Application of Machine Learning in Knowledge Discovery for Pharmaceutical Drug-drug Interactions

TY - JOUR

T1 - Application of Machine Learning in Knowledge Discovery for Pharmaceutical Drug-drug Interactions

AU - Herrero Zazo, Maria

AU - Lille, Maxime

AU - Barlow, David John

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Artificial neural networks (ANNs) have been developed to predict the clinical significance of drug-drug interactions (DDIs) for a set of 35 phar-maceutical drugs using data compiled from the Web-based resources, Lexi-comp® and Vidal®, with inputs furnished by various drug pharmacokinetic (PK) and/or pharmacodynamic (PD) properties, and/or drug-enzyme interaction data. Success in prediction of DDI significance was found to vary according to the drug properties used as ANN input, and also varied with the DDI dataset used in training. The Lexicomp® dataset is found to give predictions marginal-ly better than those obtained using the Vidal® dataset, with the best prediction of minor DDIs achieved using a multi-layer perceptron (MLP) model trained using enzyme variables alone (F1 82%) and the best prediction of major DDIs achieved using a MLP model trained on PK/PD properties alone (F1 54%). Given a more comprehensive and more consistent dataset of DDI data, we con-clude that machine learning tools could be used to acquire new knowledge on DDIs, and could thus facilitate the regulatory agencies’ pharmocovigilance of newly licensed drugs.

AB - Artificial neural networks (ANNs) have been developed to predict the clinical significance of drug-drug interactions (DDIs) for a set of 35 phar-maceutical drugs using data compiled from the Web-based resources, Lexi-comp® and Vidal®, with inputs furnished by various drug pharmacokinetic (PK) and/or pharmacodynamic (PD) properties, and/or drug-enzyme interaction data. Success in prediction of DDI significance was found to vary according to the drug properties used as ANN input, and also varied with the DDI dataset used in training. The Lexicomp® dataset is found to give predictions marginal-ly better than those obtained using the Vidal® dataset, with the best prediction of minor DDIs achieved using a multi-layer perceptron (MLP) model trained using enzyme variables alone (F1 82%) and the best prediction of major DDIs achieved using a MLP model trained on PK/PD properties alone (F1 54%). Given a more comprehensive and more consistent dataset of DDI data, we con-clude that machine learning tools could be used to acquire new knowledge on DDIs, and could thus facilitate the regulatory agencies’ pharmocovigilance of newly licensed drugs.

KW - drug-drug interactions

KW - pharmacovigilance

KW - machine learning

KW - arti-ficial neural networks

M3 - Conference paper

SN - 1613-0073

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

M1 - 13

ER -