Evidence map›Paper›PMID 42897489›Full record

ArticleBMC nephrology2026

External evaluation of population pharmacokinetic models for tacrolimus in kidney transplant recipients: implications for model-informed precision dosing.

Arun Prasath Raju, Priyanka Naithani, Deepesh Kenwar, Sarbpreet Singh, Shiva Kumar Sp, Savita Verma Atri, Vivek Kumar, Anjali Agarwal, Ravindra Prabhu Attur, Mahadev Rao and 3 more

Abstract read
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Article in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Arun Prasath RajuDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India.
Priyanka NaithaniDepartment of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Deepesh KenwarDepartment of Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Sarbpreet SinghDepartment of Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Shiva Kumar SpDepartment of Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Savita Verma AtriDepartment of Pediatric Biochemistry, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Vivek KumarDepartment of Nephrology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Anjali AgarwalDepartment of Anatomy, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Ravindra Prabhu AtturDepartment of Nephrology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Mahadev RaoDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India.
Ashish SharmaDepartment of Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Smita PattanaikDepartment of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India. drs_pattanaik@yahoo.com.
Surulivelrajan MallayasamyDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India. msv.rajan@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTacrolimus, a primary immunosuppressant in kidney transplantation, exhibits variable pharmacokinetics. While model-informed precision dosing (MIPD) using population pharmacokinetic (PopPK) models improve dosing accuracy, models developed in specific populations lack applicability in other populations. We aimed to evaluate the predictive performance of published models integrated in clinical decision support systems (CDSS) in Indian adult renal transplant recipients.

methodsPredictive performance of four published PopPK models were evaluated using an independent dataset of 120 trough concentrations from 60 adult renal transplant recipients. Models were assessed using prediction based and simulation-based diagnostics. Maximum a posteriori (MAP) Bayesian prediction was also used to evaluate the model performance.

resultsA priori predictions (PRED) for all four models demonstrated systematic underprediction, with relative mean prediction error (rME) ranging from - 25.0% to -64.0%, failing clinical acceptability thresholds. However, integrating patient-specific data via MAP Bayesian estimation corrected this bias and improved precision. Notably, all models achieved higher accuracy and precision, with more than 60% of individual predictions falling within [Formula: see text] ([Formula: see text]) of the observed values. Additionally, scaling down the clearance parameter by 30% mitigated the systematic bias observed in the initial a priori predictions.

conclusionsPublished PopPK models exhibited significant systematic bias when applied to Indian renal transplant recipients, posing a substantial risk of overdosing if used without adjustment for initial dose selection. However, these models adapted using MAP Bayesian approach or scaling of clearance parameter demonstrate better predictive performance in this cohort, suggesting they can guide proactive precision dosing in the Indian cohort. Prospective studies evaluating the target attainment and clinical outcomes are required to establish their clinical applicability.

Indexed as

Immunosuppressive AgentsKidney TransplantationModels, BiologicalPrecision MedicineTacrolimusAdultBayes TheoremDose-Response Relationship, DrugFemaleHumansMaleMiddle AgedImmunosuppressive AgentsTacrolimus

Identifiers

PMID42897489
PMCPMC13678889

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.