3D Pharmacophoric Similarity improves Multi Adverse Drug Event Identification in Pharmacovigilance

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining.

[1]  D. Bates,et al.  The Costs of Adverse Drug Events in Hospitalized Patients , 1997 .

[2]  C L Brooks,et al.  Do active site conformations of small ligands correspond to low free-energy solution structures? , 1998, Journal of computer-aided molecular design.

[3]  James G. Nourse,et al.  Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..

[4]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[5]  W. DuMouchel,et al.  Comparative Performance of Two Quantitative Safety Signalling Methods , 2006, Drug safety.

[6]  Joelle M. R. Gola,et al.  Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery , 2006, Expert opinion on drug metabolism & toxicology.

[7]  A. Bender,et al.  Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure , 2007, ChemMedChem.

[8]  E. B. Wedebye,et al.  QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation† , 2008, SAR and QSAR in environmental research.

[9]  Romualdo Benigni,et al.  Predictivity and Reliability of QSAR Models: The Case of Mutagens and Carcinogens , 2008, Toxicology mechanisms and methods.

[10]  Luis G Valerio,et al.  In silico toxicology for the pharmaceutical sciences. , 2009, Toxicology and applied pharmacology.

[11]  Richard Morrison,et al.  Exploratory drug safety: a discovery strategy to reduce attrition in development. , 2009, Journal of pharmacological and toxicological methods.

[12]  M. Milik,et al.  Mapping adverse drug reactions in chemical space. , 2009, Journal of medicinal chemistry.

[13]  A Bate,et al.  Decision support methods for the detection of adverse events in post-marketing data. , 2009, Drug discovery today.

[14]  Santiago Vilar,et al.  Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors. , 2010, Journal of molecular graphics & modelling.

[15]  Navdeep Jaitly,et al.  A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying Biological Pathways , 2010, PloS one.

[16]  Luis G Valerio,et al.  In silico toxicology models and databases as FDA Critical Path Initiative toolkits , 2011, Human Genomics.

[17]  Yoshihiro Yamanishi,et al.  Predicting drug side-effect profiles: a chemical fragment-based approach , 2011, BMC Bioinformatics.

[18]  Evan Bolton,et al.  PubChem3D: a new resource for scientists , 2011, J. Cheminformatics.

[19]  Carol Friedman,et al.  Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis , 2011, J. Am. Medical Informatics Assoc..

[20]  Evan Bolton,et al.  PubChem3D: Biologically relevant 3-D similarity , 2011, J. Cheminformatics.

[21]  Carol Friedman,et al.  Enhancing Adverse Drug Event Detection in Electronic Health Records Using Molecular Structure Similarity: Application to Pancreatitis , 2012, PloS one.

[22]  C. Bell,et al.  Incidence and economic burden of adverse drug reactions among elderly patients in Ontario emergency departments: a retrospective study. , 2012, Drug safety.

[23]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[24]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[25]  Carol Friedman,et al.  Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions , 2013, J. Am. Medical Informatics Assoc..

[26]  P. Bork,et al.  Systematic identification of proteins that elicit drug side effects , 2013, Molecular systems biology.

[27]  N. Shah,et al.  Performance of Pharmacovigilance Signal‐Detection Algorithms for the FDA Adverse Event Reporting System , 2013, Clinical pharmacology and therapeutics.

[28]  D. Madigan,et al.  Medication-Wide Association Studies , 2013, CPT: pharmacometrics & systems pharmacology.

[29]  G Hripcsak,et al.  Similarity-Based Modeling Applied to Signal Detection in Pharmacovigilance , 2014, CPT: pharmacometrics & systems pharmacology.

[30]  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.

[31]  正博 頭金.  FDA adverse event reporting system (FAERS) , 2015 .