Phenotypic personalized tacrolimus dosing: a step toward precision immunosuppression in liver transplantation

Tacrolimus, a calcineurin inhibitor, remains the cornerstone of immunosuppression in liver transplantation. This narrow therapeutic index medication carries a fine balance, with a general lack of consensus regarding what drug levels are optimal. Underdosing can cause graft rejection while overdosing risks patients with severe adverse effects such as nephrotoxicity and neurotoxicity, along with opportunistic infections and post-transplant lymphoproliferative disorder. The traditional approach to tacrolimus dosing has relied on therapeutic drug monitoring (TDM), clinical judgment, pharmacokinetics, and more recently, pharmacogenetics, particularly CYP3A5 and CYP3A4 genotyping to estimate metabolic capacity (1-3). Despite TDM, it remains challenging to achieve and maintain target tacrolimus trough concentrations, especially in the early post-transplant period when immunological response is highest. Tacrolimus exhibits substantial inter- and intra-individual pharmacokinetic variability, resulting in sub-therapeutic or supra-therapeutic blood drug levels despite comparable dosing. High intra-patient variability (IPV) in blood tacrolimus level, quantified by the coefficient of variation in trough levels, is associated with poor graft outcomes including rejection and allograft loss. Variability in tacrolimus disposition and response arises from multiple factors including CYP3A4/5 polymorphisms, adherence, and concomitant medications. Fast metabolizers, often with low tacrolimus blood concentration to daily dose (C/D) ratios, require larger doses and face greater risks of nephrotoxicity and rejection (4-6). A few studies have reported that co-administration of mycophenolate mofetil may alter tacrolimus metabolism by competing for CYP3A (6). Similarly, corticosteroids can induce the expression of CYP3A4 and the efflux transporter P-glycoprotein (P-gp), potentially affecting tacrolimus clearance; however, findings remain conflicting (6). Notably, increased tacrolimus levels have been observed following corticosteroid dose reduction or withdrawal (6).

In their recent phase 2 randomized controlled trial published in Nature Communications, Khong and colleagues introduce an innovative approach to address imprecision of tacrolimus therapy using phenotypic personalized medicine (PPM) (7). Building upon prior pilot data, the authors conducted a single-center, partially blinded trial in 62 adult liver transplant recipients comparing PPM-guided tacrolimus dosing with standard-of-care (SOC) clinician-guided dosing. The primary outcome was the percentage of post-transplant days with large (>2 ng/mL) deviations from the target drug trough level. The results are both statistically and clinically significant. The PPM group experienced 24.2% of days with large deviations compared to 38.4% in the SOC group (difference −14.2%, P=0.029). Moreover, exploratory analyses demonstrated a 33.3% shorter median hospital stay (10 vs. 15 days, P=0.0026) and faster normalization of aspartate aminotransferase (AST) levels (median 6 vs. 8.5 days, P=0.014) in the PPM group. Importantly, these improvements occurred without an increase in adverse events such as rejection, neurotoxicity, or nephrotoxicity.

The innovation of the PPM platform lies in their early parabolic personalized dosing (PPD) methodology (8). Unlike pharmacogenetic or population pharmacokinetic models, PPD does not rely on mechanistic assumptions or predefined genetic data. Instead, it constructs a second-order algebraic “phenotypic response surface” that maps drug dose inputs to measurable patient-specific outputs, such as tacrolimus trough levels. These coefficients are derived directly from clinical data by fitting at least three prior dose-response points, enabling adaptive, real-time dose optimization. This inductive approach inherently accounts for underlying molecular and pharmacokinetic determinants without explicitly modeling them, allowing PPD to remain disease mechanism-independent and indication-agnostic. In the clinical trial, tacrolimus dosing was guided by patient-specific parabolic functions that were recalibrated as needed following regimen changes, resulting in reduced variability and improved maintenance of therapeutic drug levels compared with standard physician-guided dosing (8).

The implications of this work are substantial. First, it demonstrates that real-time phenotypic personalization can meaningfully improve dosing precision even in complex settings like liver transplantation, where pharmacokinetics is influenced by early tacrolimus exposure after liver transplantation, graft function, drug interactions, and fluctuating physiology (9-11). Second, unlike pharmacogenetics, which captures static genetic influences on metabolism, PPM captures dynamic inter- and intra-individual variability, offering a complementary precision tool. Third, the approach is mechanism-agnostic, meaning it can be potentially generalized to other drugs with narrow therapeutic indices, such as chemotherapeutics, antimicrobials, or anticoagulants (12).

However, several limitations should be considered. The trial was conducted at a single center with a relatively small sample size and short follow-up period, limiting generalizability and preventing assessment of long-term clinical outcomes such as chronic rejection, graft survival, or mortality. The phenotype employed in the approach was the drug trough level, which is a pharmacokinetic parameter per se. Although the reduction in large deviations of trough drug level is promising, it remains to be seen whether these pharmacokinetic improvements translate into improved long-term graft health and patient survival—outcomes that would justify widespread implementation. Another consideration for long-term application is how the formula would perform if target trough levels changed over time, and whether standard-of-care dosing would be required to achieve a new goal range. Additionally, it would have been valuable to compare this approach with other pharmacogenomic or pharmacokinetic predictive modeling strategies.

Another consideration is operational. While the authors note that PPM calculations require similar clinician time as standard dosing, broader implementation would require integration into electronic health records and transplant pharmacy workflows to ensure seamless and timely dosing recommendations. Furthermore, while this study focused on immediate post-transplant inpatient management, long-term outpatient dosing remains an untested frontier for PPM. The PPM also remain relying on tedious TDM.

Finally, the philosophical underpinnings of PPM are worth reflecting upon. The approach embraces a complex systems perspective rather than a reductionist one, recognizing that biological responses emerge from dynamic interactions rather than linear, gene-based predictions (7). This conceptual shift aligns with broader trends in systems pharmacology and precision medicine, which increasingly seek to integrate multidimensional data to optimize therapy.

In conclusion, Khong et al. present compelling evidence that PPM can improve tacrolimus dosing precision in liver transplantation, with associated benefits in hospital stay and early biochemical recovery. However, future studies are needed to evaluate how PPM integrates with CYP3A4 and CYP3A5 genotyping to account for pharmacogenetic variability, the impact of accurate timing of tacrolimus trough level measurement, patient compliance with dosing schedules, and other factors affecting PK/PD. Moreover, real-world effectiveness must consider different formulations, routes of administration (oral, G-tube, NG-tube), and bioavailability variations in pediatric and adult populations, as well as drug interactions with concomitant medications, herbs, or dietary supplements (9). Further limitations include potential confounding by other immunosuppressive agents, viral infections such as Epstein-Barr virus (EBV) or cytomegalovirus (CMV), iron overload, or anatomic complications like biliary strictures that can mimic rejection. Larger multicenter trials incorporating these variables are warranted to fully define the role of PPM in optimizing post-transplant immunosuppression and long-term graft outcomes.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, HepatoBiliary Surgery and Nutrition. The article has undergone external peer review.

Peer Review File: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2025-620/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2025-620/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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