Clinical Development
Success Rates andContributing Factors
2011–2020

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, from the entrepreneurial to the Fortune 500 multinationals, are involved in the research anddevelopment of innovative healthcare, agricultural, industrial, and environmental biotechnology products.
BIO also produces industry-leading investor and partnering events to strengthen the innovator ecosystem.

About Informa Pharma Intelligence
Biomedtracker, a subscription-based product of Informa Pharma Intelligence, tracks the clinical development
and regulatory 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, and numerous other sources. These data can be visualized,
analyzed, and interpreted in the specifically designed R&D benchmarking tool Pharmapremia. This provides
an exclusive window into drug development pipelines and paints a fuller, clearer picture of how the industry
is performing. For more information visit www.pharmaintelligence.informa.com.

About QLS Advisors
QLS Advisors LLC is a technology and advisory company based in Cambridge, MA, dedicated to fostering
innovation in the life sciences. QLS employs a unique blend of fundamental and quantitative tools to help
clients manage risk, assess reward, and develop investment and financing strategies for portfolios of
healthcare-related assets. For more information visit www.qlsadvisors.com.

2/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Executive Summary

This is both an updated study of clinical drug development success rates from our 2016 report, and an
expansion into the drivers of success with the addition of machine learning modeling to analyze the predictive
factors contributing to drug development. A total of 12,728 clinical and regulatory phase transitions wererecorded and analyzed from 9,704 development programs over the last decade (2011–2020), across 1,779
companies in the Biomedtracker database. Phase transitions occur when a drug candidate advances into
the next phase of development or is suspended 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 datain hand, we then compared groups of diseases, drug modalities, and other attributes to generate the mostcomprehensive 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
Pharma Intelligence’s Biomedtracker, which subsequently populates the purpose-built Probability ofTechnical Success (PTS) tool, Pharmapremia. BIO has long worked with Biomedtracker to calculate successrates based on this data. More recently, BIO and Biomedtracker teamed up with QLS Advisors, a team ofexpert data scientists who apply machine learning (ML) and artificial intelligence (AI) to clinical trials data
and drug properties to assess the features that contribute the most amount, positively or negatively, to theprobability of approval. The computational analyses of over 200 different drug, trial, indication, and sponsor
metrics that influence clinical success rates were applied in a predictive capacity to produce risk assessments
and evaluations of R&D assets with increased accuracy.
Key Takeaways• The overall likelihood of approval (LOA) from Phase I for all developmental candidates over 2011–2020 was
7.9%.
• Phase II development remains the largest hurdle in drug development, with just 28.9% of candidates
achieving this critical phase transition.
• Of the 14 major disease areas, Hematology therapies had the highest LOA from Phase I (23.9%), representing
a seven-fold increase over the least successful group, Urology (3.6%).
• Immuno-oncology therapies provide a rare pocket of success in oncology R&D with an overall LOA of 12.4%
vs 5.3% for all oncology approaches.
• Rare disease therapies were notably successful with an overall LOA of 17.0%.
• Chronic, high prevalence disease therapies were less successful with an overall LOA of 5.9%.
• Biological complexity in drug modalities generally leads to higher LOA, with CAR-T and RNA interference
achieving the highest LOAs of 17.3% and 13.5%, respectively.
• Development programs with trials employing patient preselection biomarkers have two-fold higher LOAs
(15.9%), driven by a Phase II success rate of nearly one-in-two.
• The top contributing factors toward phase success are disease indication, target, modality, and drug
novelty.
• On average, it takes 10.5 years for a Phase I asset to progress to regulatory approval. Disease areas withabove-average LOAs tend to have the shortest development timelines.

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)

Contents
About BIO												2About Informa Pharma Intelligence								2About QLS Advisors										2Executive Summary										3Introduction												5Part 1. Phase Transition Success and Likelihood of Approvals					6Phase Transition Success – By Disease Area							7Phase I Transition Success Rates								7Phase II Transition Success Rates								7Phase III Transition Success Rates								8NDA/BLA Submission Success Rates								9Likelihood of Approval (LOA) – By Disease Area							9Oncology and Non-Oncology Diseases								11Rare and Chronic High Prevalence Diseases							13Drug Classes and Modalities									15Patient Preselection Biomarkers									17Part 2. Predictive Analysis of Clinical Success								19Part 3. Drug Development Timelines									24Discussion												26Comparison with previous reports on attrition rates						

26Oncology: largest segment of industry, modest success rates					

27Challenges for Phase II proof-of-concept drive overall performance				27

Characteristics of rare disease R&D not found in highly prevalent chronic disease		27

Complexity of modality correlates with higher LOA						

28Increasing duration in clinical development typically carries elevated risk				29

Methods												30Appendix		 										33Contributing Authors											34

© Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Introduction

This study aimed to measure clinical development success rates, contributing factors to those outcomes, andtimelines of clinical trials. With the goal of providing current benchmarking metrics for drug development,
this study covers the most recent decade of individual drug program phase transitions from January 1,
2011, to November 30, 2020. A phase transition is defined as the movement out of a clinical phase – forexample, advancing from Phase I to Phase II development, or being suspended after completion of Phase I
development.
Three separate analyses were conducted. Part 1 of this report includes clinical development success rates
based on 12,728 transitions in the Biomedtracker database. These transitions occurred in 9,704 clinical drug
development programs over the last decade. Within this broad set of data, we segmented and analyzed
success in drug development across levels of novelty, molecular modalities, and disease indications. As thisstudy cuts across 1,779 companies, roughly the entire spectrum of biopharma companies was included. Thisis illustrated with 15 companies each contributing more than 100 transitions, while 748 smaller biotechs
contributed just one transition each. Only company-sponsored, FDA registration-enabling development
programs were included in this analysis. Investigator-sponsored studies and combinations with otherinvestigational drugs were excluded. A more detailed description of the data collection, composition, andanalysis methodologies is provided in the “Methods” section.
The second analysis applies big data analytics to uncover the underlying drivers of drug development
success. Despite the successful application of machine learning techniques to libraries of billions of chemicaland biological compounds in order to identify potential drug candidates, most drug developers and financial
analysts still rely on historical observation to derive estimates of the clinical trial success rate when analyzing
pipelines of investigational drugs. QLS Advisors, a technology and advisory company dedicated to fostering
innovation in the life sciences, applies machine-learning techniques to predict the outcomes of randomized
clinical trials. In Part 2 of this report, QLS Advisors explores how investors in the biopharma industry can use
machine learning to characterize the probability of success for regulatory approval of a novel therapy.
An analysis of clinical timelines is included in Part 3. Here we measure the time it takes for drug development
programs at each clinical phase to transition upon success to the next phase.

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Part 1. Phase Transition Success and Likelihood of Approvals

Success rates for individual phases of the drug development process were determined by dividing thenumber that successfully 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 accountwhen drawing conclusions from the success rate results.
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.1,2 Phase I and III rates were substantially higher thanPhase II, with Phase I slightly lower than Phase III. The highest success rate of the four development phases
was the New Drug Application (NDA)/ Biologic License Application (BLA) filing phase (Figure 1).
The Phase I transition success rate was 52.0% (n=4,414). As this phase is typically conducted for safety testingand is not dependent on efficacy for candidates to advance, it is common for this phase to have a higher
success rate among the clinical phases across most categories analyzed in this report. However, Phase I
success rates also may benefit from delayed reporting or omission bias, as some larger companies may notdeem failed Phase I programs as material and thereby not report them in the public domain.
The Phase II transition success rate (28.9%, n=4,933) was substantially lower than Phase I, and the lowestof the four phases studied. As this is generally the first stage where proof-of-concept is deliberately tested
in human subjects, Phase II consistently has the lowest success rate of all phases. This is also the point
in development where industry must decide whether to pursue large, expensive Phase III studies, or toterminate development – this may be done for multiple reasons, including commercial viability. The secondhighest phase transition success rate was found in Phase III (57.8%, n=1,928). 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 an NDA or BLA, taking into account re-submissions, was
90.6% (n=1,453).
Overall phase transition success rates

Probability of Success

Probability of Success
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

90.6%

57.8%

52.0%
28.9%

Phase I to II

Phase II to III

Phase III to
NDA/BLA

NDA/BLA to
Approval

Figure 1: Phase transition success rates from Phase I for all diseases, all modalities. Source: Biomedtracker® and Pharmapremia®, 2020.

