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Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs

Published onMay 14, 2020
Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs
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Abstract

A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoSs) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 nonvaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoSs for industry-sponsored nonvaccine therapeutics are smallpox (100%), cytomegalovirus (CMV) infection (31.8%), and onychomycosis (29.8%). Non-industry-sponsored vaccine and nonvaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), Ebola, and Zika—have had a combined total of only 45 nonvaccine development programs initiated over the past two decades, and no approved therapy to date. These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be underserved by private sector engagement and in need of public sector support.

Keywords: vaccines, infectious diseases, probabilities of success, randomized clinical trials, health care finance


Media Summary

The economic value of a drug or medical device development program is typically computed by assessing the program’s cumulative revenues if successful. Therefore, the probability of success (PoS) is a key input into every major decision of every biopharmaceutical company about whether or not to undertake or continue a given program and how much resources to devote to it. And because cumulative revenues are often measured in the tens of billions of dollars, small differences in PoS estimates can lead to very large differences in estimated profitability, which, in turn, can lead to very different investment decisions and funding levels. Therefore, having timely measures of PoS that are as accurate as the data will allow is a prerequisite for managing biopharma assets. These issues are particularly relevant for deciding among the many responses to the COVID-19 pandemic currently being contemplated by all biomedical stakeholders.

In this article, we provide PoS estimates of clinical trial outcomes for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 nonvaccine programs targeting infectious diseases, the largest data set of its kind.

The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoSs for industry-sponsored nonvaccine therapeutics are smallpox (100%), cytomegalovirus (CMV) infection (31.8%), and onychomycosis (29.8%). Non-industry-sponsored vaccine and nonvaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), Ebola, and Zika—have had a combined total of only 45 nonvaccine development programs initiated over the past two decades, and no approved therapy to date.

As governments around the world begin to formulate a more systematic strategy for dealing with pandemics beyond COVID-19, these estimates can be used by policymakers to identify areas most likely to be underserved by private sector engagement and in need of public sector support.



1. Introduction

In this article, we provide estimates of the historical probabilities of success (PoSs) of clinical trials for vaccines and other therapeutic drugs for infectious diseases to inform discussions on the planning and financing of the fight against one of humanity’s oldest foes. This is of particular importance in light of the recent havoc wreaked by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease (COVID-19).

While the PoSs of therapeutic drugs for various disease groups like oncology are well-documented (Abrantes-Metz et al., 2004; DiMasi et al., 2010; Hay et al., 2014; MIT Laboratory for Financial Engineering, 2020; Smietana et al., 2016; Thomas et al., 2016; Wong et al., 2019a, 2019b), relatively little has been published on treatments for infectious diseases and vaccine development despite their importance (Davis et al., 2011; Pronker et al., 2013). Prior studies have focused on narrower subsets relevant to their specific interests and have relied on much more limited data sets. For example, Young et al. (2020) employed 10 to 25 data points per estimated value from the Bill and Melinda Gates Foundation to estimate the PoS of vaccines for neglected diseases, and DiMasi et al. (2020) reported PoS estimates on a per-drug basis using 2,575 trials for diseases of interest to the Gates Foundation. In contrast, we employ a much larger and broader data set of 16,328 unique clinical trials to estimate the PoSs of vaccines and nonvaccine therapeutics targeting 29 different infectious diseases using all available drug-indication pairs—a methodology that has been argued to be more relevant for evaluating drug development R&D risk and productivity (Wong et al., 2019b).

Vaccination is commonly recognized as one of the most cost-effective public health measures for combatting infectious diseases (André, 2002; Ehreth, 2003; Kieny & Girard, 2005; Organisation for Economic Co-operation and Development [OECD], 2013; Pronker et al., 2013; Rémy et al., 2015). In developed countries, routine prophylactic vaccination and effective treatment options have led to the control or complete elimination of several deadly infectious diseases through individual and herd immunity, preventing millions of deaths and untold suffering each year. This prophylaxis dramatically reduces the burden on the health care system and society as a whole. In addition, the deaths, hospitalizations, and treatment costs avoided by these measures have led to significant economic savings (Ehreth, 2003; Rémy et al., 2015; U.S. Department of Health and Human Services, 2017).

As technology continues to advance, one expects that the human species will be better able to cope with these diseases. The fact remains, however, that we still do not have effective treatments or vaccines for many infectious diseases. While the discovery of antibiotics has reduced the mortality rate of bacterial infection, and the development of the smallpox vaccine has led to the eradication of the devastating disease (World Health Organization, 1980), other infectious diseases, such as acquired immunodeficiency syndrome (AIDS) and malaria, still take the lives of tens of millions every year. According to the World Health Organization, there are currently only 26 infectious diseases that are preventable by available vaccines (World Health Organization, 2020).

By developing better risk measures for therapeutic development, we hope to facilitate greater investment, identification of underserved areas that require public sector support, and more efficient business and financing models in this critical field.

2. Methods

We apply the method of Wong et al. (2019b) to estimate the PoSs of drug development programs using historical clinical trial data. This method was also applied in Wong et al. (2019a) to investigate the clinical success rates of oncology development programs. We briefly describe this method, with parts reproduced from the aforementioned articles for expositional convenience.

A drug development program, also known as a drug development path, is the clinical investigation of the use of a drug for a disease. It typically consists of a sequence of clinical trials, separated into three phases. Phase 1 trials test mainly the safety and tolerance of a drug while phase 2 trials test the efficacy of the drug for a given indication. Phase 3 trials attempt to confirm the drug’s efficacy for larger populations and against alternatives. Some trials involve the combination of two phases into a single protocol, denoted `Phase 1/2' (a combination of Phases 1 and 2) and `Phase 2/3' (a combination of Phases 2 and 3).

We say that a drug development program has reached phase i if it is observed, or can be inferred, that there is at least one trial in phase i. It is possible that a clinical trial can be repeated in multiple development paths. In Figure 1, we show an example in which a single phase 1 trial for a drug is involved in four different development paths, each targeting a different disease. It is not uncommon that the result of the phase 1 trial is used as supporting evidence for the safe use of a drug, allowing that drug to be used for different indications without additional phase 1 testing. For example, hydroxychloroquine—already approved for the treatment of malaria—is being tested for effectiveness against COVID-19 without another phase 1 clinical trial. There also exist clinical trials where different drug combinations are tested for the same indication in different arms. Because of these multiplicities, computing PoSs cannot be done simply by dividing the number of phase i+1 trials by the number of phase i trials for the same drug-indication pair—we need to identify specific drug development paths.


<p><strong>Figure</strong> <strong>1. We define a drug development path as the development of a drug for a specific indication.</strong> A single clinical trial can belong to multiple drug development programs. We illustrate a hypothetical example where four drug development paths, all using the same drug, share the same phase 1 clinical trial.</p>

Figure 1. We define a drug development path as the development of a drug for a specific indication. A single clinical trial can belong to multiple drug development programs. We illustrate a hypothetical example where four drug development paths, all using the same drug, share the same phase 1 clinical trial.


Specifically, we make the assumption that each program must transition from phase 1 to phase 2 to phase 3 to approval, in this order, and model the possible states in a drug development program as a Markov chain, shown in Figure 2.


<p><strong>Figure</strong> <strong>2. Observed and unobserved states of a drug development program, from phase 1 to approval.</strong> A drug development program is in phase i if it has at least one trial in phase i. The “missing” states represent phases where we do not observe any clinical trial in that phase for the drug-indication pair, but where we know it must have occurred. Every drug development path in our study must start from phase 1 (or “missing” phase 1) and end up in one of the nodes labeled as “in progress,” “terminated,” or “approval.”</p>

Figure 2. Observed and unobserved states of a drug development program, from phase 1 to approval. A drug development program is in phase i if it has at least one trial in phase i. The “missing” states represent phases where we do not observe any clinical trial in that phase for the drug-indication pair, but where we know it must have occurred. Every drug development path in our study must start from phase 1 (or “missing” phase 1) and end up in one of the nodes labeled as “in progress,” “terminated,” or “approval.”


