Physiologically-based pharmacokinetic model predictions of inter-ethnic differences in imatinib pharmacokinetics and dosing regimens

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Kinase Inhibitors FDA Approved 2018-2023: Drug Targets, Metabolic Pathways, and Drug-Induced Toxicities

Small molecule kinase inhibitors are one of the fastest growing classes of drugs, which are approved by the US Food and Drug Administration (FDA) for cancer and noncancer indications. As of September 2023, there were over 70 FDA-approved small molecule kinase inhibitors on the market, 42 of which were approved in the past five years (2018-2023). This minireview discusses recent advances in our understanding of the pharmacology, metabolism, and toxicity profiles of recently approved kinase inhibitors with a central focus on tyrosine kinase inhibitors (TKIs). In this minireview we discuss the most common therapeutic indications and molecular target(s) of kinase inhibitors FDA approved 2018-2023. We also describe unique aspects of the metabolism, bioactivation, and drug-drug interaction (DDI) potential of kinase inhibitors; discuss drug toxicity concerns related to kinase inhibitors, such as drug-induced liver injury; and highlight clinical outcomes and challenges relevant to TKI therapy. Case examples are provided for common TKI targets, metabolism pathways, DDI potential, and risks for serious adverse drug reactions. The minireview concludes with a discussion of perspectives on future research to optimize TKI therapy to maximize efficacy and minimize drug toxicity. SIGNIFICANCE STATEMENT: This minireview highlights important aspects of the clinical pharmacology and toxicology of small molecule kinase inhibitors FDA approved 2018-2023. We describe key advances in the therapeutic indications and molecular targets of TKIs. The major metabolism pathways and toxicity profiles of recently approved TKIs are discussed. Clinically relevant case examples are provided that demonstrate the risk for hepatotoxic drug interactions involving TKIs and coadministered drugs.

Therapeutic target mapping from the genome of Kingella negevensis and biophysical inhibition assessment through PNP synthase binding with traditional medicinal compounds

Kingella negevensis belongs to the Neisseriaceae family. It is implied that it has significant virulence potential due to RTX toxin production, which can cause hemolysis. It usually colonizes the orophayrynx of pediatric population, along with Kingella kingae but has also been isolated from vagina. Todate no report on its drug targets is present, therefore putative therapeutic targets were identified from its genomic sequence data. Traditional Chinese (n > 36,000) and Indian medicinal compounds (n > 2000) were then screened against its pyridoxine 5'-phosphate synthase, a vital therapeutic target. Prioritized TCM compounds included ZINC02525131, ZINC33833737 and ZINC85486932, and Cadiyenol, 9,11,13-Octadecatrienoic acid and 6-Gingerol from Indian medicinal library. Molecular dynamics simulation of top compounds revealed ZINC02525131 as having best stability for 100 ns, compared to Cadiyenol. ADMET profiling was then done, along with physiologically based pharmacokinetic simulation of these compounds in a population of 200 individuals, for 12 h to see fate of the ingested compound. Additionally, the impact of these compounds in a population with cirrhosis and renal impairment was also simulated. We imply in light of all the studied parameters of safety and bioavailability, etc., that 6-Gingerol from Zingiber officinalis rhizome must be proceeded further for in vitro and in vivo testing for inhibition of K. negevensis.

Ultrasensitive detection of imatinib in human serum using a gold-based paper sensor

Imatinib is the tyrosine kinase inhibitor of choice for the treatment of chronic myeloid leukemia and gastrointestinal stromal tumors. However, imatinib has drawbacks such as drug resistance and significant differences in pharmacokinetics within patients. Therefore, a colloidal gold-based immunochromatographic assay (CG-IA) was developed for measuring and monitoring imatinib in human serum. An imatinib derivative containing carboxyl groups was used for the synthesis of the immunogen, and 4-(4-methyl-1-piperazinylmethyl) benzoic acid was selected as the hapten for the heterologous coating antigen. Next, a highly sensitive and specific monoclonal antibody (mAb), 2F7 was screened for the construction of a CG-IA, with an IC50 value of 0.091 ng/mL. For the qualification of imatinib in human serum, the visual limit of detection (vLOD) and cut-off values of the CG-IA were 2 and 20 ng/mL, respectively. For quantitative detection, the calculated LOD value of the CG-IA was 0.068 ng/mL, with a linearity range of 1.004 and 23.087 ng/mL. The recovery rate of spiked serum samples was between 88.24 % and 104.75 %. In addition, the concentration of imatinib in the serum samples from 10 patients was detected by CG-IA and revealed a good correlation with those from LC-MS/MS. These results indicated that the developed gold-based paper sensor could become an effective tool for the rapid monitoring of imatinib in human serum samples.

Prediction of ROS1 and TRKA/B/C occupancy in plasma and cerebrospinal fluid for entrectinib alone and in DDIs using physiologically based pharmacokinetic (PBPK) modeling approach

PURPOSE

Entrectinib (ENT) is a potent c-ros oncogene 1(ROS1) and neurotrophic tyrosine receptor kinase (NTRKA/B/C) inhibitor. To determine the optimum dosage of ENT using ROS1 and NTRKA/B/C occupancy in plasma and cerebrospinal fluid (CSF) in drug-drug interactions (DDIs), physiologically-based pharmacokinetic (PBPK) models for healthy subjects and cancer population were developed for ENT and M5 (active metabolite).