Hay M, Thomas D, Craighead JL, Economides C, Rosenthal J (2014). Clinical development success rates for investigational drugs. Nature Biotechnology, 32(1), 40–51. doi: 10.1038/
nbt.27862
Thomas D, Burns J, Audette J, Carroll A, Dow-Hygelund C, Hay M (2016) Clinical Development Success Rates 2006-2015. Available here [Accessed 15 January 2021].
1

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase Transition Success – By Disease Area
Major disease areas were segmented according to the convention used by Biomedtracker and categorized
into 21 major diseases and 623 indications for the 2011–2020 timeframe. For reporting at the disease
area level, in Figure 2 we analyzed 14 major groupings: Allergy, Autoimmune, Cardiovascular, Endocrine,
Gastroenterology (non-IBD), Hematology, Infectious disease, Metabolic, Neurology, Oncology, Ophthalmology,
Psychiatry, Respiratory, and Urology. The remaining disease areas were placed into the “Other” category. This
includes Dermatology, Renal, Obstetrics, Rheumatology (for non-autoimmune indications), ENT/Dental, andOrthopedics. Beneath these major disease areas are 573 indications, which will be analyzed and discussed
in subsequent reports.
Phase transition success rates by disease area

Phase Success
Hematology
Metabolic
Infectious disease
OthersOphthalmology
Autoimmune
Allergy
Gastroenterology
All indications
Respiratory
Psychiatry
Endocrine
Neurology
Oncology
Cardiovascular
UrologyPhase I
to II
n
92
136
403
154
88
413
55
45
4414
179
150
319
516
1628
214
22

Phase
POS
69.6%
61.8%
57.8%
63.6%
71.6%
55.2%
56.4%
46.7%
52.0%
55.9%
52.7%
43.3%
47.7%
48.8%
50.0%
40.9%

Phase II
to III

Phase III
to NDA/BLA

NDA/BLAto Approval

n

n

n

106
149
414
228
200
471
92
73
4933
215
164
293
504
1732
252
40

Phase
POS
48.1%
45.0%
38.4%
38.6%
35.5%
31.4%
28.3%
34.2%
28.9%
21.9%
26.8%
26.6%
26.8%
24.6%
21.0%
15.0%

82
66
197
90
82
219
34
35
1928
62
71
151
226
495
105
13

Phase
POS
76.8%
63.6%
64.0%
60.0%
51.2%
65.3%
64.7%
57.1%
57.8%
64.5%
56.3%
66.2%
53.1%
47.7%
55.2%
69.2%

72
48
156
69
45
202
20
33
1453
45
57
124
165
324
80
13

Phase
POS
93.1%
87.5%
92.9%
88.4%
91.1%
94.1%
100.0%
90.9%
90.6%
95.6%
91.2%
86.3%
86.7%
92.0%
82.5%
84.6%

Figure 2: Phase transition success rates by disease area. The n value is the total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘POS’ is
the probability of successfully advancing to the next phase. The ordering of disease areas is consistent with the overall likelihood of approval from Phase I, which isanalyzed later in Figure 5. Source: Biomedtracker® and Pharmapremia®, 2020

Phase I Transition Success Rates
Success rates by disease area for Phase I ranged from 40.9% to 71.6%, with the average for all disease
indications coming in at 52.0%. Ophthalmology and Hematology were both well above the average rate,achieving respective successful Phase I transitions of 71.6% (n=88) and 69.6% (n=92). With the exception ofthese two major disease areas, the remainder were all within a reasonable distance from the mean.
Phase II Transition Success Rates
In every disease area, Phase II had the lowest transition success rate of the four phases. As shown in Figure
7/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)3, Phase II success rates ranged from a high of 48.1% (Hematology, n=106) to a low of 15.0% (Urology, n=40).
This 33% range of disparity between major disease areas at the Phase II transition is the major contributor tothe observed divergences in overall likelihood of approval (LOA), as discussed in the next section. With only
Hematology and Metabolic (45.0%) coming close to achieving a one-in-two success rate, these two disease
areas are also the leaders when calculating the overall LOA from Phase I, as shown later in Figure 5.
Similar to Phase I transition success, the lowest-performing disease groups were Urology, Cardiovascular(21.0%), and Oncology (24.6%). Relative to its placement for Phase I success rate, the Gastroenterology
category performed better in Phase II (34.2%), above the average for all indications.
Phase II transition success rates by disease area

Probability of Phase II Success
Hematology

48.1%

Metabolic

45.0%

Others38.6%

Infectious disease

38.4%

Ophthalmology

35.5%

Gastroenterology

34.2%

Autoimmune

31.4%

All indications

28.9%

Allergy

28.3%

Psychiatry

26.8%

Neurology

26.8%

Endocrine

26.6%

Oncology

24.6%

Respiratory

21.9%

Cardiovascular

21.0%

Urology

15.0%
0%

10%

20%

30%

40%

50%

60%

Probability of SuccessFigure 3: Phase II transition success rates by disease area. Source: Biomedtracker® and Pharmapremia®, 2020

Phase III Transition Success Rates
For Phase III transition success rates, Oncology had the lowest transition success rate (47.5%, n=495). As
seen in Figure 4, the Phase III success rates for the remaining 13 specific disease areas were each above 50%.
Five other disease groups besides Oncology ranked below the average Phase III transition success rateof 57.8%, including Ophthalmology (51.2%, n=82) which in the earlier phases performed better than the
average across all diseases. Conversely, Urology, which had the lowest transition rates in Phase I and II,
had the second-highest Phase III success rate behind Hematology at 69.2%, albeit with a very low n of 13
transitions. All of the disease groups with below-average Phase III success rates had disease indications withlarge patient populations. Later in this report, we will analyze these high prevalence diseases and compare
their success rates to those of therapies for rare diseases.

8/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase III transition success rates by disease area

Probability of Phase III Success
Hematology

76.8%

Urology

69.2%

Endocrine

66.2%

Autoimmune

65.3%

Allergy

64.7%

Respiratory

64.5%

Infectious disease

64.0%

Metabolic

63.6%

Others60.0%

All indications

57.8%

Gastroenterology

57.1%

Psychiatry

56.3%

Cardiovascular

55.2%

Neurology

53.1%

Ophthalmology

51.2%

Oncology

47.7%
0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Probability of SuccessFigure 4: Phase III transition success rates by disease area. Source: Biomedtracker® and Pharmapremia®, 2020

NDA/BLA Submission Success Rates
NDA/BLA transition success rates (approval rates) for the major disease areas ranged from the low end of
82.5% (Cardiovascular) to a high of 100.0% (Allergy). This distribution had the tightest range among the fourphases analyzed in this report. These rates are the result of eventual success, not success on the first review.
In some cases, programs have more than two Complete Response Letters (CRLs) and attempts at approval.
This unlimited allowance of submission attempts pushes the overall success above 90.6% across all diseases,
with only 137 drugs suspended by their sponsors at the regulatory transition over 2011–2020 (thus, 1,316
approved).
Among the clinical development programs analyzed, there was an approximately even split of innovative
drugs and non-originator products that were successful in obtaining regulatory approval. These included
740 innovative drug programs (435 new molecular entities (NMEs), 278 new biologics, and 27 vaccines) and
576 non-originator products (453 non-NMEs and 123 biosimilars). There is a more detailed discussion about
success rates by for drug modality and classification later in the report.
Likelihood of Approval (LOA) – By Disease Area
One of the key measures of success used in this report is the LOA from Phase I. The LOA success rate issimply a multiplication of success rates from all four phases, 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%.

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Multiplying the individual phase probabilities across all disease areas (found in Figure 1), the compounded
probability of progressing from Phase I to U.S. FDA approval reveals that only 7.9% of drug development
programs (n=12,728) successfully make it to market.
As can be seen in Figure 5, there is a wide range of LOAs from Phase I. At the high end, Hematology towers
over the other disease groups at 23.9% (n=352). Hematology therapies had an LOA from Phase I seven times
higher than Urology therapies, which had the lowest Phase I LOA of all the major disease areas (3.6%, n=88).
After Hematology, the next highest LOA from Phase I was Metabolic with 15.5% (n=399). Five other disease
areas were above the overall average of 7.9% (Infectious Disease > Ophthalmology > Autoimmune > Allergy> Gastroenterology) which ranged from 13.2% down to 8.3%. Falling under the overall LOA of 7.9%, but very
close, were Respiratory (7.5%) and Psychiatry (7.3%). Therapies for five disease categories were well below
the average (Endocrine > Neurology > Oncology > Cardiovascular > Urology). The fact that Oncology and
Neurology have the two largest n values, while also having low LOA values, indicates that these two diseasecategories are significant contributors in bringing down the overall industry LOA.
Overall likelihood of approval by disease area

Likelihood of Approval from Phase I
Hematology
Metabolic
Infectious disease
OthersOphthalmology
Autoimmune
Allergy
Gastroenterology
All indications
Respiratory
Psychiatry
Endocrine
Neurology
Oncology
Cardiovascular
Urology

23.9%

15.5%
13.2%
13.0%
11.9%
10.7%
10.3%
8.3%
7.9%
7.5%
7.3%
6.6%
5.9%
5.3%
4.8%
3.6%
0%

5%

10%

15%

20%

25%

30%

Probability of SuccessFigure 5a: Chart of LOA from Phase I, displayed highest to lowest by disease area. Source: Biomedtracker® and Pharmapremia®, 2020

10/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase I
to Approval

Phase II
to Approval

Phase IIIto Approval

NDA/BLAto Approval

LOA
n

Phase
LOA

LOA
n

Phase
LOA

LOA
n

Phase
LOA

LOA
n

Phase
LOA

Hematology

352

23.9%

260

34.4%

154

71.5%

72

93.1%

Metabolic

399

15.5%

263

25.0%

114

55.7%

48

87.5%

Infectious disease

1170

13.2%

767

22.8%

353

59.4%

156

92.9%

Others

541

13.0%

387

20.5%

159

53.0%

69

88.4%

Ophthalmology

415

11.9%

327

16.6%

127

46.7%

45

91.1%

Autoimmune

1305

10.7%

892

19.3%

421

61.4%

202

94.1%

Allergy

201

10.3%

146

18.3%

54

64.7%

20

100.0%

Gastroenterology*

186

8.3%

141

17.8%

68

51.9%

33

90.9%

All indications

12728

7.9%

8314

15.1%

3381

52.4%

1453

90.6%

Respiratory

501

7.5%

322

13.5%

107

61.6%

45

95.6%

Psychiatry

442

7.3%

292

13.8%

128

51.4%

57

91.2%

Endocrine

887

6.6%

568

15.2%

275

57.1%

124

86.3%

Neurology

1411

5.9%

895

12.3%

391

46.0%

165

86.7%

Oncology

4179

5.3%

2551

10.8%

819

43.9%

324

92.0%

Cardiovascular

651

4.8%

437

9.6%

185

45.6%

80

82.5%

Urology

88

3.6%

66

8.8%

26

58.6%

13

84.6%

Likelihood ofApproval

Figure 5b: Table likelihood of approval by disease area with corresponding n values. The n value is the total ‘Advanced or Suspended’ transitions of all phases used
to calculate LOA. ‘Phase LOA’ is the probability of FDA approval for drugs from this phase of development. Source: Biomedtracker® and Pharmapremia®, 2020

