Clinical Development
Success Rates
2006-2015
Biomedtracker
Pharma intelligence |June 2016

About BIO
BIO is the world’s largest trade association representing biotechnology companies, academic institutions, state biotechnology
centers and related organizations across the United States and in more than 30 other nations. BIO members are involved in
the research and development of innovative healthcare, agricultural, industrial and environmental biotechnology products. BIO
also produces the BIO International Convention, the world’s largest gathering of the biotechnology industry, along with industryleading investor and partnering meetings held around the world.

About Biomedtracker
BioMedTracker, a subscription-based product of Informa, tracks the clinical development and regulatory history of investigational
drugs to assess its Likelihood of Approval (LOA) by the FDA. BioMedTracker is populated in near real-time with updated
information from press releases, corporate earnings calls, investor and medical meetings and numerous other sources.

About Amplion
Amplion is the leading biomarker business intelligence company, and its flagship product BiomarkerBase™, alongwith consulting services and free reports, deliver insights that inform key strategic decisions for drug and diagnostic
test developers. Since 2012 Amplion has helped large and small companies alike make the best use of biomarkers inadvancing precision therapeutics and next generation diagnostics. BiomarkerBase is a subscription-based service thattracks biomarker usage in clinical trials, drug labels, and tests (including laboratory-developed, FDA-cleared, and FDAapproved tests). BiomarkerBase is updated weekly with information from these sources and publications, using supervisedmachine learning algorithms for natural language processing (Amplion BiomarkerEngine) to identify biomarkers.

2 | BIO Industry Analysis

Executive Summary
This is the largest study of clinical drug development success rates to date. Over the last decade, 2006-2015, a total of 9,985
clinical and regulatory phase transitions were recorded and analyzed from 7,455 development programs, across 1,103 companies
in the Biomedtracker database. Phase transitions occur when a drug candidate advances into the next phase of development or issuspended by the sponsor. By calculating the number of programs progressing to the next phase vs. the total number progressing
and suspended, we assessed the success rate at each of the four phases of development: Phase I, II, III, and regulatory filing.
Having phase-by-phase data in hand, we then compared groups of diseases, drug modalities and other attributes to generate
the most comprehensive analysis yet of biopharmaceutical R&D success.
This work was made possible due to the years of clinical program monitoring and data entry by Informa’s Biomedtracker service.
BIO has long partnered with Biomedtracker to calculate success rates based on this data. More recently, BIO and Biomedtracker
partnered with Amplion, the inventors of BiomarkerBase, to analyze the effects of biomarkers in clinical trial success.
Key takeaways:•

The overall likelihood of approval (LOA) from Phase I for all developmental candidates was 9.6%, and 11.9% for
all indications outside of Oncology.

•

Rare disease programs and programs that utilized selection biomarkers had higher success rates at eachphase of development vs. the overall dataset.

•

Chronic diseases with high populations had lower LOA from Phase I vs. the overall dataset.

•Of the 14 major disease areas, Hematology had the highest LOA from Phase I (26.1%) and Oncology had thelowest (5.1%).

•

Sub-indication analysis within Oncology revealed hematological cancers had 2x higher LOA from Phase I
than solid tumors.

•

Oncology drugs had a 2x higher rate of first cycle approval than Psychiatric drugs, which had the lowest percent
of first-cycle review approvals. Oncology drugs were also approved the fastest of all 14 disease areas.

•

Phase II clinical programs continue to experience the lowest success rate of the four development phases, with
only 30.7% of developmental candidates advancing to Phase III.

Biomedtracker
Pharma intelligence |BIO Industry Analysis | 3

Disease areas covered in this report:• Allergy• Autoimmune• Cardiovascular• Chronic High Prevalence Diseases• Endocrine• Gastroenterology• Hematology• Infectious Disease• Metabolic• Neurology• Oncology• Ophthalmology• Psychiatry• Rare Diseases• Respiratory• Urology

4 | BIO Industry Analysis

Table of Contents
Introduction.................................................................................................................................................................... 6
Phase Success and Likelihood of Approval (LOA) – Overall..............................................................7
Phase Success and Likelihood of Approval (LOA) – by Disease.................................................... 8
Oncology and Non-Oncology Diseases.........................................................................................13
Rare and Chronic High Prevalence Disease................................................................................16
Patient Selection Biomarkers...............................................................................................................18
Phase Success and Likelihood of Approval (LOA) – by Drug Classification ........................ 20
Discussion.....................................................................................................................................................................22
Methods......................................................................................................................................................................... 24
References................................................................................................................................................................... 26

BIO Industry Analysis | 5

Introduction
This study aimed to measure clinical development success rates to strengthen benchmarking metrics for drug development. To
measure success rates for investigational drugs, we analyzed individual drug program phase transitions from January 1, 2006 to
December 31, 2015. For the ten years studied, 9,985 transitions in the Biomedtracker database were analyzed. A phase transitionis the movement out of a clinical phase – for example, advancing from Phase I to Phase II development, or being suspended
after completion of Phase I development.
These transitions occurred in 7,455 clinical drug development programs, across 1,103 companies (both large and small), making
this the largest study of its kind. With this broad set of data, we aimed to capture the diversity in drug development across levelsof novelty, molecular modalities, and disease indications.
Only company-sponsored, FDA registration-enabling development programs were considered; investigator-sponsored studies
were excluded from this analysis. A more detailed description of the data collection, composition, and analysis methodology
are described at the end of this report under “Methods.”
Individual Phase transition success rates were determined by dividing the number that advanced to the next phase by the total
number advanced and suspended. This “advanced and suspended” number is often referred to as “n” in this report, and should
be taken into account when drawing conclusions from the success rate results.
One of the key measures of success used in this report is the Likelihood of Approval (LOA) from Phase I. This LOA success rate
is simply a multiplication of all four Phases success rates, a compounded probability calculation. For example, if each phase had
a 50% chance of success, then the LOA from Phase I would be 0.5 x 0.5 x 0.5 x 0.5 = 6.25%.

