Abstract B142: A “moneyball” approach to predicting clinical trial failures and successes

Abstract

Abstract Over the past decade, significant strides have been made in the management and treatment of various diseases. Despite this progress, drug attrition rates due to clinical trial failure have continued to substantially rise. Clinical trials can fail for various reasons, ranging from design issues to drug efficacy and safety problems. Drug-likeness approaches, as first proposed by Lipinski almost two decades ago, have become a key tool for the pre-selection of compounds that are likely to have manageable toxicity in clinical studies. However all these methods consider molecular properties of the drug itself alone. In general, these approaches struggle to simultaneously well-characterize the properties of both FDA approved drugs (sensitivity) (and drugs that fail clinical trials (specificity). We introduce a novel data-driven approach (PrOCTOR) that integrates chemical properties of a compound, along with that of its targets, to provide a new measure, the PrOCTOR score, that helps predict whether drugs in clinical trials will fail for toxicity reasons. When trained on failed Phase I clinical trials and FDA approved drugs, the PrOCTOR score performs at a high accuracy, specificity and sensitivity (∼0.75), as well as high area under the ROC curve (>0.80). In comparison, none of the drug-likeness approaches were able to successfully maintain both high sensitivity and specificity. Robust feature analysis of the model indicates that it is critical to consider properties of the drug target, with the target's network connectivity and liver toxicity as two of the most important features. The approach was further evaluated by testing the predictions of the trained model on an established independent dataset. We found that our method was able to significantly distinguish a representative set of bioavailable drugs from a representative set of toxic drugs (D = 0.2133, p<2.2e-16, Kolmogorov-Smirnov test). Additionally, we found that the measure is strongly correlated with severe toxicity events, such as pleural effusion (ρ = −0.9792) and neutropenia (ρ = −0.9613). Altogether, our method provides a novel, data-driven and broadly applicable strategy that is able to identify drugs likely to possess manageable toxicity in clinical trials. Citation Format: Kaitlyn Gayvert, Neel Madhukar, Olivier Elemento. A “moneyball” approach to predicting clinical trial failures and successes. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B142.