Oncology and Non-Oncology Diseases
Oncology drug development program transitions in the 2011–2020 period accounted for 33% of the 12,728
total transitions. With the third-lowest LOA from Phase I (5.3%, n=4,179), Oncology had an outsized effecton 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(Figure 6).
The LOA from Phase I across non-oncology indications, 9.3% (n=8,549), was nearly twice that for Oncology
alone, at 5.3% (n=4,179). Comparing individual phase transition success rates in Figure 2, Oncology consistently
had one of the lowest success rates compared to the other 14 disease categories for every developmental
clinical transition. In particular, there were relative differentials of approximately 20% between Oncology andnon-oncology groupings at the Phase II and III stages. One notable exception was the NDA/BLA to approval
success rate. Oncology performed slightly better than non-oncology at the regulatory transition between
NDA/BLA and approval, with a 92.0% (n=324) success rate, as opposed to 90.2% (n=1,129).

11/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Oncology vs. non-oncology phase transition success rates and LOA

Probability of Success

Probability of Success: Oncology vs. Others
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

92.0% 90.2%

48.8%

61.3%

53.9%

47.7%
31.2%
24.6%
9.3%
5.3%

Phase I to II

Phase II to III
Oncology

Phase Success

Phase I to II

Oncology

1628

Phase
POS
48.8%

Non-oncology

2786

53.9%

POS n

Phase III to
NDA/BLA

NDA/BLA to
Approval

Phase I to
Approval

Non-oncology

1732

Phase
POS
24.6%

Phase III to
NDA/BLA
Phase
POS n
POS
495
47.7%

NDA/BLA to
Approval
Phase
POS n
POS
324
92.0%

3201

31.2%

1433

1129

Phase II to III
POS n

61.3%

90.2%

Oncology

Phase I to
Approval
Phase
LOA n
LOA
4179
5.3%

Phase II to
Approval
Phase
LOA n
LOA
2551
10.8%

Phase III to
Approval
Phase
LOA n
LOA
819
43.9%

NDA/BLA to
Approval
Phase
LOA n
LOA
324
92.0%

Non-oncology

8549

5763

2562

1129

Likelihood ofApproval

9.3%

17.2%

55.3%

90.2%

Figure 6: Oncology vs non-oncology phase transition success rates and LOA. Top: Chart of phase transition success rates and LOA from Phase I for Oncology vs nonOncology. Bottom: Table of phase transition success and likelihood of approval by Oncology vs non-Oncology with corresponding n values. The n value is the total‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase LOA’ isthe probability of FDA approval for drugs from this phase of development. Source: Biomedtracker® and Pharmapremia®, 2020

Oncology drugs were further categorized into two main types of cancer: solid tumors and hematologic
cancers. Drugs for solid tumors had more than twice as many transitions in the data set (2,982 vs 1,094), but
a much smaller LOA from Phase I vs hematological cancers (4.6% vs 7.5%).
The 2011–2020 time period witnessed the rise of drugs that harness the immune system to treat theunderlying cancer, spanning both solid and hematologic types. The first successful immuno-oncology (IO)
drugs spawned intense growth in this field over a short period of time, with 679 total phase transitions
included in the Biomedtracker database. The success rates of IO drugs far exceed the traditional oncology
averages, with a Phase I LOA of 12.4%. The notably high Phase II transition rate of 42.0% for IO drugs –contrasting 24.6% as a whole for oncology – is the main factor underpinning the overall LOA success for the
IO class. These oncology groupings are shown in Figure 7.

12/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Oncology sub-category phase transition success rates and LOA
Phase I to II

Phase Success

449

Phase
POS
27.8%

Phase III to
NDA/BLA
Phase
POS n
POS
120
60.0%

NDA/BLA to
Approval
Phase
POS n
POS
100
90.0%

Phase II to III

Hematologic

425

Phase
POS
50.1%

Solid

1145

48.9%

1261

23.4%

364

42.9%

212

92.9%

IO

275

64.0%

244

40.2%

98

49.0%

62

98.4%

POS n

POS n

Phase I to
Approval

Likelihood ofApproval

Phase II to
Approval

Phase III to
Approval

NDA/BLA to
Approval

LOA n

Phase
LOA

LOA n

Phase
LOA

LOA n

Phase
LOA

LOA n

Phase
LOA

Hematologic

1094

7.5%

669

15.0%

220

54.0%

100

90.0%

Solid

2982

4.6%

1837

9.3%

576

39.8%

212

92.9%

IO

679

12.4%

404

19.4%

160

48.2%

62

98.4%

Figure 7: Oncology sub-category phase transition success rates and LOA. The n value is the total ‘Advanced or Suspended’ transitions of all phases used to calculate
LOA. ‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs from this phase ofdevelopment. Note: Hematologic and solid tumor types are mutually exclusive categories, while the IO drug class includes transitions from across both categories.
IO drugs for solid tumors had 3x the transitions as IO hematological cancers but have similar success rates. Source: Biomedtracker® and Pharmapremia®, 2020

Rare and Chronic High Prevalence Diseases
Within the Biomedtracker database, rare diseases can be identified based on meeting either or both of thefollowing standard criteria: affecting fewer than 200,000 people in the US, or prevalence of 1 in 2,000 people
in the EU. As 43% of the Oncology transitions are for rare indications, all Oncology indications were removed
to make this rare disease analysis more concentrated on inborn genetic disorders. For chronic diseases, wefirst obtained a list of conditions from the CMS Chronic Conditions Data Warehouse (CCW). We removed any
cancer indications, then identified those diseases with greater than 1 million patients affected in the UnitedStates.
Rare disease vs. highly prevalent chronic disease success rates

Probability of Success

Probability of Success: Rare vs Chronic Diseases
93.6% 92.6% 90.6%

100%
80%
60%

60.4% 59.5%

67.4%

52.0%
46.0%
44.6%

40%

23.1%

57.8%

28.9%

17.0%

20%
0%

Phase I to II
Rare diseases

Phase II to III

Phase III to
NDA/BLA

NDA/BLA to
Approval

Chronic, high prevalence diseases

5.9%

7.9%

Phase I to
Approval

All indications

Figure 8a: Non-oncology rare disease and highly prevalent chronic disease phase transition success rates and LOA.
Chart of phase transition success rates and LOA from Phase I. Source: Biomedtracker® and Pharmapremia®, 2020

13/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase SuccessRare diseases
Chronic, highprevalence diseases
All diseases

Likelihood ofApproval
Rare diseases
Chronic, highprevalence diseases
All diseases

Phase I to II

464

Phase
POS
44.6%

Phase III to
NDA/BLA
Phase
POS n
POS
240
60.4%

NDA/BLA to
Approval
Phase
POS n
POS
172
93.6%

Phase II to III

380

Phase
POS
67.4%

745

46.0%

737

23.1%

279

59.5%

217

92.6%

4414

52.0%

4933

28.9%

1928

57.8%

1453

90.6%

POS n

POS n

Phase I to
Approval
Phase
LOA n
LOA
1256
17.0%

Phase II to
Approval
Phase
LOA n
LOA
876
25.2%

Phase III to
Approval
Phase
LOA n
LOA
412
56.6%

NDA/BLA to
Approval
Phase
LOA n
LOA
172
93.6%

1978

5.9%

1233

12.7%

496

55.1%

217

92.6%

12728

7.9%

8314

15.1%

3381

52.4%

1453

90.6%

Figure 8b: Non-oncology rare disease and highly prevalent chronic disease phase transition success rates and LOA. Table of phase transition success and likelihoodof approval by disease prevalence with corresponding n values. The n value is the total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA.
‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs from this phase of development.
Source: Biomedtracker® and Pharmapremia®, 2020

After identifying programs for non-oncology rare diseases and highly prevalent chronic diseases, we
compared their phase transition success rates and LOA as shown in Figure 8. At 17.0%, the overall LOA from
Phase I for rare diseases (17.0%) was almost three times higher than for chronic, high prevalence diseases(5.9%). As both of these groups exclude Oncology indications, we can compare these results with those of theoverall non-oncology figures. The 9.3% LOA (n=8,549) value for all non-oncology indications is meaningfully
higher than for chronic, high prevalence diseases, suggesting that these indications are having a negative
impact on overall success rates outside Oncology. Chronic, high prevalence diseases accounted for 16% of
total transitions, with Oncology providing a further 33%. These two groups, having LOAs well below average,
together comprise half of the total dataset, thus negatively affecting the overall industry LOA of 7.9%.
Although excluded from the above analysis, rare disease Oncology indications had a higher LOA than
non-rare Oncology indications (6.8% vs 4.4%), largely driven by the higher overall success for hematologic
malignancies. Blood cancers typically have lower prevalence and are classified as rare diseases.
Success rates for all four transitions were higher for the rare disease group than any of the overall transition
success rates. The largest difference was found in Phase II transition success rates (44.6% for rare diseasevs 28.9% overall). There was also a notable gap between Phase I transition rates (67.4% vs 52.0%), although
success in Phase III and in the NDA/BLA to Approval transitions were broadly comparable.
Conversely, the success rates for transitions in the highly prevalent chronic disease group were lower thanthe overall transition success rates in Phase I (46.0% vs 52.0%) and Phase II (23.1% vs 28.9%). The opposite
was seen in Phase III and NDA/BLA transitions, where slightly higher success rates were observed: 59.5% vs57.8% for Phase III and 92.6% vs 90.6% for NDA/BLA.

