Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Factoring fu Variability Into Estimates of Unbound Drug Concentrations Negatively Biases the MIC Versus % Probability of Target Attainment Relationship of Antimicrobial Agents

The clinical breakpoint for a drug-pathogen combination reflects the drug susceptibility of the pathogen wild-type population, the location of the infection, the integrity of the host immune response, and the drug-pathogen pharmacokinetic (PK)/pharmacodynamic (PD) relationship. That PK/PD relationship, along with the population variability in drug exposure, is used to determine the probability of target attainment (PTA) of the PK/PD index at a specified minimum inhibitory concentration (MIC) for a selected target value. The PTA is used to identify the pharmacodynamic cutoff value (COPD), which is one of the three components used to establish the clinical breakpoint. A challenge encountered when defining the COPD is that the available PK information typically reflects total (free plus protein-bound) plasma concentrations. However, it is the unbound drug concentrations that exert the therapeutic effects and how the population fraction unbound (fu) incorporated into the COPD assessments can markedly influence the COPD. Factors examined included the estimated population fu mean (risk of bias) and the incorporation of estimated fu population variability into the Monte Carlo simulations when converting total to unbound plasma concentrations (risk of inflating variability). In this in silico study, the drug fu, systemic clearance, and the variability of both were altered so that the relative impact of each could be explored. We demonstrate that incorporating fu variability into the estimation of fAUCback can bias the COPD assessment and that the magnitude of bias reflects the relative variability in systemic clearance and fu.

Nonlinear Pharmacokinetics of Topical Flurbiprofen Gel in a Phase I Study Among Chinese Healthy Adults

INTRODUCTION

PDX-02 (Flurbiprofen sodium) is a topical nonsteroidal anti-inflammatory drug in gel formulation for local analgesia and anti-inflammation. A Phase I clinical trial was conducted to assess the safety, tolerability, and pharmacokinetics of single and multiple doses of PDX-02 gel in Chinese healthy adults.

METHODS

The trial comprised three parts: (1) a single-dose ascending study with three dose levels (0.5%, 1% to 2% PDX-02 gel) applied on a 136 cm2 skin area; (2) a multiple-dose study with either 1% or 2% PDX-02 gel applied on a 136 cm2 skin area for 7 consecutive days; and (3) a high dose group with 2% PDX-02 gel on an 816 cm2 skin area and a frequent multiple dose group with 2% PDX-02 gel on a 272 cm2 skin area four times a day for 7 consecutive days. The safety, tolerability and pharmacokinetics of the PDX-02 gel were evaluated in each part.

RESULTS

A total of sixty participants completed the trial, with all adverse events recovered and all positive skin reaction being transient and recovered. The overall absorption of topical PDX-02 gel was slow with a mean peak time exceeding 9 h. The elimination rate remained consistent between dose groups. A less-than-dose-proportional nonlinear pharmacokinetics relationship was observed within the studied dose range, and this is likely due to the autoinduction of skin first-pass metabolism.

CONCLUSION

The topical PDX-02 gel showed favorable safety and tolerability in both single and multiple dosing studies, with a less-than-dose-proportional nonlinear pharmacokinetics observed.

Integrating In Vitro Biopharmaceutics into Physiologically Based Biopharmaceutic Model (PBBM) to Predict Food Effect of BCS IV Zwitterionic Drug (GSK3640254)

A strategy followed to integrate in vitro solubility and permeability data into a PBBM model to predict the food effect of a BCS IV zwitterionic drug (GSK3640254) observed in clinical studies is described. The PBBM model was developed, qualified and verified using clinical data of an immediate release (IR)-tablet (10-320 mg) obtained in healthy volunteers under fasted and fed conditions. The solubility of GSK3640254 was a function of its ionization state, the media composition and pH, whereas its permeability determined using MDCK cell lines was enhanced by the presence of mixed micelles. In vitro data alongside PBBM modelling suggested that the positive food effect observed in the clinical studies was attributed to micelle-mediated enhanced solubility and permeability. The biorelevant media containing oleic acid and cholesterol in fasted and fed levels enabled the model to appropriately capture the magnitude of the food effect. Thus, by using Simcyp^® v20 software, the PBBM model accurately predicted the results of the food effect and predicted data were within a two-fold error with 70% being within 1.25-fold. The developed model strategy can be effectively adopted to increase the confidence of using PBBM models to predict the food effect of BCS class IV drugs.

