Effect of CYP3A5 and ABCB1 polymorphisms on the interaction between tacrolimus and itraconazole in patients with connective tissue disease

Impact of ABCG2 and ABCB1 Polymorphisms on Imatinib Plasmatic Exposure: An Original Work and Meta-Analysis

Adequate imatinib plasma levels are necessary to guarantee an efficacious and safe treatment in gastrointestinal stromal tumor (GIST) and chronic myeloid leukemia (CML) patients. Imatinib is a substrate of the drug transporters ATP-binding cassette subfamily B member 1 (ABCB1) and ATP-binding cassette subfamily G member 2 (ABCG2) that can affect its plasma concentration. In the present study, the association between three genetic polymorphisms in ABCB1 (rs1045642, rs2032582, rs1128503) and one in ABCG2 (rs2231142) and the imatinib plasma trough concentration (C_trough) was investigated in 33 GIST patients enrolled in a prospective clinical trial. The results of the study were meta-analyzed with those of other seven studies (including a total of 649 patients) selected from the literature through a systematic review process. The ABCG2 c.421C>A genotype demonstrated, in our cohort of patients, a borderline association with imatinib plasma trough levels that became significant in the meta-analysis. Specifically, homozygous carriers of the ABCG2 c.421 A allele showed higher imatinib plasma C_trough with respect to the CC/CA carriers (C_trough, 1463.2 ng/mL AA, vs. 1196.6 ng/mL CC + AC, p = 0.04) in 293 patients eligible for the evaluation of this polymorphism in the meta-analysis. The results remained significant under the additive model. No significant association could be described between ABCB1 polymorphisms and imatinib C_trough, neither in our cohort nor in the meta-analysis. In conclusion, our results and the available literature studies sustain an association between ABCG2 c.421C>A and imatinib plasma C_trough in GIST and CML patients.

Effect of drug combination on tacrolimus target dose in renal transplant patients with different CYP3A5 genotypes

Various factors, including genetic polymorphisms, drug-drug interactions, and patient characteristics influence the blood concentrations of tacrolimus in renal transplant patients. In the present study, we established a population pharmacokinetic model to explore the effect of combined use of Wuzhi capsules/echinocandins and the patients' biochemical parameters such as hematocrit on blood concentrations and target doses of tacrolimus in renal transplant patients with different CYP3A5 genotypes. The aim of the study was to propose an individualized tacrolimus administration regimen for early renal transplant recipients.In this retrospective cohort study, we included 240 renal transplant recipients within 21 days of surgery (174 males and 66 females, mean age 39.4 years), who received tacrolimus alone (n = 54), in combination with Wuzhi capsules (99) or caspofungin (57) or micafungin (30). We collected demographic characteristics, clinical indicators, CYP3A5 genotypes, and 1950 steady-state trough concentrations of tacrolimus and included them in population pharmacokinetic model. An additional 110 renal transplant recipients and 625 steady-state trough concentrations of tacrolimus were included for external validation of the model. The population pharmacokinetic model was established and Monte Carlo was used to simulate probabilities for achieving the target concentration for individual tacrolimus administration.A two-compartment model of first-order absorption and elimination was developed to describe the population pharmacokinetics of tacrolimus. CYP3A5 genotypes and co-administration of Wuzhi capsules, as well as time after renal transplantation and hematocrit, were important factors affecting the clearance of tacrolimus. We found no obvious change in trend in the scatter plot of tacrolimus clearance rate vs. hematocrit. The Monte Carlo simulation indicated the following recommended doses of tacrolimus alone: 0.14 mg·kg^-1·d^-1 for genotype CYP3A5*1*1, 0.12 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.10 mg·kg^-1·d^-1 for CYP3A5*3*3. For patients receiving the combination with Wuzhi capsules, the recommended doses of tacrolimus were 0.10 mg·kg^-1·d^-1 for CYP3A5*1*1, 0.08 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.06 mg·kg^-1·d^-1 for CYP3A5*3*3 genotypes. Caspofungin or micafungin had no effect on the clearance of tacrolimus in renal transplant recipients.The population pharmacokinetics of tacrolimus in renal transplant patients was evaluated and the individual administration regimen of tacrolimus was simulated. For early kidney transplant recipients receiving tacrolimus treatment, not only body weight, but also CYP3A5 genotypes and drugs used in combination should be considered when determining the target dose of tacrolimus.

