HUME: large‐scale detection of causal genetic factors of adverse drug reactions

Motivation: Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously. Results: To address this challenge, we propose HUME, a multi‐phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co‐prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic‐Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records. Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[2]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[3]  Russ B. Altman,et al.  PharmGKB: the Pharmacogenetics Knowledge Base , 2002, Nucleic Acids Res..

[4]  Patrick Aloy,et al.  IntSide: a web server for the chemical and biological examination of drug side effects , 2014, Bioinform..

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

[6]  D. Classen,et al.  Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. , 1997, JAMA.

[7]  Morten Wang Fagerland,et al.  The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional , 2013, BMC Medical Research Methodology.

[8]  Nicholas A. Christakis,et al.  Social contagion theory: examining dynamic social networks and human behavior , 2011, Statistics in medicine.

[9]  K Enslein,et al.  An Overview of Structure-Activity Relationships as an Alternative To Testing in Animals for Carcinogenicity, Mutagenicity, Dermal and Eye Irritation, and Acute Oral Toxicity , 1988, Toxicology and industrial health.

[10]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[11]  S. Ebrahim,et al.  Data dredging, bias, or confounding , 2002, BMJ : British Medical Journal.

[12]  M. Rieder.  In vivo and in vitro testing for adverse drug reactions. , 1997, Pediatric clinics of North America.

[13]  P Ryan,et al.  Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis , 2012, Clinical pharmacology and therapeutics.

[14]  M. Fakruddin,et al.  Pharmacogenomics- The Promise of Personalized Medicine , 2013 .

[15]  Shashi Amur,et al.  Pharmacogenomic Biomarker Information in Drug Labels Approved by the United States Food and Drug Administration: Prevalence of Related Drug Use , 2008, Pharmacotherapy.

[16]  Andreas Antoniou,et al.  Practical Optimization: Algorithms and Engineering Applications , 2007, Texts in Computer Science.

[17]  Peter Bühlmann,et al.  Causal Inference Using Graphical Models with the R Package pcalg , 2012 .

[18]  J. Chen,et al.  Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures , 2013, Proteomics.

[19]  Jie Shen,et al.  Adverse Drug Events: Database Construction and in Silico Prediction , 2013, J. Chem. Inf. Model..

[20]  Thomas S. Richardson,et al.  Learning high-dimensional directed acyclic graphs with latent and selection variables , 2011, 1104.5617.

[21]  J. Chen,et al.  Predicting adverse side effects of drugs , 2011, BMC Genomics.

[22]  Darrell R Abernethy,et al.  Systems pharmacology to predict drug toxicity: integration across levels of biological organization. , 2013, Annual review of pharmacology and toxicology.

[23]  Hua Xu,et al.  Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs , 2012, J. Am. Medical Informatics Assoc..

[24]  Patrick Aloy,et al.  Analysis of chemical and biological features yields mechanistic insights into drug side effects. , 2013, Chemistry & biology.

[25]  Cheng Cheng,et al.  Rapid Assessment of Adverse Drug Reactions by Statistical Solution of Gene Association Network , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  R. Real,et al.  The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .

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

[28]  Manuel Gomez,et al.  Evaluation of the Extent of Under-Reporting of Serious Adverse Drug Reactions , 2004, Drug safety.

[29]  Smita H. Karande,et al.  A Coding Variant in RARG Confers Susceptibility to Anthracycline-Induced Cardiotoxicity in Childhood Cancer , 2015, Nature Genetics.

[30]  G. Danan,et al.  Causality assessment of adverse reactions to drugs--I. A novel method based on the conclusions of international consensus meetings: application to drug-induced liver injuries. , 1993, Journal of clinical epidemiology.

[31]  Quan Xu,et al.  ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms , 2014, Nucleic Acids Res..

[32]  R. Sharan,et al.  Metabolic Network Prediction of Drug Side Effects. , 2016, Cell systems.

[33]  Jiuyong Li,et al.  From Observational Studies to Causal Rule Mining , 2015, ACM Trans. Intell. Syst. Technol..

[34]  Yoshihiro Yamanishi,et al.  Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces , 2012, J. Chem. Inf. Model..

[35]  Ben Y. Reis,et al.  Predicting Adverse Drug Events Using Pharmacological Network Models , 2011, Science Translational Medicine.

[36]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[37]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[38]  Alessio Farcomeni,et al.  A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion , 2008, Statistical methods in medical research.

[39]  M. Hayden,et al.  Recommendations for genetic testing to reduce the incidence of anthracycline‐induced cardiotoxicity , 2016, British journal of clinical pharmacology.

[40]  M. Hayden,et al.  Adverse drug reaction active surveillance: developing a national network in Canada's children's hospitals , 2009, Pharmacoepidemiology and drug safety.

[41]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[42]  Shah Ebrahim,et al.  Data dredging , bias , or confounding They can all get you into the BMJ and the Friday papers , 2002 .

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

[44]  J E Ridings,et al.  Computer prediction of possible toxic action from chemical structure: an update on the DEREK system. , 1996, Toxicology.

[45]  Thomas H. Scheike,et al.  Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model , 2012 .

[46]  J. Leeder.  Developmental and pediatric pharmacogenomics. , 2003, Pharmacogenomics.

[47]  Aarash Bordbar,et al.  Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis , 2015, Nature Communications.

[48]  P. Corey,et al.  Incidence of Adverse Drug Reactions in Hospitalized Patients , 2012 .