Showing papers on "Physiologically based pharmacokinetic modelling published in 2023"

Gene expression databases for physiologically based pharmacokinetic modeling of humans and animal species

Abstract: In drug research, developing a sound understanding of the key mechanistic drivers of pharmacokinetics (PK) for new molecular entities is essential for human PK and dose predictions. Here, characterizing the absorption, distribution, metabolism, and excretion (ADME) processes is crucial for a mechanistic understanding of the drug–target and drug–body interactions. Sufficient knowledge on ADME processes enables reliable interspecies and human PK estimations beyond allometric scaling. The physiologically based PK (PBPK) modeling framework allows the explicit consideration of organ‐specific ADME processes. The sum of all passive and active ADME processes results in the observed plasma PK. Gene expression information can be used as surrogate for protein abundance and activity within PBPK models. The absolute and relative expression of ADME genes can differ between species and strains. This is affecting both, the PK and pharmacodynamics and is therefore posing a challenge for the extrapolation from preclinical findings to humans. We developed an automated workflow that generates whole‐body gene expression databases for humans and other species relevant in drug development, animal health, nutritional sciences, and toxicology. Solely, bulk RNA‐seq data curated and provided by the Swiss Institute of Bioinformatics from healthy, normal, and untreated primary tissue samples were considered as an unbiased reference of normal gene expression. The databases are interoperable with the Open Systems Pharmacology Suite (PK‐Sim and MoBi) and enable seamless access to a central source of curated cross‐species gene expression data. This will increase data transparency, increase reliability and reproducibility of PBPK model simulations, and accelerate mechanistic PBPK model development in the future.

Regulatory utility of physiologically based pharmacokinetic modeling for assessing food impact in bioequivalence studies: A workshop summary report

Abstract: This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled “Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches.” The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled “Oral PBPK for Evaluating the Impact of Food on BE” presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.

Mechanistic modeling of ophthalmic, nasal, injectable, and implant generic drug products: A workshop summary report

Abstract: For approval, a proposed generic drug product must demonstrate it is bioequivalent (BE) to the reference listed drug product. For locally acting drug products, conventional BE approaches may not be feasible because measurements in local tissues at the sites of action are often impractical, unethical, or cost‐prohibitive. Mechanistic modeling approaches, such as physiologically‐based pharmacokinetic (PBPK) modeling, may integrate information from drug product properties and human physiology to predict drug concentrations in these local tissues. This may allow clinical relevance determination of critical drug product attributes for BE assessment during the development of generic drug products. In this regard, the Office of Generic Drugs of the US Food and Drug Administration has recently established scientific research programs to accelerate the development and assessment of generic products by utilizing model‐integrated alternative BE approaches. This report summarizes the presentations and panel discussion from a public workshop that provided research updates and information on the current state of the use of PBPK modeling approaches to support generic product development for ophthalmic, injectable, nasal, and implant drug products.

Investigation of simplified physiologically‐based pharmacokinetic models in rat and human

Abstract: Whole‐body physiologically‐based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug‐dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug‐independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

A “middle‐out approach” for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Abstract: Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue‐to‐unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k‐means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7‐fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Using mechanistic models to support development of complex generic drug products: European Medicines Agency perspective

Abstract: Model-informed drug development (MIDD) approaches receive wide regulatory acceptance in the European Medicines Agency (EMA) to support new drug development. For generic drugs, the European regulators have not reached a common position on how to use these methods. This commentary expands on the existing EMA regulatory framework for bioequivalence and physiological based pharmacokinetic (PBPK) modeling to propose conditions where mechanistic models could support or potentially waive clinical bioequivalence (BE)/bioavailability (BA) studies.