Physiologically Based Absorption Modelling to Explore the Formulation and Gastric pH Changes on the Pharmacokinetics of Acalabrutinib

Acalabrutinib, a selective Bruton's tyrosine kinase inhibitor, is a biopharmaceutics classification system class II drug. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption of immediate release capsule formulation of acalabrutinib in humans. Integration of in vitro biorelevant measurements, dissolution studies and in silico modelling provided clinically relevant inputs for the mechanistic absorption PBPK model. The batch specific dissolution data were integrated in two ways, by fitting a diffusion layer model scalar to the drug product dissolution with integration of drug substance laser diffraction particle size data, or by fitting a product particle size distribution to the dissolution data. The latter method proved more robust and biopredictive. In both cases, the drug surface solubility was well predicted by the Simcyp simulator. The model using the product particle size distribution (P-PSD) for each clinical batch adequately captured the PK profiles of acalabrutinib and its active metabolite. Average fold errors were 0.89 for both Cmax and AUC, suggesting good agreement between predicted and observed PK values. The model also accurately predicted pH-dependent drug-drug interactions between omeprazole and acalabrutinib, which was similar across all clinical formulations. The model predicted acalabrutinib geometric mean AUC ratios (with omeprazole vs acalabrutinib alone) were 0.51 and 0.68 for 2 batches of formulations, which are close to observed values of 0.43 and 0.51~0.63, respectively. The mechanistic absorption PBPK model could be potentially used for future applications such as optimizing formulations or predicting the PK for different batches of the drug product.

Physiologically Based Pharmacokinetic Modeling for Quantitative Prediction of Exposure to a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Substrate of Cytochrome P450 and Aldehyde Oxidase, Using Chimeric Mice With Humanized Liver

In a previous study on the human mass balance of DS-1971a, a selective NaV1.7 inhibitor, its CYP2C8-dependent metabolite M1 was identified as a human disproportionate metabolite. The present study assessed the usefulness of pharmacokinetic evaluation in chimeric mice grafted with human hepatocytes (PXB-mice) and physiologically based pharmacokinetic (PBPK) simulation of M1. After oral administration of radiolabeled DS-1971a, the most abundant metabolite in the plasma, urine, and feces of PXB-mice was M1, while those of control SCID mice were aldehyde oxidase-related metabolites including M4, suggesting a drastic difference in the metabolism between these mouse strains. From a qualitative perspective, the metabolite profile observed in PXB-mice was remarkably similar to that in humans, but the quantitative evaluation indicated that the area under the plasma concentration-time curve (AUC) ratio of M1 to DS-1971a (M1/P ratio) was approximately only half of that in humans. A PXB-mouse-derived PBPK model was then constructed to achieve a more accurate prediction, giving an M1/P ratio (1.3) closer to that in humans (1.6) than the observed value in PXB-mice (0.69). In addition, simulated maximum plasma concentration and AUC values of M1 (3429 ng/ml and 17,116 ng·h/ml, respectively) were similar to those in humans (3180 ng/ml and 18,400 ng·h/ml, respectively). These results suggest that PBPK modeling incorporating pharmacokinetic parameters obtained with PXB-mice is useful for quantitatively predicting exposure to human disproportionate metabolites. SIGNIFICANCE STATEMENT: The quantitative prediction of human disproportionate metabolites remains challenging. This paper reports on a successful case study on the practical estimation of exposure (C max and AUC) to DS-1971a and its CYP2C8-dependent, human disproportionate metabolite M1, by PBPK simulation utilizing pharmacokinetic parameters obtained from PXB-mice and in vitro kinetics in human liver fractions. This work adds to the growing knowledge regarding metabolite exposure estimation by static and dynamic models.

CYP2C9 Polymorphism Influence in PK/PD Model of Naproxen and 6-O-Desmethylnaproxen in Oral Fluid