6 | BIO Industry Analysis

Phase Transition Success and Likelihoodof Approval (LOA) - Overall
Consistent with previous studies of drug development phase transition success rates, we found Phase II success rates to be far
lower than any other phase.1 Phase I and III rates were substantially higher than Phase II, with Phase I slightly higher than Phase
III. The highest success rate of the four development phases was the NDA/BLA filing phase.
The Phase I transition success rate was 63.2% (n=3,582). As this Phase is typically conducted for safety testing and is notdependent on efficacy results for candidates to advance, it is common for this phase to have the highest success rate among theclinical phases across most categories analyzed in this report. Phase I success rates may also benefit from delayed reporting bias,
as some larger companies may not deem failed Phase I programs as material and thereby not report them in the public domain.
The Phase II transition success rate (30.7%, n=3,862) was substantially lower than Phase I, and the lowest of the four phasesstudied. As this is generally the first stage where proof-of-concept is deliberately tested in human subjects, Phase II consistently
had the lowest success rate of all phases. This is also the point in development where industry must decide whether to pursue
the large, expensive Phase III studies and may decide to terminate development for multiple reasons including commercial
viability. The second-lowest phase transition success rate was found in Phase III (58.1%, n=1,491). This is significant as mostcompany-sponsored Phase III trials are the longest and most expensive trials to conduct.
The probability of FDA approval after submitting a New Drug Application (NDA) or Biologic License Application (BLA), taking
into account re-submissions, was 85.3% (n=1,050). Multiplying these individual phase components to obtain the compoundprobability of progressing from Phase I to U.S. FDA approval (LOA) reveals that only 9.6% (n=9,985) of drug development
programs successfully make it to market (Figure 1).

Probablity of Success
90%

85.3%

Probability of Success

80%
70%

63.2%

58.1%

60%
50%
40%
30.7%

30%
20%

9.6%

10%
0%

Phase I to
Phase II

Phase II to
Phase III

Phase III to NDA/BLA to
NDA/BLA
Approval

Phase I to
Approval

All Diseases, All Modalities

Figure 1. Phase transition success rates and LOA from Phase I for all diseases, all modalities.

BIO Industry Analysis | 7

Phase Transition Success and Likelihoodof Approval (LOA) - by Disease
We segmented major disease areas according to the convention used by Biomedtracker, and categorized 21 major diseases
and 558 indications for the 2006-2015 timeframe. For reporting at the disease area level, we analyzed only major diseases
with more than 100 total transitions from Phase I to NDA/BLA approval. This resulted in 14 categorized disease areas: Allergy,
Autoimmune, Cardiovascular, Endocrine, Hematology, Infectious disease, Gastroenterology (non-IBD), Metabolic, Neurology,
Oncology, Ophthalmology, Psychiatry, Respiratory, and Urology. Disease areas with n <100 were placed into the “Other” category.
This includes Dermatology, Renal, Obstetrics, Rheumatology (for non-autoimmune indications), Dental, and Orthopedics.
As can be seen in Figures 2a, there is a wide range of Likelihood of Approval (LOA) from Phase I. At the high end, Hematology
towers over the other groups at 26.1% (n=283). A large portion of Hematology transitions came from Hemophilia, Anemia,
and Blood Protein Deficiencies, Thrombocytopenia, and Hemostasis. Some of these Hemophilia indications had overall LOA
that reached above 50%. This more than offset some of the weaker Hematology success rates that were observed in Venous
Thromboembolism and Neutropenia. Hematology’s LOA from Phase I was 5x the success rate for Oncology, which at 5.1%
(n=3,163) had the lowest of all the major disease areas.
The next-highest LOA from Phase I under Hematology’s 26.1% was Infectious Disease with an impressive 19.1% (n=916).
Five disease areas follow closely in the 14-17% range: Ophthalmology > Other > Metabolic > Gastroenterology > Allergy.
Below 14% there is a third group of diseases that was slightly above the overall average of 9.6%: Endocrine > Respiratory >Urology > Autoimmune. Falling under the overall LOA of 9.6% was a fourth group made up of four disease areas: Neurology >Cardiovascular > Psychiatry > Oncology. The fact that Oncology and Neurology had the two highest n values while also having
low LOA values suggests that these two disease categories are a significant factor in bringing down the overall industry LOA.
Likelihood of Approval from Phase I
30%
26.1%

LOA from Phase I

25%
19.1%

20%

17.1%

15%

16.3%

15.3% 15.1% 14.7%

13.2% 12.8%

11.4% 11.1%

10%

9.6%

8.4%
6.6%

6.2%

5%

5.1%

0%

Phase I to Phase II

Phase Success

Phase II to Phase III

Phase III to NDA/BLA

Figure 2a. Chart of LOA from
Phase I, displayed highest
to lowest by disease area.
Advanced or
Advanced or
Advanced or
Suspended