Developing a mechanistic understanding of the nonlinear pharmacokinetics of letermovir and prospective drug interaction with everolimus using physiological‐based pharmacokinetic modeling

Abstract: Letermovir is approved for use in cytomegalovirus‐seropositive hematopoietic stem cell transplant recipients and is investigated in other transplant settings. Nonlinear pharmacokinetics (PKs) were observed in clinical studies after intravenous and oral dosing across a wide dose range, including the efficacious doses of 240 and 480 mg. A physiologically‐based PK (PBPK) model for letermovir was built to develop a plausible explanation for the nonlinear PKs observed in clinical studies. In vitro studies suggested that letermovir elimination and distribution are mediated by saturable uridine glucuronosyltransferases (UGT)‐metabolism and by saturable hepatic uptake via organic anion‐transporting polypeptides (OATP) 1B. A sensitivity analysis of parameters describing the metabolism and distribution mechanisms indicated that the greater than dose‐proportional increase in letermovir exposure is best described by a saturable OATP1B‐mediated transport. This PBPK model was further used to evaluate the drug interaction potential between letermovir and everolimus, an immunosuppressant that may be co‐administered with letermovir depending on regions. Because letermovir inhibits cytochrome P450 (CYP) 3A and everolimus is a known CYP3A substrate, an interaction when concomitantly administered is anticipated. The drug–drug interaction simulation confirmed that letermovir will likely increase everolimus are under the curve by 2.5‐fold, consistent with the moderate increase in exposure observed with midazolam in the clinic. The output highlights the importance of drug monitoring, which is common clinical practice for everolimus to maintain safe and efficacious drug concentrations in the targeted patient population when concomitantly administered with letermovir.

A linked physiologically based pharmacokinetic model for hydroxychloroquine and metabolite desethylhydroxychloroquine in SARS‐CoV‐2(−)/(+) populations

Abstract: Hydroxychloroquine (HCQ) is Food and Drug Administration (FDA)‐approved for malaria, systemic and chronic discoid lupus erythematosus, and rheumatoid arthritis. Because HCQ has a proposed multimodal mechanism of action and a well‐established safety profile, it is often investigated as a repurposed therapeutic for a range of indications. There is a large degree of uncertainty in HCQ pharmacokinetic (PK) parameters which complicates dose selection when investigating its use in new disease states. Complications with HCQ dose selection emerged as multiple clinical trials investigated HCQ as a potential therapeutic in the early stages of the COVID‐19 pandemic. In addition to uncertainty in baseline HCQ PK parameters, it was not clear if disease‐related consequences of SARS‐CoV‐2 infection/COVID‐19 would be expected to impact the PK of HCQ and its primary metabolite desethylhydroxychloroquine (DHCQ). To address the question whether SARS‐CoV‐2 infection/COVID‐19 impacted HCQ and DHCQ PK, dried blood spot samples were collected from SARS‐CoV‐2(−)/(+) participants administered HCQ. When a previously published physiologically based pharmacokinetic (PBPK) model was used to fit the data, the variability in exposure of HCQ and DHCQ was not adequately captured and DHCQ concentrations were overestimated. Improvements to the previous PBPK model were made by incorporating the known range of blood to plasma concentration ratios (B/P) for each compound, adjusting HCQ and DHCQ distribution settings, and optimizing DHCQ clearance. The final PBPK model adequately captured the HCQ and DHCQ concentrations observed in SARS‐CoV‐2(−)/(+)participants, and incorporating COVID‐19‐associated changes in cytochrome P450 activity did not further improve model performance for the SARS‐CoV‐2(+) population.

PBPK modeling of ivermectin—Considerations for the purpose of developing alternative routes to optimize its safety profile

Abstract: Although single‐dose ivermectin has been widely used in mass‐drug administration programs for onchocerciasis and lymphatic filariasis for many years, ivermectin may have utility as an endectocide with mosquito‐lethal effects at dosages greater and longer than those used to treat helminths. The final physiologically‐based pharmacokinetic (PBPK) model for ivermectin described here was able to capture, with reasonable accuracy, observed plasma drug concentration‐time profiles and exposures of ivermectin after a single oral dose of the drug in healthy male (dose range 6–30 mg) and female subjects, in both fasted and fed states, in African patients with onchocerciasis (150 μg/kg) and in African children. The PBPK model can be used for further work on lactation, pediatric dosing (considering CYP3A4 and Pg‐p ontogenies), and pregnancy, especially if nonstandard doses will be used. The key findings of our study indicate that absorption of ivermectin may be highly dependent on bile micelle‐mediated solubility. The drug is highly lipophilic and permeable, and its plasma exposure appears to be associated with the body mass index of an individual. These are all factors that need to be considered when extrapolating to more complex oral formulations or alternative routes of administration. Administering lower doses over a longer period may attenuate the dependence on bile micelle‐mediated solubility. With relevant inputs, the verified PBPK model developed here could be used to simulate plasma exposures following administration of ivermectin by complex generics in development.

