Prediction of the Impact of CYP2C19 Polymorphism on Drug-Drug Interaction between Voriconazole and Tacrolimus Using Physiologically-Based Pharmacokinetic Modelling

Abstract Voriconazole increases tacrolimus blood concentration significantly when coadministrated. The recommendation of reducing tacrolimus to 1/3 in voriconazole package insert seems not to be satisfactory in clinical practice. In vitro studies demonstrated that the magnitude of inhibition depends on the concentration of voriconazole, while voriconazole exposure is determined by the genotype status of CYP2C19. CYP2C19 gene polymorphism challenges the management of drug-drug interactions(DDIs) between voriconazole and tacrolimus. This work aimed to predict the impact of CYP2C19 polymorphism on the DDIs by using physiologically based pharmacokinetics (PBPK) models. The precision of the developed voriconazole and tacrolimus models was reasonable by evaluating the pharmacokinetic parameters fold error, such as AUC0-24, Cmax and tmax. Voriconazole increased tacrolimus concentration immediately in all population. The simulated duration of DDIs disappearance after voriconazole withdrawal were 146h, 90h and 66h in poor metabolizers (PMs), intermediate metabolizers (IMs) and extensive metabolizers(EMs), respectively. The developed and optimized PBPK models in this study can be applied to assit the dose adjustment for tacrolimus with and without voriconazole.

Physiologically based pharmacokinetic modeling of the inhibitory effect of dapagliflozin on intestinal and renal SGLT / 药学学报

This study establishes and optimizes the physiologically based pharmacokinetics (PBPK) model for dapagliflozin, predicts the drug distribution into relevant tissues, and calculates the inhibitory effect on the sodium-glucose cotransporters (SGLTs) in the intestine and renal proximal tubule. Based on literature data, a PBPK model for oral administration in healthy adults was established and the predicted blood concentration-time curve characteristics, the main pharmacokinetic parameters (PK), and drug excretion in urine were compared with the published data. To verify and optimize the model and verify the accuracy of the tissue distribution and concentration predictions, a pharmacodynamics model (PD) was established. Urine glucose excretion (UGE) was simulated at the corresponding times. The characteristics of the drug-time curve predicted by the model are similar to those of the measured curve, and the ratio of the main PK parameters to the measured values is within a two-fold range; the accuracy of the established PBPK model is good. The maximal inhibition obtained with 10 mg of dapagliflozin on the duodenum and jejunum segment sodium-glucose co-transporter 1 (SGLT1s) was 1.6%-4.7%, and the inhibition rate of the sodium-glucose co-transporter 2 (SGLT2s) in the proximal tubule of the kidney was as high as 99.9%. At a dose of 10 mg, dapagliflozin delayed intestinal glucose absorption while occupying most of the sites (99.9%) of the renal sodium-glucose cotransporter 2 and inhibiting its glucose reabsorption. This physiological-pharmacokinetic model for dapagliflozin in healthy adults can provide meaningful guidance for exploring pharmacological mechanisms and potential toxicity of gliflozin by simulating drug distribution in different tissues.

Changes of pharmacokinetics and application of physiologically based pharmacokinetic modeling in pharmacotherapy during pregnancy / 药学学报

The rational medication in pregnant women is a clinical issue that clinicians and pharmacists must take seriously. Most tissues and organs undergo anatomical and physiological changes during pregnancy that affect the absorption, distribution, metabolism, and excretion of drugs in vivo, which ultimately lead to changes in bioavailability. In order to achieve an effective therapeutic concentration, dose adjustment might be required during this period. In the past ten years, the application of modeling and simulation methods in the field of drug development and clinical therapy has continued to expand, for instance, using population pharmacokinetic (PPK) and physiologically based pharmacokinetic (PBPK) modeling to adjust dosage regimen in special populations. Rigorously designed and validated models will effectively make up for the deficiencies of clinical trials, provide valuable references for the design of clinical research, and even replace part of them. This article will introduce the physiological changes that affect the pharmacokinetic properties of the drug during pregnancy and review the progress in the application of PBPK modeling in pharmacokinetic studies in pregnant women.

Development and application progress of physiologically based pharmacokinetic modeling and its combined use with other modeling methods / 中国临床药理学与治疗学

Physiologically based pharmacokinetics (PBPK) is one of the main research fields of pharmacometrics, and it plays an important role at all the stages of drug development and clinical practice. In early drug discovery and development, human pharmacokinetics (PK) could be predicted by PBPK modeling using in silico, in vitro and preclinical in vivo data. During clinical studies, PBPK model could be used to investigate the effects of various physiological and pathological factors on PK, such as age, gender, liver/kidney impairment, and to guide dose adjustment of special population (pregnant women, children, etc.). Furthermore, PBPK modeling is now becoming more appealing with the ability to predict drug-drug interaction (DDI) in the case of co-administration of multiple drugs. In recent years, the application of PBPK modeling in industry has increased widely. Also, regulatory agencies have recognized the potential of PBPK and its impact on labeling recommendations. As the popularity of model-informed drug development, the combination of PBPK modeling with other commonly used modeling methods, such as population pharmacokinetics (PopPK), pharmacokinetic/pharmacodynamic (PK/PD) modeling and model-based meta-analysis (MBMA), has shown attractive advantages. In this paper, the origin and development, as well as the application status of PBPK are introduced briefly, and the application of PBPK modeling merged with PopPK, PK/PD and MBMA is reviewed.

Physiologically-based pharmacokinetic predictions of intestinal BCRP-mediated drug interactions of rosuvastatin in Koreans

It was recently reported that the C(max) and AUC of rosuvastatin increases when it is coadministered with telmisartan and cyclosporine. Rosuvastatin is known to be a substrate of OATP1B1, OATP1B3, NTCP, and BCRP transporters. The aim of this study was to explore the mechanism of the interactions between rosuvastatin and two perpetrators, telmisartan and cyclosporine. Published (cyclosporine) or newly developed (telmisartan) PBPK models were used to this end. The rosuvastatin model in Simcyp (version 15)'s drug library was modified to reflect racial differences in rosuvastatin exposure. In the telmisartan–rosuvastatin case, simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without telmisartan) ratios were 1.92 and 1.14, respectively, and the T(max) changed from 3.35 h to 1.40 h with coadministration of telmisartan, which were consistent with the aforementioned report (C(maxI)/C(max): 2.01, AUCI/AUC:1.18, T(max): 5 h → 0.75 h). In the next case of cyclosporine–rosuvastatin, the simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without cyclosporine) ratios were 3.29 and 1.30, respectively. The decrease in the CL(int,BCRP,intestine) of rosuvastatin by telmisartan and cyclosporine in the PBPK model was pivotal to reproducing this finding in Simcyp. Our PBPK model demonstrated that the major causes of increase in rosuvastatin exposure are mediated by intestinal BCRP (rosuvastatin–telmisartan interaction) or by both of BCRP and OATP1B1/3 (rosuvastatin–cyclosporine interaction).

Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in C(max) of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.