14/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Drug Classes and Modalities
Drugs in the dataset were annotated as novel or off-patent, with the novel category including new molecular
entities (NMEs; mainly small molecules), biologics, and vaccines; and the off-patent group comprised of
non-NMEs and biosimilars. As noted in Figure 9, transition success rates for novel drugs confirm that they
face a more difficult pathway to approval than for off-patent products. The overall Phase I LOA for offpatent therapies (14.7%, n=2,161) was twice as high as that for novel therapies (6.8%, n=10,527).This two-foldincrease in success for non-originator products was driven by higher success rates in all three clinical phasetransitions, with the greatest divergence observed during Phase III (52.9% vs 70.3%).
Within the novel drugs segment, NMEs had a lower probability of approval from Phase I than either biologics
or vaccines. While there was a 5.7% (n=6,803) chance of a Phase I NME gaining approval, the probabilities forbiologics and vaccines were 9.1% (n=3,412) and 9.7% (n=312), respectively. Vaccines in particular have been
100% successful in the NDA/BLA to Approval stage (n=27). In the off-patent category, biosimilars performed
well, with an overall Phase I LOA of 32.3% (n=277).
Phase transition success rates and LOA from Phase I for drugs based on class and novelty

Phase Success"Novel"NME
Biologic
Vaccine"Off-Patent"
Non-NME
Biosimilar

Likelihood ofApproval"Novel"NME
Biologic
Vaccine"Off-Patent"
Non-NME
Biosimilar

Phase I to II
POS n
3933
2505
1301
127
455
395
60

Phase
POS
51.3%
50.6%
52.5%
52.0%
60.4%
57.5%
80.0%

Phase I to
Approval
Phase
LOA n
LOA
10527
6.8%
6803
5.7%
3412
9.1%
312
9.7%
2161
14.7%
1884
13.3%
277
32.2%

Phase
POS
27.9%
25.6%
32.4%
32.2%
37.6%
37.5%
50.0%

Phase III to
NDA/BLA
Phase
POS n
POS
1374
52.9%
869
50.6%
462
56.7%
43
58.1%
552
70.3%
471
67.5%
81
86.4%

NDA/BLA to
Approval
Phase
POS n
POS
826
89.6%
505
86.1%
294
94.6%
27
100.0%
627
91.9%
495
91.5%
132
93.2%

Phase II to
Approval
Phase
LOA n
LOA
6594
13.2%
4298
11.2%
2111
17.4%
185
18.7%
1706
24.3%
1489
23.2%
217
40.3%

Phase III to
Approval
Phase
LOA n
LOA
2200
47.4%
1374
43.6%
756
53.6%
70
58.1%
1179
64.6%
966
61.8%
213
80.5%

NDA/BLA to
Approval
Phase
LOA n
LOA
826
89.6%
505
86.1%
294
94.6%
27
100.0%
627
91.9%
495
91.5%
132
93.2%

Phase II to III
POS n
4394
2924
1355
115
527
523
4

Figure 9: Phase transition success rates and LOA from Phase I for drugs based on class and novelty. The n value is the total ‘Advanced or Suspended’ transitions of
all phases used to calculate LOA. ‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for drugs
from this phase of development. Source: Biomedtracker® and Pharmapremia®, 2020

15/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Looking a little deeper into drug modality, chimeric antigen receptor T cell (CAR-T) development has seen
the most success among selected novel modalities (Figure 10). At 17.3% (n=67), the Phase I LOA for CAR-T
therapies is more than twice the 7.9% average across all diseases, and more than three times higher than the
5.3% Phase I LOA for all of oncology. Since 2017, three separate CAR-Ts have gained approval, with a total offour successful BLA transitions. The strong R&D progress in RNA interference (RNAi) therapies is reflected in
the Phase I LOA for this category, second behind CAR-T at 13.5% (n=70). This class includes three approvals asof December 31, 2020, the first of which came in 2018. Monoclonal antibodies, antibody drug conjugates(ADCs), and gene therapies also each have over a 10% likelihood of approval at the Phase I stage.
Success rates by modality

Likelihood of Approval: Novel Modalities
CAR-T

17.3%

siRNA/RNAi

13.5%

Monoclonal antibody

12.1%

ADCs

10.8%

Gene therapy

10.0%

Vaccine9.7%

Protein

9.4%

Peptide8.0%

Small molecule

7.5%

Antisense

5.2%
0%

5%

10%

15%

20%

Probability of SuccessFigure 10a: LOA from Phase I for drugs based on modality. Chart of LOA from Phase I, displayed highest to lowest by drug modality.

16/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase Success

Phase I to II

CAR-T

43

Phase
POS
44.2%

siRNA/RNAi

40

Monoclonal antibody

Phase II to III

Phase III to
NDA/BLA
Phase
POS n
POS
3
66.7%

NDA/BLA to
Approval
Phase
POS n
POS
4
100.0%

17

Phase
POS
58.8%

70.0%

38

28.9%

6

66.7%

3

100.0%

804

54.7%

740

34.1%

310

68.1%

282

95.4%

ADCs

103

41.7%

53

41.5%

16

62.5%

12

100.0%

Gene therapy

27

51.9%

57

38.6%

10

50.0%

2

100.0%

Vaccine

129

52.7%

117

31.6%

43

58.1%

27

100.0%

Protein

246

51.6%

288

33.0%

149

61.7%

117

89.7%

Peptide

234

53.0%

218

28.4%

100

60.0%

67

88.1%

Small molecule

2308

52.6%

2896

28.0%

1118

56.9%

849

89.5%

Antisense

69

60.9%

70

20.0%

14

64.3%

9

66.7%

Likelihood of Approval
CAR-T

POS n

Phase I to
Approval
Phase
LOA n
LOA
67
17.3%

POS n

Phase II to
Approval
Phase
LOA n
LOA
24
39.2%

Phase III to
Approval
Phase
LOA n
LOA
7
66.7%

NDA/BLA to
Approval
Phase
LOA n
LOA
4
100.0%

siRNA/RNAi

87

13.5%

47

19.3%

9

66.7%

3

100.0%

Monoclonal antibody

2136

12.1%

1332

22.1%

592

64.9%

282

95.4%

ADCs

184

10.8%

81

25.9%

28

62.5%

12

100.0%

Gene therapy

96

10.0%

69

19.3%

12

50.0%

2

100.0%

Vaccine

316

9.7%

187

18.4%

70

58.1%

27

100.0%

Protein

800

9.4%

554

18.3%

266

55.4%

117

89.7%

Peptide

619

8.0%

385

15.0%

167

52.8%

67

88.1%

Small molecule

7171

7.5%

4863

14.3%

1967

50.9%

849

89.5%

Antisense

162

5.2%

93

8.6%

23

42.9%

9

66.7%

Figure 10b: LOA from Phase I for drugs based on modality. Table of phase transition success and likelihood of approval by modality with corresponding n values. The nvalue is the total ‘Advanced or Suspended’ transitions of all phases used to calculate LOA. ‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase
LOA’ is the probability of FDA approval for drugs from this phase of development. *n values for vaccine differs slightly from Figure 9 owing to variable classification of cellular
vaccines. Source: Biomedtracker® and Pharmapremia®, 2020

Patient Preselection Biomarkers
A greater understanding of human disease – whether at the molecular or genomic level – ultimately leads to theinvestigation of personalized medicine. Indications are increasingly segmented by biomarkers in order to match
patients with the treatments most likely to show the greatest benefit, according to the underlying drug mechanismand disease pathophysiology. We identified 767 phase transitions out of 12,728 (6%) that incorporated patientpreselection biomarkers in their corresponding clinical trial design. This was accomplished by mapping Informa
Pharma Intelligence’s Biomedtracker and Trialtrove databases, to provide the supplemental level of clinical trial
detail. For a detailed description please refer to the Methods section.
The LOA from Phase I segmented by biomarkers can be found in Figure 11. Drug development programs withtrials employing patient preselection biomarkers have a two-fold higher LOA (15.9%) than those that do not(7.6%).

