# Presentation: 815943

## Use of Biomarker Information in Drug Product Labels to Individualize Pharmacotherapy

- Lawrence J. Lesko, Ph.D., FCP
- Director of the Office of Clinical Pharmacology and
- Biopharmaceutics
- Center for Drug Evaluation and Research
- Food and Drug Administration

- Clinical Pharmacology Subcommittee (CPSC) of the Advisory Committee for Pharmaceutical SciencesNovember 15, 2005Rockville, Maryland

## Recap of Yesterday and Introduction to Today

- Biomarkers:	- 2C9 and VKORC1	- Viral load	- Blood glucose
- Purpose:	- Matching patients to dosing	- Maximizing success in clinical trials
- Innovation:	- How can biomarkers be better utilized?	- What types of innovation can help?

## Critical Path Initiative:  Seeking Solutions to Productivity Problem

- “The goal of critical path research is to develop new, publicly available scientific and technical tools – including assays, standards, computer modeling techniques, _*biomarkers*_ and clinical trial endpoints.....”
- “The emerging techniques of PGx and proteomics show great promise for contributing _*biomarkers to target responders, monitor clinical response and serve as biomarkers of drug effectiveness*_.”

## Biomarker Definition and Pharmacodiagnostics

- A characteristic that is objectively measured and evaluated as an indicator of normal processes, pathogenic processes or pharmacological responses to a therapeutic intervention
-  *Most common biomarkers use a single feature**	**- **INR (confirm activity), s-warfarin levels (predict activity)*
* **More accurate biomarkers use multiple features**	**- multivariate analysis of warfarin co-factors (prognostic of **	**disease outcome)*
* **More precise biomarkers possible with genomics**	**- **haplotypes, gene expression (type of diagnostic)*

## Biomarker Discovery Programs Growing at a Rapid Pace

- Explosive growth in the number and scope of biomarker knowledge in drug development.
- Related to new technologies such as PGx and imaging and better understanding of diseases.
- Creates potential for individualization of treatment, i.e., scientific basis to the “art of medicine”
*Question is how to obtain biomarker information effectively and efficiently, and when is it critical to translate into labels for directing patient treatment?*

## Heavy Emphasis on Disease Pathways and Drug MOA in Drug Development

- Target plasma drug conc in POC trials
- Risk factors to select patients for trials
- Individual measurements to direct therapy
- Dose finding data to select phase 3 doses
- Plasma drug conc associated with PD
- Derived parameters to adjust dosing
- Identify patients at risk for AEs

- Qualification of biomarkers through disease-drug-statistical models has been done infrequently thus limiting potential use of biomarkers as tests or diagnostics in clinical practice

## Randomized Controlled Trials:  Integrating Biomarkers* Into Drug Development

- Provide best evidence for rejecting null hypothesis of no treatment effect
- Goal to demonstrate efficacy (and safety)
- Assumes homogeneous population
- Not designed to qualify relevant efficacy and safety biomarkers prospectively
- Need to address a different set of questions
- Focus on heterogeneity of patients
- How can “better” biomarkers/diagnostics be incorporated into drug development?
- Prospective hypothesis-testing beyond early trials

**** Not referring to biomarkers as surrogate endpoints***

## Types of Questions To Be Answered

- D/R for benefit and risk
- Inherent variability, changes with patient co-factors and impact of dosing regimens
- Biomarkers most suitable to use to adjust doses in clinical practice
- Basis for TDM monitoring and diagnostic test development
- Quantitative models to qualify relevant biomarker associations with clinical outcomes

## Model-Based Drug Development:  A Critical Path Innovation to Integrate Data Analysis

- Quantitative pharmacology ~ mathematical explanation of relationships to explain clinical outcomes over timeframe of interest
- Extensive use of biomarkers
- E/R modeling (D/R and/or PK/PD)
- Disease progression modeling
- Clinical trial simulation (e.g., Phase III)
- Enrichment and other innovative study designs
- Goal ~ to improve decision-making on dosing, trial design options, risk/benefit and transfer knowledge to label

## Conceptual Framework for MBDD

## Drug Safety in the Post-Vioxx Era:  What Can Be Done About This?

## “ New drugs are inherently more risky because of the relatively small amount of data about their effects.”
Goodman and Gilman,The Pharmacological Basis of Therapeutics, 2001

***Goodman and Gilman,******The Pharmacological Basis of Therapeutics, 2001***

**Origin of ADRs Is No Secret:  *****Inherent Predisposition and Environmental Factors***

## Many ADRs Are Avoidable:  High Incidence, Less Severe

-  Most occur within range of approved doses
-  70% ~ extension ofpharmacological effects
-  90% ~ differences in exposure (PK) or response (PD)

***Nebeker et al, Arch Intern Med, 2005, 165:1111 – 1116******Korn, Drug Development Science, AAMC, 2005***

***What is needed are new pre-marketing approaches to using biomarkers in trial design, dose selection and translation of information into labels.***

## Risk Management Cannot Be Monitored In – It Must Be Built In

## Drug Product Label

- Information in a drug product label is an integral part of the drug development process.
- Goal of labeling is to assure that prescribers have access to useful data than suggest ways to optimize efficacy and manage risk.
- Information on D/R or PK/PD relationships are of particular importance to individualizing therapy

