Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

Factors and interventions affecting tacrolimus intrapatient variability: A systematic review and meta-analysis

BACKGROUNDS

Tacrolimus is a cornerstone of posttransplantation immunosuppressive regimens. Despite routine monitoring, the efficacy of its trough concentrations in reflecting drug concentration fluctuations is limited. Intrapatient variability (IPV) emerges as a novel monitoring marker for predicting clinical outcomes. However, understanding the factors affecting IPV and assessing interventions to address it remain enigmatic, posing a conundrum in clinical management.

OBJECTIVES

This systematic review aimed to investigate a spectrum of factors affecting IPV and assess the effect of strategic interventions, thereby charting a course for enhanced clinical stewardship.

METHODS

We electronically searched of PubMed, Embase, and the Cochrane Library databases for studies investigating factors and interventions affecting IPV up to October 2023. Two reviewers independently screened literature, extracted data, and assessed quality, using RevMan 5.4.1 software for meta-analysis.

RESULTS

A total of 15 randomized controlled trials (RCTs), 34 cohort studies, and 20 self-controlled studies were included. The results indicated that IPV was significantly higher in cytochrome P450 3A5 (CYP3A5) expressers, nonadherent patients, patients taking proton pump inhibitors or statins, and Black or African American recipients, whereas recipients consuming extended-release formulation exhibited lower IPV. Additionally, the participation of pharmacists had a positive effect on improving IPV.

CONCLUSIONS

Factors affecting IPV encompassed genotype, formulation, adherence, drug combinations, and ethnicity, with each factor exerting varying degrees of effect. Identifying these factors was crucial for developing targeted intervention strategies. While the participation of pharmacists held a promise in improving IPV, further investigation of interventions such as mobile technology, educational measures to enhance adherence, and personalized dosing regimens was warranted.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Evaluating the evidence for genotype-informed Bayesian dosing of tacrolimus in children undergoing solid organ transplantation: A systematic literature review

Tacrolimus, a calcineurin inhibitor, is a highly effective immunosuppressant used in solid organ transplantation (SOT). However, it is characterized by a narrow therapeutic range and high inter-patient variability in pharmacokinetics. Standard weight-based dosing followed by empiric dose titration is suboptimal in controlling drug concentrations, increasing risk of rejection or toxicity, particularly in the initial months post transplantation. This review explores the potential of combined pre-transplant genotyping and pharmacokinetic (PK) modelling to improve tacrolimus dosing in paediatric SOT recipients. A systematic search of Medline, Embase and Cochrane databases identified studies published between March 2013 and March 2023 that investigated genotype- and PK model-informed tacrolimus dosing in children post-SOT. The Newcastle-Ottawa Scale assessed study quality. Seven studies encompassing paediatric kidney, heart, liver and lung transplants reported using genotype and model-informed dosing. A combination of clinical and genetic factors significantly impacts tacrolimus clearance and thus initial dose recommendation. Body size, transplant organ and co-medications were consistently important, while either time post-transplant or haematocrit emerged in some studies. Several models were identified, however, with limitations evident in some and with absence of evidence for their effectiveness in optimizing initial and subsequent dosing. This review highlights the development of PK models in paediatric SOT that integrate genotype and clinical covariates to personalize early tacrolimus dosing. While promising, prospective studies are needed to validate and confirm their effectiveness in improving time to therapeutic concentrations and reducing under- or overexposure. This approach has the potential to optimize tacrolimus therapy in paediatric SOT, thereby improving outcomes.

A descriptive study of the single-nucleotide polymorphisms known to affect the Tacrolimus trough concentration per dose, among a population of kidney failure patients in a tertiary hospital in Ghana

BACKGROUND

The burden of chronic kidney disease (CKD) and kidney failure in Ghana is on the ascendency, with the prevalence of CKD estimated at 13.3%. Patients with CKD who progress to kidney failure require life sustaining kidney replacement therapy (KRT) which is almost exclusively available in Ghana as haemodialysis. Kidney transplantation is considered the best KRT option for patients with irreversible kidney failure due to its relative cost efficiency as well as its superiority in terms of survival and quality of life. However, because transplants may trigger an immune response with potential organ rejection, immunosuppressants such as tacrolimus dosing are required.

OBJECTIVE

This study sought to determine single nucleotide polymorphisms in CYP3A5, CYP3A4 and MDR1 genes that affect the pharmacokinetics of Tacrolimus in a population of Ghanaian patients with kidney failure.

METHOD

This cross-sectional study comprised of 82 kidney failure patients undergoing maintenance haemodialysis at the Renal and Dialysis unit of Korle-Bu Teaching Hospital (KBTH). Clinical and demographic data were collected and genomic DNA isolated. Samples were genotyped for specific SNPs using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP).

