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

External evaluation of tacrolimus population pharmacokinetic models in adult lung transplant patients: How to enhance the predictive ability of the model?

PURPOSE

Tacrolimus is the cornerstone of current immunosuppressive strategies after lung transplantation. However, its narrow therapeutic range and considerable pharmacokinetic variability pose challenges for individualized treatment. Several tacrolimus population pharmacokinetic (popPK) models have been developed for precision dosing in adult lung transplant patients. However, their applicability across different clinical settings remains uncertain. The aim of this study was to evaluate the external predictability of these models and identify influential factors.

METHODS

Published models were systematically retrieved and assessed based on an external dataset of 39 patients (1240 tacrolimus trough concentrations) using three approaches: (1) prediction-based diagnosis using dosing records and patient characteristics; (2) simulation-based diagnosis, with prediction- and variability-corrected visual predictive checks (pvcVPC) and normalized prediction distribution error tests (NPDE); and (3) Bayesian forecasting using one to four observations for posterior predictions. We also investigated the impact of model structure and covariates on predictability.

RESULTS

The predictive performance of six published models was externally evaluated, but none demonstrated satisfactory accuracy in prediction- and simulation-based diagnosis. Bayesian forecasting yielded satisfactory results with only one prior observation and optimal predictive performance with 2-3 priors for all included models. The structural model parameterized on plasma tacrolimus concentration outperformed others. Significant correlations were observed between prediction-error and daily tacrolimus dose, postoperative day, and voriconazole co-administration.

CONCLUSIONS

The overall predictive performance of all published models was unsatisfactory, making direct extrapolation inappropriate. However, Bayesian forecasting significantly improves predictive performance. Utilizing plasma tacrolimus concentration for parameter estimation can improve the predictive ability of tacrolimus popPK models.

Model-Informed Precision Dosing of Tacrolimus: A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools

BACKGROUND AND OBJECTIVE

Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment.

METHODS

We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration.

RESULTS

We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/ , allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey.

CONCLUSIONS

Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment.

Tacrolimus Variability and Clinical Outcomes in the Early Post-lung Transplantation Period: Oral Versus Continuous Intravenous Administration

BACKGROUND AND OBJECTIVE

High variability in tacrolimus pharmacokinetics directly after lung transplantation (LuTx) may increase the risk for acute kidney injury (AKI) and transplant rejection. The primary objective was to compare pharmacokinetic variability in patients receiving tacrolimus orally versus intravenously early after LuTx.

METHODS

Pharmacokinetic and clinical data from 522 LuTx patients transplanted between 2010 and 2020 in two university hospitals were collected to compare orally administered tacrolimus to intravenous tacrolimus early post-transplantation. Tacrolimus blood concentration variability, measured as intrapatient variability (IPV%) and  percentage of time within the therapeutic range (TTR%), was analyzed within the first 14 days after LuTx. Secondary outcomes were AKI, acute rejection, length of stay in the intensive care unit (ICU), and mortality in the ICU and during hospital admission.

RESULTS

We included 224 patients in the oral and 298 in the intravenous group. The mean adjusted IPV% was 10.8% (95% confidence interval [CI] 6.9-14.6; p < 0.001) higher in the oral group (27.2%) than the intravenous group (16.4%). The mean TTR% was 7.3% (95% CI - 11.3 to - 3.4; p < 0.001) lower in the oral group (39.6%) than in the intravenous group (46.9%). The incidence of AKI was 46.0% for oral and 42.6% for intravenous administration (adjusted odds ratio [OR] 1.2; 95% CI 0.8-1.8; p = 0.451). The frequencies of clinically diagnosed acute rejection in the oral and intravenous groups were nonsignificant (24.6% vs 17.8%; OR 1.5 [95% CI 1.0-2.3; p = 0.059]). ICU and hospital mortality rate and ICU length of stay were similar.

CONCLUSIONS

Administering tacrolimus orally directly after LuTx leads to a higher variability in blood concentrations compared to intravenous administration. There was no difference in the occurrence of AKI or transplant rejection.

