Physiologically Based Pharmacokinetic Modeling Approach to Identify the Drug-Drug Interaction Mechanism of Nifedipine and a Proton Pump Inhibitor, Omeprazole

Use of physiologically-based pharmacokinetic modeling to understand the effect of omeprazole administration on the pharmacokinetics of oral extended-release nifedipine

Proton pump inhibitors (PPIs) can affect the release of drugs from their dosage forms in vivo by elevating the gastric pH. Our recent clinical study has demonstrated that drug-drug interactions (DDIs) exist between a PPI, omeprazole, and nifedipine extended-release formulations, where systemic exposure of nifedipine was increased in subjects after multiple-dose pretreatment of omeprazole. However, the mechanism of the observed DDIs between omeprazole and nifedipine has not been well-understood, as the DDI may also be mediated through CYP3A4 enzyme inhibition in addition to the elevated gastric pH caused by omeprazole. This study used physiologically-based pharmacokinetic (PBPK) modeling and simulations to investigate the underlying mechanism of these complex DDIs. A formulation exhibiting differences in in vitro dissolution across physiological pH range and another formulation where pH does not impact dissolution appreciably (e.g., an osmotic pump) were chosen to characterize the potential impact of pH. The PBPK models incorporated two-stage in vitro release profiles via US Pharmacopeia 2 apparatus. PBPK simulations suggest that the elevated gastric pH following multiple-dose administration of omeprazole has a minimal effect on nifedipine pharmacokinetics (PKs), whereas CYP3A4-mediated DDI is likely the main driver to the observed change of nifedipine PKs in the presence of omeprazole. Compared to the osmotic formulation, the slightly increased exposure of nifedipine can be accounted for by the enhanced drug release in the pH-dependent formulation. The reported model-based approach may be useful in DDI risk assessments, product formulation designs, and bioequivalence evaluations.

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

OBJECTIVE

This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models.

METHODS

The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin.

RESULTS

The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range.

CONCLUSION

The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development

Physiologically based biopharmaceutics modeling (PBBM) emphasizes the integration of physicochemical properties of drug substance and formulation characteristics with system physiological parameters to predict the absorption and pharmacokinetics (PK) of a drug product. PBBM has been successfully utilized in drug development from discovery to postapproval stages and covers a variety of applications. The use of PBBM facilitates drug development and can reduce the number of preclinical and clinical studies. In this review, we summarized the major applications of PBBM, which are classified into six categories: formulation selection and development, biopredictive dissolution method development, biopharmaceutics risk assessment, clinically relevant specification settings, food effect evaluation and pH-dependent drug-drug-interaction risk assessment. The current state of PBBM applications is illustrated with examples from published studies for each category of application. Despite the variety of PBBM applications, there are still many hurdles limiting the use of PBBM in drug development, that are associated with the complexity of gastrointestinal and human physiology, the knowledge gap between the in vitro and the in vivo behavior of drug products, the limitations of model interfaces, and the lack of agreed model validation criteria, among other issues. The challenges and essential considerations related to the use of PBBM are discussed in a question-based format along with the scientific thinking on future research directions. We hope this review can foster open discussions between the pharmaceutical industry and regulatory agencies and encourage collaborative research to fill the gaps, with the ultimate goal to maximize the applications of PBBM in oral drug product development.

Use of Modeling and Simulation to Predict the Influence of Triazole Antifungal Agents on the Pharmacokinetics of Crizotinib

Crizotinib is used for the treatment of c-ros oncogene 1-positive advanced non-small-cell lung cancer. Triazole antifungal agents are widely used for invasive fungal infections in clinical practice. To predict the potential influence of different triazoles (voriconazole, fluconazole, and itraconazole) on the pharmacokinetics of crizotinib by modeling and simulation the physiologically based pharmacokinetic models were established and validated in virtual cancer subjects through Simcyp software based on the essential physicochemical properties and pharmacokinetic data collected. The validated physiologically based pharmacokinetic models were applied to predict the drug-drug interactions between crizotinib and different triazoles (voriconazole, fluconazole, or itraconazole) in patients with cancer. Crizotinib and triazole antifungal agents were administered orally. The predicted plasma concentration vs time profiles of crizotinib, voriconazole, fluconazole, and itraconazole showed good agreement with observed, respectively. The geometric mean area under the plasma concentration-time curve (AUC) of crizotinib was increased by 84%, 58%, and 79% when coadministered with voriconazole, fluconazole, or itraconazole at multiple doses, respectively. The drug-drug interaction results showed increased pharmacokinetic exposure (maximum plasma concentration and area under the plasma concentration-time curve) of crizotinib when coadministrated with different triazoles (voriconazole > itraconazole > fluconazole). Among the 3 triazoles, voriconazole exhibited the most significant influence on the pharmacokinetic exposure of crizotinib. In clinic, adverse drug reactions and toxicity related to crizotinib should be carefully monitored, and therapeutic drug monitoring for crizotinib is recommended to guide dosing and optimize treatment when coadministered with voriconazole, fluconazole, or itraconazole.

