaging and protein detection are expanding the number of 

useful biomarkers. Researchers hope to use biomarkers to 

shorten clinical trials, increase overall research efficiency, and 

provide improved diagnostic techniques. Before the benefits of 

novel biomarkers can be realized, researchers must overcome 

significant challenges related to validation methods of new 

biomarkers and regulatory acceptance of drug study results that 

are based on biomarker measurements. 
Executive Summary 

ES-7 

These infratechnologies have applications throughout the drug 

development process and cut across a number of different therapeutic 

categories. In addition to the four drug-development TFAs, two processbased TFAs were included: 

5. Infratechnologies to enhance scale-up and commercial 

manufacturing, including improvements in upstream and 

downstream processing and process monitoring/quality 

assurance. 

6. Infratechnologies to support postmarket surveillance activities, 

including product surveillance, tracking, and Phase IV clinical 

trials. 

 ES.3 ANALYSIS METHODOLOGY AND RESULTS 

The economic analysis has two objectives: (1) estimate current privatesector spending on the biopharmaceutical technology infrastructure and 

(2) calculate the savings an improved technology infrastructure could 

yield the industry. 

The first objective entailed surveying the industry to capture spending 

and then extrapolating the results to estimate industry-level 

expenditures. To meet the second objective, RTI leveraged recent drug 

development cost studies and built an economic model that recalculated 

the cost of bringing a new drug to market under a future scenario with an 

enhanced technology infrastructure. The model included both the cost of 

failed INDs and the time value of money. Experts estimated how costs, 

stage length, and the probability of success would improve given the 

feasible infrastructure improvements RTI identified. 

Industry representatives who participated in the study represented firms 

whose combined annual R&D spending accounted for 42% of total 

industry R&D spending2

 and 49% of annual product sales.3 

 

2

RTI calculated market share for the technologies applied in the R&D phases by dividing 

company-reported R&D expenditures in 2004 by the total R&D expenditures for all 

publicly traded biotechnology firms. Market share for the manufacturing and postmarket 

segment was calculated by dividing net sales of biopharmaceuticals of the individual 

firm by the total sales for all commercialized biopharmaceuticals using data for 2004. 3

Responses to the Internet survey were anonymous and hence could not be linked to R&D 

expenditures or sales. Thus, Internet respondents are not represented in the market 

share figures. As a result, the market share of firms participating in this study is greater 

than reported here. For example, the 12 commercial manufacturing firms that 

completed the Internet survey produce 30 of the 264 domestically approved 

biopharmaceutical drugs. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-8 

 ES.3.1 Estimating Current Technology Infrastructure 

Expenditures by the Biopharmaceutical Industry 

Current private-sector expenditures on technology infrastructure were 

estimated by surveying biopharmaceutical drug developers and their 

supply chain, including service vendors, consultants, and academics who 

support them. Research directors and managers at biopharmaceutical 

companies estimated the relative proportions of labor, capital, and 

materials expenses comprising a category of infrastructure expenditure 

and the distribution of those costs among the TFAs that RTI identified. 

They also discussed the timing of costs within the product development 

stage. 

Aggregated responses by stage were scaled to national expenditures 

using activity measures relevant for each drug development. Study 

participants provided several measures for comparing and aggregating 

their data with other participants. Measures used to aggregate responses 

included 

• the number of scientists and engineers comprising the business 

unit’s research staff, 

• the number of FDA-approved biopharmaceutical products, and 

• the percentage of sales corresponding to the unit for which they 

are responding. 

RTI estimates that the biopharmaceutical industry currently spends 

$1,219 million annually on technology infrastructure-related products and 

services, including $884 million in support of drug R&D-related activities, 

and $335 million to support commercial manufacturing and postmarket 

surveillance activities. These estimates represent current 

biopharmaceutical industry expenditures. 

In reviewing the data in Table ES-1, we can make a number of 

observations concerning infrastructure spending in the R&D segment: 

• Gene expression systems and biomarkers accounted for over 

half of total technology infrastructure spending in the R&D 

segment at 30% and 24%, respectively. 

• Bioimaging accounts for 15% and informatics for 22%. 

• The remaining 8% of the $884 million is distributed among all 

other technology areas in the R&D segment.4

 

4

Some respondents were reluctant to classify infrastructure expenditures in the TFA 

categories provided and hence classified them as “other.” 
Executive Summary 

ES-9 

Table ES-1. Annual Technology Infrastructure Expenditures by Technology Focus 

Area, 2005 

Technology Focus Area 

Annual Technology 

Infrastructure Spending

(millions) 

Percentage 

Distribution Relative Spending 

Bioimaging $136 15% $4,011 per scientist 

Biomarkers $212 24% $6,240 per scientist 

Bioinformatics $198 22% $5,813 per scientist 

Gene expression analysis $265 30% $7,800 per scientist 

Other $73 8% $2,136 per scientist 

Subtotal of R&D Activities $884 100%

Commercial manufacturing $162 48% 

$613,000 per 

approved drug 

Postmarket surveillance $173 52% 

$656,000 per 

approved drug 

Subtotal of Commercial Activities $335 100% 

Industry Total $1,219 

Source: RTI estimates. 

The commercial segment expenditures were $335 million, broken out as 

follows: 

• Commercial manufacturing accounted for 48% of total 

infrastructure expenditures in the commercial segment. 

• Postmarket surveillance accounted for 52% of total infrastructure 

expenditures in the commercial segment. 

 ES.3.2 Estimating Potential R&D Efficiency Gains from an 

Improved Technology Infrastructure 

Rather than ask the industry how expenditures could have been different 

in 2005, this study took the approach of evaluating how a specific set of 

improvements could increase efficiency going forward. The same experts 

who provided data to estimate current infrastructure-related spending 

quantified the impact a series of feasible infrastructure improvements 

would have on R&D, manufacturing and post-FDA approval activities. 

They offered their views on how specific improvements to the technology 

infrastructure could 

• lower the development cost of the average biopharmaceutical 

drug, 

• increase the probability the drug would be approved by FDA by 

enhancing data quality and analytical methods, 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-10 

• shorten the drug’s time to market; 

• lower the ongoing costs for manufacturing that drug and improve 

manufacturing tolerances, and 

• make the postmarket surveillance infrastructure more efficient 

and responsive. 

The metric most commonly used for assessing industry’s spending is the 

“R&D cost per approved drug.”5

 The economic model recalculates 

average R&D cost per approved drug using the baseline costs from a 

2007 study by Joseph DiMasi and Henry Grabowski. Baseline estimates 

included biopharmaceutical drug development costs, times, and the 

probability a candidate drug moved from one R&D stage to the next. 

The cost model offered two output measures: 

• the change in actual R&D expenditures, per investigational new 

drug (IND) and per FDA-approved drug, and 

• the change in the present value of these R&D expenditures, 

including and excluding the cost of failures. 

The time-value of money concept—where the present value of $1 

invested 10 years ago is worth more than the same nominal $1 invested 

today—takes into account investment options, inflation, and other timebased factors affecting the value of money. Compressing the schedule of 

the drug development cycle greatly affects the true cost of drug 

development. Thus, the model was developed to show both changes in 

actual and present-value R&D expenditures. 

Table ES-2 presents the overall changes in success and failure rates 

across clinical trial phases predicted by survey respondents who were 

asked to assess how the typical distribution of failed INDs would change 

under the improved infrastructure scenario. Identifying poor drug 

candidates (as measured by the probability of FDA approval) sooner in 

the pipeline has the most significant impact on costs. 

The results in Table ES-2 are in percentage terms because it is not 

possible to predict the absolute number of INDs that would enter clinical 

trials under the improved infrastructure scenarios. In terms of the 

distribution of INDs that fail during clinical trials, the percentage of 

 

5

RTI followed this approach, rather than looking at annual R&D costs at the firm level, for 

multiple reasons. First, and most importantly, the models for R&D cost per approved 

drug build in the cost of failure—a very important component of the industry’s total R&D 

spending and a major focus of efforts to streamline drug development. Second, private 

firms may respond to reductions in drug failure rates, not by lowering their R&D 

spending but by producing more drugs. If this is the case, total industry R&D spending 

would remain unchanged, but the R&D cost per approved drug would decrease. 
Executive Summary 

ES-11 

Table ES-2. Estimated Potential New-Drug Approval and Failure Rates 

For INDs Failing in Clinical Trials, 

Percentage Failing by Phasea

Technology Focus 

Area 

IND Approval

Probability Phase I Phase II Phase III Total 

Probability 

of Recall 

Baseline 30.2% 23.4% 52.4% 24.2% 100.0% 0.40% 

Individual Scenarios 

Biomarkers 41.0% 39.2% 37.0% 23.8% 100.0% 0.30% 

Bioinformatics 40.0% 30.0% 40.5% 29.5% 100.0% 0.30% 

Gene expression 45.0% 37.5% 35.5% 27.0% 100.0% 0.10% 

Combined Scenarios 

Lower bound 40% 30% 41% 30% 100% 0.30% 

Upper bound 45% 39% 37% 24% 100% 0.10% 

a

If 100 INDs enter clinical trials, the IND approval probability suggests that ~30 INDs will eventually receive FDA 

approval. The remaining 70 INDs will then fail in one of the three clinical trial phases. During Phase I, ~16 INDs or 

23% of the 70 would fail. In Phase II, an additional 37 INDs or 53% of 70 are likely to fail. The remaining 17 or 24% 

of the 70 IND failures would occur during Phase III. 

Note: Comparable data for bioimaging could not be calculated; thus, bioimaging was excluded from this table. RTI 

estimates based on DiMasi and Grabowski (2007). 

failures shifted toward earlier failures. That is, a greater proportion of 

failures would occur in Phase I, which in turn leads to a lower rate of 

failures in Phase II where efficacy is evaluated for the first time. The 

proportion that fail in Phase III remains relatively constant or increases 

slightly in the improved infrastructure scenarios relative to the baseline. 

Such muted variation in Phase III failures between scenarios may reflect 

the limitations of our current scientific knowledge base of the interactions 

between chemical compounds and the human body. An increased 

understanding of biological systems and processes would likely have a 

greater impact on Phase III failures than an improved technology 

infrastructure. 

The baseline expected R&D expense for an approved biopharmaceutical 

is $560 million; however, when this figure is adjusted to account for the 

time value of money and firms’ opportunity costs (i.e., the true presentvalue expense), the expected R&D expense is $1,241 million. 

Tables ES-3 and ES-4 illustrate how improvements in the technology 

infrastructure for specific TFAs could affect the cost, development time, 

and success rates for a new drug under several scenarios. These 

scenarios include one for each TFA individually and two scenarios with 

combined results from all TFAs. The most and least optimistic gains in 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-12 

Table ES-3. Potential R&D Cost Reductions in Biopharmaceutical Development with 

an Improved Technology Infrastructure 

Technology Focus 

Area 

Estimated 

Actual Cost 

per Approved 

Drug 

(millions) 

Percentage 

Change from 

Baseline 

Estimated 

Present-Value

Cost per 

Approved 

Drug 

(millions) 

Percentage 

Change from 

Baseline 

Development 

Time 

(months) 

Baseline $559.6 — $1,240.9 — 133.7 

Individual Scenarios 

Bioimaging — — — — — 

Biomarkers $347.9 –38% $676.9 –45% 108.2 

Bioinformatics $375.0 –33% $746.3 –40% 116.6 

Gene expression $345.8 –38% $676.0 –45% 111.9 

Combined Scenarios 

Lower bound $421.2 –25 $869.6 –30 122.4 

Upper bound $289.2 –48 $533.1 –57 98.1 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. The period between completion of Phase III clinical trials and FDA 

approval is assumed to be 16 months in present-value calculations. Source: RTI estimates based on DiMasi and 

Grabowski (2007). 

Table ES-4. Potential Manufacturing Efficiency Gains from an Improved Technology 

Infrastructure 

Baseline Production Costs 

Potential Change 

in Cost by Phase/Activity 

Phase/Activity Cost 

Percentage 

of Totala

Baseline Total

(millions) 

Percentage 

Change 

Change in 

Cost 

(millions) 

Costs under 

an Improved 

Infrastructure 

(millions) 

Preproduction 30% $1,900 –29% –$551 $1,349 

Upstream processing 20% $1,267 –18% –$228 $1,035 

Downstream processing 40% $2,533 –22% –$557 $1,976 

Process monitoring and 

quality assurance testing 

10% $633 –23% –$146 $491 

Total commercial 

manufacturing costs 

 $6,333 –$1,482 $4,851 

a

From Frost and Sullivan (2004). 

Source: RTI estimates. 
Executive Summary 

ES-13 

time, cost, and approval rates were collected from each TFA to estimate 

this range. 

The counterfactual technology infrastructure could potentially 

• increase the probability of FDA approval for the average IND 

from 30.2% to 40%; 

• reduce the expected direct R&D cost for a new FDA-approved 

drug to between $289 million and $421 million, which is 25% to 

48% less than the baseline cost estimated by DiMasi and 

Grabowski (2007); and 

• reduce the average time to take a candidate from discovery 

through Phase III clinical trials from 11 years to 10 years under 

the lower bound scenario and to slightly more than 8 years under 

the upper bound scenario. 

Reducing R&D expenses while simultaneously increasing the likelihood 

of FDA approval generates significant savings. Given that R&D 

expenses for an approved drug can accrue over a period of 12 years, 

changing both the amount and timing of expenses can have a significant 

impact on benefits calculations (see Figure ES-1). 

Figure ES-1. Baseline and Counterfactual Estimated Cost per Approved Drug 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-14 

A 50% improvement is significant and would require both substantial and 

broad-based advances in a range of technical infrastructures, particularly 

gene expression analysis and biomarkers. However, these results are 

consistent with a 2004 FDA report in which one expert suggested that 

biomarkers could reduce the cost of developing a new drug by 50% 

(FDA, 2004b, p. 19). 

 ES.3.3 Estimating Potential Manufacturing Efficiency Gains 

from an Improved Technology Infrastructure 

Whereas benefits related to drug development activities from an 

improved infrastructure were measured relative to the average cost per 

IND and per FDA-approved drug, for manufacturing and postmonitoring 

activities, the analysis estimated efficiency gains relative to total industry 

manufacturing expenses. The estimated efficiency gains in this report for 

manufacturing and R&D are therefore not additive. This is because 

information on average or “typical” product costs was not publicly 

available, requiring RTI to measure potential gains relative to estimated 

total industry manufacturing costs. 

Study participants provided estimates of how an improved infrastructure 

would reduce manufacturing costs by phase. They estimated a 29% 

reduction in preproduction costs and a 22% reduction in downstream 

processing costs, among other impacts, that would result from the 

improved infrastructure (see Table ES-4). 

 ES.4 CONCLUDING REMARKS 

Implicit in this study’s findings is that each biopharmaceutical company’s 

technology infrastructure varies. The relative sophistication of any one 

firm’s infrastructure is a function of the amount of intellectual capital it 

has invested as much as its acquired resources such as instruments and 

software. Differences across firms result from different strategies and 

abilities for overcoming individual technical barriers in the absence of 

well-coordinated industry technical assistance and standardization. In 

addition, these differences go beyond R&D stages and are manifest in 

varying quality assurance and control programs (QC/QA) as well as 

adverse event reporting. 

Most experts interviewed over the course of this study conceptualized 

technology infrastructure expenditures into two general categories. The 

first are expenditures that constitute an investment in current and future 

R&D efficiency. The labor effort, systems, and instruments expended are 
Executive Summary 

ES-15 

essential, unavoidable, and integral to a firm’s primary economic activity. 

The second category are expenditures that represent costs incurred to 

develop workarounds and overcome technical barriers stemming from a 

pervasive lack of industry-wide technical assistance and standardization. 

Challenges in the biopharmaceutical technology infrastructure and the 

differences in how organizations respond to these challenges are rooted 

in 

• the historical trial-and-error approach to drug discovery and 

development; 

• development times averaging 12 years, which are compounded 

by changes in regulatory requirements, information systems, and 

procedures; 

• variability in methodologies and protocols that acquire 

information and variations in how that information is described 

and characterized; 

• few industry standards for ontologies, data formats, and data 

communications systems; 

• the rapid introduction and adoption of data acquisition 

technologies (which far outpace the development of industry’s 

ability to manage, communicate, analyze, and synthesize data); 

and 

• changes in the regulatory environment in a diverse set of 

countries and foreign languages (see Table ES-5 for 

stakeholders’ recommendations). 

Interviewees and survey respondents stated that NIST participation in 

standardization activities for biopharmaceuticals would be welcome. 

These experts believe that NIST has a natural role, given its status as an 

independent, neutral body, its greater access to consistent funding, and 

its mission to provide measurement-intensive and other technical 

infrastructure to industry. Companies’ reporting requirements to FDA add 

a regulatory driver to nearly all research initiatives. Thus, senior 

scientists and directors at biopharmaceutical companies cited FDA’s 

regulatory authority as a potential constraint and suggested that NIST 

and FDA collaborate to identify and develop standards that are 

congruent with the Critical Path initiative. 

Although the entire biopharmaceutical industry would benefit from an 

improved infrastructure, emerging companies that have yet to adopt or 

develop an internal infrastructure stand to gain the most. Such small 

start-up firms should have greater chances for success with access to a 

deeper and more efficient technical infrastructure. from the resulting 

overall increase in operating efficiency should enable these firms to 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

ES-16 

Table ES-5. Stakeholders’ Comments on Technology Infrastructure Needs 

Technology 

Focus Area 

Within the TFAs Studied in this Report, Company Representatives, Academics, 

and Government Researchers Recommended that Needed Improvements to the 

Technology Infrastructure: 

• Include consistent taxonomies for medical and anatomical regions of observations 

• Standardize image labeling procedures and ontologies 

• Develop formats for exchanging imaging data among data systems 

• Improve the image archival, retrieval, and management infrastructure 

Bioimaging 

• Improve access to imaging technology, including image capture and interpretation systems 

• Address the need for greater sensitivity in detection of protein expression levels 

• Develop traceable standards for currently known immunoassayed biomarkers 

• Standardize existing protocols for generating gene expression results 

• Develop standardized methods and tools to hasten validation of technology platforms 

Biomarkers 

• Standardize statistical methodologies for data analysis in biomarker validation studies 

• Improve data visualization and analysis techniques, 

• Develop common (neutral) data formats and analysis tools 

• Set standard protocols for investigating and transforming data 

Bioinformatics 

• Standardize ontologies for characterizing data 

• Make available reference materials that mimic the biological complexity of tissue and blood 

samples 

• Establish sample quality standards, including tools to evaluate the extent to which samples 

may have degraded 

• Create sample acquisition, handling, and preparation techniques given the amount of time 

samples may spend in transit between research sites 

• Establish systems, data, and analysis mechanisms to benchmark microarray performance 

• Develop calibration tools and techniques for scanning equipment 

Gene Expression 

• Provide standard calibration curves for genes as well as standard control techniques, 

assays, protocols, and investigative algorithms 

• Develop standardized data formats for production equipment and instrumentation 

• Create on-line measurement methodologies to improve process understanding and 

establish standard QA/QC measures 

• Improve inspection and validation methodologies 

Commercial 

Manufacturing 

• Develop reference standards analogous to cellular material for future production cell and 

gene therapies 

• Standardize protocols and descriptions adverse event data to engender greater efficiency in 

ongoing safety and efficacy monitoring and FDA reporting 

• Standardize the syntax and interchange between clinical safety databases 

• Improved statistical methodologies to enable multivariate analysis of safety data 

Postmarket 

Surveillance 

• Develop uniform standards for data formats for clinical records 
Executive Summary 

ES-17 

avoid the obstacles and costs associated with surmounting technical 

barriers, many of which would likely otherwise prevent the company from 

succeeding. 

The broader biopharmaceutical and biotechnology industry would benefit 

from greater efficiency and effectiveness with a nationally coordinated 

standardization effort supported by an independent research 

organization with proven technical expertise, technology transfer abilities 

and access to financial and technical resources. The ultimate 

beneficiaries are patients who gain access to a broader array of novel 

therapies where development is supported by an effective technology 

infrastructure. 

1-1 

 1 Introduction 

Traditionally, drug companies developed new pharmaceutical products 

through the trial-and-error investigative processes of medicinal 

chemistry. They created small chemical compounds―referred to as 

small-molecule drugs―developed through the analysis of symptoms that 

characterize certain illnesses and diseases.1

 Although these small 

chemical entities still account for a significant share of the drug industry’s 

development pipeline, they no longer dominate the focus of new drug 

development. 

Over the last 2 decades, the drug development industry has shifted 

toward larger more complex molecular compounds that target 

biochemical mechanisms instead of symptoms. These “large-molecule” 

drugs are called biopharmaceuticals because they take advantage of 

how human biological systems function. Researchers capitalize on the 

specific, precise, and predictable attributes of subcellular molecules, like 

DNA and proteins, across a diverse set of species and cell types to 

develop medicines. 

Biopharmaceuticals are medicines produced from the living cells of 

mammals, plants, viruses, and bacteria. They consist of products like 

recombinant proteins like human insulin, monoclonal antibodies like 

several cancer therapies, and vaccines like those for measles, mumps, 

and rubella. A distinguishing characteristic of biopharmaceuticals is that 

 

1

An alternate, more technical definition is that traditional pharmaceutical drugs are 

characterized as small chemical compounds with a molecular weight typically less than 

500 Daltons that were developed through medicinal chemistry and are metabolized in 

the liver. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-2 

they are not tablets that patients take orally2

 but are intravenous and 

subcutaneous products administered by injection (King, 2006). 

The term “biotechnology industry” is widely used to describe a cluster of 

firms using a diverse set of techniques from engineering and the life 

sciences to enable novel products and services, such as new drugs and 

medical diagnostic devices. The Biotechnology Industry Organization 

(BIO), the leading trade association that spans the many industry sectors 

engaged in biotechnology-related activities, reports that there were 

nearly 1,500 such firms employing over 198,000 people in the United 

States in 2004 (BIO, 2004). 

The biopharmaceutical industry’s origins are rooted in research by 

Stanley Cohen and Herbert Boyer that led to a process called 

recombinant DNA, in which pieces of DNA from cultured cells are 

artificially combined in a controlled setting. Recombinant DNA was a 

novel technique that enabled large-scale production of 

biopharmaceutical products like human insulin. From these origins, the 

industry has grown into one that currently spends $21 billion on R&D 

annually and has commercialized over 400 products. 

The dramatic growth in the number of large-molecule drug candidates in 

clinical development can be attributed to exponential growth in our 

scientific understanding of the biological systems associated with human 

health and disease. This increase in scientific knowledge allowed 

researchers to expand the list of potential disease targets and advance 

genetic/protein engineering and modification technologies (Reichert and 

Valge-Archer, 2007). 

An emerging research model for biotechnology products is systems 

biology, an integrated approach to analyzing biologic interactions and 

understanding how biological systems function. Systems biology 

integrates scientific disciplines and the computational power of 

information technologies to analyze the biological components and 

bioprocesses associated with human development, wellness, disease, 

and aging. By expanding our scientific understanding of human systems 

and their functional interaction, it is anticipated that systems biology will 

enable more effective innovation in diagnostics and therapeutics. Leroy 

Hood, president and cofounder of the Institute for Systems Biology, 

 

2

Biotechnology has enabled the development of novel delivery mechanisms that carry 

small-molecule drugs to their therapeutic target. These delivery mechanisms greatly 

improve drug efficacy by preventing of- target metabolism of the drug, which results in 

more of the drug dose reaching the intended therapeutic target. 
Chapter 1 — Introduction 

1-3 

predicts that this powerful combination of knowledge and tools will 

ultimately enable a new era of predictive, preventative, and personalized 

medicine. Systems biology has the potential to increase the productivity 

of R&D for both biotechnology-based drugs and traditional smallmolecule drugs. 

In the biopharmaceutical industry, systems biology has the potential to 

lower the cost of drug development and shorten the time it takes to bring 

a drug to market. Having a clearer understanding of the mechanism of 

disease will enable researchers to optimize drug efficacy. Moreover, 

better biological information about patients and potential toxicity 

indicators will allow researchers to streamline their clinical trials, 

ultimately reducing the number of costly clinical trial failures. For 

patients, systems biology approaches may reveal new ways to monitor 

health, identify and characterize transitions from healthy to diseased 

states, and provide information that helps patients and clinicians take 

preventative measures. 

However, the technology infrastructure—the core data, methods, 

measurements, and technologies that enable effective R&D—must be 

sufficiently developed to accommodate widespread, effective use of 

systems biology approaches. Identifying and quantifying relationships 

and interactions between biological components and processes will 

require dynamic measurement technologies and the ability to process 

terabyte quantities of experimental data, representing an enormous 

investment in computing systems and laboratory instrumentation and 

software. Information must be able to be communicated among data 

sets, technology platforms, and organizations. This implies that the 

protocols and assays used to acquire these data be transparent and 

standardized within and across organizations. Researchers must have 

confidence in and assurance of the accuracy and precision of the data 

and processes informing drug development decisions. There is a need 

for a technology infrastructure that will improve the resolution, precision, 

and certainty around the experimental data these analytical tools create. 

The purpose of this study is to inform the National Institute of Standards 

and Technology’s (NIST’s) Strategic Planning and Economic Analysis 

group of 

• the biopharmaceutical industry’s annual expenditures on 

technology infrastructure–related investments and activities; 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-4 

• key areas within the technology infrastructure in which industry 

has underinvested because of market and technical barriers or 

because of unattractive risk-reward ratios at the firm level; and 

• the potential efficiency gains an improved technology 

infrastructure offers the industry, as represented by potential cost 

reductions, shorter time to market for new drugs, and greater 

probabilities of developing successful products approved by the 

Food and Drug Administration (FDA). 

 1.1 THE NATURE AND ROLES OF TECHNOLOGY 

INFRASTRUCTURE 

The concept of technology infrastructure warrants an explanation 

because the technology infrastructure’s attributes are this study’s 

principal topic. Chapter 2 delves deeply into infrastructural issues, but a 

top-level understanding is imperative for understanding the scope of this 

report. 

As the term “infrastructure” implies, the technology infrastructure refers 

to the tools, methods, measurements, and data that enable or support 

R&D, products, and services. These are considered infrastructural 

because they are not necessarily commercial products themselves; 

rather, they support or embody processes and components that make 

many advanced technology products and services possible. 

Many elements of the technology infrastructure are unseen or taken as a 

given because they are deeply embedded in or underlie research 

methodologies and instruments. For example, techniques that control 

process quality or verify the accurate calibration of laboratory 

instruments are part of the infrastructure, as are standard reference 

materials and measurements that researchers use to increase their 

confidence and assurance of the accuracy and precision of their work. 

More visible components of the technology infrastructure include 

analytical instruments and advanced software systems and algorithms. 

The technology infrastructure enables productivity and efficiency in each 

of all major stages of drug development. Although some researchers 

may not notice their presence, gaps in the technology infrastructure are 

often readily apparent to other researchers because they hamper 

productivity and collaboration and thus present additional obstacles to 

the development of new drugs. 
Chapter 1 — Introduction 

1-5 

Technology infrastructure expenditures may consist of expenses 

incurred through internal activities; external purchases; and participation 

in consortia, partnerships, or other research activities: 

• the purchase of measurement-related equipment, software, 

reference materials, or services; 

• activities required for setup and validation of analytical 

instruments, reagents, or other research tools; examples of 

these activities may include developing calibration test 

methodologies, standard operating procedures, or process 

standards in measurement and manufacturing practices; 

• in-house customization of technology platforms purchased from 

third-party vendors; 

• efforts to develop interoperability between different software or 

equipment systems; and 

• license fees or any other spending on enhancements to routinely 

used processes or equipment intended to increase productivity, 

reduce redundancy, or improve the confidence in results. 

 1.2 THE BIOPHARMACEUTICAL INDUSTRY 

This report’s scope is limited to the biopharmaceutical industry because 

the diversity and varying definitions of biotechnology make it difficult to 

study at the level of clarity and specificity required by an economic 

analysis. 

 1.2.1 Finding a Focus within Biotechnology: Approaching a 

Broad Industry Category 

The biotechnology industry spans many disciplines from agriculture and 

the environment to health care and industrial applications. It eludes 

categorization according to traditional classification systems like the 

North America Industry Classification System (NAICS) because of the 

diversity of firms’ research applications and the breadth of their market 

foci. No one grouping of homogeneous firms or organizations defines 

this rapidly evolving industry (Toole, 2003). 

The industry is often differentiated by the applications its R&D seeks to 

enhance: therapeutics (e.g., drugs and devices), diagnostic/detection 

applications (e.g., medical, national security), chemicals (e.g., pesticides, 

insecticides, and new chemicals), agricultural (e.g., seed, plant, and 

animal applications), food and cosmetics, environmental, and energy 

(e.g., biomass). 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-6 

Researchers in the biopharmaceutical industry and other knowledgeable 

stakeholders will note that the report also has a focus on protein-based 

biopharmaceuticals. The decision was made to recognize how the 

diversity of these drugs translates into diverse needs and practices, but 

also to narrow the product focus to streamline technology infrastructure 

discussion. Many of the underlying research tools and methods are 

generic in their application across the different categories of 

biopharmaceuticals. Given that recombinant proteins and monoclonal 

antibodies represent the majority of compounds currently in the 

development pipeline, this report focuses more on the development 

processes for these therapies. 

 1.2.2 Biopharmaceutical Products 

Biopharmaceuticals address a diverse set of biological problems 

associated with very different parts of the human anatomy. It is useful to 

define these categories of drugs to set the stage here for the more 

technical description of the technologies that support their discovery and 

development that follows in later chapters. Currently, there are 

inconsistent definitions, taxonomies, and classification schemes applied 

to the term “biopharmaceutical.” Inconsistent terminology and definitions 

make it difficult to analyze this industry: the number of approved biotech 

products is highly inconsistent among industry sources that include BIO 

and PhRMA. 

For the purposes of this report, we define biopharmaceuticals as the 

subset of biotechnology-based products explicitly used for therapeutic 

purposes in humans. Biopharmaceuticals include the more traditional 

therapeutic proteins derived from recombinant DNA technologies, as well 

as monoclonal antibodies, vaccines, gene therapies, and cell therapies. 

Table 1-1 presents one potential categorization of all approved 

biopharmaceuticals marketed as of 2005. 

Therapeutic Proteins 

Therapeutic proteins were the first class of biopharmaceuticals to be 

commercially developed and approved for therapeutic use in humans. 

Therapeutic proteins include enzymes, hormones, and proteins that are 

naturally produced by the human body under normal conditions. 

Therapeutic proteins are frequently labeled as biologics because they 

are intended to replicate or replace proteins, enzymes, or hormones that 

would otherwise be produced naturally within the human body. 
Chapter 1 — Introduction 

1-7 

Table 1-1. Number of Biopharmaceutical Class Types in 2005 

Biopharmaceutical Class Type 

Number of Approved 

Products 

Recombinant DNA products Therapeutic Proteins 9 

 Monoclonal antibodies 19 

 Gene therapies 1 

 Monoclonal antibodies 13 

Non-recombinant DNA products Vaccines 128 

 Toxins 4 

 Enzymes 19 

 Cultured cells and tissues 10 

 Human blood products 108 

 Animal blood products 24 

Source: Adapted from Rader, 2005. 

Human insulin is one example of a therapeutic protein that is used to 

treat diabetes in patients who can no longer produce the enzyme 

naturally. Epotein is another example of a therapeutic protein used to 

stimulate the production of red blood cells in the body for patients who 

suffer from anemia or low red blood cell count. Epotein is intended to 

replace erythropoietin, the naturally occurring protein produced in the 

kidney that regulates red blood cell production. Therapeutic proteins are 

the product of genetic engineering of human or animal cells and 

produced through recombinant DNA technologies. 

Monoclonal Antibodies 

Monoclonal antibodies (mAbs) are the second category of 

biopharmaceuticals that capitalizes on cell receptors present on the 

surface of almost all cells to effect changes in cell growth or 

reproduction. Antibodies or other proteins can bind to the cell receptor 

and cause changes to occur in the cell. In the cases of tumor cells, 

antibodies can slow the growth or stop the production of new cancer 

cells. 

Rituximab is one example of a monoclonal antibody that binds to a cell 

receptor on B-cell non-Hodgkin’s lymphomas (tumor cells) that causes 

the tumor cells to disintegrate and in some cases prevents the 

production of new tumor cells. Herceptin is another example of a 

monoclonal antibody that is used in breast cancer patients when the 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-8 

cancer has spread or metastasized. Herceptin binds to a cell surface 

receptor HER2, present on approximately 33% of all breast cells, to 

block tumor growth and development. Both, Rituximab and Herceptin are 

produced using molecular cloning and recombinant DNA technology. 

Vaccines 

Vaccines are preventative treatments that build an immune response to 

disease by introducing nonlethal doses of the viruses to the body to 

stimulate the production of natural antibodies that kill the invading 

disease agent. Vaccines have traditionally been given as a series of 

subcutaneous injections. Vaccines can be prophylactic or can prevent or 

modify future infections from a natural pathogen (a foreign agent that 

enters the body), or therapeutic as is the case with vaccines for diseases 

such as AIDS, HPV, and hepatitis B. 

Traditional vaccines containing killed or live attenuated viruses stimulate 

an immune response in the body. The immune system recognizes the 

foreign pathogen and develops antibodies that will destroy it. Many 

common vaccines for viruses such as influenza, cholera, yellow fever, 

and measles are created using the killed or live attenuated vaccine 

method. Tetanus and diphtheria vaccines are examples of toxoid 

vaccines that include an inactive toxic agent from a specific virus rather 

than the entire virus. 

Gene Therapies 

Gene therapies are defined as the insertion of a corrected or normal 

gene into individual cells and tissues to replace abnormal diseasecausing genes. A viral vector such as an adenovirus or retrovirus is used 

to deliver the normal gene to individual cells. Once inside the cell, the 

normal gene stimulates the production of proteins needed to restore the 

cell to a normal healthy state. 

Gene therapies were originally delivered directly to targeted cells outside 

of the body and then delivered to the body through some surgical 

procedure. More recently, researchers have inserted corrected modified 

DNA into adenovirus vectors, which is used to deliver the gene therapy 

to targeted cells. One significant problem with gene therapies is immune 

response. Similar to organ transplant, when corrected genes are 

introduced to the cell, the immune system no longer recognizes the cells 

as normal and begins to destroy them. 
Chapter 1 — Introduction 

1-9 

Cell Therapies 

Cell therapies, similar to gene therapies in approach, deliver new normal 

cells or tissue to a diseased part of the body. Cell therapies to date have 

focused on treating hereditary diseases. Forms of cell therapies include 

the transplantation of stem cells and the transplantation of functional or 

mature cells. 

Limitations to Biopharmaceuticals 

Limitations to biopharmaceuticals include delivery, immunogenicity, and 

cost. Because of their higher molecular weight and sensitivity to harsh 

conditions, many biopharmaceuticals cannot be ingested like smallmolecule drugs since their proteins or antibodies denature in gastric 

acids. Hence, these drugs are typically administered through intravenous 

or subcutaneous injection. In the case of cell therapies, delivery of new 

cells to a host system such as the brain requires an invasive surgical 

procedure. 

Immunogenicity, which is an undesired immune system response to a 

biopharmaceutical once inside the body, is also a major limitation of 

biopharmaceuticals. Biopharmaceutical antibodies typically use an 

antibody known as an adenovirus to locate and bind to specific cell 

surface receptors. In some cases, the body’s immune system may create 

antibodies that try to destroy the foreign adenovirus. This immunogenic 

response can reduce or eliminate the drug’s efficacy. If the immunogenic 

response is nonspecific, the immune system may destroy other proteins 

in the body that are similar to the biopharmaceutical antibody but needed 

for other functions. Under some conditions, such an immune response 

may result in major organ failure and death.3

Cost is another limitation of biopharmaceuticals. Typically 

biopharmaceuticals are more expensive than their counterpart smallmolecule drugs because of special storage and handling requirements, 

smaller patient populations, and high administration costs. 

Biopharmaceutical injections may require the assistance of skilled 

clinicians or even surgical procedures in the case of cell therapies. 

Another key issue impacting biopharmaceuticals in the near future is 

follow-on biologics. Follow-on biologics are “generic” formulations of an 

 

3

A noted example of this was Jesse Gelsinger, who died in 1999 as a result of 

immunogenic response during a gene therapy clinical trial to treat a rare genetic liver 

disease. He died 4 days after suffering a massive immunogenic response to the 

adenovirus that carried the gene therapy to his liver cells. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-10 

existing biologic drug; approval and regulation of follow-on biologics is a 

key issue facing the industry. Follow-on biologics depend on the same 

mechanism of action as their predecessor but may or may not be exact 

replicates of the original version. Similar to a class of drugs like statins, 

follow-on biologics are innovative products that closely resemble each 

other. Measurement needs related to follow-on biologics center primarily 

on the need to characterize the molecular profiles of these drugs. The 

high molecular variability that can occur during the manufacturing 

process has raised safety concerns, and measurement technologies that 

more accurately characterize biologic profiles are not well developed. 

 1.2.3 Defining the Biopharmaceutical Industry 

As of 2006, the biopharmaceutical industry accounted for over 260 

therapeutics and vaccines approved for over 380 indications (BIO, 2006). 

FDA approved the first biopharmaceutical for marketing in 1982, marking 

the inception of the biopharmaceutical industry 25 years ago. 

Conventional pharmaceutical firms have seen R&D spending continually 

increase, while the number of new drug applications (NDAs) has 

declined over the same period. In light of the declining R&D productivity 

of more conventional “small molecule” research, biopharmaceutical 

leaders such as Amgen, Genentech, Millennium Pharmaceuticals, 

Biogen, Genzyme, and others offer the promise of improving R&D 

productivity through large molecule-based biologics. However, advances 

in the technology infrastructure are needed to improve the probability of 

success for this class of compounds, ultimately improving attrition/failure 

rates at each stage of the R&D process and thereby lowering the perdrug cost of R&D. 

The length of time from initial discovery of a biopharmaceutical through 

approval can average 8 to 15 years, with most costs incurred during 

clinical trials (BIO, 2004). In addition, revenue from successful products 

must cover the costs from failed efforts as researchers seek to recoup 

investment costs. 