Phase Success

Suspended

Phase Success

Suspended

Phase Success

NDA/BLA to Approval
Advanced or
Suspended

Phase Success
84.0%

Hematology

86

73.3%

83

56.6%

64

75.0%

50

Infectious disease

347

69.5%

286

42.7%

150

72.7%

133

88.7%

Ophthalmology

66

84.8%

101

44.6%

60

58.3%

40

77.5%

Other

96

66.7%

116

39.7%

46

69.6%

43

88.4%

Metabolic

95

61.1%

84

45.2%

35

71.4%

27

77.8%

Gastroenterology*

41

75.6%

56

35.7%

33

60.6%

26

92.3%

Allergy

37

67.6%

40

32.5%

14

71.4%

16

93.8%

Endocrine

299

58.9%

242

40.1%

143

65.0%

107

86.0%

Respiratory

150

65.3%

196

29.1%

45

71.1%

37

94.6%

Urology

21

57.1%

52

32.7%

21

71.4%

14

85.7%

65.7%

319

31.7%

135

62.2%

86

86.0%
85.3%

Autoimmune
8 | BIO Industry
Analysis297
All Indications

3582

63.2%

3862

30.7%

1491

58.1%

1050

Neurology

462

59.1%

465

29.7%

216

57.4%

161

83.2%

Cardiovascular

209

58.9%

237

24.1%

110

55.5%

76

84.2%

LOA f

9.6%

10%

8.4%
6.6%

6.2%

5%

5.1%

0%

Phase I to Phase II

Phase Success

Advanced or
Suspended

Phase Success

Phase II to Phase III
Advanced or
Suspended

Phase Success

Phase III to NDA/BLA
Advanced or
Suspended

Phase Success

NDA/BLA to Approval
Advanced or
Suspended

Phase Success
84.0%

Hematology

86

73.3%

83

56.6%

64

75.0%

50

Infectious disease

347

69.5%

286

42.7%

150

72.7%

133

88.7%

Ophthalmology

66

84.8%

101

44.6%

60

58.3%

40

77.5%

Other

96

66.7%

116

39.7%

46

69.6%

43

88.4%

Metabolic

95

61.1%

84

45.2%

35

71.4%

27

77.8%

Gastroenterology*

41

75.6%

56

35.7%

33

60.6%

26

92.3%

Allergy

37

67.6%

40

32.5%

14

71.4%

16

93.8%

Endocrine

299

58.9%

242

40.1%

143

65.0%

107

86.0%

Respiratory

150

65.3%

196

29.1%

45

71.1%

37

94.6%

Urology

21

57.1%

52

32.7%

21

71.4%

14

85.7%

Autoimmune

297

65.7%

319

31.7%

135

62.2%

86

86.0%

All Indications

3582

63.2%

3862

30.7%

1491

58.1%

1050

85.3%

Neurology

462

59.1%

465

29.7%

216

57.4%

161

83.2%

Cardiovascular

209

58.9%

237

24.1%

110

55.5%

76

84.2%

Psychiatry

154

53.9%

169

23.7%

70

55.7%

58

87.9%

Oncology

1222

62.8%

1416

24.6%

349

40.1%

176

82.4%

Likelihood of Approval

Phase I to Approval

Phase II to Approval

Phase III to Approval

NDA/BLA to Approval

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

Hematology

283

26.1%

197

35.7%

114

63.0%

50

84.0%

Infectious disease

916

19.1%

569

27.5%

283

64.5%

133

88.7%

Ophthalmology

267

17.1%

201

20.1%

100

45.2%

40

77.5%

Other

301

16.3%

205

24.4%

89

61.5%

43

88.4%

Metabolic

241

15.3%

146

25.1%

62

55.6%

27

77.8%

Gastroenterology*

156

15.1%

115

20.0%

59

55.9%

26

92.3%

Allergy

107

14.7%

70

21.8%

30

67.0%

16

93.8%

Endocrine

791

13.2%

492

22.4%

250

55.9%

107

86.0%

Respiratory

428

12.8%

278

19.6%

82

67.3%

37

94.6%
85.7%

Urology

108

11.4%

87

20.0%

35

61.2%

14

Autoimmune

837

11.1%

540

17.0%

221

53.5%

86

86.0%

All Indications

9985

9.6%

6403

15.3%

2541

49.6%

1050

85.3%

Neurology

1304

8.4%

842

14.2%

377

47.8%

161

83.2%

Cardiovascular

632

6.6%

423

11.2%

186

46.7%

76

84.2%

Psychiatry

451

6.2%

297

11.6%

128

49.0%

58

87.9%

Oncology

3163

5.1%

1941

8.1%

525

33.0%

176

82.4%

Figure 2b. Phase transition success and LOA by disease. Table of phase transition success and LOA by disease with corresponding n values.
‘Advanced or Suspended’ refers to the total number of transitions used to calculate each success rate, with the n value noted in the text.
The LOA n value is the total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability ofsuccessfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs from this phase of development.
*Gastroenterology does not include IBD.

BIO Industry Analysis | 9

Phase I Transition Success Rates by Disease
Success rates for Phase I ranged from 53.9% to 84.8%, with the average for all disease indications coming in at 63.2%. Looking
at the distribution, we find that most disease area Phase I success rates cluster within +/-10% of the overall Phase I successrate. Two disease areas were outliers, and they are both to the upside. Ophthalmology registered an 84.8% (n=66) success rate,
which was substantially higher (by >20%) than the overall Phase I success rate. Gastroenterology programs also exhibited an
above average rate of successfully overcoming initial clinical safety hurdles with a 75.6% (n=41) Phase I success rate.

Phase II Transition Success Rates by DiseaseIn every disease area, Phase II had the lowest transition success rate of the four phases. As shown in Figure 3, Phase II successrates ranged from a high of 56.6% (Hematology, n=83) to a low of 23.7% (Psychiatry, n=169). Although it is widely known that
drug program attrition is high in Phase II, it is interesting to find that the rate of success can vary by 33% among disease groups.
The only Phase II success rate above 50% was seen in Hematology, which largely explains how that indication attained thehighest LOA from Phase I.
Excluding Hematology, we can group the Phase II success rates into three clusters: success rates below the 30% overall
success rate, those 31-36%, and those in the 40-45% range. Unlike what is observed in the LOA from Phase I, Oncology does
not have the lowest success rate for Phase II. Cardiovascular and Psychiatry both registered slightly below the 25% successrate seen for Oncology.

Probability of Phase II Success

Phase II Success Rate

60%
50%

57%
45%

40%
30%

45%

43%

40%

40%

36%

33%

33%

32%

31%

30%

29%
25%

24%

24%

20%
10%
0%

Figure 3. Phase II transition success rates by disease area. Categories are listed from highest to lowest based on the probability oftransitioning from Phase II to Phase III. *Gastroenterology does not include IBD.

10 | BIO Industry Analysis

Phase III Transition Success Rates by Disease
For Phase III transition success rates, Oncology was the outlier with the lowest transition success rate. As seen in Figure 4, thePhase III success rates for 14 specific disease areas clustered into two ranges: near 70% and 55-65%. This places Oncology
into a group of its own at just 40.1% (n=349).
In addition to Oncology, Neurology, Psychiatry and Cardiovascular were also below the overall Phase III success rate of 58.1%(n=1,491) at 57.4%, 55.7%, and 55.5%, respectively. Each of these areas included disease indications with large patient populations.
Later in this report, we break down these high prevalence diseases and compare them with low prevalence disease areas.

Probability of Phase III Success
80%

Phase III Success

70%
60%

75%

73%

71%

71%

71%

71%

70%

65%

62%

61%

58%

58%

57%

56%

55%

50%
40%

40%

30%
20%
10%
0%

Figure 4. Phase III transition success rates by disease area. Categories are listed from highest to lowest based on the probability of transitioning
from Phase II to NDA/BLA filing. *Gastroenterology does not include IBD.

BIO Industry Analysis | 11

NDA/BLA Submission Success Rates
NDA/BLA transition success rates (approval rates) for the disease areas listed in Figure 2b ranged from the low end of 77.5%(Ophthalmology) to a high of 94.6% (Respiratory). The distribution of rates (17.1%) were within the tightest range among thefour phases analyzed in this report. These rates are the result of eventual success, not success on first review, meaning some
programs may have as many as four Complete Response Letters (CRLs) and attempts at approval. This unrestricted time-frameand number of re-submissions pushes the overall success above 85% across all diseases.
When looking at how many original NDA/BLA filings were approved on the first review by FDA, the rates are far from concentrated(Figure 5). In fact, Psychiatry had only a 37% chance of first-cycle approval vs. Oncology at nearly 80%. Although this is anextreme range, upon subsequent submissions and reviews, both of these disease areas ended up with 91% of original drug
indication applications being approved. There was a large increase in cumulative success rates after the second submission,but only marginal increases after the third review.
Time from filing to approval also varied by disease area. Neurology drugs took the longest to approve on average, at 2 years,
while Oncology drugs were approved almost twice as fast at 1.1 years. Many Oncology drugs for unmet medical need may havebenefited from expedited approval pathways and associated increased interactions with FDA such as Breakthrough Therapy
and Accelerated Approval, contributing to the faster overall time to approval. As might be expected, calculating time to approval
for all disease areas put the time to approval in the middle of these extremes, at 1.6 years.