RESULTS

Participants, 58/82 (70.73%) harbored the wildtype CYP3A5*1/*1 AA genotype, 20/82 (24.39%) carried the heterozygous CYP3A5*1/*3 AG genotype, and 4/82 (4.88%) had the homozygous mutant CYP3A5*3/*3 GG genotype. Also, 6/82 (7.32%) carried the wildtype AA genotype, 11/82 (13.41%) had the heterozygous AG genotype, and 65/82 (79.27%) harbored the homozygous mutant GG genotype of CYP3A4*1B (-290 A>G). For MDR1_Ex21 (2677 G>T), 81/82 (98.78%) carried the wildtype GG genotype, while 1/82 (1.22%) had the heterozygous GT genotype. For MDR1_Ex26 (3435 C>T), 63/82 (76.83%) had the wildtype CC genotype, while 18/82 (21.95%) carried the heterozygous CT genotype, and 1/82 (1.22%) harbored the mutant TT genotype.

CONCLUSION

SNPs in CYP3A4, CYP3A5, and MDR1 genes in a population of Ghanaian kidney failure patients were described. The varying SNPs of the featured genes suggest the need to consider the genetic status of Ghanaians kidney failure patients prior to transplantation and tacrolimus therapy.

Removing the physician from the equation: Patient-controlled, home-based therapeutic drug self-monitoring of tacrolimus

The dosing of tacrolimus, which forms the backbone of immunosuppressive therapy after kidney transplantation, is complex. This is due to its variable pharmacokinetics (both between and within individual patients), narrow therapeutic index, and the severe consequences of over- and underexposure, which may cause toxicity and rejection, respectively. Tacrolimus is, therefore, routinely dosed by means of therapeutic drug monitoring (TDM). TDM is performed for as long as the transplant functions and frequent and often lifelong sampling is therefore the rule. This puts a significant burden on patients and transplant professionals and is associated with high healthcare-associated costs. Furthermore, by its very nature, TDM is reactive and has no predictive power. Finally, the current practice of TDM does not foresee in an active role for patients themselves. Rather, the physician or pharmacist prescribes the next tacrolimus dose after obtaining the concentration measurement test results. In this article, we propose a strategy of patient-controlled, home-based, self-TDM of the immunosuppressant tacrolimus after transplantation. We argue that with the combined use of population tacrolimus pharmacokinetic models, home-based sampling by means of dried blood spotting and implementation of telemedicine, this may become a feasible approach in the near future.

Precise infliximab exposure and pharmacodynamic control to achieve deep remission in paediatric Crohn's disease (REMODEL-CD): study protocol for a multicentre, open-label, pragmatic clinical trial in the USA

INTRODUCTION

The only biologic therapy currently approved to treat moderate to severe Crohn's disease in children (<18 years old) are those that antagonise tumour necrosis factor-alpha (anti-TNF). Therefore, it is critically important to develop novel strategies that maximise treatment effectiveness in this population. There is growing evidence that rates of sustained corticosteroid-free clinical remission, endoscopic healing and drug durability considerably improve when patients receive early anti-TNF dose optimisations guided by reactive or proactive therapeutic drug monitoring and pharmacodynamic monitoring. In response, our team has developed a personalised and scalable infliximab dosing intervention that starts with dose selection and continues throughout maintenance to optimise drug exposure. We hypothesise that a precision dosing strategy starting from induction and targeting dose-specific pharmacokinetic and pharmacodynamic endpoints throughout therapy will significantly improve outcomes compared with a conventional dosing strategy.

METHODS AND ANALYSIS

Conduct a clinical trial to assess rates of deep remission between Crohn's disease patients receiving infliximab with precision dosing (n=90) versus conventional care (n=90). Patients (age 6-22 years) will be recruited from 10 medical centres in the USA. Each centre has been selected to provide either precision dosing or conventional care dosing. Precision dosing includes the use of a clinical decision support tool (RoadMAB) from the start of infliximab to achieve specific (personalised) trough concentrations and specific pharmacodynamic targets (at doses 3, 4 and 6). Conventional care includes the use of a modified infliximab starting dose (5 or 7.5 mg/kg based on the pretreatment serum albumin) with a goal to achieve maintenance trough concentrations of 5-10 µg/mL. The primary endpoint is year 1 deep remission defined as a combination of clinical remission (paediatric Crohn's disease activity index<10 (child) or a Crohn's disease activity index<150 (adults)), off prednisone>8 weeks and endoscopic remission (simple endoscopic severity-Crohn's disease≤2).

ETHICS AND DISSEMINATION

). The study protocol has been approved by the Cincinnati Children's Hospital Medical Centre Institutional Review Board. Study results will be disseminated in peer-reviewed journals and presented at scientific meetings.

TRIAL REGISTRATION NUMBER

NCT05660746.

Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review

Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.

A prospective validation of a population pharmacokinetic model of tacrolimus in Tunisian kidney transplant patients

BACKGROUND

A prospective validation of pharmacokinetic population (Pk pop) of Tacrolimus (Tac) for dose adjustment in kidney transplant patients was assessed in only one study. The present study was aimed at prospectively evaluating the performance of our previously developed Tac- Pk pop model in predicting trough concentration (C0) in Tunisian kidney transplant patients.