17/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Success rates by use of patient preselection biomarkers

Probability of Success

Probability of Success: Preselection Biomarkers
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

96.0%

68.2%
52.4% 52.0%

57.1%
46.3%
28.3%
15.9%

Phase I to II

Phase II to III

Phase III to
NDA/BLA

Preselection biomarkers

Phase Success

90.3%

Phase I to II

Preselection biomarkers

414

Phase
POS
52.4%

No biomarkers

4000

52.0%

POS n

NDA/BLA to
Approval

7.6%

Phase I to
Approval

No biomarkers

149

Phase
POS
46.3%

Phase III to
NDA/BLA
Phase
POS n
POS
129
68.2%

NDA/BLA to
Approval
Phase
POS n
POS
75
96.0%

4784

28.3%

1799

1378

Phase II to III
POS n

57.1%

90.3%

Preselection biomarkers

Phase I to
Approval
Phase
LOA n
LOA
767
15.9%

Phase II to
Approval
Phase
LOA n
LOA
353
30.3%

Phase III to
Approval
Phase
LOA n
LOA
204
65.5%

NDA/BLA to
Approval
Phase
LOA n
LOA
75
96.0%

No biomarkers

11961

7961

3177

1378

Likelihood of Approval

7.6%

14.6%

51.5%

90.3%

Figure 11: Patient preselection biomarker phase transition success rates and LOA. Top: Chart of phase transition success rates and LOA from Phase I. Bottom: Tableof phase transition success and likelihood of approval by biomarker status with corresponding n values. The n value is the total ‘Advanced or Suspended’ transitionsof all phases used to calculate LOA. ‘POS’ is the probability of successfully advancing to the next phase, whereas ‘Phase LOA’ is the probability of FDA approval for
drugs from this phase of development. Source: Biomedtracker®, Pharmapremia®, and Trialtrove®, 2020

The advantage of patient preselection biomarkers varies according to the trial stage. At Phase I, there is no
discernible difference between the two discrete sets, with both biomarker and non-biomarker programs
achieving close to the average success rate of 52.0%. The benefit is clearly greatest at Phase II, where the useof preselection biomarkers enables 46.3% of programs to advance (n=149), compared to just 28.3% of those
without. A similar advantage, albeit smaller in magnitude, is also observed with Phase III transitions – 68.2%
for biomarker-supported programs vs 57.1% for those without. Success at the NDA/BLA transition is largely
contingent on Phase III trial design and so this benefit carries over from the largest clinical phase.
While this analysis only represents a small subset of the overall data, the degree of difference between
individual phase success rates and overall LOA from Phase I builds confidence in the pursuit of drug
development programs targeted at biomarker-enriched patient populations. Such assets are likely toadvance through clinical development with lower levels of attrition, and should in theory improve patient
outcomes via the advent of increasingly personalized medicine.

18/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Part 2. Predictive Analysis of Clinical Success

In Part 1, we estimated success rates using the drug-indication pathways gathered from the Biomedtracker
database. We can now incorporate additional information, called “features,” for each drug development
program using machine learning methods to produce forward-looking measures—forecasts—of the outcomeof ongoing programs. This type of analysis provides more timely and relevant estimates than standalone
analyses of historical success rates.
Some examples of features included in this predictive analysis are the characteristics of the drug, its indication,
its sponsor, and its clinical trial design. There are more than 200 features used in the QLS forecasts, a sample
of which can be found in Figure 12. In much the same way that linear regression models employ regressors,
or “right-hand-side” variables, to predict the “left-hand-side” or the dependent variable, these featurescontain useful signals about drug development that allow the outcomes of drug development programs to
be more accurately predicted than before.
Sample of the 200-plus features driving success rate probabilities

Figure 12: Sample of the 200-plus features extracted from Pharmaprojects, Trialtrove, and Biomedtracker.

The QLS approach uses state of the art machine-learning techniques to analyze clinical trial data. While thedetails of the QLS model are proprietary, an example of a commonly used machine-learning algorithm is arandom forest classifier. The random forest classifier is an algorithm that takes the average prediction froma multitude of yes-no decision trees (i.e., a “forest”) that are applied to the features (see Figure 13).
When training a decision tree, one task is to learn the most informative questions to ask at each branch
point of the tree. For example, the algorithm might ask, “Is the drug sponsor a big pharma company?” If theanswer is “yes,” then the algorithm would move to the “yes” branch of the decision tree and then ask, “Is
the trial double-blind?” On the other hand, if the answer is “no,” then the algorithm would move to the “no”
branch of the decision tree, and ask, “Is the drug a small molecule?” and so on, until enough information
has been collected about the drug-program to make a prediction about its success or failure. By identifying
subtle patterns in a large enough database of historical drug transitions, machine-learning algorithms canoften make remarkably accurate predictions. Additional details on these techniques can be found in the
Methodology section.

Figure 13: A hypothetical representation of a single, simplified decision tree. The percentages at the “leaves” of the tree denote the fraction of training data samples
that are categorized by a given pathway.

19/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)

Feature Importance
Given the stakes involved in the drug development process, machine learning forecasts must not only be
accurate, but also interpretable to stakeholders charged with the responsibility of making go/no-go decisions.
These decision-makers need to understand why a given forecast differs from the historical average, andwhich features were most important in driving the difference or delta with respect to the disease-groupbaseline LOA. To increase the transparency of our forecasts, the QLS machine learning algorithm reports themost important features and their individual contributions to the forecast’s delta.
As an illustration, we decompose our probability of approval (POA) prediction of an oncology drug thatis currently in phase 3 clinical trials into its key components. (Note that we use the term POA for the QLS
machine-learning forecasts to differentiate from the empirical “LOA” presented in Part I.) Figure 14 reports
the top five features that increase this program’s estimated probability of approval from its therapeuticarea historical baseline of 35.0% from phase 3 to approval, to 71.8%. The top positive feature driving thishigher than average probability is its breakthrough therapy designation (+20.6%). This designation is granted
when the FDA has determined that preliminary clinical evidence indicates that the drug may demonstrate
substantial clinical improvement over available therapies. Further positive clinical evidence in phase 2
increases the estimate by an additional +6.4%. Next, because it is a PARP inhibitor (+4.6%) that has beenpreviously approved for another indication (+3.6%), the POA is augmented by an additional 8.2%. Finally, its
treatment of a solid tumor type (-2.0%) and the net aggregate contribution of other features (-2.8%) penalize
the overall POA score slightly.
Example of probability decomposition

Figure 14: Decomposition of the probability of approval estimate for an anti-cancer drug that is currently in phase 3 clinical trials for prostate cancer. The top fivefeatures that increase this program’s estimated probability of approval from the historical phase 3 to approval baseline in oncology of 35.0% to 71.8% are reported.
Abbreviations: POA=probability of approval; PARP= poly ADP-ribose polymerase.

20/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)

Although each individual prediction may have unique drivers, we can also extract the most informative
variables across all predictions to gain insight into some of the most common predictors of success. Figure
15 summarizes our results.
Feature importance by phase to approval
Phase I to Approval

Phase II to Approval

Phase III to Approval

NDA/BLA

45%
40%

Feature Importance

35%
30%
25%
20%
15%
10%
5%
0%

Figure 15: The most important features of our random forest classifier across all predictions. Feature importance is normalized to sum to 100%. Note that individual
predictions may have unique drivers specific to a given therapeutic area, trial design, or drug profile that are not listed here. Source: QLS Advisors.

The indication was consistently ranked the top variable across all clinical development phases. Indeed, successrates vary substantially across indications even within a therapeutic area. For example, in oncology, the overall
phase 1 to approval LOA ranges from a minimum of 1.1% (n=275) for pancreatic cancer to a maximum of 15.2%(n=33) for alimentary cancers. We also observe that lead indication status, prior approval of a drug for anotherindication, and biological target validation all have a significant impact on the probability of approval. In somecases, developing an already approved drug for a new indication—one that has a controlled manufacturing
process, and has already been shown to be safe in humans—has a greater likelihood of success than a novel
indication. In other cases, the success rates for lead indications may be higher if a sponsor initiates clinical trials
for multiple follow-on indications for which the drug was not originally intended, and many of the initiatedclinical trials for the same drug fail.
Analysis showed that the trial outcome (whether the trial was completed, with its primary endpoints met) has
significant associations with late-stage clinical success. It is easy to imagine that a drug-indication pair whose
trial failed to meet its endpoints has a low probability of success in advancing to approval. For example, asof December 31, 2020, our algorithm predicts that one specific BLA for Alzheimer’s disease has only a 65%
chance of progressing from regulatory review to approval, even though historically 83% of neurology drugs
have made the transition to approval once they have reached this stage. The observation that the biologic
failed to meet its primary endpoints in phase 3 is the top contributor (-15%) to this adjustment from the21/ February 2021

© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)baseline. In contrast, candidates that achieve positive outcomes have a higher probability of success.
The use of patient pre-selection biomarkers in clinical trials has become more common, and it has beenreported that trials using biomarkers are more likely to succeed.3 Consistent with these findings, Figure
15 shows that the use of patient pre-selection biomarkers has an important impact on success rates. For
example, in the past decade, the number of non-targeted therapies in oncology has declined, while theuse of targeted agents and pre-selection biomarkers has risen dramatically. Between 2016 and 2020, 85%(n=136) of oncology approvals were targeted agents. Moreover, 37% (n=51) involved either pre-selection
against a driver mutation (26%; n=36) or pre-selection using a tumor-specific antigen (11%; n=15).
In Figure 15, drug modality can be seen as another important predictor of success. As an illustrative example,
monoclonal antibodies have had higher phase-1-to-approval success rates (5.9%; n=935) than othermodalities within oncology. Moreover, immunotherapies focused on PD-1 or PD-L1 have been tremendously
successful from phase I (23.9%; n=71). In contrast, other immunotherapies, while numerous, have beenmuch more likely to result in failure from phase I (1.9%; n=945).
Finally, we find that sponsor track record, quantified by the number of previous successful phase transitions
in a given therapeutic area, is also a useful indicator for clinical program predictions. The positive correlation
between track record and future success is likely associated with operational experience in conducting
clinical trials and navigating the regulatory review process.
Performance
How well does machine learning predict success? A number of criteria have been proposed to measuremachine learning performance, some of them highly complex, but perhaps the most straightforward metricis to see how many correct and incorrect predictions there are on an out-of-sample data set. We analyzed
the accuracy of our machine learning predictions by first splitting our dataset into a training set (80%) andan out-of-sample test set that consisted of the most recent 20% of data. Figure 16 summarizes the resultsfor predictions for drug development programs transitioning through approval. We found that drugs withhigher probability estimates were more likely to be approved. Moreover, the predictions were relatively wellcalibrated; when the model predicted a drug-indication pair had a certain probability of approval, the drug
was subsequently approved at approximately the same rate.

