Impact of Switching From Immediate- or Prolonged-Release to Once-Daily Extended-Release Tacrolimus (LCPT) on Tremor in Stable Kidney Transplant Recipients: The Observational ELIT Study.

Once-daily extended-release tacrolimus (LCPT) exhibits increased bioavailability versus immediate-release (IR-TAC) and prolonged release (PR-TAC) tacrolimus. Improvements in tremor were previously reported in a limited number of kidney transplant patients who switched to LCPT. We conducted a non-interventional, non-randomized, uncontrolled, longitudinal, prospective, multicenter study to assess the impact of switching to LCPT on tremor and quality of life (QoL) in a larger population of stable kidney transplant patients. The primary endpoint was change in The Essential Tremor Rating Assessment Scale (TETRAS) score; secondary endpoints included 12-item Short Form Survey (SF-12) scores, tacrolimus trough concentrations, neurologic symptoms, and safety assessments. Subgroup analyses were conducted to assess change in TETRAS score and tacrolimus trough concentration/dose (C0/D) ratio by prior tacrolimus formulation and tacrolimus metabolizer status. Among 221 patients, the mean decrease of TETRAS score after switch to LCPT was statistically significant (p < 0.0001 vs. baseline). There was no statistically significant difference in change in TETRAS score after switch to LCPT between patients who had received IR-TAC and those who had received PR-TAC before switch, or between fast and slow metabolizers of tacrolimus. The overall increase of C0/D ratio post-switch to LCPT was statistically significant (p < 0.0001) and from baseline to either M1 or M3 (both p < 0.0001) in the mITT population and in all subgroups. In the fast metabolizers group, the C0/D ratio crossed over the threshold of 1.05 ng/mL/mg after the switch to LCPT. Other neurologic symptoms tended to improve, and the SF-12 mental component summary score improved significantly. No new safety concerns were evident. In this observational study, all patients had a significant improvement of tremor, QoL and C0/D ratio post-switch to LCPT irrespective of the previous tacrolimus formulation administered (IR-TAC or PR-TAC) and irrespective from their metabolism status (fast or slow metabolizers).

Tools for a personalized tacrolimus dose adjustment in the follow-up of renal transplant recipients. Metabolizing phenotype according to CYP3A genetic polymorphisms versus concentration-dose ratio.

BACKGROUND AND JUSTIFICATION: The strategy of the concentration-dose (C/D) approach and the different profiles of tacrolimus (Tac) according to the cytochrome P450 polymorphisms (CYPs) focus on the metabolism of Tac and are proposed as tools for the follow-up of transplant patients. The objective of this study is to analyse both strategies to confirm whether the stratification of patients according to the pharmacokinetic behaviour of C/D corresponds to the classification according to their CYP3A4/5 cluster metabolizer profile. MATERIALS AND METHODS: 425 kidney transplant patients who received Tac as immunosuppressive treatment have been included. The concentration/dose ratio (C/D) was used to divide patients in terciles and classify them according to their Tac metabolism rate (fast, intermediate, and slow). Based on CYP3A4 and A5 polymorphisms, patients were classified into 3 metabolizer groups: fast (CYP3A5*1 carriers and CYP34A*1/*1), intermediate (CYP3A5*3/3 and CYP3A4*1/*1) and slow (CYP3A5*3/*3 and CYP3A4*22 carriers). RESULTS: When comparing patients included in each metabolizer group according to C/D ratio, 47% (65/139) of the fast metabolizers, 85% (125/146) of the intermediate and only 12% (17/140) of the slow also fitted in the homonym genotype group. Statistically lower Tac concentrations were observed in the fast metabolizers group and higher Tac concentrations in the slow metabolizers when compared with the intermediate group both in C/D ratio and polymorphisms criteria. High metabolizers required approximately 60% more Tac doses than intermediates throughout follow-up, while poor metabolizers required approximately 20% fewer doses than intermediates. Fast metabolizers classified by both criteria presented a higher percentage of times with sub-therapeutic blood Tac concentration values. CONCLUSION: Determination of the metabolizer phenotype according to CYP polymorphisms or the C/D ratio allows patients to be distinguished according to their exposure to Tac. Probably the combination of both classification criteria would be a good tool for managing Tac dosage for transplant patients.

Association of CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T Polymorphisms with Tacrolimus Dose, Serum Concentration, and Biochemical Parameters in Mexican Patients with Kidney Transplant.

