Physiologically Based Pharmacokinetic Modeling to Evaluate the Systemic Exposure of Gefitinib in CYP2D6 Ultrarapid Metabolizers and Extensive Metabolizers

In Vitro Assessment of Inhibitory Effects of Kinase Inhibitors on CYP2C9, 3A and 1A2: Prediction of Drug-Drug Interaction Risk with Warfarin and Direct Oral Anticoagulants

OBJECTIVE

This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs).

METHODS

An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models.

RESULTS

Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 μM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 μM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure.

CONCLUSIONS

Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Physiologically based pharmacokinetic modeling to predict the pharmacokinetics of codeine in different CYP2D6 phenotypes

Objectives

Codeine, a prodrug used as an opioid agonist, is metabolized to the active product morphine by CYP2D6. This study aimed to establish physiologically based pharmacokinetic (PBPK) models of codeine and morphine and explore the influence of CYP2D6 genetic polymorphisms on the pharmacokinetics of codeine and morphine.

Methods

An initial PBPK modeling of codeine in healthy adults was established using PK-Sim® software and subsequently extrapolated to CYP2D6 phenotype-related PBPK modeling based on the turnover frequency (Kcat) of CYP2D6 for different phenotype populations (UM, EM, IM, and PM). The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between the predicted and observed values of the pharmacokinetic parameters to evaluate the accuracy of PBPK modeling. The validated models were then used to support dose safety for different CYP2D6 phenotypes.

Results

The developed and validated CYP2D6 phenotype-related PBPK model successfully predicted codeine and morphine dispositions in different CYP2D6 phenotypes. Compared with EMs, the predicted AUC0-∞ value of morphine was 98.6% lower in PMs, 60.84% lower in IMs, and 73.43% higher in UMs. Morphine plasma exposure in IMs administered 80 mg of codeine was roughly comparable to that in EMs administered 30 mg of codeine. CYP2D6 UMs may start dose titration to achieve an optimal individual regimen and avoid a single dose of over 20 mg. Codeine should not be used in PMs for pain relief, considering its insufficient efficacy.

Conclusion

PBPK modeling can be applied to explore the dosing safety of codeine and can be helpful in predicting the effect of CYP2D6 genetic polymorphisms on drug-drug interactions (DDIs) with codeine in the future.

Distribution of CYP2D6 and CYP2C19 gene polymorphisms in Han and Uygur populations with breast cancer in Xinjiang, China

The aim of this study was to investigate the frequency distribution of the cytochrome P450 (CYP450) enzymes, CYP2D6 and CYP2C19, and the form of tamoxifen metabolisation in premenopausal patients with breast cancer in the Han and Uygur ethnic groups of Xinjiang to guide rational clinical drug use. A total of 125 Han patients and 121 Uygur patients with premenopausal hormone-receptor-positive breast cancer treated at the Xinjiang Uygur Autonomous Region Cancer Hospital between 1 June 2011 and 1 December 2013 were selected. The common mutation sites in CYP450 were analysed using TaqMan® minor groove binder technology. Genetic testing was performed to determine other metabolic types of tamoxifen, and the genotypes and metabolic types were compared using a Chi-squared test. Between the Han and Uygur groups, there were significant differences in the frequencies of the CYP2D6 (*10/*10) and CYP2C19 (*1/*1) genotypes, with P-values of 0.002 and 0.015, respectively. Genotypes of CYP2D6 (*1/*1), CYP2D6 (*1/*5), CYP2D6 (*5/*5), CYP2D6 (*5/*10) and CYP2C19 (*3/*3) were expressed in the two patient groups, and the difference was not statistically significant (P > 0.05). In the Han patients, the proportions of extensive, intermediate and poor metabolisers of tamoxifen were 72, 24 and 4%, respectively, whereas those in the Uygur patients were 76.9, 17.4 and 5.7%, respectively, with no significant difference (P > 0.05). In conclusion, There were partial differences in the CYP2D6 and CYP2C19 gene polymorphisms of CYP450 between the Han and Uygur patients with premenopausal breast cancer, but there was no significant difference between the CYP2D6 and CYP2C19 phenotypes. Further research is needed to determine the relationship between the enzyme genetic differences of CYP450 and the pharmacokinetics and efficacy of tamoxifen. Although there were some differences in genotypes, these did not result in differences in the predicted tamoxifen metabolisation phenotype between the Han and Uygur patients with breast cancer. Therefore, the doses should be adjusted according to the individual genotype data.