3

Wong CH, Siah KW, Lo A (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273–286. doi: 10.1093/biostatistics/kxx069

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Forecasted vs. actual success rates

60%
50%
40%
30%
20%
10%
0%

Phase II to Approval(n=1,381)
50.3%

21.2%
1.7% 3.7%

9.5%

Observed Frequency

Observed Frequency

Phase I to Approval(n=1,739)

70%
60%
50%
40%
30%
20%
10%
0%

QLS Prediction

25.4%

NDA/BLA (N=444)

90%76.6% 80.8%
80%
61.3%
70%
60%
50%
40% 28.2% 33.1%
30%
20%
10%
0%

Observed Frequency

Observed Frequency

2.2% 4.3%

14.9%

QLS Prediction

Phase III to Approval(n=621)QLS Prediction

62.8%

98.9%
96.6%

100%
95%

91.1%
88.5%
90%
85%

83.1%

80%
75%

QLS Prediction

Figure 16: Histogram of machine-learning forecasts of drug development programs transitioning to approval using the most recent 20% of our dataset. Higher
forecasts are associated with higher rates of approval, which indicates positive forecast power. Estimates for each phase are grouped into quintiles.

However, the results also demonstrate that these predictions are by no means perfect. There is considerable
noise in these estimates. In certain cases, our model tends to slightly underestimate the approval rates
in the most recent out-of-sample period. For example, success rates in oncology have been increasing,concurrently with the emergence of genomic technologies, an improved scientific understanding of cancer
biology and ways to disrupt cancer cells, as well as an evolving regulatory environment that includes a higher
number of accelerated approvals. As we incorporate more data, more features, and the latest scientific andmedical knowledge into the algorithm, these forecasts will improve, which will occur naturally over time asthese methods become more popular. The QLS model is trained on the full dataset, which includes the mostrecent development programs and approvals.

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Part 3. Drug Development Timelines

In addition to determining whether drugs are advanced or suspended at the end of a phase transition, theBiomedtracker and Pharmapremia data can also be analyzed to yield time spent at each clinical stage, andoverall drug development timelines. These are valuable metrics as there are considerable opportunity costs
associated with investing in an R&D process that may take up to a decade, and a simple analysis of clinical
trial durations excludes the contribution of internal decision making and strategic execution from the overall
timeline.
Based on 6,151 successful phase transitions over the 2011–2020 period, it took an average of 10.5 years fora drug to successfully progress from Phase I development to regulatory approval. This includes 2.3 years at
Phase I, 3.6 years at Phase II, 3.3 years at Phase III, and 1.3 years at the regulatory stage.
Phase duration can vary greatly according to numerous factors, such as disease area and indication, bestpractices of clinical trial design, and patient availability. Accordingly, Figure 17 evaluates drug development
timelines for the major 14 categorized disease areas.
Disease areas with above-average LOAs tend to be associated with shorter development timelines. Five of
the seven best-performing disease areas by LOA fall beneath the 10.5-year average development duration,
including all four groups with a duration of less than 10 years (Allergy, Metabolic, Infectious disease, andOphthalmology). Conversely, the remaining disease areas, with below average LOAs, either have durations
that lie very close to the 10.5-year average duration, or in the case of Urology, Cardiovascular and Neurology,
notably exceed the average.
Phase transition durations by disease area

Phase Transition Durations from Phase I
1.5
2.0
2.0
2.1
2.1
2.7
2.1
2.3
1.9
2.3
1.8
2.2
1.6
2.1
2.4
2.7

Allergy
Metabolic
Infectious disease
Ophthalmology
Autoimmune
Oncology
Respiratory
Psychiatry
OthersAll indications
Endocrine
Hematology
Gastroenterology
Neurology
Cardiovascular
Urology0

1.1 9.2
2.9
3.8
1.2 9.5
3.1
3.2
1.2 9.7
3.1
3.5
Phase I to II
1.3 9.8
3.4
2.9
1.1 10.0
3.2
3.6
0.8 10.3
3.1
3.7
Phase II to III
1.5 10.4
3.3
3.5
1.8 10.4
2.8
3.4
Phase III to
1.8 10.5 NDA/BLA
3.2
3.5
1.3 10.5
3.3
3.6
1.8 10.7 NDA/BLA to
3.7
3.4
Approval
1.5 10.7
3.6
3.4
1.4 10.8
3.9
3.9
1.6 11.1
3.7
3.7
1.2 11.5
4.2
3.8
1.6 12.2
2.9
5.0
3

6

9

12

15

Duration of Phase Transition (Years)
Figure 17a: Phase transition durations from Phase I by disease area. Chart of phase transition duration from Phase I for all diseases. Source: Biomedtracker® andPharmapremia®, 2020

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Phase Duration
AllergyPhase III to
NDA/BLA
NDA/BLAto Approval
Advanced Duration Advanced Duration Advanced Duration Advanced Duration
31
1.5
26
3.8
22
2.9
20
1.1
Phase I to II

Phase II to III

Metabolic

84

2.0

67

3.2

42

3.1

42

1.2

Infectious disease

233

2.0

159

3.5

126

3.1

145

1.2

Ophthalmology

63

2.1

71

2.9

42

3.4

41

1.3

Autoimmune

228

2.1

148

3.6

143

3.2

190

1.1

Oncology

795

2.7

426

3.7

236

3.1

298

0.8

Respiratory

100

2.1

47

3.5

40

3.3

43

1.5

Psychiatry

79

2.3

44

3.4

40

2.8

52

1.8

Others

98

1.9

88

3.5

54

3.2

61

1.8

All indications

2296

2.3

1424

3.6

1115

3.3

1316

1.3

Endocrine

138

1.8

78

3.4

100

3.7

107

1.8

Hematology

64

2.2

51

3.4

63

3.6

67

1.5

Gastroenterology

21

1.6

25

3.9

20

3.9

30

1.4

Neurology

246

2.1

135

3.7

120

3.7

143

1.6

Cardiovascular

107

2.4

53

3.8

58

4.2

66

1.2

Urology

9

2.7

6

5.0

9

2.9

11

1.6

Figure 17b: Table of phase transition duration by disease area with corresponding n values. The n value is the total ‘Advanced’ transitions within each phase used to
calculate duration. The ordering of disease areas is equivalent to the total years overall in clinical development. Source: Biomedtracker® and Pharmapremia®, 2020

Within each individual phase, there are some noteworthy performers. Oncology shares the longest Phase I
transition at 2.7 years with Urology, but it is remarkably close to the Phase II and III averages with durationsof 3.7 and 3.1 years, respectively. It is also the only disease area to have an average regulatory review ofless than 1 year; the 0.8-year duration is almost half as short as the cumulative total for all non-Oncology
indications (1.4 years).
Urology drug candidates experience the longest Phase II transition (5.0 years), although this average iscalculated from a sample size of just six. Excluding Urology, the remaining disease areas all lie close to theaverage transition of 3.6 years. Ophthalmology emerges as the fastest disease area for Phase II research (2.9
years).
When looking at Phase III timelines, Cardiovascular drug programs have the longest duration, extending
to 4.2 years. The large cardiovascular patient population sizes in trials and the long-term evaluation ofcardiovascular outcomes contribute to longer timelines than seen in other highly prevalent diseases, suchas Psychiatry (2.8 years), which typically assesses short-term symptomatic improvement using rating-scalequestionnaires.

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Discussion
Comparison with previous reports on attrition rates
In 2016, BIO published a major report on industry attrition rates using Biomedtracker’s data from 2006 to2015 for 9,985 phase transitions, revealing an overall LOA of 9.6% for an asset entering Phase I development.
This report analyzed 12,728 transitions from 2011 to 2020 and revealed a lower overall LOA of 7.9%. Whilethe underlying data and analyses are consistent, we caution against direct comparison between the two topline LOA numbers.
Among the additional ~2,750 phase transitions in this analysis, a sizable portion can be attributed to industry
expansion, with more companies (1,779 vs 1,103) and a larger pipeline (9,704 programs vs 7,455) in thedataset. Additionally, increased demand from investors for disclosures on drug pipeline progress has resulted
in timelier reporting of suspended programs. Furthermore, the time period examined in this report includes
more rigorous scrutiny of the Biomedtracker database. The result is a stronger account of failed transitionsin the 2011–2020 analysis vs the 2006–2015 analysis. In particular, this has resulted in a notable correctionin the calculation of success rates for the Phase I transition, where drugs can lie dormant or unreported formany years, lowering the success rate from 63.2% during 2006–2015 to 52.0% in 2011–2020. Transition rates
at Phase II and III are closer, albeit slightly lower in this update (28.9% vs 30.7%; 57.8% vs 58.1%). Regulatory
approval moved in the other direction, increasing from 85.3% to 90.6%
Comparing current success rates with prior publication
Likelihood ofApproval
Hematology
Metabolic
Infectious disease
OthersOphthalmology
Autoimmune
Allergy
Gastroenterology
All indications
Respiratory
Psychiatry
Endocrine
Neurology
Oncology
Cardiovascular
Urology

2016

2021

Phase I to
ApprovalN

Phase I to
Approval

N

26.1%
15.3%
19.1%
16.3%
17.1%
11.1%
14.7%
15.1%
9.6%
12.8%
6.3%
13.2%
8.4%
5.1%
6.6%
11.4%

283
241
916
301
267
837
107
156
9985
428
451
791
1304
3163
632
108

23.9%
15.5%
13.2%
13.0%
11.9%
10.7%
10.3%
8.3%
7.9%
7.5%
7.3%
6.6%
5.9%
5.3%
4.8%
3.6%

352
399
1170
541
415
1305
201
186
12728
501
442
887
1411
4179
651
88

Figure 18: Comparing current success rates (2011–2020) with prior publication (2006–2015). Disease areas ranked by the latest LOA results from high to low. See
Appendix for a breakdown of individual phase success rates

The observations and takeaways of the 2016 report remains as relevant today as they did in 2016, evenaccounting for the changes from these improved calculations. The leading factors that correlate with higher
success rates remain consistent: clinical validation of a target (e.g., whether a drug with the same mechanismof action has been previously approved), biologic modality (e.g., mAbs), patient population and selection
strategy (e.g., rare diseases with detectable gene mutations).