PATIENTS AND METHOD

It was a prospective study including patients who had undergone kidney transplantation at Monastir-Nephrology Department. The population study was divided into adherence and control groups.

RESULTS

A total of 198 C0 (30 patients) were analyzed. The proportion of C0 within TR was 63.9% and 38.0% in the adhesion and control group, respectively. The percentage of C0 within TR was significantly higher in the adherence group during both early and late post-transplant period (p = 0.03 and 0.04, respectively). This percentage was found to be significantly higher during the third C0 monitoring and thereafter in the adherence group compared with the control group (65.8% vs 41%, respectively).

CONCLUSION

Tac dose proposal based on this model could be helpful to improve clinical outcomes in our population by reducing the risk of acute rejection and this immunosuppressant's toxic side effects.

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

The Role of Therapeutic Drug Monitoring in Children

The use of biologic therapies has changed the treatment landscape for children with inflammatory bowel disease. While the novel biologics have improved clinical outcomes, there remains a significant gap in achieving endoscopic remission, prolonged steroid-free remission, and drug durability. Contributing to this gap is the paucity of real-world pharmacokinetic studies in children and a failure to dose optimize therapy during induction. Emerging data from a pediatric clinical trial and several observational studies have shown that the combination of proactive therapeutic drug monitoring and achievement of early therapeutic concentrations is effective in achieving improved outcomes. The next steps will be to leverage these past studies to develop more innovative clinical trials to properly assess the safety and effectiveness of proactive therapeutic drug monitoring in children.

Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation

The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.

Individualized dosing algorithms for tacrolimus in kidney transplant recipients: current status and unmet needs

INTRODUCTION

Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus' pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations.

AREAS COVERED

Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023.

EXPERT OPINION

Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

AIM

Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.

METHODS

Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.

RESULTS

Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing.

CONCLUSION

A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

The Appropriately Designed TDM Clinical Trial: Endpoints, Pitfalls, and Perspectives

BACKGROUND

Appropriately designed clinical trials can provide the evidence needed to broadly implement therapeutic drug monitoring (TDM). In the past 30 years, some stunning successes but also some fascinating failures in demonstrating the benefits of TDM have been observed. Future TDM studies can be designed based on this experience.

METHODS

The manuscript is based on a combination of personal experience and published articles and discusses several aspects of the design and conduct of TDM studies.

RESULTS

Recommendations are provided to reduce the risk of protocol violations and to maximize the potential impact of a TDM study on clinical practice.

CONCLUSIONS

There are lessons that can be learned from previous experience, and this article gives an overview of potential TDM study designs, endpoints, pitfalls, and perspectives.

Longitudinal Exposure to Tacrolimus and New-Onset Diabetes Mellitus in Renal Transplant Patients

PURPOSE

Tacrolimus is an immunosuppressant widely used in transplantations requiring mandatory concentration-controlled dosing to prevent acute rejection or adverse effects, including new-onset diabetes mellitus (NODM). However, no relationship between NODM and tacrolimus exposure has been established. This study aimed to evaluate the relationship between cumulative tacrolimus exposure and NODM occurrence.

METHODS

A total of 452 kidney transplant patients were included in this study. Sixteen patients developed NODM during the first 3 months after transplant. We considered all tacrolimus concentration (C0) values collected until the diagnosis of NODM in these patients and until 3 months after transplant in the others. New tacrolimus cumulative exposure metrics were derived from the time profile of the tacrolimus morning predose concentration, C0: the percentage of C0 values > cutoff, the average of C0 values above the cutoff, and the percentage of the area under C0 versus time curve, AUCC0, above the cutoff. The cutoff chosen was 15 ng/mL, corresponding to the higher end of the therapeutic range for the early post-transplant period. The influence of these metrics on NODM and other clinical and biological characteristics was investigated using the Cox models.

RESULTS

The percentage of C0 > 15 mcg/L was statistically different between patients with and without NODM (P = 0.01). Only these tacrolimus C0-derived metrics were significantly associated with an increased risk of NODM [HR: 1.73 (1.43-2.10, P < 0.001)].

CONCLUSION

This study shows that tacrolimus concentrations >15 mcg/L affect the incidence of NODM.

Machine learning in lung transplantation: Where are we?

Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Body composition is associated with tacrolimus pharmacokinetics in kidney transplant recipients

PURPOSE

A population pharmacokinetic (popPK) model may be used to improve tacrolimus dosing and minimize under- and overexposure in kidney transplant recipients. It is unknown how body composition parameters relate to tacrolimus pharmacokinetics and which parameter correlates best with tacrolimus exposure. The aims of this study were to investigate which body composition parameter has the best association with the pharmacokinetics of tacrolimus and to describe this relationship in a popPK model.