Dispersive solid phase extraction of tacrolimus from biological samples using curcumin and iron-based metal organic frameworks nanocomposite followed by LC-MS/MS determination

Tacrolimus is a potent immunosuppressive drug used in the prevention of tissue rejection. It has a narrow therapeutic index. Therefore, the determination of its concentration in biological fluids like plasma and urine is a very crucial issue. In this research, tacrolimus concentrations in plasma and urine samples were determined with a dispersive solid phase extraction procedure coupled to high-performance liquid chromatography-tandem mass spectrometry. For this purpose, a curcumin modified metal-organic framework was synthesized and used in extraction procedure. Tacrolimus was adsorbed onto the sorbent surface with aid of vortexing. Then, the adsorbed tacrolimus was eluted by a suitable solvent. Important parameters in extraction procedure were optimized by "one-variable-at-a-time" approach and reported as below: sorbent amount, 10 mg; sample solution pH, 2; agitation mode, vortexing; adsorption and desorption times, 1 min, and eluent (volume), methanol (200 µL). Under the optimized conditions and according to the International Council for Harmonization guidelines, the validation of the method was performed, and the results showed acceptable accuracy and precision (relative standard deviations ≤14 %), good linearity in a wide range (4-200 ng mL-1), and low limits of detection (1.2 ng mL-1 in plasma and 0.34 ng mL-1 in urine) and quantification (4.7 ng mL-1 in plasma and 1.12 ng mL-1 in urine). Finally, the validated method was successfully applied for the determination of tacrolimus in the plasma samples of the patients.

Tacrolimus-why pharmacokinetics matter in the clinic

The calcineurin inhibitor (CNI) Tacrolimus (Tac) is the most prescribed immunosuppressant drug after solid organ transplantation. After renal transplantation (RTx) approximately 95% of recipients are discharged with a Tac-based immunosuppressive regime. Despite the high immunosuppressive efficacy, its adverse effects, narrow therapeutic window and high intra- and interpatient variability (IPV) in pharmacokinetics require therapeutic drug monitoring (TDM), which makes treatment with Tac a major challenge for physicians. The C/D ratio (full blood trough level normalized by daily dose) is able to classify patients receiving Tac into two major metabolism groups, which were significantly associated with the clinical outcomes of patients after renal or liver transplantation. Therefore, the C/D ratio is a simple but effective tool to identify patients at risk of an unfavorable outcome. This review highlights the challenges of Tac-based immunosuppressive therapy faced by transplant physicians in their daily routine, the underlying causes and pharmacokinetics (including genetics, interactions, and differences between available Tac formulations), and the latest data on potential solutions to optimize treatment of high-risk patients.

Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation

Background

Tacrolimus therapy is standard of care for immunosuppression after lung transplantation. However, tacrolimus exposure variability during the early postoperative period may contribute to poor outcomes in this population. Few studies have examined tacrolimus pharmacokinetics (PK) during this high-risk time period.

Methods

We conducted a retrospective pharmacokinetic study in lung transplant recipients at the University of Pennsylvania who were enrolled in the Lung Transplant Outcomes Group (LTOG) cohort. We derived a model in 270 patients using NONMEM (version 7.5.1) and examined validity in a separate cohort of 114 patients. Covariates were examined with univariate analysis and multivariable analysis was developed using forward and backward stepwise selection. Performance of the final model in the validation cohort was examined with calculation of mean prediction error (PE).

Results

We developed a one-compartment base model with a fixed rate absorption constant. Significant covariates in multivariable analysis were postoperative day, hematocrit, transplant type, CYP3A5 genotype, total body weight, and time-varying postoperative day, hematocrit, and CYP inhibitor drugs. The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than threefold over the 14 day study period. In the validation cohort, the final model showed a mean PE of 36.4% (95%CI 30.8%-41.9%) and a median PE of 7.2% (IQR -29.3%-70.53%).