Quantitative systems pharmacology of the eye: Tools and data for ocular QSP

Abstract: Good eyesight belongs to the most‐valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug‐response in specific segments of the eye. In particular, ocular physiologically‐based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose–response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part III

Abstract: The development of in vitro/in vivo translational methods and a clinical trial framework for synergistically acting drug combinations are needed to identify optimal therapeutic conditions with the most effective therapeutic strategies. We performed physiologically based pharmacokinetic–pharmacodynamic (PBPK/PD) modelling and virtual clinical trial simulations for siremadlin, trametinib, and their combination in a virtual representation of melanoma patients. In this study, we built PBPK/PD models based on data from in vitro absorption, distribution, metabolism, and excretion (ADME), and in vivo animals’ pharmacokinetic–pharmacodynamic (PK/PD) and clinical data determined from the literature or estimated by the Simcyp simulator (version V21). The developed PBPK/PD models account for interactions between siremadlin and trametinib at the PK and PD levels. Interaction at the PK level was predicted at the absorption level based on findings from animal studies, whereas PD interaction was based on the in vitro cytotoxicity results. This approach, combined with virtual clinical trials, allowed for the estimation of PK/PD profiles, as well as melanoma patient characteristics in which this therapy may be noninferior to the dabrafenib and trametinib drug combination. PBPK/PD modelling, combined with virtual clinical trial simulation, can be a powerful tool that allows for proper estimation of the clinical effect of the above-mentioned anticancer drug combination based on the results of in vitro studies. This approach based on in vitro/in vivo extrapolation may help in the design of potential clinical trials using siremadlin and trametinib and provide a rationale for their use in patients with melanoma.

Safety of elexacaftor/tezacaftor/ivacaftor dose reduction: Mechanistic exploration through physiologically based pharmacokinetic modeling and a clinical case series

Abstract: Elexacaftor/tezacaftor/ivacaftor (ETI) treatment is associated with significant improvement in lung function in people with cystic fibrosis (pwCF); however, some patients experience adverse effects (AEs) including hepatotoxicity. One potential strategy is dose reduction in ETI with the goal of maintaining therapeutic efficacy while resolving AEs. We report our experience of dose reduction in individuals who experienced AEs following ETI therapy. We provide mechanistic support for ETI dose reduction by exploring predicted lung exposures and underlying pharmacokinetics–pharmacodynamics (PK‐PD) relationships.

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl

Abstract: Physiologically‐based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well‐suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl.

Physiologically‐based pharmacokinetic model to investigate the effect of pregnancy on risperidone and paliperidone pharmacokinetics: Application to a pregnant woman and her neonate

Abstract: This study aimed to determine the effects of pregnancy and ontogeny on risperidone and paliperidone pharmacokinetics by assessing their serum concentrations in two subjects and constructing a customized physiologically‐based pharmacokinetic (PBPK) model. Risperidone and paliperidone serum concentrations were determined in a pregnant woman and her newborn. PBPK models for risperidone and paliperidone in adults, pediatric, and pregnant populations were developed and verified using the Simcyp simulator. These models were then applied to our two subjects, generating their “virtual twins.” Effects of pregnancy on both drugs were examined using models with fixed pharmacokinetic parameters. In the neonatal PBPK simulation, 10 different models for estimating the renal function of neonates were evaluated. Risperidone was not detected in the serum of both pregnant woman and her newborn. Maternal and neonatal serum paliperidone concentrations were between 2.05–3.80 and 0.82–1.03 ng/ml, respectively. Developed PBPK models accurately predicted paliperidone's pharmacokinetics, as shown by minimal bias and acceptable precision across populations. The individualized maternal model predicted all observed paliperidone concentrations within the 90% prediction interval. Fixed‐parameter simulations showed that CYP2D6 activity largely affects risperidone and paliperidone pharmacokinetics during pregnancy. The Flanders metadata equation showed the lowest absolute bias (mean error: 22.3% ± 6.0%) and the greatest precision (root mean square error: 23.8%) in predicting paliperidone plasma concentration in the neonatal population. Our constructed PBPK model can predict risperidone and paliperidone pharmacokinetics in pregnant and neonatal populations, which could help with precision dosing using the PBPK model‐informed approach in special populations.