% Approved on
1st Review% Approved by
2nd Review% Ultimately
Approved

Filing toApproval Time(Years)Oncology

79%

89%

89%

1.1

Allergy

71%

93%

93%

1.3

Respiratory

71%

94%

94%

1.6

Cardiovascular

69%

83%

85%

1.4

Infectious disease

69%

86%

92%

1.4

Urology

64%

73%

82%

1.7

Autoimmune

63%

82%

86%

1.6

Metabolic

63%

83%

83%

1.5

Ophthalmology

62%

69%

73%

1.3

All Diseases

61%

80%

86%

1.6

Hematology

60%

76%

90%

1.6

Disease Area

Gastroenterology

56%

84%

92%

1.8

Endocrine

56%

77%

83%

1.8

Neurology

45%

70%

81%

2.0

Psychiatry

37%

70%

91%

1.6

Figure 5. Time to FDA approval and percent approved by FDA for original NDA/BLA filings only. Data shown does not include
supplemental applications.

12 | BIO Industry Analysis

Oncology and Non-Oncology Diseases
Oncology drug development program transitions in the 2006-2015 period accounted for 31% of the 9,985 total transitions. With
the lowest LOA from Phase I (5.1%, n=3,163), Oncology had an outsized effect on the overall industry success rate. To further
understand this contribution, we compared phase transition success rates and LOA for non-oncology development programs
against oncology development programs alone. Figure 6 shows Phase success rates and LOA from Phase I for oncology andnon-oncology development programs. The LOA from Phase I across non-oncology indications was twice that for oncology alone,at 11.9% (n=6,822). Looking at individual phase transition success rates, it is clear that Phase III transition success rates were
the reason Oncology ended up with the lowest overall success across our 14 disease categories. Oncology Phase III successwas 23% lower than Non-Oncology disease areas.
Probability of SuccessOncology vs. Non-Oncology
100%
85.9%
82.4%

Probability of Success

90%
80%
70%
60%

63.5%
62.8%

63.7%

50%
40%

40.1%

34.3%

30%

24.6%

20%

11.9%
5.1%

10%
0%

Phase I to
Phase II

Phase II to
Phase III

Phase III to
NDA/BLA

Non-Oncology
Phase I to Phase II

Phase Success

Phase II to Phase III

Phase
Success

Advanced or
Suspended

Phase
Success

Oncology

1222

62.8%

1416

Non-Oncology

2360

63.5%

2446

Phase I to Approval
LOA n

LOA n

Phase III to NDA/BLA

NDA/BLA to Approval

Advanced or
Suspended

Phase
Success

Advanced or
Suspended

Phase Success

24.6%

349

40.1%

176

82.4%

34.3%

1142

63.7%

874

85.9%

Phase II to Approval

Phase LOA

Phase I to
Approval

Oncology

Advanced or
Suspended

Likelihood of Approval

NDA/BLA to
Approval

Phase LOA

Phase III to Approval

NDA/BLA to Approval

LOA nPhase LOA

LOA nPhase LOA

Oncology

3163

5.1%

1941

8.1%

525

33.0%

176

82.4%

Non-Oncology

6822

11.9%

4462

18.7%

2016

54.7%

874

85.9%

Figure 6. Oncology vs. Non-Oncology phase transition success rates and LOA. Top: Chart of LOA from Phase I. Bottom: Table of phasetransition success rates and LOA for Oncology vs. Non-Oncology indications, with corresponding n values. ‘Advanced or Suspended’refers to the total number of transitions used to calculate each success rate, with the n value noted in the text. The LOA n value isthe total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability of successfully
advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs in this phase of development.

BIO Industry Analysis | 13

Oncology Sub-Indication Phase Transition Success Rates and LOA
Oncology drugs were further categorized into two main types of cancer: solid tumors and hematological cancers. Solid tumors
had twice as many transitions in the data set (2,283 vs. 805), but only half the LOA from Phase I vs. hematological cancers (4.0%
vs. 8.1%). These are shown in Figure 7 in more detail.

Phase III Success Rate
0%

10%

20%

30%

40%

50%

60%

70%

Oncology Indications
Hematologic Cancers
CLL/SLL - NHLMultiple Myeloma
Indolent NHL
AML
Solid Tumors
Renal Cell Cancer
Breast Cancer
Melanoma
Prostate Cancer
Colorectal Cancer
NSCLC
Ovarian Cancer
Gastric Cancer
Pancreatic Cancer

Phase I to Phase II

Phase Success

Phase II to Phase III

Phase III to NDA/BLA

NDA/BLA to Approval

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase SuccessOncology

1222

62.8%

1416

24.6%

349

40.1%

176

82.4%

Solid

860

64.1%

1055

23.0%

260

34.2%

108

79.6%

Hematologic

327

61.8%

341

28.7%

78

52.6%

59

86.4%

Likelihood of Approval

Phase I to Approval

Phase II to Approval

Phase III to Approval

NDA/BLA to Approval

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

Oncology

3163

5.1%

1941

8.1%

525

33.0%

176

82.4%

Solid

2283

4.0%

1423

6.3%

368

27.3%

108

79.6%

Hematologic

805

8.1%

478

13.1%

137

45.4%

59

86.4%

Figure 7. Phase transition success rates and LOA for Oncology indications with corresponding n values. ‘Advanced or Suspended’refers to the total number of transitions used to calculate each success rate, with the n value noted in the text. The LOA n value isthe total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability of successfully
advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs in this phase of development.

14 | BIO Industry Analysis

Phase II transition success rates by sub-indication tended to range close to the overall 25% Oncology calculation for Phase II, +/-10%.
Narrowing in on Phase III transition success rates, only 34.2% of the 260 drug programs in solid tumor cancers were deemedsufficiently successful to file an NDA/BLA with the FDA. This was the underlying cause for the 2x difference we see in overall LOA,
as the hematological cancer programs recorded a 52.6% success rate in Phase III. Since Phase III was identified as the weakest
phase for Oncology, Phase III transition success rates for a number of major oncology sub-indications were included in Figure 7.
The solid tumor Phase III transition success rate (34.2%, n=260) ended up as the lowest for any of the major disease categories
studied. Solid tumor drugs for pancreatic cancer seemed to have the toughest challenges in Phase III studies (13.3%, n=15).
However, Phase III success rates for Ovarian and Gastric cancers also fell below 30%.
Hematological cancer Phase III transition success rates benefited from transition successes in CLL/SLL (66.7%, n=12) andMM (63.7%, n=11). ALL, Hodgkin’s and CML had Phase III success rates of 100% but only had fewer than five completed clinicaldevelopment programs each (data not shown). Only AML (36.4%, n=11) came in below 40% for Phase III, which helped the overall
hematologic cancer Phase III success rate remain above 50%.
The NDA/BLA to approval success rate for all hematological cancers (86.4%, n=59) was impacted positively by Multiple Myeloma,
ALL, and CML success, as each had more than five completed filings and 100% approval rates. The NDA/BLA success rate forall sold tumors was lower at 79.6% (n=108).

