Dr. Sarangdhar is a bioinformatics-trained computational scientist interested in unraveling the underlying causes and mechanisms of drug toxicity. His research focuses on integrating high-dimensional computational approaches with systems biology knowledgebase to accelerate the discovery of novel drug-toxicity relationships buried in heterogeneous big data. Dr. Sarangdhar developed a novel platform, AERSMine, to mine the clinical responses of millions of patients to all FDA-approved drugs in order to identify unexpected clinical harm, benefits and alternative treatment choices for individual patients. AERSMine provides an insight into sub-population-specific differential therapeutic risks, and creates an avenue to improve our understanding of the molecular basis of adverse drug reactions.
Dr. Sarangdhar is also a member of the Children’s Oncology Group and is leading the effort to delineate differential treatment- and age-specific toxicity profiles within pediatric and young adult cancer patients across multiple studies and disease groups. He is designing computational approaches that facilitate effective analysis of large-scale datasets including clinical trials so we can identify a) the true regimen-specific differential risks associated with chemotherapy, b) the underlying genetic factors that drive exacerbation of toxicities, and c) recognize effective personalized therapeutic strategies for individuals at highest risk of complications.
His group is developing integrative analytical approaches that combine machine learning techniques with toxicity data, genotype-phenotype relationships, and gene-regulatory mechanisms, to help facilitate modelling novel and effective therapeutics.
BE: Computer Science, University of Mumbai, Mumbai, India, 2004.
MRes: Computer Science and Artificial Intelligence, University of Sussex, UK, 2005.
PhD: Computer Science, University of Hull, UK, 2013.
Post-doctoral: Cincinnati Children's Hospital Medical Center, 2015.
Systems pharmacology; developmental pharmacology; cardio-oncology; drug-induced toxicities; drug repositioning
Oncology, Biomedical Informatics
Glucagon-like peptide-1 receptor agonists are not associated with retinal adverse events in the FDA Adverse Event Reporting System. BMJ Open Diabetes Research and Care. 2018; 6.
Dipeptidyl peptidase-4 inhibitors moderate the risk of genitourinary tract infections associated with sodium-glucose co-transporter-2 inhibitors. Diabetes, Obesity and Metabolism. 2018; 20:740-744.
Data mining differential clinical outcomes associated with drug regimens using adverse event reporting data. Nature Biotechnology. 2016; 34:697-700.
Using Systems Biology-based Analysis Approaches to Identify Mechanistically Significant Adverse Drug Reactions: Pulmonary Complications from Combined Use of Anti-TNFα Agents and Corticosteroids. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science. 2013; 2013:151-155.
A systems approach points to a therapeutic role for retinoids in asparaginase-associated pancreatitis. Science Translational Medicine. 2023; 15.
Pneumocystis jirovecii pneumonia associated with immune checkpoint inhibitors: A systematic literature review of published case reports and disproportionality analysis based on the FAERS database. Frontiers in Pharmacology. 2023; 14.
Association of Pulmonary Sepsis and Immune Checkpoint Inhibitors: A Pharmacovigilance Study. Cancers. 2023; 15.
Do antibody-drug conjugates increase the risk of sepsis in cancer patients? A pharmacovigilance study. Frontiers in Pharmacology. 2022; 13.
FVEstimator: A novel food volume estimator Wellness model for calorie measurement and healthy living. Measurement: Journal of the International Measurement Confederation. 2022; 198.
Mayur Sarangdhar, PhD, Maisam A. Abu-El-Haija, MD, MS ...3/15/2023
Mayur Sarangdhar, PhD, Anil Goud Jegga, DVM, MRes ...6/29/2019