For the pharmaceutical industry as a whole, only 1 in roughly 10,000 

compounds screened during the drug discovery stage succeeds through 

the clinical trial stage and results in an FDA-approved drug (PhRMA, 

2005). Of drugs entering clinical trials, FDA reports only about 8% ever 

reach the market to recoup those expenditures. Furthermore, the failure 

rate in the most expensive Phase III stage is nearly 50% (FDA, 2006). 
Chapter 1 — Introduction 

1-11 

Similar statistics are not readily available for the biopharmaceutical 

sector alone, but some studies suggest clinical trial success rates are 

higher for biopharmaceutical therapeutics.4

 Even if attrition is less of a 

problem for biopharmaceuticals, the development stages are more 

complex. These factors translate to a high price for prescription drugs, 

and according to recent reports, spending on biopharmaceuticals “is 

growing twice as fast as traditional prescription drugs” (Anand, 2005). 

In 2004, the average biotechnology company spent more than 24% of its 

revenues on R&D, compared with 13.3% for large pharmaceutical 

manufacturers (Ho and Gibaldi, 2003). (see Table 1-2).5

 Many 

biopharmaceutical companies are still in the development stage and 

have yet to commercialize drugs that take more than a decade to 

develop. 

Table 1-2. 10 Largest Biopharmaceutical Companies by Net Sales in 2004 

Company Name NAICS 

R&D Total 

Expenditures 

($million) 

Net Sales 

($million) 

Relative R&D 

Intensity 

(R&D/Sales) 

Amgen 325414 $2,582 $10,550 24% 

Genentech 325412 $816 $4,621 18% 

Serono 325414 $595 $2,458 24% 

Biogen-IDEC 325414 $688 $2,212 31% 

Genzyme 325414 $646 $2,201 29% 

Chiron 325412 $441 $1,605 27% 

Gilead 325414 $224 $1,325 17% 

Medimmune 325414 $357 $1,141 31% 

Millennium Pharmaceuticals 325413 $266 $448 59% 

Intermune 325414 $81 $151 54% 

Source: Compustat North America. 

Note: NAICS Industry Descriptions: 

325412—Pharmaceutical Preparation Manufacturing 

325413—In-Vitro Diagnostic Substance Manufacturing 

325414—Biological Product (except Diagnostic) Manufacturing 

 

4

See, for example, Pavlou and Riechert (2004), who report a 35% approval rate for 

recombinant protein therapeutics entering clinical trials from 1990 to 1997. 5

While the 13% R&D-to-sales ratio is the industry average, the top 10 drug manufacturers’ 

average was slightly higher at 16%. The top 10 drug manufacturers include Pfizer, 

Novartis, Johnson & Johnson, GlaxoSmithKline, AstraZeneca, and Merck. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-12 

Changing demands in consumer markets are also driving 

biopharmaceutical R&D as a result of an increasingly health-conscious 

population and a shift toward preventive and predictive medical 

treatments. Traditionally, R&D expenditures targeted primarily cancer 

and cardiovascular disease markets. However, emerging markets for the 

treatment of metabolic disease (e.g., obesity and diabetes), treatment of 

memory disease (e.g., Alzheimer’s), and wellness (i.e., preventative or 

predictive) therapies are expanding the targeted areas for drug 

development. In addition, R&D is targeting new approaches to the 

traditional cancer and cardiovascular diseases, such as the development 

of therapeutic vaccines to treat heart disease and certain types of 

cancer. Other diseases targeted include neurological, pulmonological, 

orthopedic, metabolic, renal, and infectious diseases. 

Medicare’s recent adoption of prescription drug coverage combined with 

the “baby boom” generation approaching retirement will result in the 

Centers for Medicare & Medicaid Services (CMS) becoming a dominant 

consumer (representing 40% of the market) of prescription drug 

purchases (Burrill & Company, 2006). Demand for generic drugs is 

expected to increase as CMS becomes a larger consumer. As a result, 

new R&D programs are focused on reducing the time to market of new 

drugs and increasing years of peak sales over the life of a drug’s patent. 

 1.2.4 The Biopharmaceutical Product Development Cycle 

The exact path a biopharmaceutical firm takes in developing a human 

therapeutic varies greatly depending on the disease application and the 

type of product.6

 For example, the R&D process for monoclonal 

antibodies and recombinant proteins may look quite different from the 

R&D process for vaccines, DNA (gene-based) therapies, or cell-based 

therapies. However, all these drugs start with a foundation in basic 

research and pass through the following generic stages:7

• drug discovery, 

• preclinical development and testing, 

• clinical trials, 

 

6

A biotechnology firm may not even be the sole organization involved in developing such 

products. Often a firm will license intellectual property from universities or other 

research organizations, or the firm may license discoveries to large pharmaceutical 

organizations for further development. 7

These five stages are presented in chronological order, with the exception that scale-up to 

commercial manufacturing is undertaken simultaneously with preclinical development 

and clinical trials. 
Chapter 1 — Introduction 

1-13 

• scale-up and commercial manufacturing, and 

• postmarket surveillance. 

The following discussion is intentionally oversimplified to provide readers 

unfamiliar with the drug development process a brief overview. The drug 

development process is revisited in Chapter 2, where the technology 

infrastructure supporting drug development stages is discussed. 

Drug Discovery 

This stage in the development cycle involves four primary phases: target 

identification, target validation, lead identification, and lead optimization. 

Target identification marks the beginning of the discovery process. 

Researchers use their understanding of molecular biology, genomics, 

and proteomics to identify pathways to disease or disease “targets.” 

Researchers identify and validate drug targets and identify compounds 

that have the ability to affect the activity of a target (e.g., a protein). 

Those leads demonstrating the highest probability of success—typically 

250 optimized leads for every one approved drug, according to PhRMA 

(2005)—then enter the preclinical stage. 

Preclinical Development and Testing 

Once researchers have identified a lead compound, they must conduct a 

number of preclinical studies before testing the drug therapy in humans. 

Researchers perform experiments to determine the biochemical and 

physiological effects and properties of the compound. At the same time 

that researchers are investigating the pharmacological properties of the 

drug candidate, they are beginning to develop the process for 

manufacturing the compound. 

Clinical Trials 

After developing a promising drug, a developer will submit an 

Investigational New Drug (IND) application to FDA. Drug companies file 

applications for gene therapies, cellular products, vaccines, and plasmaderived products with the Center for Biologics Evaluation and Research 

(CBER), and protein therapies, such as monoclonal antibodies, 

cytokines, enzymes, and growth factors with the Center for Drug 

Education and Research (CDER). FDA also has an Office of 

Combination Products to oversee approvals for those products. 
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Clinical trials typically include three phases: 

• Phase I trials are primarily meant to test the safety, tolerance, 

and behavior of the IND in human subjects. These trials have the 

smallest patient group. If the therapy is found to be safe, it 

progresses to the next trial phase. 

• Phase II trials expand the patient group significantly. 

Researchers continue to monitor the safety of the therapy 

because of the increase in genetic diversity present in a larger 

patient group, but the focus of this stage is on learning how the 

IND behaves and interacts within the human organism at 

different dosage levels. 

• Phase III trials monitor safety and drug behavior but are meant to 

serve as the definitive assessment of the IND’s efficacy. These 

trials have the largest patient group and are generally the last 

round of trials before the IND is submitted for FDA approval. 

Scale-Up and Commercial Manufacturing 

Production can be divided into two major stages: scale-up and 

commercial manufacturing. Scale-up includes manufacturing-process 

design, product specification, and optimizing production activities. Bulk 

manufacturing includes commercial-scale upstream and downstream 

processing. These two stages are discussed together in this section for 

ease of presentation. However, in practice, scale-up manufacturing 

activities occur in tandem with the preclinical and clinical development 

stages. Commercial manufacturing occurs following FDA approval. 

The commercial production of biopharmaceuticals includes upstream 

and downstream processing. Upstream processing is defined as the 

fermentation process that results in the initial core product material. 

Downstream processing refers to the actual purification of the product 

and formulation, bottling, and labeling activities. 

Postmarket Surveillance 

Postmarket surveillance refers to the on-going, indefinite monitoring of 

the efficacy and safety of an FDA-approved product. Once clinical trials 

are complete, the biopharmaceutical drug or therapy sponsor submits an 

NDA containing all scientific information collected during preclinical and 

clinical studies. Among other documents, the sponsor must submit an 

integrated summary of efficacy (ISE) and an integrated summary of 

safety (ISS), which in combination document the benefit-risk trade-off for 

the candidate drug. 

For some therapies, FDA will approve the drug provisionally and will 

require additional “Phase IV” studies to evaluate long-term effects. Even 
Chapter 1 — Introduction 

1-15 

if approval is not conditional, sponsors must produce a Periodic Safety 

Update Report (PSUR) quarterly for the first 3 years and annually in 

years that follow. These updates report adverse drug reaction (ADR) 

cases and alert FDA to potential safety issues that may prompt a 

reevaluation of therapy labeling. A particular concern related to the 

postapproval performance of a biopharmaceutical involves stability of the 

therapy and the conditions under which the therapy must be handled, 

stored, and dispensed to remain effective. 

Postmarket surveillance for safety and efficacy, also known as 

pharmacovigilance, will always be necessary despite even the most 

rigorous preclinical and clinical testing phase evaluations. These earlier 

phases usually will have involved only a few thousand human patients, 

and certain therapy responses or interactions are so statistically rare that 

they only manifest once the therapy reaches the large market population. 

The costs of collecting such data and producing these reports can be 

sizeable, but the private and social costs of an unsafe therapy that must 

be recalled or remarketed under restricted criteria are much more 

substantial. 

 1.2.5 Biopharmaceutical Technology Infrastructure 

Complexity 

Looking toward the future, biotechnology is enabling a new era of human 

therapeutics that builds on advances in nanosciences and systems 

biology. The biopharmaceutical industry’s ability to create these 

innovative therapies will largely be determined by several factors, 

including the productivity and efficiency of R&D, manufacturing quality 

control and assurance programs, and postmarket surveillance activities. 

Large variations in productivity and efficiency currently exist at each step 

in the biopharmaceutical development life cycle. For complex sciencebased technologies like biopharmaceuticals, major gains in these 

performance attributes require a complex and ubiquitous technical 

infrastructure. 

While biopharmaceuticals capitalize on the specific and predictable 

attributes of subcellular organic molecules, like DNA and proteins, 

inherent random variation associated with producing drugs from living 

organisms demands an even more complex technical infrastructure than 

is needed for inorganic complex science-based technologies like 

semiconductors. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

1-16 

 1.3 PROJECT SCOPE AND GOALS 

This report characterizes the infratechnologies used to develop new 

biopharmaceuticals and estimates the biopharmaceutical industry’s 

current annual spending on the technology infrastructure. The report also 

identifies technological inadequacies or gaps that may exist and 

estimates the potential efficiency gains an improved infrastructure would 

hold for developing and producing new biopharmaceutical products. 

Accordingly, subsequent chapters 

• define and characterize the range and roles of technology 

infrastructure in the biopharmaceutical industry, 

• estimate the annual expenditures for this infrastructure incurred 

by biopharmaceutical firms, 

• estimate the “excessive” costs incurred because of inadequacies 

of existing infratechnologies, and 

• assess research priorities and industry’s awareness of NIST’s 

capabilities. 

Although benefits ultimately accrue to patients, the project methodology 

concentrated on quantitatively assessing the economic benefits to firms. 

The inherent difficulty in linking investments in infrastructure technologies 

to health outcomes precludes providing quantitative estimates of 

consumer benefit. 

 1.4 REPORT ORGANIZATION 

The report is organized as follows: 

• Chapter 2 discusses the biopharmaceutical technology 

infrastructure in detail and includes stakeholders’ comments on 

technical and market barriers to an improved infrastructure. 

• Chapter 3 presents the approach to quantifying annual 

infrastructure-related spending and estimating the potential an 

improved infrastructure holds for the costs of developing and 

manufacturing biopharmaceuticals. 

• Chapter 4 describes interview protocols and provides summary 

statistics on the sample pool of study participants that 

contributed to this research. 

• Chapter 5 presents quantitative results, including the potential 

efficiency gains from an improved infrastructure. 

• Chapter 6 summarizes research findings and offers concluding 

remarks on opportunities for NIST. 
2-1 

 The Technology 

 Infrastructure 

 Supporting the 

 Biopharmaceutical 2 Industry 

This chapter presents a broad characterization of the biopharmaceutical 

industry’s technology infrastructure and relates stakeholders’ views on 

the technical and market barriers that impede greater efficiency in 

biopharmaceutical R&D and production. Study participants’ insights 

provide a context for the cost model and the economic methodology 

presented in Chapter 3. Also included in this chapter is a discussion of 

six technology focus areas (TFAs) for which counterfactual technology 

infrastructures were investigated. 

 2.1 DEFINING THE BIOPHARMACEUTICAL 

TECHNOLOGY INFRASTRUCTURE 

This chapter begins by defining the technology infrastructure. It includes 

several illustrative examples of the infrastructure components of the 

biopharmaceutical industry’s development and production processes. 

Feedback from study participants on the TFAs and efforts to bridge those 

gaps are discussed later in the sections of this chapter devoted to 

technical and market barriers. 

In addition to defining this technology infrastructure, we offer preliminary 

comments on technology gaps and inefficiencies informed by the 

National Institutes of Health’s (NIH’s) Roadmap for Medical Research 

and FDA’s Critical Path report. These documents represent strategic 

initiatives in the areas of technology development that experts believe 

will alleviate bottlenecks and reduce the costs of biotech drugs. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-2 

 2.1.1 Technology Infrastructure Components 

The technology infrastructure has been defined as a suite of 

infratechnologies used jointly by competing firms within a specific 

industry, often in the form of standards (Tassey, 1997). In the 

biopharmaceutical industry, this suite of infratechnologies enables the 

advancement of scientific knowledge through proof of concept (generic 

technology) research and then applied R&D, the results of which lead to 

innovation and ultimately new products and process techniques. 

Technology infrastructure consists of three interlocked components: 

generic technologies, proprietary technologies, and infratechnologies. 

Infratechnologies are defined as a set of “technical tools and 

processes” that enable efficiency increases in R&D, production, and 

market development activities. They can include measurement and test 

methods, materials characterization, standard reference materials, 

process models, analytical methods, and standards in data formats and 

systems interoperability. Infratechnologies are methods employed to 

conduct testing and analyze results using a combination of various tools, 

techniques, and procedures. 

Infratechnologies have a role in every stage of economic activity. In 

R&D, infratechnologies support the development of generic and 

proprietary technologies and improve the efficiency, precision, and 

repeatability of R&D tests. Infratechnologies also improve the quality and 

reliability of production processes. These improvements affect the 

efficiency of market transactions by reducing the performance risk to 

market participants. 

Infratechnologies leverage the productivity of the processes of 

developing generic technologies (also called “technology platforms”) and 

subsequent proprietary technologies (innovations). 

Generic technologies represent the first attempts to construct a 

laboratory proofs-of-concept derived from basic science. Generic 

technologies have no specific market applications as stand-alone 

technologies; rather, they represent a potential trajectory toward a new 

market application. Tassey (1997) suggests that generic technologies 

must exist before innovative market applications can be identified. This 

inherent quality means that generic technologies may reduce the 

technical risk associated with pursuing additional applied R&D. Generic 

technologies are considered infrastructural because competing firms 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-3 

draw on the same generic technologies in pursuit of innovation, 

ultimately leading to new market applications. 

Proprietary technologies consist of methods, processes, and 

techniques firms develop to achieve their strategic objectives in 

conducting the development and production processes required to bring 

new products and services to market. Competing firms differentiate 

themselves through their implementation of these proprietary 

technologies. Typically, proprietary technologies are traceable to 

industrial standards to ensure market acceptance. 

 2.1.2 The Biopharmaceutical Technology Infrastructure 

Taxonomy 

The biopharmaceutical technology infrastructure comprises the 

aforementioned infratechnology categories. Many of them are embodied 

in broader technical infrastructure categories such as analytical systems, 

bioinformatics, and consumables and reagents used every day.1

 These 

include the following: 

• Analytical Systems incorporate the generic and proprietary 

technologies embodied in the equipment and hardware used in 

determining biological samples’ physical and behavioral 

characteristics. Analytical systems include

– sequencing platforms; 

– expression platforms, including microarrays; 

– spectrometry platforms; 

– spectroscopy platforms; 

– imaging technologies (MRI, PET, CT); 

– chromatography platforms; 

– electrophoresis platforms; 

– fluorescence and flow cytometry; and 

– screening platforms, including microfluidics. 

• Bioinformatics includes all computational-based technologies, 

such as bioinformatic and chemoinformatic databases, predictive 

modeling software, statistical analysis software, and data capture 

systems. Bioinformatics encompasses

– computing systems, 

 

1

This taxonomy leverages NIH’s Roadmap for Medical Research and FDA’s Critical Path 

report and the U.S. Measurement System (USMS) assessment, which is currently 

conducted by the NIST. In addition, professional associations, such as the Analytical 

Life Science Systems Association (ALSSA), a trade association representing the 

scientific and technical instrument industry, offer some guidance on organizing the 

variety of technologies that are applied in the biopharmaceutical product development 

life cycle. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-4 

– databases, 

– work management software, 

– analytical software and algorithms, 

– data acquisition technologies, and 

– statistical methodologies. 

• Consumables and Reagents for Analysis capture all chemical 

and biological aids that facilitate laboratory analysis of drug 

targets and their potential lead compounds (i.e., drugs):

– molecular biomarkers; 

– nucleic acids; 

– radiochemicals; 

– labeling kits; 

– vectors, such as viral vectors for delivering gene therapies; 

– gas/liquid chromatographic supplies; 

– modifying/restriction enzymes; and 

– transfer membranes. 

More generally, several infratechnologies are often combined to provide 

measurement and test methods, standards, reference materials and 

other critically evaluated data, models, algorithms, and interface 

specifications. For example, the use of mass spectrometry in proteomic 

research clearly involves measurement, but it also requires precise 

calibration, standards for sample preparation, computer interfaces for 

reading output, and quality databases for matching that output to known 

peptide sequences. Researchers often take infratechnologies for 

granted; the consequences of not having technologies such as 

calibrants, advanced chemical methods, and standard operating 

protocols―all of which researchers rely on every day―demonstrate the 

value they contribute to the overall body of technology enabling 

biopharmaceutical R&D. 

 2.1.3 The Economic Role of Biopharmaceutical Technology 

Infrastructure 

The methods, techniques, and data discussed above form a complex 

technology infrastructure that enables productivity and efficiency in each 

of the major stages of economic activity related to biopharmaceuticals 

(such as basic and applied research, clinical trials, commercial 

manufacturing, and market development). Improvements in this 

infrastructure can have numerous potential economic impacts, including 

the following: 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-5 

• Cost reductions 

– Lower labor and materials costs for discovery and 

development of a given therapy 

– Lower labor, equipment, and materials costs for production 

– Lower transaction costs promoting market penetration and 

postmarket tracking/assessments 

• Accelerated time to market 

– Shorter time between discovery and FDA approval 

– Shorter time between new market demand and production 

response 

• Quality improvements 

– Detection of drug failures in earlier clinical trial phases 

– Investigational New Drugs (INDs) have greater probability of 

receiving FDA approval 

– Reduced uncertainty in drug efficacy and safety in the 

population targeted for prescription, longer shelf lives, and 

less restrictive storage and handling needs 

Improvements in the technology infrastructure can lower total costs by 

enabling go or no-go decisions with respect to research strategies in 

earlier stages of the R&D cycle. For example, one way to reduce overall 

costs might be to conduct more extensive in vitro (in test tubes or Petri 

dishes) and in silico studies before testing a therapy in animal models. 

Additionally, accelerating the time to market by realizing productivity 

gains results in both a cost reduction (fewer labor hours) and a revenue 

increase (as firms take better advantage of a limited patent life). 

Despite the private and social benefits resulting from improvements in 

the technology infrastructure, economic theory suggests that the 

biopharmaceutical industry will underinvest in improvements. Generally 

speaking, the private return on generic technology or infratechnology 

investments is too low because of a lack of appropriability of the 

knowledge produced and the relative high risk inherent in these areas of 

research. Such market failures are often cited as a motivation for 

industry initiatives through trade associations, joint ventures, and public 

funding, which include the R&D investments made by NIST and other 

government organizations. 

Investments to improve the technology infrastructure will result in 

benefits to firms in the biopharmaceutical and other industries developing 

and using biotechnology, as well as to consumers. However, there are 

inherent difficulties in quantifying the benefits to consumers. The 

uncertain nature of drug R&D makes predicting health outcomes difficult; 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-6 

moreover, quantifying those health outcomes (i.e., quality-of-life metrics) 

is beyond the scope of this study. As a result, our approach focuses on 

estimating the potential benefits accruing directly to biopharmaceutical 

firms from reductions in R&D, manufacturing, and postmarket 

surveillance costs (in large part from greater success in getting approval 

for new drugs and getting them into the marketplace). 

 2.2 TECHNOLOGY FOCUS AREAS IN THIS STUDY 

Discussions with RTI’s internal experts, experts at the FDA and the 

nonprofit Critical Path Institute, and other industry participants and a 

literature review identified four compelling infrastructure technologies that 

could serve as TFAs for this study. The potential an improved technology 

infrastructure holds for the biopharmaceutical R&D is greatest with these 

TFAs. Thus, the following areas helped define what exactly is meant by 

an improved infrastructure by presenting study participants with a 

defined baseline against which to estimate the impact potential 

improvements may have. 

For discovery, preclinical, and clinical (Phase I through Phase III) 

activities, four TFAs were selected: 

1. Enhanced bioimaging techniques 

2. Standards and metrology in gene and protein expression 

analysis 

3. Improved bioinformatics and in silico predictive modeling 

4. Identification and validation of molecular biomarkers 

These infratechnologies have applications throughout the drug 

development process and cut across a number of different therapeutic 

categories. 

In addition to the four drug-development-specific TFAs, two processspecific TFAs were included: 

5. Infratechnologies to enhance scale-up and commercial 

manufacturing, including improvements in upstream and 

downstream processing and process monitoring/quality 

assurance 

6. Infratechnologies to support postmarket surveillance activities, 

including product surveillance, tracking, and Phase IV clinical 

trials 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-7 

 2.2.1 Enhanced Bioimaging Techniques 

Bioimaging refers to using imaging techniques to review biological 

structures and functions and to evaluate phenomena in a noninvasive 

manner. Although often used for diagnostic purposes, bioimaging and 

structural characterization can also play an important role in drug 

discovery, preclinical testing, and clinical trials. 

In the discovery stage, bioimaging can be used to identify and map 

potential drug targets. In preclinical development, imaging can be used 

to inform pharmacokinetic and pharmacodynamic (PK/PD) studies, for 

example, by dosing a brain tumor with a highly fluorescent drug while 

monitoring accumulation of the drug in the tumor. PK/PD studies refer to 

the metabolism of a drug (pharmacokinetics) and how the drug behaves 

in the body (pharmacodynamics). 

Furthermore, in preclinical and clinical stages, bioimaging can serve as a 

biomarker, signaling future response in animal models or actual 

response patterns in human trials. Improved structural characterization 

may reduce costs during manufacturing by increasing the homogeneity 

in populations produced for each therapeutic category. The ability to 

create a homogenous population could increase manufacturing yields 

and potentially reduce downstream purification and processing costs. 

However, infrastructural advances must be made in the diverse imaging 

technologies (including CT, MRI, PET, and ultrasound)2

 to ensure the 

quantitative data produced by these technologies are accurate, reliable, 

and reproducible. Biopharmaceutical executives and researchers have 

also indicated that the throughput and resolution of these technologies 

can be too low (and the expense too high) for use in preclinical studies. 

Some technologies offering higher throughput and better resolution 

require labeling compounds that cannot be used in humans in a clinical 

trial setting (Chapman, 2005). 

 2.2.2 Gene and Protein Expression Analysis 

Gene and protein expression analysis and profiling provide a complex 

picture of a cellular state by determining whether genes are turned off or 

on in a cell, or their expression level, for tens of thousands of genes at a 

specific time for a specific biological sample. 

 

2

These acronyms stand for computed tomography (CT or “CAT scan”), magnetic resonance 

imaging (MRI), and positron emission tomography (PET). 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-8 

Expression analysis can be performed on blood, tumors, normal tissue, 

and many other types of samples. One example of gene expression 

analysis is comparing the gene expression levels between normal and 

cancerous tissue to identify genes that are differentially expressed only 

in the disease state. Not only could this information be used as a 

potential molecular diagnostic, but it could also direct new research to 

enlighten our basic understanding of these disease states. 

Expression analysis is predominantly performed on DNA or RNA 

microarrays. Microarrays are small glass or silicon surfaces that are 

divided into tens of thousands of “features,” where each feature is a 

small area that contains DNA or RNA sequences that are 

complementary to a specific gene sequence. These features are also 

known as “probes.” 

In a microarray experiment, researchers introduce a specially prepared 

blood or tissue sample to a microarray and track gene expression 

patterns by analyzing matches between the sample and the microarray’s 

probes. Each microarray contains thousands of probes that are singlestranded DNA reference sequences. Where an RNA or DNA sequence 

from an experimental sample is complementary to a probe’s sequence, 

the two will bind and emit a fluorescent signal (see Figure 2-1). 

DNA microarrays are an important component of medical research 

because they document cells’ responses to diseases and the effects of 

drug treatments (Gerhold, Jensen, and Gullans, 2002). They also take 

the vast amount of information from large-scale sequencing efforts and 

provide a platform from which DNA and mRNA samples can be tested 

against entire genomes. Until microarrays’ introduction in 1994, scientists 

used $100 blood tests and other laborious diagnostic procedures to 

identify individual genes. A microarray permits thousands of those 

experiments to run simultaneously. Experiments that only a decade ago 

took weeks, months, or perhaps years to run now take only a couple of 

days (Malik, 2003). This speed has had a profound effect on the way 

medical research is conducted. 

Repeatability in gene and protein expression analysis has been identified 

as a bottleneck in efficacy and toxicology testing during all drug 

development stages. A standardized set of reference materials and 

standard sample collection and management protocols would streamline 

this process by reducing variability of results across laboratories and 

multiple platforms. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-9 

Figure 2-1. DNA Hybridization in a Microarray Experiment 

Source: Courtesy of Affymetrix. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-10 

Reference materials would ultimately improve robustness of results and 

reduce the quantity of data submissions. In addition, standard protocols 

or “best practices” would improve repeatability and allow results from 

different laboratories to be shared and compared. These would include 

unifying aspects of experimental design, reducing variability in the 

quantity and quality of RNA or protein sequence examined in a 

microarray experiment, using similar statistical and bioinformatic 

algorithms to analyze the experiment’s raw data, and reporting final data 

in standard formats. 

 2.2.3 Bioinformatics and In Silico Predictive Modeling 

Bioinformatics is analogous to what many scientists call computational 

biology, which means the use of advanced computing techniques to 

create databases of biological and chemical information, investigate 

relationships among data, and model biological systems mathematically. 

This report uses the term bioinformatics. 

Bioinformatics has had a turbulent history in the biopharmaceutical 

sector. The fields of genomics, proteomics, and metabolomics—aided by 

advances in instrumentation and high-throughput data analysis—have 

produced a vast amount of data for use in the early stages of drug 

discovery. Yet the various databases researchers use lack integration—

both across content types (i.e., DNA microarray data and screening data) 

and across stages of drug development. The effort to enhance 

interoperability is important for moving basic research results more 

quickly into a clinical setting. 

This research also requires analysis servers or computing clusters for 

computational biology algorithms that require multiple CPUs and large 

amounts of memory. Data format standards do not currently exist, but 

they are becoming essential, including consistent ways to record the 

outputs of microarrays and bioimaging devices and ways to describe 

experimental protocols, which can greatly increase the value of legacy 

data (Quackenbush, 2004). 

Outside of R&D, manufacturing process control and quality monitoring 

systems constitute another area where interoperable bioinformatics tools 

are needed. Reductions in lot-to-lot variation for all therapeutic 

categories may lower quality assurance costs in meeting good 

manufacturing practice standards. Additionally, data integration and 

format standards are needed to provide a link between preclinical, 

clinical, and postmarket safety and efficacy studies. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-11 

In silico (computer-based simulation) modeling may be applied to 

therapeutic proteins, vaccines, and cell and gene therapies. In silico 

models can be used for target validation and lead identification, 

predicting the ability of compounds to bind effectively at the appropriate 

site. By using results from previous in vitro or animal tests, computer 

programs build metabolite models that are virtually screened against 

known compounds to “test” interactions (McGee, 2005b). 

The main advantages of in silico modeling are the high throughput and 

low labor and materials costs. These features make it attractive for 

screening thousands of lead compounds in a matter of hours, and doing 

this earlier in the process allows researchers to prioritize compounds for 

development. Although in silico modeling will not replace in vivo models 

(in whole organisms, such as mice or larger animals), it could improve 

the quality of compounds being tested in vivo, reducing failure rates and 

lowering overall costs. 

Costs may be substantial, given that failures due to toxicity, for example, 

range from 30% to 40% (McGee, 2005b). The need for predictive 

toxicology is more pressing than the need for predictive absorption, 

distribution, metabolism, and elimination (ADME) for candidate drug 

compounds (where in silico tools are reportedly regularly in use), and this 

had been expressed in increased research expenditures and workshops, 

such as the NIH Summit Workshop on Predictive Drug Toxicology, held 

in June 2004. 

 2.2.4 Molecular Biomarkers 

Biomarkers are molecular or cellular indicators of susceptibility to a 

disease or condition. Researchers are actively seeking out and defining 

biomarkers in the hopes of using them to make drug discovery and 

testing more efficient. Predictive biomarkers may one day serve as 

surrogate endpoints to shorten clinical trials or stratify patient populations 

by identifying those individuals with certain predispositions toward either 

responsiveness or adverse reactions. In earlier stages, biomarkers may 

allow drug makers to optimize their selection of drug candidates, 

avoiding compounds that show early signs of liver toxicity, for example. 

Molecular biomarkers are especially cross-cutting in nature, because 

they have applications throughout discovery, preclinical development, 

clinical trials, and potentially manufacturing and postapproval market 

penetration. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

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Biomarkers are a diverse set of biological measurements that can be 

tissue based, serum based, or image based and have equally sweeping 

applications. Types of biomarkers include 

• exposure biomarkers, which indicate a genetic predisposition to 

develop a disease; 

• disease biomarkers, which measure a clinical outcome or 

progression of disease; 

• surrogate biomarkers, which are thought to be valid substitutes 

for clinical outcomes or results; 

• efficacy biomarkers, which measure the positive effect of the 

drug treatment; 

• toxicity biomarkers, which measure the toxic intensity of a drug in 

the body; and 

• target biomarkers, which detect the interaction between a drug 

and its desired target. 

Biomarkers’ infrastructural needs relate primarily to the lack of validation 

standards (to verify these markers provide a relevant and reliable signal 

of future response) and the need for more sensitive detection. At this 

point, speculating on the exact impact that improvements in biomarker 

validation and detection would have on the industry is difficult, but the 

most expensive phases of therapeutic drug development are prime 

targets improvements in this new area of technical infrastructure. 

For example, establishing predictive biomarkers as surrogate endpoints 

could allow drug sponsors to reduce the lengths of evaluation periods in 

clinical trials. Moreover, biomarkers could be used to identify 

subpopulations of trial patients who are predisposed to be responsive to 

the drug or to have toxicity complications. 

This information, if validated, could be used by sponsors to stratify 

patient populations and better target those who could benefit from the 

therapy. By excluding certain types of patients from trials, sponsors could 

lower the incidence of adverse effects and increase their approval rates, 

thereby getting useful therapies to market, even if they only help niche 

populations. Even earlier in the development process, improved 

sensitivity in biomarkers could allow drug makers to optimize their 

selection of drug candidates, avoiding compounds that show early signs 

of liver toxicity, for example. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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 2.2.5 Commercial Manufacturing 

The manufacturing process for biopharmaceuticals is more complex than 

the process for traditional small-molecule pharmaceuticals. This TFA 

addresses some of the most pressing issues related to the technology 

infrastructure supporting manufacturing. It includes the need for better 

models of scale-up to ease the expensive and often unpredictable 

transition between clinical trial volumes and the larger quantities needed 

for commercial-scale production. Needs are also associated with new 

upstream processing platforms and bottlenecks in the downstream 

processing stage. 

This RTI analysis considered integration and interoperability issues 

related to implementing Process Analytical Technology (PAT) to enable 

better monitoring and control throughout the production process. The 

PAT initiative is part of a larger regulatory guidance document released 

in 2002 entitled “Pharmaceutical CGMPs for the 21st Century: A RiskBased Approach.” This regulatory guidance document was developed 

through a collaborative effort involving FDA’s CDER, the Center for 

Veterinary Medicine (CVM), and the Office of Regulatory Affairs (ORA). 

Informing this process were extensive public discussions at the FDA 

Science Board, the Advisory Committee for Pharmaceutical Science 

(ACPS), the PAT-Subcommittee of ACPS, and several scientific 

workshops. Discussions covered a wide range of topics, including 

opportunities for improving pharmaceutical manufacturing, existing 

barriers to innovation, and potential approaches for removing both real 

and perceived barriers (FDA, 2004b). 

 2.2.6 Postmarket Surveillance 

Postmarket surveillance refers to adverse event reporting and monitoring 

the safety and efficacy of drugs. Concerns about safety and product 

quality are obviously elevated when the product is a human therapeutic. 

The technology infrastructure supporting postmarket activities may 

reduce transaction costs and lower barriers to adoption by helping 

alleviate those concerns. 

Some drugs are given FDA approval, but FDA requires ongoing longterm clinical trials to monitor safety, efficacy, and adverse reactions in 

patients. Known as Phase IV testing, this postapproval clinical phase 

may have several objectives, such as 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-14 

• comparing a drug’s performance to others on the market, 

• monitoring long-term effectiveness and impact on patient quality 

of life, and 

• determining the cost-effectiveness of a drug therapy relative to 

alternatives. 

This TFA considered potential improvements to quality assurance testing 

to provide confidence that products are pure and potent. It also 

addressed the infratechnologies that drug sponsors rely on when 

monitoring product performance in the marketplace. 

 2.3 TRENDS IN INFRASTRUCTURE RESEARCH 

AND DEVELOPMENT 

The stages of drug development and commercialization provide a natural 

framework for understanding the total cost of the drug life cycle and 

understanding where bottlenecks or excessive costs may hinder 

efficiency and growth. Figure 2-2 illustrates the process and identifies 

several of the major technical infrastructure categories that influence 

each stage. Many of the same infratechnologies are used throughout the 

five general stages. However, the implementation of the technology is 

somewhat specific to each stage; hence, the infratechnology needs may 

be different. This figure highlights the fact that advances in screening 

capabilities in the preclinical stages can have significant impacts on 

research costs during the clinical and bulk manufacturing stages. 

Identifying problems earlier in the process increases the probability of a 

drug’s success and hence reduces costs incurred from subsequent 

stages. 

This section reviews some of the common methods employed in each 

stage of drug development and discusses technology infrastructure 

improvements under way today. The diversity of biopharmaceuticals 

parallels that of the technology infrastructure supporting their 

development. As mentioned previously, this report simplifies the 

presentation of the industry’s diversity by focusing on therapeutic 

proteins. Many of the processes, technologies, and trends discussed 

below may be relevant for some biopharmaceutical products but not for 

others. 

Section 2.4 complements this section by summarizing stakeholders’ 

views on technology gaps, and Section 2.5 addresses obstacles the 

marketplace sets in the way of developing what otherwise would be 

socially beneficial advances. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-15 

Figure 2-2. Summary R&D Framework for Human Therapeutics 

250 compoundsb

Basic R&D and Drug Discovery

 Basic research in biology, genomics, proteomics, 

and metabolomics to suggest targets

 Target identification

 Target validation

 Lead identification

 Lead optimization

Preclinical 

Development 

In vitro, in vivo, and in 

silico pharmacokinetic 

(ADME), toxicity 

studies

Clinical Trials

 Phase I: safety

 Phase II: safety, 

efficacy

 Phase III: long-term 

safety, efficacy

Market Penetration

 Adverse event 

reporting and postmarket surveillance

 Handling, storage, and 

distribution

Scale-Up

Bioavailability studies

Process engineering

Manufacturing

Upstream processing 

involving cell culturing 

or fermentation

Downstream 

processing, 

purification via 

chromatography

Quality control using 

LC-MS, GC-MS, 

spectroscopy

1 approved

NDA or BLAb

 High-quality protein and gene 

databases

 Data integration and 

interoperability

 Mass spectrometry, patch 

clamp, and high-resolution 

bioimaging

 High-throughput screening, 

and in silico models

 Standards across gene 

expression platforms

 High-throughput and highcontent screening

 Bioimaging to monitor ADME 

and in vivo response

 Structural biology data to 

inform algorithms used in 

predictive in silico models

 Data formats and standards 

to capture experimental 

conditions and results

Validated databases 

compliant with FDA 

guidelines

Formats and standard 

reporting statistics, 

compatible with preclinical 

and clinical trial data

Sterility, protein degradation 

studies to inform standards 

for storage and handling

 Lower cost of 

development

 Increased probability 

that an IND coming 

out of this stage is 

ultimately successful

 Lower cost of 

discovery

 Increased probability 

that a compound 

selected at this stage 

for preclinical 

development is 

ultimately successful

Stages of Biopharmaceutical Supporting Infrastructure: a Development:

 Lower cost of clinical 

trials

 Reduced probability 

that an approved 

drug coming out of 

this stage will not be 

recalled later 

(through identifying 

subpopulation risks)

 Qualification of surrogate 

endpoints (biomarkers and 

bioimaging)

 Validation of standard 

protocols for microarray 

experiments

 Good Clinical Practice (GCP) 

standards

 Lower costs of postmarket surveillance, 

holding quality 

constant

 Reduced waste due 

to protein 

denaturation or 

product expiration

5 INDsb

Potential Impacts:

5,000-10,000 compoundsb

a

 As described in Chapter 1, this study focuses on the segment 

of the biopharmaceutical industry involved in producing 

products for therapeutic applications, including recombinant 

proteins, monoclonal antibodies, and gene and cell-based 

therapies.

b These numbers reflect typical ratios of initial compounds 

screened and Indus filed to NDAs/Biologic License Applications 

(Blass) approved. 

