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

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin in relation to SLCO1B1 genetic polymorphism

Pitavastatin, a potent 3-hydroxymethylglutaryl coenzyme A reductase inhibitor, is indicated for the treatment of hypercholesterolemia and mixed dyslipidemia. Hepatic uptake of pitavastatin is predominantly occupied by the organic anion transporting polypeptide 1B1 (OATP1B1) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is a polymorphic gene that encodes OATP1B1. SLCO1B1 genetic polymorphism significantly alters the pharmacokinetics of pitavastatin. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict pitavastatin pharmacokinetics according to SLCO1B1 genetic polymorphism. PK-Sim® version 10.0 was used to establish the whole-body PBPK model of pitavastatin. Our pharmacogenomic data and a total of 27 clinical pharmacokinetic data with different dose administration and demographic properties were used to develop and validate the model, respectively. Physicochemical properties and disposition characteristics of pitavastatin were acquired from previously reported data or optimized to capture the plasma concentration-time profiles in different SLCO1B1 diplotypes. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and profiles to the observed data. Predicted plasma concentration-time profiles were visually similar to the observed profiles in the non-genotyped populations and different SLCO1B1 diplotypes. All fold error values for AUC and Cmax were included in the two fold range of observed values. Thus, the PBPK model of pitavastatin in different SLCO1B1 diplotypes was properly established. The present study can be useful to individualize the dose administration strategy of pitavastatin in individuals with various ages, races, and SLCO1B1 diplotypes.

PBPK modeling to predict the pharmacokinetics of pantoprazole in different CYP2C19 genotypes

Pantoprazole is used to treat gastroesophageal reflux disease (GERD), maintain healing of erosive esophagitis (EE), and control symptoms related to Zollinger-Ellison syndrome (ZES). Pantoprazole is mainly metabolized by cytochrome P450 (CYP) 2C19, converting to 4'-demethyl pantoprazole. CYP2C19 is a genetically polymorphic enzyme, and the genetic polymorphism affects the pharmacokinetics and/or pharmacodynamics of pantoprazole. In this study, we aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of pantoprazole in populations with various CYP2C19 metabolic activities. A comprehensive investigation of previous reports and drug databases was conducted to collect the clinical pharmacogenomic data, physicochemical data, and disposition properties of pantoprazole, and the collected data were used for model establishment. The model was evaluated by comparing the predicted plasma concentration-time profiles and/or pharmacokinetic parameters (AUC and Cmax) with the clinical observation results. The predicted plasma concentration-time profiles in different CYP2C19 phenotypes properly captured the observed profiles. All fold error values for AUC and Cmax were included in the two-fold range. Consequently, the minimal PBPK model for pantoprazole related to CYP2C19 genetic polymorphism was properly established and it can predict the pharmacokinetics of pantoprazole in different CYP2C19 phenotypes. The present model can broaden the insight into the individualized pharmacotherapy for pantoprazole.

Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

Effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on the pharmacokinetics of tolperisone

Tolperisone, a muscle relaxant used for post-stroke spasticity, is metabolized to its main metabolite by CYP2D6 and to a lesser extent by CYP2C19 and CYP1A2. We investigated the effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on tolperisone pharmacokinetics. A 150 mg oral dose of tolperisone was given to 184 healthy Korean subjects and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A 3.14-fold significant increase in AUC0-∞ was observed in the CYP2D6*10/*10 group compared with the CYP2D6*wt/*wt group, whereas a 3.59-fold increase in AUC0-∞ was observed in CYP2C19PMs compared to CYP2C19EMs. Smokers had a 38.5% decrease in AUC0-∞ when compared to non-smokers. When these effects were combined, CYP2D6*10/*10-CYP2C19PM-Non-smokers had a 25.9-fold increase in AUC0-∞ compared to CYP2D6*wt/*wt-CYP2C19EM-Smokers. Genetic polymorphisms of CYP2D6 and CYP2C19 and cigarette smoking independently and significantly affected tolperisone pharmacokinetics and these effects combined resulted in a much greater impact on tolperisone pharmacokinetics.

