In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data

High Throughput. 2018 Nov 23;7(4):37. doi: 10.3390/ht7040037.


We present a novel approach for the molecular transformation and analysis of patient clinical phenotypes. Building on the fact that drugs perturb the function of targets/genes, we integrated data from 8.2 million clinical reports detailing drug-induced side effects with the molecular world of drug-target information. Using this dataset, we extracted 1.8 million associations of clinical phenotypes to 770 human drug-targets. This collection is perhaps the largest phenotypic profiling reference of human targets to-date, and unique in that it enables rapid development of testable molecular hypotheses directly from human-specific information. We also present validation results demonstrating analytical utilities of the approach, including drug safety prediction, and the design of novel combination therapies. Challenging the long-standing notion that molecular perturbation studies cannot be performed in humans, our data allows researchers to capitalize on the vast tomes of clinical information available throughout the healthcare system.

Keywords: adverse events; clinical phenotypes; computational biology; drug safety prediction; large-scale approaches; mode of action; outcome analytics; phenotypic screening; real world data; side-effects; systems pharmacology.