Source: Pharmaceutical Research and Manufacturers of America 

(Parma) March 2005. Pharmaceutical Industry Profile 2005. 

Washington, DC: Parma.

 2.3.1 Drug Discovery 

The technical infrastructure supporting drug discovery is diverse and 

cross-cutting in nature. Infratechnologies used in target identification 

include gene expression microarrays, bioinformatics databases, and 

computer algorithms that search for proteins related to known drug 

targets that are different but perform related functions. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

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This stage in the development cycle involves four primary phases: target 

identification, target validation, lead identification, and lead optimization. 

Target identification marks the beginning of the discovery process. 

Researchers use their understanding of molecular biology, genomics, 

and proteomics to identify pathways to disease or disease “targets.” 

Once identified, a drug target must be characterized and validated 

through gain-and-loss-of-function studies of the target protein in the 

disease process. Targets are validated using animal models imitating 

disease states as an in vivo method to demonstrate the relevance of the 

target protein in the disease process. 

In lead identification, compounds are identified that have the ability to 

affect the activity of a target (e.g., a protein). Various screening 

techniques are applied to develop a list of potential leads. Screening is 

followed by further testing to fully characterize each lead’s properties. 

Leads must meet certain criteria specified by the research team, 

including pharmacodynamic properties (e.g., efficacy, potency, and 

selectivity) and pharmacokinetic properties (e.g., metabolic stability, 

toxicological aspects, chemical or physiological optimization potential, 

and patentability). Those leads demonstrating the highest probability of 

success—typically 250 optimized leads for every one approved drug, 

according to PhRMA (2005)—then enter the preclinical stage. 

Lead identification relies on large compound libraries used to determine 

lead compounds using high throughput screening that uses solutionbased biochemical assays or cell-based assays. In silico, or virtual 

screening, is an alternative technology that uses three-dimensional 

modeling to simulate real or predicted reactions between the target 

protein and lead compounds. Empirical scoring then ranks the lead 

compounds by how well they bind to the target protein (Giersiefen, 

Hilgenfeld, and Hillisch, 2003). In silico screening is cost-effective and 

allows for high throughput. However, the impact of this method is 

constrained by the availability of structural data for both the target protein 

and the ligand (compound) under investigation. 

Mass spectrometry is a leading analytical technique in the drug discovery 

process, and rigorous calibrations are required to ensure it meets the 

tolerances required in biopharmaceutical research. 

Proteomic techniques are becoming increasingly important in target 

identification. Proteins expressed at different levels between diseased 

and healthy tissues suggest that the protein may be a causal agent and 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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therefore could be a potential drug target (Giersiefen, Hilgenfeld, and 

Hillisch, 2003). 

Although there is considerable interest in using proteomics-based 

approaches to drive the drug discovery process, the infancy of the 

infratechnologies for large-scale protein identification and quantitation 

presents barriers to its adoption. Recently, a large number of start-up 

organizations were formed with the objective of using proteomic 

techniques for biomarker-driven drug discovery (GeneProt and Large 

Scale Biology are two examples). It is telling that both of these 

organizations have either eliminated or greatly diminished their 

proteomic efforts. 

Research efforts through NIH Roadmap initiatives are addressing some 

of the technology gaps present in drug discovery through advances in 

bioinformatics, bioimaging, and in silico modeling. To reduce costs and 

improve the efficiency of drug discovery and development, the 

pharmaceutical industry has placed a greater emphasis on early ADME 

and toxicology evaluation. Researchers are attempting to address ADME 

and toxicity issues earlier by using in silico methods in the lead 

identification and optimization processes (NIH, 2006). These methods 

employ a collection of infratechnologies, spanning chemical 

bioinformatics (e.g., chemical libraries), molecular modeling, and 

structure prediction. 

NIST is developing standards in gene expression analysis across 

multiple gene expression platforms to improve reproducibility and reduce 

the uncertainty of experiment results across multiple expression 

platforms. These efforts are directed at enhancing the efficiency of target 

identification studies through standard reference materials and 

experiment protocols. 

 2.3.2 Preclinical Development and Testing 

Once a lead compound has been identified, a number of preclinical 

studies must be conducted before testing the drug therapy in humans. 

Researchers perform experiments to determine the compound’s 

biochemical and physiological effects and properties. 

These tests are primarily performed in vitro or in vivo. Although in vitro

studies are usually cheaper and easier to control, in vivo studies using 

animal models are better suited for understanding how the drug or 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-18 

device will interact with an entire biosystem. Increasingly, testing can be 

conducted in silico using computer models to predict behavior. 

At the same time that researchers are investigating the pharmacological 

properties of the drug candidate, they are beginning to develop the 

process for manufacturing the compound. For conceptual reasons, we 

discuss the issues involved with formulation and process design in the 

section on manufacturing. 

Researchers use a range of analytical instruments, molecular 

biomarkers, assays, and other tools and technologies during the 

preclinical stage of drug development. In preclinical and clinical studies 

mass spectrometry and immunoassays are the principal detectors for 

quantitative analysis of drug levels in biological samples. 

Another set of tools used for determining a candidate drug’s efficacy and 

safety during the preclinical testing phase involves biomarkers. For 

example, alanine aminotransferase (ALT) and aspartate 

aminotransferase (AST) are two enzymes that have worked well in 

detecting toxicity in the kidney and liver (McGee, 2005d). Information 

received on interactions at the preclinical stage can have implications for 

how a candidate therapeutic will react in the human body. 

Bioimaging techniques, which have traditionally played a role as a 

diagnostic in clinical testing, are moving into the preclinical stage. PET is 

being used to track the ability of a given therapy to modify tumor growth 

and magnetic resonance imaging (MRI) is used to study lungs, joints, 

and brain tissue. 

Several initiatives, including those driven by the FDA Critical Path and 

the NIH Roadmap, have called attention to the infrastructure needs 

related to preclinical testing technologies; however, these are only a 

beginning and the technical barriers remain. Industry participants have 

noted that current imaging technologies may require higher throughput 

and better resolution to be used effectively for preclinical testing 

(Chapman, 2005). Thus, the Molecular Libraries and Imaging component 

of the NIH Roadmap has identified predictive toxicology as an important 

piece of the technology infrastructure needing further development. 

The NIH Roadmap cites structural biology as a priority. Structural biology 

will make important contributions in the area of in silico drug design and 

lead optimization by providing scientists with accurate structural data of 

proteins representing potential targets or biomarkers. But researchers 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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consider the necessary computations expensive and time consuming. 

Innovation in structural biology could provide foundations for in silico

drug design and predictive toxicology because they permit scientists to 

simulate how a given drug molecule will interact (or “dock”) with the 

binding site of a target protein. 

Other areas of need include protocols for sample preparation and for 

recording experimental data. In addition to documenting quantitative 

results of experiments, researchers must record the qualitative 

conditions under which the experiments were conducted. 

 2.3.3 Clinical Trials 

After developing a promising therapy, a developer will submit an IND 

application to FDA. Gene therapies, cellular products, vaccines, and 

plasma-derived products are filed with the Center for Biologics 

Evaluation and Research (CBER), while protein therapies, such as 

monoclonal antibodies, cytokines, enzymes, and growth factors, are filed 

with the Center for Drug Education and Research (CDER). 

Regardless of the product candidate, clinical trials are typically 

conducted in three phases. First, the therapy enters Phase I trials 

designed to determine drug safety and dosage. Specifically, investigators 

monitor how the drug behaves in humans using a small group of healthy 

individuals. Efficacy, drug interactions, and side effects are evaluated on 

a larger sample of patients during Phase II. In Phase III, trials are 

expanded to verify efficacy and monitor long-term effects in 1,000 to 

5,000 patients. 

A wide array of infrastructure technologies are currently used to monitor 

volunteers’ responses during clinical trials. In addition to standard 

laboratory tests, the use of bioimaging and biomarkers shows promise as 

ways to evaluate drug safety and efficacy. 

Part of a biomarker’s value lies in its ability to identify patient 

subpopulations. In particular, a biochemical compound or substance may 

indicate whether an individual is likely to respond positively or adversely 

to treatment, and this information may be useful in shaping the 

composition of a clinical trial’s patient population. Alternatively, if 

biomarkers can be linked to clinical outcomes, or endpoints, then their 

presence can be used as a surrogate and, in theory, allow trial lengths to 

be shortened. Although laboratories have measured biomarkers as part 

of clinical trials and used them to do internal assessments, these 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-20 

markers lack the FDA validation that would be required to formally use 

them as surrogate endpoints. 

Likewise, imaging technologies have the potential to serve as biomarkers 

or surrogate endpoints, but further qualification is required. The industry 

recognizes that bioimaging has the potential to provide quality data 

earlier in the trial phases. For example, various modalities are being 

used in cancer treatment trials to see if a tumor shrinks in response to 

the investigational therapy. PET is used to determine if the tumor is using 

less of the body’s resources, while magnetic resonance spectroscopy 

can detect chemical changes in the tumor. 

Another technology that has been discussed with regard to improving 

clinical trials is the microarray. For example, microarrays are being used 

in clinical trials to generate gene expression data to be used in 

evaluating drug safety and efficacy. However, microarrays have yet to 

achieve the necessary levels of standardization and reproducibility for 

their data output results to be acceptable to the regulatory community. 

Finally, because of the large quantities of data that must be collected, 

analyzed, and presented to FDA during the approval process, 

information technology plays an important role in the clinical trials stage. 

For example, electronic data capture (EDC) technology and tools help 

streamline the collection and submission process. 

Validation and standards are necessary for biomarkers and bioimages to 

be accepted by FDA as surrogate endpoints. The National Cancer 

Institute (NCI) calls for improving on this methodology with validated 

clinical trial-acceptable standards. Janet Woodcock, the FDA’s Deputy 

Commissioner for Operations, suggests that, in general, biomarker 

qualification for formal use in the drug approval process will require 

collaborative action. Existing data must be pooled and analyzed, 

followed by identifying gaps in the understanding of marker performance 

and executing studies or add-on trials to obtain new data (Woodcock, 

2005). 

Such a collaborative process is under way to provide validation and 

standards for microarray expression data, such as the MicroArray Quality 

Control (MAQC) Project and the External RNA Control Consortium 

(ERCC). These groups seek to establish an environment for future 

standardization by analyzing comparability among platforms. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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 2.3.4 Scale-Up and Commercial Manufacturing 

The manufacturing process for biopharmaceuticals varies greatly from 

the process for traditional small-molecule pharmaceuticals. Generally 

speaking, production can be divided into two major stages: scale-up and 

bulk manufacturing. Scale-up includes process design, product 

specification, and optimization production. Bulk manufacturing includes 

commercial-scale upstream and downstream processing. We present 

these two stages together in this section for ease of presentation. In 

practice, the scale-up manufacturing stages are developed in tandem 

with the preclinical and clinical development stages and commercial bulk 

manufacturing occurs following FDA approval. 

The commercial production of biopharmaceuticals includes upstream 

and downstream processing. Upstream processing is defined as the 

fermentation process that results in the initial core product material. 

Once a therapeutic protein is harvested from a cell or medium, it enters 

the downstream processing stage. Downstream processing refers to the 

actual purification of the product and formulation, bottling, and labeling 

activities. 

The primary tool used in the scale-up stage of biopharmaceutical 

manufacturing is chromatography. Chromatography on a laboratory 

scale creates homogenous samples in small amounts that enable full 

characterization of a lead compound. This information provides insights 

needed to design the upstream manufacturing process. Mass 

spectrometry, gel electrophoresis, and immunological assays verify 

purity and activity. 

Process control monitoring is an important component in manufacturing 

recombinant proteins. The environmental conditions (e.g., temperature, 

acidity) of cells and surrounding media must be maintained while 

proteins are being expressed. Additional measurements must be made 

during each step of downstream processing to ensure proteins retain 

their activity and structure. 

Bioprocess measurements currently include both off-line and on-line 

measurements. Off–line process measurements include biomass, 

substrate, and product concentration valuation. On-line measurement 

values are limited to basic physical quantities that include temperature; 

culture weight, feed rates, and pH or O2 concentrations. Because of a 

lack of adequate online measurement sensors these measurements 

must be made off line using samples that are sent to an analytical lab. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-22 

Biomass concentration values can be generated using a dry-weight 

measurement, substrate concentration relies on enzymatic analysis 

techniques, and product concentration uses chromatography or 

electrophoresis to conduct measurement. Limited sampling frequency is 

a draw back to off-line measurement, which makes it difficult to detect 

real-time changes in concentration levels and prevents manufacturers 

from making in-line adjustments to the process. Process analytics 

technology and improvements in in-line measurement devices would 

benefit the commercial manufacturing of biopharmaceuticals such as 

proteins and monoclonal antibodies. 

Chromatography separation is a critical technique used in the production 

of biopharmaceuticals to ensure homogeneity in successive output 

batches. The creation of the homogenous samples requires purification 

that typically involves three or more high-resolution chromatographic 

steps (Walsh, 2003). The purification protocol developed at this stage 

defines the procedures for pilot and full-scale commercial purification 

systems. A variety of chromatographic methods are available, including 

affinity chromatography, high-performance liquid chromatography 

(HPLC), and gas chromatography (GC). Purification represents a suite of 

tools that allow the production of a high-quality, GMP-grade product that 

is free of bacterial endotoxin or potentially immunogenic proteins derived 

from the host system. 

Maintaining quality through the manufacturing process is at the heart of 

FDA’s recent Process Analytical Technology Initiative, which has been 

incorporated into current Good Manufacturing Practice (cGMP) (FDA, 

2004a). Such guidelines and protocols play an important role in 

supporting all pharmaceutical manufacturing, including biological 

products like recombinant proteins. 

However, because of inherent differences in proteins, industry 

participants do not believe it is possible to standardize the production 

process entirely (Aldridge, 2006). For example, each protein has unique 

properties like hydrophilic nature, structure, and ability to bind to 

particular antibodies. However, it is exactly this set of characteristics that 

determines the best approach to purification, so a broad knowledge base 

must be developed to optimize production. 

 2.3.5 Postmarket Surveillance 

The important and complex issues associated with biopharmaceutical 

discovery and development do not end when a product receives FDA 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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approval and is manufactured on a commercial scale. Postmarket 

surveillance, in which manufacturers and regulatory agencies monitor the 

quality, safety, and efficacy of a therapy, is an essential component of 

the process. 

Once clinical trials are complete, the biopharmaceutical drug or therapy 

sponsor submits a drug application (i.e., NDA) or biologic license 

application (BLA) containing all scientific information collected during 

preclinical and clinical studies. Among other documents, the sponsor 

must submit an integrated summary of efficacy (ISE) and an integrated 

summary of safety (ISS), which in combination document the benefit-risk 

trade-off for the candidate. For some therapies, FDA will approve the 

drug provisionally and will require additional “Phase IV” studies to 

evaluate long-term effects. Even if approval is not made conditional, 

sponsors must produce a Periodic Safety Update Reports (PSUR) 

quarterly for the first 3 years and annually in years that follow. These 

updates incorporate adverse drug reaction (ADR) cases and alert FDA to 

potential safety issues that may prompt a reevaluation of therapy 

labeling. 

Such postmarket surveillance for safety and efficacy, also known as 

pharmacovigilance, will always be necessary despite even the most 

rigorous preclinical and clinical testing phase evaluations. These earlier 

phases usually will have involved only a few thousand human patients, 

and certain therapy responses or interactions are so statistically rare that 

they only manifest once the therapy reaches the large market population. 

The costs of collecting such data and producing these reports can be 

sizeable, but the private and social costs of an unsafe therapy that must 

be recalled or remarketed under restricted criteria are much more 

substantial. 

A concern related to the postapproval performance of a 

biopharmaceutical involves stability of the therapy and the conditions 

under which the therapy must be handled, stored, and dispensed to 

remain effective. Unlike traditional small-molecule pharmaceuticals that 

are usually dispensed in pill form, biopharmaceuticals are almost 

exclusively administered via injection. 

Proteins by their nature can be unstable and subject to denaturation (i.e., 

unfolding of the protein structure) when lyophilized or exposed to 

extreme temperatures or agitation during shipping. Some therapies are 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

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subject to light degradation or adherence to glass or plastic containers, 

which limits effectiveness. 

There is also the issue of shelf life. Proteins stored in liquid form are less 

stable and have considerably shorter shelf lives than traditional 

pharmaceuticals. They can show greater stability if stored in a freezedried form for later reconstitution, but care must be taken during the 

freeze-drying process to prevent degradation and ensure proper protein 

folding upon reconstitution. DNA material, tissues, cell-based products, 

and other biologics are also sensitive to improper storage and handling. 

Many of the technical methods used to evaluate safety and efficacy of a 

biopharmaceutical during clinical trials can also be used to monitor its 

safety and efficacy in the marketplace following approval. That is, the 

same laboratory tests and techniques can be used to determine whether 

a patient responds favorably to treatment. Drug manufacturers do not 

intend to document the response of every patient; instead, they rely on 

reports of ADRs from consumers and health care providers. 

In the case of monoclonal antibodies, there will be a significant need for 

improved immunogenic testing to evaluate patients’ immune system 

response to the foreign substances measured by the production of 

antibodies specific to the biopharmaceutical. If an immunogenic 

response is triggered, the biopharmaceuticals efficacy may be reduced 

or mitigated entirely. Standardized immunogenic testing would provide 

reliable and comparable information on patients’ response to treatment 

and help mitigate this measurement technology barrier to the use of 

monoclonal antibodies. 

Validated databases, managed either by the drug/therapy sponsor or an 

outsourced provider, are essential. These must comply with FDA’s 

electronic record requirements outlined in Title 21 CFR Part 11 and the 

International Conference on Harmonization (ICH) E2B data guidelines on 

clinical safety data management. Adverse experiences collected into 

these databases must also be submitted to FDA’s Adverse Event 

Reporting System, an E2B-compliance database that also tracks ADRs 

reported by consumers and health care professionals via MedWatch. 

The primary infrastructure needs associated with postmarket surveillance 

involve standard data formats, data management protocols, and 

statistical analysis models. A certain degree of interoperability is required 

to submit data collected by a biopharmaceutical firm’s or subcontracted 

organization’s to FDA’s safety surveillance databases. In addition, 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

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conversations with a postmarket drug safety services provider suggest 

that time and cost could be saved if the data standards used 

postapproval were compatible with those used in clinical trials and even 

preclinical stages. Some suggestions are provided by the following 

FDA’s guidelines for industry: “Development and Use of Risk 

Minimization Action Plans” and “Good Pharmacovigilance Practices and 

Pharmacoepidemiologic Assessment.” 

Finally, it may be important to provide the infrastructure for data 

management and reporting today so that these systems will be able to 

accommodate the increasing volumes of data that may be processed in 

the future. The concept of personalized medicine, though many years 

away from implementation in a postmarket setting, may eventually 

require tracking of detailed genetic information about patients—both in 

the clinical trial phases and in the marketplace when adverse reactions 

occur (Keating, 2003). 

Health care providers often have developed their own practices for 

maintaining stability of biopharmaceuticals. For example, they may 

determine that the maximum time they are willing to store a reconstituted 

protein is 72 hours after preparation. However, Crommelin and Sindelar 

(2002) argue that these guidelines are somewhat arbitrary because, in 

theory, a sterile product reconstituted under asceptic conditions should 

remain sterile for a longer period of time (depending on other 

compatibility issues). Thus, they identify the potential need for sterility 

studies to determine the acceptable storage period for a protein product 

once reconstituted—studies that would reduce unnecessary waste 

(Crommelin and Sindelar, 2002). 

Aggregation monitoring studies are also needed during the 

manufacturing process and as part of a stability testing program. 

Aggregation is one factor that has been commonly associated with the 

biopharmaceutical immunogenicity. Aggregation occurs when proteins 

interact to form large clusters of molecules. Several methods exist to 

characterize and analyze the level of protein or viral vector aggregation 

with a biopharmaceutical solution. However, no standardized 

measurement system exists for conducting aggregation studies for 

existing or future biopharmaceuticals. Improvements to aggregation 

methods could minimize the risk of immunogenicity as well as improve 

our understanding of the effects of environmental conditions during 

biopharmaceutical manufacturing and storage. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

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 2.3.6 Relevant Federal Technology Infrastructure 

Development Efforts 

A number of federal agencies have funded public investments in 

technology infrastructure, including the National Science Foundation 

(NSF), NIH, FDA, and NIST. Some of these organizations have primarily 

contributed to the basic science underlying the industry’s technology 

platforms, while others have been more active in supporting generic 

technology and infratechnology research. 

NSF’s and NIH’s investments have enriched the basic science and 

applied research disciplines that underlie the biotechnology industry. In 

addition to biology and chemistry research that informs drug discovery, 

NSF’s support of engineering, physics, and computing contributes to the 

technical infrastructure knowledge base (e.g., for mass spectrometry, 

imaging, and microarray production). Much of this support is through 

funding of university researchers, who also receive private and state 

funding for these activities. NIH’s research emphasis is more applicationspecific because it concentrates on understanding disease mechanisms. 

Some of its institutes, such as the National Institute for Biomedical 

Imaging and Bioengineering (NIBIB), also conduct generic technology 

research that the biopharmaceutical industry uses as new technology 

platforms that lead to innovative drugs through private applied R&D. 

In 2002, NIH took a step further in addressing the needs of the medical 

industry, including biopharmaceuticals, with its Roadmap for Medical 

Research. The overall goal of the roadmap is the promotion of 

translational research, which focuses on bringing basic research results 

more quickly to clinical settings. Several of the Roadmap’s initiatives 

address gaps in the technology infrastructure along the path to drug 

discovery and therapy development. The total budget for the NIH 

Roadmap’s stated research initiatives has grown from $239,716,000 in 

FY2005 to an appropriation of $329,462,000 for FY2006. 

Complementing the NIH Roadmap initiative for translational research is 

FDA’s initiative for critical-path research, which emphasizes efforts to 

improve the product development process itself through better evaluation 

tools. The most recent budget request for FY2007 calls for $5.94 million 

(out of a total of $1.95 billion requested by the agency) to support its 

Critical Path Initiative. These funds are intended to support partnerships 

and targeted research to “modernize medical product development” 

(FDA, 2006). Examples of these partnerships include a medical imaging 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-27 

initiative to validate PET as a surrogate endpoint in cancer drug clinical 

trials and an electrocardiogram data warehouse to help address safety 

issues during trials and in postmarket review. 

The technology research being conducted through the NIH Roadmap 

and FDA Critical Path Initiative calls for supporting infrastructure 

investments, particularly in measurement, data, standards, and models. 

As discussed in the following two sections, NIST could play an important 

role in developing these infratechnologies through its Chemical Science 

and Technology Laboratory (CSTL) and vast expertise in developing 

standards. 

 2.4 TECHNICAL BARRIERS TO AN IMPROVED 

INFRASTRUCTURE 

This section synthesizes the comments study participants made during 

in-depth interviews when asked about technology gaps that inhibit their 

work currently and technical problems they foresee on the horizon. 

Chapter 4 discusses primary data collection efforts in detail, but 

stakeholder comments are presented in this earlier section to articulate 

views on the infrastructural needs in advance of developing the cost 

model that quantifies potential efficiency gains. 

 2.4.1 Bioimaging 

Increasingly, FDA is requesting imaging studies to be included in the 

regulatory submissions for new drug approvals. However, respondents 

indicated that there is inadequate technical infrastructure in place to 

support a large-scale adoption of imaging in preclinical and clinical trials 

industry-wide. Bioimaging experts interviewed during this study identified 

technical barriers and efforts to overcome them. These included the 

following: 

• Internal Standardization Efforts. Maintaining consistency in the 

processes to capture imaging data is critical to the validity of 

imaging study results. Firms must develop calibration procedures 

that are consistent across multiple imaging devices, among 

images taken by the same device, and images taken across 

time. 

For example, a Phase III medical imaging study may include 

1,000 patients at 100 different clinical sites. A dispersed data 

collection strategy demands strict adherence to study protocols 

for calibration or imaging equipment as well as anatomical 

positioning of research subjects. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-28 

• Internal Evaluation and Validation. The software used to 

interpret images has not been able to keep pace with the rate of 

assay development in high-content screening. Experts stated 

that imaging device manufacturers have a “box” mentality and 

they have not provided adequate image interpretation. 

This is particularly important in molecular imaging where no 

generic software tool can be applied universally to all cell-based 

assays. Commercially available systems are only suitable for a 

small number of assay types, leading researchers to develop 

customized computational algorithms to interpret images and 

meet their specific needs. 

One scientist interviewed suggested that developing an 

automated computer algorithm for a specific assay type could 

take anywhere from 1 week to 1 year to complete depending on 

the complexity of the assay developed and the cell type. 

• Quality Control Programs and Tools. Researchers spend time 

aligning data definitions in interpretation software because of 

differing taxonomies surrounding medical or anatomical regions 

of images (e.g., a specific cell, receptor, or lobe of the brain). 

• Reagents. Imaging assays may require contrast agents to reveal 

bioactivity at the drug target site or to follow the drug as it moves 

through the body. These reagents must be purchased or 

produced internally. Bioimaging reagents—chemical solutions 

that carry out chemical or enzymatic reactions—require 

advanced chemistries that may be underdeveloped within an 

organization. 

• Data Transfer and Archival Systems. Imaging data are 

transferred from clinics or labs to several different sites for 

analysis, interpretation, and validation. Currently, no online 

digital transfer system exists to securely send imaging data 

across the Internet. As a result, firms rely on couriers to transfer 

digital imaging data from the clinical sites to their internal sites 

for analysis. 

• Data Management in a Regulatory Environment. Long-term 

data storage and retrieval systems must be created to archive 

imaging data. Current image file size is between 500 megabytes 

(MBs) and 10 gigabytes (GBs). File sizes will trend toward 120 

GB over the next few years. 

Storage capacity quickly becomes an issue when dealing with 

large files in a regulatory environment. Image files that are 

changed or manipulated are always saved as new files to 

maintain transparency and documentation for FDA review, 

thereby multiplying storage requirements. 

• Industry Comparability and Testing Programs and 

Standardization Efforts. Researchers follow or participate in 

efforts that seek to study and evaluate comparability issues or 

that seek to establish industry standards. For example, Digital 

Imaging and Communications in Medicine (DIACOM) is an 

industry committee working to standardize clinical imaging data 

formats and transfer procedures. Differences in transfer 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-29 

procedures lead to data comparability and integration during 

later analysis stages. 

 2.4.2 Gene and Protein Expression Analysis 

Study participants provided consistent accounts of the benefits and 

challenges of gene expression and proteomics analyses, particularly in 

an era of production-style genomics studies. Problems often arise from a 

lack of consistent agreement among technology platforms (e.g., 

microarrays vs. flow cytometry), manufacturers (e.g., Affymetrix v. 

Agilent), laboratories, time, and protocols, among other factors. One 

interviewee characterized the situation as “distressing” because the 

platforms and laboratory infrastructure are expensive; however, the 

implications of carrying poor data forward in the discovery process dwarf 

those costs. 

In the emerging genomic era, firms frequently find themselves in a 

position that is at odds with classical statistical studies: studies used to 

have few variables but many replicates. Although commoditization is 

bringing microarray experiment costs down, they remain sufficiently 

expensive at $500 to $1,000 per assay that few replications are run 

(O’Connor et al., 2007). Microarrays yield huge volumes of data 

variables, but cost limitations prevent a large number of data points for 

each variable. 

Internal standardization initiatives at many firms are driven by the need 

to increase the confidence researchers have in the data output because 

of the many assumptions that must be made. Some of these 

assumptions are as follows: 

• the concentration of the gene of interest in the sample is 

sufficient, 

• there is little competition between the genes of interest and the 

probes on the microarray, 

• there is a linear relationship between fluorescence and gene 

expression, 

• the microarray is accurate and true, and 

• the microarray scanner is calibrated correctly and reading the 

fluorescence correctly, among many other assumptions. 

Differences between assays emerge because laboratories (even within 

the same organization) may have different equipment, use different 

calibration and assay protocols, or run different analyses on data output 

using different analytical methods. Other factors that complicate the 

reproducibility and reliability of results include the volatile nature of 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-30 

enzymes, tissue degradation, and the amount of noise inherent in gene 

expression assays. Gene expression studies are subjective, and human 

error can be introduced into the experiment during multiple steps. 

Firms are aware of the above limitations to gene expression studies and 

have responded by standardizing internally and investing in tools and 

programs that increase their confidence and assurance in the accuracy 

and quality of their experiments. 

Interviewees discussed the following efforts: 

• Internal Standardization Efforts. Most firms have established 

uniform internal protocols and operating procedures that labs are 

required to follow that dictate proper sample preparation, 

handling, assay, and data output procedures. However, these 

often differ among firms, inhibiting collaboration. 

• Internal Evaluation and Validation. Formal and informal 

committees review processes, materials, and protocols to 

evaluate or validate best practices. For example, one firm 

identified 15 different analytical methods that were appropriate 

for evaluating microarray data output. Because each method 

yielded different results, the firm sponsored a project that 

evaluated all methods, selecting the most appropriate method. 

Standards would have greatly facilitated the process by reducing 

the number of replicated assays needed. Another example 

includes a firm that analyzes each microarray it receives from its 

supplier for “dim spots” and other quality defects. 

• Quality Control Programs and Tools. Firms research, develop, 

implement, and maintain quality control programs and tools. 

Tools include algorithms that evaluate data quality and Webbased tools that track real-time data on analyses. This activity 

category would exist even in the absence of standards; however, 

respondents indicated that efforts would be more productive and 

efficient if industry standards were established. One company 

develops new quality control metrics for each new array type it 

receives. 

• Reference Materials. Many firms purchase from a limited 

number of providers or develop in-house reference materials that 

are “spiked into” samples. These reference materials have 

known values, which can be used as benchmarks. However, the 

materials that are available do not reflect “real-world” sample 

complexity, even though they provide researchers with some 

degree of confidence in the accuracy of their instruments. 

• Industry Comparability and Testing Programs and 

Standardization Efforts. Researchers follow or participate in 

efforts that seek to study and evaluate comparability issues or 

that seek to establish industry standards. For example, the 

Microarray Quality Control (MAQC) Project is evaluating the 

comparability of analysis results across microarray platforms 

(brands), laboratories, and time. 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-31 

Table 2-1 illustrates the labor effort one large microarray laboratory 

devotes annually to its technology infrastructure. Inadequate industry 

standardization requires technical staff to proactively manage data 

quality and assurance risks on an ongoing basis. In addition, senior 

research staff attend conferences on standardization and devise 

methods for managing quality. 

The lab foresaw no changes to its quality control staff given that this 

function would exist regardless of the scope and quality of industry 

standardization. However, industry standards for protocols, sample 

management, and analysis methods and tools, such as standard 

reference materials, would lower resources assigned to positions that 

contribute to or use gene expression analyses. 

 2.4.3 Bioinformatics and In Silico Predictive Modeling 

The industry’s massive data production capacity relies on information 

technologies to manage and analyze data output, transmission and 

storage. In an ideal world, information flowing from external and internal 

sources, and across development stages (where possible), would 

converge in software applications that would evaluate drug candidates 

on an ongoing basis. 

However, interviewees reported that in reality data production capacity in 

the posthuman genome era outstrips the industry’s ability to manage, 

coordinate, analyze, and communicate the resulting data across software 

systems and technology platforms. Yet these analyses inform decision 

making on a daily basis. 

Table 2-1. Potential Labor Allocation Involved in Quality Control at One Microarray 

Laboratory with an Improved Technology Infrastructure 

Position 

Current Staffing 

Allocation (FTE) 

Allocation with 

Improved 

Standardization 

(FTE) Employee Description 

Quality Control Manager 1 1 Senior Ph.D. scientist 

Laboratory Director 0.5 0.25 Ph.D. scientist 

Informaticist 1 0.5 M.A. scientist 

Laboratory Manager 0.3 0 B.A., with 10 years’ experience 

Director of Bio Stats 0.5 0 Senior Ph.D. scientist 

Director of R&D 0.5 0 Senior Ph.D. scientist 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-32 

Visualizing and combining high-throughput data streams from multiple 

sources, including legacy data, is the standardization dilemma most 

frequently mentioned during interviews. Problems often arise when 

combining multiple data streams from different sources, even within the 

same biopharmaceutical company. Legacy data issues frustrate 

organizations with large data repositories. In many respects, start-up 

firms are better off because they can build systems from the ground up 

that use the latest in information architecture theories. 

Current efforts are fundamentally about the ability to exchange, interpret, 

and analyze data. In the words of one interviewee, a lack of standards 

“creates a constant struggle.” Nearly all interviewees voiced a need for 

“middle layer” software tools to communicate data between systems, and 

standardized ontologies do not simply describe the data but make 

statements about them and their relationship to other data. Stakeholders 

offered the following insights: 

• Internal Standardization Efforts. Expenditures on internal 

standardization efforts include establishing standard analytical 

processes, evaluating and validating existing and proposed 

systems, and troubleshooting inconsistencies in data design and 

analysis across organizational boundaries. 

• Data Translation and Communication Tools. At least two of 

the firms interviewed cited the use of customized software tools 

to communicate and exchange data between database systems. 

One company is working with its software vendor, while the other 

is developing its own in-house tools. 

Data become locked into definitions and formats when they are 

transferred from industrial-scale data production tools like 

sequencers and flow cytometers into software applications. New 

instrumentation, competing software platforms, and differences 

in how researchers prefer to define data elements combine to 

impede information sharing. 

• Industry Comparability and Testing Programs and 

Standardization Efforts. Firms fund senior research staff to 

participate or follow industry standardization efforts. The majority 

of interorganizational interaction is through ad-hoc meetings and 

semiformal working groups comprising academics and industry 

researchers. One interviewee characterized these endeavors as 

“grass roots efforts” undertaken by groups “on the cutting edge 

of research.” 

Integrating data is difficult because the processes that capture that data 

may have defined the sample parameters differently or may have used 

different terms to describe variables. One senior scientist in 

bioinformatics stated that it could take 1 to 2 weeks to capture data from 

gene expression, 3 to 4 weeks from protein expression, a few weeks for 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-33 

text mining, and then an additional 3 to 4 weeks to integrate the 

information. 

 2.4.4 Molecular Biomarkers 

When discussing activities and investments that comprise this category 

of infrastructure spending, stakeholders offered the following insights: 

• Internal Evaluation and Validation. Firms must apply gene or 

protein expression profiling techniques to validate potential 

biomarkers that may be associated with disease or drug targets. 

Following biomarker discovery, firms develop assays to test 

biomarkers’ association with a disease or drug target’s response. 

The outcomes of the association studies must then be externally 

validated. 

• Quality Control Programs and Tools. In the biomarker assay 

development process, firms must develop internal standard 

operating procedures (SOPs) and good laboratory practices 

(GLPs) associated with conducting biomarker assays. 

Similar to gene expression, firms must validate the technology 

platform and the reagents used in the biomarker assay. The 

large data outputs generated from these assays require firms to 

identify potentially new analytical techniques capable of eliciting 

information from large data sets. Some examples include cluster 

profiling, self mapping, or other relationship or pattern 

recognition analytical methods. 

• Industry Comparability and Testing Programs and 

Standardization Efforts. Firms participate in industry consortia 

that work to resolve interplatform differences, comparability of 

data sets, sample quality, user variability, and data analysis 

methodologies. These standards are often adopted by peerreviewed journals as requirements for publication of biomarker 

discovery experiments. 

Currently there is no standard or accepted process for validating 

biomarker association studies. Validation methods include 

replicating association tests with an independent patient and 

control samples or employing a second technology platform to 

confirm the initial association findings (Ginsburg and Haga, 

2006). 

 2.4.5 Commercial Manufacturing 

Participants identified that excess technology infrastructure expenditures 

in commercial manufacturing arise from data verification, validation, and 

QA/QC processes. Related to these technology infrastructures, problems 

may arise from data transfer from the development lab bench to 

production, data assembly, and interpretation at manufacturing facilities. 

Thus, an inadequate data infrastructure impedes the efficient transfer of 

scientific, process, and regulatory compliance data from R&D to 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-34 

manufacturing. These data transfer complications can result in delays in 

production and additional labor costs associated with tracking down or 

regenerating lost information. 

• Data Transfer from Lab to Production. Batch testing during 

preproduction scale-up is costly. Processes that do not transfer 

from the lab to the production floor result in multiple trial batches 

produced before the process is perfected. During process scaleup, batches are tested to ensure they meet the development 

specifications. Batches failing during production can cost as 

much as $1 million each. 

 One participant provided the example of cell culture testing. 

Current cell culture development may take 5 to 10 days to attain 

sufficient yields. Process engineers then need to know what 

factors or variables to adjust in the process to improve the yields. 

It then takes another 5 to 10 days for samples taken from the cell 

culture to be sent to an analytical department for analysis. Once 

results of the analyses are returned, process engineers repeat 

the previous steps until the process is perfected. 

• Internal Data Integration. The labor and time associated with 

data transfer, integration, validation, and continued process 

monitoring represent significant cost and productivity losses 

during the manufacturing stages of the biopharmaceutical life 

cycle. 

 Much of the industry relies on unstructured and disparate data 

systems. Such inadequate information infrastructure creates 

inefficiencies and costs due to labor and time associated with 

tracking down information, conducting redundant tests, and 

replicating analysis. 

• Supporting Multiple Proprietary Data Formats. One line 

manager interviewed said that systems and equipment original 

equipment manufacturers (OEMs) are at the root of a significant 

share of their plants’ data integration costs. Manufacturing plants 

include numerous measurement instruments and types of 

processing equipment. OEMs develop their own proprietary data 

formats for both process monitoring and diagnostic data on the 

instrument itself. 

 OEMs will only support the maintenance of purchased 

equipment if OEM proprietary data formats are supported 

continuously by the biopharmaceutical manufacturing plants. As 

a result, the plant often has redundant data systems, one to 

support the proprietary data formats of the OEM and a second 

system to integrate the multiple OEM data formats across the 

manufacturing processes. 

Manufacturing operations operate in a regulatory environment that 

requires validation of manufacturing processes. The data infrastructure 

inadequacies increase the cost of demonstrating regulatory compliance 

as well as increasing manufacturing costs by limiting firms’ 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-35 

understanding of their manufacturing processes and constraining their 

ability to efficiently conduct process monitoring. 