Abbreviated cancer indications:ALL- Acute Lymphocytic Leukemia

AML- Acute Myelogenous Leukemia

CLL- Chronic Lymphocytic Leukemia

CML- Chronic myelogenous Leukemia

MM- Multiple MyelomaNHL- Non-Hodgkin’s Lymphoma

NSCLC - Non-Small Cell Lung Cancer
SLL- Small Lymphocytic Lymphoma

BIO Industry Analysis | 15

Rare Diseases and Chronic High Prevalence Diseases
In recent years, there has been an increase in funding for companies focused on rare diseases.2 This is welcome news as there
are reportedly 7,000 rare diseases and most do not have an approved therapeutic treatment.4 One question that is often asked
is if the probabilities of success are any better for rare diseases, especially for those in which a particular defective gene has
been confirmed as the sole contributor. On the other extreme, we have observed less venture funding for high prevalence,chronic diseases.2 The question we wanted to explore is whether investors may have scaled back funding because there is ahigher hurdle to developing and gaining approval for medicines that treat highly prevalent conditions.

Probability of SuccessRare Disease and High Prevalence Diseases
100%

Probability of Success

90%

85%
76%

80%
70%

89%

73%

63%
58%

60%

87%

58%

61%

50%

50%
40%

30%
27%

30%

25.3%

20%

9.6%
8.7%

10%
0%

Phase I to
Phase II

Phase II to
Phase IIIAll Diseases

NDA/BLA to
Approval

Chronic High Prevalence

Phase I to Phase II

Phase Success

Phase III to
NDA/BLA

Phase II to Phase III

Phase I to
Approval

Rare Diseases

Phase III to NDA/BLA

NDA/BLA to Approval

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase
Success

Advanced or
Suspended

All Diseases

3582

63.2%

3862

30.7%

1491

58.1%

1050

85.3%

Chronic High Prevalence

732

58.7%

726

27.7%

268

61.6%

196

87.2%

150

76.0%

168

50.6%

110

73.6%

93

89.2%

Rare Diseases
Likelihood of Approval

Phase I to Approval

Phase II to Approval

Phase III to Approval

Phase Success

NDA/BLA to Approval

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOAAll Diseases

9985

9.6%

6403

15.3%

2541

49.6%

1050

85.3%

Chronic High Prevalence

1922

8.7%

1190

14.9%

464

53.7%

196

87.2%

Rare Diseases

521

25.3%

371

33.3%

203

65.7%

93

89.2%

Figure 8. Non-Oncology Rare disease and high prevalence, chronic disease. Top: Chart of phase transition success rates and LOA from
Phase I, for rare and chronic, high prevalence. Bottom: Table of phase transition success and LOA by disease with corresponding n values.
‘Advanced or Suspended’ refers to the total number of transitions used to calculate each success rate, with the n value noted in the text.
The LOA n value is the total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability ofsuccessfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs in this phase of development.

16 | BIO Industry Analysis

We isolated rare disease programs in the Biomedtracker database by first identifying all prior FDA Orphan-designated indications,then pooling all drug programs in these indications, regardless of whether they obtained FDA’s Orphan designation. All Oncology
indications were removed to make this rare disease analysis more concentrated on inborn genetic disorders. For chronic
diseases, we first obtained a list of conditions from the Center for Medicare & Medicaid Services (CMS) Chronic Conditions
Data Warehouse (CCW). We removed any cancer indications, then identified those disease with > 1 million patients afflictedwith the disease in the United States.
With programs from both groups identified, we compared phase transition success rates and LOA as shown in Figure 8. At 25.3%,
the overall LOA from Phase I for Non-Oncology rare diseases was 2.6x higher than the LOA for all diseases and 3x higher than the
8.7% LOA for chronic, high prevalence diseases. Chronic, high prevalence diseases accounted for almost 20% of the transitions,and Oncology, 31% of the transitions. The combined weighting of 50% of the dataset for these two categories with low LOAs
(Chronic, high prevalence disease, 8.7% and Oncology, 5.7%) contributed significantly to the low overall industry LOA of 9.6%.
All four transition success rates were higher for the rare disease group than the overall dataset. The largest difference between
was found in Phase II transition success rates (50.6% for rare disease vs 30.7% overall). Phase III and Phase I each had higher
transition success rates by at least 10%.
Chronic, high prevalence diseases transition success rates were lower in Phase I (58.7% vs 63.2%) and Phase II (27.7% vs. 30.7%)
vs. the overall dataset. The opposite was seen in Phase III and NDA/BLA, where slightly higher rates were observed: 61.6% vs.
58.1% for Phase III and 87.2% vs. 85.3% for NDA/BLA.
As the chronic, high prevalence group in this study does not include Oncology, we compared these results with the Non-Oncology
rates found in Figure 6. Although the chronic, high prevalence Phase III transition success rate is higher than that seen with all
diseases, it is lower than the Non-Oncology transition success rates (61.6% vs. 63.7%). Furthermore, the LOA of 11.9% (n=6,822)
for all Non-Oncology indications is 3% higher than for chronic, high prevalence diseases (8.7%), suggesting that these chronic
indications are negatively impacting overall success rates outside Oncology.

BIO Industry Analysis | 17

Patient Selection Biomarker Programs
The use of biomarkers as inclusion or exclusion criteria, or ‘selection biomarkers’, for enrolling patients into clinical studies
has increased dramatically since the sequencing of the human genome. We identified 512 phase transitions out of 9,985(5%) that incorporated a selection biomarker for patient stratification. This was accomplished by mapping the NCT numbers(clinicaltrial.gov identifier) from Amplion’s BiomarkerBase to programs in the Biomedtracker transition database.6 For
programs that filed an NDA/BLA, we only included filings that used selection biomarkers in the Phase III study design.
The LOA from Phase I can be found in Figure 9. The benefit from selection biomarker use raises the LOA from Phase I to one
in four compared to less than one in 10 when no selection biomarker was used.

