Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

Identification of a Safe and Tolerable Carbamazepine Dosing Paradigm that Facilitates Effective Evaluation of CYP3A4 Induction

Carbamazepine (CBZ) is the recommended alternative to rifampicin as a CYP3A4 inducer in drug-drug interaction studies. However, the traditional CBZ dosing paradigm can lead to several adverse events (AEs). This study tested a shorter CBZ dosing regimen using the CYP3A4-sensitive index substrate midazolam (MDZ). This was a fixed-sequence arm of an open-label, phase I study (NCT04840888). Healthy participants (n = 15) aged 18-63 years received oral doses of 1.2 mg MDZ alone (Day 1), CBZ b.i.d. alone (100 mg Days 2-4; 200 mg Days 5-7; 300 mg Days 8-10 and 12-13), and 300 mg CBZ b.i.d. plus 1.2 mg MDZ (Days 11 and 14). One participant (6.7%) experienced constipation due to treatment with CBZ plus MDZ on Day 11. One participant (6.7%) experienced urticaria (Days 12-13), and two participants (13.3%) experienced somnolence (Days 8-10) due to treatment with 300 mg CBZ b.i.d. alone. All AEs were mild. For MDZ, the geometric mean (90% CI) ratio (vs. Day 1) of the area under the curve (AUC 0-∞) was 0.28 (0.24-0.31) on Day 11 and 0.26 (0.23-0.29) on Day 14. The AUC (0-12 hours) of CBZ was 114,000 ng∙h/mL on Day 11 and 105,000 ng∙h/mL on Day 14. Steady-state concentrations of CBZ and induction of CYP3A4 were achieved on Day 11. The data are consistent with predictions of physiologically-based pharmacokinetic models in Simcyp. The 9-day dosing regimen for CBZ induction was well-tolerated by healthy participants, supporting the use of a shorter CBZ regimen for CYP3A4 induction studies.

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.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care

It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.

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 Modelling to Predict Imatinib Exposures in Cancer Patients with Renal Dysfunction: A Case Study

Imatinib is mainly metabolised by CYP3A4 and CYP2C8 and is extensively bound to α-acid glycoprotein (AAG). A physiologically based pharmacokinetic (PBPK) model for imatinib describing the CYP3A4-mediated autoinhibition during multiple dosing in gastrointestinal stromal tumor patients with normal renal function was previously reported. After performing additional verification, the PBPK model was applied to predict the exposure of imatinib after multiple dosing in cancer patients with varying degrees of renal impairment. In agreement with the clinical data, there was a positive correlation between AAG levels and imatinib exposure. A notable finding was that for recovery of the observed data in cancer patients with moderate RI (CrCL 20 to 39 mL/min), reductions of hepatic CYP3A4 and CYP2C8 abundances, which reflect the effects of RI, had to be included in the simulations. This was not the case for mild RI (CrCL 40 to 50 mL/min). The results support the finding of the clinical study, which demonstrated that both AAG levels and the degree of renal impairment are key components that contribute to the interpatient variability associated with imatinib exposure. As indicated in the 2020 FDA draft RI guidance, PBPK modelling could be used to support an expanded inclusion of patients with RI in clinical studies.

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50-900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

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.

Another Step Toward Qualification of Pediatric Physiologically Based Pharmacokinetic Models to Facilitate Inclusivity and Diversity in Pediatric Clinical Studies

Robust prediction of pharmacokinetics (PKs) in pediatric subjects of diverse ages, ethnicities, and morbidities is critical. Qualification of pediatric physiologically-based pharmacokinetic (P-PBPK) models is an essential step toward enabling precision dosing of these vulnerable groups. Twenty-two manuscripts involving P-PBPK predictions and corresponding observed PK data (e.g., area under the curve and clearance) for 22 small-molecule compounds metabolized by CYP (3A4, 1A2, and 2C9), UGT (1A9 and 2B7), FMO3, renal, non-renal, and complex routes were identified; ratios of mean predicted/observed (P/O) PK parameters were calculated. Seventy-eight of 115 mean predicted PK parameters were within 0.8 to 1.25-fold of observed data, 98 within 0.67 to 1.5-fold, 109 within 2-fold, and only 6 P/O ratios were outside of these bounds. A set of 12 CYP3A4-metabolized compounds and a set of 6 metabolized by other enzymes, CYP1A2 (1 compound), CYP2C9 (2 compounds), UGT1A9 (1 compound) and UGT2B7 (2 compounds) had 56 of 59 and 22 of 25 mean P/O ratios, respectively, that fell within the > 0.5 and < 2.0-fold boundaries. For compounds covering renal, non-renal, complex, and FM03 routes of elimination, 29 of 31 mean P/O ratios fell within the 0.67 to 1.5-fold bounds, including 4 of 5 P/O ratios from newborns. P-PBPK modeling and simulation is a strategic component of the complement of precision dosing methods and has a vital role to play in dose adjustment in vulnerable pediatric populations, such as those with disease or in different ethnic groups. Qualification of such models is an essential step toward acceptance of this methodology by regulators.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Understanding the Risk of Drug Interactions Between Ritonavir-Containing COVID-19 Therapies and Small-Molecule Kinase Inhibitors in Patients With Cancer