METHODS

Body composition was assessed using bio-impedance spectroscopy (BIS). Pharmacokinetic analysis was performed using nonlinear mixed effects modeling (NONMEM). Lean tissue mass, adipose tissue mass, over-hydration, and phase angle were measured with BIS and then evaluated as covariates. The final popPK model was evaluated using goodness-of-fit plots, visual predictive checks, and a bootstrap analysis.

RESULTS

In 46 kidney transplant recipients, 284 tacrolimus concentrations were measured. The base model without body composition parameters included age, plasma albumin, plasma creatinine, CYP3A4 and CYP3A5 genotypes, and hematocrit as covariates. After full forward inclusion and backward elimination, only the effect of the phase angle on clearance (dOFV =  - 13.406; p < 0.01) was included in the final model. Phase angle was positively correlated with tacrolimus clearance. The inter-individual variability decreased from 41.7% in the base model to 34.2% in the final model. The model was successfully validated.

CONCLUSION

The phase angle is the bio-impedance spectroscopic parameter that correlates best with tacrolimus pharmacokinetics. Incorporation of the phase angle in a popPK model can improve the prediction of an individual's tacrolimus dose requirement after transplantation.

Important lack of difference in tacrolimus and mycophenolic acid pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients

AIM

To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians.

METHODS

Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit.

RESULTS

No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p = .07 and p = .08).

CONCLUSION

There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.

Predicting model‐informed precision dosing: A test‐case in tacrolimus dose adaptation for kidney transplant recipients

Before investing resources into the development of a precision dosing (model‐informed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one‐compartment pharmacokinetic (PK) model with time‐dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13.5 ng/ml and an acceptable range of 12–15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post‐transplant and beyond. By incorporating correlated within‐patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit. This yielded a Bayesian feedback dosing algorithm accurately predicting future trough concentrations and adapting each dose to reach a target concentration. Simulated concentration‐time profiles were then used to quantify MIPD‐based improvement on three end points: average PTA increased from 28% to 39%, median time to three concentrations in target decreased from 10 to 7 days, and mean log‐squared distance to target decreased from 0.080 to 0.055. A study of 200 patients was predicted to have sufficient power to demonstrate these nuanced PK end points reliably. These simulations supported our decision to develop a precision dosing tool for tacrolimus and test it in a prospective trial.

The Relationship between Initial Tacrolimus Metabolism Rate and Recipients Body Composition in Kidney Transplantation

There are several premises that the body composition of kidney transplant recipients may play a role in tacrolimus metabolism early after transplantation. The present study aimed at analyzing the relationship between the body composition parameters assessed by bioimpedance analysis (BIA) and initial tacrolimus metabolism. Immediately prior to transplantation, BIA using InBody 770 device was performed in 122 subjects. Tacrolimus concentration-to-dose (C/D) ratio was calculated based on the first blood trough level measurement. There was no difference in phase angle, visceral fat area, lean body mass index (LBMI) and the proportion of lean mass as a percentage of total body mass between the subgroups of slow and fast metabolizers. However, subjects with LBMI ≥ median value of 18.7 kg/m², despite similar initial tacrolimus dose per kg of body weight, were characterized by a significantly lower tacrolimus C/D ratio (median 1.39 vs. 1.67, respectively; p < 0.05) in comparison with the subgroup of lower LBMI. Multivariate regression analysis confirmed that age (r_partial = 0.322; p < 0.001) and LBMI (r_partial = −0.254; p < 0.01) independently influenced the tacrolimus C/D ratio. A LBMI assessed by BIA may influence the tacrolimus metabolism in the early post-transplant period and can be a useful in the optimization of initial tacrolimus dosing.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

BACKGROUND

Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.

METHODS

A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.

RESULTS

The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.

CONCLUSIONS

Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

Towards next-generation personalization of tacrolimus treatment: a review on advanced diagnostic and therapeutic approaches

The benefit of personalized medicine is that it allows the customization of drug therapy - maximizing efficacy while avoiding side effects. Genetic polymorphisms are one of the major contributors to interindividual variability. Currently, the only gold standard for applying personalized medicine is dose titration. Because of technological advancements, converting genotypic data into an optimum dose has become easier than in earlier years. However, for many medications, determining a personalized dose may be difficult, leading to a trial-and-error method. On the other hand, the technologically oriented pharmaceutical industry has a plethora of smart drug delivery methods that are underutilized in customized medicine. This article elaborates the genetic polymorphisms of tacrolimus as case study, and extensively covers the diagnostic and therapeutic technologies which aid in the delivery of personalized tacrolimus treatment for better clinical outcomes, thereby providing a new strategy for implementing personalized medicine.

Influence of a low-dose tacrolimus protocol on the appearance of de novo donor-specific antibodies during 7 years of follow-up after renal transplantation

BACKGROUND

Tacrolimus (TAC) is a key immunosuppressant drug for kidney transplantation (KTx). However, the optimal serum trough level of TAC for good long-term outcomes remains unclear. This study aimed to investigate the relationship between the maintenance TAC trough level and the appearance of de novo donor-specific anti-human leukocyte antigen (HLA) antibodies (dnDSAs).