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Oncology: largest segment of industry, modest success rates
Oncology continues to possess the largest portion of the 12,728 phase transitions over 2011–2020, eventhough it has the lowest Phase I LOA (5.3%). Despite our caution against direct comparisons with the 2016
paper, Oncology was one of the few major disease areas to increase its LOA, growing marginally from 5.1% in
the previous analysis. In addition to the emergence of IO therapeutics, other drivers of improved success may
be due to an increase in biomarker-defined patient populations, and the success of novel drug modalities(CAR-T, ADCs, etc.). That being said, the overall performance of Oncology is still considerably below theindustry average (7.9%) and non-oncology indications (9.3%). There is also no substantial benefit to many
Oncology indications being classified as rare diseases, as the rare Oncology subset only carries a Phase I LOA
of 6.8%. These findings indicate that scientific complexity and the competitive dynamic that characterize thistherapy area continue to have an adverse impact on the success rates for oncology therapies. This has notdissuaded investment within Oncology, with nearly half of all venture capital going into cancer companies by
the end of the last decade.4
Challenges for Phase II proof-of-concept drive overall performance
The Phase II transition is clearly the main translational and limiting step from bench to bedside, as proof-ofconcept is only established in 28.9% of drug programs at this stage. Phase II also sees the greatest distribution
between major disease areas, with a three-fold difference spanning Urology (15.0%) and Hematology (48.1%)
at opposite ends of the spectrum. Within these disease areas, it is the performance of “novel” drug classes –NMEs (New Molecular Entities), biologics, and vaccines – that have the most significant negative contribution
to low overall LOA success rates, as the “off-patent” grouping relies on duplication of established science.
Hematology retains a dominant position largely on the strength of hemophilia research, which accounts forthe largest number of transitions within the disease area. In the case of hemophilia A, where the underlying
biology is extremely well characterized, 32 of 34 of phase transitions have been successful, including all six
at Phase II development. The other main hematologic disease, anemia (which includes several different subindications), also has notably above-average success rates.
Conversely, Cardiovascular saw a large numbers of failed Phase II transitions for novel drug candidates, withan overall rate of just 21.0%. Compared to the other major disease areas such as Oncology and Autoimmune,
there is relatively little penetration of biologics within Cardiovascular, where novel drug discovery is commonly
centered on oral small molecules. Of the 507 Cardiovascular development programs in the Biomedtracker
dataset, there are just 98 biologics vs 321 NMEs. This approximate 1:3 ratio is far lower than Autoimmune
therapies which have a ratio of 1:1 and a LOA of 10.7%. As noted in Figure 9, NMEs underperform biologics
considerably, including at the critical Phase II transition. For biologics, there is typically a stronger relationshipbetween disease state and drug mechanism, enabling a stronger proof-of-concept.
Neurology is an interesting example where the Phase II transition rate (26.8%) is relatively close to theindustry average, with most of the developmental risk realized later in Phase III studies. Clinical development
programs for neurodegenerative diseases are often able (or designed) to find adequate justification frombiomarker data to progress into late-stage trials, without having a clinical proof-of-concept on clinicallyvalidated endpoints. The resulting effect is underperformance in the Phase III and NDA/BLA transitions,dragging down the overall Phase I to LOA success rate for this disease category to 5.9%
Characteristics of rare disease R&D not found in highly prevalent chronic disease
Throughout the last decade, industry investment and drug development have pivoted towards rare,congenital diseases. Specific examples of clinical and commercial successes have encouraged this transition.
Drivers of these successes include targeting molecularly defined causes of disease, regulatory incentives,and favorable reimbursement environments. However, as these drivers of success are absent from chronic,
highly prevalent diseases, investment for these indications has waned over the same period, with notable
4

BIO (2020) Emerging Therapeutic Company Investment and Deal Trends. Available here [Accessed 15 January 2021].

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)large company exits in psychiatric and cardiovascular medicine. BIO has covered the state of innovation inthese areas in a series of industry analysis reports.6,7,8
One large difference between this 2011–2020 dataset and the previous 2006–2015 iteration is theintensifying focus on rare diseases. Our latest analysis includes 1,256 phase transitions within rare diseases,
a considerable increase over the 521 noted in the previous study. This spans 685 different lead developers(not including those listed solely as partners). This indicates that companies view pivoting to rare disease
clinical development as a sound strategy.
While notably more successful than industry averages – and in particular chronic, highly prevalent diseases– the increasing breadth of rare disease R&D is leading to a reduction in overall success. Our calculated
Phase I LOA of 17.0%, slightly over double the industry average (7.9%), represents a notable fall from the
25.3% reported in 2016. As the number of programs has grown, so has the number of rare diseases thatare being addressed for the first time by therapeutic modalities where less is known about their biology andpathology. In other cases, there are new difficulties in targeting the root cause. Nevertheless, rare disease
R&D remains an attractive area for investment, combining a large unmet need with commercial potential,
supported by regulatory incentives and above-average clinical success rates.
Complexity of modality correlates with higher LOA
As an approximate rule, the increasing chemical complexity of the therapeutic being tested is associatedwith higher rates of phase success, as shown in Figure 10. In the past ten years the proportion of complex
biologics in the clinical pipeline has steadily expanded from 25% to 40%.9 However, this trend has not beenenough to boost overall industry rates beyond a 10% LOA, as simpler small molecules remain the in the
majority. These account for 57% of the 12,728 phase transitions included in this analysis, with a modest 7.5%
Phase I LOA.
Monoclonal antibodies, which entered routine clinical practice three decades ago, have considerably
improved LOAs (12.1%) when compared to the drug classes that came before – small molecules (7.5%),
peptides (8.0%), and proteins (9.4%). Since then, numerous new classes have emerged that have not yet had
a comparable transformational impact, including gene therapies and antibody-drug conjugates. However,recent advances with CAR-T cell therapies and RNA interference are yielding industry-leading LOAs of 17.3%
and 13.5%, respectively.
Advanced biologic therapies possess the intrinsic benefit of being precise tools for manipulating humanbiology and are reserved for indications where there is a clear pathophysiology. Additionally, they arealso more likely to benefit indirectly by association with other factors that are highly predictive of success,including regulatory designation, biomarker-enriched patient populations, and reduced competition. On thelast point, commercial maturity is one important factor that leads to pipeline attrition. Investment decisions
and portfolio prioritization reflect the competitive scenario, so we should realistically expect strategic andclinical suspensions to increase over time. It will be interesting to see whether CAR-T and RNA interference
approaches can sustain such remarkable LOAs as fast-followers enter the clinic and attempt to extend thetechnology into new, unproven treatment settings. Success rates can eventually wane over time as sample
sizes increases.
Contributors of clinical success and predictive analytics
While recent breakthrough therapeutics offer new hope for patients, they have also made biomedical
innovation more complex, riskier, and more expensive. In the face of multiple growing uncertainties, theThomas D, Wessel C (2019). The State of Innovation in Highly Prevalent Chronic Diseases Volume V: Systemic Hypertension and Heart Failure. Available here [Accessed
15 January 2021].
6
Thomas D, Wessel C (2018). The State of Innovation in Highly Prevalent Chronic Diseases Volume II: Pain and Addiction Therapeutics. Available here [Accessed 15 January 2021].
7
Thomas D, Wessel C (2017). The State of Innovation in Highly Prevalent Chronic Diseases Volume I: Depression Therapeutics. Available here [Accessed 15 January 2021].
8
Lloyd I (2020) Pharma RD Annual Review 2020 Whitepaper. Available here [Accessed 15 January 2021].
5