PURPOSE

The introduction of COVID-19 therapies containing ritonavir has markedly expanded the scope of use for this medicine. As a strong cytochrome P450 3A4 inhibitor, the use of ritonavir is associated with a high drug interaction risk. There are currently no data to inform clinician regarding the likely magnitude and duration of interaction between ritonavir-containing COVID-19 therapies and small-molecule kinase inhibitors (KIs) in patients with cancer.

METHODS

Physiologically based pharmacokinetic modeling was used to conduct virtual clinical trials with a parallel group study design in the presence and absence of ritonavir (100 mg twice daily for 5 days). The magnitude and time course of changes in KI exposure when coadministered with ritonavir was evaluated as the primary outcome.

RESULTS

Dosing of ritonavir resulted in a > 2-fold increase in steady-state area under the plasma concentration-time curve and maximal concentration for six of the 10 KIs. When the KI was coadministered with ritonavir, dose reductions to between 10% and 75% of the original dose were required to achieve an area under the plasma concentration-time curve within 1.25-fold of the value in the absence of ritonavir.

CONCLUSION

To our knowledge, this study provides the first data to assist clinicians' understanding of the drug interaction risk associated with administering ritonavir-containing COVID-19 therapies to patients with cancer who are currently being treated with KIs. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with KIs who require treatment with ritonavir-containing COVID-19 antiviral therapies.

The Application of Virtual Therapeutic Drug Monitoring to Assess the Pharmacokinetics of Imatinib in a Chinese Cancer Population Group

PURPOSE

Imatinib is used in gastrointestinal stromal tumours (GIST) and chronic myeloid leukaemia (CML). Oncology patients demonstrate altered physiology compared to healthy adults, e.g. reduced haematocrit, increased α-1 acid glycoprotein, decreased albumin and reduced glomerular filtration rate (GFR), which may influence imatinib pharmacokinetics. Given that Chinese cancer patients often report raised imatinib plasma concentrations and wider inter-individual variability reported in trough concentration when compared to Caucasian cancer patients, therapeutic drug monitoring (TDM) has been advocated.

METHOD

This study utilised a previously validated a Chinese cancer population and assessed the impact of imatinib virtual-TDM in Chinese and Caucasian cancer populations across a dosing range from 200-800 mg daily.

RESULTS

Staged dose titration to 800 mg daily, resulted in recapitulation to within the target therapeutic range for 50 % (Chinese) and 42.1% (Caucasian) subjects possessing plasma concentration < 550 ng/mL when dosed at 400 mg daily. For subjects with plasma concentrations >1500 ng/mL when dosed at 400 mg daily, a dose reduction to 200 mg once daily was able to recover 67 % (Chinese) and 87.4 % (Caucasian) patients to the target therapeutic range.

CONCLUSION

Virtual TDM highlights the benefit of pharmacokinetic modelling to optimising treatments in challenging oncology population groups.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

A PBPK Model of Ternary Cyclodextrin Complex of ST-246 Was Built to Achieve a Reasonable IV Infusion Regimen for the Treatment of Human Severe Smallpox

ST-246 is an oral drug against pathogenic orthopoxvirus infections. An intravenous formulation is required for some critical patients. A ternary complex of ST-246/meglumine/hydroxypropyl-β-cyclodextrin with well-improved solubility was successfully developed in our institute. The aim of this study was to achieve a reasonable intravenous infusion regimen of this novel formulation by a robust PBPK model based on preclinical pharmacokinetic studies. The pharmacokinetics of ST-246 after intravenous injection at different doses in rats, dogs, and monkeys were conducted to obtain clearances. The clearance of humans was generated by using the allometric scaling approach. Tissue distribution of ST-246 was conducted in rats to obtain tissue partition coefficients (K_p). The PBPK model of the rat was first built using in vivo clearance and K_p combined with in vitro physicochemical properties, unbound fraction, and cyclodextrin effect parameters of ST-246. Then the PBPK model was transferred to a dog and monkey and validated simultaneously. Finally, pharmacokinetic profiles after IV infusion at different dosages utilizing the human PBPK model were compared to the observed oral PK profile of ST-246 at therapeutic dosage (600 mg). The mechanistic PBPK model described the animal PK behaviors of ST-246 via intravenous injection and infusion with fold errors within 1.2. It appeared that 6h-IV infusion at 5 mg/kg BID produced similar C_max and AUC as oral administration at 600 mg. A PBPK model of ST-246 was built to achieve a reasonable regimen of IV infusion for the treatment of severe smallpox, which will facilitate the clinical translation of this novel formulation.