Probability of SuccessWith or Without Selection Biomarkers
100%

94%

Probability of Success

90%

83%
76%

80%
70%

76%

63%

60%

55%
46%

50%
40%

28%

30%

25.9%

20%

8.4%

10%
0%

Phase I to
Phase II

Phase II to
Phase III

Without Biomarkers
Phase I to Phase II

Phase Success

Phase III to
NDA/BLA

NDA/BLA to
Approval

Phase I to
Approval

With Selection Biomarkers

Phase II to Phase III

Phase III to NDA/BLA

NDA/BLA to Approval

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase
Success

Advanced or
Suspended

Phase Success

No Biomarkers

3480

63.0%

3396

28.8%

1254

55.0%

882

83.9%

Selection Biomarkers

43

76.7%

246

46.7%

132

76.5%

91

94.5%

Likelihood of Approval

Phase I to Approval

Phase II to Approval

Phase III to Approval

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

NDA/BLA to Approval

No Biomarkers

9012

8.4%

5532

13.3%

2136

46.2%

882

83.9%

Selection Biomarkers

512

25.9%

469

33.8%

223

72.3%

91

94.5%

Figure 9. Selection Biomarker phase transition success rates and LOA. Top: Chart of individual phase transition successrates and LOA from Phase I for all indications without and with selection biomarkers. Bottom: Table of Phase transition
success and LOA by disease with corresponding n values. ‘Advanced or Suspended’ refers to the total number oftransitions used to calculate each success rate, with the n value noted in the text. The LOA n value is the total ‘Advanced
or Suspended’ transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability of successfully advancing
to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs in this phase of development.

18 | BIO Industry Analysis

As shown in the table of Figure 9, a three-fold higher LOA from Phase 1 was calculated for programs that utilized selection
biomarkers (25.9%, n=512) vs. programs that did not (8.4%, n=9,012).
Individual phase transition success rates can be found in Figure 9. All four phase transition success rates were muchhigher for programs incorporating selection biomarkers vs. those not incorporating selection biomarkers. Phase
III transition success rates for selection biomarker programs were 76.5% (n=132) vs. only 55.0% (n=1,254) for nonselection biomarker programs - the largest percentage difference among the four phases of development.
There were fewer completed Phase 1 trials that utilized a selection biomarker (43) than those observed in NDA/BLA filings (91).
This is likely due to the fact that biomarkers are more commonly used in Phase II and III.
The large differences in Phase II and III transition success rates are quite convincing, quantitatively, of what many drug developers
have long argued anecdotally – enrichment of patient enrollment at the molecular level is a more successful strategy thanheterogeneous enrollment.
With respect to statistical rigor, while having a smaller number of transitions for programs that utilized a selection biomarker(512) vs. for non-biomarker phase transitions (9,012), when analyzed for statistical significance, all phases, save for Phase I, were
found to be statistically significant. This is in large part due to the low n value for Phase I, a clinical phase in which few selection
biomarkers are actually used.

BIO Industry Analysis | 19

Drug Classification and Modality
Drugs in the dataset were annotated by: new molecular entity (NME), non-NME, biologic and vaccine.
Most NMEs are small molecules, although in some cases FDA will designate larger molecules, such as proteins and peptides,
as NMEs. When comparing small-molecule NMEs to all NMEs we found very little difference in success rates. The Non-NME
classification often includes drugs with the same molecular properties as NMEs, but are frequently reformulations or combinationsof approved products. Non-NMEs often use the 505(b)(2) pathway to gain FDA approval. Monoclonal antibodies made up the
majority of biologics in the dataset. Other biologics in the dataset included gene therapy, cell therapy, RNAi, and other large
molecules. Generic and over-the-counter (OTC) drugs were not included.

Probability of Success
NME vs. Biologic vs. Non-NME
100%

90%
88%

Probability of Success

90%
80%
70%
60%

74%

70%
66%
61%

78%

57%
48%

50%
40%

49%

34%
26%

30%

22.6%

20%

11.5%
6.2%

10%
0%

Phase I to
Phase II

Phase II to
Phase III
NME

Phase I to Phase II

Phase Success

Phase III to
NDA/BLA

Biologic

NDA/BLA to
Approval

Phase I to
Approval

Non-NME

Phase II to Phase III

Phase III to NDA/BLA

NDA/BLA to Approval

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase Success

Advanced or
Suspended

Phase
Success

Advanced or
Suspended

Phase Success

NME

2236

61.3%

2482

26.5%

731

48.7%

409

78.0%

Biologic

882

66.0%

883

34.4%

313

57.2%

199

88.4%

Non-NME

314

70.1%

406

48.3%

398

73.9%

406

90.4%

Vaccine

95

66.3%

76

32.9%

35

74.3%

32

100.0%

Phase I to Approval

Phase II to Approval

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

LOA nPhase LOA

NME

5858

6.2%

3622

10.1%

1140

38.0%

409

78.0%

Biologic

2277

11.5%

1395

17.4%

512

50.6%

199

88.4%

Non-NME

1524

22.6%

1210

32.2%

804

66.8%

406

90.4%

Vaccine

238

16.2%

143

24.4%

67

74.3%

32

100.0%

Likelihood of Approval

Phase III to Approval

NDA/BLA to Approval

Figure 10. Phase transition success rates and LOA by drug type and class. Top: Chart of phase transition success ratesand LOA from Phase I by FDA classification for NME, Biologic, Non-NME. Bottom: Table of phase transition success ratesand LOAs for FDA’s classifications and vaccines. ‘Advanced or Suspended’ refers to the total number of transitions used
to calculate each success rate, with the n value noted in the text. The LOA n value is the total ‘Advanced or Suspended’
transitions of all phases used to calculate LOA. ‘Phase Success’ is the probability of successfully advancing to thenext phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs in this phase of development.

20 | BIO Industry Analysis

NMEs were found to have the lowest transition success rates in every phase of development and an LOA from Phase I of just 6.2%
(n=5,858). Biologics had almost 2x this LOA (11.6%, n=2,277), as shown in Figure 10. The breakout of Monoclonal Antibodies
yielded the exact same 11.6% LOA from Phase I (n= 1,258).
Non-NMEs had the highest LOA from Phase I of 22.6% (n=1,524), with transition success rates well above the NME and biologic
classifications in every phase. However, many non-NMEs begin development in Phase II or Phase III, so the actual approval rate
is likely higher (assuming that successful Phase I outcomes would contribute positively to the LOA from Phase I).
Although a much smaller subset of the modality breakout, vaccines generated a healthy 16.1% (n=238) LOA from Phase I.

BIO Industry Analysis | 21

Discussion
During the time frame of this study (2006-2015), programs entering clinical development in Phase I were found to have only a
one in ten (9.6%) chance of advancing all the way to FDA approval. Although this is an overall probability calculation and does
not follow each program from its first trial to final outcome (which is often be outside of a 10-year time-frame), we believe thatthese low success rates for individual drug programs accurately reflect overall industry realities. This is particularly importantin the context of cost and time of unsuccessful clinical trials.
Potential factors that may have impacted success rates in this report include:
Clinical and regulatory related:•

Patient population and selection strategy. The impact that a targeted, well-defined patient population can have
was observed in the success rates for both selection biomarker usage and rare disease studies.