Tacrolimus (TAC) is an immunosuppressant drug that prevents organ rejection after transplantation. This drug is transported from cells via P-glycoprotein (ABCB1) and is a metabolic substrate for cytochrome P450 (CYP) 3A enzymes, particularly CYP3A4 and CYP3A5. Several single-nucleotide polymorphisms (SNPs) have been identified in the genes encoding CYP3A4, CYP3A5, and ABCB1, including CYP3A4-392A/G (rs2740574), CYP3A5 6986A/G (rs776746), and ABCB1 3435C/T (rs1045642). This study aims to evaluate the association among CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T polymorphisms and TAC, serum concentration, and biochemical parameters that may affect TAC pharmacokinetics in Mexican kidney transplant (KT) patients. METHODS: Forty-six kidney transplant recipients (KTR) receiving immunosuppressive treatment with TAC in different combinations were included. CYP3A4, CYP3A5, and ABCB1 gene polymorphisms were genotyped using qPCR TaqMan. Serum TAC concentration (as measured) and intervening variables were assessed. Logistic regression analyses were performed at baseline and after one month to assess the extent of the association between the polymorphisms, intervening variables, and TAC concentration. RESULTS: The GG genotype of CYP3A5-6986 A/G polymorphism is associated with TAC pharmacokinetic variability OR 4.35 (95%CI: 1.13-21.9; p = 0.0458) at one month of evolution; in multivariate logistic regression, CYP3A5-6986GG genotype OR 9.32 (95%CI: 1.54-93.08; p = 0.028) and the use of medications or drugs that increase serum TAC concentration OR 9.52 (95%CI: 1.79-88.23; p = 0.018) were strongly associated with TAC pharmacokinetic variability. CONCLUSION: The findings of this study of the Mexican population showed that CYP3A5-6986 A/G GG genotype is associated with a four-fold increase in the likelihood of encountering a TAC concentration of more than 15 ng/dL. The co-occurrence of the CYP3A5-6986GG genotype and the use of drugs that increase TAC concentration correlates with a nine-fold increased risk of experiencing a TAC at a level above 15 ng/mL. Therefore, these patients have an increased susceptibility to TAC-associated toxicity.

Follow the Area Under the Curve Not the Trough Concentration: A Case Study of Tacrolimus Monitoring in a Kidney Transplant Recipient Cotreated With Phenobarbital.

ABSTRACT: The authors described tacrolimus dosing in a kidney transplant patient concurrently treated with phenobarbital, where measuring the tacrolimus area under the curve was necessary to achieve adequate drug exposure and improve kidney function.

Effect of Tacrolimus Formulation (Prolonged-Release vs Immediate-Release) on Its Susceptibility to Drug-Drug Interactions with St. John's Wort.

Tacrolimus is metabolized by cytochrome P450 3A (CYP3A) and is susceptible to interactions with the CYP3A and P-glycoprotein inducer St. John's Wort (SJW). CYP3A isozymes are predominantly expressed in the small intestine and liver. Prolonged-release tacrolimus (PR-Tac) is largely absorbed in distal intestinal segments and is less susceptible to CYP3A inhibition. The effect of induction by SJW is unknown. In this randomized, crossover trial, 18 healthy volunteers received single oral tacrolimus doses (immediate-release [IR]-Tac or PR-Tac, 5 mg each) alone and during induction by SJW. Concentrations were quantified using ultra-high performance liquid chromatography coupled with tandem mass spectrometry and non-compartmental pharmacokinetics were evaluated. SJW decreased IR-Tac exposure (area under the concentration-time curve) to 73% (95% confidence interval 60%-88%) and maximum concentration (Cmax ) to 61% (52%-73%), and PR-Tac exposure to 67% (55%-81%) and Cmax to 69% (58%-82%), with no statistical difference between the 2 formulations. The extent of interaction appeared to be less pronounced in volunteers with higher baseline CYP3A4 activity and in CYP3A5 expressors. In contrast to CYP3A inhibition, CYP3A induction by SJW showed a similar extent of interaction with both tacrolimus formulations. A higher metabolic baseline capacity appeared to attenuate the extent of induction by SJW.

Pharmacokinetics of Tacrolimus in Pregnant Solid-Organ Transplant Recipients: A Retrospective Study.