 2.4.6 Postmarket Surveillance 

Industry participants spend a considerable amount of time conducting 

surveillance, tracking adverse events, and filing Periodic Safety Update 

Reports for approved biopharmaceuticals. Advanced techniques for 

managing and integrating the data from these activities have the 

potential to reduce the costs of these postmarket surveillance activities, 

as well as provide the health care community with assurances that any 

unanticipated risks are detected as quickly as possible. 

Thus, technical barriers related to postmarket surveillance were primarily 

associated with data collection, data entry and analysis, quality control, 

data systems design and validation activities, training on reporting 

adverse events, and product relabeling activities motivated by safety 

issues. Specific examples include the following: 

• Data Collection and Integration. Data on adverse event 

reporting and postmarket safety studies must be collected across 

regional sites that may have different data formats, have data 

collection forms, and potentially include multiple languages. 

Firms dedicate labor and resources to developing reporting 

protocols and training clinicians on data entry. In addition to 

multiple sites reporting adverse events, a variety of data 

collection mechanisms may be used to submit data. Collection 

methods may include paper forms, automated call centers, and 

electronic data capture devices. Lack of a common data 

dictionary across sites may result in errors or delays due to 

misreporting of data. 

These factors require a staff of data entry and quality control 

technicians to manually enter paper reports into centralized 

databases and perform quality checks to ensure full integration 

of reported data with consistency across data elements. 

• Analysis. FDA requires periodic safety update reporting. 

Adverse events are investigated and the adverse event data 

must be compiled and analyzed by statisticians and a scientific 

panel. 

• Validation of systems. Data systems infrastructure must be 

constructed and validated. This can cost in the millions of dollars 

and requires 6 to 12 months to develop. Once in place, the core 

database and system must be validated through rigorous initial 

testing; testing is continued periodically to ensure functionality.
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

2-36 

 2.5 MARKET BARRIERS TO AN IMPROVED 

INFRASTRUCTURE 

Constraints familiar to researchers in the private sector hamper the 

development of the technology infrastructure, particularly the 

development of infratechnologies. Industry stakeholders discussed that, 

although overcoming technical barriers holds significant economic 

potential for the industry, resource and time constraints combine with 

competing investment priorities and poor pay-off ratios to inhibit privatesector investment. 

Interviewees stated that industry has been interested in improving 

infratechnologies for some time, and although improvements have been 

made in the last decade, much work remains to be done. Several 

semiformal groups have formed to strategize these issues, including 

ones with some measure of representation from NIST, but significant 

market barriers persist. These barriers are rooted in the following: 

• Intellectual property rights: Biopharmaceutical companies 

guard their intellectual property positions fiercely. As a result, 

when these companies participate in research consortia that 

address infrastructure issues, they can be reluctant to share 

technical knowledge, even when it has an infrastructure 

character. Companies also may question the motives of 

coalitions and industry groups that advocate industry-wide 

adoption of a standard set of procedures, protocols, ontologies, 

or data formats. 

• Financial and time commitment: Most efforts are currently 

“grass roots” efforts with inconsistent funds and schedules. 

Business and technology life cycle effects erode progress on 

topics that require dedication and long-term investment. If it 

takes 5 to 10 years, at one group’s current pace, to develop a 

standard, will that standard be relevant when it is ready? More 

funding and commitment of personnel by participating 

companies would allow acceleration of standards development, 

but the public-good nature of the needed infratechnologies 

reduces investment incentives. 

• Coordination Issues: Multiple experts interviewed told of 

instances in which one semiformal group was working on issues 

concurrently with another, without either being aware of the 

other’s progress. The extent of duplication of effort between the 

two groups was not fully known, but such occurrences illustrate 

the importance of coordination. 

In summary, “ad-hoc” standardization groups often duplicate each 

other’s efforts, “bumping into each other” in the research community. 

These groups also face hurdles posed by the business cycle, competing 

R&D priorities, and the length of commitment (of both time and 
Chapter 2 — The Technology Infrastructure Supporting the Biopharmaceutical Industry 

2-37 

resources) required to enact real progress on standardization. Also, 

because participating firms tend to rotate staff, these ad hoc groups lack 

continuity and institutional memory. 

Because of the public-good nature of technical infrastructure (its 

economic value comes from common and uniform use), economic theory 

suggests that individual firms will underinvest in such infrastructure 

because private returns to needed investments in infratechnologies are 

either low or negligible for individual companies. Lacking complete and 

equitable participation by all stakeholders means that some portion of the 

industry will “free ride” on the investments of others. This is the case 

despite collective (industry-level) private benefits resulting from 

investments in the technology infrastructure. In general, if innovators do 

not believe they will gainfully appropriate economic returns and control 

the generation of economic value from the knowledge they developed, 

they will likely avoid the risk inherent in infrastructure research. The only 

way for individual companies to capture an adequate return on 

investment in infratechnologies is to use them as proprietary protocols. 

Not only is this inefficient from an investment perspective but the 

demand (buyer) side of the market objects strenuously to the lack of 

uniform industry standards (Tassey, 2000, 2005). 

Industry stakeholders interviewed over the course of this project saw a 

natural role for NIST to accelerate the development and further the 

sophistication of the biopharmaceutical industry’s technology 

infrastructure, given NIST’s reputation and NIST’s expertise in 

standardization and chemical and biological sciences. 

3-1 

 Economic Analysis 3 Methodology 

The economic analysis has two objectives: to estimate current privatesector expenditures on biopharmaceutical technology infrastructure and 

to estimate the efficiency gains an improved technology infrastructure 

could yield the industry. 

The first objective entailed surveying the industry to capture spending 

and then extrapolating the results to estimate industry-level 

expenditures. To meet the second objective, RTI leveraged recent drug 

development cost studies and built an economic model that recalculated 

the cost of bringing a new drug to market. The model included both the 

cost of failed INDs and the time value of money. Expert opinions on 

feasible improvements in costs, stage length, and the probability of 

success were used to estimate counterfactual cost-model scenarios. 

The cost model offers two output categories: 

• the change in actual R&D expenditures, per IND and per FDAapproved drug and 

• the change in the present value of R&D expenditures. Including 

and excluding the cost of failures. 

The time value of money concept—where $1 today is worth less in real 

terms than the same nominal $1 was 10 years ago—takes into account 

investment options, inflation, and other time-based factors affecting the 

value of money. The discount rate used to calculate the present value 

can also be used to adjust for risk and uncertainty. Compressing the 

schedule of the drug development cycle greatly affects the current, or 

present, value of the cost of drug development. Thus, the model was 

developed to show both actual and present-value savings. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-2 

This chapter presents the theory and technical approach to meeting 

these two objectives. It begins by reviewing existing cost studies, 

including a 2007 DiMasi and Grabowski paper on biopharmaceutical 

drug development whose analytical results were used as the baseline 

parameters in the cost model. 

 3.1 EXISTING BIOPHARMACEUTICAL R&D COST 

STUDIES 

Few published studies focus exclusively on the R&D cost of 

biopharmaceutical drugs. Consequently, the model employed in this 

study referenced previous models constructed for traditional 

pharmaceuticals, particularly a series of studies conducted by the Tufts 

Center for the Study of Drug Development (CSDD) (DiMasi et al., 1991; 

2003).1

 These studies were updated in 2007 to account for differences in 

biotechnology drug R&D (DiMasi and Grabowski, 2007). 

 3.1.1 Small-Molecule Pharmaceutical Development Cost 

Studies 

Several research groups have attempted to estimate the cost of drug 

development over the years. The three groups whose work was most 

relevant to this analysis were the CSDD at Tufts University, the Boston 

Consulting Group (BCG), and the Global Alliance for TB Drug 

Development. Each study estimated the total cost of bringing a single 

traditional (small-molecule) pharmaceutical drug to market. 

Cost estimates ranged between $240 million and $880 million 

(cumulative in 2000 dollars) per drug developed over a period spanning 

8 to 15 years. Discovery research and clinical trials were consistently the 

two most expensive stages of R&D, while preclinical represented the 

smallest share of total cost. Table 3-1 summarizes the cost estimates for 

each study across the R&D stages. 

The total cost estimates varied in absolute terms, and the different 

definitions of cost categories make it difficult to compare estimates 

across studies. However, the issue of failure rates and the R&D stage at 

which failure occurs has a significant impact on both the total and relative 

costs measured. The three studies have significantly different 

motivations and therefore inherent structural characteristics that must be 

considered when comparing their results presented in Table 3-1. 

 

1

See also DiMasi (2002) and Adams and Brantner (2006). 
Chapter 3 — Economic Analysis Methodology 

3-3 

Table 3-1. Summary of Cost Studies: Estimated Absolute Costs and Relative Share by 

R&D Stage 

R&D Stage 

Tuftsa 

(millions) BCGb TB Alliancec

Discovery N/A $530 (60%) $60 (25%)

Preclinicald $121 (30%) $90 (10%) $10 (4%)

Clinical $282 (70%) $260 (30%) $26 (11%)

Failuree

 N/A N/A $144 (60%)

Total Cost Per Drug $403 (100%) $880 (100%) $240 (100%) 

N/A = Not reported as separate costs 

a

Source: DiMasi, Joseph A., Ronald W. Hansen, and Henry G. Grabowski. 2003. “The Price of Innovation: New 

Estimates of Drug Development Costs.” Journal of Health Economics 22:151-185. b

Source: Tollman, Peter, Philipe Guy, Jill Altshuler, Alastair Flanagan, and Michael Steiner. 2001. “A Revolution in 

R&D: How Genomics and Genetics are Transforming the Biopharmaceutical Industry.” Boston: The Boston 

Consulting Group. Available at: http://www.bcg.com/publications/files/eng_genomicsgenetics_rep_11_01.pdf. 

Obtained on January 12, 2006. c

Source: Global Alliance for TB Drug Development. 2001. “The Economics of TB Drug Development.” Nancy Pekar 

(ed.) Available at: http://www.tballiance.org/3_2_C_BalancingIncentivesandAccess.asp. As obtained on January 

10, 2006. d

Tufts preclinical cost includes the costs of drug discovery research. e

Tufts and BCG incorporated failure costs into the costs by R&D stages. 

The CSDD at Tufts, a research group funded by members of the 

pharmaceutical industry, focused on the cost of late-stage clinical trials. 

The study is based on a dataset of drug candidates in clinical trials. For 

this reason, the clinical-stage costs are highly detailed with low 

uncertainty, while a more approximate approach was employed to 

quantify the cost estimates in the discovery and preclinical stages using 

annual aggregated R&D expenditure data over a 20-year period. 

The BCG report is primarily focused on new genomic approaches and 

demonstrating the potential cost savings that these technologies might 

bring to drug development. As discussed earlier, most benefits from 

genomics and proteomics research trajectories would occur in the earlier 

stages of the drug development process. Given the focus of the BCG 

report, costs may be overreported in the earlier stages of development. 

Finally, the Global Alliance report (the most detailed in cost categories) 

was focused on estimating the development cost of an orally ingested TB 

drug using a conventional small-molecule approach. The costs related to 

small-molecule drug development can be significantly different from the 

costs of biopharmaceuticals using protein-based approaches to 

development. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-4 

 3.1.2 Large-Molecule Biopharmaceutical Development Cost 

Studies 

It is not the intent of this study to estimate the cost of developing a new 

biopharmaceutical product, but rather to estimate the potential an 

improved infrastructure holds for the industry. Thus, this study relies 

heavily on a recently completed study by DiMasi and Grabowski at 

CSDD (2007). Given the importance of this study to results presented in 

Chapter 5, the DiMasi and Grabowski study is discussed in detail below. 

Recent studies have suggested that biopharmaceuticals may cost more 

in preclinical stages and may be more difficult to manufacture, but that 

their clinical trials require fewer patients and have higher success rates 

(see Reichert [2004]). Compared with a traditional success rate around 

20%, the Tufts CSDD reports 30% to 35% success rates for rDNA and 

mAbs that entered clinical trials between 1990 and 1997 (Tufts, 2005). 

Reichert has also published summary data on stage lengths for these 

classes of biopharmaceuticals. 

Biopharmaceutical trials have fewer subjects and fewer studies 

compared with small-molecule drugs. Some biopharmaceutical trials are 

relatively small because they are targeting diseases with small patient 

populations. In addition, biotechs target hard-to-treat illnesses, which 

means that even moderate efficacy may induce FDA to approve such 

drugs based on smaller trials. Finally, because major classes of disease 

exhibit distinctly different levels of difficulty in proving efficacy, the size 

and duration of clinical trials vary accordingly. 

The baseline drug development cost estimate was $1,241 million (DiMasi 

and Grabowski, 2007). The results were presented at the BIO2007 

international convention in Boston, Massachusetts. This study in 

Managerial and Decisions Economics represents the first published 

study that estimates the R&D costs related to biopharmaceuticals. 

DiMasi and Grabowski computed their total capitalized R&D cost 

estimate using data for 17 compounds2

 tested in humans between 1990 

and 2003. The project cost data on these 17 compounds come from two 

sources: data on four compounds are from a previous study (DiMasi et 

 

2

The authors do not provide a distribution by recombinant proteins and monoclonal 

antibodies. 
Chapter 3 — Economic Analysis Methodology 

3-5 

al., 2003),3

 while project data for the remaining 13 compounds were 

provided by a single biotech firm.4 

In addition to detailed expenditures for the 17 compounds, the authors’ 

analysis used a much larger data set to estimate average development 

times, clinical success rates (receiving FDA approval), and clinical stage 

transition probabilities. This information was pulled from the Tufts CSDD 

database5

 for 522 compounds that included 278 therapeutic recombinant 

proteins and 244 monoclonal antibodies tested in humans between 1990 

and 2003. 

Components of the Total Capitalized R&D Cost 

Estimate 

The full cost of drug development includes not only the direct costs for 

successful INDs, but also the net costs associated with failures (attrition 

rates) and the opportunity cost of engaging in long-term development 

cycles. To characterize the full costs of drug development, the following 

information is needed: 

1. actual (i.e., out-of-pocket or direct expenditures) costs per IND 

for the preclinical period, 

2. actual costs per IND for the clinical period, 

3. clinical success and stage attrition rates, and 

4. the discount rate used to capitalize the time series of costs over 

the duration of the development process up to the date of market 

approval. 

Preclinical development costs are all R&D expenditures incurred before 

submitting an IND application and the entry of a candidate therapy into 

clinical trials. These include expenditures on disease mechanism 

research, screening, animal models, and PK/PD tests, as well as 

process engineering studies and the production of small volumes of 

biological product to use in preclinical testing. 

Subsequently, the clinical period covers the time starting with first-inhuman testing in Phase I up to the market approval date. Also included 

in the cost estimates are costs incurred during the clinical period for long-

 

3

Four recombinant proteins and two monoclonal antibodies part of a larger sample of 68 

compounds from 10 multinational pharmaceutical firms. 4

Biotech firms provided detailed project data for 13 compounds as part of a consulting 

project. The premise of the project was to test the firms’ hypotheses that their R&D 

costs were lower than the estimated $802 million for traditional pharmaceutical firms 

from DiMasi et al. (2003) 5

The Tufts CSDD database aggregates information from a collection of business 

intelligence databases, trade press news, company annual reports, and company Web 

sites. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-6 

term animal model testing conducted during that period; regulatory 

submission costs; and chemistry, manufacturing, and control costs. 

Table 3-2 summarizes baseline R&D costs used in this study, which are 

reported in constant 2005 dollars. 

Stage Costs per IND. Costs for the preclinical stage were developed 

using times series data on aggregate annual R&D costs per drug. 

Clinical stage costs for clinical periods were calculated using the actual 

expenditure data from the project dataset for the 17 compounds for 

Phase I, Phase II, and Phase III (see DiMasi et al. [2007]). 

Transition Probabilities and Clinical Success Rates. Transition 

probabilities reflect the probability of any one IND progressing from one 

clinical trial phase to the next, which is conditional on the IND’s success 

in the current phase. Table 3-2 illustrates how biopharmaceutical 

companies apply a portfolio approach to derive the estimated cost per 

approved drug when it is known that not all INDs will be successful and it 

is not known in which stages unsuccessful INDs will fail. 

As shown in Table 3-2, (A) is the average actual (sometimes called “out 

of pocket”) cost a firm incurs for an IND that progresses through the 

entire stage. Some INDs will progress through all stages and, therefore, 

incur all the stage costs, while others will fail after proceeding through 

perhaps one stage or a portion of a stage. Transition probabilities are 

calculated by multiplying (B) the percentage entering each phase by (C) 

the probability of success in that same phase. Transition probabilities 

have a cumulative effect on the share of INDs that progress to the next 

clinical stage. 

The computation is as follows: the study’s authors multiply the actual 

stage cost (A) by the probability of an IND entering the stage (B), which 

represents the expected outlay per IND in that stage. The sum of the 

values in (D) represents the cumulative expected stage costs for the 

IND. Knowing some INDs will fail and being able to project failure rates 

based on historical information, a biopharmaceutical firm would expect 

the outlay for any one IND to complete Phase III trials to be $169.0 

million. 

A clinical success rate of 30.2% (E) reflects the overall probability that an 

IND that enters clinical trials will eventually receive FDA approval. 

Dividing $169.0 million by 30.2% yields $559.6 million; this is the 

estimated cost per approved drug. The successful drug carries its costs 
Chapter 3 — Economic Analysis Methodology 

3-7 

Table 3-2. Actual and Expected Costs per IND by Stage 

Transition Probability 

Development Stage 

Actual Stage 

Cost per 

Successful INDa 

(millions) 

Share of 

INDs 

entering 

Each Stage 

Probability 

of INDs 

Success in 

Each Stage 

Expected 

Stage Cost 

per INDa

Expected 

INDa

Success 

Rate 

Estimated Stage 

Cost per 

Approved Drug 

Computational 

Reference 

A B C D = A*B E F = D /E 

Preclinical $59.9 N/A 100% $59.9 30.2% $198.3 

Phase I $32.3 100% 83.7% $32.3 30.2% $106.9 

Phase II $37.7 83.7% 56.3% $31.5 30.2% $104.5 

Phase III $96.1 47.1% 64.1% $45.3 30.2% $149.9 

Total Out-of-Pocket $169.0 $559.6 

a

IND = Investigational New Drug 

Source: Adapted from DiMasi et al. (2007). 

as well as the costs of those that fail. Column (F), therefore, represents 

the expected stage cost per approved drug, taking into account the 

probability of success for a portfolio of INDs when it is not known in 

advance whether any particular IND in a portfolio will be successful. 

The baseline estimates of expected R&D costs per approved drug 

reported in Table 3-2 are spread over many years. Preclinical costs may 

accrue over a period of 4 to 5 years on average, and it may take another 

8 years to move through clinical trials and regulatory review. Investments 

made 12 years in advance of a return clearly have a different implicit cost 

than investments made immediately prior to a return. To take this “time 

value of money” into account, RTI followed the literature in reversediscounting up to the date of marketing approval. 

DiMasi and Grabowski estimated the cost of capital for a sample of 

biotechnology companies at 5-year intervals beginning in 1989 using the 

capital asset pricing model (CAPM). The CAPM framework is used to 

estimate the rate of return investors require and, thus, the minimum 

expected rate of return a company’s financial managers require when 

deciding whether to approve a candidate project at the company. The 

authors’ capitalized cost estimates were derived using a benchmark of 

11.5% cost of capital from the late 1990s to mid-2000s. This discount 

rate took into consideration fluctuations in prevailing interest rates and 

equity-market premiums. They also confirmed the appropriateness of this 

rate through conversations with financial managers active in the industry. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-8 

R&D costs were spread evenly over each development stage’s average 

length (estimated in months) and then compounded to the point of 

marketing approval to estimate total capitalized costs. Monthly costs in 

Table 3-3 were calculated by dividing the expected stage costs per IND 

from Table 3-2 by the mean stage length. The annual costs from 

Table 3-2 were broken down further into monthly costs to better 

apportion stage costs over time. 

DiMasi and Grabowski capitalized the expected monthly costs by first 

converting the discrete annual discount rate to an equivalent continuous 

rate, changing the period to monthly, and then compounding 

continuously up to FDA approval. The capitalized cost per stage is 

calculated and presented in Table 3-3. Dividing this capitalized expected 

cost by the mean success rate of 30.2% results in the expected 

capitalized cost per approved drug. 

 3.2 ESTIMATING CURRENT EXPENDITURES ON 

THE TECHNOLOGY INFRASTRUCTURE 

While business analysts and economists have investigated the costs of 

developing traditional pharmaceutical products, this study is the first to 

estimate annual industry spending on the technology infrastructure and 

among the first to narrow the research scope to encompass only the 

biopharmaceutical industry. 

Table 3-3. Capitalized R&D Costs per IND by Stage 

Development 

Stage 

Expected 

Stage 

Cost per 

INDa

(millions) 

Stage 

Length 

(months) 

Monthly 

Cost 

(millions)

Start of 

Stage to 

Approval

(months) 

End of 

Stage to 

Approval 

(months) 

Expected 

Capitalized 

Stage Cost 

per IND 

(millions) 

Mean INDa

Success 

Rate 

Expected 

Capitalized 

Stage Cost 

per Approved 

Drug 

(millions) 

Preclinical $59.9 52.0 $1.15 149.7 97.7 $185.6 30.2% $614.7 

Phase I $32.3 19.5 $1.66 97.7 78.2 $71.8 30.2% $237.7 

Phase II $31.5 29.3 $1.08 78.2 48.9 $56.3 30.2% $186.5 

Phase III $45.3 32.9 $1.38 48.9 16.0b $61.0 30.2% $202.0 

Total Present 

Value of Costs 

$169.0 $347.7 $1,240.9 

a

IND = Investigational New Drug 

b

Average number of months for FDA to complete its regulatory review for biopharmaceuticals. 

Source: Adapted from DiMasi and Grabowski (2007). 
Chapter 3 — Economic Analysis Methodology 

3-9 

Current private-sector expenditures on technology infrastructure were 

estimated by interviewing and surveying biopharmaceutical drug 

developers and the technology suppliers, service vendors, consultants, 

and academics who support them. Reviewing corporate financial 

statements and industry R&D expenditure surveys offers little in the way 

of estimates of technology infrastructure spending. Technology 

infrastructure expenditures span R&D, capital investments, and many 

other line items in corporate financial reports. Thus, this study relied on 

the input collected from in-depth interviews and surveys of industry 

researchers, experts, and others to estimate spending. Chapter 4 

provides more detail on this study’s data collection activities. 

Research directors and managers at biopharmaceutical companies 

identified their title, business unit, and technical background and 

provided estimates of their firms’ (or business units’) total annual 

spending for RTI-defined categories. They estimated the relative 

proportions of labor, capital, and materials expenses comprising the 

estimate and the distribution of those costs among the TFAs RTI 

identified. They also discussed the timing of all of those costs within the 

product development period. 

Aggregated responses by stage were scaled to annual national 

expenditures using activity measures relevant for each drug 

development stage (see Table 3-4). Study participants provided several 

measures for comparing to and aggregating their data with other 

participants. Measures used to aggregate responses included 

• the number of scientists and engineers comprising the business 

unit’s research staff, 

• the number of FDA-approved biopharmaceutical products, and 

• the percentage of sales corresponding to the unit for which they 

are responding. 

 3.2.1 Technology Infrastructure Supporting Drug 

Discovery, Development, and Clinical Trials 

Annual technology infrastructure spending supporting drug discovery, 

development, and clinical trials was estimated using data supplied by 

participants engaged in these areas. 

Many businesses have specialty groups that strategize infrastructural 

issues, and these groups often span drug development stages. To 

capture spending appropriately, RTI asked groups to provide total 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-10 

Table 3-4. Estimating Current Expenditures on the Technology Infrastructure 

Activity Measures Factor Used for Scaling Available Breakdowns 

Drug discovery, 

development, and clinical 

trial activities: Average 

annual expenditures per 

scientist 

Number of scientists 

involved in discovery, 

preclinical, and clinical trial 

activities (~34,000)a

By type of expenditure (capital, 

materials, labor) 

By technology areas (bioimaging, 

bioinformatics, gene/protein 

expression platforms, molecular 

biomarkers, other) 

Manufacturing activities:

Average annual expenditures 

per drug 

Number of approved 

biopharmaceutical drugs 

(~264)b

By type of expenditure (capital, 

materials, labor) 

By infratechnologies related to 

activities (preproduction, upstream and 

downstream processing, process 

monitoring, transaction costs) 

Postmarket activities:

Average annual expenditures 

per drug 

Number of approved 

biopharmaceutical drugs 

(~264)b

By type of expenditure (capital, 

materials, labor) 

By infratechnologies related to 

activities (adverse event monitoring, 

database management, drug safety 

reporting, labeling, other) 

a

The number of scientists involved was calculated by taking the 2001 estimate of the number of scientists involved in 

biotechnology activities for human-health applications and applying a 1.9% annual growth rate to obtain a 2006 

estimate. Sources: Department of Commerce (2003), Battelle (2006), and PhRMA (2006b). b

Source: BIO (2006) and PhRMA (2006a), based on list of FDA approvals as of June 2006. 

spending rather than attempt to parse spending out too finely. To 

properly weight the responses, respondents reported the number of 

scientists6

 on their unit’s research staff. 

It was assumed that the average annual spending per scientist captured 

by the survey was representative of national spending. The average 

infrastructure spending per scientist was multiplied by the total estimated 

population of scientists to derive national expenditures. 

The biopharmaceutical industry’s scientific research staff totaled about 

34,000 scientists in 2006. RTI derived this estimate by adjusting the 

Department of Commerce’s 2001 estimate of the number of scientists 

active in biotechnology activities for human-health applications by the 

industry’s average annual employment growth rate. PhRMA reported 

average annual employment growth of 1.9% between 2001 and 2004, 

and this rate was used to grow the 2001 estimate to 2006 (Battelle, 

2006; PhRMA, 2006b). 

 

6

Specifically, respondents indicated the number of scientists, engineers, clinical laboratory 

technicians, and computer scientists involved in biotechnology activities—a definition 

that matches the one used by our secondary data source. 
Chapter 3 — Economic Analysis Methodology 

3-11 

 3.2.2 Technology Infrastructure Supporting Commercial 

Manufacturing Activities 

Interviews with and completed surveys submitted from manufacturing 

operations managers were used to estimate technology infrastructure 

spending supporting commercial-scale manufacturing activities. 

Rather than aggregate by the number of scientists, RTI used the number 

of FDA-approved product lines currently manufactured by the 

respondent’s facility and the total national number of product lines 

currently in production. 

Values from respondents were analyzed to obtain the “mean 

manufacturing technology infrastructure expenditures per approved 

biopharmaceutical,” which was then multiplied by the number of 

approved biopharmaceuticals available from Biotechnology Industry 

Organization (BIO) publications. 

Recognizing that each individual drug requires specific technology 

infrastructure investments that must be made to support manufacturing, 

the number of approved biopharmaceuticals was nevertheless deemed 

the best available factor by which to scale estimates.7

 

To this end, survey data were reviewed to assess if any particular 

respondents’ drug types were unlikely to be representative of the 

biopharmaceutical drug population as a whole. Individual production 

processes are unique even for similar types of therapies (e.g., two types 

of therapeutic proteins may require vastly different upstream processing 

and the downstream purification process will be similarly tailored). No 

significant inconsistencies in the data reported warranted adjusting the 

extrapolation approach. 

 3.2.3 Technology Infrastructure Supporting Postmarket 

Surveillance Activities 

The approach to estimating private-sector expenditures on the 

infratechnologies that support efficiency in these activities was similar to 

that for commercial manufacturing. The mean technology infrastructure 

expenditure per approved biopharmaceutical was multiplied by the total 

approved number of biopharmaceuticals to obtain total technology 

infrastructure expenditures supporting postmarket activities. 

 

7

The number of scientists working in commercial-scale manufacturing also was collected in 

RTI’s survey, but the Department of Commerce (2003) study does not include these 

individuals in the total number of scientists. Instead, they are classified as “production 

workers” and reported in combination with managers and other administrative positions. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-12 

 3.3 ESTIMATING POTENTIAL REDUCTIONS IN 

BIOPHARMACEUTICAL DEVELOPMENT AND 

PRODUCTION COSTS 

The second objective of this study was to measure the “excessive” costs 

incurred by the industry because of an inadequate technology 

infrastructure. 

Excessive costs are those that could be avoided if the industry had 

access to an improved, and therefore “more adequate,” infrastructure. 

Industry experts helped define hypothetical improvements to the 

technology infrastructure supporting each TFA that are feasible within 

the next 10 years. These improvements were included in the survey, and 

respondents provided their assessments based on the scenarios RTI 

presented. (Section 3.3.7 concludes this chapter with an accounting of 

these improvements.) This section outlines the economic model that 

estimated potential reductions in R&D and production costs by 

recalculating the average cost to develop and manufacture a drug. 

The metric most commonly used for assessing industry’s spending is the 

“R&D cost per approved drug.”8

 The economic model recalculates 

average R&D cost per approved drug using the baseline costs from the 

DiMasi and Grabowski study. Baseline estimates included 

biopharmaceutical drug development costs, times, and the probability a 

candidate drug moved from one R&D stage to the next. 

Respondents’ estimates of the impact the improvements would have on 

costs, time, and quality were inputted into the model. Respondents also 

offered their estimates of changes in the probability that a drug candidate 

moves from one stage to the next and related measures, relative to 

industry averages.9

 The percentage change in costs possible given the 

 

8

RTI followed this approach, rather than looking at annual R&D costs at the firm level, for 

multiple reasons. First, and most importantly, the models for R&D cost per approved 

drug build in the cost of failure—a very important component of the industry’s total R&D 

spending and a major focus of efforts to streamline drug development. Second, private 

firms may respond to reductions in drug failure rates, not by lowering their R&D 

spending but by producing more drugs. If this is the case, total industry R&D spending 

would remain unchanged, but the R&D cost per approved drug would decrease. 9

There are likely substantial benefits to patients’ quality of life from receiving better 

treatments earlier. However, it is beyond the scope of this study to assess those 

quality-of-life metrics quantitatively. It is also beyond the scope of this study to quantify 

the additional profits that firms may earn by reaching the market earlier and thus being 

able to take better advantage of their limited patent lives. Finally, improvements to the 

technology infrastructure supporting the biopharmaceutical industry may affect 

bioagriculture, bioenergy, or biodefense. Quantifying those impacts is also outside the 

scope of this study. 
Chapter 3 — Economic Analysis Methodology 

3-13 

hypothetical improvements for each stage of drug development and 

production was used to estimate excessive costs. 

 3.3.1 Baseline Preclinical Cost per Approved Drug 

Preclinical costs are the R&D expenditures incurred before submitting an 

IND and entering a candidate therapy into clinical trials. The total costs of 

preclinical development, TCpreclinical, at a firm for a specific year or set of 

years is divided by the number of biopharmaceuticals that were 

eventually approved out of that cohort: 

Discovery/Preclin.

Cost per Approved Drug = 

1

D [ ] TCpreclinical (3.1) 

where D is the number of approved drugs that eventually result from 

these discovery and preclinical research efforts. 

During the survey, respondents were asked how baseline costs, stage 

length, and the probability of success could change with an improved 

technology infrastructure. The data in Table 3-5 both summarize and 

present an alternative view of the DiMasi and Grabowski results 

discussed in Section 3.1.2. 

 3.3.2 Baseline Clinical Trials Cost per Approved Drug 

Clinical trial costs are easier to quantify than the costs of earlier R&D 

stages, because drug sponsors often track them individually for each 

compound filed as an IND. However, not all INDs become approved 

drugs. The clinical trial cost per approved drug is given by 

Clinical Trial Cost

per Approved Drug = 

1

D [Total Clinical Trial Costs Incurred] 

= 

1

D [CI·IND + CII·IND(1 – p1) + 

 CIIIIND(1 – p1)(1 – p2)] (3.2) 

where CI, CII, and CIII represent the average cost of taking a candidate 

through Phase I, II, and III trials, respectively, regardless of whether 

those compounds are successful. 

These costs include expenses related to conducting clinical procedures, 

as well as the costs associated with manufacturing small quantities of 

drugs for trial use, managing the trials, and analyzing the resulting data. 

The remaining terms in Eq. (3.2) track the expected number of individual 

compounds incurring those phase costs. IND represents the total 

number of drugs entering Phase I trials (the number of INDs filed). All of 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-14 

Table 3-5. Baseline Parameters for the Biopharmaceutical R&D Cost Model 

Stage 

Stage Costs per IND 

(millions) 

Distribution of Failed 

INDs, by Clinical Trial 

Phase 

Stage Length 

(Time to Next Stage in 

Months) 

Discovery/preclinical $59.89a 52.0a

Phase I $32.28a 23.4% 19.5a

Phase II $37.69a

 52.4% 29.3a

Phase III $96.09 a 24.2% 32.9a

a

Adapted from DiMasi et al. (2007). 

these compounds would incur costs for Phase I. The probabilities that a 

drug entering Phase I trials fails in Phase I or in Phase II are given by p1

and p2, respectively. IND(1 – p1) represents the expected number of 

compounds incurring Phase II costs, and IND(1 – p1)(1 – p2) represents 

the expected number incurring Phase III costs. 

Noting that (D/IND) reflects the probability that an IND entering Phase I 

clinical trials is successfully approved, p(approval), the above expression 

can be rewritten as follows: 

Clinical Trial Cost

per Approved Drug = 

CI + IND(1 – p1) + IND(1 – p)(1 – p2)

p(approval) . (3.3) 

This value represents the average actual cost of clinical trials for one 

approved drug, taking into account the cost of failures that firms 

experience along the way. 

As with the discovery and preclinical development costs, this analysis 

relied on the DiMasi and Grabowski paper to determine a baseline value 

for the cost of conducting clinical trials. In this way, the baseline 

estimates are a function of within-stage costs (CI, CII, and CIII) and

success and failure probabilities (p1, p2, and p(approval)).

The timing of investments is important to calculating the total capitalized 

cost per approved drug. The periods over which costs were reverse 

discounted using the average biopharmaceutical cost of capital were 

adjusted using participants’ expected stage length reductions. These 

adjustments affected the capitalized cost by shortening firms’ investment 

horizons. 
Chapter 3 — Economic Analysis Methodology 

3-15 

 3.3.3 Baseline Commercial Manufacturing Costs 

The baseline model of manufacturing costs is less complex than the 

baseline model for preclinical/clinical R&D costs because it does not 

account for the probability of failure. Manufacturing costs include any 

expenses incurred to produce commercial-scale volumes of an approved 

biopharmaceutical drug or therapy:10 

Annual Manufacturing Costs = 

Annual Preproduction Costs + 

Annual Upstream Processing Costs + 

Annual Downstream Processing Costs + 

Annual Process Monitoring Costs (3.4) 

Preproduction costs refer to all costs incurred to scale up from clinical 

trial volumes to the volumes needed in the marketplace. Upstream and 

downstream processing costs refer to the costs of producing the raw 

product material (usually through fermentation or cell culturing) and the 

costs of extracting, filtering, and purifying that raw product, respectively. 

Process monitoring costs include the costs of tracking and modifying 

environmental parameters, as well as testing batch characteristics. 

Survey respondents determined the percentage of total annual 

manufacturing costs accounted for by each of these activities. 

Total annual manufacturing costs for biopharmaceuticals are calculable 

from the Department of Commerce (2003) report detailing estimates of 

biopharmaceutical sales. Survey respondents were asked to estimate 

manufacturing costs as a percentage of annual sales. The average of 

their responses was applied to total sales to estimate potential 

reductions. RTI verified the appropriateness of the average using 

company annual reports and additional secondary resources. 

 3.3.4 Baseline Postmarket Surveillance Costs 

Postmarket surveillance costs include commercializing a drug and 

verifying that it behaves in the marketplace as designed. Surveillance 

includes testing product quality and stability, as well as continuing to 

monitor the product for adverse events in the wider population. The 

annual cost of postmarket activities is as follows: 

 

10Recall that costs to produce small batches for use in preclinical studies or clinical trials, 

though very important, are counted by the industry as R&D expenditures. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-16 

Annual Postmarket Transactions Costs = 

Annual Quality Assurance Costs + 

Annual Adverse Event (AE) Monitoring Costs + 

Annual AE and Safety Data Management Costs + 

Annual Regulatory Drug Safety Reporting Costs + 

Annual Product Label Updating Costs (3.5) 

As with manufacturing costs, the baseline postmarket costs were 

determined through a combination of surveys, interviews, and secondary 

sources. These costs were expressed as a percentage of total annual 

sales of biopharmaceuticals.11 The shares reported in the online survey 

informed the baseline value for each type of transaction cost. In practice, 

some of these transaction costs are incurred by manufacturers, who 

must verify quality, while other transaction costs are incurred by 

individuals performing postmarket safety surveillance. 

 3.3.5 Hypothesized Economic Impact of an Improved 

Technology Infrastructure 

RTI considered the cost savings that would be associated with an 

improved (more adequate) infrastructure. The definition of “improved 

infrastructure” was described for survey respondents and interview 

participants in terms of a suite of infratechnologies that could feasibly be 

made available within the next 10 years. 

In some instances, new infratechnologies or processes can be described 

for which it is reasonably clear what the improved attributes will be. 

However, for others the advancement might be a relative improvement in 

accuracy, reliability, and stability of existing infratechnologies. In the 

latter case, respondents were asked to consider advancements of 50% 

over the current state of the art in those areas. 

When considering the impact of this improved technology infrastructure 

on biopharmaceutical development and production costs, the model 

anticipated three possible effects: 

1. The advancements may change the within-stage costs (e.g., 

TCpreclinical, CI, upstream processing costs). 

For example, standardization may reduce the need for redundant 

tests to match and verify results. Better approaches to scale up 

and enhanced process control may reduce manufacturing costs. 

 

11A recent economic study estimated the postmarket safety surveillance costs at 

approximately 0.3% of sales, though this estimate is for all pharmaceuticals, not just 

biopharmaceuticals (Ridley et al., 2006). 
Chapter 3 — Economic Analysis Methodology 

3-17 

Other infrastructure improvements may lower transaction costs 

experienced postproduction. 

2. The advancements may change the transition probabilities or 

probability of overall approval. 

An improvement in infrastructure used in the preclinical stage 

may allow researchers to better characterize the properties of 

their investigational compounds, which may affect the success 

probabilities in later stages (e.g., by increasing the probability of 

passing Phase I and, therefore, the overall approval rate). 

