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Machine-Learning Models for Predicting Drug Approvals and Clinical-Phase Transitions

60 Pages Posted: 25 May 2017 Last revised: 26 Jul 2017

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Kien Wei Siah

Massachusetts Institute of Technology (MIT)

Chi Heem Wong

Massachusetts Institute of Technology (MIT)

Date Written: July 25, 2017

Abstract

We apply machine-learning techniques to predict drug approvals and phase transitions using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. Imputation methods are used to deal with missing data, allowing us to fully exploit the entire dataset, the largest of its kind. We achieve predictive measures of 0.74, 0.78, and 0.81 AUC for predicting transitions from phase 2 to phase 3, phase 2 to approval, and phase 3 to approval, respectively. Using five-year rolling windows, we document an increasing trend in the predictive power of these models, a consequence of improving data quality and quantity. The most important features for predicting success are trial outcomes, trial status, trial accrual rates, duration, prior approval for another indication, and sponsor track records. We provide estimates of the probability of success for all drugs in the current pipeline.

Keywords: biotech; pharmaceuticals; risk management; machine learning

JEL Classification: I10, I11, G11, G32, C55, C13

Suggested Citation

Lo, Andrew W. and Siah, Kien Wei and Wong, Chi Heem, Machine-Learning Models for Predicting Drug Approvals and Clinical-Phase Transitions (July 25, 2017). Available at SSRN: https://ssrn.com/abstract=2973611

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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National Bureau of Economic Research (NBER) ( email )

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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Stata Center
Cambridge, MA 02142
United States

Kien Wei Siah

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Chi Heem Wong

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

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