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)need for greater accuracy in predicting clinical trial outcomes has also grown. More accurate forecasts mean
fewer drug failures, faster approval times, a lower cost of capital, and more funding available to bring new
and better therapies to patients sooner.
When managing their portfolios of investigational drugs, biopharma companies typically use simple historical
averages of regulatory approval rates, based on backward-looking relative frequencies of approval. Whilethese historical averages contain useful insights, they do not take into account the many important predictive
features related to therapeutic modality, trial design, sponsor track record, or other characteristics specific to
the indication. Drug and device developers need to be able to accurately evaluate the impact of key drivers on
the probability of success to efficiently allocate capital to opportunities with the highest potential. In this report,
we demonstrate how machine learning techniques can be used to fully exploit all available data.
The relative importance of individual features in our predictive models find that trial outcome, regulatory
designation, prior approval for another indication, the use of patient pre-selection biomarkers, drug modality,
and sponsor track record are all critical features for predicting success in our set of more than 200 features.
Because these classifiers are nonlinear, there is no simple interpretation of the contribution of each predictor
to the forecast. However, the intuition behind some of these factors is clear. For example, drug-indication pairs
with trials that achieve positive outcomes should have a greater chance of approval; candidates sponsoredby companies with strong track records and diverse expertise in drug development should have a higher
likelihood of success; and already approved drugs should have higher chances of approval for a second related
indication.
When applied to out-of-sample data, the QLS forecasts show that candidates with higher probabilities wereindeed more likely to be approved, indicating that our models are able to discriminate between high- andlow-probability candidates. These promising results suggest the possibility of even more powerful predictivemodels with the inclusion of more refined drug development data, including patient-level clinical trial data.
Ultimately, such predictive analytics have the potential to facilitate more informed data-driven decisions about
the risk assessment and portfolio management of investigational drugs at all clinical stages.
Increasing duration in clinical development typically carries an elevated risk
The average timeline from Phase I to approval over 2011–2020 was 10.5 years. This lengthy timeframe is asignificant risk to investors and entrepreneurs, and speaks to the current complexities of today’s clinical drug
development. This risk is compounded with the correlation of longer timelines in certain disease areas withgreater pipeline attrition rates. For example, Urology and Cardiovascular carry both the longest clinical program
durations (12.2 years and 11.5 years) and the lowest LOAs from Phase I (3.6% and 4.8%). This may partly be
attributed to highly prevalent diseases requiring longer enrollment times and more complex endpoints. The
average time to run an Alzheimer’s Phase II trial is not going to be a short as a Phase II trial for a Covid-19
antibody. However, for some of the more complex diseases, the failure to terminate R&D programs when thedata do not support further progression can add significantly to the duration recorded for a particular phase.
There are countless examples of studies yielding inconclusive results, necessitating further clinical investigationand prolonging the duration within a given phase, only for the drug to subsequently fail again.
Oncology timelines were found to have unique characteristics. Although the overall oncology development
time is close to the average (10.3 years vs 10.5 years), the length of each constituent phase varies considerablyfrom the average. Its Phase I duration (2.7 years) is the longest of all major disease areas, while its NDA/
BLA filing transition is the shortest (0.8 years). Oncology drug developers have broadly adopted the basket
trial design at Phase I, whereby multiple different indications are studied in a single trial to evaluate potential
efficacy signals, before expansion cohorts are initiated, either within the same trial or in a new Phase II study. At
the US FDA, the Office of Oncologic Diseases in particular has led the use of expedited pathways and predictive
endpoints, as well as Real Time Oncology Review (RTOR), facilitating the transition of new cancer drugs that
address unmet clinical needs.

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)

Methods
Drug Development Programs analyzed in this report track a specific indication for each drug. For example,
Rituxan in non-Hodgkin’s lymphoma (NHL) qualifies as a different development path than Rituxan in multiple
sclerosis (MS). Biomedtracker assigns a unique internal identifier that 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 specificdrug. Drugs can only have one lead development program (with some rare exceptions). For example, cancer
drugs developed in multiple indications will have the most advanced program assigned as the lead, and therest as “non-lead”. However, in this report we do not differentiate the most 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 andthe number of drugs that were suspended. The n value associated with the phase transition success rates
represents the number of drugs that have advanced plus the number of drugs that have been suspended,
which we label as phase transitions. Phase transition success rates reported in this study were based ontransition 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 andportfolio management decisions.
Biomedtracker further classifies events by phase of development, summarized in the table below:

Generic products were not included, but generic manufacturers developing novel investigational drugs wererepresented. Biosimilars, which require thorough clinical development, were also included.
Likelihood of Approval (LOA) denotes the probability of reaching FDA approval from the current phaseand 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 transition
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

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using Pharmapremia, a purpose-built Probability of Technical Success (PTS) tool, which identified all ‘Advanced’and ‘Suspended’ drugs by development phase from January 1, 2011, to November 30, 2020. Biomedtracker
and Pharmapremia, subscription-based products of Informa, track the clinical development and regulatory
history of investigational drugs to assess their Likelihood of Approval (LOA) by the FDA. Biomedtracker ispopulated in near real-time with updated information from press releases, corporate earnings calls, investor
and medical meetings, and numerous other sources. These data are recorded in Biomedtracker and taggedwith a date. Biomedtracker also uses other sources, including regular communication with companies
conducting clinical trials, to enhance the accuracy and timeliness of the data.
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 the FDA, can be sugars, proteins,or nucleic acids, or complex combinations of these substances, or may be living entities such as cells andtissues.
Patient Preselection Biomarkers. A subset of the Biomedtracker phase transition data was matched withcorresponding entries in Trialtrove, an analyst-curated clinical trial database produced by Informa Pharma
Intelligence. Where available, NCT numbers (ClinicalTrials.gov) from Biomedtracker for each drug-indication
pair was matched according to the corresponding entry in Trialtrove, which contains additional information
on whether patient preselection biomarkers featured in the trial design. This includes trials incorporating
pharmacogenomic and/or pharmacogenetic analysis, including the use of genomic biomarkers (RNA, DNA)for patient selection or stratification. Qualifying trials were also matched by their corresponding phaseof clinical development and timing, with completion dates occurring in advance of the subsequent phasetransition. Phase transitions for which an associated biomarker-augmented trial could be identified wereassigned accordingly, while those that either featured trials without confirmed patient preselection, or forwhich no information could be identified, were marked as not having patient preselection biomarkers. NDA/
BLA transitions were assumed to feature patient preselection biomarker data if the Phase III transitions
qualified as such.
Drug Development Timelines. In addition to denoting whether a drug was advanced or suspended at
the end of a phase, Pharmapremia also calculates the time spent at each clinical or regulatory phase forall successful transitions. Suspended program timelines were not included to assess timelines. This is theduration between public disclosures of the initiation of appropriate stages, and as such does not map exactly
to clinical trial timings. Each clinical phase transition may encompass more than one clinical trial, which may
or may not run sequentially. Furthermore, there may be an additional delay between planning development
strategy, reporting of trial results, the decision to progress or suspend, and the public disclosure of thisinformation.
QLS Methodology. As in the case of estimating historical success rates, missing data is a significant challenge
when applying machine-learning algorithms to predicting clinical trial outcomes. Gathering and filling inthis missing data manually is expensive, time-consuming, susceptible to error, and, in many cases, simply
not possible because the missing data no longer exist. A typical solution is to discard any clinical trials or
drug-indication pathways with missing features. However, this approach eliminates a great deal of useful
information, and can lead to biased inferences in the analysis. A more elegant solution involves replacing
it with substituted values obtained through various imputation methods. One approach is a statisticaltechnique called the k-nearest neighbors algorithm, in which the missing data are replaced with the averageof the k “most similar” (i.e., closest) examples in the data set. For example, if one were replacing a square of
missing bathroom tile, the k-nearest neighbors algorithm using a k of 8 would suggest the replacement tile
should have the color average of the eight tiles surrounding the missing piece.
Once the missing information has been imputed, the QLS approach uses a proprietary machine-learningalgorithm to analyze the clinical trial data, including its additional features. While the details of the QLS model
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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)are proprietary, an example of a commonly used machine-learning algorithm is a random forest classifier.
The motivation for a random forest is to aggregate the predictions of many different decision trees, eachof which is trained over a randomized subset of variables (see figure below). While each individual tree isa weak predictor, their collective average forms a strong predictor, similar to the ‘wisdom of crowds’ that
determines the market price of a financial asset. For example, consider a specific dataset of clinical trials inwhich the successful trials are more frequently associated with big pharma companies having three or more
oncology drugs approved over the past five years, all of which were biologics rather than small molecules.
The collection of decision trees that form a random forest might recognize this pattern and predict thata small biotech company with no previous drug approvals will have a lower probability of success than acompany that more closely fit the profile described in the previous sentence.

A “forest” of decision trees. Each individual tree is a weak predictor, but similar to the wisdom of crowds, their ensemble average forms a strong predictor.

By identifying subtle patterns in a large enough database of historical drug transitions, machine-learningalgorithms can often make remarkably accurate predictions. An illustration of the components of the QLS
approach is provided below.9

Modeling methodology adopted by QLS. Abbreviations: CV=cross-validation.

We use Citeline data provided by Informa Pharma Intelligence, a superset of the most commonly used data sources in the industry. It combines individual clinical trial
information from Trialtrove and drug approval data from Pharmaprojects and Biomedtracker, in addition to incorporating multiple data streams, including nightly feeds fromofficial sources such as ClinicalTrials.gov. We restrict our analysis to drugs being developed for the U.S. market and seeking U.S. FDA approval using Pharmaprojects’ manuallycurated Drug Program Landscape data field.
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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)Appendix
Comparison of phase success for the 2016 vs. 2021 publications, ranked by 2021 phase success

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)

Contributing Authors
Biotechnology Innovation Organization (BIO): David Thomas
Informa Pharma Intelligence: Daniel Chancellor, Amanda Micklus, Sara LaFever, Michael Hay
QLS: Shomesh Chaudhuri, Robert Bowden, Andrew W. Lo

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© BIO | QLS Advisors | Informa UK Ltd 2021 (Unauthorized photocopying prohibited.)