We infer missing transitions in the development paths arising from incomplete records. This is plausible since each of these stages involves distinct predefined tests, all of which are required by regulators in any new drug application (NDA). If we observe data for phases 1 and 3 but not phase 2 for a given drug-indication pair, our idealized process implies that there was at least one phase 2 trial that occurred, but is missing from our data set. Accordingly, we impute the successful completion of phase 2 in these cases. There exist some rare cases where phase 2 trials are skipped, as with the example of aducanumab (BIIB037), Biogen’s Alzheimer’s candidate, as reported by Root (2014). Since skipping phase 2 trials is motivated by compelling phase 1 data and is approved by the regulatory authorities, imputing the successful completion of phase 2 trials in these cases to trace drug development paths is a reasonable approximation. We make the standard assumption that phase 1/2 and phase 2/3 trials are to be considered as phase 2 and phase 3 trials, respectively.

We call the estimated probability of a drug development program transitioning from phase i to phase i + 1 the “phase i PoS,” and the “estimated overall PoS” is defined as the estimated probability of a drug development program going from phase 1 to regulatory approval in at least one country. To simplify terminology, we will henceforth omit the qualifier ‘estimated’ when referring to the PoS, so it should be understood that all PoS values reported in this article are statistical estimates of unobservable population parameters.

The probability of a drug development program transitioning from phase i to phase j (PoSij) can be computed using the simple ratio Nj/Ni, where Ni is the number of drug development programs that have reached phase i (where i = 1, 2, or 3) of the drug development process and are not in active development between phase i and phase j (where j = 2, 3, or “A,” which denotes regulatory approval, i < j), and Nj is the number of drug development programs among the former that made it to phase j. PoS1A is also known as the “overall PoS.” We provide simple numerical examples in the Appendix (Section A1) to clarify our algorithms.

The estimated probability of a drug development program transitioning from phase 1 to approval—estimated directly using the method described above—is called the ‘path-by-path’ estimate of the overall PoS, and is reported for all PoS calculations. Our method of inferring unobserved states and computing the PoS using the simple ratio defined above applies to both vaccines and nonvaccine therapeutics. In fact, since it is common for vaccine candidates to skip phase 1 and move directly to phase 2 or 3 based on initial safety of the vaccine base (e.g., egg, etc.) after changing the virus within it, filling in unobserved phases will lead to a more accurate PoS.

It should be emphasized that because of the treatment of in-progress drug development programs, path-by-path PoS estimates are not multiplicative, that is, PoS12×PoS23×PoS3APoS1A\text{PoS}_{12} \times \mathrm{\text{PoS}}_{23} \times \mathrm{\text{PoS}}_{3\mathrm{A}} \neq \mathrm{\text{PoS}}_{1\mathrm{A}}. In contrast, the phase-by-phase estimates used in prior studies (DiMasi et al., 2010, 2020; Hay et al., 2014; Thomas et al., 2016) do multiply, that is, PoS12×PoS23×PoS3A=PoS1A\text{PoS}_{12} \times \mathrm{\text{PoS}}_{23} \times \mathrm{\text{PoS}}_{3\mathrm{A}} = \mathrm{\text{PoS}}_{1\mathrm{A}}. The latter two studies do not fill in unobserved clinical development phases. We elaborate on the differences between the path-by-path and phase-by-phase methods in the Appendix (see Section A2).

We compute the path-by-path PoSs using an algorithm that recursively considers all possible drug-indication pairs and determines the maximum observed phase. As the Markov chain model implies, reaching phase i would imply that all prior phases were completed. To determine if a drug development program has been terminated in the last observed phase or is still ongoing, we use a simple heuristic: If the time elapsed between the end date of the most recent phase i and the end of our sample exceeds a certain threshold ti, we conclude that the trial has terminated. Based on practical considerations, we set ti, to be 360, 540, and 900 days for phases 1, 2, and 3, respectively. For example, we assume that it takes approximately 6 months to prepare documents for an NDA filing after a phase 3 trial has been completed. Since the U.S. Food and Drug Administration (FDA) has a 6-month period to decide if it wishes to follow up on a filing, and an additional 18 months to deliver a verdict, this places the overall time between phase 3 and approval to about 30 months, hence we set t3 = 900 days. Based on these criteria, we will consider only drug development programs that have seen at least one trial with a definite outcome in the PoS computations. More detailed exposition of and pseudocode for this algorithm can be found in Wong et al. (2019b).

3. Data

We extracted clinical trials metadata from the January 7, 2020, snapshots of Citeline’s PharmaProjects and TrialTrove databases, provided by Informa Pharma Intelligence. These data are widely available commercially as well as through an academic license. Clinical trial metadata was retrieved from the TrialTrove database while the approval data was obtained from the PharmaProjects database, both of which are required to identify the drug development programs. In addition to incorporating multiple data streams, including nightly feeds from official sources such as ClinicalTrials.gov, Citeline contains data from primary sources such as institutional press releases, financial reports, study reports, drug marketing label applications, and secondary sources such as analyst reports by consulting companies. Secondary sources are particularly important for reducing potential biases that may arise from the tendency of organizations to report only successful trials, especially those prior to the FDA Amendments Act of 2007, which requires all clinical trials conducted in the U.S. to be registered and tracked via ClinicalTrials.gov. The databases we use contain information from both U.S. and non-U.S. sources. We consider a drug approved if it is approved in any country. All clinical trials used in this analysis have end dates after January 1, 2000, and start dates before January 7, 2020.

We filter our data to include only trials that have been tagged by Citeline as being in the “Infectious Disease” or “Vaccines (Infectious Diseases)” therapeutic areas. The vaccine types and diseases are provided by the databases. The database encodes each unique triplet of trial identification number, drug, and disease as a datapoint. Therefore, a single trial may appear as multiple datapoints. Since the two therapeutic areas may overlap in datapoints, we define clinical trials that are involved in any vaccine development as part of a vaccine development program. In addition, we process the data such that more specific diseases (e.g., rabies) can be identified instead of broad vaccine classes (e.g., vector borne disease vaccines). Clinical trials that are not involved in any vaccine development program will be deemed to be part of a nonvaccine drug development program. We derive 43,414 datapoints in total. We define an industry-sponsored development program as one where there is at least one commercial company involved in any stage of clinical development. The complement—in which there is no commercial company involved in any stage of the vaccine or drug development program—shall be referred to as non-industry-sponsored. Given these definitions, a drug or vaccine development program (and the clinical trials in the program) can belong to only one of these mutually exclusive sets: industry-sponsored vaccines, industry-sponsored nonvaccine therapeutics, non-industry-sponsored vaccines, and non-industry-sponsored nonvaccine therapeutics.

The vaccines in TrialTrove are identified by broad categories such as “respiratory vaccines,” “other viral vaccines,” or “hepatitis vaccines.” We attempt to infer the diseases targeted by the vaccines by cross-referencing the disease tags for each clinical trial. For example, a clinical trial may be tagged with both “hepatitis vaccines” and “HBV,” allowing us to conclude that the vaccine is indicated for HBV (hepatitis B virus). Those clinical trials that have only vaccine tags will have their disease labeled as “others.” Manual inspection of some of the clinical trial titles shows that this category includes diseases such as measles and tuberculosis.