METHODS

The PBPK models were built using the modeling parameters of ENT and M5 that were mainly derived from the published paper on the ENT PBPK model, and then validated by the observed pharmacokinetics (PK) in plasma and CSF from healthy subjects and patients.

RESULTS

The PBPK model showed that AUC, Cmax, and Ctrough ratios between predictions and observations are within the range of 0.5-2.0, except that the M5 AUC ratio is slightly above 2.0 (2.34). Based on the efficacy (> 75% occupancy for ROS1 and NTRKA/B/C) and safety (AUC < 160 μM·h and Cmax < 8.9 μM), the appropriate dosing regimens were identified. The appropriate dosage is 600 mg once daily (OD) when administered alone, reduced to 200 mg and 400 mg OD with itraconazole and fluconazole, respectively. ENT is not recommended for co-administration with rifampicin or efavirenz, but is permitted with fluvoxamine or dexamethasone.

CONCLUSION

The PBPK models can serve as a powerful approach to predict ENT concentration as well as ROS1 and NTRKA/B/C occupancy in plasma and CSF.

Pharmacokinetics of Imatinib Mesylate and Development of Limited Sampling Strategies for Estimating the Area under the Concentration-Time Curve of Imatinib Mesylate in Palestinian Patients with Chronic Myeloid Leukemia

BACKGROUND AND OBJECTIVE

Imatinib is a tyrosine kinase inhibitor used in the treatment of chronic myeloid leukemia (CML). The area under the concentration-time curve (AUC) is a pharmacokinetic parameter that symbolizes overall exposure to a drug, which is correlated with complete cytogenetic and treatment responses to imatinib, as well as its side effects in patients with CML. The limited sampling strategy (LSS) is considered a sufficiently precise and practical method that can be used to estimate pharmacokinetic parameters such as AUC, without the need for frequent, costly, and inconvenient blood sampling. This study aims to investigate the pharmacokinetic parameters of imatinib, develop and validate a reliable and practical LSS for estimating imatinib AUC0-24, and determine the optimum sampling points for predicting the imatinib AUC after the administration of once-daily imatinib in Palestinian patients with CML.

METHOD

Pharmacokinetic profiles, involving six blood samples collected during a 24-h dosing interval, were obtained from 25 Palestinian patients diagnosed with CML who had been receiving imatinib for at least 7 days and had reached a steady-state level. Imatinib AUC0-24 was calculated using the trapezoidal rule, and linear regression analysis was performed to assess the relationship between measured AUC0-24 and concentrations at each sampling time. All developed models were analyzed to determine their effectiveness in predicting AUC0-24 and to identify the optimal sampling time. To evaluate predictive performance, two error indices were employed: the percentage of root mean squared error (% RMSE) and the mean predictive error (% MPE). Bland and Altman plots, along with mountain plots, were utilized to assess the agreement between measured and predicted AUC.

RESULTS

Among the one-timepoint estimations, predicted AUC0-24 based on concentration of imatinib at the eighth hour after administration (C8-predicted AUC0-24) demonstrated the highest correlation with the measured AUC (r2 = 0.97, % RMSE = 6.3). In two-timepoint estimations, the model consisting of C0 and C8 yielded the highest correlation between predicted and measured imatinib AUC (r2 = 0.993 and % RMSE = 3.0). In three-timepoint estimations, the combination of C0, C1, and C8 provided the most robust multilinear regression for predicting imatinib AUC0-24 (r2 = 0.996, % RMSE = 2.2). This combination also outperformed all other models in predicting AUC. The use of a two-timepoint limited sampling strategy (LSS) for predicting AUC was found to be reliable and practical. While C0/C8 exhibited the highest correlation, the use of C0/C4 could be a more practical and equally accurate choice. Therapeutic drug monitoring of imatinib based on C0 can also be employed in routine clinical practice owing to its reliability and practicality.

CONCLUSION

The LSS using one timepoint, especially C0, can effectively predict imatinib AUC. This approach offers practical benefits in optimizing dose regimens and improving adherence. However, for more precise estimation of imatinib AUC, utilizing two- or three-timepoint concentrations is recommended over relying on a single point.

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.