METHODS

A total of 584 KTx recipients were enrolled in this study, of whom 164 developed dnDSAs during the follow-up period and 420 did not.

RESULTS

We found no significant relationship between TAC trough level during the follow-up period and dnDSA incidence. Patients who developed dnDSAs had a significantly greater number of HLA-A/B/DR mismatches (3.4 ± 1.3 versus 2.8 ± 1.5; P < 0.001), were more likely to have preformed DSAs (48.2% versus 27.1%; P < 0.001) and showed poor allograft outcome.

CONCLUSIONS

There was no clear relationship between TAC trough level and dnDSA incidence for KTx recipients whose TAC trough levels were kept within the narrow range of 4-6 ng/mL during the immunosuppression maintenance period.

Therapeutic Drug Monitoring Strategies for Envarsus in De Novo Kidney Transplant Patients Using Population Modelling and Simulations

INTRODUCTION

Tacrolimus, the cornerstone of transplantation immunosuppression, is a narrow therapeutic index drug with a low and highly variable bioavailability. Therapeutic drug monitoring based on trough level assessment is mandatory in order to target a personalised exposure and avoid both rejection and toxicity. Population pharmacokinetic (POPPK) models might be a useful tool for improving early attainment of target range by guiding initial doses until steady state is reached and trough levels can be reliably used as surrogate marker of exposure. Here we present the first POPPK for predicting the initial doses of the once-daily prolonged release tacrolimus Envarsus (LCPT) in adult kidney recipients.

METHODS

The model was developed exploiting the data from a recent pharmacokinetic randomised clinical study, in which 69 de novo kidney recipients, 33 of whom treated with LCPT, underwent an intensive blood sampling strategy for tacrolimus including four complete pharmacokinetic profiles.

RESULTS

The complex and prolonged absorption of LCPT is well described by the three-phase model that incorporates body weight and CYP3A5 genotype as significant covariates accounting for a great proportion of the inter-patient variability: in particular, CYP3A5*1/*3 expressors had a 66% higher LCPT clearance. We have then generated by simulation a personalised dosing strategy based on the model that could improve the early attainment of therapeutic trough levels by almost doubling the proportion of patients within target range (69.3% compared to 36.1% with the standard body weight-based approach) on post-transplantation day 4 and significantly reduce the proportion of overexposed patients at risk of toxicity.

CONCLUSIONS

A POPPK model was successfully developed for LCPT in de novo kidney recipients. The model could guide a personalised dosing strategy early after transplantation. For the model to be translated into clinical practice, its beneficial impact of earlier attainment of therapeutic trough levels should be demonstrated on hard clinical outcomes in further studies.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

BACKGROUND AND OBJECTIVES

Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions.

METHODS

The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020.

RESULTS

Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies).

CONCLUSION

The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

Therapeutic drug monitoring of immunosuppressive drugs in hepatology and gastroenterology

Immunosuppressive drugs have been key to the success of liver transplantation and are essential components of the treatment of inflammatory bowel disease (IBD) and autoimmune hepatitis (AIH). For many but not all immunosuppressants, therapeutic drug monitoring (TDM) is recommended to guide therapy. In this article, the rationale and evidence for TDM of tacrolimus, mycophenolic acid, the mammalian target of rapamycin inhibitors, and azathioprine in liver transplantation, IBD, and AIH is reviewed. New developments, including algorithm-based/computer-assisted immunosuppressant dosing, measurement of immunosuppressants in alternative matrices for whole blood, and pharmacodynamic monitoring of these agents is discussed. It is expected that these novel techniques will be incorporate into the standard TDM in the next few years.

Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Evaluation of two software using Bayesian methods for monitoring exposure and dosing once-daily intravenous busulfan in paediatric patients receiving haematopoietic stem cell transplantation

AIM

To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment.

METHODS

Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies.

RESULTS

Thirty-two paediatric patients (0.2-16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1-14.6% using InsightRX and from 3.4-7.8% using NextDose. Precision ranged from median RMSE of 0.19-0.32 mg·h·L^-1 for InsightRX and 0.08-0.1 mg·h·L^-1 for NextDose. A median reduction in busulfan clearance from day 1 to day 4 was observed in the clinical data (-10.9%), when using InsightRX (-18.6%) and with NextDose (-14.7%).

CONCLUSION

Bayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Tacrolimus Area Under the Concentration Versus Time Curve Monitoring, Using Home-Based Volumetric Absorptive Capillary Microsampling

BACKGROUND

Therapeutic drug monitoring (TDM) of tacrolimus (Tac) is mandatory in renal transplant recipients (RTxR). Area under the concentration versus time curve (AUC) is the preferred measure for Tac exposure; however, for practical purposes, most centers use trough concentrations as a clinical surrogate. Limited sampling strategies in combination with population pharmacokinetic model-derived Bayesian estimators (popPK-BE) may accurately predict individual AUC. The use of self-collected capillary microsamples could simplify this strategy. This study aimed to investigate the potential of AUC-targeted Tac TDM using capillary microsamples in combination with popPK-BE.