## Assessment of Pharmacometrics in Regulatory Decisions

** **244 ~ number of NDAs surveyed in cardio-renal, oncology and neuropharmacology
** **42 ~ NDAs with pharmacometric (PM) analysis**
** **26 ~ PM pivotal or supportive of NDA approval
-  32 ~ PM provided evidence for label language

**Assessment of Pharmacometrics in Regulatory Decisions**

_***October 3, 2005, Volume 67, Number 40, Page 15***_

**** *****Number not higher because sponsor application lacked necessary data***

## Perspective on Current Situation

** ****Much of the value inherent in the development of a new drug product is lost in uninformative labels**

**“****A physician without information cannot take responsibility;  a physician who is given information *****cannot help but take responsibility”***
***- Paraphrased from Wilbert Leo Gore (Founder of Gore-Tex)***

** ****Put *****knowledge (information + skills + experience)*****in the value chain of drug development**

## D/R or PK/PD Data

- Data obtained from phase 1 and 2 trials
- D/R relationships for efficacy (sometimes safety) used to pick phase 3 dose(s) and design trials
- Data obtained from phase 2/3 trials
- Extensive data on PD biomarkers and PK drug levels associated with clinical outcomes
- Data obtained from targeted special population studies
- PK drug level rich with changes in levels associated with dosing adjustments

## Development of Exposure-Response Modeling Throughout Drug Development 

-  *Integrate* D/R across trials more complete use of data
-  Use to *interpret and predict *differences in exposure between patients
-  Use to *design and simulate* clinical trials
-  Use to *estimate the probabilities* of clinical outcomes

## Very Few Labels Contain E/R Relationships:  Two Examples

- Irbesartan: D/R for effects on DBP

- “...the maximal beta blocking effects have been estimated to produce a 30% reduction in exercise HR.  Beta blocking effects in the 30-80% of maximal effect occur at metoprolol plasma ranging from 30-540 nmol/L.  The conc-effect curve plateaus at 200-300 nmol/L.”

- Metoprolol CR: PK/PD effects on exercise HR

## Example:  Development of Tipranavir for Treatment of HIV

-  Plasma drug levels: remain above IC50 or    IC90 to achieve sustained viral suppression and avoid resistance development
-  Inhibitory quotient (IQ): Cmin / IC50  proposed as a metric to predict efficacy (% responders at week 24)
-  Potential basis for individualizing dosing in patients receiving tipranavir/ritonavir

## Clinical Efficacy and Safety:  Relationship to IQ and Cmin

*Benefit*

*Risk*

***From Dr. Jenny Zheng (OCPB), FDA Antiviral Drug AC Meeting, May 19, 2005***

## Issues With Use of IQ and Cmin to Guide Dosing

- Protein binding adjustment factor ~ lack of consensus on estimate to calculate IC50
- Estimate of IC50 ~ patient-dependent on prior treatment history, generalizability
- Sensitivity and specificity of Cmin to predict grade 3-4 ALT toxicity
- Relative variability in Cmin/IC50
- Future studies of drugs in class designed to determine usefulness of IQ and Cmin

## Final Tipranavir Label

- Approved for combination antiretroviral treatment of HIV-1 in treatment-experienced patients or those having resistance to PTs
- Large interindividual variability in PK, many DDIs and other uncertainties (28 post-marketing studies)
- Some reports of clinical hepatitis and hepatic decompensation
- Label advocates genotype/phenotype testing *a priori* for viral resistance
- FDA recommended *individualized dosing based on the relationship between IQ and probability of efficacy *(pilot study will be conducted in 2006)

***See The Pink Sheet, June 30, 2005***

## Example:  Optimizing B/R of Zoledronic Acid in Cancer Patients

- Pain and fracture reduction in patients metastatic bone disease
- Major clinical concern about risk what dose would optimized clinical benefit without causing unnecessary renal deterioration
- Apply quantitative pharmacology to understand trade-offs and select dosing

## Step 1:  Collecting Prior Information on PK to Characterize Excretion

-   Constructed dose-PK model that included intersubject variability
-   Used to identify significant co-variates affecting clearance of zoledronic acid
-   Renal function identified as only major co-variate of interest

## Step 2:  Bone Turnover Response Biomarkers as a Function of Dose

** ****Urinary excretion of deoxypyridinoline, corrected for creatinine clearance, as a measure of bone turnover**
** ****Response biomarker previously shown to be predictive of metastasis and fracture risk reduction**
** ****Predicted dose-response was flat from 2 – 16 mg**

## Step 3:  Construct a Drug-Disease-Outcome Model to Guide Dosing

** ****Risk of renal deterioration in any given patient is dependent on exposure to zoledronic acid (dose-dependent)**
** ****Risk of renal deterioration at any given dose is dependent on renal function at baseline pre-dosing**

**NR**

**4 mg**

**3 mg**

## Potential Value of MBDD:  Informative Labeling for Risk Management

## Summary:  Critical Path Opportunity for Innovation

- Systematic effort should be made to include biomarker data in labeling (translational science) when:
- - It is available from drug development- Validated assays can be available- Associated with meaningful clinical outcome- Potentially useful in guiding dose adjustments- Adjunct to clinical monitoring of patient

## Acknowledgments

- Dr. Joga Gobburu
- Dr. Bob Powell
- Dr. Don Stanski
- Dr. Jenny Zheng
- Pharmacometrics Division
- Regulatory Review Staff

- Jadhav P et al, “How Biomarkers Can Improve Clinical Drug Development?, Amer.Pharm.Res., July 2004