•

Clinical validation of a target, drug class, or mechanism of action. Note that non-NME drugs presented in thisreport are drugs developed for already approved classes and targets and have 3x higher LOA from Phase I
vs. novel NMEs.

•

Clinical trial complexity. In chronic, high prevalence diseases, where large heterogeneous patient populations
will be treated, additional safety and efficacy studies may be required. These areas have experienced below
average LOA from Phase I.

Corporate related:•

Lack of funding. This is a common issue for emerging companies, especially during times of economic stresssuch as in the years following the global financial of 2008. Programs terminated due to lack of resources orfunding would be counted as “suspended” in this study and not differentiated from programs terminated basedon disappointing clinical data.

•

Commercial decisions and portfolio prioritization. Competition, IP litigation, and other market factors could
result in the termination of a program. Internal programs at companies may also compete during pipeline review
and prioritization, potentially leading to program termination.

The overall low rates for Phase III programs to successfully transition to an NDA/BLA filing is concerning, as 35% of all R&D
spending is now spent on Phase III development, and Phase III trials account for 60% of all clinical trial costs.3 A main contributor
to the overall low Phase III transition success rate was found to be Oncology clinical programs. While Oncology remains a very
challenging area in which to develop drugs, some more recent successes in immunotherapies give hope that there will be highly
successful drug programs in Oncology in coming years.
Outside of Oncology, clinical programs targeting heterogeneous patient populations in chronic, high prevalence indications
also contributed to lower overall success rates. For this group, all three clinical-stage transition success rates were lower thanthe Non-Oncology disease group.
As selection biomarkers are used more frequently in clinical development (presently, they are only being used in a small proportionof studies), phase transition success rates in high prevalence diseases should improve as patient selection improves. The higher
success rates for trials run with biomarker-selected patients suggests that the broader industry is already on the right path. For
example, Phase III transition success rates in programs utilizing selection biomarkers in the last decade were 76.5% (n=132)
compared to only 55.0% (n=1,254) for non-biomarker trials. Experiencing one in four failures (with selection biomarkers) vs. two
in four Phase III failures (without biomarkers) could have significant cost implications.

22 | BIO Industry Analysis

As many rare diseases are identified by specific genetic mutations, it is not surprising that success rates in rare disease indications
closely match the success rates observed in clinical trials that utilized selection biomarkers. Phase transition success rates forrare disease candidates and candidates utilizing selection biomarkers were very similar for every stage of development. Transition
success rates for each classification, rare disease programs and programs with selection biomarkers, respectively, were; Phase
I: 76% and 76.7%; Phase II: 50.6% and 46.7%; Phase III: 73.6% and 76.5%; and NDA/BLA: 89.2% and 94.5%. Both of these specificclassifications significantly outpaced the success rates seen for chronic, high prevalence disease drug development at 58.7%
in Phase I, 27.7% in Phase II, 61.6% in Phase III, and 87.2% in the NDA/BLA stage.
Greater flexibility with alternative and novel surrogate endpoints, the utilization of adaptive clinical trial design, improved
methodologies for assessing patient benefit-risk, and improvements in communication between sponsors and regulators could
help improve the success rates reported in this study. Simultaneously, improvements in basic science can enable better successrates. For example, more predictive animal models, earlier toxicology evaluation, biomarker identification and new targeted
delivery technologies may increase future success in the clinic.
Advancing industry-wide understanding of the evidentiary standards or considerations governing regulatory acceptance of drug
development tools including biomarkers would increase adoption and use of innovative drug development tools, and serve to
expedite development and review timelines and improve chances of success.
The ability to utilize these modern approaches to drug development and modern regulatory review processes combined withhealthy capital markets supporting private-sector investment will enable biopharmaceutical companies to develop the next
generation of innovative medicines.

BIO Industry Analysis | 23

Methods
Drug Development Programs analyzed in this report track a specific indication for each drug. For example, Rituxan in nonHodgkin’s lymphoma (NHL) qualifies as a different development path than Rituxan in multiple sclerosis (MS). Biomedtracker
assigns a unique internal identifier which can be used to isolate all development paths.
In addition to tracking the phase of development, Biomedtracker also assigns “lead” status to certain development paths.
This is used to denote the most advanced indication in clinical development for a specific drug. Drugs can only have one leaddevelopment program (with some rare exceptions). For example, cancer drugs developed in multiple indications will have themost advanced program assigned as the lead, and the rest as “non-lead”. However, in this report we do not differentiate themost advanced programs, and analyze the data on a program/development path level, which more accurately reflects company
resource utilization.
Individual Phase Transition Success Rates were calculated as the number of drugs that moved from one phase to the next
phase divided by the sum of the number of drugs that progressed to the next phase and the number of drugs that were suspended.
The n value associated with the phase transition success rates represent the number of drugs that have advanced plus thenumber of drugs that have been suspended, which we label as phase transitions. Phase transition Success rates reported inthis study were based on transition rates, not necessarily resulting from safety or efficacy data. Transition rates are negatively
impacted by early development termination due to commercial and regulatory uncertainty as well as economic and portfolio
management decisions.
Biomedtracker further classifies events by phase of development, summarized in the table below:
Biomedtracker phase
I
I/II, II, IIb
II/III, III

Description for purposes of the study
Drug is currently in Phase I
Drug is currently in Phase II
Drug is currently in Phase III

NDA/BLA

Application for approval has been submitted to the FDA andis currently under review
Approved, Withdrawn From Market, Drug has been approved for marketing in the United States
Approved (Generic Competition)
Suspended
Drug is no longer in development
Approved in Europe, Approved in The company developing this drug does not plan to market
other than U.S./E.U., Development, it in the United States
Development Outside U.S.
Generic products were not included, but generic manufacturers developing novel investigational drugs were represented.
Likelihood of Approval (LOA) denotes the probability of reaching FDA approval from the current phase, and is also expressed
as a percentage. LOA is calculated as the product of each Phase Success probability leading to FDA approval. The n value
associated with LOA is the sum of the n values for each Phase Success included in the LOA calculation.
For example, if a drug is currently in Phase II, and the phase success for Phase II is 50% (n=10), Phase III is 50% (n=10), and FDA
Approval is 50% (n=10), then the LOA for the Phase II drug would be 12.5% (50%×50%×50%=12.5%, n=30).
Data Source for Drug Program Transitions. Data used for this study were extracted from Biomedtracker using a Probabilityof Technical Success (PTS) tool, which identified all ‘Advanced’ and ‘Suspended’ drugs by development phase from January
1, 2006 to December 31, 2015. Biomedtracker, a subscription-based product of Informa, tracks the clinical development andregulatory history of investigational drugs to assess their Likelihood of Approval (LOA) by the FDA. Biomedtracker is populated
in near real-time with updated information from press releases, corporate earnings calls, investor and medical meetings andnumerous other sources. These data are recorded in BioMedTracker and tagged with a date. BioMedTracker also uses othersources, including regular communication with companies conducting clinical trials, to ensure accuracy and timeliness of the data.
24 | BIO Industry Analysis