We plot the number of development programs known to start in each month from January 2000 through December 2019 in Figure 3. There are 1,838 and 706 industry-sponsored and non-industry-sponsored vaccine development programs, respectively, and, 3,851 and 2,978 industry-sponsored and non-industry-sponsored nonvaccine drug development programs targeting infectious diseases, respectively. As can be seen in Figure 3A, the number of industry-sponsored clinical programs attempting to treat infectious diseases is often greater than the number of vaccine development programs. We see a precipitous fall in the number of infectious disease treatment development programs initiated between late 2018 and mid-2019, which is likely related to declining investment in the research and development (R&D) of novel antibiotics, precipitated by the closure of antibiotics biotechnology firms and the withdrawal of pharmaceutical companies from the antibiotics business (Hu, 2018; Langreth, 2019).

Between January 2000 and June 2011, the number of non-industry-sponsored vaccine development programs initiated is on par with the number of non-industry-sponsored, nonvaccine anti-infective drug development programs initiated (see Figure 3B). However, the number of nonvaccine drug development programs initiated begins to outpace the number of vaccine development programs after January 2012, and such programs experience a rapid boom between mid-2015 and mid-2018 before declining rapidly between October 2018 and January 2019.


<p>Figure 3A</p>

Figure 3A

<p>Figure 3B</p>

Figure 3B

Figure 3. The number of development programs initiated per month from January 2000 through December 2019 in the areas of vaccine and nonvaccine treatment for infectious diseases (thin, light colored lines). The darker, thicker lines represent the 6-month moving average of the individual series. A. Number of industry-sponsored development programs initiated. B. Number of non-industry-sponsored development programs initiated.


4. Results

4.1 Vaccines

Overall, 2,544 vaccine development programs are observed in our data set, of which 1,838 are sponsored by industry and 706 do not involve any industry sponsor in any stage of development. For industry-sponsored drug development programs, respiratory infections is the most actively researched vaccine category, comprising 34.8% (n = 640) of all vaccine development programs (see Figure 4). HBV and human immunodeficiency virus (HIV) vaccines represent 11.6% (n = 213) and 9.8% (n = 181) of all vaccine development programs, respectively, whereas intra-abdominal infections, monkeypox, and severe acute respiratory syndrome (SARS) vaccines are the least researched fields, with only one development path observed per disease.

A similar pattern can be seen for the non-industry-sponsored vaccine development programs; excluding the others category, the top three most researched vaccine categories are also respiratory infections (24.8%), HIV (20.4%), and HBV (8.2%), whereas Middle East respiratory syndrome (MERS) and SARS are the least researched diseases with one development program each.


<p>Figure 4A</p>

Figure 4A

<p>Figure 4B</p>

Figure 4B

Figure 4. Number of vaccine development programs observed for each vaccine type. A. Number of industry-sponsored vaccine development programs. B. Number of non-industry-sponsored vaccine development programs.


From Table 1, we can see that the overall PoS for industry-sponsored vaccine development programs is 39.6% (SE = 1.2%), which is substantially higher than the average overall PoS of 11.0% (SE = 0.2%) across all industry-sponsored drug development programs (see Table A2 in the Appendix). These findings are largely in line with the results of Wong et al. (2019b), who first observed this trend, and of DiMasi et al. (2020), despite the fact that the latter computed their estimates using a different method (a phase-by-phase approach) and considered only lead indications. We estimate PoS12, PoS23, and PoS3A to be 82.5% (SE = 0.9%), 65.4% (SE = 1.3%), and 80.1% (SE = 1.4%), respectively.

Across all industry-sponsored vaccine development programs, we can see that monkeypox vaccines have had the most developmental success, followed by rotavirus and Japanese encephalitis vaccines (see Table 1). Their overall success rates are 100% (SE = 0.0%), 78.7% (SE = 5.2%), and 67.6% (SE = 8.0%), respectively. The overall PoS for monkeypox is based on only one sample. Only 12 diseases out of the 27 disease categories with at least one development path observed have seen at least one approved vaccine.


Table 1. The Probabilities of Success (PoSs) of Industry-Sponsored Vaccine Drug Development Programs.1

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Bacterial Skin Infection

12

83.3

10.8

7

14.3

13.2

0.0

0.0

1

0.0

0.0

9

0.0

0.0

Chikungunya

6

83.3

15.2

0

-

-

-

-

0

-

-

1

0.0

0.0

Clostridium difficile

6

100.0

0.0

6

33.3

19.2

0.0

0.0

0

-

-

4

0.0

0.0

Cytomegalovirus (CMV) Infection

14

57.1

13.2

3

33.3

27.2

0.0

0.0

0

-

-

8

0.0

0.0

Ebola

13

53.8

13.8

7

57.1

18.7

28.6

20.2

2

100.0

0.0

11

18.2

11.6

Hepatitis B Virus (HBV)

213

94.8

1.5

187

74.9

3.2

54.5

3.7

132

77.3

3.6

190

53.7

3.6

Hepatitis C Virus (HCV)

27

70.4

8.8

15

0.0

0.0

0.0

0.0

0

-

-

23

0.0

0.0

Human Immunodeficiency Virus (HIV)

181

65.2

3.5

95

36.8

4.9

0.0

0.0

21

0.0

0.0

144

0.0

0.0

Human Papillomavirus (HPV)

120

88.3

2.9

69

52.2

6.0

36.2

6.1

30

83.3

6.8

77

32.5

5.3

Intra-abdominal Infections

1

100.0

0.0

1

100.0

0.0

0.0

0.0

1

0.0

0.0

1

0.0

0.0

Japanese Encephalitis

35

100.0

0.0

35

71.4

7.6

65.7

8.1

24

95.8

4.1

34

67.6

8.0

Marburg

3

0.0

0.0

0

-

-

-

-

0

-

-

3

0.0

0.0

Middle East Respiratory Syndrome (MERS)

4

50.0

25.0

0

-

-

-

-

0

-

-

2

0.0

0.0

Monkeypox

1

100.0

0.0

1

100.0

0.0

100.0

0.0

1

100.0

0.0

1

100.0

0.0

Norovirus

6

100.0

0.0

5

0.0

0.0

0.0

0.0

0

-

-

5

0.0

0.0

Otitis Media

23

95.7

4.3

22

81.8

8.2

45.5

10.6

18

55.6

11.7

23

43.5

10.3

Rabies

47

91.5

4.1

40

87.5

5.2

65.0

8.1

30

86.7

6.2

39

66.7

7.5

Respiratory Infections

640

79.1

1.6

465

66.9

2.2

50.1

2.4

287

81.2

2.3

575

40.5

2.0

Rotavirus

72

97.2

1.9

70

91.4

3.3

68.6

6.0

53

90.6

4.0

61

78.7

5.2

Sepsis

13

38.5

13.5

5

80.0

17.9

0.0

0.0

4

0.0

0.0

13

0.0

0.0

Severe Acute Respiratory Syndrome (SARS)

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Smallpox

11

81.8

11.6

8

62.5

17.1

50.0

17.7

5

80.0

17.9

10

40.0

15.5

Urinary Tract Infections

3

100.0

0.0

3

100.0

0.0

0.0

0.0

1

0.0

0.0

1

0.0

0.0

West Nile Virus (WNV)

4

25.0

21.7

1

100.0

0.0

0.0

0.0

1

0.0

0.0

4

0.0

0.0

Yellow Fever

30

90.0

5.5

26

73.1

8.7

57.7

10.5

15

100.0

0.0

25

60.0

9.8

Zika

2

0.0

0.0

0

-

-

-

-

0

-

-

2

0.0

0.0

Others

350

87.1

1.8

268

63.4

2.9

47.0

3.2

142

88.7

2.7

285

44.2

2.9

Total

1,838

82.5

0.9

1,339

65.4

1.3

45.9

1.4

768

80.1

1.4

1552

39.6

1.2

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


In contrast, non-industry-sponsored vaccine development programs have an overall PoS of only 6.8% (SE = 1.0%), with PoS12, PoS23, and PoS3A estimates of 63.3% (SE = 1.8%), 37.3% (SE = 2.6%), and 39.8% (SE = 4.9%), respectively (Table 2). The top three indications with the highest overall success rates for non-industry-sponsored drug development programs are otitis media (28.6%, SE = 17.1%), rabies (25.0%, SE = 10.8%), and Japanese encephalitis (25.0%, SE = 21.7%). The latter estimates are derived from only a handful of samples and must be interpreted with caution as their large standard errors suggest.


