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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical Pharmacokinetics and Pharmacodynamics of Voclosporin.

Voclosporin is an approved option for the long-term treatment of lupus nephritis. We aimed to provide a narrative review of the pharmacokinetics and pharmacodynamics of voclosporin. In addition, we derived values for pharmacokinetic and pharmacodynamic parameters by graphical analysis of published diagrams. Compared with cyclosporin, low-dose voclosporin is associated with a lower nephrotoxicity risk and, compared to tacrolimus, with a lower diabetes risk. After repetitive dosing of 23.7 mg twice daily and at target trough concentrations of 10-20 ng/mL, the dominant or effect-indicative half-life is estimated at 7 hours. Compared with the pharmacodynamics of cyclosporin, the potency of voclosporin is stronger, with a lower concentration CE50 of 50 ng/mL already producing the half-maximum immunosuppressive effect. The Hill coefficient can be predicted to be low at H = 1.3, indicating a concentration-dependent effect on the immune system. The corresponding effect bisection time of 10 hours allows for dosing every 12 hours. Accordingly, the trough concentration will be above the threshold concentration that produces 5% of the maximum effect of 5.2 ng/mL for immunosuppression but below both the predicted threshold of 30 ng/mL for nephrotoxicity and the predicted threshold of 40 ng/mL for new-onset diabetes. The pharmacokinetic and pharmacodynamic properties suggest the use of low-dose voclosporin combined with mycophenolate and low-dose glucocorticoids for immunosuppressive maintenance therapy.

Multidrug resistant 1 (MDR1) C3435T and G2677T gene polymorphism: impact on the risk of acute rejection in pediatric kidney transplant recipients.

BACKGROUND: Tacrolimus is the backbone drug in kidney transplantation. Single nucleotide polymorphism of Multidrug resistant 1 gene can affect tacrolimus metabolism consequently it can affect tacrolimus trough level and incidence of acute rejection. The aim of this study is to investigate the impact of Multidrug resistant 1 gene, C3435T and G2677T Single nucleotide polymorphisms on tacrolimus pharmacokinetics and on the risk of acute rejection in pediatric kidney transplant recipients. METHODS: Typing of Multidrug resistant 1 gene, C3435T and G2677T gene polymorphism was done using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) for 83 pediatric kidney transplant recipients and 80 matched healthy controls. RESULTS: In Multidrug resistant 1 gene (C3435T), CC, CT genotypes and C allele were significantly associated with risk of acute rejection when compared to none acute rejection group (P = 0.008, 0.001 and 0.01 respectively). The required tacrolimus doses to achieve trough level were significantly higher among CC than CT than TT genotypes through the 1st 6 months after kidney transplantation. While, in Multidrug resistant 1 gene (G2677T), GT, TT genotypes and T allele were associated with acute rejection when compared to none acute rejection (P = 0.023, 0.033 and 0.028 respectively). The required tacrolimus doses to achieve trough level were significantly higher among TT than GT than GG genotypes through the 1st 6 months after kidney transplantation. CONCLUSION: The C allele, CC and CT genotypes of Multidrug resistant 1 gene (C3435T) and the T allele, GT and TT genotypes of Multidrug resistant 1 gene (G2677T) gene polymorphism may be risk factors for acute rejection and this can be attributed to their effect on tacrolimus pharmacokinetics. Tacrolimus therapy may be tailored according to the recipient genotype for better outcome.

Impact of genetic polymorphisms on tacrolimus trough blood concentration in Chinese liver transplant recipients.

Purpose: The aim of this study was to analyze the effects of various genetic polymorphisms and clinical factors on tacrolimus (TAC) concentration in the convalescence period (CP) and stabilization period (SP) post-liver transplantation. Patients & methods: A total of 13 SNPs were genotyped in 97 Chinese liver transplant recipients. Associations between SNPs and TAC trough blood concentration/dose ratio (C0/D) were analyzed using different genetic models in both CP and SP. Results: Only five SNPs were significantly associated with TAC log (C0/D) in the CP, and none showed a significant association in the SP. We identified rs15524 (CYP3A5), rs9200 (C6), albumin and creatinine as independent predictors of TAC C0/D in the CP. Furthermore, a final model in the CP explained a total of 30.5% TAC variation. Conclusion: Our study results suggest that in the early stages post-transplantation surgery, recipients' genetic and clinical factors exert a short-term impact on TAC metabolism that gradually decreases with time.

Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach.

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.