Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs
Author(s)Lo, Andrew W; Siah, Kien Wei; Wong, Chi Heem
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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.
DepartmentSloan School of Management; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Harvard Data Science Review
Lo, Andrew W et al. "Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs." Harvard Data Science Review (May 2020): dx.doi.org/10.1162/99608f92.e0c150e8
Final published version