Effects of CYP2C9 and CYP2C19 genetic polymorphisms on the pharmacokinetics and pharmacodynamics of gliclazide in healthy subjects

Gliclazide metabolism is mediated by genetically polymorphic CYP2C9 and CYP2C19 enzymes. We investigated the effects of CYP2C9 and CYP2C19 genetic polymorphisms on the pharmacokinetics and pharmacodynamics of gliclazide. Twenty-seven Korean healthy volunteers were administered a single oral dose of gliclazide 80 mg. The plasma concentration of gliclazide was quantified for the pharmacokinetic analysis and plasma concentrations of glucose and insulin were measured as pharmacodynamic parameters. The pharmacokinetics of gliclazide showed a significant difference according to the number of defective alleles of combined CYP2C9 and CYP2C19. The two defective alleles group (group 3) and one defective allele group (group 2) showed 2.34- and 1.46-fold higher AUC_0-∞ (P < 0.001), and 57.1 and 32.3% lower CL/F (P < 0.001), compared to those of the no defective allele group (group 1), respectively. The CYP2C9IM-CYP2C19IM group had AUC_0-∞ increase of 1.49-fold (P < 0.05) and CL/F decrease by 29.9% (P < 0.01), compared with the CYP2C9 Normal Metabolizer (CYP2C9NM)-CYP2C19IM group. The CYP2C9NM-CYP2C19PM group and CYP2C9NM-CYP2C19IM group showed 2.41- and 1.51-fold higher AUC_0-∞ (P < 0.001), and 59.6 and 35.4% lower CL/F (P < 0.001), compared to those of the CYP2C9NM-CYP2C19NM group, respectively. The results represented that CYP2C9 and CYP2C19 genetic polymorphisms significantly affected the pharmacokinetics of gliclazide. Although the genetic polymorphism of CYP2C19 had a greater effect on the pharmacokinetics of gliclazide, the genetic polymorphism of CYP2C9 also had a significant effect. On the other hand, plasma glucose and insulin responses to gliclazide were not significantly affected by the CYP2C9-CYP2C19 genotypes, requiring further well-controlled studies with long-term dosing of gliclazide in diabetic patients.

Effects of CYP2C19 genetic polymorphism on the pharmacokinetics of tolperisone in healthy subjects

Tolperisone hydrochloride is a centrally-acting muscle relaxant used for relieving spasticities of neurological origin and muscle spasms associated with painful locomotor diseases. It is metabolized to the inactive metabolite mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. In our previous study, the pharmacokinetics of tolperisone was significantly affected by the genetic polymorphism of CYP2D6, but the wide interindividual variation of tolperisone pharmacokinetics was not explained by genetic polymorphism of CYP2D6 alone. Thus, we studied the effects of CYP2C19 genetic polymorphism on tolperisone pharmacokinetics. Eighty-one subjects with different CYP2C19 genotypes received a single oral dose of 150 mg tolperisone with 240 mL of water, and blood samples were collected up to 12 h after dosing. The plasma concentration of tolperisone was measured by a liquid chromatography-tandem mass spectrometry system. The CYP2C19PM group had significantly higher C_max and lower CL/F values than the CYP2C19EM and CYP2C19IM groups. The AUC_inf of the CYP2C19PM group was 2.86-fold and 3.00-fold higher than the CYP2C19EM and CYP2C19IM groups, respectively. In conclusion, the genetic polymorphism of CYP2C19 significantly affected tolperisone pharmacokinetics.

