Santiago Vilar, PhD
Dr. Vilar graduated with a Ph.D. in Organic & Medicinal Chemistry from the University of Santiago de Compostela, Spain (2006) where he focused on organic synthesis, molecular docking and statistical studies of quantitative structure-activity relationship (QSAR). In 2007, he worked at the Department of Pharmaceutical Sciences at the University of Padova (Italy) on the rational design of new Hsp90 inhibitors. From 2008 to 2010, he was a researcher at National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (USA) working on molecular modeling of G protein-coupled receptors (GPCRs). He concentrated on the development of computational strategies for the virtual screening and quantitative affinity prediction of GPCR ligands and on molecular dynamics studies of the activation mechanism in adrenergic receptors. Since 2010 he has been conducting research at the Department of Biomedical Informatics at Columbia University (New York, USA). Dr. Vilar is interested in the implementation of molecular similarity methods in pharmacovigilance databases, such as the Electronic Health Records of the Presbyterian Hospital (New York), to detect adverse drug effects and drug-drug interactions. He is applying drug similarity-based methods to improve adverse event signal detection and facilitate decision making.
Vilar S, Harpaz R, Chase HS, Costanzi S, Rabadan R, Friedman C. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis. J. Am. Med. Inform. Assoc. 2011, 18, Suppl 1:i73-80.
Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C. Drug-drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc. 2012, 19, 1066-74.
Vilar S, Harpaz R, Santana L, Uriarte E, Friedman C. Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis. PLoS One 2012, 7, e41471.
Harpaz R, Vilar S, Dumouchel W, Salmasian H, Haerian K, Shah NH, Chase HS, Friedman, C. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J. Am. Med. Inform. Assoc. 2013, 20, 413-9.
Vilar S, Uriarte E, Santana L, Tatonetti N. P, Friedman C. Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints. PLoS One 2013, 8(3), e58321.
Li Y, Salmasian H, Vilar S, Chase H, Friedman C, Wei Y. A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records. J. Am. Med. Inform. Assoc. 2014, 21, 308-14.
Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti N. P. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protoc. 2014, doi:10.1038/nprot.2014.151.
Vilar S, Ryan P. B, Madigan D, Stang P. E, Schuemie M. J, Friedman C, Tatonetti N. P, Hripcsak G. Similarity-Based Modeling Applied to Signal Detection in Pharmacovigilance. CPT Pharmacometrics Syst. Pharmacol. 2014, 3, e1, doi:10.1038/psp.2014.35.
Vilar S, Uriarte E, Santana L, Friedman C, Tatonetti, N. P. State of the Art and Development of a Drug-Drug Interaction Large Scale Predictor Based on 3D Pharmacophoric Similarity. Curr. Drug Metabolism 2014, 15, (in press).