METHODS

A single-center prospective pharmacokinetic study was conducted in standard-risk RTxR (n = 27) receiving Tac twice daily. Both venous and capillary microsamples (Mitra; Neoteryx, Torrance, CA) were obtained across 2 separate 12-hour Tac dosing intervals (n = 13 samples/AUC). Using popPK-BE, reference AUC (AUCref) was determined for each patient using all venous samples. Different limited sampling strategies were tested for AUC predictions: (1) the empiric sampling scheme; 0, 1, and 3 hours after dose and (2) 3 sampling times determined by the multiple model optimal sampling time function in Pmetrics. Agreement between the predicted AUCs and AUCref were evaluated using C-statistics. Accepted agreement was defined as a total deviation index ≤±15%.

RESULTS

The AUC from capillary microsamples revealed high accuracy and precision compared with venous AUCref, and 85% of the AUCs had an error within ±11.9%. Applying microsamples at 0, 1, and 3 hours after dose predicted venous AUCref with acceptable agreement. Patients performed self-sampling with acceptable accuracy.

CONCLUSIONS

Capillary microsampling is patient-centered, making AUC-targeted TDM of Tac feasible without extended hospital stays. Samples obtained 0, 1, and 3 hours after dose, combined with popPK-BE, accurately predict venous Tac AUC.

Monitoring Tacrolimus Trough Concentrations During the First Year After Kidney Transplantation: A National Retrospective Cohort Study

Background: There is a lack of data in the literature on the evaluation of tacrolimus (TAC) dosage regimen and monitoring after kidney transplantation (KT) in Kuwait. The aim of the present study was to evaluate TAC dosing in relation to the hospital protocol, the achievement of target TAC trough concentration (C₀), the prevalence of TAC side effects (SEs), namely, posttransplant diabetes mellitus (PTDM), denovo hypertension (HTN), and dyslipidemia, and factors associated with the occurrence of these SEs among KT recipients.

Methods: A retrospective study was conducted among 298 KT recipients receiving TAC during the first year of PT. Descriptive and multivariate logistic regression analyses were used.

Results: The initial TAC dosing as per the local hospital protocol was prescribed for 28.2% of patients. The proportion of patients who had C₀ levels within the target range increased from 31.5 to 60.3% during week 1 through week 52. Among patients who did not have HTN, DM, or dyslipidemia before using TAC, 78.6, 35.2, and 51.9% of them were prescribed antihypertensive, antidiabetic, and antilipidemic medications during the follow-up period. Age of ≥40 years was significantly associated with the development of de novo HTN, dyslipidemia, and PTDM (p < 0.05). High TAC trough concentration/daily dose (C₀/D) ratio was significantly associated with the development of PTDM (p < 0.05).

Conclusion: Less than two-fifths of patients achieved target TAC C₀ levels during the first month of PT. Side effects were more common in older patients. These findings warrant efforts to implement targeted multifaceted interventions to improve TAC prescribing and monitoring after KT.

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Bodyweight‐based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady‐state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C₀). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug‐related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post‐transplant phase, a previously developed dosing algorithm to predict an individual’s tacrolimus starting dose was tested prospectively. In this single‐arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight‐based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C₀, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post‐transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C₀ within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy‐proven acute rejection occurred in three patients (5%). In conclusion, algorithm‐based tacrolimus dosing leads to the achievement of the tacrolimus target C₀ in as many as 58% of the patients on day 3 after kidney transplantation.

Advances in personalized medicine and noninvasive diagnostics in solid organ transplantation

Personalized medicine has been a mainstay and in practice in transplant pharmacotherapy since the advent of the field. Decisions pertaining to the diagnosis, selection, and monitoring of transplant pharmacotherapy are aimed toward the individual, the allograft, and the overall immunologic needs of the patient. Recent advances in pharmacogenomics, noninvasive biomarkers, and artificial intelligence (AI) technologies have the promise of transforming the way we individualize treatment and monitor allograft function. Pharmacogenomic testing can provide clinicians with additional data that can minimize toxicity and maximize therapeutic dosing in high-risk patients, leading to more informed decisions that may decrease the risk of rejection and adverse outcomes related to immunosuppressive therapies. Development of noninvasive strategies to monitor allograft function may offer safer and more convenient methods to detect allograft injury. Cell free DNA and gene expression profiling offer the potential to serve as "liquid biopsies" minimizing the risk to patients and providing clinicians with useful molecular data that may help individualize immunosuppression and rejection treatment. Use of big data in transplant and novel AI platforms, such as the iBox, hold tremendous promise in providing clinicians a "glimpse into the future" thereby allowing for a more individualized approach to immunosuppressive therapy that may minimize future adverse outcomes. Advances in diagnostics, laboratory science, and AI have made the application of personalized medicine even more tailored for solid organ transplant recipients. In this perspective, we summarize the current and emerging tools available, literature supporting use, and the horizon for future personalization of transplantation.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Tacrolimus intrapatient variability in solid organ transplantation: A multiorgan perspective