A physiologically based pharmacokinetic model for [68Ga]Ga-(HA-)DOTATATE to predict whole-body distribution and tumor sink effects in GEP-NET patients

Abstract: Little is known about parameters that have a relevant impact on (dis)similarities in biodistribution between various 68Ga-labeled somatostatin analogues. Additionally, the effect of tumor burden on organ uptake remains unclear. Therefore, the aim of this study was to describe and compare organ and tumor distribution of [68Ga]Ga-DOTATATE and [68Ga]Ga-HA-DOTATATE using a physiologically based pharmacokinetic (PBPK) model and to identify factors that might cause biodistribution and tumor uptake differences between both peptides. In addition, the effect of tumor burden on peptide biodistribution in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients was assessed.A PBPK model was developed for [68Ga]Ga-(HA-)DOTATATE in GEP-NET patients. Three tumor compartments were added, representing primary tumor, liver metastases and other metastases. Furthermore, reactions describing receptor binding, internalization and recycling, renal clearance and intracellular degradation were added to the model. Scan data from GEP-NET patients were used for evaluation of model predictions. Simulations with increasing tumor volumes were performed to assess the tumor sink effect.Data of 39 and 59 patients receiving [68Ga]Ga-DOTATATE and [68Ga]Ga-HA-DOTATATE, respectively, were included. Evaluations showed that the model adequately described image-based patient data and that different receptor affinities caused organ uptake dissimilarities between both peptides. Sensitivity analysis indicated that tumor blood flow and blood volume impacted tumor distribution most. Tumor sink predictions showed a decrease in spleen uptake with increasing tumor volume, which seemed clinically relevant for patients with total tumor volumes higher than ~ 550 mL.The developed PBPK model adequately predicted tumor and organ uptake for this GEP-NET population. Relevant organ uptake differences between [68Ga]Ga-DOTATATE and [68Ga]Ga-HA-DOTATATE were caused by different affinity profiles, while tumor uptake was mainly affected by tumor blood flow and blood volume. Furthermore, tumor sink predictions showed that for the majority of patients a tumor sink effect is not expected to be clinically relevant.

A Physiologically-Based Pharmacokinetic Model for Cannabidiol in Healthy Adults, Hepatically-Impaired Adults, and Children

Abstract: Cannabidiol (CBD) is available as a prescription oral drug that is indicated for the treatment of some types of epilepsy in children and adults. CBD is also available over-the-counter and is used to self-treat a variety of other ailments, including pain, anxiety, and insomnia. Accordingly, CBD may be consumed with other medications, resulting in possible CBD-drug interactions. Such interactions can be predicted in healthy and hepatically-impaired (HI) adults and in children through physiologically based pharmacokinetic (PBPK) modeling and simulation. These PBPK models must be populated with CBD-specific parameters, including the enzymes that metabolize CBD in adults. In vitro reaction phenotyping experiments showed that UDP-glucuronosyltransferases (UGTs, 80%), particularly UGT2B7 (64%), were the major contributors to CBD metabolism in adult human liver microsomes. Among the cytochrome P450s (CYPs) tested, CYP2C19 (5.7%) and CYP3A (6.5%) were the major CYPs responsible for CBD metabolism. Using these and other physicochemical parameters, a CBD PBPK model was developed and validated for healthy adults. This model was then extended to predict CBD systemic exposure in HI adults and children. Our PBPK model successfully predicted CBD systemic exposure in both populations within 0.5- to 2-fold of the observed values. In conclusion, we developed and validated a PBPK model to predict CBD systemic exposure in healthy and HI adults and children. This model can be used to predict CBD-drug or CBD-drug-disease interactions in these populations. SIGNIFICANCE STATEMENT Our PBPK model successfully predicted CBD systemic exposure in healthy and hepatically-impaired adults, as well as children with epilepsy. This model could be used in the future to predict CBD-drug or CBD-drug-disease interactions in these special populations.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators

Abstract: The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing’s syndrome, is prone to drug–food interactions (DFIs) and is well known for its strong drug–drug interaction (DDI) potential. Some of ketoconazole’s potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole’s metabolites on its DDI potential. The parent–metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.

Physiologically based pharmacokinetic modeling for development and applications of a virtual celiac disease population using felodipine as a model drug

Abstract: In celiac disease (CeD), gastrointestinal CYP3A4 abundance and morphology is affected by the severity of disease. Therefore, exposure to CYP3A4 substrates and extent of drug interactions is altered. A physiologically‐based pharmacokinetic (PBPK) population for different severities of CeD was developed. Gastrointestinal physiology parameters, such as luminal pH, transit times, morphology, P‐gp, and CYP3A4 expression were included in development of the CeD population. Data on physiological difference between healthy and CeD subjects were incorporated into the model as the ratio of celiac to healthy. A PBPK model was developed and verified for felodipine extended‐release tablet in healthy volunteers (HVs) and then utilized to verify the CeD populations. Plasma concentration‐time profile and PK parameters were predicted and compared against those observed in both groups. Sensitivity analysis was carried out on key system parameters in CeD to understand their impact on drug exposure. For felodipine, the predicted mean concentration‐time profiles and 5th and 95th percentile intervals captured the observed profile and variability in the HV and CeD populations. Predicted and observed clearance was 56.9 versus 56.1 (L/h) in HVs. Predicted versus observed mean ± SD area under the curve for extended release felodipine in different severities of CeD were values of 14.5 ± 9.6 versus 14.4 ± 2.1, 14.6 ± 9.0 versus 17.2 ± 2.8, and 28.1 ± 13.5 versus 25.7 ± 5.0 (ng.h/mL), respectively. Accounting for physiology differences in a CeD population accurately predicted the PK of felodipine. The developed CeD population can be applied for determining the drug concentration of CYP3A substrates in the gut as well as for systemic levels, and for application in drug–drug interaction studies.

Minimal physiologically‐based hybrid model of pharmacokinetics in pregnant women: Application to antenatal corticosteroids

Abstract: Minimal physiologically‐based pharmacokinetic (mPBPK) models are an alternative to full physiologically‐based pharmacokinetic (PBPK) models as they offer reduced complexity while maintaining the physiological interpretation of key model components. Full PBPK models have been developed for pregnancy, but a mPBPK model eases the ability to perform a “top‐down” meta‐analysis melding all available pharmacokinetic (PK) data in the mother and fetus. Our hybrid mPBPK model consists of mPBPK models for the mother and fetus with connection by the placenta. This model was applied to describe the rich PK data of antenatal corticosteroid betamethasone (BET) jointly with the limited data for dexamethasone (DEX) in the mother and fetus. Physiologic model parameters were obtained from the literature while drug‐dependent parameters were estimated by the simultaneous fitting of all available data for DEX and BET. Maternal clearances of DEX and BET confirmed the literature values, and the expected fetal‐to‐maternal plasma ratios ranged from 0.3 to 0.4 for both drugs. Simulations of maternal plasma concentrations for the dosing regimens of BET and DEX recommended by the World Health Organization based on our findings revealed up to 60% lower exposures than found in nonpregnant women and offers a means of devising alternative dosing regimens. Our hybrid mPBPK model and meta‐analysis approach could facilitate assessment of other classes of drugs indicated for the treatment of pregnant women.

Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia

Abstract: Abstract We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady‐state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predict imatinib AUCss, Css,min and Css,max for patients with CML (n = 68) from a real‐world retrospective observational study. Differences in imatinib exposure were evaluated based on clinical outcomes, (a) Early Molecular Response (EMR) achievement and (b) occurrence of grade ≥3 adverse drug reactions (ADRs), using the Kruskal‐Wallis rank sum test. Sensitivity analyses explored the influence of patient characteristics and drug interactions on imatinib exposure. Simulated imatinib exposure was significantly higher in patients who achieved EMR compared to patients who did not (geometric mean AUC0‐24,ss 51.2 vs. 42.7 μg h mL−1, p < 0.05; Css,min 1.1 vs. 0.9 μg mL−1, p < 0.05; Css,max 3.4 vs. 2.8 μg mL−1, p < 0.05). Patients who experienced grade ≥3 ADRs had a significantly higher simulated imatinib exposure compared to patients who did not (AUC0‐24,ss 56.1 vs. 45.9 μg h mL−1, p < 0.05; Css,min 1.2 vs. 1.0 μg mL−1, p < 0.05; Css,max 3.7 vs. 3.0 μg mL−1, p < 0.05). Simulations identified a range of patient (sex, age, weight, abundance of hepatic CYP2C8 and CYP3A4, α1‐acid glycoprotein concentrations, liver and kidney function) and medication‐related factors (dose, concomitant CYP2C8 modulators) contributing to the inter‐individual variability in imatinib exposure. Relationships between imatinib plasma exposure, EMR achievement and ADRs support the rationale for therapeutic drug monitoring to guide imatinib dosing to achieve optimal outcomes in CML.

Vinblastine pharmacokinetics in mouse, dog, and human in the context of a physiologically based model incorporating tissue‐specific drug binding, transport, and metabolism

Abstract: Vinblastine (VBL) is a vinca alkaloid‐class cytotoxic chemotherapeutic that causes microtubule disruption and is typically used to treat hematologic malignancies. VBL is characterized by a narrow therapeutic index, with key dose‐limiting toxicities being myelosuppression and neurotoxicity. Pharmacokinetics (PK) of VBL is primarily driven by ABCB1‐mediated efflux and CYP3A4 metabolism, creating potential for drug–drug interaction. To characterize sources of variability in VBL PK, we developed a physiologically based pharmacokinetic (PBPK) model in Mdr1a/b(−/−) knockout and wild‐type mice by incorporating key drivers of PK, including ABCB1 efflux, CYP3A4 metabolism, and tissue‐specific tubulin binding, and scaled this model to accurately simulate VBL PK in humans and pet dogs. To investigate the capability of the model to capture interindividual variability in clinical data, virtual populations of humans and pet dogs were generated through Monte Carlo simulation of physiologic and biochemical parameters and compared to the clinical PK data. This model provides a foundation for predictive modeling of VBL PK. The base PBPK model can be further improved with supplemental experimental data identifying drug–drug interactions, ABCB1 polymorphisms and expression, and other sources of physiologic or metabolic variability.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug–Drug Interactions between Elexacaftor/Tezacaftor/Ivacaftor and Tacrolimus in Lung Transplant Recipients

Abstract: Elexacaftor/tezacaftor/ivacaftor (ETI) treatment has potential benefits in lung transplant recipients, including improvements in extrapulmonary manifestations, such as gastrointestinal and sinus disease; however, ivacaftor is an inhibitor of cytochrome P450 3A (CYP3A) and may, therefore, pose a risk for elevated systemic exposure to tacrolimus. The aim of this investigation is to determine the impact of ETI on tacrolimus exposure and devise an appropriate dosing regimen to manage the risk of this drug–drug interaction (DDI). The CYP3A-mediated DDI of ivacaftor–tacrolimus was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach, incorporating CYP3A4 inhibition parameters of ivacaftor and in vitro enzyme kinetic parameters of tacrolimus. To further support the findings in PBPK modeling, we present a case series of lung transplant patients who received both ETI and tacrolimus. We predicted a 2.36-fold increase in tacrolimus exposure when co-administered with ivacaftor, which would require a 50% dose reduction of tacrolimus upon initiation of ETI treatment to avoid the risk of elevated systemic exposure. Clinical cases (N = 13) indicate a median 32% (IQR: −14.30, 63.80) increase in the dose-normalized tacrolimus trough level (trough concentration/weight-normalized daily dose) after starting ETI. These results indicate that the concomitant administration of tacrolimus and ETI may lead to a clinically significant DDI, requiring the dose adjustment of tacrolimus.