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

AIMS

This study implements a physiologically-based pharmacokinetic (PBPK) modelling approach to investigate inter-ethnic differences in imatinib pharmacokinetics and dosing regimens.

METHODS

A PBPK model of imatinib was built in the Simcyp Simulator (version 17) integrating in vitro drug metabolism and clinical pharmacokinetic data. The model accounts for ethnic differences in body size and abundance of drug-metabolising enzymes and proteins involved in imatinib disposition. Utility of this model for prediction of imatinib pharmacokinetics was evaluated across different dosing regimens and ethnic groups. The impact of ethnicity on imatinib dosing was then assessed based on the established range of trough concentrations (C_ss,min ).

RESULTS

The PBPK model of imatinib demonstrated excellent predictive performance in describing pharmacokinetics and the attained C_ss,min in patients from different ethnic groups, shown by prediction differences that were within 1.25-fold of the clinically-reported values in published studies. PBPK simulation suggested a similar dose of imatinib (400-600 mg/d) to achieve the desirable range of C_ss,min (1000-3200 ng/mL) in populations of European, Japanese and Chinese ancestry. The simulation indicated that patients of African ancestry may benefit from a higher initial dose (600-800 mg/d) to achieve imatinib target concentrations, due to a higher apparent clearance (CL/F) of imatinib compared to other ethnic groups; however, the clinical data to support this are currently limited.

CONCLUSION

PBPK simulations highlighted a potential ethnic difference in the recommended initial dose of imatinib between populations of European and African ancestry, but not populations of Chinese and Japanese ancestry.

Critical Considerations for the Design of Multi-Organ Microphysiological Systems (MPS)

Identification and approval of new drugs for use in patients requires extensive preclinical studies and clinical trials. Preclinical studies rely on in vitro experiments and animal models of human diseases. The transferability of drug toxicity and efficacy estimates to humans from animal models is being called into question. Subsequent clinical studies often reveal lower than expected efficacy and higher drug toxicity in humans than that seen in animal models. Microphysiological systems (MPS), sometimes called organ or human-on-chip models, present a potential alternative to animal-based models used for drug toxicity screening. This review discusses multi-organ MPS that can be used to model diseases and test the efficacy and safety of drug candidates. The translation of an in vivo environment to an in vitro system requires physiologically relevant organ scaling, vascular dimensions, and appropriate flow rates. Even small changes in those parameters can alter the outcome of experiments conducted with MPS. With many MPS devices being developed, we have outlined some established standards for designing MPS devices and described techniques to validate the devices. A physiologically realistic mimic of the human body can help determine the dose response and toxicity effects of a new drug candidate with higher predictive power.

Ontogeny of Drug-Metabolizing Enzymes

Almost 50% of prescription drugs lack age-appropriate dosing guidelines and therefore are used "off-label." Only ~10% drugs prescribed to neonates and infants have been studied for safety or efficacy. Immaturity of drug metabolism in children is often associated with drug toxicity. This chapter summarizes data on the ontogeny of major human metabolizing enzymes involved in oxidation, reduction, hydrolysis, and conjugation of drugs. The ontogeny data of individual drug-metabolizing enzymes are important for accurate prediction of drug pharmacokinetics and toxicity in children. This information is critical for designing clinical studies to appropriately test pharmacological hypotheses and develop safer pediatric drugs, and to replace the long-standing practice of body weight- or surface area-normalized drug dosing. The application of ontogeny data in physiologically based pharmacokinetic model and regulatory submission are discussed.

Physiologically based pharmacokinetic modelling of acetaminophen in preterm neonates-The impact of metabolising enzyme ontogeny and reduced cardiac output

In preterm neonates, physiologically based pharmacokinetic (PBPK) models are suited for studying the effects of maturational and non-maturational factors on the pharmacokinetics of drugs with complex age-dependent metabolic pathways like acetaminophen (APAP). The aim of this study was to determine the impact of drug metabolising enzymes ontogeny on the pharmacokinetics of APAP in preterm neonates and to study the effect of reduced cardiac output (CO) on its PK using PBPK modelling. A PBPK model for APAP was first developed and validated in adults and then scaled to paediatric age groups to account for the effect of enzyme ontogeny. In preterm neonates, CO was reduced by 10%, 20%, and 30% to determine how this might affect APAP PK in preterm neonates. In all age groups, the predicted concentration-time profiles of APAP were within 5th and 95th percentile of the clinically observed concentration-time profiles and the predicted Cmax and AUC were within 2-folds of the reported parameters in clinical studies. Sulfation accounted for most of APAP metabolism in children, with the highest contribution of 68% in preterm neonates. A reduction in CO by up to 30% did not significantly alter the clearance of APAP in preterm neonates. The model successfully incorporated the ontogeny of drug metabolising enzymes involved in APAP metabolism and adequately predicted the PK of APAP in preterm neonates. A reduction in hepatic perfusion as a result of up to 30% reduction in CO has no effect on the PK of APAP in preterm neonates.

