Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes

In recent years, there has been an explosion in predictive technologies to help researchers select only the most promising candidates for clinical development. The need for such tools is driven by the disastrous economic consequences of late-stage failures, which account for over 60% of all drug terminations. This report describes a powerful and novel predictive tool called Bayesian network modeling and demonstrates its application in clinical forecasting.

• Reduce drug development costs
• Increase median cumulative 7-year revenue per Phase III trial
• Redirect capital and human resources to development programs with the greatest likelihood of success
• Expose clinical trial subjects to fewer unsafe or ineffective drugs
• Improve the accuracy and decision-making utility of market forecasts (which currently assume that all drugs in the projection period will achieve NDA approval)
• Increase industry' s and society' s confidence in including pediatric subjects in clinical trials

Moreover, unlike existing predictive technologies such as microdosing, toxicogenomics, or ultra high-throughput screening (HTS), all of which entail significant costs in capital equipment, training, and ongoing maintenance, clinical forecasting based on Bayesian statistics is comparatively inexpensive.

Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes begins by summarizing existing predictive technologies with particular reference to their limitations. Gene expression arrays, while providing useful prognostic information, are limited by the lability of mRNA and inconsistencies across microarray platforms. Microdosing is disadvantaged by limited databases required for the studies, unclear regulatory guidelines, and, in the case of PET studies, short trace half-lives and limited ability to distinguish between the compound and its metabolites.

With complete transparency as to data sources and assumptions, the authors show how the Bayesian network model predicted outcomes (NDA approval or failure) based on an independent dataset of 503 new chemical entities (NCEs) with an optimal accuracy of 78%. The author emphasizes that, with more complete and historical datasets of in vivo and in vitro compound data including therapeutic index ranges, the model' s performance can be even further improved.