Physiologically-based pharmacokinetic modeling for optimal dosage prediction of olaparib when co-administered with CYP3A4 modulators and in patients with hepatic/renal impairment

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict the maximum plasma concentration (Cmax) and trough concentration (Ctrough) at steady-state of olaparib (OLA) in Caucasian, Japanese and Chinese. Furthermore, the PBPK model was combined with mean and 95% confidence interval to predict optimal dosing regimens of OLA when co-administered with CYP3A4 modulators and administered to patients with hepatic/renal impairment. The dosing regimens were determined based on safety and efficacy PK threshold Cmax (< 12,500 ng/mL) and Ctrough (772-2500 ng/mL). The population PBPK model for OLA was successfully developed and validated, demonstrating good consistency with clinically observed data. The ratios of predicted to observed values for Cmax and Ctrough fell within the range of 0.5 to 2.0. When OLA was co-administered with a strong or moderate CYP3A4 inhibitor, the recommended dosing regimens should be reduced to 100 mg BID and 150 mg BID, respectively. Additionally, the PBPK model also suggested that OLA could be not recommended with a strong or moderate CYP3A4 inducer. For patients with moderate hepatic and renal impairment, the dosing regimens of OLA were recommended to be reduced to 200 mg BID and 150 mg BID, respectively. In cases of severe hepatic and renal impairment, the PBPK model suggested a dosing regimen of 100 mg BID for OLA. Overall, this present PBPK model can determine the optimal dosing regimens for various clinical scenarios involving OLA.

Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

(1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C _trough) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f _up), α1-acid glycoprotein (AGP) level, and organic cation transporter 1 (OCT1) activity/expression in clinical efficacy. (2) Methods: The population PBPK model was built using physicochemical and biochemical properties, metabolizing and transporting kinetics, tissue distribution, and human physiological parameters. (3) Results: The PBPK model successfully predicted the C _trough of IMA administered alone in chronic phase (CP) and accelerated phase (AP) patients, the C _trough of IMA co-administered with six modulators, and C _trough in CP patients with hepatic impairment. Most of the ratios between predicted and observed data are within 0.70-1.30. Additionally, the recommendations for dosing adjustments for IMA have been given under multiple clinical uses. The sensitivity analysis showed that exploring the f _up and AGP level had a significant influence on the plasma C _trough of IMA. Meanwhile, the simulations also revealed that OCT1 activity and expression had a significant impact on the intracellular C _trough of IMA. (4) Conclusion: The current PBPK model can accurately predict the IMA C _trough and provide appropriate dosing adjustment recommendations in a variety of clinical situations.

Clinicopathological, immunohistochemical, molecular-genetic and risk profiles of gastrointestinal stromal tumors in a cohort of Sudanese patients

Background

Determining the risk of malignant behaviour and mutational status of gastrointestinal stromal tumours (GISTs) guide the management decision and allow optimal individualized patient treatment.

Objectives

To determine clinicopathological, immunohistochemical (IHC), risk and KIT mutational findings of GISTs in Sudanese patients.

Methods

Histological slides were reviewed, IHC for DOG-1 and CD117 performed and hotspot KIT mutations examined. The risk group was assigned using combined risk criteria.

Results

21 of the 36 patients (58.3%) were males (mean age, 54.83 ±12.57; range, 26-71). Abdominal pain and mass were the most frequent symptoms. Mean tumor size (±SD) was 11.6(±5.82) cm. Either CD117, DOG1 or both were positive in all cases. Using risk criteria, 33.3% (n=12) were clinically malignant at presentation, 13.9% (n=5) high risk, 16.7% (n=6) intermediate, 27.8% (n=10) low risk and 2.8% (n=1) very low risk. Sixteen of 23 (70%) tested cases had KIT (14 exon 11 and two exon 9) mutations. Six tumors were wild type. Exon 11 deletions (p.I563-L576 del and p.V559-N566delinsD) significantly correlate with disease recurrence (p-value: 0.028).

Conclusions

Sudanese patients with GIST tend to present late. Nearly half of them correspond to the malignant/high-risk category. The frequency of KIT mutations (79.31%) is in line with the literature.

Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach.

Physiologically-Based Pharmacokinetic Modeling Approaches for Patients with SARS-CoV-2 Infection: A Case Study with Imatinib

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, which causes coronavirus disease 2019 (COVID‐19), manifests as mild respiratory symptoms to severe respiratory failure and is associated with inflammation and other physiological changes. Of note, substantial increases in plasma concentrations of α₁‐acid‐glycoprotein and interleukin‐6 have been observed among patients admitted to the hospital with advanced SARS‐CoV‐2 infection. A physiologically based pharmacokinetic (PBPK) approach is a useful tool to evaluate and predict disease‐related changes on drug pharmacokinetics. A PBPK model of imatinib has previously been developed and verified in healthy people and patients with cancer. In this study, the PBPK model of imatinib was successfully extrapolated to patients with SARS‐CoV‐2 infection by accounting for disease‐related changes in plasma α₁‐acid‐glycoprotein concentrations and the potential drug interaction between imatinib and dexamethasone. The model demonstrated a good predictive performance in describing total and unbound imatinib concentrations in patients with SARS‐CoV‐2 infection. PBPK simulations highlight that an equivalent dose of imatinib may lead to substantially higher total drug concentrations in patients with SARS‐CoV‐2 infection compared to that in patients with cancer, while the unbound concentrations remain comparable between the 2 patient populations. This supports the notion that unbound trough concentration is a better exposure metric for dose adjustment of imatinib in patients with SARS‐CoV‐2 infection, compared to the corresponding total drug concentration. Potential strategies for refinement and generalization of the PBPK modeling approach in the patient population with SARS‐CoV‐2 are also provided in this article, which could be used to guide study design and inform dose adjustment in the future.