BACKGROUND

Tacrolimus therapy in solid organ transplant (SOT) recipients is challenging due to its narrow therapeutic window and pharmacokinetic variability both between patients and within a single patient. Intrapatient variability (IPV) of tacrolimus trough concentrations has become a novel marker of interest for predicting transplant outcomes. The purpose of this review is to evaluate the association of tacrolimus IPV with graft and patient outcomes and identify interventions to improve IPV in SOT recipients.

METHODS

A systematic review of the literature was performed using PubMed and Embase from database inception to September 20, 2020. Studies were eligible only if they evaluated an association between tacrolimus IPV and transplant outcomes. Both pediatric and adult studies were included. Measures of variability were limited to standard deviation, coefficient of variation, and time in therapeutic range.

RESULTS

Forty-four studies met the inclusion criteria. Studies were published between 2008 and 2020 and were observational in nature. Majority of data were published in adult kidney transplant recipients and identified an association with rejection, de novo donor specific antibody (dnDSA) formation, graft loss, and patient survival. Evaluation of IPV-directed interventions was limited to small preliminary studies.

CONCLUSIONS

High tacrolimus IPV has been associated with poor outcomes including acute rejection, dnDSA formation, graft loss, and patient mortality in SOT recipients. Future research should prospectively explore IPV-directed interventions to improve transplant outcomes.

High Variability of Whole-Blood Tacrolimus Pharmacokinetics Early After Thoracic Organ Transplantation

Background and Objective

Oral tacrolimus is initiated perioperatively in heart and lung transplantation patients. There have been few studies on oral tacrolimus pharmacokinetics early post-transplantation, even though tacrolimus-related toxicity may occur early, potentially leading to morbidity and mortality. Therefore, we aimed to study the pharmacokinetics of oral tacrolimus in thoracic organ recipients during the first days after transplantation.

Methods

We conducted a pharmacokinetic study in 30 thoracic organ transplants at intensive care at the University Medical Center Utrecht in the first week post-transplantation. Twelve-hour whole-blood tacrolimus profiles were examined using high-performance liquid chromatography tandem mass spectrometry (HPLC–MS/MS) and analysed via population pharmacokinetic modelling.

Results

The concentration–time profiles showed high variability. Concentrations at 12 h were outside the target range in 69% of the cases. A two-compartment model with mixed first-order and zero-order absorption adequately described tacrolimus concentrations. The typical value of the apparent clearance was 19.6 L/h (95% CI 16.2–22.9), and the apparent distribution volumes of central and peripheral compartments, V1 and V2, were 231 L (95% CI 199–267) and 521 L (95% CI 441–634), respectively. Inter-occasion (dose-to-dose) variability far exceeded the interindividual variability (IIV), with an estimated variability in relative bioavailability of 55% (95% CI 48.5–64.4).

Conclusions

The high variability of tacrolimus pharmacokinetics early after thoracic organ transplantation is largely due to excessive variability in bioavailability, making individualised dosing based on measured concentrations futile. To bypass this bioavailability issue, we suggest administering tacrolimus intravenously and aiming below the upper therapeutic range early post-transplantation.

Clinical Trial Registraion: NTR 3912/EudraCT 2012-001909-24.

Electronic supplementary material

The online version of this article (10.1007/s13318-019-00591-7) contains supplementary material, which is available to authorized users.

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

Fasting Status and Circadian Variation Must be Considered When Performing AUC-based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC‐guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice‐daily tacrolimus in a real‐life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting‐state and under real‐life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting‐conditions: 45% higher peak‐concentration and 20% higher AUC following the morning dose. In the real‐life nonfasting setting, the PK‐profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting‐state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real‐life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting‐state, whereas flatter PK‐profiles and no circadian variation was present in a real‐life, nonfasting setting.

TDM is dead. Long live TCI!

Twenty years ago, target concentration intervention (TCI) was distinguished from therapeutic drug monitoring (TDM). It was proposed that TCI would bring more clinical benefit because of the precision of the approach and the ability to link TCI to principles of pharmacokinetics and pharmacodynamics to predict the dose required by an individual (1). We examine the theory and clinical trial evidence supporting the benefits of TCI over TDM and conclude that in the digital age TDM should be abandoned and replaced by TCI.

