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Andrew W. Lo. Siah, Kien Wei Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2017-10-30T15:29:16Z 2017-10-30T15:29:16Z 2017 2017 http://hdl.handle.net/1721.1/112049 1006507955 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 105-106). 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. by Kien Wei Siah. S.M. 106 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Machine-learning models for predicting drug approvals and clinical-phase transitions Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="dc99fa08-7541-4ab4-91db-bb6f2c5b4460"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Machine-learning models for predicting drug approvals and clinical-phase transitions</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Siah, Kien Wei</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>