Anil Maharaj BSc (Pharm), PhD
Assistant Professor (tenure-track)
Anil Maharaj is a Canadian pharmacist and researcher focused on clinical pharmacology and pharmacometrics. He obtained his Bachelor of Science at the University of Manitoba’s College of Pharmacy in 2008. After graduation and licensure, he practiced as a clinical pharmacist with the Winnipeg Regional Health Authority, providing pharmaceutical care as part of an integrated clinical care team. To pursue his ongoing research interest in the field of pharmacokinetics, he enrolled in the University of Waterloo’s School of Pharmacy graduate program. Following his doctorate in 2017, Dr. Maharaj pursued two postdoctoral fellowships at St. Jude Children’s Research Hospital and the Duke Clinical Research Institute. Recent career milestones include receiving the Thrasher Research Fund’s Early Career Award and serving as a member of the editorial board of Therapeutic Drug Monitoring. Dr. Maharaj’s overarching career goal is to promote the safe and effective use of medications in children and underserved populations through advanced modeling and simulation techniques.
Appropriate drug dosing information for children and pregnant women is lacking. Often, health care providers are compelled to treat such patients using therapies and dosages that have yet to be adequately evaluated for safety and effectiveness. This can increase the risk of sub-therapeutic drug exposures or drug-related toxicities among these underserved populations. To address this knowledge gap, my laboratory focusses on the application of pharmacometrics to define appropriate dosing strategies in different patient populations.
Pharmacometrics is the field of study that resides at the intersection between drug pharmacology, organism physiology, and mathematics. Its goal is to use computational modeling to define dosing practices that increase drug safety and efficacy. To optimize pharmacotherapy in humans, developed models need to account for inherent interactions between the drug, human physiology, and disease. Model-based dosing recommendations are evaluated using simulated trials with virtual subjects who are designed to resemble real-world patients.
Data used to develop pharmacometric models can range from basic human anatomical/physiological information (e.g., organ-specific weights and blood flows) to tissue-specific drug concentrations following dose administration (e.g., blood and cerebral spinal fluid). In many instances, there remains significant information gaps that impede model development, particularly in populations such as children and pregnant women. To increase confidence in model predictions, the laboratory incorporates benchtop research to investigate and quantify biological differences between patient groups that drive changes in drug effect. Integration of these biological parameters into pharmacometric models provides a fundamental link between the laboratory and the clinic. The strength of this link is a constant driving force for the laboratory to focus on research that can readily be translated towards improving patient clinical care.
Maharaj AR, Wu H, Hornik CP, et al. Simulated Assessment of Pharmacokinetically Guided Dosing for Investigational Treatments of Pediatric Patients With Coronavirus Disease 2019. JAMA Pediatr. 2020;174(10):e202422. doi:10.1001/jamapediatrics.2020.2422
Maharaj AR, Wu H, Zimmerman KO, Autmizguine J, Kalra R, Al-Uzri A, Sherwin CMT, Goldstein SL, Watt K, Erinjeri J, Payne EH, Cohen-Wolkowiez M, Hornik CP. Population pharmacokinetics of olanzapine in children. Br J Clin Pharmacol. 2021 Feb;87(2):542-554. doi: 10.1111/bcp.14414. Epub 2020 Jul 5. PMID: 32497307.
Maharaj, A.R., Wu, H., Hornik, C.P. et al. Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn 47, 199–218 (2020). https://doi.org/10.1007/s10928-020-09684-2
Maharaj, A.R., Gonzalez, D., Cohen-Wolkowiez, M. et al. Improving Pediatric Protein Binding Estimates: An Evaluation of α1-Acid Glycoprotein Maturation in Healthy and Infected Subjects. Clin Pharmacokinet 57, 577–589 (2018). https://doi.org/10.1007/s40262-017-0576-7
Maharaj AR, Edginton AN. Physiologically based pharmacokinetic modeling and simulation in pediatric drug development. CPT Pharmacometrics Syst Pharmacol. 2014 Oct 22;3(11):e150. doi: 10.1038/psp.2014.45. PMID: 25353188; PMCID: PMC4260000.
Reviewer of the Year Award - Therapeutic Drug Monitoring (2019)
Outstanding Achievement in Graduate Studies Honour - University of Waterloo (2017)
W.B. Pearson Medal - Faculty of Science, University of Waterloo (2017)
NSERC Alexander Graham Bell Canada Graduate Scholarship - Doctoral Program (2014)
Merck Canada Ltd. Postgraduate Pharmacy Fellowship Award (2013)
"Evaluation of the Effects of Childhood Obesity on Drug Dosing.” Maharaj, AR. $24,646 USD. Thrasher Research Fund (2019-2021)
Maharaj AR, Wu H, Hornik CP, et al. Simulated Assessment of Pharmacokinetically Guided Dosing for Investigational Treatments of Pediatric Patients With Coronavirus Disease 2019. JAMA Pediatr. 2020;174(10):e202422. doi:10.1001/jamapediatrics.2020.2422
Maharaj AR, Wu H, Zimmerman KO, Autmizguine J, Kalra R, Al-Uzri A, Sherwin CMT, Goldstein SL, Watt K, Erinjeri J, Payne EH, Cohen-Wolkowiez M, Hornik CP. Population pharmacokinetics of olanzapine in children. Br J Clin Pharmacol. 2021 Feb;87(2):542-554. doi: 10.1111/bcp.14414. Epub 2020 Jul 5. PMID: 32497307.
Maharaj, A.R., Wu, H., Hornik, C.P. et al. Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn 47, 199–218 (2020). https://doi.org/10.1007/s10928-020-09684-2
Maharaj, A.R., Gonzalez, D., Cohen-Wolkowiez, M. et al. Improving Pediatric Protein Binding Estimates: An Evaluation of α1-Acid Glycoprotein Maturation in Healthy and Infected Subjects. Clin Pharmacokinet 57, 577–589 (2018). https://doi.org/10.1007/s40262-017-0576-7
Maharaj AR, Edginton AN. Physiologically based pharmacokinetic modeling and simulation in pediatric drug development. CPT Pharmacometrics Syst Pharmacol. 2014 Oct 22;3(11):e150. doi: 10.1038/psp.2014.45. PMID: 25353188; PMCID: PMC4260000.