What's new in IBD therapy: An "omics network" approach

The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social structure, and so on. Almost certainly because this rapid evolution has outpaced the ability of the body to adapt to a number of environmental and behavioral changes, there has been a parallel emergence of several chronic inflammatory diseases, among which are inflammatory bowel diseases (IBD), primarily ulcerative colitis and Crohn's disease. The ability to treat these conditions has progressively improved in the last 50 years, particularly in the last couple of decades with the introduction of biological therapy targeting primarily soluble mediators produced by inflammatory cells. A large number of biologics are now available, but all of them induce similarly unsatisfactory (<50%) rates of clinical response and remission, and most of them lose efficacy over time, requiring dose escalation or switching from one biologic to another. So, treatment of IBD still needs improvement that will occur only if different approaches are taken. A reason why even the most recent forms of IBD therapy are unsatisfactory is because they target only selected components of an exceedingly complex pathophysiological process, a reality that must be honestly considered if better IBD therapies are to be achieved. Brand new approaches must integrate all relevant factors in their totality - the "omes" - and identify the key controllers of biological responses. This can be accomplished by using systems biology-based approaches and advanced bioinformatics tools, which together represent the essence of network medicine. This review looks at the past and the present of IBD pathogenesis and therapy, and discusses how to develop new therapies based on a network medicine approach.

Tacrolimus and Indomethacin Are Safe and Effective at Reducing Pancreatitis After Endoscopic Retrograde Cholangiopancreatography in Patients Who Have Undergone Liver Transplantation

BACKGROUND & AIMS

Biliary complications occur in up to 25% of patient following liver transplantation and are often managed by endoscopic retrograde cholangiopancreatography (ERCP). Pancreatitis is the most common adverse event after ERCP (PEP). Tacrolimus and rectal indomethacin have each been reported to reduce risk of PEP. We investigated the incidence of PEP in patients who have undergone ERCP after liver transplantation and the effectiveness of tacrolimus and/or indomethacin in reducing risk of PEP.

METHODS

We performed a retrospective study of 337 patients who underwent ERCP (n = 937 procedures) for biliary complications after liver transplantation from June 1, 2007 through December 1, 2015. After June 1, 2012, rectal indomethacin (100 mg) was routinely administered at the conclusion of the ERCP unless patients had contraindications. Indomethacin was given after 286 ERCP procedures. After excluding patients with acute/chronic rejection, 323 patients were maintained on a stable dose of tacrolimus prior to ERCP (901 procedures). We collected data on demographic and clinical variables, pre-procedural tacrolimus trough levels, and development of PEP. We calculated adjusted odds ratios (ORs) for the association between tacrolimus and indomethacin use and risk of PEP using mixed-effects multivariable logistic regression. The primary outcome was development of PEP; secondary outcomes included the development moderate-to-severe PEP, cholangitis and bleeding.

RESULTS

PEP occurred after 2.2% of ERCP procedures. A trough level of tacrolimus above 2.5 ng/mL was associated with 79% lower odds of PEP (OR, 0.21; 95% CI, 0.06-0.72; P = .01). Indomethacin was associated with a 91% reduction in risk of PEP (OR, 0.09; 95% CI, 0.01-0.85; P = .03). Indomethacin use did not affect rates of bleeding or cholangitis or decrease in glomerular filtration rate. In patients with trough levels of tacrolimus above 2.5 ng/mL, addition of indomethacin reduced the odds of PEP by 93% compared with patients who were unexposed to indomethacin. (OR, 0.07; 95% CI, 0.01-0.90; P = .04).

CONCLUSIONS

In a retrospective study of patients who underwent ERCP for biliary complications after liver transplantation, we found trough levels of tacrolimus above 2.5 ng/mL to significantly reduce risk for PEP. Rectal administration of indomethacin after ERCP significantly decreased rates of pancreatitis, and reduced risk further in patients given tacrolimus. Administration of both drugs prevented patients from developing moderate or severe pancreatitis. Indomethacin did not worsen renal function in patients with chronic kidney disease.

Intrapatient Variability of Tacrolimus Exposure in Solid Organ Transplantation: A Novel Marker for Clinical Outcome

The calcineurin-inhibitor tacrolimus (Tac) provides an acceptable balance between prevention of allograft rejection and drug-related adverse effects, making it the standard of care in all types of solid organ transplantation for the last 2 decades. Recent data have demonstrated that high intrapatient variability (IPV) in Tac predose trough concentrations has deleterious effects on allograft survival. The underlying mechanisms by which a high Tac IPV shortens allograft survival are acute and chronic rejection, donor-specific anti-HLA antibodies, and progressive fibrotic damage to the graft. Modifiable causes of high Tac IPV include medication nonadherence (MNA), drug interactions, nutritional interferences, and concurrent diseases. Recognizing high Tac IPV as an important prognostic risk factor after solid organ transplantation requires understanding of the definitions, the use of correct diagnostic metrics, and methodology. Therapeutic interventions aimed at reducing Tac IPV are targeted on improving MNA, avoiding or adjusting drug interactions, drug dosing assists, and educational support of recipients.