Effects of CYP2D6*10 allele on the pharmacokinetics of tolperisone

Tolperisone, a muscle relaxant used for post-stroke spasticity, has been reported to have a very wide interindividual pharmacokinetic variability. It is metabolized mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. CYP2D6 is a highly polymorphic enzyme, and CYP2D6*wt/*wt, CYP2D6*wt/*10 and CYP2D6*10/*10 genotypes constitute more than 90% of the CYP2D6 genotypes in the Korean population. Thus, effects of the CYP2D6*10 on tolperisone pharmacokinetics were investigated in this study to elucidate the reasons for the wide interindividual variability. Oral tolperisone 150 mg was given to sixty-four healthy Koreans, and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The CYP2D6*10/*10 and CYP2D6*wt/*10 groups had significantly higher C_max and lower CL/F values than the CYP2D6*wt/*wt group. The AUC_inf of CYP2D6*10/*10 and CYP2D6*wt/*10 groups were 5.18-fold and 2.25-fold higher than the CYP2D6*wt/*wt group, respectively. There were considerable variations in the C_max and AUC values within each genotype group, and the variations were greater as the activity of CYP2D6 decreased. These results suggest that the genetic polymorphism of CYP2D6 significantly affected tolperisone pharmacokinetics and factor(s) other than CYP2D6 may also have significant effects on the pharmacokinetics of tolperisone.

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 modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes

Metoprolol, a selective β₁-adrenoreceptor blocking agent used in the treatment of hypertension, angina, and heart failure, is primarily metabolized by the CYP2D6 enzyme, which catalyzes α-hydroxylation and O-desmethylation. As CYP2D6 is genetically highly polymorphic and the enzymatic activity differs greatly depending on the presence of the mutant allele(s), the pharmacokinetic profile of metoprolol is highly variable depending on the genotype of CYP2D6. The aim of study was to develop the physiologically based pharmacokinetic (PBPK) model of metoprolol related to CYP2D6 genetic polymorphism for personalized therapy with metoprolol. For PBPK modelling, our previous pharmacogenomic data were used. To obtain kinetic parameters (K_m, V_max, and CL_int) of each genotype, the recombinant CYP enzyme of each genotype was incubated with metoprolol and metabolic rates were assayed. Based on these data, the PBPK model of metoprolol was developed and validated in different CYP2D6 genotypes using PK-Sim^® software. As a result, the input values for various parameters for the PBPK model were presented and the PBPK model successfully described the pharmacokinetics of metoprolol in each genotype group. The simulated values were within the acceptance criterion (99.998% confidence intervals) compared with observed values. The PBPK model developed in this study can be used for personalized pharmacotherapy with metoprolol in individuals of various races, ages, and CYP2D6 genotypes.

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.

Effects of CYP2C9*3 and *13 alleles on the pharmacokinetics and pharmacodynamics of glipizide in healthy Korean subjects

Glipizide is a second-generation sulfonylurea antidiabetic drug. It is principally metabolized to inactive metabolites by genetically polymorphic CYP2C9 enzyme. In this study, we investigated the effects of CYP2C9*3 and *13 variant alleles on the pharmacokinetics and pharmacodynamics of glipizide. Twenty-four healthy Korean volunteers (11 subjects with CYP2C9*1/*1, 8 subjects with CYP2C9*1/*3, and 5 subjects with CYP2C9*1/*13) were recruited for this study. They were administered a single oral dose of glipizide 5 mg. The plasma concentration of glipizide was quantified for pharmacokinetic analysis and plasma glucose and insulin concentrations were measured as pharmacodynamic parameters. The results represented that CYP2C9*3 and *13 alleles significantly affected the pharmacokinetics of glipizide. In subjects with CYP2C9*1/*3 and CYP2C9*1/*13 genotypes, the mean AUC_0-∞ were increased by 44.8% and 58.2%, respectively (both P < 0.001), compared to those of subjects with CYP2C9*1/*1 genotype, while effects of glipizide on plasma glucose and insulin levels were not significantly different between CYP2C9 genotype groups. In conclusion, individuals carrying the defective CYP2C9*3 and CYP2C9*13 alleles have markedly elevated plasma concentrations of glipizide compared with CYP2C9*1/*1 wild-type.