Data on the pharmacokinetics of tacrolimus during pregnancy are limited. Therefore, the aim of this retrospective study was to characterize the whole-blood pharmacokinetics of tacrolimus throughout pregnancy. In this single-center retrospective cohort study, whole-blood tacrolimus trough concentrations corrected for the dose (concentration-to-dose [C/D] ratios) were compared before, monthly during, and after pregnancy in kidney, liver, and lung transplant recipients who became pregnant and gave birth between 2000 and 2022. Descriptive statistics and linear mixed models were used to characterize changes in tacrolimus C/D ratios before, during, and after pregnancy. The total study population included 46 pregnancies (31 pregnant women). Nineteen, 21, and 6 pregnancies were following kidney, liver, and lung transplantation, respectively. Immediate-release or extended-release formulations were used in 54.5% and 45.5% of the women, respectively. Tacrolimus C/D ratios significantly (P < .001) decreased (-48%) compared to the prepregnancy state at 7 months of pregnancy. These ratios recovered within 3 months postpartum (P = .002). C/D ratios tended to be lower during treatment with an extended-release formulation than with an immediate-release formulation (P = .071). Transplantation type did not significantly affect C/D ratios during pregnancy (P = .873). In conclusion, we found that tacrolimus whole-blood pharmacokinetics change throughout pregnancy, with the lowest C/D ratios (48% decrease) in the 7th month of pregnancy. In general, the decrease in C/D ratios seems to stabilize from month 4 onward compared to prepregnancy.

Tacrolimus Pharmacokinetics is Associated with Gut Microbiota Diversity in Kidney Transplant Patients: Results from a Pilot Cross-Sectional Study.

Clinical use of tacrolimus (TAC), an essential immunosuppressant following transplantation, is complexified by its high pharmacokinetic (PK) variability. The gut microbiota gains growing interest but limited investigations have evaluated its contribution to TAC PKs. Here, we explore the associations between the gut microbiota composition and TAC PKs. In this pilot cross-sectional study (Clinicaltrial.gov NCT04360031), we recruited 93 CYP3A5 non-expressers stabilized kidney transplant recipients. Gut microbiota composition was characterized by 16S rRNA gene sequencing, TAC PK parameters were computed, and additional demographic and medical covariates were collected. Associations between PK parameters or diabetic status and the gut microbiota composition, as reflected by α- and ß-diversity metrics, were evaluated. Patients with higher TAC area under the curve AUC/(dose/kg) had higher bacterial richness, and TAC PK parameters were associated with specific bacterial taxa (e.g., Bilophila) and amplicon sequence variant (ASV; e.g., ASV 1508 and ASV 1982 (Veillonella/unclassified Sporomusaceae); ASV 664 (unclassified Oscillospiraceae)). Building a multiple linear regression model showed that ASV 1508 (co-abundant with ASV 1982) and ASV 664 explained, respectively, 16.0% and 4.6% of the interindividual variability in TAC AUC/(dose/kg) in CYP3A5 non-expresser patients, when adjusting for hematocrit and age. Anaerostipes relative abundance was decreased in patients with diabetes. Altogether, this pilot study revealed unprecedented links between the gut microbiota composition and diversity and TAC PKs in stable kidney transplant recipients. It supports the relevance of studying the gut microbiota as an important contributor to TAC PK variability. Elucidating the causal relationship will offer new perspectives to predict TAC inter- and intra-PK variability.

Population pharmacokinetic models of tacrolimus in paediatric solid organ transplant recipients: A systematic review.

AIMS: This study aimed to provide up-to-date information on paediatric population pharmacokinetic models of tacrolimus and to identify factors influencing tacrolimus pharmacokinetic variability. METHODS: Systematic searches in the Web of Science, PubMed, Scopus, Science Direct, Cochrane, EMBASE databases and reference lists of articles were conducted from inception to March 2023. All population pharmacokinetic studies of tacrolimus using nonlinear mixed-effect modelling in paediatric solid organ transplant patients were included. RESULTS: Of the 21 studies reviewed, 62% developed from liver transplant recipients and 33% from kidney transplant recipients. Most studies used a 1-compartment model to describe tacrolimus pharmacokinetics. Body weight was a significant predictor for tacrolimus volume of distribution (Vd/F). The estimated Vd/F for 1-compartment models ranged from 20 to 1890 L, whereas the peripheral volume of distribution (Vp/F) for 2-compartment models was between 290 and 1520 L. Body weight, days post-transplant, CYP3A5 genotype or haematocrit were frequently reported as significant predictors of tacrolimus clearance. The estimated apparent clearance values range between 0.12 and 2.18 L/h/kg, with inter-individual variability from 13.5 to 110.0%. Only 29% of the studies assessed the generalizability of the models with external validation. CONCLUSION: This review highlights the potential factors, modelling approaches and validation methods that impact tacrolimus pharmacokinetics in a paediatric population. The clinician could predict tacrolimus clearance based on body weight, CYP3A5 genotype, days post-transplant or haematocrit. Further research is required to determine the relationship between pharmacogenetics and tacrolimus pharmacodynamics in paediatric patients and confirm the applicability of nonlinear kinetics in this population.

Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation: A retrospective cohort study.

AIMS: Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. METHODS: Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. RESULTS: A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance. CONCLUSIONS: Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is required.

A longitudinal study of long-term renal outcome after pediatric liver transplantation in relation to CNI exposure.

BACKGROUND: Chronic kidney disease (CKD) is reported in 20%-30% of children after liver transplantation (LT). One of the proposed underlying causes is the long-term exposure to tacrolimus, a calcineurin inhibitor (CNI), which is the main immunosuppressive drug used after LT. Variation in tacrolimus absolute exposure and relative dose requirements are believed to be important risk factors for developing CNI-associated nephrotoxicity. AIM: To describe the long-term renal outcome of pediatric LT recipients and determine the effects of tacrolimus exposure on renal outcome parameters. METHODS: Retrospective single center study of renal function (GFR, proteinuria) and pharmacokinetic parameters (C0 , AUC0-12h ) obtained during annual follow-up in children after liver transplantation, between 1998 and 2019. Relevant pharmacogenetic variants for tacrolimus disposition (CYP3A5 and ABCB1) were determined in recipients and donors. The evolution of individual renal function and tacrolimus exposure was evaluated using linear mixed models for repeated measurements. RESULTS: Twenty-six children were included (mean follow-up: 10.4 years (range 2-18.9)). Mean estimated GFR was 109.3 (SE: 7.4), vs. measured: 91.3 mL/min/1.73 m2 (SE: 6.3), which remained stable during follow-up. CKD stage ≥2 was observed in 32.8% of the visits based on eGFR versus 50.0% on mGFR. CKD stage ≥3 was uncommon (4.1% and 6.2% resp.). Mean tacrolimus C0 was 5.3 ng/mL (SE: 2.5) with a AUC0-12h of 72.7 ng*h/mL (SE: 30.3), which demonstrated a small decrease during follow-up. There was a negative correlation between C0 and mGFR (rS = -0.3; p < .001). We found no correlation between GFR and tacrolimus dose requirements ((ng/mL)/(mg/kg)) or pharmacogenetic background. CONCLUSION: Renal function during long-term follow-up after pediatric LT remained stable for the majority of our cohort. However, mild CKD was relatively common, warranting follow-up into adulthood. Although absolute tacrolimus exposure has a small depressing effect on concurrent GFR, there is no progressive deterioration of GFR due to long-term exposure, dose requirements or genetic background under the current target levels. These findings should be confirmed in a larger sample set, ideally including data from multiple centers.

A model-based pharmacokinetic assessment of drug-drug interaction between tacrolimus and nirmatrelvir/ritonavir in a kidney transplant patient with COVID-19.

We experienced a patient with a remarkable and prolonged increase in tacrolimus blood concentrations when nirmatrelvir/ritonavir was concomitantly used. The inhibitory intensity and duration of nirmatrelvir/ritonavir on tacrolimus pharmacokinetics were examined using a model-based analysis. A renal transplant patient taking oral tacrolimus continuously was treated with nirmatrelvir/ritonavir for 5 days. The baseline tacrolimus trough blood concentration was 4.2 ng/mL. Tacrolimus was discontinued on Day 6 after the concomitant administration of nirmatrelvir/ritonavir, and the trough concentration increased to 96.4 ng/mL on Day 7. The model-based analysis showed that tacrolimus clearance decreased to 35% and bioavailability increased by 18.7-fold after the coadministration of nirmatrelvir/ritonavir, compared with before the coadministration. Therefore, nirmatrelvir/ritonavir drastically decreased both the apparent clearance and apparent volume of distribution. Simulated tacrolimus concentrations could be best fitted to the observed concentrations when the inhibitory effects of nirmatrelvir/ritonavir were modeled to disappear over about 10 days by first-order elimination. In conclusion, nirmatrelvir/ritonavir greatly increases tacrolimus concentrations by not only reducing clearance, but also increasing bioavailability. Interactions between nirmatrelvir/ritonavir and low-bioavailability drugs which are substrates for CYP3A and P-glycoprotein, such as tacrolimus, are harmful, and concomitant use of these medicines should be avoided.

