Objective: To evaluate the economic impact of a Bayesian network model designed to predict clinical success of a new chemical entity (NCE) based on pre-phase III data.
Methods: We trained our Bayesian network model on publicly accessible data on 503 NCEs, stratified by therapeutic class. We evaluated the sensitivity, specificity and accuracy of our model on an independent data set of 18 NCE-indication pairs, using prior probability data for the antineoplastic NCEs within the training set. We performed Monte Carlo simulations to evaluate the economic performance of our model relative to reported pharmaceutical industry performance, taking into account reported capitalized phase costs, cumulative revenues for a postapproval period of 7 years, and the range of possible false negative and true negative rates for terminated NCEs within the pharmaceutical industry.
Results: Our model predicted outcomes on the independent validation set of oncology agents with 78% accuracy (80%sensitivity and 76% specificity). In comparison with the pharmaceutical industry's reported success rates, on average our model significantly reduced capitalized expenditures from $727 million/successful NCE to $444 million/successful NCE (P < 0.001), and significantly improved revenues from $347 million/phase III trial to $507 million/phase III trial (P < 0.001) during the first 7 years post launch. These results indicate that our model identified successful NCEs more efficiently than currently reported pharmaceutical industry performances.
Conclusions: Accurate prediction of NCE outcomes is computationally feasible, significantly increasing the proportion of successful NCEs, and likely eliminating ineffective and unsafe NCEs.