Table 2. The Probabilities of Success (PoSs) of Non-Industry-Sponsored Vaccine Development Programs.2

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Bacterial Skin Infection

3

100.0

0.0

0

-

-

-

-

0

-

-

0

0.0

0.0

Clostridium difficile

3

66.7

27.2

0

-

-

-

-

0

-

-

1

0.0

0.0

Cytomegalovirus (CMV) Infection

14

50.0

13.4

5

40.0

21.9

40.0

21.9

2

100.0

0.0

12

16.7

10.8

Ebola

8

12.5

11.7

0

-

-

-

-

0

-

-

7

0.0

0.0

Hepatitis B Virus (HBV)

58

91.4

3.7

48

47.9

7.2

8.3

4.3

16

25.0

10.8

46

8.7

4.2

Hepatitis C Virus (HCV)

10

70.0

14.5

5

0.0

0.0

0.0

0.0

0

-

-

8

0.0

0.0

Human Immunodeficiency Virus (HIV)

144

48.6

4.2

62

3.2

2.2

0.0

0.0

2

0.0

0.0

136

0.0

0.0

Human Papillomavirus (HPV)

32

87.5

5.8

16

56.3

12.4

6.3

6.5

7

14.3

13.2

18

5.6

5.4

Intra-abdominal Infections

3

100.0

0.0

0

-

-

-

-

0

-

-

0

0.0

0.0

Japanese Encephalitis

4

100.0

0.0

4

100.0

0.0

25.0

21.7

4

25.0

21.7

4

25.0

21.7

Middle East Respiratory Syndrome (MERS)

1

100.0

0.0

0

-

-

-

-

0

-

-

0

0.0

0.0

Otitis Media

7

100.0

0.0

7

28.6

17.1

28.6

17.1

2

100.0

0.0

7

28.6

17.1

Rabies

16

81.3

9.8

13

53.8

13.8

30.8

12.8

7

57.1

18.7

16

25.0

10.8

Respiratory Infections

175

66.9

3.6

101

51.5

5.0

16.8

3.9

41

41.5

7.7

148

11.5

2.6

Rotavirus

5

80.0

17.9

4

50.0

25.0

0.0

0.0

1

0.0

0.0

4

0.0

0.0

Sepsis

7

42.9

18.7

2

0.0

0.0

0.0

0.0

0

-

-

6

0.0

0.0

Severe Acute Respiratory Syndrome (SARS)

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Smallpox

11

63.6

14.5

6

16.7

15.2

16.7

15.2

1

100.0

0.0

10

10.0

9.5

Urinary Tract Infections

3

100.0

0.0

1

0.0

0.0

0.0

0.0

0

-

-

1

0.0

0.0

West Nile Virus (WNV)

4

0.0

0.0

0

-

-

-

-

0

-

-

4

0.0

0.0

Yellow Fever

9

66.7

15.7

6

33.3

19.2

0.0

0.0

1

0.0

0.0

8

0.0

0.0

Zika

5

40.0

21.9

0

-

-

-

-

0

-

-

3

0.0

0.0

Others

183

57.9

3.6

71

35.2

5.7

9.9

3.8

14

50.0

13.4

137

5.1

1.9

Total

706

63.3

1.8

351

37.3

2.6

11.1

1.8

98

39.8

4.9

577

6.8

1.0

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


4.2 Nonvaccine Anti-Infective Therapeutics

In contrast to vaccines, which are intended to prevent disease, a number of alternatives have been developed to treat—and, in some cases, cure—patients afflicted with an infectious disease. According to our data set, 3,851 and 2,978 industry-sponsored and non-industry-sponsored nonvaccine drug development programs, respectively, have been initiated in the area of infectious disease (see Figure 5). The top three diseases with the greatest number of industry-sponsored drug development programs are respiratory infections (21.8%), HIV (15.7%) and hepatitis C virus, or HCV (14.1%). Together, they comprise 51.6% of all industry-sponsored nonvaccine development programs. Non-industry anti-infectious-disease drug development programs focus on treating respiratory infections (20.5%), HIV (13.9%), and bacterial skin infection (12.1%).

With respect to addressing the most recent virus outbreaks—MERS, SARS, Ebola, and Zika—a total of nine industry-sponsored and 36 non-industry-sponsored nonvaccine drug development programs were initiated over the past 20 years, and there have been no approved therapies to date.


<p>Figure 5A</p>

Figure 5A

<p>Figure 5B</p>

Figure 5B

Figure 5. Number of nonvaccine drug development programs for each disease in the “Infectious Disease” category. A. Number of industry-sponsored, nonvaccine drug development programs. B. Number of non-industry-sponsored, nonvaccine drug development programs.


From Table 3, we can see that the overall PoS across all industry-sponsored drug development programs treating infectious diseases is 16.3% (SE = 0.7%). The PoS12, PoS23, and PoS3A are 65.0% (SE = 0.8%), 64.3% (SE = 1.0%), and 51.1% (SE = 1.6%), respectively. Based on our data, the highest success rates for industry-sponsored nonvaccine development programs have been for smallpox (100.0%, SE = 0.0%), cytomegalovirus (CMV) infection (31.8%, SE = 7.0%), and onychomycosis (29.8%, SE = 6.7%). There are currently no nonvaccine therapies approved for rotavirus, SARS, rabies, Ebola, West Nile virus, Marburg, yellow fever, chikungunya, MERS, monkeypox, or norovirus. With the exception of norovirus and MERS, these diseases without any vaccine are predominantly prevalent in nonindustrialized nations, and thus represent neglected diseases. It is also interesting that for the latter eight diseases, even the PoS12 is low. Since phase 1 trials in the development of anti-infective therapies focus primarily on safety, understanding the pharmacokinetics of the compound, and maximum tolerable dose levels, it can be inferred that the drugs tested are either of high toxicity or lack the necessary characteristics required for optimal absorption, distribution, metabolism, and excretion (ODME), or perhaps failed to advance due to financial constraints.


