Application of Solubility and Dissolution Profile Comparison for Prediction of Gastric pH-Mediated Drug-Drug Interactions.

The objective of this study was to assess how solubility and dissolution profile comparisons under different pH conditions can be used to predict gastric pH-mediated drug-drug interaction (DDI) potential. We collected information for new molecular entities (NMEs) approved from 2003 to 2019 by the U.S. Food and Drug Administration (FDA) that had dedicated clinical DDI studies with acid-reducing agents (ARAs). Among these, 67 NMEs with solubility under different pHs and dissolution profiles generated in pH 1.2, 4.5, and 6.8 aqueous media were included for analysis. Similarity factor (f2) was used to compare dissolution profiles at different pHs for pH-mediated DDI prediction (e.g., f2<50 predicts positive DDI). Prediction accuracy was calculated based on the outcome comparison between predicted and observed DDIs. Based on dissolution profile comparisons and observed DDI data, weak base drugs (WBDs) (n = 49) showed 72.5% prediction accuracy under the fasted conditions, and 66.7% prediction accuracy under fed conditions. While using solubility and clinical dose for prediction, the prediction accuracy was 80% under fasted conditions and 66.7% under fed conditions, respectively. Comparison of dissolution profiles generated at pH 1.2, 4.5, and 6.8 can be used to predict gastric pH-mediated DDI potential for WBDs. It demonstrated comparable prediction accuracy under both fasted and fed conditions when compared to the prediction using solubility and clinical dose. Furthermore, dissolution profile comparison could add an additional understanding of possible impact of pH change on the release behavior of the drug product. Graphical abstract.

Exploring the Relationship of Drug BCS Classification, Food Effect, and Gastric pH-Dependent Drug Interactions.

Food effect (FE) and gastric pH-dependent drug-drug interactions (DDIs) are both absorption-related. Here, we evaluated if Biopharmaceutics Classification System (BCS) classes may be correlated with FE or pH-dependent DDIs. Trends in FE data were investigated for 170 drugs with clinical FE studies from the literature and new drugs approved from 2013 to 2019 by US Food and Drug Administration. A subset of 38 drugs was also evaluated to determine whether FE results can inform the need for a gastric pH-dependent DDI study. The results of FE studies were defined as no effect (AUC ratio 0.80-1.25), increased exposure (AUC ratio ≥1.25), or decreased exposure (AUC ratio ≤0.8). Drugs with significantly increased exposure FE (AUC ratio ≥2.0; N=14) were BCS Class 2 or 4, while drugs with significantly decreased exposure FE (AUC ratio ≤0.5; N=2) were BCS Class 1/3 or 3. The lack of FE was aligned with the lack of a pH-dependent DDI for all 7 BCS Class 1 or 3 drugs as expected. For the 13 BCS Class 2 or 4 weak base drugs with an increased exposure FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). Among the 16 BCS Class 2 or 4 weak base drugs with no FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). FE appears to have limited correlation with BCS classes except for BCS Class 1 drugs, confirming that multiple physiological mechanisms can impact FE. Lack of FE does not indicate absence of pH-dependent DDI for BCS Class 2 or 4 drugs. Graphical Abstract.

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

BACKGROUND AND OBJECTIVES: Proton pump inhibitors (PPIs) can affect the intragastric release of other drugs from their dosage forms by elevating the gastric pH. They may also influence drug absorption and metabolism by interacting with P-glycoprotein or with the cytochrome P450 (CYP) enzyme system. Nifedipine is a Biopharmaceutics Classification System (BCS) class II drug with low solubility across physiologic pH and high permeability. Previous studies have demonstrated that drug-drug interaction (DDI) existed between omeprazole and nifedipine with significantly increased systemic exposure of nifedipine in subjects after pre-treatment for 7 days with omeprazole compared to the subjects without omeprazole treatment. It was shown that omeprazole not only induced an increase in intragastric pH, but also inhibited the CYP3A4 activity, while CYP3A4-mediated oxidation is the main metabolic pathway of nifedipine. The purpose of this study is to apply a physiologically based pharmacokinetic (PBPK) modeling approach to investigate the DDI mechanism for an immediate release formulation of nifedipine with omeprazole. METHODS: A previously published model for omeprazole was modified to integrate metabolites and to update CYP inhibition based on the most updated published in vitro data. We simulated the nifedipine pharmacokinetics in healthy subjects with or without the multiple-dose pretreatment of omeprazole (20 mg) following oral administrations of immediate-release (IR) (10 mg) nifedipine. Nifedipine solubility at different pHs was used to simulate the nifedipine pharmacokinetics for both clinical arms. Multiple sensitivity analyses were performed to understand the impact of gastric pH and the CYP3A4-mediated gut and liver first pass metabolism on the overall nifedipine pharmacokinetics. RESULTS: The developed PBPK model properly described the pharmacokinetics of nifedipine and predicted the inhibitory effect of multiple-dose omeprazole on CYP3A4 activity. With the incorporation of the physiologic effect of omeprazole on both gastric pH and CYP3A4 to the PBPK model, the verified PBPK model allows evaluating the impact of the increase in gastric pH and/or CYP3A4 inhibition. The simulated results show that the nifedipine metabolic inhibition by omeprazole may play an important role in the DDI between nifedipine and omeprazole for IR nifedipine formulation. CONCLUSION: The developed full PBPK model with the capability to simulate DDI by considering gastric pH change and metabolic inhibition provides a mechanistic understanding of the observed DDI of nifedipine with a PPI, omeprazole.

Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs.

Weak-base drugs are susceptible to drug-drug interactions (DDIs) when coadministered with gastric acid-reducing agents (ARAs). We developed PBPK models to evaluate the potential of such pH-dependent DDIs for four weak-base drugs, i.e., tapentadol, darunavir, erlotinib, and saxagliptin. The physiologically-based pharmacokinetic (PBPK) models of these drugs were first optimized using pharmacokinetic (PK) data following oral administration without ARAs, which were then verified with data from additional PK studies in the presence and absence of food. The models were subsequently used to predict the extent of DDIs with ARA coadministration. Sensitivity analysis was conducted to explore the impact of gastric pH on quantitative prediction of drug exposure in the presence of ARA. The results suggested that the PBPK models developed could adequately describe the lack of the effect of ARA on the PK of tapentadol, darunavir, and saxagliptin and could qualitatively predict the effect of ARA in reducing the absorption of erlotinib. Further studies involving more drugs with positive pH-dependent DDIs are needed to confirm the findings and broaden our knowledge base to further improve the utilization of PBPK modeling to evaluate pH-dependent DDI potential.

Evaluation of transporters in drug development: Current status and contemporary issues.

Transporters govern the access of molecules to cells or their exit from cells, thereby controlling the overall distribution of drugs to their intracellular site of action. Clinically relevant drug-drug interactions mediated by transporters are of increasing interest in drug development. Drug transporters, acting alone or in concert with drug metabolizing enzymes, can play an important role in modulating drug absorption, distribution, metabolism and excretion, thus affecting the pharmacokinetics and/or pharmacodynamics of a drug. The drug interaction guidance documents from regulatory agencies include various decision criteria that may be used to predict the need for in vivo assessment of transporter-mediated drug-drug interactions. Regulatory science research continues to assess the prediction performances of various criteria as well as to examine the strength and limitations of each prediction criterion to foster discussions related to harmonized decision criteria that may be used to facilitate global drug development. This review discusses the role of transporters in drug development with a focus on methodologies in assessing transporter-mediated drug-drug interactions, challenges in both in vitro and in vivo assessments of transporters, and emerging transporter research areas including biomarkers, assessment of tissue concentrations, and effect of diseases on transporters.

Exposure-Response of Palonosetron for Prevention of Chemotherapy-Induced Nausea and Vomiting in Pediatric Patients.

OBJECTIVES: Extrapolation of efficacy from adult populations to pediatrics may be appropriate if it is reasonable to assume that the 2 populations have similar disease progression and response to intervention. When full extrapolation of efficacy is deemed appropriate, the pediatric dose can be determined by "matching" exposure to a drug with that observed in adult patients. This approach has been used in certain therapeutic areas to alleviate the burden of pediatric clinical trials. We present here a case in which exposure matching is not appropriate. METHODS: Data analyses including pharmacokinetics and exposure-response were performed using data obtained from 2 pediatric chemotherapy-induced nausea and vomiting trials for intravenously administered palonosetron (Aloxi; a 5-HT3 receptor antagonist) injection and the results were compared with adult findings. RESULTS: At the approved doses for adults (0.25 mg) and pediatric patients (20 µg/kg), mean systemic exposure (area under the curve) of palonosetron in pediatric patients was approximately 3-fold higher than that in adults, whereas the response rate was similar between the 2 populations. Across pediatric patients, those younger than 6 years of age appeared to have a higher response than those ages 6 years or older, even though estimated systemic exposure was comparable between these age groups. CONCLUSIONS: Overall, these analyses provide an example in which pediatric and adult exposure data alone are insufficient to adequately identify effective pediatric doses and raise questions about the appropriateness of exposure matching for other drugs in the same therapeutic class. In such cases, pediatric dose-ranging and efficacy studies are needed.

Predicting nonlinear pharmacokinetics of omeprazole enantiomers and racemic drug using physiologically based pharmacokinetic modeling and simulation: application to predict drug/genetic interactions.

PURPOSE: The objective of this study is to develop a physiologically-based pharmacokinetic (PBPK) model for each omeprazole enantiomer that accounts for nonlinear PK of the two enantiomers as well as omeprazole racemic drug. METHODS: By integrating in vitro, in silico and human PK data, we first developed PBPK models for each enantiomer. Simulation of racemic omeprazole PK was accomplished by combining enantiomer models that allow mutual drug interactions to occur. RESULTS: The established PBPK models for the first time satisfactorily predicted the nonlinear PK of esomeprazole, R-omeprazole and the racemic drug. The modeling exercises revealed that the strong time-dependent inhibition of CYP2C19 by esomeprazole greatly altered the R-omeprazole PK following administration of racemic omeprazole as in contrast to R-omeprazole given alone. When PBPK models incorporated both autoinhibition of each enantiomer and mutual interactions, the ratios between predicted and observed AUC following single and multiple dosing of omeprazole were 0.97 and 0.94, respectively. CONCLUSIONS: PBPK models of omeprazole enantiomers and racemic drug were developed. These models can be utilized to assess CYP2C19-mediated drug and genetic interaction potential for omeprazole and esomeprazole.