Drug-drug-gene interactions and adverse drug reactions

The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug–drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug–drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug–drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug–drug–gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug–drug–gene interactions.

Effects of MDR1 1236C > T-2677G > T-3435C > T polymorphisms on the intracellular accumulation of tacrolimus, cyclosporine A, sirolimus and everolimus

1. Overexpression of P-glycoprotein (P-gp, encoded by MDR1) mediates resistance to multiple immunosuppressors. Several common MDR1 variants (1236C > T, 2677G > T, 3435C > T) impact the efflux activity of P-gp-mediated substrates. We assessed the effect of these polymorphisms on the sensitivity, intracellular accumulation, and efflux of tacrolimus, cyclosporine A, sirolimus and everolimus in transfected LLC-PK1 cells. 2. LLC-PK1 cell lines were transfected with empty vector (pcDNA3.1) and recombinant MDR1_T-T-T, MDR1_C-T-T, MDR1_C-G-T and MDR1_C-G-C vectors, respectively and further screened in the presence of puromycin. The IC₅₀ values, intracellular accumulation, and apparent permeability ratios of tacrolimus, cyclosporine A, sirolimus and everolimus were evaluated. 3. MDR1 overexpression increased the resistance of LLC-PK1 cells to tacrolimus, cyclosporine A, sirolimus and everolimus. The resistance of cells expressing MDR1_C-G-C wild-type haplotypes to tacrolimus were increased compared to MDR1_T-T-T, MDR1_C-T-T, MDR1_C-G-T variant haplotypes. The efflux ability of P-gp-mediated tacrolimus in cells transfected with MDR1_C-G-C was higher than cells overexpressing MDR1_T-T-T, MDR1_C-T-T, MDR1_C-G-T variant haplotypes. In addition, the resistance of cells expressing MDR1_C-G-C wild-type haplotypes to sirolimus were increased compared to MDR1_C-T-T, MDR1_C-G-T variant haplotypes. The efflux ability of P-gp-mediated sirolimus in cells overexpressing MDR1_C-G-C was higher than cells transfected with MDR1_C-T-T, MDR1_C-G-T variant haplotypes. 4. These findings indicate that wild-type MDR1 exports tacrolimus and sirolimus more efficiency than the MDR1_T-T-T, MDR1_C-T-T, MDR1_C-G-T variant protein. This observation indicates that 1236C > T, 2677G > T, 3435C > T variant haplotypes drastically decrease the efflux ability of P-gp-mediated tacrolimus and sirolimus in a substrate-specific manner.

Use of a semi-physiological pharmacokinetic model to investigate the influence of itraconazole on tacrolimus absorption, distribution and metabolism in mice

1. The aim of this study was to investigate the influence of itraconazole (ITCZ) on tacrolimus absorption, distribution and metabolism by developing a semi-physiological pharmacokinetic model of tacrolimus in mice. 2. Mice were randomly divided into four groups, namely control group (CG, taking 3 mg kg^-1 tacrolimus only), low-dose group (LDG, taking tacrolimus with 12.5 mg kg^-1 ITCZ), medium-dose group (MDG, taking tacrolimus with 25 mg kg^-1 ITCZ) and high-dose group (HDG, taking tacrolimus with 50 mg kg^-1 ITCZ). 3. Liver clearance (CL_li) decreased significantly (**p < 0.01) in LDG (35.3%), MDG (45.2%) and HDG (58.7%) mice compared to CG mice. With respect to gut clearance (CL_gu), significant (**p < 0.01) decrease was also revealed in LDG (35.9%), MDG (50.2%) and HDG (64.6%) mice. A significant (**p < 0.01) higher tacrolimus brain-to-blood partition coefficient (K_t,br) was found in MDG (25.3%) and HDG (55.9%) mice than in CG mice. Moreover, a significant (*p < 0.05) increase (16.3%) was found in the absorption rate constant (K_a) in HDG mice compared to CG mice. There was a significant (**p < 0.01) association between ITCZ dose and the change in CL_gu (ΔCL_gu, r= -0.790), the change in CL_li (ΔCL_li, r= -0.787) and the change in K_t,br (ΔK_t,br, r = 0.727), while the association between ITCZ dose and the change in K_a (ΔK_a) was not significant (p > 0.05). 4. These findings could be useful in predicting the efficacy and toxicity of tacrolimus, and drug-drug interaction of ITCZ and tarcolimus in human.