Calendar Period Estimation of Probabilities of Transition in Drug Development

This paper examines the intricate and high-risk process of drug development. A compounds journey from target validation to market launch can span 10 to 15 years, with an average cost of one to two billion USD per successful drug. Despite extensive pre-clinical work, only about 10% of drug candidates entering human clinical trials achieve US Federal Drug Administration approval. The primary causes of failure during the three phases of clinical trials include lack of clinical efficacy, unmanageable toxicity, poor drug-like properties, and lack of commercial needs or strategic planning. The study aims to measure the transition probability of drugs from Phase I to clinical approval and offers a broad comparison between drugs with different characteristics (therapeutic in-dication, mechanism of action, target, and modality). We employ a large industry database, Pharmaprojects, which tracks the development of drug candidates globally from pre-clinical stages through market launch and withdrawal or discontinuation (if it occurs). Thus, it provides a complete picture of both successful and unsuccessful drugs, albeit limited to publicly available information. Nearly all prior work on the estimation of transition probabilities has used a traditional longitudinal design. We take a cross-sectional approach which allows for a more dynamic understanding of the process as it unfolds over time and offers the possibility of detecting and measuring the calendar year impacts of policy and practice changes in a timely manner. We construct life tables for each phase of clinical trials between 2002 and 2022 to estimate the raw hazard of transition and graduate them with a generalized additive model to produce a smooth hazard and corresponding conditional probabilities of transition and the overall probability of success. Our analysis shows that the propensity to transition out of each of the three phases of clinical trials behaves differently, changes over time, and is heavily influenced by certain drug characteristics, such as, the therapeutic indication or the mechanism of action. Moreover, we find positive trends in the overall probability of success for certain classes of drugs suggesting that the industry is improving its productivity. Practical Applications SummaryWith our results we can estimate various conditional transition probabilities given that a drug has been under development for some time and combine them to provide probabilistic results for a portfolio of drugs.