Area under the curve of tacrolimus using microsampling devices: towards precision medicine in solid organ transplantation?

PURPOSE: Therapeutic drug monitoring of tacrolimus using trough concentration (Cmin) is mandatory to ensure drug efficacy and safety in solid organ transplantation. However, Cmin is just a proxy for the area under the curve of drug concentrations (AUC) which is the best pharmacokinetic parameter for exposure evaluation. Some studies suggest that patients may present discrepancies between these two parameters. AUC is now easily available through mini-invasive microsampling approach. The aim of this study is to evaluate the relationship between AUC and Cmin in patients benefiting from a complete pharmacokinetic profile using a microsampling approach. METHODS: Fifty-one transplant recipients benefited from a complete pharmacokinetic profile using a microsampling approach, and their 24-h AUC were calculated using the trapezoidal method. The correlation with Cmin was then explored. In parallel, we estimated AUC using the sole Cmin and regression equations according to the post-transplantation days and the galenic form. RESULTS: Weak correlations were found between 24-h AUC observed and the corresponding Cmin (R2 = 0.60) and between AUC observed and expected using the sole Cmin (R2 = 0.62). Therapeutic drug monitoring of tacrolimus using Cmin leads to over- or under-estimate drug exposure in 40.3% of patients. CONCLUSION: Tacrolimus Cmin appears to be an imperfect reflection of drug exposure. Evaluating AUC using a microsampling approach offers a mini-invasive strategy to monitor tacrolimus treatment in transplant recipients.

Drug interactions of tacrolimus with letermovir and azole antifungals following hematopoietic stem cell transplantation: A retrospective observational analysis.

Tacrolimus interacts with letermovir and azole antifungals, whereas letermovir has nonuniform effects on the pharmacokinetics of azole antifungals. We retrospectively investigated the interaction of tacrolimus (continuous infusion) with letermovir considering co-administered azole antifungals in adult hematopoietic stem cell transplantation patients. The extent of intraindividual variation in the ratio of tacrolimus concentration to dose normalized by body weight (C/D ratio) was investigated. The correlation between the C/D ratio and estimated glomerular filtration rate (eGFR) was analyzed. In 35 patients (795 points), the C/D ratio was higher in the tacrolimus plus letermovir period than in the tacrolimus alone period (1234.7 [566.2-2721.0] ng/mL/mg/kg vs. 564.4 [245.3-1861.3] ng/mL/mg/kg, p < .001). This trend was observed when co-administered with azole antifungals (n = 30, 1285.5 [662.7-2506.7] ng/mL/mg/kg vs. 547.1 [245.3-1861.3] ng/mL/mg/kg, p < .001), but not without azole antifungals (n = 5, 809.9 [566.2-1573.3] ng/mL/mg/kg vs. 616.1 [350.6-979.8] ng/mL/mg/kg, p = .125). For patients co-administered fluconazole, the tacrolimus C/D ratio increased in patients with letermovir than those without letermovir (n = 28, 1215.0 [662.7-2506.7] ng/mL/mg/kg vs. 529.9 [245.3-1654.4] ng/mL/mg/kg, p < .001). Tacrolimus C/D ratio did not correlate with eGFR under letermovir and fluconazole administrations (y = 0.1x + 1307.1, r = .008, p = .968). Close blood concentration monitoring of intravenous tacrolimus is required when patients administered letermovir and azole antifungals.

Effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplant recipients: The PRETHI study.

Data comparing long-term effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplantation are scarce. We compared the effectiveness, pharmacokinetic profile, and safety of LCPT (Envarsus) and PR-Tac (Advagraf) for up to 12 months post-transplant. Adult de novo liver transplant recipients who started IR-Tac (Prograf) and were converted to LCPT or PR-Tac 3-5 days post-transplant were included. Data from 163 patients were analyzed, 87 treated with LCPT and 76 with PR-Tac. The incidence of treatment failure was 30.5% in the LCPT group versus 23.0% in the PR-Tac group (p = .291). Biopsy-proven acute rejection (BPAR) was reported in 26.8% of patients in the LCPT group and 17.6% in the PR-Tac group (p = .166). Graft loss was experienced in one patient (1.2%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p = .346). Death was registered in three patients (3.7%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p > .999). Patients in the LCPT group showed 45.7% higher relative bioavailability (Cmin /total daily dose [TDD]; p < .01) with similar Cmin and 33.3% lower TDD versus PR-Tac (p < .01). The evolution of renal function, safety profile, and the incidence of post-transplant renal failure, dyslipidemia, obesity, hypertension, and diabetes mellitus were similar in patients treated with LCPT and PR-Tac. In de novo liver transplant patients, LCPT and PR-Tac showed comparable effectiveness with higher relative bioavailability, similar Cmin and lower TDD in the LCPT group. Renal function, safety, and post-transplant complications were comparable in LCPT and PR-Tac groups.

