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High-Throughput 2018, 7(4), 37; https://doi.org/10.3390/ht7040037

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

Molecular Health GmbH, Kurfuersten Anlage 21, 69115 Heidelberg, Germany
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Received: 23 October 2018 / Revised: 9 November 2018 / Accepted: 19 November 2018 / Published: 23 November 2018
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Abstract

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. View Full-Text
Keywords: computational biology; large-scale approaches; systems pharmacology; adverse events; side-effects; outcome analytics; real world data; mode of action; clinical phenotypes; phenotypic screening; drug safety prediction computational biology; large-scale approaches; systems pharmacology; adverse events; side-effects; outcome analytics; real world data; mode of action; clinical phenotypes; phenotypic screening; drug safety prediction
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Soldatos, T.G.; Taglang, G.; Jackson, D.B. In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data. High-Throughput 2018, 7, 37.

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