Conclusion

Postoperative day was the strongest predictor of tacrolimus exposure in the early post-lung transplant period. Future multicenter studies employing intensive sampling to examine a broad set of variables related to critical illness physiology are needed to understand determinants of clearance, volume of distribution and absorption in this population.

Pharmacokinetic Evaluation of Tacrolimus in Chinese Adult Patients during the Early Stages Post-Lung Transplantation

BACKGROUND

Although tacrolimus has been widely used in patients undergoing lung transplantation, few studies have reported the pharmacokinetics of tacrolimus in Chinese patients after lung transplantation. Thus, we aimed to investigate the pharmacokinetics and influential factors in this patient cohort in the early stage after lung transplantation.

METHODS

We enrolled 14 adult lung transplant recipients who were treated with tacrolimus and then intensively collected blood samples within a 12-h dosing interval. The pharmacokinetic parameters of tacrolimus were calculated using non-compartmental analysis, and the influence of pathophysiological characteristics and CYP3A5*3 and CYP3A4*1G genotypes on the pharmacokinetics of tacrolimus was assessed. Using linear regression analysis, we investigated the correlation between tacrolimus concentration at different sampling points and measured the area under the time-concentration curve (AUC_0-12h).

RESULTS

Geometric mean of apparent clearance (CL/F) was 18.13 ± 1.65 L/h in non-CYP3A5*3/*3 carriers, five times higher than that in CYP3A5*3/*3 carriers (p < 0.001). Furthermore, the tacrolimus concentration 4 h after administration had the strongest correlation with AUC_0-12h (R² = 0.979).

CONCLUSION

The pharmacokinetics of tacrolimus varied largely between patients during the early stage post-transplantation, which could be partially explained by CYP3A5*3 genetic polymorphisms.

Population Pharmacokinetic Analysis for Model-Based Therapeutic Drug Monitoring of Tacrolimus in Chinese Han Heart Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus has become the first-line immunosuppressant for preventing rejection after heart transplantation. The present study aimed to investigate genetic variants and clinical factors affecting the variability of tacrolimus in Chinese Han heart transplant patients using a population pharmacokinetic approach.

METHODS

The retrospective study included 53 hospitalized patients with 547 tacrolimus concentrations for analysis. Nonlinear mixed-effects modeling was used to develop the population pharmacokinetics model for tacrolimus in patients with heart transplants, followed by Monte Carlo simulations to design initial dosing regimens.

RESULTS

In our study, the mutation rate of CYP3A4*18B (C>T) was 27.36%. An oral one-compartment model with first-order absorption and elimination was used to describe the pharmacokinetics of tacrolimus in heart transplant patients. In the final model, the estimated apparent clearance (CL/F) and volume of distribution (V/F) were 532.5 L/h [12.20% interindividual variability, IIV] and 16.87 L (23.16% IIV), respectively. Albumin, postoperative time, and rs2242480 (CYP3A4*18B) gene polymorphisms were the significant covariates affecting CL/F, and creatinine clearance had significant effects on the V/F.

CONCLUSION

The population pharmacokinetic model of tacrolimus in heart transplant patients can better estimate the population and individual pharmacokinetic parameters of patients and can provide a reference for the design of individualized dosing regimens.

Effects Of Voriconazole Exposure on The Pharmacokinetics of Tacrolimus in Lung Transplantation Patients: Based on Therapeutic Drug Monitoring Data