Table 3. The Probabilities of Success (PoSs) of Industry-Sponsored, Nonvaccine Anti-Infective Drug Development Programs for the Treatment of Infectious Diseases.3

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Bacterial Skin Infection

406

54.9

2.5

207

72.9

3.1

19.8

3.2

104

39.4

4.8

343

12.0

1.8

Chikungunya

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Clostridium difficile

91

83.5

3.9

66

53.0

6.1

4.5

2.8

25

12.0

6.5

71

4.2

2.4

Cytomegalovirus (CMV) Infection

64

87.5

4.1

43

60.5

7.5

32.6

7.8

19

73.7

10.1

44

31.8

7.0

Ebola

7

28.6

17.1

1

0.0

0.0

0.0

0.0

0

-

-

6

0.0

0.0

Hepatitis B Virus (HBV)

186

77.4

3.1

105

68.6

4.5

36.2

5.2

54

70.4

6.2

129

29.5

4.0

Hepatitis C Virus (HCV)

542

68.8

2.0

348

52.3

2.7

23.6

2.4

155

52.9

4.0

490

16.7

1.7

Human Immunodeficiency Virus (HIV)

604

63.2

2.0

326

59.8

2.7

39.3

2.8

167

76.6

3.3

520

24.6

1.9

Human Papillomavirus (HPV)

63

85.7

4.4

34

23.5

7.3

11.8

5.7

6

66.7

19.2

41

9.8

4.6

Intra-abdominal Infections

182

68.7

3.4

113

72.6

4.2

2.7

2.0

35

8.6

4.7

123

2.4

1.4

Marburg

3

0.0

0.0

0

-

-

-

-

0

-

-

3

0.0

0.0

Middle East Respiratory Syndrome (MERS)

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Monkeypox

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Non-tuberculous Mycobacteria (NTM) Infection

23

87.0

7.0

16

62.5

12.1

6.3

7.7

4

25.0

21.7

13

7.7

7.4

Norovirus

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Onychomycosis

60

85.0

4.6

44

63.6

7.3

31.8

7.6

22

63.6

10.3

47

29.8

6.7

Otitis Media

152

48.0

4.1

68

80.9

4.8

51.5

6.2

51

68.6

6.5

143

24.5

3.6

Rabies

4

75.0

21.7

0

-

-

-

-

0

-

-

1

0.0

0.0

Respiratory Infections

841

64.2

1.7

476

70.0

2.1

22.9

2.2

222

49.1

3.4

666

16.4

1.4

Rotavirus

2

100.0

0.0

2

0.0

0.0

0.0

0.0

0

-

-

2

0.0

0.0

Sepsis

334

66.8

2.6

206

64.6

3.3

10.2

2.4

81

25.9

4.9

265

7.9

1.7

Severe Acute Respiratory Syndrome (SARS)

1

100.0

0.0

1

0.0

0.0

0.0

0.0

0

-

-

1

0.0

0.0

Smallpox

2

100.0

0.0

2

100.0

0.0

100.0

0.0

2

100.0

0.0

2

100.0

0.0

Urinary Tract Infections

276

55.1

3.0

143

72.0

3.8

10.5

3.2

51

29.4

6.4

215

7.0

1.7

West Nile Virus (WNV)

2

50.0

35.4

1

0.0

0.0

0.0

0.0

0

-

-

2

0.0

0.0

Yellow Fever

2

0.0

0.0

0

-

-

-

-

0

-

-

2

0.0

0.0

Total

3,851

65.0

0.8

2,202

64.3

1.0

23.2

1.0

998

51.1

1.6

3,133

16.3

0.7

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


For non-industry-sponsored nonvaccine development programs, the overall PoS is 8.2% (SE = 0.6%) while PoS12, PoS23, and PoS3A are 61.0% (SE = 0.9%), 65.2% (SE = 1.2%), and 30.0% (SE = 1.8%), respectively (see Table 4). The top three indications with the highest overall success rates for non-industry-sponsored nonvaccine development programs are CMV infection (23.5%, SE = 5.9%), clostridium difficile (20.5%, SE = 6.5%), and sepsis (17.4%, SE = 2.6%).


Table 4. The Probabilities of Success (PoSs) of Non-Industry-Sponsored, Nonvaccine Anti-Infective Drug Development Programs.4

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Bacterial Skin Infection

360

46.4

2.6

151

81.5

3.2

19.2

3.7

85

34.1

5.1

306

9.5

1.7

Chikungunya

2

100.0

0.0

2

50.0

35.4

0.0

0.0

1

0.0

0.0

2

0.0

0.0

Clostridium difficile

83

94.0

2.6

51

76.5

5.9

15.7

6.2

22

36.4

10.3

39

20.5

6.5

Cytomegalovirus (CMV) Infection

77

83.1

4.3

51

51.0

7.0

23.5

6.9

13

92.3

7.4

51

23.5

5.9

Ebola

30

96.7

3.3

28

14.3

6.6

0.0

0.0

2

0.0

0.0

27

0.0

0.0

Hepatitis B Virus (HBV)

171

49.1

3.8

73

47.9

5.8

1.4

1.4

31

3.2

3.2

156

0.6

0.6

Hepatits C Virus (HCV)

139

84.2

3.1

112

43.8

4.7

8.9

2.9

33

30.3

8.0

118

8.5

2.6

Human Immunodeficiency Virus (HIV)

414

61.1

2.4

195

49.2

3.6

13.3

2.6

75

34.7

5.5

335

7.8

1.5

Human Papillomavirus (HPV)

71

88.7

3.8

42

42.9

7.6

2.4

2.7

8

12.5

11.7

40

2.5

2.5

Intra-abdominal Infections

189

66.1

3.4

112

76.8

4.0

12.5

3.8

51

27.5

6.2

141

9.9

2.5

Japanese Encephalitis

2

100.0

0.0

2

0.0

0.0

0.0

0.0

0

-

-

2

0.0

0.0

Middle East Respiratory Syndrome (MERS)

3

100.0

0.0

3

66.7

27.2

0.0

0.0

0

-

-

1

0.0

0.0

Non-tuberculous Mycobacteria (NTM) Infection

13

84.6

10.0

9

44.4

16.6

11.1

11.9

2

50.0

35.4

9

11.1

10.5

Norovirus

2

100.0

0.0

1

0.0

0.0

0.0

0.0

0

-

-

1

0.0

0.0

Onychomycosis

20

75.0

9.7

15

66.7

12.2

0.0

0.0

6

0.0

0.0

16

0.0

0.0

Otitis Media

186

30.1

3.4

53

56.6

6.8

7.5

3.9

24

16.7

7.6

177

2.3

1.1

Rabies

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Respiratory Infections

610

58.5

2.0

323

72.8

2.5

11.5

2.1

141

26.2

3.7

482

7.7

1.2

Sepsis

310

80.0

2.3

227

77.5

2.8

15.9

3.0

94

38.3

5.0

207

17.4

2.6

Severe Acute Respiratory Syndrome (SARS)

3

100.0

0.0

3

100.0

0.0

0.0

0.0

2

0.0

0.0

2

0.0

0.0

Urinary Tract Infections

291

46.7

2.9

126

73.8

3.9

10.3

3.4

49

26.5

6.3

237

5.5

1.5

West Nile Virus (WNV)

1

100.0

0.0

1

0.0

0.0

0.0

0.0

0

-

-

1

0.0

0.0

Total

2,978

61.0

0.9

1,580

65.2

1.2

12.2

0.9

639

30.0

1.8

2,351

8.2

0.6

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.

5. Industry-Sponsored Development Programs

In an attempt to shed more light on the industry-sponsored vaccine and nonvaccine drug development programs, we classify the diseases by their biological family and transmission type. The classifications are presented in Table A1 in the Appendix. We then compute PoSs using these classifications.

Looking at the vaccine PoSs by transmission route (see Table 5), we see that vaccines for diseases transmitted through animal bites have the highest overall PoS (61.3%, SE = 4.7%), whereas no vaccine has been developed for diseases transmitted through contaminated food or water.