3. The advancements may change the duration of any of the 

stages, possibly bringing products to the point of marketing 

approval more quickly.

 This does not affect the expenses incurred by biopharmaceutical 

development firms, but it does affect the timing of those 

expenses. A shorter time to approval will be reflected by a lower 

present value cost. 

A single improvement to the infrastructure may affect any or all of these 

dimensions of the total cost of biopharmaceutical development and 

production. Standardization or calibration techniques that increase 

consistency of results may lower stage costs or shorten the time spent in 

one stage by eliminating redundant tests, for example. However, 

improved consistency alone might not directly improve the probability of 

success. Alternatively, improvements in the technical capabilities of a 

particular infratechnology may increase the quantity of known information 

about a compound and thereby improve the probability of success, yet 

this benefit might be offset by an increase in the mean stage costs if it 

means new tests or equipment. With any change there may be one-time 

costs of adoption; therefore, the total cost of development could increase 

during the transition.12

 3.3.6 Model Implementation Using Impact Metrics 

In a recent study, DiMasi (2002) explored the effect that the above 

impacts could have on pharmaceutical drug R&D costs. His results 

suggest that hypothetical reductions in all stage lengths of 25% would 

lower the future-value total cost per approved drug by 16%. Additionally, 

a hypothetical increase in the overall success rate for compounds 

entering clinical trials from 21.5% to 33.3% would lower the present 

value cost per approved drug by up to 30.2% (DiMasi, 2002). 

 

12We will not estimate these transition costs, because we are not trying to describe exactly 

what it will take or cost to achieve hypothetical infratechnology improvements. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-18 

A similar approach was used in this analysis to determine potential 

changes in the total cost of biopharmaceutical development—starting 

with a baseline estimate and re-estimating that cost in response to 

changes in the underlying parameters. However, the model’s estimates 

rely on expert opinions about potential infrastructural improvements to 

determine the appropriate changes to those parameters. The survey 

asked respondents to estimate impact values that were then used to 

reestimate the cost of biopharmaceutical drug development (see 

Table 3-6). 

Potential Impact on Discovery/Preclinical 

Development and Clinical Trial Costs 

The impact metrics are relatively straightforward when the effect of an 

infratechnology improvement is on within-stage costs or durations. For 

example, if an improved infrastructure allows the same task to be 

performed at a lower cost, this is captured by estimating the percentage 

change in the cost of processing a compound (e.g., Cpreclinical, CI) 

attributable to the supporting infratechnologies. Likewise, if validation 

procedures allow for the use of biomarkers as surrogate endpoints that 

shorten clinical trials, this is captured by estimating the percentage 

change in the length of the stage. These metrics are shown for the R&D 

stages in Table 3-7. 

These percentage reductions were applied to the baseline stage costs 

and stage lengths. To verify that estimates from survey respondents 

were reasonable, we used in-depth interviews to collect information on 

some intermediate metrics (e.g., number of redundant tests eliminated, 

cost per test, and length of time required for each test). 

The assessment of the impact on success probabilities was more 

complicated, because the improvements in one stage can have effects 

that filter into later stages. For example, the likelihood that a drug fails in 

Phase I determines the quality of drugs entering Phase II and is itself 

determined by the quality of compounds coming out of preclinical 

development. 

First, the approach assumed that the probability that an IND succeeds in 

clinical trials is essentially determined by the end of the preclinical stage. 

By this point, the drug sponsor has established a target, optimized the 

lead, and identified the patient populations. What remains is a screening 

process. If a drug has short-term safety problems, it assumed those are 
Chapter 3 — Economic Analysis Methodology 

3-19 

Table 3-6. Estimated Potential Impact of an Improved Technology Infrastructure 

Final Estimates Based on 

Potential Reduction in Annual R&D Costs 

Potential reduction in R&D cost per drug 

= a function of 

Potential reductions related to bioimaging, 

Potential reductions related to bioinformatics, 

Potential reductions related to gene/protein expression, 

and 

Potential reductions related to molecular biomarkers 

A baseline figure of $1.3 billion for the 

R&D cost per approved drug and 

associated baseline stage costs and 

probabilitiesa

Total annual R&D for the industry 

Impact metrics: percentage reductions in 

stage costs and lengths, revised 

success and failure rates 

Potential Reduction in Annual Manufacturing Costs Total annual sales for the industry 

Estimated manufacturing costs as a 

percentage of salesb

Impact metrics: percentage reductions in 

stage costs 

Potential Reduction in Annual Postmarket Costs Total annual sales for the industry 

Estimated postmarket costs as a 

percentage of salesb

Impact metrics: percentage reductions in 

stage costs 

a

Adapted from DiMasi et al. (2007) in Managerial and Decision Economics. b

Survey respondents provided information about their current manufacturing and postmarket surveillance costs as a 

percentage of their total sales. These percentages, along with industry estimates of annual sales, were used to 

estimate a baseline figure for manufacturing costs and for postmarket surveillance costs. 

Table 3-7. Metrics for Assessing the Impact on R&D Costsa

Stage 

Percentage Reduction in Stage Length

(Time to Next Stage) 

Percentage Reduction in Stage 

Cost 

Discovery/preclinical _____% _____% 

Phase I _____% _____% 

Phase II _____% _____% 

Phase III _____% _____% 

a

This table appeared in the online survey for respondents to complete. The survey allowed both preclinical and 

clinical trial scientists to fill out all rows of the table, so that they could account for some of the interactions between 

these R&D stages. For example, in silico modeling used by preclinical scientists to assess toxicity may allow for the 

reduction of certain clinical trial procedures. 

detected in Phase I. If a drug is ineffective or has long-term safety 

problems, the model assumed those are detected in Phases II and III. 

Yet, regardless of where failures occur, the inherent probability of 

approval conditional on entry in clinical trials, p(approval), was assumed 

to be determined during discovery and preclinical trials. Therefore, the 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-20 

value of enhancements to the technology infrastructure supporting these 

two initial stages was measured by asking respondents to estimate a 

new p(approval). They also estimated a new p(recall | approval), the 

probability that an FDA-approved biopharmaceutical is recalled, 

withdrawn, or relabeled in response to adverse effects. 

Holding p(approval) constant, the distribution of the remaining failures 

across the three trial stages was estimated. The industry average 

p(approval) is estimated at 35%, meaning that 35 of 100 candidates 

entering Phase I clinical trials ultimately receive FDA approval. The 

remaining 65 candidates are expected to fail during one of the three trial 

phases. The survey provided firms with the industry average distribution 

of failures within clinical trials and asked them how this distribution would 

change under the improved infrastructure scenario. 

A matrix for these types of responses appears in Table 3-8. The 

estimates about the distribution of failures given in the middle of 

Table 3-8 were converted to the estimates about p1 and p2 needed for 

the theoretical model. 

Using this information on improved probabilities of success and failure 

and the improved stage costs and lengths (calculated using the baseline 

figures and percentage reductions reported in the surveys), the model 

calculated an improved discovery/preclinical cost per approved drug and 

an improved clinical trial cost per approved drug. By comparing the 

difference between the baseline cost and the improved cost estimates, 

the model identified the potential impact of an improved (adequate) 

infrastructure: 

Potential Impact on

Disc/Preclin. Costs = 

Improved Disc/Preclin.

Cost per Approved Drug – Baseline Disc/Preclin.Cost per Approved Drug

Baseline Disc/Preclin.

Cost per Approved Drug

 .

Applying this percentage to the current annual R&D expenditures on 

discovery/preclinical biopharmaceutical development yields a dollar 

value for the potential industry-wide impact. The same calculation and 

logic apply to the potential impact on clinical trial costs. 
Chapter 3 — Economic Analysis Methodology 

3-21 

Table 3-8. Metrics for Assessing the Impact on Success and Failure Rates 

Change in IND Overall Success Ratesa

Typical 

Percentagesa 

Estimated Percentages in 

World with Improved 

Infrastructureb

p(approval) 

Probability that an IND ultimately receives 

FDA approval 35% _____% 

Change in Clinical Trial Failure Rates 

Of the remaining INDs that fail in clinical trials, what percentage fail in each stage? 

Percentage of failures occurring in Phase I 15% _____% 

Percentage of failures occurring in Phase II 46% _____% 

Percentage of failures occurring in Phase III 39% _____% 

Total 100% 100%

Change in Postmarket Failure Rates 

p(recall | approval) 

Probability that an approved drug is recalled 0.4% _____% 

a

Based on estimates from Pavlou and Reichert (2004) and PAREXEL’s Pharmaceutical R&D Sourcebook (Mathieu, 

2005). b

The definition of “improved infrastructure” was detailed for respondents in terms of a suite of infratechnologies that 

could feasibly be made available in the next 5 to 10 years. For infratechnologies that already exist to some degree, 

respondents considered advancements of 50% over the current state of the art.

Comparing Impact Assessments across the R&DRelated Technology Focus Areas 

In some cases, the proposed potential improvements may be 

complementary, but in other cases, they may be substitutes that 

researchers will choose between when it comes to adoption. If the TFAs 

involve infratechnologies that are complementary, then the cost 

reduction from implementing different technologies may be more than 

the sum of the individual impacts.13 

For example, consider the bioimaging and bioinformatics TFAs. Suppose 

standards and improved change measurements that allow for enhanced 

bioimaging techniques reduce the cost of conducting Phase III clinical 

trials by an estimated 10%. Suppose also that advancements in image 

formatting and search capabilities alone would improve Phase III costs 

by 5%. Should both advancements be available simultaneously the net 

 

13Likewise, if the TFAs involve infratechnologies that are substitutes, the net impact may be 

less than the sum of the individual cost reductions. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-22 

impact may be slightly lower than 15% because some bioimaging and 

bioinformatics infratechnologies may be addressing the same 

inefficiency. 

Thus, the greatest potential improvements in cost and time per stage, 

irrespective of TFA, were used to determine the extent to which the 

hypothetical improved infrastructure would affect total R&D costs. 

Shortening the investment horizon while simultaneously reducing cash 

outlays significantly reduces capitalized costs. Model results thus 

illustrate how R&D costs per IND and the expected capitalized costs per 

approved drug were affected by anticipated lower costs from an 

improved infrastructure as well as shorter development times. 

It is important to note that these calculations hold all other costs and 

production factors constant. Outside of this model, real world shifts and 

reassignment of resources is possible if companies identify opportunities. 

Thus, any total potential cost reduction is based on historical, observed 

costs that do not reflect future costs. 

Potential Impact on Manufacturing Costs 

A similar approach was used to estimate the potential impacts on 

manufacturing costs, although the model did not have to account for 

changing probabilities or stage lengths and did not have to combine 

TFAs. Table 3-9 reports the impact estimates collected from the survey 

and interviews. 

These reductions were applied to the baseline estimates for 

manufacturing costs, and these elements were summed to estimate a 

total potential reduction in manufacturing costs due to an improved 

(adequate) infrastructure. 

Table 3-9. Metrics for Assessing the Impact on Manufacturing Costs 

Phase/Activity Cost 

Percentage Reduction in Cost by 

Phase/Activity 

Preproduction (scale-up from clinical trial volumes) costs _____% 

Upstream processing costs _____% 

Downstream processing costs _____% 

Process monitoring and quality assurance testing costs _____% 
Chapter 3 — Economic Analysis Methodology 

3-23 

Potential Impact on Postmarket Costs 

Estimated reductions in postmarket costs were applied to the baseline 

estimates and summed to estimate a total potential reduction in 

postmarket costs due to an improved infrastructure (see Table 3-10). 

 3.3.7 Hypothetical Improvements by TFA Posed to Survey 

Respondents and Interviewees 

This section discusses the hypothetical improvements posed to 

respondents for each TFA. Respondents chose one TFA area to explore 

in the survey and were requested to explore the one with which they 

were most familiar. 

Bioimaging 

This TFA focused on the inadequacies in bioimaging capabilities, 

including the need for novel reagents to produce high-quality images and 

better software algorithms to resolve these images. 

For bioimaging, RTI asked respondents about the potential R&D cost 

savings that could occur given hypothetical improvements in the 

technology infrastructure supporting the use of bioimaging in 

biopharmaceutical discovery, development, and testing. The 

advancements listed come from the NIH Technology Road Map, the FDA 

Critical Path Opportunities List, and other industry sources. 

Participants were asked to consider the following advancements: 

A. Qualified imaging biomarkers in a disease or therapeutic 

category of interest. 

B. Availability of labeling and contrast agents that are proven to be 

stable in vivo, nondestructive to the molecule being tested, and 

nontoxic in humans. 

C. Reconstruction algorithms that improve image quality (e.g., 

reduce fuzziness, define clear borders). 

Table 3-10. Metrics for Assessing the Impact on Postmarket Costs 

Phase/Activity Cost Percentage Reduction in Cost by Phase/Activity 

Quality assurance testing _____% 

Adverse event (AE) monitoring _____% 

AE and safety data management _____% 

Regulatory drug safety reporting _____% 

Product label updating _____% 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-24 

D. Automated or computer-assisted interpretation of images, 

including edge detection and size measurements. 

E. National image test bed and benchmarking methodology to 

assess the quality of the image analysis algorithms and software. 

F. Standard protocols for patient/specimen positioning, instrument 

calibration, and settings to reduce variability and allow images to 

be compared across tests (in discovery) or trials (in the clinical 

testing phases). 

Advancements in these areas could lead to a number of impacts on R&D 

productivity. Examples include the following: 

• R&D labor and materials cost savings associated with shorter 

clinical trials or smaller trial populations. 

• Elimination of alternative tests required to identify and validate a 

target or response. 

• Reduction in the number of animal subjects needed (since 

noninvasive imaging would allow the same animal to be followed 

for longer periods of time). 

• Reduction in the number and skill level of technicians required to 

verify the analysis of an image. 

• Elimination of additional (redundant) tests needed to verify 

results, because of poor image quality. 

• Reduction in R&D labor costs for time spent assessing 

performance capabilities of image analysis software. 

• Reduction in R&D labor costs associated with calibrating 

equipment because of the availability of standardized procedures 

and reference phantoms. 

Biomarkers 

Respondents considered the R&D cost savings that could occur given 

hypothetical improvements in the technology infrastructure supporting 

the use of biomarkers in biopharmaceutical discovery, development, and 

testing.

Survey respondents and interviewees were presented with the following 

hypothetical improvements: 

A. Standardized and accepted validation process for biomarker 

discovery. 

B. Safety biomarkers that predict human toxicity in preclinical 

stages of drug development. 

C. Validated efficacy biomarkers for diseases or therapeutic 

categories in an area of interest that could be used as surrogate 

endpoints in clinical trial activities. 

D. Enhanced ability to predict biological response to treatment (e.g., 

drug efficacy measurement). 
Chapter 3 — Economic Analysis Methodology 

3-25 

Advancements in these areas could lead to a number of impacts on R&D 

productivity. Examples include the following: 

• Reduction in the number of tests conducted in preclinical studies 

due to the reduced uncertainty of drug efficacy. 

• Avoidance of future R&D costs due to early detection of toxicity 

that eliminates drug candidates before entering clinical trials. 

• Shorter clinical trial periods due to faster detection of biologic 

response to drugs. 

• Reduced number of patients in clinical studies due to more 

accurate selection criteria, resulting in improved stratified patient 

populations. 

• Reduction in labor and material costs during clinical trials due to 

the ability to set dosage levels on a per-patient basis. 

Bioinformatics 

The study addressed a host of issues related to using databases and 

information technology in laboratories, clinical settings, and production 

facilities. These issues ranged from the need for reliable databases in 

drug discovery to data management techniques that link preclinical and 

clinical trial results and new ways to store high-content data such as 

images. 

In addition, the study addressed the need for refined in silico models 

based on such data. Researchers need improved algorithms capable of 

simulating cell and protein behavior. These algorithms would ultimately 

improve the predictions of drug efficacy at earlier stages of development 

such as target identification and lead optimization studies. Models of 

predictive ADME and toxicity are also important needs for improved 

candidate selection. 

Respondents reflected on the potential R&D cost savings that could 

occur given hypothetical improvements in the technology infrastructure 

supporting the use of bioinformatics in biopharmaceutical discovery, 

development, and testing. 

Participants were asked to consider: 

A. Shared data standards for formatting and content. In addition to 

formatting standards for storing different types of data (such as 

image data), this advancement would include standards for the 

metadata that record information on the conditions of an 

experiment, similar to the content standards used as Minimum 

Information About a Microarray Experiment (MIAME). 

B. Greater availability of currently gathered data through publicly 

accessible, curated databases. These databases would include 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-26 

(but not be limited to) data concerning the natural history of rare 

diseases, adverse events, toxicology properties of drug 

candidates, and ADME properties of drug candidates. 

C. Improved data mining applications and algorithms. Potential 

improvements include the incorporation of natural language 

processing into a text search. 

D. Improved accuracy of in silico model predictions, including (but 

not limited to) models of protein structure and binding, cellular 

localization, PK/PD properties, clinical trial simulation, and 

disease modeling. 

Advancements in these areas could lead to a number of impacts on R&D 

productivity. Examples include the following: 

• Reduction in time and labor costs spent on finding and 

interpreting data from previous experiments. 

• Reduction in labor and material costs related to redundant 

experiments. 

• Reduction in the number of animal subjects needed in preclinical 

trials (since in silico models can be used to predict how drug 

candidates will interact with the subject). 

• R&D labor and material cost savings associated with fewer 

product redesign-synthesize-test cycles (since more accurate in 

silico models could be used to predict the effect of a proposed 

structural modification of a therapeutic product). 

• Increased clinical trial success rates due to early detection of 

toxicity using improved in silico models of information obtained 

from expanded adverse event databases; these models may 

eliminate drug candidates before entering clinical trials. 

• Reduction in the number of trials and patients through the 

simulation of clinical trials using in silico modeling. 

Gene Expression 

The TFA on standards and metrology in gene and protein expression 

examined costs incurred during discovery stages and preclinical and 

clinical investigation stages that result from the absence of a 

standardized set of reference materials. 

In addition, this TFA considered the costs incurred during the 

manufacturing stage as a result of inadequate methods and standards to 

optimize expression systems at the upstream processing stages of 

manufacturing for therapeutic proteins and vaccines. 

The gene expression component queried respondents about potential 

R&D cost savings that could occur given hypothetical improvements in 

the technology infrastructure supporting the use of gene and/or protein 
Chapter 3 — Economic Analysis Methodology 

3-27 

expression analysis in biopharmaceutical discovery, development, and 

testing. 

Survey respondents and interviewees were presented with the following 

hypothetical improvements: 

For gene expression: 

• Publicly available synthetic mRNA reference materials for 

microarray performance assurance. 

• Standard technical protocols for microarray experiments. 

• Standard protocols for RNA and DNA extraction. 

• Data and analysis to benchmark microarray performance. 

• Microarray scanning equipment calibration tools. 

For protein expression: 

• Techniques for measuring the presence of low-abundance 

proteins. 

• Improved sensitivity and lower coefficients of variation (≤10%) in 

mass spectrometry analysis to allow for its use in later stages of 

drug development. 

• Improved availability of antibodies with high affinity, specificity, 

and selectivity for use with protein microarrays. 

• Standards for protein microarray experiments. 

Advancements in these areas could lead to a number of impacts on R&D 

productivity. Examples include the following: 

• Reduction in labor, microarray, and consumables costs for 

redundant experiments and avoided downstream data capture 

and analysis costs for those experiments. 

• Avoided downstream R&D costs for investigating genes and 

proteins mistakenly identified as potential targets. 

• Greater confidence in and comparability among results from 

microarray experiments allowing for the elimination of redundant 

tests. 

• Reduction in labor costs associated with calibrating equipment 

due to the availability of standardized procedures and reference 

phantoms. 

• Reduction in or elimination of microarray scanning errors due to 

poor equipment alignment and calibration and associated 

downstream impacts of avoidable data capture errors. 

• Elimination of additional (redundant) tests needed to verify 

results due to poor image quality. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

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Commercial-Scale Manufacturing 

The NIH Technology Road Map, the FDA Critical Path Opportunities List, 

and industry sources identified a number of areas that could benefit from 

additional research. The survey asked individuals involved in 

manufacturing biopharmaceuticals to consider advancements in the 

following areas related to the technology infrastructure supporting 

different stages of manufacturing. 

Preproduction:

• Predictive modeling and underlying data for determining batch 

yield given a specified level of inputs and production parameters 

(which would reduce the number of test batches required during 

process scale-up). 

Upstream Processing: 

• Robust expression systems that produce raw proteins in higher 

yields with fewer impurities (for example, the use of transgenic 

plants and animals). 

• Technologies, such as disposable bioreactors and mixing 

systems, which can accommodate rapid changes in 

manufacturing processes and can reduce the risk of biological 

product contamination. 

Downstream Processing: 

• Purification technology that can improve flow rates and increase 

capacity over current methods. Examples of new technology 

might include membrane chromatography or improved, highpressure affinity chromatography among other methods. 

Process Monitoring (crossing over all phases of production):

• Implementation of PAT to better understand, monitor, and control

production processes in real time. For example, this might 

involve the following: 

– Improved detection of contamination in biological products 

(e.g., viruses, bacteria, and other organisms) through the 

use of microarrays or proteomics infratechnologies. 

– Uniform standards for spectroscopic instruments; for 

example, standards for appropriate instrument qualification 

and calibration standards for techniques such as Raman and 

Terahertz spectroscopy. 

• Improved methods for product characterization, including 

enhanced potency assays and appropriate statistical and 

sampling techniques. For example, this might involve the 

following: 

– More reliable and quantitative nonanimal-based tests of 

vaccine potency. 

– Potency measurements that provide reliable information 

about the quality of cells or tissues to be used in therapies. 
Chapter 3 — Economic Analysis Methodology 

3-29 

Postproduction: 

• Improved certainty about product characteristics, including 

potency, sterility, purity, and handling requirements. 

Enhancements in these areas could lead to a number of impacts on 

manufacturing productivity, such as the following: 

• Reduced time to market and higher product quality as a result of 

better scale-up procedures, shorter downstream processing 

purification periods, and faster and more reliable batch testing 

procedures. 

• Improved production yields for a given amount of raw material. 

• Reduced labor and material costs with fewer trial and error 

iterations and fewer contaminated batches. 

• Labor saved in sterilization activities between batches. 

• Lower transaction costs to customers as a result of greater 

reliability of data and fewer instances of inactive product 

released. 

• Smaller amount of product required for testing. 

Postmarket Surveillance 

Respondents considered advancements in the following areas related to 

the technology infrastructure supporting postmarket surveillance 

activities: 

• Improved statistical methods for signal detection in adverse 

event monitoring. 

• Development of standards for adverse event reporting systems. 

• A standardized process and protocol for ensuring data 

consistency across international adverse event databases. 

• Improved interoperability between safety and clinical database 

management systems. 

• Automated monitoring software tools to provide notification of 

required product label changes based on changes in adverse 

event frequencies. 

• Improved integration linking data entry, clinical databases, 

randomization databases, and regulatory systems. 

Enhancements in these areas could lead to a number of impacts on the 

productivity of postmarket surveillance activities, such as the following: 

• Reduced labor costs of physicians required to conduct adverse 

event monitoring and data analysis. 

• Reduced labor costs in reconciling differences across 

international adverse event databases. 

• Reduced transaction costs in updating product labels based on 

adverse event frequency changes. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

3-30 

• Reduced labor costs to generate electronic submissions to 

regulator agencies. 

• Reduced risk due to the mitigation of potential errors in analyzing 

and interpreting adverse event data. 
4-1 

 Primary Data 4 Collection 

This study’s data collection activities focused on individual researchers 

and organizations that apply biotechnology to developing human 

therapeutic drug products. Primary data collection methods included onsite and telephone interviews with technical experts from the 

biopharmaceutical industry and an Internet survey of biopharmaceutical 

companies. Secondary data sources included the professional literature 

and economic surveys conducted by the federal government and 

research organizations. This chapter describes the data collection 

process and the key stakeholders surveyed for this study. 

Table 4-1 summarizes the impact metrics respondents were asked to 

quantify during interviews and in the Internet survey. The metrics include 

percentage cost reductions for preclinical, clinical, and manufacturing 

activities; changes in the percentage distribution of where drugs fail; and 

changes in the length of time for preclinical and clinical phases. 

Interaction effects between these impact metrics were captured using a 

spreadsheet model employing the methodology described in Chapter 3. 

Table 4-2 summarizes the number of responses received from our 

interviews and survey by TFA. We conducted 44 in-depth interviews with 

technical experts, senior scientists, and research directors from the 

biopharmaceutical industry. These interviews provided a wealth of 

information relating to current technology infrastructure expenditures and 

the potential impact of improvements on the technology infrastructure. 

Technical experts who participated in the study represented firms whose 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

4-2 

Table 4-1. Impact Metrics Respondents Quantified 

Impact Category Impact Metric Comment 

Cost Reductions in 

Discovery/Preclinical Stage 

and Clinical Trials 

Percentage change in R&D costs 

per successful drug 

(see Table 3-7) 

Standardization may reduce 

the need for redundant tests to 

match and verify results 

Cost Reductions in 

Manufacturing 

Percentage change in manufacturing 

costs 

(see Table 3-9) 

Better approaches to scale-up 

and enhanced process control 

may reduce manufacturing 

costs 

Cost Reductions in 

Postmarket Surveillance 

Percentage change in postmarket 

surveillance costs 

(see Table 3-10) 

Infrastructure improvements 

may lower transaction costs 

experienced in postproduction 

Change in the Probability of 

Success of INDs 

Change in the success rate (%) of 

INDs (see Table 3-8) 

Screening out more drugs in 

the preclinical stage reduces 

the need for clinical trials 

Changes in When Drugs Fail 

in the Clinical Stages 

Change in the percentage 

distribution of where drugs fail (see 

Table 3-8) 

Identifying failed drugs sooner 

rather than later in the clinical 

stage reduces the use of 

expensive Phase II and Phase 

III trials 

Shortening of Drug 

Development Time 

Percentage time reduction of 

preclinical and clinical phases (see 

Table 3-7) 

Decreasing drug development 

time reduces present value 

costs (holding actual costs 

constant) 

Table 4-2. Number of Responses by Technology Focus Area 

Technology Focus Area Interview Internet Survey Total 

R&D Sector 

Bioimaging 7 7 14 

Biomarkers 9 9 18 

Bioinformatics 10 6 16 

Gene expression 7 19 26 

Commercial Sector 

Commercial manufacturing 8 12 20 

Postmarket surveillance 3 5 8 

Total 44 58 102 
Chapter 4 — Primary Data Collection 

4-3 

combined annual R&D spending accounted for as much as 42% of total 

industry R&D spending1

 and 49% of annual R&D sales.2 

 4.1 TECHNICAL EXPERT INTERVIEWS 

RTI conducted detailed on-site and telephone interviews with 44 

technical experts knowledgeable in at least one of the six TFAs. We 

used three information sources to identify prospective participants for the 

telephone interviews: conference proceedings, professional and 

technical literature, and referrals. Several professional societies sponsor 

workshops and conferences on technology state of the art and 

application to one or more components of the biopharmaceutical product 

life cycle. 

 4.1.1 Technical Expert Interview Methodology 

RTI obtained the attendance lists and conference proceedings from 

several recent biopharmaceutical technology conferences and 

workshops. We also searched the professional and technical literature 

for authors of recent articles on each TFA. Interviewees often referred us 

to other individuals knowledgeable about activities in their realm of 

expertise or in complementary research areas. In some cases, RTI 

interviewed several representatives from the same organization to 

acquire as broad a perspective as possible on the organization’s 

infratechnology investments. 

The technical experts RTI contacted represented academia, government, 

and public and private enterprises. The experts we formally interviewed 

are among the most active in the field, as indicated by their conference 

attendance, number of articles published in the professional and 

technical literature, number of corporate white papers authored, and 

participation in formal societies. Interviewees represented professors, 

directors of R&D programs, and product line managers, as well as both 

executive and technical staff (see Table 4-3). 

 

1

RTI calculated market share for the technologies applied in the R&D phases by dividing 

company-reported R&D expenditures in 2004 by the total R&D expenditures for all 

publicly traded biotechnology firms. Market share for the manufacturing and postmarket 

segment was calculated by dividing net sales of biopharmaceuticals of the individual 

firm by the total sales for all commercialized biopharmaceuticals. 2

Responses to the Internet survey were anonymous and hence could not be linked to R&D 

expenditures or sales. Thus, Internet respondents are not represented in the market 

share figures. As a result, the market share of firms participating in this study is greater 

than reported here. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

4-4 

Background 

Research Director Vice President, R&D 

Senior Scientist Chief Medical Officer 

Executive Facility Manager 

Program Director Quality Manager 

Consultant Global Unit Lead 

Each interview was conducted over the telephone and lasted between 30 

and 90 minutes. Typical interviews consisted of a brief overview of the 

study and its objectives, followed by a series of questions aimed at 

assessing the impacts that potential improvements to the technical 

infrastructure would have on different stages of a biopharmaceutical 

product’s life cycle. If further clarification was needed after the initial 

interview about particular comments or issues raised, the interviewee 

was recontacted. 

RTI worked diligently to corroborate anecdotes about the challenges that 

inadequate infrastructures pose, areas of particular need for investment, 

and views on the current state of the industry. Some experts were 

unavailable for lengthy discussions but did discuss general industry 

trends and their perspectives on infrastructural needs. These short 

conversations confirmed comments and insights gathered from other 

interviews but are not included in the responses in Table 4-2. 

 4.1.2 Topics Covered in Technical Interviews 

RTI asked experts to reflect on the current spending, the current 

distribution of costs across different TFAs or processes, and any 

potential role NIST or industry associations might play in infratechnology 

development and adoption. 

The questionnaire consisted of three sections, each containing a series 

of table-format questions. Appendix B contains a copy of the survey. 

The first section asked for background information on the firm, such as 

the firm’s primary line of business, size, and the respondent’s business 

unit’s life-cycle stage. The remaining sections asked about the following: 

• Current Infratechnology Costs: Respondents were asked to 

estimate their annual expenditure on infratechnology, including 

all labor, materials, and equipment expenditures for in-house 

Table 4-3. Examples of 

Interviewed Technical 

Experts’ Job Titles 
Chapter 4 — Primary Data Collection 

4-5 

activities as well as contributions to consortia or partnership 

initiatives. This section also asked for information on 

– distribution of costs by expenditure category (i.e., labor, 

capital, and materials) and enabling technology category, 

– historical trends in infratechnology expenditures, and 

– use of and experience with technologies included in the 

TFAs. 

• Impacts of Technology Improvements: This section asked 

respondents to consider how future improvements in one specific 

TFA might affect the key drivers of drug development and 

commercialization costs. Questions asked about the following: 

– percentage changes in stage length and cost by R&D stage, 

manufacturing stage, or postmarket stage depending on 

which TFA the respondent selected; 

– changes in the overall success rates (percentage of all drugs 

entering clinical trials that receive FDA approval); 

– changes in the distribution of attrition rates (percentage for 

all drugs failing in clinical trials that fail during a specific 

clinical phase); and 

– changes in the probability of recall once a drug receives 

approval. 

• Potential Role for NIST: Interviewees were asked to what extent, 

if any, NIST might play a role in facilitating the technology 

improvements presented in the survey. 

The survey instrument served as the general structure for the interviews. 

However, in many cases the unanticipated information, comments, and 

anecdotal examples obtained during the interviews proved most 

insightful. 

 4.2 INTERNET SURVEY OF BIOPHARMACEUTICAL 

FIRMS 

RTI conducted an Internet survey to reach a broad audience of industry 

participants. The information obtained from this survey was intended to 

complement the more detailed qualitative information obtained through 

the expert interviews. The Internet survey’s objectives were to 

• obtain a national industry average of current infratechnology 

costs, 

• identify the technology areas where the costs of inadequate 

infratechnology are greatest, and 

• identify which components of the technology infrastructure could 

best be served by NIST’s involvement. 

The information gathered from the Internet survey helped quantify the 

magnitude of the current costs associated with the technology 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

4-6 

infrastructure and confirm the impact estimates gathered from our 

technical expert interviews. 

The primary channel used to distribute the online survey was BIO, a 

national trade association representing the biotechnology industry. BIO 

represents companies that apply biotechnology in a number of different 

industries, including alternative fuels, agriculture, and human 

therapeutics. BIO agreed to support our research efforts by distributing 

an invitational e-mail to a subset of the organization’s members 

requesting their participation by completing the Internet survey. 

BIO sent an e-mail message to a select list of its membership to inform 

them of the study and request their participation. The e-mail message 

was targeted to biotechnology firms affiliated with the application of 

biotechnology to human health applications. The invitational e-mail 

directed recipients to an RTI Web page that housed the survey. 

Because BIO’s membership is not limited to any particular type of 

biotechnology firm, we anticipated that companies that responded to the 

survey might represent an array of firms, including biopharmaceutical 

manufacturing, small biotechnology start-ups, and contract research 

organizations (CROs), as well as service firms providing support to larger 

biopharmaceutical firms in analytical, raw materials, or managerial 

support services. 

RTI attempted to increase the number of Internet survey responses by 

sending similar invitational e-mails to various Internet-based professional 

networking communities and technology user groups such as a 

molecular profiling (gene/protein expression) technology users group. 

Many individuals responded to the invitational e-mail and completed at 

least some portion of the Internet survey; however, only 58 responses 

were sufficiently complete to be included in the impact analysis. 

The Internet survey was anonymous (except if the respondent chose to 

self-identify), so we do not know what portion of industry sales or R&D 

expenditures was represented by these completed surveys. 

Seventy-one percent of respondents to the Internet survey were involved 

with one or more of the R&D stages of the biopharmaceutical life cycle. 

Twenty-one percent were involved in commercial manufacturing, and the 

remaining 9% of respondents were associated with postmarket 

surveillance activities (see Table 4-2). Chapter 5 provides a discussion of 

the combined results of the interviews and survey. 
5-1 

5 Analysis Results 

This chapter presents the analytical results from the infrastructure 

expenditure analysis and the counterfactual cost model described in 

Chapter 3. Study participants provided insights and data that permitted 

the calculation of three sets of results: 

• estimated annual expenditures on the technology infrastructure 

and related activities by the biopharmaceutical sector; 

• potential impacts an improved technology infrastructure holds for 

each TFA individually, including cost reductions; changes in 

stage length; and the probability of FDA approval from a cohort 

of INDs; and 

• lower bound and upper bound cost model scenarios that account 

for the uncertainty surrounding participants’ views. 

The quantified potential impact calculations hold all other costs and 

production factors constant. Outside of this project’s model, real-world 

shifts and reassignment of resources are possible and likely, as are 

unrelated process improvements that may lead to overall reductions in 

industry costs. 

 5.1 ANNUAL BIOPHARMACEUTICAL INDUSTRY 

EXPENDITURES ON THE TECHNOLOGY 

INFRASTRUCTURE 

RTI estimates that the biopharmaceutical industry currently spends 

$1,219 million annually on technology infrastructure–related issues, 

encompassing $884 million in spending on drug R&D-related activities 

and $335 million on commercial manufacturing and postmarket 

surveillance activities. The empirical results presented represent a 

snapshot of the biopharmaceutical industry. Estimates were developed 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-2 

using cross-sectional data informed by companies and experts working 

in the biopharmaceutical industry and therefore represent only a single 

point in time. 

 5.1.1 Annual Expenditures by Technology Focus Area 

Study participants provided their companies’ infrastructure-related 

expenditures by TFA. Assuming that their aggregated spending parallels 

the balance of the industry’s, total annual expenditures by TFA can be 

estimated and are presented in Table 5-1: 

• Gene expression systems and biomarkers accounted for over 

half of total technology infrastructure spending in the R&D 

segment at 30% and 24%, respectively. 

• Bioimaging accounts for 15% and informatics for 22%. 

• Study participants allocated the remaining 8% of infrastructure 

expenditures to other R&D activities and technology areas. 

Table 5-1 also includes estimates of annual R&D spending and sales 

that participants attributed to the TFAs. Companies do not necessarily 

categorize infrastructure-related expenditures as R&D expenses, and a 

direct comparison ignores the fact that spending by TFA falls into 

multiple corporate spending accounts. However, the relative shares do 

provide a measure of the intensity with which firms invest in these TFAs. 

Survey data indicate that infrastructure spending accounted for 8.4% of 

R&D. 

The commercial segment had spending of $335 million (approximately 

8% of sales), broken out as follows: 

• Commercial manufacturing accounted for 48% of total 

infrastructure expenditures in the commercial segment. 

• Postmarket surveillance accounted for 52% of total infrastructure 

expenditures in the commercial segment. 

The R&D segment clearly incurs the majority of expenditures; however, 

this distribution does not reflect any relative level of inadequacy among 

TFAs. Many environmental factors must be considered when comparing 

the magnitude of costs across these areas. For example, regulatory 

requirements may be significantly different in terms of testing frequency, 

number of measurements, tests, or analyses placed on the R&D effort 

than on commercial activities. 
Chapter 5 — Analysis Results 

5-3 

Table 5-1. Technology Infrastructure Expenditures by Technology Focus Area, 2005 

Technology Focus Area 

Technology 

Infrastructure 

Spending 

(millions) 

Percentage 

Distribution Relative Spending Intensity 

Bioimaging $136 15% $4,011 per scientist 1.3% of R&D 

Biomarkers $212 24% $6,240 per scientist 2.0% of R&D 

Bioinformatics $198 22% $5,813 per scientist 1.9% of R&D 

Gene expression analysis $265 30% $7,800 per scientist 2.5% of R&D 

Other $73 8% $2,136 per scientist 0.7% of R&D 

Subtotal: R&D Activities $884 100% 

Commercial manufacturing $162 48% $613,000 per approved drug 6.4% of sales

Postmarket surveillance $173 52% $656,000 per approved drug 2.0% of sales

Subtotal: Commercial Activities $335 100% 

Industry Total $1,219 

Source: RTI estimates. 

 5.1.2 Annual Expenditures by Cost Component 

Table 5-2 disaggregates the total estimate of $1,219 million into capital, 

labor, and materials expenditures categories: 

• $218 million or 18% in capital costs (includes all equipment, 

software, and licensing fees related to technology infrastructure), 

• $817 million or 67% in labor costs (includes all research, 

implementation support activities, and participation in consortia 

related to technology infrastructure), and 

• $184 million or 15% in materials costs (includes materials, 

reagents, and any other materials consumed in QA/QC 

processes). 