Prediction of cytochromes P450 3A and 2C19 modulation by both inflammation and drug interactions using physiologically based pharmacokinetics

Xenobiotics can interact with cytochromes P450 (CYPs), resulting in drug-drug interactions, but CYPs can also contribute to drug-disease interactions, especially in the case of inflammation, which downregulates CYP activities through pretranscriptional and posttranscriptional mechanisms. Interleukin-6 (IL-6), a key proinflammatory cytokine, is mainly responsible for this effect. The aim of our study was to develop a physiologically based pharmacokinetic (PBPK) model to foresee the impact of elevated IL-6 levels in combination with drug interactions with esomeprazole on CYP3A and CYP2C19. Data from a cohort of elective hip surgery patients whose CYP3A and CYP2C19 activities were measured before and after surgery were used to validate the accurate prediction of the developed models. Successive steps were to fit models for IL-6, esomeprazole, and omeprazole and its metabolite from the literature and to validate them. The models for midazolam and its metabolite were obtained from the literature. When appropriate, a correction factor was applied to convert drug concentrations from whole blood to plasma. Mean ratios between simulated and observed areas under the curve for omeprazole/5-hydroxy omeprazole, esomeprazole, and IL-6 were 1.53, 1.06, and 0.69, respectively, indicating an accurate prediction of the developed models. The impact of IL-6 and esomeprazole on the exposure to CYP3A and CYP2C19 probe substrates and respective metabolites were correctly predicted. Indeed, the ratio between predicted and observed mean concentrations were <2 for all observations (ranging from 0.51 to 1.7). The impact of IL-6 and esomeprazole on CYP3A and CYP2C19 activities after a hip surgery were correctly predicted with the developed PBPK models.

The Influence of Wuzhi Capsule on the Pharmacokinetics of Cyclophosphamide

BACKGROUND

Cyclophosphamide is approved for the treatment of a variety of tumors, yet the use of cyclophosphamide is limited by kidney and liver toxicity. In the clinic, the Wuzhi capsule is approved to attenuate cyclophosphamide toxicity in the kidney and liver.

OBJECTIVE

We aimed to investigate the effects of the principal ingredients of Wuzhi capsule, schisandrin A (SIA) and schisantherin A (STA), on the pharmacokinetics of cyclophosphamide.

METHODS

The essential pharmacokinetic data and physicochemical parameters of SIA, STA, and cyclophosphamide were collected. Physiologically based pharmacokinetic (PBPK) models of SIA, STA, and cyclophosphamide were built in Simcyp Simulator and verified using published clinical pharmacokinetic data. The verified PBPK models were used to predict potential herb-drug interactions (HDIs) between cyclophosphamide and SIA and STA in cancer patients.

RESULTS

The area under the plasma concentration-time curve (AUC) of cyclophosphamide was increased by 18% and 1% when co-administered with STA and SIA at a single dose, respectively, and increased by 301% and 29% when co-administered with STA and SIA at multiple doses, respectively. The maximum concentration (Cmax) of cyclophosphamide was increased by 75% and 7% when co-administered with STA and SIA at multiple doses, respectively.

CONCLUSION

The AUC and Cmax of cyclophosphamide were increased when cyclophosphamide was combined with the Wuzhi capsule, compared to cyclophosphamide alone. Our study shows that the adverse drug reactions and toxicity of cyclophosphamide should be closely monitored and an effective dosage adjustment of cyclophosphamide may need to be considered when co-administered with the Wuzhi capsule.

Gastroprotective Effect of Enteral Nutrition Formula in Mice Injected Subcutaneously with Indomethacin

We have previously shown that two enteral nutrition formulas suppressed gastric lesions induced by the oral administration of indomethacin (IND) in mice. However, the mechanism of their protective effect is unknown. In this study, the effect of the two enteral nutrition formulas on gastric lesions induced by subcutaneous IND injection was investigated, with the objective of exploring the possibility that they may interact directly with IND in the gastrointestinal tract. Ten-week-old, male, ICR mice were fasted, then orally given either purified water, Mermed^® One, or 2-fold diluted Terumeal^® 2.0α as enteral nutrition formula (25 mL/kg). IND was injected subcutaneously at 20 mg/kg after 30 min, and the stomach was removed 6 h later and fixed in formalin. The number and area of lesions in the stomachs of mice given enteral nutrition formula was reduced to 56–89% and 34–61%, respectively, compared with the mice given purified water. The time courses of plasma IND concentrations were comparable among the three groups. These results suggested that the effect of these enteral nutrition formulas on gastric lesions did not originate from their direct interaction with IND in the gastrointestinal tract or their effect on the disposition of IND.