Table 5. The Probabilities of Success (PoSs) of Industry-Sponsored Vaccine Development Programs, Grouped by Transmission Route.5

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Animal bites

125

89.6

2.7

103

78.6

4.0

63.1

5.0

71

91.5

3.3

106

61.3

4.7

Contaminated food or water

6

100.0

0.0

5

0.0

0.0

0.0

0.0

0

-

-

5

0.0

0.0

Human-human (Others)

643

82.4

1.5

446

62.8

2.3

39.7

2.4

238

74.4

2.8

517

34.2

2.1

Human-human (Airborne)

16

68.8

11.6

8

62.5

17.1

50.0

17.7

5

80.0

17.9

13

30.8

12.8

Multiple or others

1,048

81.9

1.2

777

65.6

1.7

47.5

1.9

454

81.3

1.8

911

40.5

1.6

Total

1,838

82.5

0.9

1,339

65.4

1.3

45.9

1.4

768

80.1

1.4

1,552

39.6

1.2

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


We find that companies have been most successful in developing nonvaccine treatments for diseases transmitted between humans through the air, with 50.0% (SE = 25.0%) of all drug development programs making it from phase 1 to regulatory approval (see Table 6). Unfortunately, this is based on only four drug development programs and may not be indicative of the general trend. Treatments for diseases that transmit through “human to human (others)” have an overall PoS of 21.5% (SE = 1.2%) while no approval is observed for diseases transmitted through animal bites or contaminated food or water.


Table 6. The Probabilities of Success (PoSs) of Industry-Sponsored, Nonvaccine Anti-Infective Drug Development Programs, Grouped by Transmission Route.6

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Animal bites

10

40.0

15.5

1

0.0

0.0

0.0

0.0

0

-

-

7

0.0

0.0

Contaminated food or water

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Human-human (Others)

1,471

68.9

1.2

859

56.2

1.7

31.0

1.7

401

66.3

2.4

1,235

21.5

1.2

Human-human (Airborne)

4

75.0

21.7

3

66.7

27.2

66.7

27.2

2

100.0

0.0

4

50.0

25.0

Multiple or others

2,365

62.7

1.0

1,339

69.5

1.3

18.1

1.2

595

40.7

2.0

1,886

12.8

0.8

Total

3,851

65.0

0.8

2,202

64.3

1.0

23.2

1.0

998

51.1

1.6

3,133

16.3

0.7

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


When we classify the vaccines by the biological family of the infectious agent (Table 7), we see that reoviridae (e.g., rotavirus), rhabdoviridae (e.g., rabies), and hepadnaviridae (e.g., HBV) are the three biological families with the highest overall PoSs for vaccines at 78.7%, (SE = 5.2%), 66.7% (SE = 7.5%), and 53.7% (SE = 3.6%), respectively. We have yet to see a vaccine for diseases caused by agents in the biological families of retroviridae (e.g., HIV), caliciviridae (e.g., norovirus), clostridiaceae (e.g., clostridium difficile), coronaviridae (e.g., SARS, MERS), herpesviridae (e.g., CMV infection), or togaviridae (e.g., chikungunya).


Table 7. The Probabilities of Success (PoSs) of Industry-Sponsored Vaccine Development Programs, Grouped by Biological Family.7

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Caliciviridae

6

100.0

0.0

5

0.0

0.0

0.0

0.0

0

-

-

5

0.0

0.0

Clostridiaceae

6

100.0

0.0

6

33.3

19.2

0.0

0.0

0

-

-

4

0.0

0.0

Coronaviridae

5

40.0

21.9

0

-

-

-

-

0

-

-

3

0.0

0.0

Filoviridae

16

43.8

12.4

7

57.1

18.7

28.6

20.2

2

100.0

0.0

14

14.3

9.4

Flaviviridae

218

86.2

2.3

146

55.5

4.1

43.2

4.3

70

90.0

3.6

165

38.2

3.8

Hepadnaviridae

213

94.8

1.5

187

74.9

3.2

54.5

3.7

132

77.3

3.6

190

53.7

3.6

Herpesviridae

14

57.1

13.2

3

33.3

27.2

0.0

0.0

0

-

-

8

0.0

0.0

Multiple or others

1,042

81.8

1.2

771

65.9

1.7

47.9

1.9

454

81.3

1.8

907

40.7

1.6

Poxviridae

12

83.3

10.8

9

66.7

15.7

55.6

16.6

6

83.3

15.2

11

45.5

15.0

Reoviridae

72

97.2

1.9

70

91.4

3.3

68.6

6.0

53

90.6

4.0

61

78.7

5.2

Retroviridae

181

65.2

3.5

95

36.8

4.9

0.0

0.0

21

0.0

0.0

144

0.0

0.0

Rhabdoviridae

47

91.5

4.1

40

87.5

5.2

65.0

8.1

30

86.7

6.2

39

66.7

7.5

Togaviridae

6

83.3

15.2

0

-

-

-

-

0

-

-

1

0.0

0.0

Total

1,838

82.5

0.9

1,339

65.4

1.3

45.9

1.4

768

80.1

1.4

1,552

39.6

1.2

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


When we consider nonvaccine PoSs by biological family of the infectious agent (see Table 8), we see that nonvaccine therapies for poxviridae (e.g., smallpox), herpesviridae (e.g., CMV infection), and hepadnaviridae (e.g., HBV) have the highest overall PoSs at 66.7% (SE = 27.2%), 31.8% (SE = 7.0%), and 29.5% (SE = 4.0%), respectively. For viruses in the reoviridae (e.g., rotavirus), coronaviridae (e.g., SARS, MERS), caliciviridae (e.g., norovirus), rhabdoviridae (e.g., rabies), and togaviridae (e.g., chikungunya) families, there have been less than five development programs each, and no approved treatment.


Table 8. The Probabilities of Success (PoSs) of Industry-Sponsored, Nonvaccine Anti-Infective Drug Development Programs, Grouped by Biological Family.8

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

Paths

PoS1A

SE

Caliciviridae

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Clostridiaceae

91

83.5

3.9

66

53.0

6.1

4.5

2.8

25

12.0

6.5

71

4.2

2.4

Coronaviridae

2

50.0

35.4

1

0.0

0.0

0.0

0.0

0

-

-

2

0.0

0.0

Filoviridae

10

20.0

12.6

1

0.0

0.0

0.0

0.0

0

-

-

9

0.0

0.0

Flaviviridae

609

70.3

1.9

383

49.6

2.6

22.5

2.2

161

53.4

3.9

535

16.1

1.6

Hepadnaviridae

186

77.4

3.1

105

68.6

4.5

36.2

5.2

54

70.4

6.2

129

29.5

4.0

Herpesviridae

64

87.5

4.1

43

60.5

7.5

32.6

7.8

19

73.7

10.1

44

31.8

7.0

Poxviridae

3

66.7

27.2

2

100.0

0.0

100.0

0.0

2

100.0

0.0

1,815

66.7

27.2

Reoviridae

2

100.0

0.0

2

0.0

0.0

0.0

0.0

0

-

-

3

0.0

0.0

Retroviridae

604

63.2

2.0

326

59.8

2.7

39.3

2.8

167

76.6

3.3

2

24.6

1.9

Rhabdoviridae

4

75.0

21.7

0

-

-

-

-

0

-

-

520

0.0

0.0

Togaviridae

1

0.0

0.0

0

-

-

-

-

0

-

-

1

0.0

0.0

Multiple or others

2,274

61.9

1.0

1,273

70.3

1.3

18.8

1.3

570

41.9

2.1

1

13.2

0.8

Total

3,851

65.0

0.8

2,202

64.3

1.0

23.2

1.0

998

51.1

1.6

3,133

16.3

0.7

Note. A = regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.