Labor is the dominant cost component, accounting for two-thirds of the 

industry’s total technology infrastructure expenditures, while the 

combined capital and materials represent the remaining 34%. Labor 

intensity is high in the R&D and postmarket segments, which rely heavily 

on human capital, and is lower in the capital-intensive manufacturing 

sector. 

The cost per scientist (for R&D expenditures) and the cost per approved 

drug (for manufacturing and postmarket surveillance) were scaled to total 

industry spending using the total number of scientists and the total 

number of approved drugs, as discussed in the methodology chapter. 

The scaling factors are included in the table for reference. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-4 

Table 5-2. Annual Technology Infrastructure Expenditures by Cost Category, 2005 

Expenditure Category 

Percentage 

Category 

Distribution 

Cost per 

Scientista

Scaling Factor 

(Scientists) 

Total Costs 

(millions) 

Capital 17% $4,389 34,000 $149 

Labor 68% $17,558 34,000 $597 

Materials 16% $4,053 34,000 $138 

Subtotal of R&D Activities 100% $26,000 $884 

Expenditure Category 

Percentage 

Distribution 

Cost per 

Approved 

Druga

Scaling Factor 

(Approved 

Drugs) 

Total Costs 

(millions) 

Capital 28% $173,513 264 $46 

Labor 47% $286,407 264 $76 

Materials 25% $153,080 264 $40 

Commercial Manufacturing 100% $613,000 $162 

 

Capital 13% $87,467 264 $23 

Labor 83% $546,667 264 $144 

Materials 3% $21,867 264 $6 

Postmarket Surveillance 100% $656,000 $173 

Commercial Activities $1,295,000 $335 

Industry Total $1,219 

a

R&D activities = $/scientist; commercial activities = $/approved FDA drug 

Note: Totals may not add because of rounding. 

 5.1.3 Annual Expenditures on R&D-Related Activities 

The biopharmaceutical industry currently spends approximately $136 

million annually on the technology infrastructure supporting 

bioimaging. Bioimaging is playing an increasingly important role in drug 

development partly because of increased technical capabilities and a 

focus by the industry on chronic diseases (Wang, 2005). Increasingly, 

FDA is requesting imaging studies to be included in the regulatory 

submissions for new drug approvals. 

Spending on biomarkers totals $212 million annually for technology 

infrastructure to support biomarker discovery and validation. 

Researchers are using genomic or proteomic technologies such as SNP 

screening, RNA profiling, protein profiling, and pathway analysis to 

discover new biomarkers in DNA, RNA, and proteins. As stated earlier, 

the intent is for biomarkers to be used as substitutes or surrogates for 

clinical outcomes. They can provide early drug safety or efficacy 
Chapter 5 — Analysis Results 

5-5 

information to researchers on the biological response to treatments, 

potentially reducing the duration and cost of clinical trials (Gingsburg and 

Haga, 2006). The technology infrastructure supporting biomarkers is still 

in the early stages of development. 

Bioinformatics spending was estimated at $198 million annually.

Current efforts are fundamentally about the ability to exchange, interpret, 

and analyze data. In the words of one interviewee, a lack of standards 

“creates a constant struggle.” 

The industry’s massive data production capacity relies on information 

technologies to manage and analyze data output. The other TFAs, 

particularly gene expression, rely on bioinformatics to investigate 

hypotheses, identify gene targets, and guide research strategies. 

Analysis results indicate that gene expression was the largest 

infrastructural spending area at $265 million annually. 

In an ideal world, information flowing from external and internal sources, 

and across development stages (where possible), would converge in 

software applications that would evaluate drug candidates on an ongoing 

basis. However, interviewees reported that in reality data production 

capacity in the posthuman genome era outstrips the industry’s ability to 

manage, coordinate, analyze, and communicate the resulting data 

across software systems and technology platforms. Yet these analyses 

inform decision making on a daily basis. 

Many interviewees stated that their companies continually invest in their 

bioinformatics systems—software, hardware, and procedures—to make 

as effective use as possible of the genomic data produced internally or 

acquired from external databanks. 

 5.1.4 Annual Expenditures on Commercial Activities 

Analysis results indicate that commercial manufacturing 

infrastructure spending was $162 million annually. Participants 

identified that technology infrastructure expenditures arise from data 

verification, validation, and QA/QC processes. Related to these 

technology infrastructures, problems may arise from data transfer from 

the development lab bench to production, data assembly, and 

interpretation at manufacturing facilities. 

Manufacturing operations operate in a regulatory environment that 

requires validation of manufacturing processes. The data infrastructure 

inadequacies increase the cost of demonstrating regulatory compliance 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-6 

and manufacturing costs by limiting firms’ understanding of their 

manufacturing processes and constraining their ability to efficiently 

conduct process monitoring. 

Results indicate that technology infrastructure spending associated with 

postmarket surveillance was $173 million annually in 2005.

Participants in the study indicated that infrastructure expenditures 

accounted for 0.5% of annual sales. This result is somewhat higher than 

a recent empirical estimate of $56 million (0.3% of annual sales) for the 

average pharmaceutical company’s expenditures on postmarket 

surveillance in 2003, though their average market size is larger (Ridley et 

al., 2006). 

Previous studies calculated postmarket surveillance spending as a 

percentage of global sales, whereas the current study calculates it as a 

percentage of domestic sales in the United States. The denominator is 

smaller, yet because most biopharmaceutical companies are American, 

the postmarket surveillance infratechnology expenses accrue at R&D 

facilities located in the United States. Thus, overall results from this study 

are consistent with recent published estimates given the 3-year 

difference between estimates and the use of domestic rather than 

international sales. 

Technology infrastructure costs related to postmarket surveillance were 

primarily associated with data collection, data entry and analysis, quality 

control, data systems design and validation activities, training on 

reporting adverse events, and product relabeling activities motivated by 

safety issues. 

Participants we interviewed confirmed that labor expenses accounted for 

83% of postmarket surveillance infratechnology spending. Capital 

spending was only 13% and consisted primarily of database design and 

information collection technologies. The remaining 3% was associated 

with materials expenses. 

Postmarket surveillance costs have been increasing because of greater 

complexity in regulatory compliance, divergence between global 

regulatory bodies, and lower levels of data element harmonization 

through increased complexity in safety study design and reporting 

requirements. Participants suggested that, although overall infrastructure 

costs have risen over time, postmarket activities have been underfunded 

in previous years and are only recently reaching adequate levels as a 

result of increased attention to risk management. 
Chapter 5 — Analysis Results 

5-7 

 5.2 POTENTIAL EFFICIENCY GAINS FROM AN 

IMPROVED TECHNOLOGY INFRASTRUCTURE 

This section presents the potential efficiency gains from the 

counterfactual scenario detailing an improved technology infrastructure 

for each TFA. RTI presents results both as actual expenses and as 

present-value costs, which takes into account the time value of money 

and the opportunity cost savings to firms as the length of the drug 

development process is shortened due to an improve technology 

infrastructure. 

Reducing R&D expenses while simultaneously shortening development 

time and improving research effectiveness generates savings on actual 

expenses. Given that R&D expenses for an approved drug can accrue 

over a period of 12 years, changing both the amount and timing of 

expenses can have a significant impact on benefits calculations (see 

Figure 5-1). 

Figure 5-1. Illustration of How Expected Costs per Approved Drug Change with an 

Improved Technology Infrastructure 

Discovery/Preclinical Phase I Trials Phase II Trials

C0

C1

Baseline

Counterfactual

C0

C1

Baseline expected R&D costs per approved drug

Expected R&D costs per approved drug with an improved technology infrastructure

Approval1 Approval0

Discovery/Preclinical Phase I Trials Phase II Trials

 Phase III Trials

Phase III Trials

Time

Expected Cost 

per Approved 

Drug

($)

FDA Review

FDA Review
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-8 

Present-value efficiency gains were calculated using the same 11.5% 

hurdle rate employed by the 2007 DiMasi and Grabowski study. Although 

the discount rate in this study is higher than the 7% social discount rate 

recommended by the Office of Management and Budget (OMB) for 

public benefits, the 11.5% rate better approximates the return-oninvestment requirements faced by biopharmaceutical companies. Refer 

to Section 3.1 for a discussion of the 11.5% discount rate. 

This section begins with a comparison of survey results on 

counterfactual drug approval rates, transition probabilities, and potential 

stage length and time reductions for each TFA except bioimaging. 

Survey results for bioimaging were inconclusive as respondents were not 

willing to speculate on the potential impacts for several key model 

parameters, although all were in agreement that improvements would 

generate significant benefits. In the absence of sufficient survey data, 

anecdotal evidence from study participants in this TFA was compiled to 

illustrate potential efficiency gains. 

Study participants assessed the potential improvements with respect to a 

single TFA and improvements are presented on that basis, except in the 

summary section in which improvements are compiled to assess the 

potential simultaneous improvements to all TFAs’ technology 

infrastructures. 

 5.2.1 Counterfactual Success Rates and Cost and 

Development Time Reductions for Biopharmaceutical 

INDs 

Survey results indicate that an improved infrastructure would increase 

the probability that FDA would approve an IND. They also indicate that a 

greater share of INDs that fail would do so earlier in clinical trials. 

Although each clinical trial has the intent of studying a different aspect of 

an IND, each subsequent phase has a larger patient group, and the 

analytical studies conducted are cumulative. Greater confidence and 

assurance, conveyed through improved statistical power in 

comparatively smaller patient groups, translate into lower clinical trial 

costs under the counterfactual scenario. 

Table 5-3 presents the overall changes in success and failure rates 

across clinical trial phases predicted by survey respondents by TFA. 

Participants were asked to assess how the typical distribution of failures 

within clinical trials would change under the improved infrastructure 
Chapter 5 — Analysis Results 

5-9 

Table 5-3. Comparison of Estimated Potential Success and Failure Rates by TFA 

For INDs Failing in Clinical Trials, Percentage 

Failing by Phasea

Technology Focus 

Area 

IND Approval 

Probability Phase I Phase II Phase III Total 

Probability 

of Recall 

Baseline 30.2% 23.4% 52.4% 24.2% 100.0% 0.40% 

Biomarkers 41.0% 39.2% 37.0% 23.8% 100.0% 0.30% 

Bioinformatics 40.0% 30.0% 40.5% 29.5% 100.0% 0.30% 

Gene expression 45.0% 37.5% 35.5% 27.0% 100.0% 0.10% 

a

Using the baseline case as an example, if 100 INDs enter clinical trials, the IND approval probability suggests that 

~30 INDs will eventually receive FDA approval. The remaining 70 INDs will then fail in one of the three clinical trial 

phases. During Phase I, ~16 INDs or 23% of the 70 INDs would fail. In Phase II, an additional 37 INDs or 53% of 

70 are likely to fail. The remaining 17 or 24% of the 70 IND failures would then fail during Phase III. 

Note: Comparable data for bioimaging could not be calculated; thus, bioimaging was excluded from this table. RTI 

estimates based on DiMasi and Grabowski (2007). 

scenario. Holding the probability of FDA approval constant, the 

distribution of the failures across the three trial stages shifted toward 

earlier failures. 

The DiMasi and Grabowski study forms the baseline for this analysis, 

which is that FDA ultimately approves 30.2% of biopharmaceutical INDs 

that enter clinical trials. Of the INDs that fail, 52.4% fail during Phase II 

and 24.2% fail in Phase III. 

Several interviewees stated that improvements to each counterfactual 

technology infrastructure would improve researchers’ knowledge of a 

drug’s mechanism and its likely reception by the human body or the drug 

target. Overall, improved confidence in and assurance of candidates 

would result in better INDs entering clinical trials, suggesting that the 

drugs in clinical trials would be more likely to gain FDA approval. 

An improved gene expression infrastructure holds the greatest potential 

(45.0% versus 30.2%), in terms of an anticipated improvement in FDA 

approval rates. Improved infrastructures for bioinformatics and 

biomarkers could boost IND approval probabilities to 40.0% and 41.0%, 

respectively. It is important to note, however, that no statements were 

made on how the absolute number of INDs submitted to the FDA for 

clinical trials may change. 

A small number of larger biopharmaceutical firms reported that they 

would not adopt new industry standards after investing in and developing 

systems, protocols, and procedures that they view as effective. Yet, an 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-10 

improved infrastructure may improve accuracy within the same protocol. 

Such firms may not adopt a new series of protocols, but if the 

measurement systems supporting their proprietary methods were to 

improve, the methods would also improve. Other respondents reported 

that improvements would have little to no impact on success rates. 

These individuals believed that improvements in technical infrastructure 

would not address the fact that it is not always possible to predict exactly 

how a novel compound would react until it is inside the body. However, 

an improved infrastructure would yield significant improvements in time 

and cost. 

The results in Table 5-3 are in percentage terms because it is not 

possible to predict the absolute number of INDs that would enter clinical 

trials under the improved infrastructure scenarios. In terms of the 

distribution of INDs that fail during clinical trials, the percentage of 

failures shifted toward earlier failures. That is, a greater proportion of 

failures would occur in Phase I, which in turn leads to a lower rate of 

failures in Phase II where efficacy is evaluated for the first time. The 

proportion that fails in Phase III remained relatively constant or increased 

slightly in the improved infrastructure scenarios relative to the baseline. 

Such muted variation in Phase III failures between scenarios may reflect 

the limitations to our current scientific knowledge base of the interactions 

between chemical compounds and the human body. An increased 

understanding of biological systems and processes would likely have a 

greater impact on Phase III failures than an improved technology 

infrastructure. 

Finally, respondents believed that the overall probability of recall once a 

drug had received approval was likely to decrease. Overall, most 

participants believed that the improved infrastructure scenario presented 

would have a positive effect, thus reducing the likelihood that a drug, 

once approved, would be recalled. 

The magnitudes of the reductions in cost and stage length were typically 

associated with the stage where the technology was applied or used 

most often. For example, biomarkers were predicted to have little net 

impact on early discovery/preclinical and Phase I clinical trials because 

improvements could increase upfront costs, even though such 

expenditures could achieve costs and time savings in later Phase II and 

Phase III clinical trials, when researchers would have more efficient 

surrogates for clinical end points. Biomarkers have the ability to shorten 

the length of clinical trials by providing researchers with clinical 
Chapter 5 — Analysis Results 

5-11 

information on drug safety and efficacy more quickly. For example, 

surrogate biomarkers may provide information on the progression of a 

disease following treatment more quickly than without the biomarker. 

Biomarkers may also improve patient stratification when selecting 

volunteers for clinical trials. This may lead to more targeted clinical trials 

with higher success rates. 

Conversely, gene expression impacts were concentrated in earlier 

stages of development. Improvements in comparability standards for 

gene expression and other infratechnologies could potentially improve 

the efficiency of discovery screening and preclinical PD/PK and ADME 

toxicity analysis. Study participants were consistent in their belief that an 

improved gene expression technology infrastructure would not have the 

same magnitude of impact on Phase I and II clinical trials as on earlier 

stage activities. Whereas survey results indicate the counterfactual 

technology infrastructure could yield discovery/preclinical phase cost 

reductions of 19% and time reductions of 26%, those same benefits are 

comparatively lower in Phase III clinical trials (8% reduction in both time 

and cost). 

The trend for the counterfactual bioinformatics infrastructure parallels 

that for gene expression, but the data for bioimaging were inconclusive; 

therefore, bioimaging was not included in Table 5-4. 

 5.2.2 Potential Gains from Improvements in Bioinformatics 

Table 5-5 presents the baseline data from the 2007 DiMasi and 

Grabowski study, and Table 5-6 illustrates how the counterfactual 

infrastructure for bioinformatics could affect the cost, development time, 

and probability of success for a new drug. According to the data supplied 

by experts, the counterfactual technology infrastructure could increase 

the probability of FDA approval for the average IND from 30.2% to 40% 

(see Section 3.3.7 for an accounting of the counterfactual bioinformatics 

infrastructure). 

Participants stated that improved standardization in measurements, data 

formats, procedures, and protocols could reduce costs associated with 

redundant testing and thereby reduce stage length, while improvements 

in automation for data acquisition, interpretation, and feedback could 

also reduce costs. The reduction in current costs would result from 

efficiency gains and productivity improvements via improved access to 

more timely and accurate information, better understanding of biological 

processes, and more insightful analysis enabled by higher-quality data. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-12 

Table 5-4. Estimated Reductions in R&D Cost and Stage Length 

Percentage Change from Baseline 

Technology Focus Area Stage 

Expected Stage Cost 

per Investigational 

Compound (IND) 

Stage Length 

(in months) 

Bioinformatics 

Discovery/preclinical –20% –17% 

Phase I –15% –12% 

Phase II –13% –11% 

Phase III –13% –8% 

Biomarkers 

Discovery/preclinical –1% –7% 

Phase I –6% –14% 

Phase II –16% –16% 

Phase III –46% –44% 

Gene expression analysis 

Discovery/preclinical –19% –26% 

Phase I –15% –15% 

Phase II –11% –10% 

Phase III –8% –8% 

Note: Comparable data for bioimaging could not be calculated; thus, bioimaging was excluded from this table. 

Table 5-5. Baseline Expected Cost per IND and per Approved Drug 

Stage 

Baseline 

Stage Cost, 

per IND 

(millions) 

Probability of 

IND Entering 

Stage 

Expected 

Baseline 

Stage Cost, 

per IND 

(millions) 

Baseline 

Probability of 

IND Approval

Estimated 

Baseline Stage 

Cost, per 

Approved Drug 

(millions) 

Baseline 

Stage 

Length 

(months) 

Discovery/ 

preclinical 

$59.9 $59.9 30.2% $198.3 52.0 

Phase I $32.3 100% $32.3 30.2% $106.9 19.5 

Phase II $37.7 83.7% $31.5 30.2% $104.5 29.3 

Phase III $96.1 47.1% $45.3 30.2% $149.9 32.9 

Total $169.0 $559.6 133.7 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. The period between completion of Phase III clinical trials and FDA 

approval is assumed to be 16 months. 

Source: DiMasi and Grabowski (2007). 
Chapter 5 — Analysis Results 

5-13 

Table 5-6. Potential Efficiency Gains from an Improved Bioinformatics Infrastructure 

Stage 

Improved 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability of 

IND Entering 

Stage 

Expected 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability of 

IND Approval

Estimated 

Stage Cost, 

per Approved 

Drug 

(millions) 

Improved 

Stage 

Length 

(months) 

Discovery/ 

preclinical 

$47.7 $47.7 40.0% $119.3 43.2 

Phase I $27.4 100% $27.4 40.0% $68.6 17.2 

Phase II $32.6 79.4% $26.8 40.0% $67.0 26.1 

Phase III $83.3 59.9% $48.1 40.0% $120.1 30.2 

Total $150.0 $375.0 116.6 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. Table 5-5 includes percent changes in cost and phase length 

relative to the baseline. The period between completion of Phase III clinical trials and FDA approval is assumed to 

be 16 months. 

Source: RTI estimates based on DiMasi and Grabowski (2007). 

Bioinformatics activities are closely linked to microarray experiments, 

flow cytometry, and other data acquisition systems because data 

production systems feed databases. The quality and accuracy of the 

data and the confidence researchers have in it directly affect the 

confidence they have in the subsequent statistical analyses. Thus, 

infratechnology activities and investments discussed in the gene 

expression TFA would have a symbiotic relationship with the 

bioinformatics TFA. 

The expected reductions in costs and stage lengths change the stage 

cost per IND and the stage length per IND, which are represented in the 

first and last data columns in Table 5-7. But the improved infrastructure 

is estimated to also increase the probability of FDA approval and cause 

INDs that fail to do so earlier in clinical trials. 

Companies take a portfolio approach to drug development, meaning that 

they expect that some INDs to fail. Each IND that becomes an FDAapproved drug carries the costs of failed INDs from its cohort, thereby 

representing the total actual cost of developing a new drug. 

Mathematically, the expected cost per IND is the sum of each stage cost 

multiplied by the probability that the IND reaches that stage. 

Once the clinical trial data and final application are submitted to FDA, 

there is still the probability that FDA will not approve the IND. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-14 

Table 5-7. Potential Cost Reductions per IND and per Approved Drug: Bioinformatics 

Baseline 

(millions) 

Improved 

Infrastructure 

(millions) 

Percentage 

Change 

Expected actual R&D cost per IND $169.0 $150.0 –11% 

Expected actual R&D cost per approved drug $559.6 $375.0 –33% 

Expected present-value R&D cost per IND $374.7 $298.5 –20% 

Expected present-value R&D cost per approved drug $1,240.9 $746.3 –40% 

Source: RTI estimates based on DiMasi and Grabowski (2007). 

Participants believe that an improved gene expression infrastructure 

would improve R&D effectiveness to such an extent that it would be 

possible to have an average approval probability of 45%. Given that 

companies expect each IND to have an average cost of $150 million, but 

also expect that only 45% of INDs that complete clinical trials would be 

approved, they expect the average cost of actually receiving an approval 

to be $375 million. This figure is the expected cost per IND divided by the 

probability of FDA approval. 

Note that, although the cost per IND for all stages has decreased from 

the baseline in the improved scenario, the probability of entering Phase 

III trials has increased because the probability of the IND being a 

success has increased. As a result, more INDs may enter Phase III trials 

and incur additional costs. A larger number of INDs from a portfolio 

would be commercialized. Total costs for the portfolio would increase but 

so would the return on the total portfolio. 

Thus, when taking into consideration improvements to the probability of 

success, cost, and development times, the estimated actual cost per 

approved drug drops from $559.6 million to $375.0 million, a 33% 

reduction (see Table 5-7). In comparison, the expected actual R&D 

expense per IND is $19.0 million lower. 

Accounting for firms’ investment horizons and their opportunity costs with 

the weighted average 11.5% cost of capital provides a more dynamic 

view of these savings. Development times are expected to decrease 

from just over 11 years to just under 10 years, on average. Baseline and 

counterfactual present-value costs were adjusted to account for 

difference in timing and relative magnitude of cash flows. The efficiency 

gains result in a present-value cost per IND of $298.5 million under the 

counterfactual scenario, which is a reduction of 20% from the baseline. 
Chapter 5 — Analysis Results 

5-15 

The estimated present-value cost per approved drug changes from 

$1,241 million to $746 million, a reduction of 40%, all else held equal. 

The FDA Critical Path reports that “...some believe extensive use of in 

silico technologies could reduce the overall cost of drug development by 

as much as 50%” (FDA, 2004b). The results from this analysis, while 

lower than suggested in the FDA report, are reasonably close to the 

FDA’s rough estimate. 

 5.2.3 Potential Gains from Improvements in Biomarkers 

Biomarkers have the potential to lower the cost of developing new drugs 

by providing potential safety and efficacy information to researchers with 

greater precision and in less time. Biomarkers could have the largest 

impact on Phase II and III clinical trials, as respondents estimated that 

biomarkers could reduce these costs by as much as 46% and shorten 

stage length by 44%. During Phase III, researchers must validate the 

efficacy of drug treatment in the largest patient group. Improved 

biomarkers would allow trial design to better stratify the patient cohorts 

and reduce uncertainty with respect to clinical endpoints. 

The improved infrastructure scenario could also increase the success 

rate for new drug candidates entering clinical trials. However, the largest 

impact would come from the improvement in researchers’ ability to 

predict biological responses in humans earlier in the drug development 

process. While the improved infrastructure scenario could increase 

productivity in discovery and preclinical testing phases, the largest 

impact would come from the ability to shorten the clinical trials. If 

researchers are able to obtain critical drug efficacy information faster 

during clinical trials, it would greatly reduce the number of months and 

potentially the number of patients required to conduct a clinical trial. 

One participant cited that with improved biomarkers clinicians and drug 

researchers could better understand the immune system response, as 

well as body tolerances for drug dosages. However, another participant 

commented that, although safety biomarkers could potentially lower 

clinical trial costs, the complexity of the human body and disease 

pathways make it highly unlikely that such biomarkers could exist within 

the next 10 years. 

Table 5-8 presents survey results on how biomarkers would affect 

development times and costs. While impacts on the discovery/preclinical 

stage are comparatively low (1% of cost and 7% of lead time), the 

benefits of biomarkers grow as clinical trial patient groups become larger 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-16 

Table 5-8. Potential Efficiency Gains from an Improved Biomarkers Infrastructure 

Stage 

Improved 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability of 

IND Entering 

Stage 

Expected 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability 

of IND 

Approval 

Estimated 

Stage Cost, per 

Approved Drug 

(millions) 

Improved 

Stage 

Length 

(months) 

Discovery/ 

preclinical 

$59.4 $59.4 41.0% $144.9 48.4 

Phase I $30.3 100% $30.3 41.0% $74.0 16.8 

Phase II $31.7 76.9% $24.3 41.0% $59.4 24.6 

Phase III $51.9 55.0% $28.6 41.0% $69.7 18.4 

Total $142.7 $347.9 108.2 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. Table 5-5 includes percentage changes in cost and phase length 

relative to the baseline. The period between completion of Phase III clinical trials and FDA approval is assumed to 

be 16 months. Source: RTI estimates based on DiMasi and Grabowski (2007). 

in each stage. Study participants believed that improved biomarker 

technology infrastructure could lead to Phase III clinical trial cost 

reductions of 46% and lead time reductions of 44%, as shown in 

Table 5-4. 

When taking into consideration improvements to the probability of 

success, cost, and development times, the estimated actual cost per 

approved drug drops from $559.6 million to $347.9 million, a reduction of 

38% due in large part to reduced clinical trial costs (see Table 5-9). 

As with bioinformatics, RTI also accounted for firms’ investment horizons 

and their opportunity costs using the weighted average 11.5% 

biopharmaceutical cost of capital. Average time from discovery through 

the completion of Phase III clinical trials is expected to decline from over 

11 years to 9 years. Baseline and counterfactual present-value costs 

were adjusted to account for differences in timing and relative magnitude 

of cash flows. The efficiency gains results in a present-value cost per 

IND of $278 million under the counterfactual scenario, which is a 

reduction of 26% from the baseline. The estimated present-value cost 

per approved drug changes from $1,241 million to $677 million, a 

reduction of 45%. 

 5.2.4 Potential Gains from Improvements in Gene and 

Protein Expression 

As discussed previously, gene expression and bioinformatics processes 

are tightly integrated. For example, microarray analyses generate data 
Chapter 5 — Analysis Results 

5-17 

Table 5-9. Potential Cost Reductions per IND and per Approved Drug: Biomarkers 

Baseline 

(millions) 

Improved 

Infrastructure 

(millions) 

Percentage 

Change 

Expected actual R&D cost per IND $169.0 $142.7 –16% 

Expected actual R&D cost per approved drug $559.6 $347.9 –38% 

Expected present-value R&D cost per IND $374.7 $277.5 –26% 

Expected present-value R&D cost per approved drug $1,240.9 $676.9 –45% 

Source: RTI estimates based on DiMasi and Grabowski (2007). 

that informaticists use to develop lists and identify clusters of genes 

expressed in tissue samples and the genes’ functions. Researchers 

evaluate the data output and determine and refine research strategies. 

Gene expression data may be developed internally or be acquired from 

outside the organization. Large genomic data sets are publicly available, 

but interviewees in this TFA stated that most gene expression data are 

produced internally. Indeed, external databases may not be usable if the 

data they contain cannot be integrated with information generated 

internally. 

Standardization has the potential to make acquiring and evaluating gene 

expression data less resource intensive. One interviewee provided the 

following example: if a lab runs three samples per patient during a 

clinical trial, and there are 300 patients, it would expect costs of 

$670,000. Expenses would be $130,000 for labor, $360,000 for 

microarrays, and $180,000 for reagents. A suite of standards and 

measurement tools could enable a process in which only 200 patients 

were needed to get the same statistical power as 300 patients, thereby 

reducing the lab’s clinical trial costs. 

Ultimately, firms want drug targets to fail as early in the process as 

possible. Failing early prevents resources from being assigned to the 

project in the first place, which is important because the further an 

erroneous target moves forward in the discovery and preclinical phase, 

the greater is the cost that will need to be distributed over successful 

projects. 

Table 5-10 shows how study participants believed the improved 

technology infrastructure for gene expression presented in Section 3.3.7 

would affect costs, lead times, and the probability of having an IND 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-18 

Table 5-10. Potential Efficiency Gains from an Improved Gene Expression 

Infrastructure 

Stage 

Improved 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability of 

IND Entering 

Stage 

Expected 

Stage Cost, 

per IND 

(millions) 

Improved 

Probability of 

IND Approval

Estimated 

Stage Cost, 

per Approved 

Drug 

(millions) 

Improved 

Stage Length

(months) 

Discovery/ 

preclinical 

$48.4 $48.4 45.0% $107.6 38.5 

Phase I $27.4 100% $27.4 45.0% $61.0 16.6 

Phase II $33.5 79.4% $26.6 45.0% $59.0 26.4 

Phase III $88.9 59.9% $53.2 45.0% $118.2 30.4 

Total $155.6 $345.8 111.9 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. Table 5-5 includes percentage changes in cost and phase length 

relative to the baseline. The period between completion of Phase III clinical trials and FDA approval is assumed to 

be 16 months. Source: RTI estimates based on DiMasi and Grabowski (2007). 

become an FDA-approved drug. One company thought that an ideal 

world would allow one lab of four technicians and six Ph.D. analysts to 

be five times as productive. That company further added that a 50% 

improvement would have the same magnitude of impact because of the 

relationship between information flows in the discovery process. 

When taking into consideration improvements to the probability of 

success, cost, and development times, the estimated actual cost per 

approved drug drops from $559.6 million to $346 million, a reduction of 

38% (see Table 5-11). The expected actual R&D expenses per IND are 

more than $13 million lower. Once again, these estimates are adjusted 

for the fact that close to 60% of INDs enter Phase II trials under the 

improved scenario. 

Accounting for firms’ investment horizons and their opportunity costs with 

the weighted average 11.5% cost of capital provides a more dynamic 

view of these savings. Average time from discovery through the 

completion of Phase III trials is expected to decline from over 11 years to 

approximately 10 years under this scenario. Baseline and counterfactual 

costs were adjusted to account for difference in timing and relative 

magnitude of cash flows. The efficiency gains result in a present-value 

cost per IND of $304 million under the counterfactual scenario, which is a 

reduction of 19% from the baseline. The expected present-value cost per 

approved drug changes from $1,241 million to $676 million, a reduction 

of 45%, all else held equal. 
Chapter 5 — Analysis Results 

5-19 

Table 5-11. Potential Cost Reductions per IND and per Approved Drug: Gene 

Expression 

Baseline 

(millions) 

Improved 

Infrastructure 

(millions) 

Percentage 

Change 

Expected actual R&D cost per IND $169.0 $155.6 –8% 

Expected actual R&D cost per approved drug $559.6 $345.8 –38% 

Expected present-value R&D cost per IND $374.7 $304.2 –19% 

Expected present-value R&D cost per approved drug $1,240.9 $676.0 –45% 

Source: RTI estimates based on DiMasi and Grabowski (2007). 

 5.2.5 Potential Gains from Improvements in Bioimaging 

Respondents were unable to quantify metrics needed to populate the 

improved infrastructure model for bioimaging. However, they did provide 

quantitative examples of how improvements in infrastructure might 

reduce current costs, identifying potential opportunities for reductions in 

R&D stage costs and duration through improving access to image 

capture and analysis technology, data standardization, and data 

management. 

For example, typical costs of a Phase III imaging study could run as high 

as $100 million. Such studies can include as many as a 1,000 patients at 

100 different clinical sites. Interviews indicated that a cost savings of 

15% could result under the improved infrastructure scenario. The 

impacts would include the following: 

• Cost reduction of 10% due to 

– reduction in number of subjects for each clinical study 

– compressed timeline to conduct study 

– reduction of internal resources and staff (i.e., CRO 

radiologist costs) 

• Cost reduction of 5% due to 

– mitigation of the courier costs to transport images to an 

analysis lab for interpretation 

Inadequate ability to acquire and effectively use bioimaging technology 

has resulted in increased errors in imaging data and time delays in 

conducting imaging analysis. To date, the industry has relied on major 

research hospitals such as MD Anderson, the Mayo Clinic, SloanKettering, Duke, and Johns Hopkins to conduct clinical imaging studies, 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-20 

and image analysis and interpretation has been largely outsourced to 

CROs. 

Increased demand for clinical imaging studies is putting additional 

burden on the imaging hospitals conducting these studies. Currently it 

takes between 3 and 4 months from image creation to image 

interpretation. This time lag makes it impossible to look at a single 

subject over time. One scientist reported that by the time a baseline scan 

is interpreted too many things have changed to isolate any positive 

treatment effects. As a result, clinical trials have to increase the number 

of subjects to obtain statistically valid results. 

The variance in imaging protocols and data requirements has caused 

costly time delays and increased errors in image capture. Errors have 

been increasing as hospitals and imaging technicians are asked to follow 

a diverse set of imaging study protocols and data requirements defined 

by individual biopharmaceutical firms. Time delays associated with 

correcting errors can be significant because analysis of imaging data is 

conducted by a CRO in a different location. The transfer of data can 

sometimes take weeks. As a result, errors are discovered long after the 

data are originally captured. 

Drug companies must validate images from large numbers of globally 

dispersed clinical trial sites. Current levels of data standardization and 

image labeling and formatting are inadequate and require additional 

effort to ensure the comparability of images across trial sites. Any 

standards also have to meet the privacy guidelines of the Health 

Insurance Portability and Accountability Act (HIPAA). 

The American College of Radiology and the National Electrical 

Manufacturers Association formed a joint committee to develop a 

Standard for Digital Imaging and Communications in Medicine known as 

DICOM. Study participants reported that this effort was a step in the right 

direction, but that it was not sufficient to meet current or future needs. 

In addition, the biopharmaceutical industry lacks the digital data storage 

capacity or bioinformatics database infrastructure to archive, store, and 

retrieve image data effectively. This problem will increase in the near 

future. Scientists predict that, as imaging technology improves, the size 

of the typical imaging file will also increase. 
Chapter 5 — Analysis Results 

5-21 

 5.2.6 Potential Impacts on Commercial Manufacturing 

Costs 

Whereas gains from an improved infrastructure were measured relative 

to the average cost per IND and per FDA-approved drug on the R&D 

side, the analysis estimated efficiency gains relative to total industry 

manufacturing expenses. The estimated efficiency gains in this report for 

manufacturing and R&D are therefore not additive. This is because 

information on average or “typical” products was not available publicly, 

requiring RTI to measure potential gains relative to estimated total 

industry manufacturing costs. 

Study participants reported that manufacturing costs amounted to 17% of 

their companies’ total annual sales. The impact results reported in 

Table 5-12 were based on the total annual domestic biopharmaceutical 

sales of $37.3 billion in 2005 as reported in company annual 10-K 

reports. Thus, baseline total biopharmaceutical production costs 

approximated $6.3 billion in 2005. RTI applied Frost and Sullivan’s 2004 

estimates to distribute total production costs across individual 

manufacturing phases. 

An inadequate data infrastructure impedes the efficient transfer of 

scientific, process, and regulatory compliance data from R&D to 

manufacturing. These data transfer complications can result in delays in 

production and additional labor costs associated with tracking down or 

regenerating lost information. 

Improvement to the technology infrastructure to support manufacturing 

could reduce manufacturing costs by 23%, according to data supplied by 

study participants. This 23% estimate represents a reduction of $1.5 

billion from estimated 2005 annual manufacturing costs. Table 5-12 

reports the percentage reductions in cost by phase from the estimated 

baseline. 

Study participants provided their estimates of how an improved 

infrastructure would reduce manufacturing costs by phase. They 

estimated a 29% reduction in preproduction costs and a 22% reduction 

in downstream processing costs, among other impacts, that would result 

from the improved infrastructure described in Section 3.3.7. That 

counterfactual technology infrastructure was the implementation of the 

FDA’s PAT initiative. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-22 

Table 5-12. Impact on Commercial Manufacturing Costs 

Baseline Production Costs 

Potential Change 

in Cost by Stage/Activity 

Stage/Activity Cost 

Percentage of 

Totala

Baseline 

Total 

(millions) 

Percentage 

Change 

Change in 

Cost 

(millions) 

Costs under an 

Improved 

Infrastructure 

(millions) 

Preproduction 30% $1,900 –29% –$551 $1,349 

Upstream processing 20% $1,267 –18% –$228 $1,035 

Downstream processing 40% $2,533 –22% –$557 $1,976 

Process monitoring and quality 

assurance testing 

10% $633 –23% –$146 $491 

Total commercial 

manufacturing costs 

 $6,333 –$1,482 $4,851 

a

From Frost and Sullivan (2004). 

Source: RTI estimates. 

Industry experts expect that PAT would increase production yields, 

reduce time delays, and reduce product quality variability through 

continuous real-time process monitoring and sample measurement. For 

example, cell culture development may take 12 to 14 days. Once 

sufficient yields are produced, a sample is taken and sent to an analytics 

lab. The analysis requires an additional 5 to 10 days to run the sample 

and generate results. 

Improved technology infrastructure through PAT would allow for real-time 

analysis, which may 

• allow process engineers to react to information as it is 

generated, 

• potentially eliminate as many as 14 days of off-line analysis, 

• eliminate a number of bioreactor runs, and 

• generate labor savings. 

One interviewee stated that the cost of batches lost during production 

because of an equipment failure could average $1 million. PAT aims to 

reduce variability in product profiles between batch runs by 

• providing a better understanding of product tolerances, 

• lowering the standard deviation and coefficient of variation in 

processes, and 

• optimizing buffer volumes required during purification stages. 
Chapter 5 — Analysis Results 

5-23 

The net impact would be fewer variations in end-product quality. Cost 

savings would accrue through such impacts as using fewer raw 

materials, incorporating fewer manufacturing processes, and lowering 

inventory and facility expenditures. 

 5.2.7 Potential Impacts on Postmarket Surveillance Costs 

Analysis of the potential that an improved infrastructure holds for 

postmarket surveillance costs did not provide sufficient insight with which 

to estimate cost reductions. In most cases, study participants agreed that 

gains were possible, but they also stated that those gains were too 

intangible for them to assign percentage reductions to. Several experts 

suggested that the variation in costs per postmarket surveillance study 

was simply too high to estimate potential across the board reductions. 