Model-Based Drug-Drug Interaction Extrapolation Strategy From Adults to Children: Risdiplam in Pediatric Patients With Spinal Muscular Atrophy

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time‐dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug–drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically‐based pharmacokinetic (PBPK) model‐based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11‐fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18‐fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK‐predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months–18 years was negligible (midazolam AUCR of 1.09–1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model‐based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.

Hsa-miR-4277 Decelerates the Metabolism or Clearance of Sorafenib in HCC Cells and Enhances the Sensitivity of HCC Cells to Sorafenib by Targeting cyp3a4

Increasing evidence has shown that the metabolism and clearance of molecular targeted agents, such as sorafenib, plays an important role in mediating the resistance of HCC cells to these agents. Metabolism of sorafenib is performed by oxidative metabolism, which is initially mediated by CYP3A4. Thus, targeting CYP3A4 is a promising approach to enhance the sensitivity of HCC cells to chemotherapeutic agents. In the present work, we examined the association between CYP3A4 and the prognosis of HCC patients receiving sorafenib. Using the online tool miRDB, we predicted that has-microRNA-4277 (miR-4277), an online miRNA targets the 3’UTR of the transcript of cyp3a4. Furthermore, overexpression of miR-4277 in HCC cells repressed the expression of CYP3A4 and reduced the elimination of sorafenib in HCC cells. Moreover, miR-4277 enhanced the sensitivity of HCC cells to sorafenib in vitro and in vivo. Therefore, our results not only expand our understanding of CYP3A4 regulation in HCC, but also provide evidence for the use of miR-4277 as a potential therapeutic in advanced HCC.

Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations

Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.

Recent advances in the ontogeny of drug disposition

Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations

The current trend to personalize anticancer therapies mostly relies on selecting the best drug or combination of drugs to achieve optimal efficacy in patients. In addition to the comprehensive genetic and molecular knowledge of each tumor before choosing the drugs to be given, there is probably much room left for improvement by further personalizing the very modes by which the drugs are given, once they have been carefully selected. In particular, shifting from standard dosing to tailored dosing should help in maintaining drug exposure levels in the right therapeutic window, thus ensuring that the efficacy/toxicity balance is optimal. This paper covers the current knowledge regarding pharmacokinetic/pharmacodynamic relationships of anticancer agents, from decades-old cytotoxics to the latest immune checkpoint inhibitors, the most frequent sources for long-neglected interpatient variability impacting on drug exposure levels, and what could be done to achieve real personalized medicine in oncology such as implementing therapeutic drug monitoring with adaptive dosing strategies or using model-driven modalities for personalized dosing and scheduling.

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically-based pharmacokinetic modelling approach

AIMS

This study aimed to investigate the potential interaction between Schisandra sphenanthera, imatinib and bosutinib combining in vitro and in silico methods.

METHODS

In vitro metabolism of imatinib and bosutinib using recombinant enzymes and human liver microsomes were investigated in the presence and absence of Schisandra lignans. Physiologically-based pharmacokinetic (PBPK) models for the lignans accounting for reversible and mechanism-based inhibitions and induction of CYP3A enzymes were built in the Simcyp Simulator (version 17) and evaluated for their capability to predict interactions with midazolam and tacrolimus. Their potential effect on systemic exposures of imatinib and bosutinib were predicted using PBPK in silico simulations.

RESULTS

Schisantherin A and schisandrol B, but not schisandrin A, potently inhibited CYP3A4-mediated metabolism of imatinib and bosutinib. All three compounds showed a strong reversible inhibition on CYP2C8 enzyme with k_i of less than 0.5 μmol L^-1 . The verified PBPK models were able to describe the increase in systemic exposure of midazolam and tacrolimus due to co-administration of S. sphenanthera, consistent with the reported changes in the corresponding clinical interaction study (AUC ratio of 2.0 vs 2.1 and 2.4 vs 2.1, respectively). The PBPK simulation predicted that at recommended dosing regimens of S. sphenanthera, co-administration would result in an increase in bosutinib exposure (AUC ratio 3.0) but not in imatinib exposure.

CONCLUSION

PBPK models for Schisandra lignans were successfully developed. Interaction between imatinib and Schisandra lignans was unlikely to be of clinical importance. Conversely, S. sphenanthera at a clinically-relevant dose results in a predicted three-fold increase in bosutinib systemic exposure.