Influence of genetic variants on the pharmacokinetics and pharmacodynamics of sirolimus: a systematic review.

Sirolimus is an antiproliferative and immunosuppressive compound inhibiting the mTOR pathway, which is often activated in congenital low-flow vascular malformations. Studies have demonstrated the efficacy of sirolimus for this disease. Studies in kidney transplant patients suggest that genetic variants can influence these pharmacokinetic parameters. Therefore, a systematic literature search was performed to gain insight into pharmacogenetic studies with sirolimus. Most studies investigated CYP3A4 and CYP3A5, with inconsistent results. No pharmacogenetic studies focusing on sirolimus have been performed for low-flow vascular malformations. We analyzed two common variants of CYP3A4 and CYP3A5 (CYP3A4*22 and CYP3A5*3, respectively) in patients (n = 59) with congenital low-flow vascular malformations treated with sirolimus. No association with treatment outcome was identified in this small cohort of patients.

Gut microbiome modulates tacrolimus pharmacokinetics through the transcriptional regulation of ABCB1.

BACKGROUND: Following solid organ transplantation, tacrolimus (TAC) is an essential drug in the immunosuppressive strategy. Its use constitutes a challenge due to its narrow therapeutic index and its high inter- and intra-pharmacokinetic (PK) variability. As the contribution of the gut microbiota to drug metabolism is now emerging, it might be explored as one of the factors explaining TAC PK variability. Herein, we explored the consequences of TAC administration on the gut microbiota composition. Reciprocally, we studied the contribution of the gut microbiota to TAC PK, using a combination of in vivo and in vitro models. RESULTS: TAC oral administration in mice resulted in compositional alterations of the gut microbiota, namely lower evenness and disturbance in the relative abundance of specific bacterial taxa. Compared to controls, mice with a lower intestinal microbial load due to antibiotics administration exhibit a 33% reduction in TAC whole blood exposure and a lower inter-individual variability. This reduction in TAC levels was strongly correlated with higher expression of the efflux transporter ABCB1 (also known as the p-glycoprotein (P-gp) or the multidrug resistance protein 1 (MDR1)) in the small intestine. Conventionalization of germ-free mice confirmed the ability of the gut microbiota to downregulate ABCB1 expression in a site-specific fashion. The functional inhibition of ABCB1 in vivo by zosuquidar formally established the implication of this efflux transporter in the modulation of TAC PK by the gut microbiota. Furthermore, we showed that polar bacterial metabolites could recapitulate the transcriptional regulation of ABCB1 by the gut microbiota, without affecting its functionality. Finally, whole transcriptome analyses pinpointed, among others, the Constitutive Androstane Receptor (CAR) as a transcription factor likely to mediate the impact of the gut microbiota on ABCB1 transcriptional regulation. CONCLUSIONS: We highlight for the first time how the modulation of ABCB1 expression by bacterial metabolites results in changes in TAC PK, affecting not only blood levels but also the inter-individual variability. More broadly, considering the high number of drugs with unexplained PK variability transported by ABCB1, our work is of clinical importance and paves the way for incorporating the gut microbiota in prediction algorithms for dosage of such drugs. Video Abstract.

Tacrolimus population pharmacokinetics in adult heart transplant patients.

INTRODUCTION: Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). MATERIAL AND METHODS: Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. RESULTS: The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor). CONCLUSION: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review.

BACKGROUND AND OBJECTIVES: Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research. METHODS: The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach. RESULTS: Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h-1 (median of 14.9 L·h-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1. CONCLUSION: The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients.

BACKGROUND AND OBJECTIVE: Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations. OBJECTIVE: We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges. METHODS: Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges. RESULTS: Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively. CONCLUSION: Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease.

BACKGROUND: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance ( CLint ) of tacrolimus as a case example. METHODS: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate ( eGFR ) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method. RESULTS: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m 2 . A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients. CONCLUSIONS: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.