Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

Drug–drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.Graphical abstract.

[1]  J. Aronson,et al.  Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature , 2016, BMC Medicine.

[2]  Paloma Martínez,et al.  DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug-Drug Interactions and Their Mechanisms , 2015, J. Chem. Inf. Model..

[3]  G. De Sarro,et al.  Pharmacokinetic drug-drug interaction and their implication in clinical management , 2013, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences.

[4]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[5]  A. G. Rao,et al.  A spectroscopic study of the interaction of isoflavones with human serum albumin , 2006, The FEBS journal.

[6]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[7]  Jianying Hu,et al.  Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects , 2015, Scientific Reports.

[8]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[9]  George Hripcsak,et al.  Similarity-based modeling in large-scale prediction of drug-drug interactions , 2014, Nature Protocols.

[10]  Ping Zhang,et al.  DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome , 2014, Nucleic Acids Res..

[11]  F. Hock,et al.  Drug Discovery and Evaluation: Methods in Clinical Pharmacology , 2018 .

[12]  A. Schinkel,et al.  A Critical Analysis of the Interplay between Cytochrome P450 3A and P-Glycoprotein: Recent Insights from Knockout and Transgenic Mice , 2011, Pharmacological Reviews.

[13]  Yurong Lai,et al.  Beyond the ITC White Paper: emerging sciences in drug transporters and opportunities for drug development. , 2014, Current pharmaceutical design.

[14]  Sean Ekins,et al.  In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. , 2015, Advanced drug delivery reviews.

[15]  P. Sanseau,et al.  Systematic prediction of drug combinations based on clinical side-effects , 2014, Scientific Reports.

[16]  Nigam H. Shah,et al.  Mining clinical text for signals of adverse drug-drug interactions , 2014, J. Am. Medical Informatics Assoc..

[17]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[18]  L. Di Bari,et al.  Interactions between quercetin and warfarin for albumin binding: A new eye on food/drug interference. , 2009, Chirality.

[19]  Y. Tanigawara,et al.  Simvastatin and lovastatin, but not pravastatin, interact with MDR1 , 2002, The Journal of pharmacy and pharmacology.

[20]  George Hripcsak,et al.  Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health Records , 2015, Drug Safety.

[21]  R. Altman,et al.  Pharmacogenomics Knowledge for Personalized Medicine , 2012, Clinical pharmacology and therapeutics.

[22]  Carol Friedman,et al.  Drug-drug interaction through molecular structure similarity analysis , 2012, J. Am. Medical Informatics Assoc..

[23]  Lei Zhang,et al.  Scientific and regulatory perspectives on metabolizing enzyme-transporter interplay and its role in drug interactions: challenges in predicting drug interactions. , 2009, Molecular pharmaceutics.

[24]  T. R. Johnson,et al.  Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model , 2015, Drug Metabolism and Disposition.

[25]  R. Sharan,et al.  INDI: a computational framework for inferring drug interactions and their associated recommendations , 2012, Molecular systems biology.

[26]  B. Okopień,et al.  The Effect of Simvastatin on Plasma Steroid Hormone Levels in Metformin-Treated Women with Non-Classic Congenital Adrenal Hyperplasia , 2016, Experimental and Clinical Endocrinology & Diabetes (Barth).

[27]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[28]  Mingwu Zheng.  Pharmacodynamic Drug–Drug Interactions , 2020, Drug Discovery and Evaluation: Methods in Clinical Pharmacology.

[29]  S. Brunak,et al.  Generating Genome‐Scale Candidate Gene Lists for Pharmacogenomics , 2009, Clinical pharmacology and therapeutics.

[30]  Zhongming Zhao,et al.  Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[31]  Ben Y. Reis,et al.  Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions , 2013, PloS one.

[32]  Carol Friedman,et al.  State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity. , 2014, Current drug metabolism.

[33]  Marc C. Nicklaus,et al.  QSAR Modeling and Prediction of Drug-Drug Interactions. , 2016, Molecular pharmaceutics.