Major cost components in postmarket studies relate to the design of a 

core reporting system, data management, clinical site management, and 

analysis. Interviewees hypothesize that the improved infrastructure 

proposed in Section 3.3.7 would at least offer the following impacts: 

• data management activities could be reduced by as much as 

35% globally, 

• clinical site management activities could be reduced by as much 

as 20%, and 

• data analysis could be reduced by as much as 25%, given 

standardized reporting and consistent data formats. 

Data on adverse events and postmarket safety must be collected across 

regional sites that may employ differing, and often competing, tracking 

systems maintained in multiple languages. The situation is made more 

complicated by reporting obligations that vary by country. Firms dedicate 

labor and resources to developing reporting protocols and training 

clinicians on data entry. 

 5.3 COMBINED POTENTIAL IMPACT OF AN 

IMPROVED TECHNOLOGY INFRASTRUCTURE 

The preceding section reviewed the efficiency gains study participants 

believed the counterfactual technology infrastructure would have for 

each TFA individually. This section presents estimates on the gains the 

combined impacts from the four R&D TFAs would have on stage length 

and costs. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-24 

There exists some potential for varying degrees of substitution among 

the TFAs that the study was not able to capture. Some counterfactual 

infrastructural improvements―like gains in gene expression and 

bioinformatics, which were evaluated independently―exhibited at least 

some influence across all phases of R&D. The efficiency gains presented 

in Section 5.2, therefore, are not mutually exclusive and cannot be 

summed across the four TFAs because summing impacts would 

overestimate potential benefits. 

Therefore, RTI developed lower- and upper-bound estimates where the 

lower bound was composed of the smallest estimated gains in each 

stage across all the TFAs and the upper bound was composed of the 

greatest estimated gains. The methodology used to develop the lowerbound (conservative) and upper-bound (optimistic) estimates entailed 

looking across all TFAs and selecting the least and most optimistic 

impact estimates for each of the four major cost parameters (i.e., direct 

stage costs, stage durations, IND approval rates, and transition 

probabilities) that drive the economic model results. The resulting ranges 

of estimates for our four key cost parameters are individual TFA 

estimates rather than an average taken across TFAs. 

Table 5-13 contains the counterfactual approval rates and distribution of 

failures for INDs during clinical trials that are implicit in the results 

depicted in Tables 5-14 and 5-15. The lower-bound scenario included 

the smallest gain in terms of time and cost by stage (e.g., biomarkers in 

preclinical and Phase III clinical trials, bioinformatics in Phase I, gene 

expression in Phase II). The inverse was true for the upper-bound 

scenario. 

The lower-bound scenario posited that (1) the probability an IND would 

be approved by FDA increased from 30.2% to 40.0%, (2) INDs failed 

slightly earlier in clinical trial phases on average, and (3) the probability 

an approved drug would be recalled declined from 0.4% to 0.3%. Study 

participants whose expertise was predominantly in gene expression and 

bioinformatics had consistent views on the possible magnitude of the 

impact. The upper-bound scenario data are strongly influenced by gains 

from biomarkers. 
Chapter 5 — Analysis Results 

5-25 

Table 5-13. Combined Scenarios’ Potential Improvement in IND Success and Failure 

Rates 

For INDs Failing in Clinical Trials, Percentage 

Failing by Phase Technology Focus 

Area 

IND Approval 

Probability Phase I Phase II Phase III Total

Probability 

of Recall

Baseline 30.2% 23.4% 52.4% 24.2% 100% 0.40%

Individual Scenarios 

Biomarkers 41.0% 39.2% 37.0% 23.8% 100% 0.30% 

Bioinformatics 40.0% 30.0% 40.5% 29.5% 100% 0.30% 

Gene expression 45.5% 37.5% 35.5% 27.0% 100% 0.10% 

Combined Scenarios 

Lower bound 40% 30% 41% 30% 100% 0.30% 

Upper bound 45% 39% 37% 24% 100% 0.10% 

% = Lower bound 

% = Upper bound 

Note: Comparable data for bioimaging could not be calculated; thus, bioimaging was excluded from this table. RTI 

estimates based on DiMasi and Grabowski (2007). 

Table 5-14. Combined Scenarios’ Potential Efficiency Gains 

 Baseline 

Improved Infrastructure 

Lower-Bound Scenario 

Improved Infrastructure 

Upper-Bound Scenario 

Stage 

Expected 

Stage Cost per 

Approved 

Drug 

(millions) 

Stage 

Length 

(months) 

Estimated 

Stage Cost per 

Approved 

Drug 

(millions) 

Stage 

Length 

(months) 

Estimated 

Stage Cost 

per Approved 

Drug 

(millions) 

Stage 

Length 

(months) 

Discovery/preclinical $198.3 52.0 $148.5 48.4 $106.0 38.5 

Phase I $106.9 19.5 $75.9 17.2 $61.0 16.6 

Phase II $104.5 29.3 $68.6 26.4 $55.2 24.6 

Phase III $149.9 32.9 $128.2 30.4 $67.0 18.4 

Total $559.6 133.7 $421.2 122.4 $289.2 98.1 

Source: RTI estimates based on DiMasi and Grabowski (2007). 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

5-26 

Table 5-15. Combined Scenarios’ Potential Cost Reductions 

Baseline 

(millions) 

Lower-Bound 

Scenario: 

Improved 

Infrastructure 

(millions) 

Upper-Bound 

Scenario: Upper 

Bound Improved 

Infrastructure 

(millions) 

Percentage 

Change 

Expected actual R&D cost per IND $169.0 $168.5 $130.1 –0 to –23% 

Expected actual R&D cost per 

approved drug 

$559.6 $421.2 $289.2 –25 to –48% 

Expected present-value R&D cost per 

IND 

$374.7 $347.8 $239.9 –7 to –36% 

Expected present-value R&D cost per 

approved drug 

$1,240.9 $869.6 $533.1 –30 to –57% 

Source: RTI estimates based on DiMasi and Grabowski (2007). 

Following this methodology, the combined technology infrastructure 

scenario could reduce the expected actual R&D cost for a new FDAapproved drug to $421 million under the lower-bound scenario and $289 

million under the upper-bound scenario (see Tables 5-14 and 5-15). 

These estimates are 48% and 25% less, respectively, than the baseline 

cost estimated by DiMasi and Grabowski (2007). 

Whereas the average time to take a candidate from discovery through 

Phase II clinical trials is currently around 11 years, the least optimistic 

results suggest that development time would be reduced to around 10 

years, and the most optimistic scenario suggests that the time could be 

reduced to slightly more than 8 years. 

The combined scenario results are highly sensitive to the impact an 

improved biomarker infrastructure could have on clinical trials, and the 

results in Tables 5-13 through 5-15 should be interpreted cautiously. 

Note, in particular, the reduction in time for Phase III. Whereas the 

baseline length is 32.9 months, the lower bound from the survey results 

is 30.4, and the upper bound is 18.4, which was a consensus view from 

biomarkers experts. These data illustrate the magnitude of impact that 

study participants believed an improved biomarker infrastructure could 

have. However, they also illustrate the sensitivity of upper-bound 

estimates to stakeholders’ views on how a nascent technology may 

mature. Their views with respect to timing and ultimate impact introduce 

relatively greater uncertainty into the results than is the case for 

estimates obtained for bioinformatics and gene expression TFAs. 
6-1 

 6 Conclusion 

This study estimates the biopharmaceutical industry’s annual spending 

on technology infrastructure–related investments and activities. It also 

identifies key areas within the technology infrastructure in which industry 

has underinvested because of market and technical barriers or because 

of unattractive risk-reward ratios at the firm level. The potential efficiency 

gains an improved technology infrastructure holds for the industry are 

estimated relative to the current costs of developing, manufacturing, and 

monitoring successful FDA-approved biopharmaceutical drugs. This 

chapter offers some summary remarks regarding the study’s findings. 

 6.1 CURRENT STATE OF THE 

BIOPHARMACEUTICAL TECHNOLOGY 

INFRASTRUCTURE 

The biopharmaceutical industry’s technology infrastructure is not only 

composed of advanced laboratory instrumentation, chemistries, and 

information technologies but also of standard operating protocols, test 

methods, data, and institutional memory that have accrued over time. 

Implicit in this study’s findings is that biopharmaceutical infrastructure 

varies across companies. The relative sophistication of any one firm’s 

infrastructure is a function of the amount of intellectual capital it has 

acquired as much as its investments in tangible assets such as 

instruments and software. These differences result from firms reacting 

differently to and overcoming individual technical barriers differently in 

the absence of well-coordinated industry standardization. In addition, 

these differences go beyond R&D stages and are manifest in varying 

quality assurance and control programs (QC/QA) as well as adverse 

event reporting. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

6-2 

One company RTI interviewed mandated strict adherence to its defined 

corporate standards and protocols and maintained program areas that 

developed tools in-house for communicating and representing 

information. External data not meeting defined screening criteria were 

routinely rejected to avoid introducing additional uncertainties into the 

company’s R&D. The screening standards go beyond those for data 

formats. This organization recognizes gaps in the broader industry 

technology infrastructure inclusive of laboratory and QA/QC procedures 

and exercises extreme caution as a consequence. 

Most experts interviewed over the course of this study conceptualized 

technology infrastructure expenditures into two general categories. The 

first is that these expenditures are, in part, an investment in current and 

future R&D efficiency. The labor effort, systems, and instruments 

expended are essential, unavoidable, and integral to a firm’s primary 

economic activity. The second is that these expenditures represent costs 

incurred to develop work-arounds and overcome technical barriers 

stemming from a pervasive lack of standardization. 

As a result, organizations view the current level of expenditures on 

infrastructure technologies to be above the level required with a more 

efficient infrastructure. Whereas the industry expended $884 million on 

the R&D technology infrastructure and $335 million on the commercial 

manufacturing and postmarket surveillance infrastructure (see 

Figure 6-1), some portion of that total $1,219 in spending could have 

been avoided with an improved infrastructure. 

Challenges in the biopharmaceutical technology infrastructure and the 

differences in how organizations respond to these challenges are rooted 

in 

• the inherent trial and error of the drug discovery and 

development process; 

• development times averaging 12 years, which is compounded by 

changes in regulatory requirements, information systems, and 

procedures; 

• variability in methodologies and protocols that acquire 

information and variations in how that information is described 

and characterized; 

• few industry standards for ontologies, data formats, and data 

communications systems; 
Chapter 6 — Conclusion 

6-3 

Figure 6-1. Annual Biopharmaceutical Industry Technology Infrastructure Spending 

$136

million

$212

million

$198 

million

$265

million

$73

million

$162

million

$173

million

Annual Biopharmaceutical Technology Infrastructure Expenditures: $1,219 million

Postmarket 

Surveillance 

Commercial 

Manufacturing 

Other 

Gene 

Expression

Bioimaging 

Biomarkers

Informatics

• the rapid introduction and adoption of data acquisition 

technologies (which far outpaces the development of industry’s 

ability to manage, communicate, analyze, and synthesize data); 

and 

• changes in the regulatory environment in a diverse set of 

countries and foreign languages; 

Semiformal industry groups have organized to study how data are 

acquired, analyzed, and interpreted differently among technology 

platforms in the hope of guiding the industry toward greater 

standardization. However, the absence of financial and technical 

resources, competing research priorities, and the length of commitment 

required impedes their progress. 

Before 2000, research methods were largely manual and time based—

large volumes of data were not being produced, stored, exchanged, or 

analyzed. Analyses had many replicates, but few variables—meaning 

that processes were iterative, time consuming, and data management 

needs were predictable. Then, there was a technology leap-frogging 

event in the late 1990s when high-throughput laboratory instrumentation 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

6-4 

went online and enabled rapid, inexpensive acquisition of genomic data, 

including the human genome (O’Connor et al., 2007). Laboratories found 

themselves generating in only 1 day 100 times or more data than they 

previously generated in 1 month. 

Many organizations have realized that standardization is needed to limit 

uncertainty, increase confidence, and give information users some 

assurance of the information’s accuracy. In some instances, companies 

and their vendors have researched, developed, and adopted ad-hoc 

standards and protocols, and these processes can work well internally. 

However, company or laboratory standards are not an efficient 

replacement for industry standards, particularly in the current 

environment in which massive volumes of data are exchanged between 

laboratories, are compared over time, and used to analyze dynamic 

biological specimens. The pragmatism of encountering an 

infratechnology problem and responding on an ad-hoc basis translates to 

multiple responses to similar problems, which in turn impedes 

comparability and information exchange. Given the complexity of 

genomic information, conceiving or characterizing data element 

descriptors differently or not imparting how data interrelate can preclude 

any meaningful comparison or integration. 

The experts RTI interviewed said that problems are growing, but that 

many companies have yet to see the extent of the problem because 

“they haven’t gotten there technically yet.” 

 6.2 CHARACTERIZING POTENTIAL EFFICIENCY 

GAINS FROM AN IMPROVED TECHNOLOGY 

INFRASTRUCTURE 

Rather than ask the industry how expenditures could have been different 

in 2005, this study took the approach of evaluating how a specific set of 

improvements could increase efficiency going forward. In-depth 

interviews with people in industry, academia, and government agencies 

and an Internet survey quantified the impact a series of feasible 

infrastructure improvements would have on R&D and post-FDA approval 

activities. Stakeholders offered their views on how specific improvements 

to the technology infrastructure could 

• lower the development cost of the average biopharmaceutical 

drug, 
Chapter 6 — Conclusion 

6-5 

• increase the probability the drug would be approved by FDA by 

enhancing data quality and analytical methods, 

• shorten the drug’s time to market, 

• lower the ongoing costs for manufacturing that drug and improve 

manufacturing tolerances, and 

• make the postmarket surveillance infrastructure more efficient 

and responsive. 

Even though industry has made significant investments in some areas of 

technical infrastructure over the past several years, the results of this 

study indicate that the expected actual expense for a new approved drug 

could be reduced by 25% under a lower bound scenario and by 48% 

under an upper bound scenario (see Table 6-1). This study also found 

that commercial manufacturing costs could be reduced by up to 22%. 

A 25% to 48% improvement is significant and would require both 

substantial and broad-based advances in a range of technical 

infrastructures. However, industry respondents to the surveys conducted 

for this study believe such advances are feasible within a reasonable 

timeframe, perhaps 5 to 10 years. These results are consistent with a 

2004 FDA report in which one expert suggested that biomarkers could 

reduce the cost of developing a new drug by 50% (FDA, 2004b). 

Moving forward, drug development—for traditional products as well as 

biopharmaceuticals—will experience a global shift away from the lab 

toward computational systems that analyze drug candidates, predict 

cellular responses, and more effectively select good candidates for 

clinical trials. The systems biology approach to the development of new 

medicines discussed in this report’s introduction requires a technology 

infrastructure that supports and integrates each of this study’s TFAs. 

Databases of accurate, well-defined information comparable across both 

time and technology platforms rely as much on how data are captured as 

on the sophistication of the algorithms, ontologies, and formats used to 

integrate and understand the information. Protocols and measurement 

systems that govern how and at what quality level information is 

integrated into databases are needed. These elements are essential if 

the potential advantages of personalized medicine are to be realized in 

which patients are prescribed drug therapies that are most effective for 

their individual genetic make-up. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

6-6 

Table 6-1. Potential Cost Reductions in Biopharmaceutical Development with an 

Improved Technology Infrastructure 

Technology Focus Area 

(TFA) 

Expected 

Actual Cost 

per Approved 

Drug 

(millions) 

Percent 

Change from 

Baseline 

Expected 

Capitalized 

Cost per 

Approved 

Drug 

(millions) 

Percent 

Change from 

Baseline 

Development 

Time 

(months) 

Baseline $559.6 — $1,240.9 — 133.7 

Individual TFA Scenarios 

Bioimaging — — — — — 

Biomarkers $347.9 –38% $676.9 –45% 108.2 

Bioinformatics $375.0 –33% $746.3 –40% 116.6 

Gene Expression $345.8 –38% $676.0 –45% 111.9 

Combined Scenarios 

Lower bound $421.2 –25% $869.6 –30% 122.4 

Upper bound $289.2 –48% $533.1 –57% 98.1 

Note: See Table 5-4 for estimated changes in FDA approval, distribution of IND failures within clinical trials, and 

probability of a recall of an FDA-approved drug. 

Table 6-2 highlights key technology infrastructure priorities that emerged 

during interviews and that informed the scenarios against which potential 

efficiency gains were quantified. Most notable among participants’ 

comments were (1) the need to establish a basic foundation of 

standardization within and across each TFA and (2) that gains in one 

TFA spill over and increase gains from others. The process of aligning 

research strategies and investments in the following improvements to the 

technology infrastructure would be best approached in a coordinated, 

interdisciplinary manner. 

 6.2.1 Advanced Bioimaging Techniques 

Bioimaging studies are becoming more important in the analysis of how 

chemical compounds impact biological systems, particularly as FDA 

increasingly requests that such studies accompany submissions and 

Phase IV clinical trials. While these studies have the ability to capture 

and analyze fluorescently coded information to seek out biomarkers, 

improvements in measurement, calibration, and metrology systems are 

needed to improve comparability, repeatability, and throughput. 
Chapter 6 — Conclusion 

6-7 

Table 6-2. Stakeholders’ Comments on Technology Infrastructure Needs 

Technology 

Focus Area 

Within the TFAs studied in this report, company representatives, academics, and 

government researchers recommended that needed improvements to the technology 

infrastructure: 

• Include consistent taxonomies for medical and anatomical regions of observations 

• Standardize image labeling procedures and ontologies 

• Develop formats for exchanging imaging data among data systems 

• Improve the image archival, retrieval, and management infrastructure 

Bioimaging 

• Improve access to imaging technology, including image capture and interpretation systems 

• Address the need for greater sensitivity in detection of protein expression levels 

• Develop traceable standards for currently known immunoassayed biomarkers 

• Standardize existing protocols for generating gene expression results 

• Develop standardized methods and tools to hasten validation of technology platforms 

Biomarkers 

• Standardize statistical methodologies for data analysis in biomarker validation studies 

• Improve data visualization and analysis techniques, 

• Develop common (neutral) data formats and analysis tools 

• Set standards for and transforming and exchanging data 

Bioinformatics 

• Standardize ontologies for characterizing data 

• Make available reference materials that mimic the biological complexity of tissue and blood 

samples 

• Establish sample quality standards, including tools to evaluate the extent to which samples 

may have degraded 

• Create sample acquisition, handling, and preparation techniques given the amount of time 

samples may spend in transit between research sites 

• Establish systems, data, and analysis mechanisms to benchmark microarray performance 

• Develop calibration tools and techniques for scanning equipment 

Gene Expression 

• Provide standard calibration curves for genes as well as standard control techniques, 

assays, protocols, and investigative algorithms 

• Develop standardized data formats for production equipment and instrumentation 

• Create on-line measurement methodologies to improve process understanding and 

establish industry standard QA/QC measures 

• Improve inspection and validation methodologies 

Commercial 

Manufacturing 

• Develop reference standards analogous to cellular material for future production cell and 

gene therapies 

• Standardize protocols and descriptions for adverse event data to attain greater efficiency in 

ongoing safety and efficacy monitoring and FDA reporting 

• Standardize the syntax and interchange between clinical safety databases 

• Improved statistical methodologies to enable multivariate analysis of safety data 

Postmarket 

Surveillance 

• Develop uniform standards for data formats for clinical records 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

6-8 

Stakeholders cited the significant benefits of using bioimaging 

techniques to seek and quantify key biomarkers in large-scale imaging 

studies. However, identification and quantification of key biomarkers for a 

disease first requires standard protocols for patient or specimen 

positioning, instrument calibration, and settings to reduce variability and 

allow images to be compared across tests (in discovery) or trials (in the 

clinical testing phases). Given the volume of imaging data that would be 

analyzed in high-throughput imaging studies, experts also cited the need 

for automated or computer-assisted interpretation of images, including 

edge detection and size measurements. 

 6.2.2 Molecular Biomarkers 

New biomarkers such as those that predict toxicity or detect 

concentrations of antibodies in cells are needed if the potential time and 

cost benefits quantified as part of this study are to be realized. The 

industry experts we interviewed stressed the benefits that using validated 

efficacy biomarkers as surrogate end points in clinical trials could have, 

particularly the ability to rank and prioritize drug candidates in a firm’s 

portfolio of potential products. Enhancements to researchers’ ability to 

predict biological response to treatment provides researchers with 

access to information that previously was only available at later stages in 

the discovery process or clinical trials. Outside of drug R&D, nextgeneration biomarkers hold promise for patients whose quality of care 

may improve because doctors have improved foresight into the transition 

from wellness to disease. 

Key improvements in the technology infrastructure to support 

identification of and widespread use of biomarkers include a 

standardized and accepted validation process for biomarker discovery. 

Sophisticated measurement tools and methods are needed as part of 

providing accurate data if new biomarkers are to assist with ranking drug 

targets and guide decision-making. 

 6.2.3 Bioinformatics and In Silico Predictive Modeling 

Improving the effectiveness and efficiency of bioinformatics as drug 

discovery shifts away from traditional laboratory science toward 

computational systems is one of the key challenges facing the industry. 

Tools and strategies for interpreting, synthesizing, and communicating 

data have not kept pace with the ability to generate it. Standardization for 

bioinformatics is essential if the full potential of advances in gene 

expression, bioimaging, and biomarkers’ improvements is to be realized. 
Chapter 6 — Conclusion 

6-9 

Achieving key priorities within informatics would increase the confidence 

and assurance analysts have in the data being used in analyses. These 

include developing 

• tools for integrating data sets between technology platforms and 

managing data volumes; 

• quality and accuracy measurements and standards for data 

validation, cleaning, and normalization; 

• standardized ontologies for describing and formatting data 

elements; 

• greater availability of currently gathered data through publicly 

accessible and maintained databases; 

• improved data mining applications and algorithms; and 

• improved accuracy of in silico model predictions, including 

models of protein structure and binding, cellular localization, 

PK/PD properties, clinical trial simulation, and disease modeling. 

 6.2.4 Gene and Protein Expression Analysis 

Standardization in gene and protein expression analysis is needed for 

drug R&D and the practice of medicine to achieve NIH’s and FDA’s goals 

of personalized medicine. Expression analysis systems capture and 

analyze information from organisms’ cells that can be used in medical 

research and health management. It will likely be a decade or more until 

medical care reaches the point of personalization, but if the underlying 

technology infrastructure supporting expression analysis remains 

fragmented and characterized by dissimilar approaches to diagnostics 

and assays, then availability of personalized medicine will be still further 

delayed. 

The development of key measurements, protocols, standards, and tools 

underlying the benefits estimates presented in Table 6-1 include 

• publicly available synthetic mRNA reference materials for 

microarray performance assurance, 

• standard technical protocols for microarray experiments, 

• standard protocols for RNA and DNA extraction, 

• data and analysis to benchmark microarray performance, 

• microarray scanning equipment calibration tools, and 

• techniques for measuring the presence of low-abundance 

proteins. 
Economic Analysis of the Technology Infrastructure Needs of the U.S. Biopharmaceutical Industry 

6-10 

 6.2.5 Commercial Manufacturing 

Improvements in data integration and in-line process measurement 

techniques are needed in commercial manufacturing. Biopharmaceutical 

manufacturing is a complex process with a propensity for high levels of 

variability in reproducing biological substances. Standardization of 

impurity testing methods and protein aggregation studies will ensure 

safer and more effective products. Over the next 10 to 20 years, 

manufacturers will continue to achieve major milestones in improving 

production yields, demonstrating a better understanding of the 

bioprocess underlying their products. However, the benefits to 

consumers resulting from these innovations could be constrained by 

continued inefficiencies if parallel improvements to the measurement and 

technical infrastructure that supports the commercial manufacturing 

process are not made. Particularly these include 

• standardized data formats for production equipment and 

instrumentation, 

• creation of on-line measurement methodologies to improve 

process understanding and establish industry standard QA/QC 

measures, 

• improve inspection and validation methodologies related to 

immunogenicity and aggregation of proteins during 

manufacturing and storage, and 

• develop reference standards analogous to cellular material for 

future production cell and gene therapies. 

 6.2.6 Postmarket Surveillance 

Improved standardization of data elements, improved data integration, 

and more powerful statistical methodologies are needed to improve the 

efficiency and precision of postmarket surveillance activities. Over the 

next decade, the scope and quantity of data required for postmarket 

surveillance is going to increase dramatically largely because of 

globalization of markets and increasing FDA requirements for long-term 

safety studies. Robust and efficient technology infrastructure in data 

collection, retrieval, and analysis methods will be required to ensure that 

these postmarket studies can be cost effectively implemented. 

Stakeholders’ priorities include 

• standardizing protocols and descriptions for adverse event data 

to attain greater efficiency in ongoing safety and efficacy 

monitoring and FDA reporting, 

• standardizing the syntax and interchange between clinical safety 

databases, 
Chapter 6 — Conclusion 

6-11 

• improving statistical methodologies to enable multivariate 

analysis of safety data, and 

• developing uniform standards for data formats for clinical 

records. 

 6.3 RATIONALE FOR NIST INVOLVEMENT 

Interviewees and survey respondents stated that NIST participation in 

standardization activities for biopharmaceuticals would be welcome. 

About half of all interviewees were familiar with NIST and cited NIST’s 

historical leadership in standards-setting and coordination. These 

experts believe that NIST has a natural role, given its status as an 

independent, neutral body, its greater access to consistent funding, and 

its mission to provide technical infrastructure to industry. 

However, senior scientists and directors at biopharmaceutical companies 

cited FDA’s regulatory authority as a potential constraint and suggested 

that NIST and FDA collaborate to identify and develop standards that are 

congruent with the Critical Path initiative. Companies’ reporting 

requirements to FDA add a regulatory driver to nearly all research 

initiatives. These individuals encouraged NIST leadership and 

participation in standards-setting but stated that any initiatives should be 

mindful of and improve companies’ ability to report to FDA, as well as 

improving data communication internally and between organizations. 

Although the entire biopharmaceutical industry would benefit from an 

improved infrastructure, emerging companies that have yet to adopt or 

develop an internal infrastructure stand to gain the most. Such adoptions 

by small start-up firms may increase their chances for success and 

thereby invigorate entrepreneurship. 

The broader biopharmaceutical and biotechnology industry would benefit 

from greater efficiency and effectiveness with a nationally coordinated 

standardization effort supported by an independent research 

organization with a proven track record, technical expertise, and access 

to financial and technical resources. The ultimate beneficiaries are 

patients who would have access to a broader array of novel therapies 

whose development was supported by an effective technology 

infrastructure. 

R-1 

 

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 Appendix A: 

 Survey 

A-1 

Electronic Survey of the Biopharmaceutical Industry 

The following text will appear in the email sent to members of BIO: 

As part of an economic study for the National Institute of Standards and Technology (NIST), RTI 

International (RTI) is conducting a survey of biotechnology-related companies involved in research, 

development, testing, and manufacturing of the next generation in human therapeutics known as 

biopharmaceuticals. 

Currently, NIST devotes $426.3 million to scientific and technical research services in support of the 

nation’s technology infrastructure. The technology infrastructure consists of a set of “technical tools and 

processes” that enable or increase the efficiency of R&D, manufacturing, and market penetration (e.g., 

measurement science, standards, and other supporting technologies). NIST has designated 

biotechnology as a strategic focus area and is planning to expand its research programs to provide the 

U.S. biotechnology industry with infrastructure technologies and standards that will increase the efficiency 

of R&D, manufacturing, and market transactions. 

You can assist NIST in its strategic planning by providing estimates of in-house and external spending 

related to the technology infrastructure supporting the biopharmaceutical industry. These estimates will 

be used to develop a national estimate of private-sector expenditures related to technology infrastructure 

in the biopharmaceutical industry. 

Your participation in this critically important survey is voluntary. However, our study would greatly benefit 

from your insights and experience, and, in exchange for your participation, you will be given access to the 

final report. Please note that your responses will be kept strictly confidential and will not be available to 

the public or shared with NIST or other survey participants. All survey responses to questions regarding 

current expenditures will be aggregated to obtain an estimate of technology infrastructure costs for the 

entire industry. Only these aggregate numbers will be reported in the final report at the conclusion of this 

research study. 

Thank you for your willingness to participate. The survey should take approximately 20 to 30 minutes to 

complete, and, if you have any questions, please contact Jeffrey Petrusa at NIST_biotech@rti.org. 

Please click here to access the survey or go to https://biotechsurvey.rti.org/. 
A-2

The following text will appear in the introduction to the online survey. 

NIST/RTI Study: 

The Technology Infrastructure Needs of the 

Biopharmaceutical Industry 

Thank you for your participation in this brief but critically important survey regarding the technology 

infrastructure supporting the research, development, production, and marketing of biopharmaceuticals. 

The results of this survey will be used by RTI International (RTI) as part of a strategic planning study 

commissioned by the National Institute of Standards and Technology (NIST). 

The purpose of this survey is to collect information on the current level of private-sector expenditures on 

the standards, measurement techniques, and technical tools that make up the technology infrastructure. 

In addition, the information will be used to assess the existing needs and potential impact of 

improvements in this infrastructure. 

Instructions: 

This survey should take approximately 20 to 30 minutes to complete. Your participation is voluntary, and 

your responses will be kept strictly confidential.30 Please answer all questions by checking the appropriate 

box(es) or providing text in the designated space. You do not need to look up any information; simply 

provide answers based on your best knowledge. 

In exchange for your participation, you will have access to a copy of the final report. If you would like to be 

notified when this report is available, please include your e-mail address and contact information at the 

end of this survey. 

If you have any questions as you complete the survey, please contact Jeffrey Petrusa at 

NIST_biotech@rti.org. 

 

30 Nondisclosure policy: 

RTI has well-established practices for handling confidential information as part of numerous projects. Any information we obtain 

through these surveys will be used solely in aggregate with information from other respondents. In no instance will specific 

individuals or organizations be identified by name in any reports or as part of information that is released publicly or to NIST. 
A-3 

Part I: Background Information 

1. Please provide your professional title: ___________________ (e.g., director, production supervisor, 

project manager, head of process engineering) 

2. Please indicate whether your parent company’s primary or secondary use of biotechnology is for 

human-health applications (e.g., therapeutics, preventatives, or diagnostics). 

□ Yes 

□ No 

If they check NO, the survey will terminate with a “Thank you, but you do not qualify for this study” 

message. 

3. What stage of the biopharmaceutical drug development and manufacturing supply chain are you most 

involved with? 

NOTE: If you work for a company that is diversified, please select the activity about which you 

personally are most knowledgeable and complete this survey from that perspective. Skip logic for the 

remainder of the survey will be guided by their response to this question. Based on their selection 

respondents will be asked to consider questions in 1 of 2 areas: product development, and 

commercial activities.

Product Development-Related Activities (Pre-FDA Approval)

□ Drug Discovery and Preclinical Development 

□ Clinical Trials (includes any activities prior to approval from FDA) 

Commercial Activities (Post-FDA approval)

□ Commercial-Scale Manufacturing 

□ Post-market Monitoring 

4. Approximately how many full-time equivalent (FTE) scientists and engineers are active in <<Activity 

from Q5>>? << Pop-up definitions: 

FTE: A measurement equal to one staff person working a full-time work schedule (35-40 hours 

per week) for one year. 

Scientists and engineers: Includes scientists, engineers, science and clinical laboratory 

technicians, and R&D focused computer specialists. >>

___________ = Total number of scientists and engineers (FTE) 

IF the respondent selects Discovery/Preclinical Development or Clinical Trial Activities in Q5 then the 

following statement will be given.

For the remainder of the survey please respond only for the activities related to the

<response to Q4 above>scientists and engineers you indicated above. 

THEN GO TO PART II-A 
A-4

IF the respondent selects Commercial Scale Manufacturing or Post-Market Monitoring in Q4 then 

the following questions will be asked. 

Manufacturing Activities

5. How many biological product lines are currently produced by you group or division? 

Product Type 

Number of Products in 

the Development 

Pipeline 

Number of FDAApproved Products 

Therapeutic protein 

Monoclonal antibody 

Vaccine 

Gene therapy 

Cellular therapy 

Tissue therapy 

Other biotechnology based 

human health applications 

Total <sum of above #s> <sum of above #s>

Display the following set of questions on a new page: 

Post-market Monitoring Activities 

6. How many biological product lines are currently monitored by your group/division? 

Product Type 

Number of FDAApproved Products 

Therapeutic proteins 

Monoclonal antibodies 

Vaccines 

Gene therapies 

Cellular therapies 

Tissue therapies 

Other biotechnology based 

human health applications 

Total <sum of above #s>

For the remainder of the survey please respond only for the activities related to the <total in 

table above> product lines currently marketed by your group/division. 

GO TO PART II-B. 

SKIP LOGIC: IF respondent chooses the “Commercial-Scale Manufacturing” or ““Post-Market Monitoring” 

response in Q4, then GO TO Part II-B on page 28. 

All other responses to Q4, then GO TO PART II-A on page 8. 
A-5 

SKIP LOGIC: Respondent will only see this section if they respond as participating in drug discovery, 

preclinical development, or clinical trials in Q4 

Each of the following paragraphs will appear in sequential screen shots on the Web survey to make it 

easier to read.

Part II-A: Current Expenditures on Technology Infrastructure 

The purpose of this section is to obtain estimates from your organization about the resources 

allocated to developing comparable test methods, standardized software models, standard 

reference materials used for measurement and calibration, and standard reference data. We will 

aggregate this information to quantify the proportion of the biopharmaceutical industry’s 

activities that are allocated to improving the “technology infrastructure” supporting product 

development (R&D).

Technology infrastructure consists of a set of “technical tools and processes” that enable or increase 

the efficiency of R&D, manufacturing, and market penetration. Collectively, NIST refers to these tools and 

processes as the technology infrastructure. Technology infrastructure provides the technical basis for 

activities ranging from drug discovery to quality control in the production process. Investments in the 

technology infrastructure have the potential to significantly lower development, production, and 

transaction costs and hence are an ongoing activity in most companies. Investment in the technology 

infrastructure may include both direct and indirect costs in the form of equipment, labor, and material 

expenditures dedicated to technology infrastructure. 

Examples of technology infrastructure include 

• certified reference materials, reference methods, and standardized procedures related to 

measurement or calibration in conducting tests during R&D stages or in any manufacturing 

process; 

• validated methods for interpreting results from different analytical platforms; 

• standardized techniques (e.g., for use with spectrometers, imaging systems, and/or measurement 

devices); 

• standardized methods for characterizing scientific and engineering data, and the algorithms to 

manipulate, search, and analyze this uniform data within a publicly available database; and 

• processing techniques (e.g., for use with 2D gel electrophoresis platforms, chromatography 

separation systems) 

Click here for examples of technology infrastructure costs.

The following bullets will appear in as a separate browser window if the respondent selects the 

link in RED above. 

Technology infrastructure Cost Examples

• Purchase of measurement-related equipment, software, reference materials, or services. 

• Activities required for setup and validation of analytical instruments, reagents, or other research 

tools. Examples of these activities may include developing test methodologies or process 

standards in measurement and manufacturing practices. 

• In-house customization of technology platforms purchased from third-party vendors. 

• Efforts to develop interoperability between different software or equipment systems. 

• License fees or any other spending on enhancements to routinely used processes or 

equipment, intended to increase productivity, reduce redundancy, or improve the confidence in 

results. 
A-6

1. Based on the definition and examples of the technology infrastructure provided above, please 

estimate the current annual level of expenditures on the technology infrastructure supporting your (Fill 

in from answer to Q5 of Part I) activities. Please consider all labor, materials, and equipment 

expenditures for in-house activities, as well as contributions to consortia or partnership initiatives.

Approximate Annual Expenditure on Technology Infrastructure = 

$______________ 

Technology infrastructure Expenditure Category 

Approximate Percentage of 

Annual Expenditure on 

Technology infrastructure 

Labor (includes all research, implementation support 

activities, and participation in consortia related to 

infrastructural technology) 

<<value 0 to 100>>

Capital (includes equipment, software, and licensing 

fees related to infrastructural technology) <<value 0 to 100>>

Materials (includes all reference materials and reagents 

and other materials related to infrastructural technology) <<value 0 to 100>>

Total 100% 

Insert “Save & Continue” button after this question. Following Question will appear on a new screen. 

2. Approximately what share of total R&D expenditures <<$ value from Q1 above>> in Question 1 

represent? 

_______ % of total current annual R&D expenditures 

SKIP LOGIC: IF Respondent answers $0 or 0% THEN the survey will terminate with a “Thank you for 

your participation in this important NIST study” message. 

3. a. Over the last 5 years, have these expenditures increased or decreased or stayed the same? 

□ Increased 

□ Decreased 

□ Stayed the same 

3b. Comments: ___________________________________________ 
A-7 

4. The four technologies listed below have been identified as critical enabling technologies for 

developing biopharmaceuticals. Please allocate a percentage of your estimated technology 

infrastructure expenditure of $(Fill in response to Q1 of Part II) to the following enabling technologies. 

If you do not use a particular technology, please enter 0%.

Enabling Technology 

(not listed in any particular order) 

Approximate 

Percentage of 

Annual 

Expenditure on 

Technology 

infrastructure

Bioimaging (PET, MRI, mass spec, other imaging modality) 

Informatics (software and databases) 

Molecular biomarkers 

Gene and/or protein expression platforms 

Other 

Total Annual Technology infrastructure Expenditure 100% 

If they enter a positive amount for “Other” in the box above THEN ask:

4a. What are some examples of the “Other” enabling technologies not listed above? 

_____________________________________________________________________________ 

_____________________________________________________________________________ 

If they allocate 100 to “Other” category, the survey will terminate with a “Thank you for your participation 

in this important NIST study” message. 

If they allocate a percent greater than zero to bioimaging, they will answer this question. 

5. Bioimaging

Which of the following imaging modalities does your group support in biotechnology-based drug discovery 

and development? Please check all that apply. 