Polymorphisms in CYP2C9 can significantly interfere with the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of nonsteroidal anti-inflammatory drugs (NSAIDs), including naproxen. The present research aimed to study the PK/PD parameters of naproxen and its metabolite, 6-O-desmethylnaproxen, associated with allelic variations of CYP2C9. In our study, a rapid, selective, and sensitive Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method was developed and validated for the determination of naproxen and its main metabolite, 6-O-desmethylnaproxen, in oral fluid. Naproxen and its main metabolite were separated using a Shim-Pack XR-ODS 75L × 2.0 column and C18 pre-column at 40 °C using a mixture of methanol and 10 mM ammonium acetate (70:30, v/v), with an injection flow of 0.3 mL/min. The total analytical run time was 3 min. The volunteers, previously genotyped for CYP2C9 (16 ancestral—CYP2C9 *1 and 12 with the presence of polymorphism—CYP2C9 *2 or *3), had their oral fluids collected sequentially before and after taking a naproxen tablet (500 mg) at the following times: 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6 8, 11, 24, 48, 72 and 96 h. Significant differences in the PK parameters (* p < 0.05) of naproxen in the oral fluid were: Vd/F (L): 98.86 (55.58−322.07) and 380.22 (261.84−1097.99); Kel (1/h): 0.84 (0.69−1.34) and 1.86 (1.09−4.06), in ancestral and mutated CYP2C9 *2 and/or *3, respectively. For 6-O-desmethylnaproxen, no PK parameters were significantly different between groups. The analysis of prostaglandin E2 (PGE2) proved to be effective and sensitive for PD parameters analysis and showed higher levels in the mutated group (p < 0.05). Both naproxen and its main metabolite, 6-O-desmethylnaproxen, and PGE2 in oral fluid can be effectively quantified using LC-MS/MS after a 500 mg oral dose of naproxen. Our method proved to be effective and sensitive to determine the lower limit of quantification of naproxen and its metabolite, 6-O-desmethylnaproxen, in oral fluid (2.4 ng/mL). All validation data, such as accuracy, precision, and repeatability intra- and inter-assay, were less than 15%. Allelic variations of CYP2C9 may be considered relevant in the PK of naproxen and its main metabolite, 6-O-desmethylnaproxen.

Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes

The aim of this study was to establish the physiologically based pharmacokinetic (PBPK) model of flurbiprofen related to CYP2C9 genetic polymorphism and describe the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes. PK-Sim® software was used for the model development and validation. A total of 16 clinical pharmacokinetic data for flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups were used for the PBPK modeling. Turnover number (k_cat) of CYP2C9 values were optimized to capture the observed profiles in different CYP2C9 genotypes. In the simulation, predicted fraction metabolized by CYP2C9, fraction excreted to urine, bioavailability, and volume of distribution were similar to previously reported values. Predicted plasma concentration-time profiles in different CYP2C9 genotypes were visually similar to the observed profiles. Predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.44-, 2.05-, and 3.67-fold higher than the CYP2C9*1/*1 genotype. The ranges of fold errors for AUC_inf, C_max, and t_1/2 were 0.84-1.00, 0.61-1.22, and 0.74-0.94 in development and 0.59-0.98, 0.52-0.97, and 0.61-1.52 in validation, respectively, which were within the acceptance criterion. Thus, the PBPK model was successfully established and described the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups. The present model could guide the decision-making of tailored drug administration strategy by predicting the pharmacokinetics of flurbiprofen in various clinical scenarios.

Physiologically based pharmacokinetic (PBPK) modeling of piroxicam with regard to CYP2C9 genetic polymorphism

Piroxicam is a non-steroidal anti-inflammatory drug used to alleviate symptoms of osteoarthritis and rheumatoid arthritis. CYP2C9 genetic polymorphism significantly influences the pharmacokinetics of piroxicam. The objective of this study was to develop and validate the piroxicam physiologically based pharmacokinetic (PBPK) model related to CYP2C9 genetic polymorphism. PK-Sim^® version 10.0 was used for the PBPK modeling. The PBPK model was evaluated by predicted and observed plasma concentration-time profiles, fold errors of predicted to observed pharmacokinetic parameters, and a goodness-of-fit plot. The turnover number (k_cat) of CYP2C9 was adjusted to capture the pharmacokinetics of piroxicam in different CYP2C9 genotypes. The population PBPK model overall accurately described and predicted the plasma concentration-time profiles in different CYP2C9 genotypes. In our simulations, predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.83-, 2.07-, and 6.43-fold higher than CYP2C9*1/*1 genotype, respectively. All fold error values for AUC, C_max, and t_1/2 were included in the acceptance criterion with the ranges of 0.57-1.59, 0.63-1.39, and 0.65-1.51, respectively. The range of fold error values for predicted versus observed plasma concentrations was 0.11-3.13. 93.9% of fold error values were within the two-fold range. Average fold error, absolute average fold error, and root mean square error were 0.93, 1.27, and 0.72, respectively. Our model accurately captured the pharmacokinetic alterations of piroxicam according to CYP2C9 genetic polymorphism.

Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series

A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.