Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation
Altynova S. Saliev T. Asanova A. Kozybayeva Z. Rakhimzhanova S. Bolatov A.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Pharmaceuticals
2026#19Issue 1
Optimizing immunosuppressant dosing presents significant challenges in kidney transplantation due to narrow therapeutic ranges and considerable inter-patient pharmacokinetic differences. Emerging strategies for precision dosing, encompassing Bayesian population pharmacokinetic models, pharmacogenomic integration, and artificial intelligence algorithms, aim to enhance drug monitoring by moving beyond traditional trough-based approaches. This review critically assesses available evidence for predictive dosing models targeting immunosuppressants, including calcineurin inhibitors, antimetabolites, and mTOR inhibitors in kidney transplant patients. Available observational and simulation studies demonstrate substantial methodological diversity, with Bayesian PopPK-guided strategies showing 15–35% better target exposure achievement compared to trough-based monitoring. The absence of pooled estimates precludes a precise summary effect size, and evidence from randomized controlled trials remains limited. Machine learning models, particularly for tacrolimus, frequently reduced prediction error relative to traditional regression approaches, but substantial heterogeneity in study design, outcome definitions, and external validation limits quantitative synthesis. Hybrid Bayesian–AI frameworks and explainable AI tools show conceptual promise but are largely supported by proof-of-concept studies rather than reproducible clinical implementations. Overall, Bayesian pharmacokinetic modelling represents the most mature and clinically interpretable approach for precision dosing in transplantation, whereas AI-driven and hybrid systems remain investigational. Key gaps include the need for standardized reporting, rigorous risk-of-bias assessment, prospective validation, and clearer regulatory and implementation pathways to support safe and equitable clinical adoption.
antimetabolites , artificial intelligence (AI) , Bayesian pharmacokinetic , calcineurin inhibitors , drug monitoring , kidney transplantation , machine learning , mTOR inhibitors
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Department of Medical and Regulatory Affairs, Corporate Fund “University Medical Center”, Astana, 010000, Kazakhstan
Institute for Fundamental and Applied Medical Research, S.D. Asfendiyarov Kazakh National Medical University, Almaty, 050000, Kazakhstan
Department of Science, Corporate Fund “University Medical Center”, Astana, 010000, Kazakhstan
Clinical Academic Department of Internal Medicine, Corporate Fund “University Medical Center”, Astana, 010000, Kazakhstan
Clinical Academic Department of Paediatrics, Corporate Fund “University Medical Center”, Astana, 010000, Kazakhstan
Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
Department of Medical and Regulatory Affairs
Institute for Fundamental and Applied Medical Research
Department of Science
Clinical Academic Department of Internal Medicine
Clinical Academic Department of Paediatrics
Shenzhen University Medical School
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