Drug Classification Methods. Biomedtracker records FDA classification (i.e. new molecular entity (NME), non-NME, biologic,
or vaccine) as well as the biochemical profile (e.g. small molecule, monoclonal antibody, peptides, natural proteins, antisense,
vaccine, etc.). Biologics, as defined by FDA, can be sugars, proteins, or nucleic acids or complex combinations of these substances,or may be living entities such as cells and tissues.
Sub-Indications. Drilling down into sub-indications within each major disease area has limitations due to lower n values foreach phase. The statistical significance of the results must be taken into account when comparing data from low sample sizes.
Although this study is over a 10-year period, some sub-indications had a limited number of Phase III programs completed or
NDA/BLA filings submitted. For example, because AML had four drugs submitted for FDA approval, and no wins, the successrate for AML at the NDA/BLA stage is 0% (n=4). That also means that the LOA calculation for AML yields 0% (n=103), due tothe compounded approach of multiplying individual phase probabilities to arrive at the overall LOA. With that caveat in mind, it
is best to approach sub-indications starting with the NDA/BLA filing phase to assess n values and % success.
Data Source for Selection Biomarkers. Selection biomarkers are gene products used as inclusion or exclusion criteria forenrolling patients into clinical studies. The biomarker subject selection data used for this study were extracted from BiomarkerBase,which identifies selection biomarkers described in clinical trials posted at ClinicalTrials.gov. BiomarkerBase is a subscriptionbased service from Amplion that tracks selection biomarker usage in clinical trials, drug labels, and tests (including laboratorydeveloped, FDA-cleared, and FDA-approved tests). BiomarkerBase is updated weekly with information from these sources
and publications, using supervised machine learning algorithms for natural language processing (Amplion BiomarkerEngine) to
identify selection biomarkers. BiomarkerBase includes primarily human genes and proteins. Other diagnostic measurements,
including clinical blood chemistry, liver enzymes, white blood cell count, heart rate, blood oxygenation, blood glucose, clotting
times, etc. are not included in BiomarkerBase.
Comparison with Previous BIO/Biomedtracker Study. The Biomedtracker database has expanded since our original
publication in Nature Biotechnology, January 2014.1 The first study contained 5,820 phase transitions from 2003-2011. The current
study contains 9,985 transitions from 2006-2015 (10 years). This more recent finding for overall LOA success rates falls within
the same statistical range as our previous report (near 10.4%, n=5,820 for 2003-2011).1,2 While the more recent study captures
four additional years (2012-2015), it begins in 2006 and thus cuts 2003-2005 data, leaving a 10-year window for evaluation.
Importantly, there are 4,165 more transitions in the current dataset. Such broad sample size comparisons makes it very difficult
for significant changes to be found in overall rates. To see significant change would require more than just a few disease areas
to experience profound improvements in productivity. For example, the impressive success rates found in Hematology mightneed to be seen in the larger groups such as Oncology or Neurology to move the needle. Narrowing the time-frame to detect
improvements, can lead to program biases as R&D timelines are typically closer to a 10-year time-frame and not less.
Lead Drug vs. Non-Lead. In our previous report, we had included “Lead” drug success rates.1 That allows us to answer thequestion: “What is the probability that a drug will reach approval for any indication?” However, the Lead program analysis will not
represent the true costs, both monetary and time, that is required for approval. Furthermore, it can be a source of confusion inthat it refers not to a company’s lead program, but rather the most advanced indication for a given drug. Lead indication status
is constantly changing within the database through time as programs fail and secondary indication then become the Lead
indication. For this reason, the current study focuses on each development path to answer the question: “What is the probability
that a drug developed for a specific indication will reach approval?”
For those interested in the Lead indication success rates, the overall Lead success rate was 16.8% (n = 5,511), 1.7x higher than
the 9.6% reported for individual development paths. This is a slightly wider difference than our previous finding (for 2003-2011)of 15.3% for the Lead indication. As the spread between Lead and subsequent individual indication programs widens, it could
be argued that more R&D is required (across multiple indications) to reach final approval of a chemical entity in at least one of
its indications.

BIO Industry Analysis | 25

References
1. Hay, M., Thomas, D., John L Craighead, J.L., Economides, C., Rosenthal, J. Clinical development success rates forinvestigational drugs. Nature Biotechnology 32, 40-51 (2014).
2. Thomas, D., Wessel, C. Emerging Therapeutic Company Inveestme and Deal Trends 2006-2016. http://www.bio.org/iareports
3. Pharmaceutical Research and Manufacturers of America. Annual Report 2011. PhRMA website [online], http://www.
phrma.org/sites/default/files/159/phrma_2011_annual_report.pdf (2011).
4. Global Genes Rare Disease Facts: http://globalgenes.org/rare-diseases-facts-statistics
5. Amplion Inc. Market Trends for Biomarker-Based IVD Tests (2003-2014). http://www.amplion.com/download-thecommercial-trends-in-ivds-report (Amplion 2015).
6. BiomarkerBase: https://biomarkerbase.com/

Acknowledgements
This research was supported by Informa, Amplion, and the Biotechnology Innovation Organization (BIO). The authors are indebted
to the analysts at Biomedtracker and Amplion who have collected these data. We would also like to thank our colleagues atInforma (special thanks to Amanda Micklus), Amplion, and BIO (special thanks to Cartier Esham and Scott McGoohan) and fortheir input, advice, and review of this study.

Authors
David W. Thomas2,4, Justin Burns1,4, John Audette3, Adam Carroll3, Corey Dow-Hygelund3, Michael Hay1
Biomedtracker, Sagient Research Systems, Informa, San Diego, California, USA.
Biotechnology Innovation Organization (BIO), Washington, District of Columbia, USA.
3
Amplion, Bend, Oregon, USA.
4
These authors contributed equally to this work.
Correspondence should be addressed to M.H. (michael.hay@informa.com).
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26 | BIO Industry Analysis

Amplion, Inc.
1011 SW Emkay Dr, Suite 208
Bend, OR 97702
www.amplion.com

Biomedtracker
Pharma intelligence |Biomedtracker
Sagient Research Systems
3655 Nobel Drive, Suite 600
San Diego, CA 92122
www.biomedtracker.com

Biotechnology Innovation Organization (BIO)
1201 Maryland Avenue SW, Suite 900
Washington, DC 20024
www.bio.org