Tacrolimus and voriconazole are usually used simultaneously in lung transplantations. Voriconazole can increase tacrolimus concentrations by inhibiting the CYP enzyme, which poses a great challenge for dose adjustment. The aim of this study is to clarify the correlation between voriconazole exposure and tacrolimus trough concentrations (C₀ ), and to establish a population pharmacokinetic model including voriconazole trough concentrations (VOZ) as a covariate for dose optimization. All data were retrospectively collected from lung transplantation patients who were subjected to the therapeutic drug monitoring of tacrolimus and voriconazole. The correlation between C₀ and VOZ or voriconazole daily doses was analyzed by Spearman's correlation. A total of 52 patients accounting for 351 pairs of tacrolimus and voriconazole trough concentrations were included. C₀ and C₀ /DD had a significant correlation with VOZ (P<0.01) rather than voriconazole daily doses. A linear one-compartment model with first-order absorption and elimination was used as basic model in population pharmacokinetic analysis. The body weight (WT), daily dose of tacrolimus (DD), VOZ, and hematocrit (HCT) were included as covariates in the final model. With the increase in voriconazole concentrations, the apparent total clearance (CL/F) of tacrolimus decreased significantly. The simulation results showed that the highest proportion of C₀ within the target range can only reach lower than 50% when optimal initial drug regimen was given. Therefore, both tacrolimus and voriconazole concentrations need to be continuously monitored during treatments in lung transplantation patients, and the tacrolimus dose can be optimized according to VOZ based on the established pharmacokinetic model. This article is protected by copyright. All rights reserved.

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.

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.

Clinical Pharmacokinetics and Impact of Hematocrit on Monitoring and Dosing of Tacrolimus Early After Heart and Lung Transplantation

The calcineurin inhibitor tacrolimus is an effective immunosuppressant and is extensively used in solid organ transplantation. In the first week after heart and lung transplantation, tacrolimus dosing is difficult due to considerable physiological changes because of clinical instability, and toxicity often occurs, even when tacrolimus concentrations are within the therapeutic range. The physiological and pharmacokinetic changes are outlined. Excessive variability in bioavailability may lead to higher interoccasion (dose-to-dose) variability than interindividual variability of pharmacokinetic parameters. Intravenous tacrolimus dosing may circumvent this high variability in bioavailability. Moreover, the interpretation of whole-blood concentrations is discussed. The unbound concentration is related to hematocrit, and changes in hematocrit may increase toxicity, even within the therapeutic range of whole-blood concentrations. Therefore, in clinically unstable patients with varying hematocrit, aiming at the lower therapeutic level is recommended and tacrolimus personalized dosing based on hematocrit-corrected whole-blood concentrations may be used to control the unbound tacrolimus plasma concentrations and subsequently reduce toxicity.

Clinical Implications of Tacrolimus Time in Therapeutic Range and Intrapatient Variability in Urban Renal Transplant Recipients Undergoing Early Corticosteroid Withdrawal

Tacrolimus demonstrates wide intrapatient and interpatient variability requiring therapeutic drug monitoring. The utility of tacrolimus time in therapeutic range (TTR) after renal transplantation (RT) under an early corticosteroid withdrawal (ECSWD) protocol is unknown. The purpose of this study is to assess the impact of tacrolimus TTR in an ECSWD RT population.

Materials

A retrospective analysis of adult RT recipients maintained on tacrolimus was conducted. Patients were excluded if they were on nonstandard protocol immunosuppression agents <12 months post-RT. Tacrolimus TTR was calculated using the Rosendaal method. Patients were divided into high (TTR-H) and low (TTR-L) TTR groups based on cohort median. The primary outcome was to compare the incidence of acute rejection 12 months post-RT. Secondary outcomes included comparing rejection subtypes, incidence of donor-specific antibody (DSA) and de novo DSA (dnDSA), risk factors for acute rejection and dnDSA development, and allograft function (serum creatinine and estimated glomerular filtration rate).

Results

A total of 193 patients were analyzed (TTR-H = 98 and TTR-L = 95). There was no difference in the incidence of acute rejection (TTR-H 20.4% versus TTR-L 20.0%; P = 0.944). Positive DSA posttransplant (odds ratio [OR], 3.62; 95% confidence interval [CI], 1.41-9.26; P = 0.007) was associated with a higher acute rejection at 12 months posttransplant. Mycophenolate dose reduction (OR, 2.82; 95% CI, 1.13-6.97; P = 0.025) and acute rejection (OR, 2.99; 95% CI, 1.09-8.18; P = 0.032) were associated with dnDSA formation. No difference in serum creatinine or estimated glomerular filtration rate was observed (P > 0.05).