6. Discussion

Companies producing vaccines and other therapeutics for infectious diseases have gradually been retreating from these spaces in recent years. The number of companies producing vaccines has dwindled over the past few decades, and the top four vaccine companies now make up more than 90% of the global market (Evaluate, 2018). Similarly, the top four companies producing antiviral drugs occupy about 80% of the global market (Evaluate, 2018). Antibiotic developers such Achaogen and Melinta Therapeutics have filed for bankruptcy in the past year, while large pharmaceutical companies such as Novartis and Sanofi have withdrawn from the space (Jacobs, 2019), leading the Infectious Diseases Society of America to sound the alarm about the availability of effective antibiotics (Infectious Diseases Society of America, 2019).

It should be no surprise that investors are unwilling to invest in companies producing vaccines and treatments for infectious diseases given the economics of this market (Vu et al., 2020). These have been generally regarded as low-margin products, and they have low expected growth potential compared to chronic treatments in other therapeutic areas, such as oncology or cardiovascular diseases. For example, Merck’s oncology assets are estimated to have contributed $11.8 billion in incremental revenues from 2017 to 2020; for the same period, the incremental contribution of its vaccines portfolio is estimated to be $2.7 billion (Trefis, 2020). And Merck is the second largest vaccine maker in the world. This lack of investment has resulted in a relatively low number of development programs for vaccines and treatments for infectious diseases; only 10.4% of all industry-sponsored drug development programs launched in the past two decades are in these areas (see Table A2 in the Appendix).

Our study indicates that the technical success rate is unlikely to be a barrier to investments in new vaccines and treatments for infectious diseases, unlike cancer drugs, where the financial risk of new R&D projects comes from the reduced chance of bringing a drug-indication pair from phase 1 to market. The overall PoS of industry-sponsored vaccines and treatments for infectious diseases are above the average for all therapeutic groups (see Table A2 in the Appendix).

It is often suggested that the fundamental issue behind this lack of investment is that the market for vaccines and treatments for infectious diseases is simply not lucrative enough. Despite the expense of R&D and the need for large-scale production (Weir & Gruber, 2016), anti-infective disease treatments are used only occasionally, while vaccine companies face an avalanche of liability lawsuits (Hensley & Wysocki, 2005). Furthermore, the companies are at the mercy of government pricing decisions (Hu, 2018).

Apart from financial considerations, the dearth of vaccines and other treatments for infectious diseases may be due to the lack of available subjects for testing these therapeutics, especially during non-epidemic periods. This may be alleviated by having faster preclinical and clinical pathways in cases of severe infectious diseases with no existing treatments. One such pathway is the Animal Rule (FDA Approval of New Drugs When Human Efficacy Studies Are Not Ethical or Feasible, 2019; FDA Approval of Biological Products When Human Efficacy Studies Are Not Ethical or Feasible, 2019) whereby the "FDA may grant marketing approval based on adequate and well-controlled animal efficacy studies when the results of those studies establish that the drug is reasonably likely to produce clinical benefit in humans” (FDA, 2015). This has been used to approve smallpox and monkeypox vaccines, and can be expanded for the investigation of therapeutics for other potentially deadly infectious diseases with low incidence rates, such as SARS.

Even though this pathway can expedite the development of vaccines and anti-infective treatments, it still requires considerable development time as one needs to establish the equivalence of the drug mechanism between animal models and humans. While it is desirable to hasten the development of vaccines and medical products during an epidemic, biological breakthroughs and science will ultimately drive the efficiency of our ability to fight pandemics of novel pathogens.

It remains to be seen if more non-industry-sponsored research can alleviate the issue. Our study shows that only 6.8% (SE = 1.0%) and 8.2% (SE = 0.6%) of non-industry-sponsored vaccines and nonvaccine infectious disease development programs transition from phase 1 to approval, respectively. However, this may be a result of selection bias: promising vaccine and therapeutics initiated in nonindustry settings are often pursued in conjunction with industry sponsors, whereas commercially less-promising projects are more likely to be pursued by nonprofit organizations.

7. Conclusion

The world today has never been in greater need of effective vaccines and other anti-infectives. As the COVID-19 crisis has shown, infectious diseases still have the potential to cause a catastrophically large number of deaths and disrupt the daily lives of billions. We hope that our research into the probability of successfully developing infectious disease therapeutics will inform all stakeholders and catalyze innovation and greater investment in this critical and underserved field.

Disclosure Statement

The views and opinions expressed in this article are those of the authors only, and do not represent the views, policies, and opinions any institution or agency, any of their affiliates or employees, or any of the individuals acknowledged below.. W. Siah and C. H. Wong report no conflicts. A. Lo has personal investments in biotechnology companies, biotech venture capital funds, and mutual funds, and is a co-founder and partner of QLS Advisors, a healthcare analytics and consulting company. He is also an advisor to BrightEdge and Thales; an advisor to and investor in BridgeBio Pharma; a director of Roivant Sciences Ltd. and Annual Reviews; chairman emeritus and senior advisor to AlphaSimplex Group; and a member of the Board of Overseers at Beth Israel Deaconess Medical Center. Finally, Lo is a member of the NIH’s National Center for Advancing Translational Sciences Advisory Council and Cures Acceleration Network Review Board. During the most recent six-year period, Lo has received speaking/consulting fees, honoraria, or other forms of compensation from: AIG, AlphaSimplex Group, BIS, BridgeBio Capital, Citigroup, Chicago Mercantile Exchange, Financial Times, Harvard University, IMF, National Bank of Belgium, Q Group, Roivant Sciences, Scotia Bank, State Street Bank, University of Chicago, and Yale University.

Acknowledgments

We thank Informa for providing us access to their data and expertise and are particularly grateful to Will Akie, Christine Blazynski, Gabrielle Gessner, Mark Gordon, Michael Hay, Ian Lloyd, and Ryan Sasaki. We also thank Christine Blazynski, John Tedrow, the editor, associate editor, and reviewers for helpful comments on this manuscript. Research support from the MIT Laboratory for Financial Engineering and funding support from The Rockefeller Foundation are gratefully acknowledged. The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of any institution or agency, any of their affiliates or employees, or any of the individuals acknowledged above.


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Appendix

A1. An Example of the Path-by-Path Probability of Success (PoS) Calculations

For clarity, we will walk our readers through some calculations using the example shown in Figure A1. In that figure, we see that 768 vaccine programs have conducted phase 1 testing, whereas 1,178 vaccine programs have skipped phase 1 and proceeded directly to phase 2 or 3 testing. This is not uncommon in vaccine development programs, where vaccine candidates move directly to the higher phases based on initial safety of the vaccine base (e.g., egg, etc.) after changing the virus within it. Among these 1,946 vaccine development programs, we know that 108 have yet to conclude phase 1 testing while 1,838 have completed phase 1. Of these 1,838 programs, 1,517 have gone on to phase 2 while 321 have failed. In the notation introduced earlier, N1= 1,838 and N2=1,517, yielding an estimate of 1,517/1,838, or 82.5%, for PoS12. Repeating the logic for the transitions between phase 2 and phase 3, and between phase 3 and approval, gives 65.4% and 80.1% as estimates of PoS23 and PoS3A, respectively.

In order to compute the probability of a vaccine development program making it all the way from phase 1 to approval, we consider only the vaccine development programs that have definite outcomes. In other words, we do not consider development programs that are “in progress” in the denominator. In our example, the number of such programs is 1,178 + 768 − 108 − 178 − 108 = 1,552. Since 615 programs made it to approval, the estimated PoS1A is 615/1552 = 39.6%.


<p><strong>Figure A1. An example of the number of transitions computed, based on the data for industry-sponsored vaccine drug development programs.</strong></p>

Figure A1. An example of the number of transitions computed, based on the data for industry-sponsored vaccine drug development programs.