□ X-Ray (for research activities other than diagnosis) 

□ Computed tomography (CT) 

□ Magnetic resonance imaging (MRI) 

□ Ultrasound 

□ Single-photon emission computed tomography (SPECT) 

□ Positron emission tomography (PET) 

□ Optical imaging, including fluorescence 

□ Combined methods (such as PET-CT) 

Please specify: ___________________________________ 

□ Other 

Please specify: ____________________________________ 

If they allocate a percent greater than zero to informatics, they will answer this question. 
A-8

6. Informatics 

Which of the following informatic tools does your organization support in drug discovery and 

development? Please check all that apply.

□ Bioinformatic databases

□ Cheminformatic databases 

□ Systems biology and pathway analysis 

□ Data management software 

□ Predictive modeling (also referred as in silico modeling) software 

If they allocate a percent greater than zero to molecular biomarkers, they will answer this question. 

7. Molecular Biomarkers 

Which of the following types of biomarkers does your organization currently support in drug research and 

development? Please check all that apply.

□ Translation biomarkers

□ Disease biomarkers 

□ Efficacy biomarkers 

□ Staging biomarkers 

□ Surrogate biomarkers 

□ Toxicity biomarkers 

□ Mechanism biomarkers 

□ Target biomarkers 

If they allocate a percent greater than zero to Gene/protein expression platforms, they will answer this 

question. 

8. Gene and/or Protein Expression Platforms 

Which of the following platforms does your organization support in measuring gene and/or protein 

expression for drug discovery and development? Please check all that apply.

Gene Expression

□ Quantitative, real-time reverse transcriptase polymerase chain reaction (QRT-PCR) 

assays 

□ Microarray experiments 

□ Other, please specify: ___________________________________________ 

Protein Expression 

□ 2D gel electrophoresis 

□ Mass spectrometry 

□ Protein arrays 

□ Other, please specify: ___________________________________________ 
A-9 

Part III-A: Assessment of the Potential Impact of Improvements in Technology Infrastructure 

The purpose of this section is to collect information that will help us assess the potential impact 

that certain improvements to the underlying technology infrastructure could have on the current 

cost of drug discovery and development. 

1. Please indicate which of these four technologies you are MOST knowledgeable about. Select only 

one of the four boxes. 

□ Bioimaging 

□ Informatics 

□ Molecular biomarkers 

□ Gene and/or protein expression analysis 

SKIP LOGIC: THE RESPONDENT WILL BE DIRECTED TO THE TECHNOLOGY THAT 

CORRESPONDS TO THE TECHNOLOGY THEY SELECTED ABOVE in Q1 of Part III. 

For Bioimaging, see pages 10-13. 

For Informatics, see pages 14-17. 

For Molecular Biomarkers, see pages 18-20. 

For Gene and/or protein expression analysis, see pages 21-24. 

Each Respondent (answering Part III-A) will only answer one of the four Technology Cases listed 

above. 
A-10

Assessment of the Potential Impact of Improvements in Bioimaging

In this case, we investigate the potential R&D cost savings that could occur given hypothetical 

improvements in the technology infrastructure supporting the use of bioimaging in biopharmaceutical 

discovery, development, and testing. The advancements listed come from the NIH Technology Road 

Map, the FDA Critical Path Opportunities List, and other industry sources. 

The following six advancements exist in varying degrees today and are expected to improve in the 

coming years: 

B. Qualified imaging biomarkers in your disease or therapeutic category of interest. 

C. Availability of labeling and contrast agents that are proven to be stable in vivo, nondestructive 

to the molecule being tested, and nontoxic in humans. 

D. Reconstruction algorithms that improve image quality (e.g., reduce fuzziness, define clear 

borders). 

E. Automated or computer-assisted interpretation of images, including edge detection and size 

measurements. 

F. National image test bed and benchmarking methodology to assess the quality of the image 

analysis algorithms and software. 

G. Standard protocols for patient/specimen positioning, instrument calibration, and settings to 

reduce variability and allow images to be compared across tests (in discovery) or trials (in the 

clinical testing phases). 

Advancements in these areas could lead to a number of impacts on R&D productivity. Examples include: 

• R&D labor and materials cost savings associated with shorter clinical trials or smaller trial 

populations. 

• Elimination of alternative tests required to identify and validate a target or response. 

• Reduction in the number of animal subjects needed (since noninvasive imaging would allow 

the same animal to be followed for longer periods of time). 

• Reduction in the number and skill level of technicians required to verify the analysis of an 

image. 

• Elimination of additional (redundant) tests needed to verify results, due to poor image quality. 

• Reduction in R&D labor costs for time spent assessing performance capabilities of image 

analysis software. 

• Reduction in R&D labor costs associated with calibrating equipment due to the availability of 

standardized procedures and reference phantoms. 
A-11 

Impact on Research and Development 

1. Consider the following “improved world.” Suppose there is an immediate advancement in the above 

areas of 50% over the current state of the art. What would you estimate to be the impact on the 

process of bringing a candidate biopharmaceutical to the next stage of development? Please 

consider impacts over the entire development process.

Stage 

Percentage 

Reduction in 

Stage Length 

(Time until Start of 

Next Stage) 

Percentage 

Reduction in 

Stage Cost 

Check Here if Not 

Familiar with this 

Stage 

Discovery & Preclinical ____% ____% □

Phase I ____% ____% □

Phase II ____% ____% □

Phase III ____% ____% □

Comments:_________________________________________________________________________-

________________________________________________________________________
A-12

Impact on Success Rates 

Now consider the success and failure rates for candidate biopharmaceuticals in this same “improved 

world” (a 50% advancement in the current the state of the art in the above areas). 

2a. The table below provides the average success rate for an Investigational New Drug (IND) 

entering clinical trials, in other words, the probability that an IND will receive FDA approval. Please 

estimate what impact the advancements above might have on this success rate in the table below. 

For example, providing an estimate that is greater than 35% implies that the advancements 

above allow researchers to make more well-informed decisions in selecting a lead candidate for 

IND submission. A higher success rate implies improved quality of all INDs; thus, there is a 

higher probability of receiving FDA approval. 

Change in IND Overall 

Success Rates Typical Ratea

Current Estimated 

Rate for Your 

Organizationb

Estimated Rate in 

Improved World 

Probability that an IND ultimately 

receives FDA approval 35% 

2b. From the table above, a 35% success rate implies that 35 out of 100 INDs will receive FDA 

approval. This suggests that the other 65 INDs will fail at some point during clinical trials. The table 

below provides the average distribution of failures occurring at each clinical trial phase. Please 

estimate what impact the advancements above might have on this distribution of failures across the 

clinical trial phases. 

For example, if you think the advancements above would allow researchers to make decisions 

about project failures earlier in clinical trials, this impact would be represented by shifting 5% of 

clinical failures from Phase III to Phase I. A shift in the occurrence of failures to an earlier phase 

implies a decrease in the overall cost of drug development. 

Change in Clinical Trial 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Of all the INDs that fail in clinical trials, what percentage fail in each stage?

Percentage failing in Phase I 15% □

Percentage failing in Phase II 46% □

Percentage failing in Phase III 39% □

Total 100% 100% 

2c. The table below provides the average failure rate for biopharmaceuticals that are currently 

approved and marketed to consumers. Please estimate the impact the advancements above might 

have on the probability that an approved drug is recalled. 

Change in Post-market 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an approved drug 

is recalled 0.4% □ a

 Based on industry estimates and published literature. 

b

 Respond only if your estimates are significantly different from typical percentages provided. 

Comments:__________________________________________________________________________

____________________________________________________________________________________

________________________________________________________________ 
A-13 

3. Which of the advancements listed above do you perceive as being the most important to achieving 

the improvements you noted in the boxes above? 

□ Qualified imaging biomarkers in your disease or therapeutic category of interest. 

□ Availability of labeling and contrast agents that are proven to be stable in vivo, nondestructive 

to the molecule being tested, and nontoxic in humans. 

□ Reconstruction algorithms that improve image quality (e.g., reduce fuzziness, define clear 

borders). 

□ Automated or computer-assisted interpretation of images, including edge detection and size 

measurements. 

□ National image test bed and benchmarking methodology to assess the quality of the image 

analysis algorithms and software. 

□ Standard protocols for patient/specimen positioning, instrument calibration, and settings to 

reduce variability and allow images to be compared across tests (in discovery) or trials (in the 

clinical testing phases). 

THIS IS THE END OF THE SURVEY for respondents who answered Bioimaging Technology Case. 

Respondent should see a Finish button following Q3 above. Finish button click GO TO page p. 33
A-14

Assessment of the Potential Impact of Improvements in Informatics

In this case, we investigate the potential R&D cost savings that could occur given hypothetical 

improvements in the technology infrastructure supporting the use of informatics in biopharmaceutical 

discovery, development, and testing. The advancements listed come from the NIH Technology Road 

Map, the FDA Critical Path Opportunities List, and other industry sources. 

The following four advancements exist in varying degrees today and are expected to improve in the 

coming years: 

A. Shared data standards for formatting and content. In addition to formatting standards for 

storing different types of data (such as image data), this advancement would include 

standards for the metadata that record information on the conditions of an experiment, similar 

to the content standards used as Minimum Information About a Microarray Experiment 

(MIAME). 

B. Greater availability of currently gathered data through publicly accessible, curated databases. 

These databases would include (but not be limited to) data concerning the natural history of 

rare diseases; adverse events; toxicology properties of drug candidates; and absorption, 

distribution, metabolism, and elimination (ADME) properties of drug candidate. 

C. Improved data mining applications and algorithms. Potential improvements include the 

incorporation of natural language processing into a text search. 

D. Improved accuracy of in silico model predictions, including (but not limited to) models of 

protein structure and binding, cellular localization, pharmacokinetic/pharmacodynamic 

(PK/PD) properties, clinical trial simulation, and disease modeling. 

Advancements in these areas could lead to a number of impacts on R&D productivity. Examples include 

the following: 

• Reduction in time and labor costs spent on finding and interpreting data from previous 

experiments. 

• Reduction in labor and material costs related to redundant experiments. 

• Reduction in the number of animal subjects needed in preclinical trials (since in silico

models can be used to predict how drug candidates will interact with the subject). 

• R&D labor and material cost savings associated with fewer product redesign-synthesizetest cycles (since more accurate in silico models could be used to predict the effect of a 

proposed structural modification of a therapeutic product). 

• Increased clinical trial success rate due to early detection of toxicity, using improved in 

silico models of information obtained from expanded adverse event databases, which 

eliminates drug candidates before entering clinical trials. 

• Reduction in number of trials and patients through the simulation of clinical trials using in 

silico modeling. 
A-15 

Impact on Research and Development 

1. Consider the following “improved world.” Suppose there is an immediate advancement in the above 

areas of 50% over the current state of the art. What would you estimate to be the impact on the 

process of bringing a candidate biopharmaceutical to the next stage of development? Please 

consider impacts over the entire development process.

Stage 

Percentage 

Reduction in 

Stage Length 

(Time until Start of 

Next Stage) 

Percentage 

Reduction in 

Stage Cost 

Check Here if Not 

Familiar with this 

Stage 

Discovery & Preclinical ____% ____% □

Phase I ____% ____% □

Phase II ____% ____% □

Phase III ____% ____% □

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-16

Impact on Success Rates 

Now consider the success and failure rates for candidate biopharmaceuticals in this same “improved 

world” (a 50% advancement in the current the state of the art in the above areas). 

2a. The table below provides the average success rate for an Investigational New Drug (IND) 

entering clinical trials, in other words, the probability that an IND will receive FDA approval. Please 

estimate what impact the advancements above might have on this success rate in the table below. 

For example, providing an estimate that is greater than 35% implies that the advancements 

above allow researchers to make more well-informed decisions in selecting a lead candidate for 

IND submission. A higher success rate implies improved quality of all INDs; thus, there is a 

higher probability of receiving FDA approval. 

Change in IND Overall 

Success Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an IND ultimately 

receives FDA approval 35% □

2b. From the table above, a 35% success rate implies that 35 out of 100 INDs will receive FDA 

approval. This suggests that the other 65 INDs will fail at some point during clinical trials. The table 

below provides the average distribution of failures occurring at each clinical trial phase. Please 

estimate what impact the advancements above might have on this distribution of failures across the 

clinical trial phases. 

For example, if you think the advancements above would allow researchers to make decisions 

about project failures earlier in clinical trials, this impact would be represented by shifting 5% of 

clinical failures from Phase III to Phase I. A shift in the occurrence of failures to an earlier phase 

implies a decrease in the overall cost of drug development. 

Change in Clinical Trial 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Of all the INDs that fail in clinical trials, what percentage fail in each stage?

Percentage failing in Phase I 15% □

Percentage failing in Phase II 46% □

Percentage failing in Phase III 39% □

Total 100% 100% 

2c. The table below provides the average failure rate for biopharmaceuticals that are currently 

approved and marketed to consumers. Please estimate the impact the advancements above might 

have on the probability that an approved drug is recalled. 

Change in Post-market 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an approved drug 

is recalled 0.4% □ a

 Based on industry estimates and published literature. 

b

 Respond only if your estimates are significantly different from typical percentages provided. 

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-17 

3. Which of the advancements listed above do you perceive as being the most important to achieving 

the improvements you noted in the boxes above? 

□ Shared data standards for formatting and content. 

□ Greater availability of currently gathered data through publicly accessible, curated databases. 

□ Improved data mining applications and algorithms. 

□ Improved accuracy of in silico model predictions. 

THIS IS THE END OF THE SURVEY for respondents who answered Informatics Technology Case. 

Respondent should see a Finish button following Q3 above. Finish button click GO TO page p. 33
A-18

Assessment of the Potential Impact of Improvements in Molecular Biomarkers

In this section, we investigate the potential R&D cost savings that could occur given hypothetical 

improvements in the technology infrastructure supporting the use of biomarkers in biopharmaceutical 

discovery, development, and testing. The advancements listed come from the NIH Technology Road 

Map, the FDA Critical Path Opportunities List, and other industry sources. 

The following four advancements exist in varying degrees today and are expected to improve in the 

coming years: 

A. Standardized and accepted validation process for biomarker discovery. 

B. Safety biomarkers that predict human toxicity in preclinical stages of drug development. 

C. Validated efficacy biomarkers for diseases or therapeutic categories in your area of interest 

that could be used as surrogate endpoints in clinical trial activities. 

D. Enhanced ability to predict biological response to treatment (e.g., drug efficacy 

measurement). 

Advancements in these areas could lead to a number of impacts on R&D productivity. Examples include 

the following: 

• Reduction in number of tests conducted in preclinical studies due to reduced uncertainty of 

drug efficacy. 

• Avoidance of future R&D costs due to early detection of toxicity that eliminates drug 

candidates before entering clinical trials. 

• Shorter clinical trial periods due to faster detection of biologic response to drug. 

• Reduced number of patients in clinical studies due to more accurate selection criteria, 

resulting in improved stratified patient populations. 

• Reduction in labor and material costs during clinical trials due to the ability to set dosage 

levels on a per-patient basis. 

Impact on Research and Development 

1. Consider the following “improved world.” Suppose there is an immediate advancement in the above 

areas of 50% over the current state of the art. What would you estimate to be the impact on the 

process of bringing a candidate biopharmaceutical to the next stage of development? Please 

consider impacts over the entire development process.

Stage 

Percentage 

Reduction in 

Stage Length 

(Time until Start of 

Next Stage) 

Percentage 

Reduction in 

Stage Cost 

Check Here if Not 

Familiar with this 

Stage 

Discovery & Preclinical ____% ____% □

Phase I ____% ____% □

Phase II ____% ____% □

Phase III ____% ____% □

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-19 

Impact on Success Rates 

Now consider the success and failure rates for candidate biopharmaceuticals in this same “improved 

world” (a 50% advancement in the current the state of the art in the above areas). 

2a. The table below provides the average success rate for an Investigational New Drug (IND) 

entering clinical trials, in other words, the probability that an IND will receive FDA approval. Please 

estimate what impact the advancements above might have on this success rate in the table below. 

For example, providing an estimate that is greater than 35% implies that the advancements 

above allow researchers to make more well-informed decisions in selecting a lead candidate for 

IND submission. A higher success rate implies improved quality of all INDs; thus, there is a 

higher probability of receiving FDA approval. 

Change in IND Overall 

Success Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an IND ultimately 

receives FDA approval 35% □

2b. From the table above, a 35% success rate implies that 35 out of 100 INDs will receive FDA 

approval. This suggests that the other 65 INDs will fail at some point during clinical trials. The table 

below provides the average distribution of failures occurring at each clinical trial phase. Please 

estimate what impact the advancements above might have on this distribution of failures across the 

clinical trial phases. 

For example, if you think the advancements above would allow researchers to make decisions 

about project failures earlier in clinical trials, this impact would be represented by shifting 5% of 

clinical failures from Phase III to Phase I. A shift in the occurrence of failures to an earlier phase 

implies a decrease in the overall cost of drug development. 

Change in Clinical Trial 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Of all the INDs that fail in clinical trials, what percentage fail in each stage?

Percentage failing in Phase I 15% □

Percentage failing in Phase II 46% □

Percentage failing in Phase III 39% □

Total 100% 100% 

2c. The table below provides the average failure rate for biopharmaceuticals that are currently 

approved and marketed to consumers. Please estimate the impact the advancements above might 

have on the probability that an approved drug is recalled. 

Change in Post-market 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an approved drug 

is recalled 0.4% □ a

 Based on industry estimates and published literature. 

b

 Respond only if your estimates are significantly different from typical percentages provided. 

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-20

3. Which of the advancements listed above do you perceive as being the most important to achieving 

the improvements you noted in the boxes above? 

□ Standardized and accepted validation process for biomarker discovery. 

□ Safety biomarkers that predict human toxicity in preclinical stages of drug development. 

□ Validated efficacy biomarkers for diseases or therapeutic categories in your area of interest that 

could be used as surrogate endpoints in clinical trial activities. 

□ Enhanced ability to predict biological response to treatment (e.g., drug efficacy measurement). 

THIS IS THE END OF THE SURVEY for respondents who answered Molecular Biomarkers 

Technology Case. 

Respondent should see a Finish button following Q3 above. Finish button click GO TO page p. 33
A-21 

Assessment of the Potential Impact of Improvements in Gene and/or Protein Expression Analysis

In this section, we investigate the potential R&D cost savings that could occur given hypothetical 

improvements in the technology infrastructure supporting the use of gene and/or protein expression 

analysis in biopharmaceutical discovery, development, and testing. The advancements listed come from 

the NIH Technology Road Map, the FDA Critical Path Opportunities List, and other industry sources. 

The following nine advancements exist in varying degrees today and are expected to improve in the 

coming years: 

For gene expression: 

A. Publicly available synthetic mRNA reference materials for microarray for performance 

assurance. 

B. Standard technical protocols for microarray experiments. 

C. Standard protocols for RNA and DNA extraction. 

D. Data and analysis to benchmark microarray performance. 

E. Microarray scanning equipment calibration tools. 

For protein expression: 

F. Techniques for measuring the presence of low-abundance proteins. 

G. Improved sensitivity and lower coefficients of variation (≤10%) in mass spectrometry analysis 

to allow for its use in later stages of drug development. 

H. Improved availability of antibodies with high affinity, specificity, and selectivity for use with 

protein microarrays. 

I. Standards for protein microarray experiments. 

Advancements in these areas could lead to a number of impacts on R&D productivity. Examples include 

the following: 

• Reduction in labor, microarray, and consumables costs for redundant experiments and 

avoided downstream data capture and analysis costs for those experiments. 

• Avoided downstream R&D costs for investigating genes and proteins mistakenly identified as 

potential targets. 

• Greater confidence in and comparability among results from microarray experiments allowing 

for the elimination of redundant tests. 

• Reduction in labor costs associated with calibrating equipment due to the availability of 

standardized procedures and reference phantoms. 

• Reduction in or elimination of microarray scanning errors due to poor equipment alignment 

and calibration and associated downstream impacts of avoidable data capture errors. 

• Elimination of additional (redundant) tests needed to verify results due to poor image quality. 
A-22

Impact on Research and Development 

1. Consider the following “improved world.” Suppose there is an immediate advancement in the above 

areas of 50% over the current state of the art. What would you estimate to be the impact on the process 

of bringing a candidate biopharmaceutical to the next stage of development? Please consider impacts 

over the entire development process.

Stage 

Percentage 

Reduction in 

Stage Length 

(Time until Start of 

Next Stage) 

Percentage 

Reduction in 

Stage Cost 

Check Here if Not 

Familiar with this 

Stage 

Discovery & Preclinical ____% ____% □

Phase I ____% ____% □

Phase II ____% ____% □

Phase III ____% ____% □

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-23 

Impact on Success Rates 

Now consider the success and failure rates for candidate biopharmaceuticals in this same “improved 

world” (a 50% advancement in the current the state of the art in the above areas). 

2a. The table below provides the average success rate for an Investigational New Drug (IND) 

entering clinical trials, in other words, the probability that an IND will receive FDA approval. Please 

estimate what impact the advancements above might have on this success rate in the table below. 

For example, providing an estimate that is greater than 35% implies that the advancements 

above allow researchers to make more well-informed decisions in selecting a lead candidate for 

IND submission. A higher success rate implies improved quality of all INDs; thus, there is a 

higher probability of receiving FDA approval. 

Change in IND Overall 

Success Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an IND ultimately 

receives FDA approval 35% □

2b. From the table above, a 35% success rate implies that 35 out of 100 INDs will receive FDA 

approval. This suggests that the other 65 INDs will fail at some point during clinical trials. The table 

below provides the average distribution of failures occurring at each clinical trial phase. Please 

estimate what impact the advancements above might have on this distribution of failures across the 

clinical trial phases. 

For example, if you think the advancements above would allow researchers to make decisions 

about project failures earlier in clinical trials, this impact would be represented by shifting 5% of 

clinical failures from Phase III to Phase I. A shift in the occurrence of failures to an earlier phase 

implies a decrease in the overall cost of drug development. 

Change in Clinical Trial 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Of all the INDs that fail in clinical trials, what percentage fail in each stage?

Percentage failing in Phase I 15% □

Percentage failing in Phase II 46% □

Percentage failing in Phase III 39% □

Total 100% 100% 

2c. The table below provides the average failure rate for biopharmaceuticals that are currently 

approved and marketed to consumers. Please estimate the impact the advancements above might 

have on the probability that an approved drug is recalled. 

Change in Post-market 

Failure Rates Typical Ratea

Current 

Estimated 

Rate for Your 

Organizationb 

Estimated 

Rate in 

Improved 

World 

Check Here 

if Not 

Familiar 

with This 

Stage 

Probability that an approved drug 

is recalled 0.4% □ a

 Based on industry estimates and published literature. 

b

 Respond only if your estimates are significantly different from typical percentages provided. 

Comments:__________________________________________________________________________

_______________________________________________________________________ 
A-24

3. Which of the advancements listed above do you perceive as being the most important to achieving 

the improvements you noted in the boxes above? 

□ Publicly available synthetic mRNA reference materials for microarray for performance 

assurance. 

□ Standard technical protocols for microarray experiments. 

□ Standard protocols for RNA and DNA extraction. 

□ Data and analysis to benchmark microarray performance. 

□ Microarray scanning equipment calibration tools. 

□ Techniques for measuring the presence of low-abundance proteins. 

□ Improved sensitivity and lower coefficients of variation (≤10%) in mass spectrometry analysis 

to allow for its use in later stages of drug development. 

□ Improved availability of antibodies with high affinity, specificity, and selectivity for use with 

protein microarrays. 

□ Standards for protein microarray experiments. 

THIS IS THE END OF THE SURVEY for respondents who answered Gene/Protein Expression 

Analysis Technology Case. 

Respondent should see a Finish button following Q3 above. Finish button click GO TO page p. 33 
A-25 

SKIP LOGIC: Respondent will only see this section if they respond as participating in commercial-scale 

manufacturing or post-market monitoring in Q4 

Each paragraph will appear in sequential screen shots on the Web survey to make it easier to read. 

Part II-B: Current Expenditures on Technology Infrastructure 

Manufacturing Activities 

The purpose of this section is to obtain estimates from your organization about the resources 

allocated to developing comparable test methods, standardized software models, standard 

reference materials used for measurement and calibration, and standard reference data. We will 

aggregate this information to quantify the proportion of the biopharmaceutical industry’s 

activities that are allocated to improving the “technology infrastructure” supporting commercialscale manufacturing.

Technology infrastructure consists of a set of “technical tools and processes” that enable or increase 

the efficiency of R&D, manufacturing, and market penetration. Collectively, NIST refers to these tools and 

processes as the technology infrastructure. The technology infrastructure provides the technical basis 

for activities ranging from drug discovery to quality control in the production process. Investments in the 

technology infrastructure have the potential to significantly lower development, production, and 

transaction costs and hence are an ongoing activity in most companies. Investment in the technology 

infrastructure may include both direct and indirect costs in the form of equipment, labor, and material 

expenditures dedicated to technology infrastructure. 

Examples of technology infrastructure include the following: 

• Certified reference materials (SRM), reference methods, and standardized procedures related to 

measurement or calibration in conducting tests during R&D stages or in any manufacturing 

process. 

• Validated methods for interpreting results from different analytical platforms. 

• Standardized techniques (e.g., for use with spectrometers, imaging systems, and/or 

measurement devices) 

• Standardized methods for characterizing scientific and engineering data and the algorithms to 

manipulate, search, and analyze these uniform data within a publicly available database. 

• Processing techniques (e.g., for use with 2D Gel electrophoresis platforms, chromatography 

separation systems) 

Click here for examples of technology infrastructure costs.

The following bullets will appear in a separate browser window if the respondent selects the link 

in RED above. 

Technology infrastructure Cost Examples

• Purchase of measurement-related equipment, software, reference materials, or services. 

• Activities required for setup and validation of analytical instruments, reagents, or other research 

tools. Examples of these activities may include developing test methodologies or process 

standards in measurement and manufacturing practices. 

• In-house customization of technology platforms purchased from third-party vendors. 

• Efforts to develop interoperability between different software or equipment systems. 

• License fees or any other spending on enhancements to routinely used processes or 

equipment, intended to increase productivity, reduce redundancy, or improve the confidence in 

results. 
A-26

1. Based on the definition and examples of technology infrastructure provided above, please estimate 

the expenditures for the technology infrastructure supporting your commercial–scale manufacturing

activities. Please consider all labor, materials, and equipment expenditures for in-house activities, as 

well as contributions to consortia or partnership initiatives. 

Approximate Annual Expenditure on Technology Infrastructure = 

$___________ 

Technology infrastructure Expenditure Category 

Approximate Percentage of 

Annual Expenditure on 

Technology infrastructure 

Labor (includes all research, implementation support 

activities, and participation in consortia related to 

infrastructure technologies) 

<<value 0 to 100>>

Capital (includes all equipment, licensing fees, and ...) <<value 0 to 100>>

Materials (includes all reagents, reference materials) <<value 0 to 100>>

Total 100% 

2. Approximately what share of total “sales” does the dollar value reported in Question 1 represent? 

□ _______ % of total current annual sales 

SKIP LOGIC: IF Respondent answers $0 or 0% THEN the survey will terminate with a “Thank you for 

your participation in this important NIST study” message. 

3. Over the last 5 years, have these expenditures increased, decreased, or stayed the same? 

□ Increased 

□ Decreased 

□ Constant 

□ Comments: ___________________________________________ 
A-27 

Part III-B: Assessment of the Potential Impact of Improvements in Technology Infrastructure

The purpose of this section is to collect information that will help us assess the potential impact 

that certain improvements to the underlying technology infrastructure could have on the current 

cost of biopharmaceutical manufacturing. 

1. Please estimate the current share of total manufacturing costs accounted for by the following phases 

of production for a typical biopharmaceutical like those your organization manufactures.

Production Phase/Activity 

Approximate Percentage of Total 

Annual Manufacturing Costs 

Preproduction 

(includes process engineering and pilot 

scale-up activities to move from clinical 

trial to commercial volumes) 

Upstream processing 

Downstream processing 

Process monitoring across all phases of 

production 

Postproduction market transactions 

(e.g., testing, handling, and distribution) 

Total Manufacturing Costs 100% 

2. To help us establish a baseline figure for these manufacturing costs, please estimate total 

manufacturing costs as a share of annual sales. The estimate may be for a particular product line(s) 

or an industry estimate for firms similar to yours. 

Manufacturing costs account for approximately _______% of annual sales. 

3. Based on your knowledge of emerging technologies and other developments in the biomanufacturing 

industry to improve the productivity, quality, and efficiency of the biopharmaceutical production 

process, which phase/activity has the greatest potential for cost reductions over the next 5 years? 

Please only check one. 

□ Preproduction 

□ Upstream processing 

□ Downstream processing 

□ Process monitoring (across all phases of production) 

□ Postproduction market transactions (e.g., testing, handling, and distribution) 

□ Other, please specify: ___________ 
A-28

4. The NIH Technology Road Map, the FDA Critical Path Opportunities List, and other industry sources 

have identified a number of areas that could benefit from additional research. Consider 

advancements in the following areas related to the technology infrastructure supporting different 

stages of manufacturing. These exist in varying degrees today and are expected to improve in the 

coming years. 

Preproduction:

 

A. Predictive modeling and underlying data for determining batch yield given a specified level of 

inputs and production parameters (which would reduce the number of test batches required 

during process scale-up). 

Upstream Processing: 

B. Robust expression systems that produce raw proteins in higher yields with fewer impurities 

(for example, the use of transgenic plants and animals). 

C. Technologies, such as (but not limited to) disposable bioreactors and mixing systems, that 

can accommodate rapid changes in manufacturing processes and can reduce the risk of 

biological product contamination. 

Downstream Processing: 

D. Purification technology that can improve flow rates and increase capacity over current 

methods. Examples of new technology might include membrane chromatography or 

improved, high-pressure affinity chromatography among other methods. 

Process Monitoring (crossing over all phases of production):

E. Implementation of process analytics technology (PAT) to better understand, monitor, and 

control production processes in real time. For example, this might involve the following: 

a. Improved detection of contamination in biological products (e.g., viruses, bacteria, 

and other organisms) through the use of microarrays or proteomics technologies. 

b. Uniform standards for spectroscopic instruments. For example, standards for 

appropriate instrument qualification and calibration standards for techniques such as 

Raman and Terahertz spectroscopy. 

F. Improved methods for product characterization, including enhanced potency assays and 

appropriate statistical and sampling techniques. For example, this might involve the following: 

a. More reliable and quantitative nonanimal-based tests of vaccine potency. 

b. Potency measurements that provide reliable information about the quality of cells or 

tissues to be used in therapies. 

Postproduction: 

G. Improved certainty about product characteristics, including potency, sterility, purity, and 

handling requirements. 

Enhancements in these areas could lead to a number of impacts on manufacturing productivity, such as 

the following: 

• Reduced time to market as a result of better scale-up procedures, shorter downstream 

processing purification periods, and faster and more reliable batch testing procedures. 
A-29 

• Improved product yields for a given amount of raw material. 

• Reduced labor and material costs with fewer trial and error iterations and fewer contaminated 

batches. 

• Labor saved in sterilization activities between batches. 

• Lower transactions costs to customers as a result of greater reliability of data and fewer 

instances of inactive product released. 

• Smaller amount of product required for testing. 

Impact on Manufacturing 

Consider the following “improved world.” Suppose there is an immediate advancement in the above areas 

of 50% over the current state of the art. What would you estimate the impact on the manufacturing 

process to be for a typical biopharmaceutical? 

Manufacturing Costs 

Percentage 

Reduction in Cost 

by Phase 

Check Here if 

Not Familiar with 

This Activity 

Preproduction (scale-up from clinical trial 

volumes) costs □

Upstream processing costs □

Downstream processing costs □

Process monitoring and quality assurance 

testing costs □

Postmanufacturing market transactions costs □

Comments:__________________________________________________________________________

_______________________________________________________________________ 

Insert “Save & Continue” button after this question. Following Question will appear on a new screen. 

THIS IS THE END PART III-B, Respondents will now be asked questions related to their postmarket monitoring activities. 
A-30

Part II-C: Current Expenditures on Technology Infrastructure 

Post-market Monitoring Activities 

The purpose of this section is to obtain estimates from your organization about the resources 

allocated to developing comparable test methods, standardized software models, standard 

reference materials used for measurement and calibration, and standard reference data. We will 

aggregate this information to quantify the proportion of the biopharmaceutical industry’s

activities that are allocated to improving the “technology infrastructure” supporting post-market 

monitoring. 

1. Based on the same definition and examples of the technology infrastructure provided in Part II-B, 

please estimate the expenditures for the technology infrastructure supporting your post-market 

activities. Please consider all labor, materials, and equipment expenditures, both for internal use and 

contributions to consortia or partnerships. 

Approximate Annual Expenditure on Technology Infrastructure = 

$_____ ______ 

Technology infrastructure Expenditure 

Category 

Approximate Percentage of Annual 

Expenditure on Technology 

infrastructure 

Labor (includes all research, implementation 

support activities and participation in consortia 

related to infrastructure technologies) 

<<value 0 to 100>>

Capital (includes all equipment, licensing fees, 

and ...) <<value 0 to 100>>

Materials (includes all reagents, reference 

materials) <<value 0 to 100>>

Total 100% 

2. What share of total sales does the dollar value in Question 1 represent? 

□ _______ % of total current annual sales 

SKIP LOGIC: IF Respondent answers $0 or 0% THEN the survey will terminate with a “Thank you for 

your participation in this important NIST study” message. 

3. Over the last 5 years, have these expenditures increased, decreased, or staved constant? 

□ Increased 

□ Decreased 

□ Constant 

3b. Comments: ___________________________________________ 
A-31 

Part III-C: Assessment of the Potential Impact of Improvements in Technology Infrastructure

The purpose of this section is to collect information that will help us assess the potential impact 

that certain improvements to the underlying technology infrastructure could have on the current 

cost of biopharmaceutical post-market monitoring activities. 

1. Please estimate the current share of total post-market monitoring costs accounted for by the following 

activities for a typical biopharmaceutical like those your organization monitors.

Post-market Monitoring Activities 

Approximate Percentage of Total 

Annual Post-market Monitoring Costs 

Adverse event (AE) monitoring 

AE and safety data management 

Regulatory drug safety reporting 

Product label updating 

Other related activities 

Total 100% 

2. Please estimate total post-market monitoring costs as a share of annual sales. The estimate may be 

for a particular product line(s) or it may be an industry estimate for firms similar to yours. 

Post-market monitoring costs account for approximately _____% of annual sales. 

3. Based on your knowledge of emerging technologies and other developments in the 

biopharmaceutical industry to improve the productivity, quality, and efficiency of the 

biopharmaceutical development process, which post-market monitoring activity has the greatest 

potential for cost reductions over the next 5 years? Please check only one. 

□ AE monitoring 

□ AE and safety data management 

□ Regulatory drug safety report 

□ Product label updating 

□ Other, please specify: ___________ 
A-32

4. The NIH Technology Road Map, the FDA Critical Path Opportunities List, and other industry sources 

have identified a number of areas that could benefit from additional research. Consider 

advancements in the following areas related to the technology infrastructure supporting post-market 

monitoring activities. These exist in varying degrees today and are expected to improve in the coming 

years. 

A. Improved statistical methods for signal detection in AE monitoring. 

B. Development of standards for AE reporting systems. 

C. A standardized process and protocol for ensuring data consistency across international AE 

databases. 

D. Improved interoperability between safety and clinical database management systems. 

E. Automated monitoring software tools to provide notification of required product label changes 

based on changes in AE frequencies. 

F. Improved integration linking data entry, clinical databases, randomization databases, and 

regulatory systems. 

Enhancements in these areas could lead to a number of impacts on the productivity of post-market 

monitoring activities, such as the following: 

• Reduced labor costs of physicians required to conduct AE monitoring and data analysis. 

• Reduced labor costs in reconciling differences across international AE databases. 

• Reduced transaction costs in updating product labels based on AE frequency changes. 

• Reduced labor costs to generate electronic submissions to regulator agencies. 

• Reduced risk due to the mitigation of potential errors in analyzing and interpreting AE data. 

Impact on Post-market Monitoring Activities 

Consider the following “improved world.” Suppose there is an immediate advancement in the above areas 

of 50% over the current state of the art. What would you estimate to be the impact on the post-market 

monitoring costs for a typical biopharmaceutical? 

Post-market Monitoring Activities 

Percentage 

Reduction in Cost 

by Activity 

Check Here if 

Not Familiar with 

This Activity 

AE monitoring □

AE and safety data management □

Regulatory drug safety report □

Product label updating □

Other related activities □

Comments:__________________________________________________________________________

_______________________________________________________________________ 

THIS IS THE END OF THE SURVEY for respondents who answered PART III-C. 

Respondent should see a Finish button following Q3 above. Finish button click GO TO page p. 33 
A-33 

5. Do you see a role for the National Institute of Standards and Technology (NIST) in helping to achieve 

the technological advancements discussed in this survey? If Yes, what role should NIST play? 

□ Yes 

□ No 

□ If Yes, what role should NIST play?________________________________

6. What is the total employment of your parent company? Please indicate the approximate range. 

□ 1–10 

□ 11–50 

□ 51–500 

□ 501–2,500 

□ 2,501–15,000 

□ >15,000 

7. Approximately how many full-time equivalent (FTE) scientists and engineers were represented in your 

responses to this survey?

This may be a subset of your company’s total employment. 

_____________ # of FTEs
A-34

Following the completion of all questions in the survey the respondent will receive the following message: 

On behalf of NIST, RTI International would like to thank you for your participation in this important 

survey of the biopharmaceutical industry. 

If you would like to be contacted when a final report is available and/or if we can contact you with any 

questions we may have, please indicate such: 

□ I am willing to have RTI contact me about my responses to this survey. 

□ Please send me a copy of the final report when it is available. 

If you clicked either box above, please provide the following contact information and any additional 

comments or questions you would like to discuss with us (NOTE: your e-mail address and contact 

information will not be shared with anyone outside RTI or used for any other purpose outside this project): 

Name: _____________________________ 

Company Name: _____________________________ 

E-mail Address: _____________________________ 

Phone Number: ____________________________(optional) 

NOTE: If you would like to refer this survey to a colleague who is active in other areas of 

biopharmaceutical development, please forward them the e-mail invitation you received or enter their email address in the space below. An e-mail will be sent to them with the survey material. Please be sure 

to include your name and e-mail address above, so we can indicate that you asked that this e-mail be 

sent. 

 Intended recipient’s e-mail address: _____________________