Comment on “Model Informed Precision Dosing of Tacrolimus in Children Following Heart Transplant”

Dear Editor, We read with great interest the study by Wagstaff et al,1 which evaluated a decision support tool (DST) based on a population pharmacokinetic (popPK) model to individualize tacrolimus dosing in pediatric heart transplant recipients. This innovative work addresses a key challenge in pediatric transplantation—the safe and timely attainment of therapeutic immunosuppression in a population marked by substantial pharmacokinetic variability. While the study represents progress toward precision dosing, several methodological and interpretive aspects merit further consideration for clinical translation. The prospective evaluation of a Bayesian DST marks an important translational step beyond model development. However, the single-center design and small sample size (n = 15) limit generalizability, given the ethnically homogeneous cohort and limited genetic characterization. The use of a historical rather than concurrent control introduces potential bias from temporal changes in clinical practice.2 Although restricting analysis to first-time pediatric transplant recipients ensures population uniformity, the post hoc exclusion of patients receiving continuous renal replacement therapy (CRRT) highlights the need for clearer inclusion criteria to ensure consistent applicability. The reported 3-day reduction in time to stable therapeutic tacrolimus concentrations (P = .03) is clinically meaningful. Nonetheless, the small sample and absence of randomization raise concerns about statistical stability and confounding factors such as age and concomitant antifungal use. Reporting confidence intervals for mean differences would strengthen interpretation by defining precision around estimates. While the DST's fidelity to NONMEM modeling was validated in silico,3 clinical performance depends on appropriate covariate weighting and sampling frequency areas underexplored in the manuscript. Reliance on retrospective data for model calibration is reasonable, yet the dependence on creatinine clearance as a surrogate for tacrolimus disposition warrants further justification. As the authors note, creatinine clearance likely reflects broader physiological influences, such as hepatic perfusion, rather than renal elimination, underscoring the need for refinement of structural assumptions. Moreover, the absence of CYP3A5 genotyping during DST calibration limits predictive robustness. Incorporating pharmacogenomic data could enhance parameter individualization and reduce misdosing in expressor phenotypes.4 Clinically, the DST offers a promising framework for model-informed precision dosing (MIPD) in pediatric transplantation. The observed reduction in time to therapeutic range may translate to shorter hospital stays, fewer blood draws, and improved graft outcomes. However, implementation outside the electronic medical record (EMR) constrains scalability. Seamless EMR integration, automated data capture, and optimized interfaces are prerequisites for widespread adoption. Caution remains warranted for patients with dynamic conditions, such as those on CRRT or interacting medications, pending further validation. The work by Wagstaff et al represents a significant advance toward actionable MIPD for tacrolimus in pediatric heart transplantation. Future studies should pursue multicenter validation with diverse cohorts, integrate pharmacogenomic data, and assess long-term outcomes such as rejection and nephrotoxicity. Aligning quantitative modeling with clinical usability can enable MIPD to evolve from research to standard-of-care in pediatric transplant immunosuppression. None. No external funding was received for this work. The authors declare no conflicts of interest. Generative AI tools (ChatGPT-5 and Paperpal) were used only for grammar, structure, and stylistic refinement. All interpretations, clinical perspectives, and critiques were independently developed and verified by the authors. Princy Kashyap contributed to conceptualization, supervision, and final review of the manuscript. Manoj Kumar contributed to validation, methodological critique, and editing. Ramenani Hari Babu contributed to drafting and clinical interpretation, while Mahesh Kumar Gupta conducted literature review, data verification, and initial manuscript preparation. All authors reviewed and approved the final version prior to submission. No new data were generated or analyzed.