A2. Differences Between the Path-By-Path Approach and the Phase-By-Phase Approach

The path-by-phase approach described in the main text carefully considers the drug development programs that are under active development and excludes them from the PoS calculations when necessary. As such, the overall probability of success, PoS1A, is not the multiplication of PoS12, PoS23, and PoS3A, that is,

PoS1A (path-by-path) PoS12×PoS23×PoS3A.\text{PoS}_{1\mathrm{A}}\mathrm{\text{ (path-by-path)} \neq \ }\text{PoS}_{12} \times \mathrm{\text{PoS}}_{23} \times \mathrm{\text{PoS}}_{3\mathrm{A}}.


In contrast, the phase-by-phase computation simply computes PoSij using the equation:

PoSij=x=i,,j1PoSx,x+1.\text{PoS}_{\text{ij}} = \prod_{x = i,\ldots,j - 1}^{}\text{PoS}_{x,x + 1}.

In particular, the PoS1A is computed using the following formula:


PoS1A (phase-by-phase)=PoS12×PoS23×PoS3A.\text{PoS}_{1\mathrm{A}}\mathrm{\text{ (phase-by-phase)}} = \text{PoS}_{12} \times \mathrm{\text{PoS}}_{23} \times \mathrm{\text{PoS}}_{3\mathrm{A}}.


The phase-by-phase approach is valid under some circumstances, such as when there are no active development programs in any of the phases. This is easily seen if one simply sets the number of “in progress” development programs in all phases in Figure A1 to zero and recomputes the PoSs.

The path-by-path approach can also obtain the same results as the phase-by-phase approach if one is willing to make an additional assumption: programs that are “in progress” in phase i will transition to phase i + 1 or to “terminated” with the same probability as going from phase i to phase i + 1, or from phase i to “terminated,” without “in progress” programs.

We illustrate this with Figure A2, which shows the different states of a drug development program with hypothetical transitions from “in progress” states to the next phase or to the “terminated” state. Without considering “in progress” programs, the probability of transitioning from phase 1 to phase 2 is 78.0/(78.0+16.5) = 82.5% and the probability of transitioning from phase 1 into the “terminated” state is 17.5%. Similarly, without considering “in progress” programs between phase 2 and phase 3 or phase 3 and approval, the probabilities of transitioning from phase 2 to phase 3 or phase 3 to approval are 65.4% and 80.0%, respectively. If we set a, b, and c to be 82.5%, 65.4%, and 80.0%, respectively, we will obtain a PoS1A of 43.2%, which is exactly PoS12×PoS23×PoS3A.\text{PoS}_{12} \times \mathrm{\text{PoS}}_{23} \times \mathrm{\text{PoS}}_{3\mathrm{A}}. In contrast, the path-by-path approach obtains a PoS1A of 39.6% as it does not make any assumptions and ignores programs that are “in progress” in phase 1, phase 2, or phase 3.

We believe that our method of inferring unobserved clinical development stages and then applying the path-by-path approach is a better measure of the PoSs of clinical development programs as it does not underestimate the PoSs, and makes no assumption about the programs that are in active development.


<p><strong>Figure A2. A Markov chain that includes hypothetical transitions from “in progress” states to the next phase or to the “terminated” state.</strong></p>

Figure A2. A Markov chain that includes hypothetical transitions from “in progress” states to the next phase or to the “terminated” state.


Table A1. List of Transmission Routes and Biological Family for Infectious Diseases

Disease

Transmission Route

Biological Family

Hepatitis B Virus (HBV)

Human-human (Others)

Hepadnaviridae

Other

Multiple or others

Multiple or others

Otitis Media

Multiple or others

Multiple or others

Bacterial Skin Infection

Multiple or others

Multiple or others

Sepsis

Multiple or others

Multiple or others

Human Papillomavirus (HPV)

Human-human (Others)

Flaviviridae

Human Immunodeficiency Virus (HIV)

Human-human (Others)

Retroviridae

Intra-abdominal Infections

Multiple or others

Multiple or others

Onychomycosis

Multiple or others

Multiple or others

Clostridium difficile

Multiple or others

Clostridiaceae

Cytomegalovirus (CMV) Infection

Human-human (Others)

Herpesviridae

Hepatitis C Virus (HCV)

Human-human (Others)

Flaviviridae

Respiratory Infections

Multiple or others

Multiple or others

Urinary Tract Infections

Multiple or others

Multiple or others

Rotavirus

Human-human (Others)

Reoviridae

Ebola

Human-human (Others)

Filoviridae

Marburg

Human-human (Others)

Filoviridae

Smallpox

Human-human (Airborne)

Poxviridae

Zika

Animal bites

Flaviviridae

Rabies

Animal bites

Rhabdoviridae

Yellow Fever

Animal bites

Flaviviridae

Chikungunya

Animal bites

Togaviridae

Norovirus

Contaminated food or water

Caliciviridae

Japanese Encephalitis

Animal bites

Flaviviridae

Non-tuberculous Mycobacteria (NTM) Infection

Multiple or others

Multiple or others

West Nile Virus (WNV)

Animal bites

Flaviviridae

Middle East Respiratory Syndrome (MERS)

Human-human (Airborne)

Coronaviridae

Severe Acute Respiratory Syndrome (SARS)

Human-human (Airborne)

Coronaviridae

Monkeypox

Animal bites

Poxviridae


Table A2. The Probabilities of Success (PoSs) of Industry-Sponsored Drug Development Programs Across All Therapeutic Groups

Disease

P1 Paths

PoS12

SE

P2 Paths

PoS23

SE

PoS2A

SE

P3 Paths

PoS3A

SE

PoS1A

SE

Oncology

27,600

65.5

0.3

10,650

37.7

0.5

6.9

0.3

2,597

28.2

0.9

3.9

0.1

Metabolic / Endocrinology

4,360

74.2

0.7

2,767

60.0

0.9

21.3

0.8

1,293

45.6

1.4

16.7

0.6

Cardiovascular

3,387

74.3

0.8

2,265

70.2

1.0

25.9

1.0

1,203

48.8

1.4

21.4

0.8

CNS

6,207

71.7

0.6

3,806

56.9

0.8

14.6

0.6

1,525

36.5

1.2

11.3

0.5

Autoimmune / Inflammation

6,272

71.7

0.6

3,704

52.7

0.8

17.3

0.7

1,332

48.0

1.4

13.1

0.5

Genitourinary

1,103

71.4

1.4

737

61.6

1.8

25.0

1.7

352

52.3

2.7

19.3

1.3

Infectious Disease (ex. vaccines)

3,851

65.0

0.8

2,202

64.2

1.0

23.1

1.0

996

51.0

1.6

16.2

0.7

Ophthalmology

697

89.1

1.2

510

55.7

2.2

17.1

1.8

191

45.5

3.6

17.6

1.7

Vaccines (Infectious Disease)

1,886

83.9

0.8

1,409

66.4

1.3

45.8

1.4

813

79.5

1.4

40.6

1.2

Total

55,363

69.1

0.2

28,050

51.6

0.3

16.1

0.2

10,302

44.0

0.5

11.0

0.2

All except Oncology

27,763

72.7

0.3

17,400

60.1

0.4

21.8

0.3

7,705

49.3

0.6

17.1

0.3

Note. The classification of vaccines used in this table is based on broader categories such as “other viral vaccines” instead of the finer ones such as “Ebola” used in this article, resulting in a slight difference in the computed PoSs. A= regulatory approval; P1 = phase 1; P2 = phase 2; P3 = phase 3.





This article is © 2020 by Andrew W. Lo, Kien Wei Siah, and Chi Heem Wong. The article is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode), except where otherwise indicated with respect to particular material included in the article. The article should be attributed to the author identified above.

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