A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug-induced toxicity

We have built a quantitative systems toxicology modeling framework focused on the early prediction of oncotherapeutic-induced clinical intestinal adverse effects. The model describes stem and progenitor cell dynamics in the small intestinal epithelium and integrates heterogeneous epithelial-related processes, such as transcriptional profiles, citrulline kinetics, and probability of diarrhea. We fitted a mouse-specific version of the model to quantify doxorubicin and 5-fluorouracil (5-FU)-induced toxicity, which included pharmacokinetics and 5-FU metabolism and assumed that both drugs led to cell cycle arrest and apoptosis in stem cells and proliferative progenitors. The model successfully recapitulated observations in mice regarding dose-dependent disruption of proliferation which could lead to villus shortening, decrease of circulating citrulline, increased diarrhea risk, and transcriptional induction of the p53 pathway. Using a human-specific epithelial model, we translated the cytotoxic activity of doxorubicin and 5-FU quantified in mice into human intestinal injury and predicted with accuracy clinical diarrhea incidence. However, for gefitinib, a specific-molecularly targeted therapy, the mice failed to reproduce epithelial toxicity at exposures much higher than those associated with clinical diarrhea. This indicates that, regardless of the translational modeling approach, preclinical experimental settings have to be suitable to quantify drug-induced clinical toxicity with precision at the structural scale of the model. Our work demonstrates the usefulness of translational models at early stages of the drug development pipeline to predict clinical toxicity and highlights the importance of understanding cross-settings differences in toxicity when building these approaches.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Model-based meta-analysis of ethnic differences and their variabilities in clearance of oral drugs classified by clearance mechanism

In this study, the ethnic ratios (ERs) of oral clearance between Japanese and Western populations were subjected to model-based meta-analysis (MBMA) for 81 drugs evaluated in 673 clinical studies. The drugs were classified into eight groups according to the clearance mechanism, and the ER for each group was inferred together with interindividual variability (IIV), interstudy variability (ISV), and inter-drug variability within a group (IDV) using the Markov chain Monte Carlo (MCMC) method. The ER, IIV, ISV, and IDV were dependent on the clearance mechanism, and, except for particular groups such as drugs metabolized by polymorphic enzymes or their clearance mechanism is not confirmative, the ethnic difference was found to be generally small. The IIV was well-matched across ethnicities, and the ISV was approximately half of the IIV as the coefficient of variation. To adequately assess ethnic differences in oral clearance without false detections, phase I studies should be designed with full consideration of the mechanism of clearance. This study suggests that the methodology of classifying drugs based on the mechanism that causes ethnic differences and performing MBMA with statistical techniques such as MCMC analysis is helpful for a rational understanding of ethnic differences and for strategic drug development.

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.

Effects of p450 Polymorphisms on the Clinical Outcomes of Gefitinib Treatment in Patients with Epidermal Growth Factor Receptor Mutation-Positive Non-Small Cell Lung Cancer

Aims: In this study, we determined whether different genotypes of drug-metabolizing enzymes are associated with the therapeutic effects of gefitinib in non-small cell lung cancer (NSCLC). Methods: A retrospective analysis of 112 patients with stage III or IV NSCLC was performed. The clinical characteristics of these patients, including progression-free survival (PFS), outcome of gefitinib treatment, and relationship between the genotypes of rs1065852/rs2242480 and prognosis, were analyzed. Results: The rs1065852 CT/TT genotype was associated with worse prognosis than the CC type (p = 0.0306), and the median PFS was lower than that with the CC type (287 days vs. 350 days). Compared with those with CC+CC genotypes, individuals carrying T alleles (CT/TT+CT/TT) at rs1065852/rs2242480 had a poorer prognosis, and the median PFS of CT/TT+CT/TT at rs1065852/rs2242480 was significantly lower than that of the CC+CC type (188 days vs. 444.5 days). Conclusions: Genotypes of the drug-metabolizing enzymes rs1065852 and rs2242480 have an impact on the prognosis of patients with NSCLC treated with gefitinib.

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids’ responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Pharmacokinetic properties and bioequivalence of gefitinib 250 mg in healthy Korean male subjects

Gefitinib is an anti-cancer drug used to treat non-small cell lung cancer. The objective of this study was to compare the pharmacokinetics and evaluate the bioequivalence of 2 orally administered gefitinib 250 mg tablets in healthy Korean subjects. A randomized, open-label, single-dose, crossover bioequivalence study was conducted. A total of 50 healthy male volunteers were randomized into 2 sequence groups. During each treatment, the subjects received the test or reference formulation of 250 mg gefitinib with a washout period of 21 days. The plasma samples were collected at pre-dose and up to 144 hours post-dose, and plasma drug concentrations were measured using validated liquid chromatography-tandem mass spectrometry. Pharmacokinetic parameters were calculated, and the formulations were considered as bioequivalent if the 90% confidence intervals (CIs) of the geometric mean ratios were within the bioequivalence limits of 0.8 to 1.25. Forty-one subjects completed the study and were included in the pharmacokinetic analysis. The 90% CIs of the geometric mean ratios of the test formulation to the reference formulation were 0.8115 to 0.9993 for maximum plasma concentration and 0.9119 to 1.0411 for area under the plasma concentration versus time curve from dosing to the last measurable concentration. There were no serious or unexpected adverse events during the study. In healthy Korean adult subjects, the test and reference formulations of gefitinib 250 mg had similar pharmacokinetic parameters and similar plasma concentration-time profiles. The test formulation of gefitinib met the regulatory criteria for assuming bioequivalence. Both formulations were safe and well-tolerated.