Conclusions

Tacrolimus TTR was not significantly different with regards to acute rejection in an ECSWD population. Future studies are still needed to determine tacrolimus TTR thresholds post-RT and identify populations that may benefit from this intrapatient variability monitoring parameter.

Identifying the association between tacrolimus exposure and toxicity in heart and lung transplant recipients: A systematic review

AIMS

Tacrolimus is the cornerstone of immunosuppression management in heart and lung transplant recipients, improving overall survival. However, tacrolimus-associated toxicities, including nephrotoxicity, neurotoxicity, new-onset diabetes mellitus after transplant (NODAT), and gastrointestinal toxicity, are known contributors to increased post-transplant morbidity outcomes and reduced graft and recipient survival rates. The aim of this systematic review was to identify correlations between pharmacokinetic measures of tacrolimus exposure in heart and lung recipients and tacrolimus toxicities.

METHODS

MEDLINE, Embase, the Cochrane Library, CENTRAL and WHO Clinical Trial Registries were searched for published studies evaluating tacrolimus toxicities and their correlation to pharmacokinetic monitoring parameters in thoracic transplant recipients. Studies were reviewed by two authors, with data extracted for evaluation. Risk of bias was assessed using the PEDro scale for randomised control trials and the Newcastle Ottawa Scale for non-randomised cohort studies.

RESULTS

Eighteen studies were eligible; a randomised control trial, 11 observational cohort studies, and 6 case series or studies. Of these, 9 studies were in heart transplant recipients alone and 5 in lung transplant recipients alone, 2 studies were in heart and lung transplant recipients and 2 were heart, lung, liver or renal transplant recipients. Studies used variable criteria to define toxicities. Tacrolimus trough concentration (C₀) was the marker of tacrolimus exposure most commonly used. Ten studies reported on nephrotoxicity. Elevated tacrolimus C₀ was associated with acute kidney injury occurrence and severity in three observational studies. Increasing C₀ was a predictor of renal impairment in 6 studies. One study found that for each 5 ng/mL per year of tacrolimus exposure, defined by consecutive AUC, eGFR declined by 1.3 mL/min/1.73m² (p < 0.001). Comparatively, 2 studies failed to find a significant association between nephrotoxicity and tacrolimus exposure. Seven studies reported on neurotoxicity, including neuro-encephalopathies, polyneuropathies and symptomatic change in neurological status. Neurotoxicity occurred both with tacrolimus C₀ within therapeutic range and with supratherapeutic C₀. No significant association was found between NODAT and tacrolimus C₀ in two studies. One study reported on gastrointestinal toxicity, with supratherapeutic C₀ and elevated peak concentration in one lung transplant recipient three days prior to symptom development.

CONCLUSION

No clearly defined relationship between tacrolimus exposure and toxicities is described in the literature. Studies with clear toxicity criteria and pharmacokinetic markers of tacrolimus exposure are required to provide valuable information that may optimise tacrolimus therapy, helping to reduce toxicities in heart and lung transplant recipients.

[Pharmacokinetic changes related to acute infection. Examples from the SARS-CoV-2 pandemic]

The knowledge of factors of pharmacokinetic variability is important in order to personalize pharmacological treatment, particularly for drugs with a narrow therapeutic range for which pharmacological therapeutic monitoring is recommended. Inflammation is a protective response against acute infections and injuries that contributes to intra- and inter-individual variability in drug exposure by modulating the activity of enzymes involved in drug metabolism, and by altering the binding of drugs to plasma proteins. The understanding of the impact of inflammation on drug metabolism and the related clinical consequences allow to better take into consideration the effect of inflammation on the variability of drug exposure. We first summarized the molecular mechanisms by which inflammation contributes to the inhibition of drug metabolism enzymes. We then presented an updated overview of the consequences of the outcome of acute infectious event on pharmacokinetic exposure of drugs with a narrow therapeutic range and that are substrates of cytochrome P450, and the related clinical consequences. Finally, in the context of the COVID-19 pandemic, we reported examples of drug overexposures in COVID- 19 infected patients.