Prediction of the Drug–Drug Interaction Potential between Tegoprazan and Amoxicillin/Clarithromycin Using the Physiologically Based Pharmacokinetic and Pharmacodynamic Model

Abstract: Tegoprazan is a novel potassium-competitive acid blocker. This study investigated the effect of drug–drug interaction on the pharmacokinetics and pharmacodynamics of tegoprazan co-administered with amoxicillin and clarithromycin, the first-line therapy for the eradication of Helicobacter pylori, using physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling. The previously reported tegoprazan PBPK/PD model was modified and applied. The clarithromycin PBPK model was developed based on the model provided by the SimCYP® compound library. The amoxicillin model was constructed using the middle-out approach. All of the observed concentration–time profiles were covered well by the predicted profiles with the 5th and 95th percentiles. The mean ratios of predicted to observed PK parameters, including the area under the curve (AUC), maximum plasma drug concentration (Cmax), and clearance, were within the 30% intervals for the developed models. Two-fold ratios of predicted fold-changes of Cmax and AUC from time 0 to 24 h to observed data were satisfied. The predicted PD endpoints, including median intragastric pH and percentage holding rate at pH above 4 or 6 on day 1 and day 7, were close to the corresponding observed data. This investigation allows evaluation of the effects of CYP3A4 perpetrators on tegoprazan PK and PD changes, thus providing clinicians with the rationale for co-administration dosing adjustment.

Prediction of gastric pH‐mediated drug exposure using physiologically‐based pharmacokinetic modeling: A case study of itraconazole

Abstract: Abnormal gastric acidity, including achlorhydria, can act as a significant source of variability in orally administered drugs especially with pH‐sensitive solubility profiles, such as weak bases, potentially resulting in an undesirable therapeutic response. This study aimed to evaluate the utility of physiologically‐based pharmacokinetic (PBPK) modeling in the prediction of gastric pH‐mediated drug exposure by using itraconazole, a weak base, as a case. An itraconazole PBPK model was developed on the mechanistic basis of its absorption kinetics in a middle‐out manner from a stepwise in vitro‐in vivo extrapolation to in vivo refinement. Afterward, an independent prospective clinical study evaluating gastric pH and itraconazole pharmacokinetics (PKs) under normal gastric acidity and esomeprazole‐induced gastric hypoacidity was conducted for model validation. Validation was performed by comparing the predicted data with the clinical observations, and the valid model was subsequently applied to predict PK changes under achlorhydria. The developed itraconazole PBPK model showed reasonable reproducibility for gastric pH‐mediated exposure observed in the clinical investigation. Based on the model‐based simulations, itraconazole exposure was expected to be decreased up to 65% under achlorhydria, and furthermore, gastric pH‐mediated exposure could be mechanistically interpreted according to sequential variation in total solubility, dissolution, and absorption. This study suggested the utility of PBPK modeling in the prediction of gastric pH‐mediated exposure, especially for drugs whose absorption is susceptible to gastric pH. Our findings will serve as a leading model for further mechanistic assessment of exposure depending on gastric pH for various drugs, ultimately contributing to personalized pharmacotherapy.

Tiered approach to evaluate the CYP3A victim and perpetrator drug–drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling

Abstract: Vonoprazan is metabolized extensively through CYP3A and is an in vitro time‐dependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug–drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically‐based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug‐ and system‐specific parameters, and clinical data and observations from a [14C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time‐dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co‐administration with moderate and strong CYP3A inducers should be avoided.