The Pharmacokinetics of Gefitinib in a Chinese Cancer Population Group: A Virtual Clinical Trials Population Study

Gefitinib, a selective inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, is used to treat non-small-cell lung cancer (NSCLC). Lung cancer rates are high in China and are expected to increase over the next decade. CYP 2D6 intermediate metaboliser (IM) phenotypes are more prevalent in the Chinese population compared to Caucasians; the increased risk of drug-drug interactions (DDI) with chemotherapy polypharmacy may lead to different clinical pharmacokinetics outcomes for Chinese patients. This study developed and validated a virtual Chinese cancer population for the pragmatic assessment of gefitinib DDI as a victim drug in Chinese and Caucasian cancer populations. When assessing the impact of 2D6 phenotypes on bupropion mediated CYP 2D6 DDI in Chinese cancer population, we found that AUC increased by at least 60% in extensive metabolizers (EM) and 30% in IM. As a result, fmCYP2D6 was reduced by 15% in IM in the presence of bupropion, translating into > 70% of EM subjects and > 48% of IM subjects with trough concentrations at steady state (Ctrough,ss) below the gefitinib target trough level. The PBPK model predicted that a 500 mg once daily dose in both EM and IM subjects successfully reduced the percent of subjects below the Ctrough,ss. Such changes in Ctrough,ss warrant further investigation and highlight the ability of pharmacokinetic modelling to investigate populations that may be difficult to recruit for traditional clinical studies.

Phenoconversion of Cytochrome P450 Metabolism: A Systematic Review

Phenoconversion is the mismatch between the individual’s genotype-based prediction of drug metabolism and the true capacity to metabolize drugs due to nongenetic factors. While the concept of phenoconversion has been described in narrative reviews, no systematic review is available. A systematic review was conducted to investigate factors contributing to phenoconversion and the impact on cytochrome P450 metabolism. Twenty-seven studies met the inclusion criteria and were incorporated in this review, of which 14 demonstrate phenoconversion for a specific genotype group. Phenoconversion into a lower metabolizer phenotype was reported for concomitant use of CYP450-inhibiting drugs, increasing age, cancer, and inflammation. Phenoconversion into a higher metabolizer phenotype was reported for concomitant use of CYP450 inducers and smoking. Moreover, alcohol, pregnancy, and vitamin D exposure are factors where study data suggested phenoconversion. The studies reported genotype–phenotype discrepancies, but the impact of phenoconversion on the effectiveness and toxicity in the clinical setting remains unclear. In conclusion, phenoconversion is caused by both extrinsic factors and patient- and disease-related factors. The mechanism(s) behind and the extent to which CYP450 metabolism is affected remain unexplored. If studied more comprehensively, accounting for phenoconversion may help to improve our ability to predict the individual CYP450 metabolism and personalize drug treatment.

Evaluation of the Drug-Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP-5862, Using a Physiologically-Based Pharmacokinetic Modeling Approach

Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically‐based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP‐5862 to predict potential drug–drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80‐fold vs. 5.21‐fold observed) and rifampicin (0.21‐fold vs. 0.23‐fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage.

Association of Variability and Pharmacogenomics With Bioequivalence of Gefitinib in Healthy Male Subjects

Objective: The aim of the study was to explore the association of pharmacokinetic variability and pharmacogenomics with the bioequivalence of orally administered gefitinib (Iressa®, AstraZeneca) provided by three sponsors in healthy subjects. Methods: The study designs were randomized, open-label, and two-period crossover studies in both fasting and fed healthy subjects. In one fasting study, the sample size was enlarged from 30 to 60 for the failing study. Each study subject received a 250-mg gefitinib tablet with a 21-day washout. The plasma concentrations were measured using LC-MS/MS, and pharmacokinetic parameters were determined by noncompartmental methods. Genetic analyses of CYP3A4, CYP3A5, and CYP2D6 alleles were carried out by the polymerase chain reaction (PCR). Results: Two hundred and sixty healthy male subjects were enrolled. The median maximum plasma concentration (T_max) was 4-5 h, and the mean elimination half-life (t_1/2) was 18-26 h. The maximum plasma concentration (C_max) and area under the curve (AUC) increased but T_max and t_1/2 were unaffected by the intake of high-fat food. Three fed and two fasting studies achieved a plausible bioequivalence. The intake of high-fat food decreased the intra-subject variability significantly. In addition, CYP2D6 was associated with gefitinib exposure and may contribute to the high inter-subject variability, but it did not influence the bioequivalence result. Conclusions: Gefitinib is well tolerated, and the bioequivalence is easier to achieve under fed conditions compared to fasting conditions. The 90% confidence interval (CI) of geometric mean ratio (GMR) can be narrowed when the sample size is enlarged without changing the formulation-related technology.