Physiologically Based Pharmacokinetic Research Articles

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.

Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Exploring the Impact of Developmental Clearance Saturation on Propylene Glycol Exposure in Adults and Term Neonates Using Physiologically Based Pharmacokinetic Modeling.

Propylene glycol (PG) is a pharmaceutical excipient which is generally regarded as safe (GRAS), though clinical toxicity has been reported. PG toxicity has been attributed to accumulation due to saturation of the alcohol dehydrogenase (ADH)-mediated clearance pathway. This study aims to explore the impact of the saturation of ADH-mediated PG metabolism on its developmental clearance in adults and neonates and assess the impact of a range of doses on PG clearance saturation and toxicity. Physiologically based pharmacokinetic (PBPK) models for PG in adults and term neonates were developed using maximum velocity (Vmax) and Michaelis-Menten's constant (Km) of ADH-mediated metabolism determined in vitro in human liver cytosol, published physicochemical, drug-related and ADH ontogeny parameters. The models were validated and used to determine the impact of dosing regimen on PG clearance saturation and toxicity in adults and neonates. The Vmax and Km of PG in human liver cytosol were 1.57 nmol/min/mg protein and 25.1 mM, respectively. The PG PBPK model adequately described PG PK profiles in adults and neonates. The PG dosing regimens associated with saturation and toxicity were dependent on both dose amount and cumulative in standard dosing frequencies. Doses resulting in saturation were higher than those associated with clinically observed toxicity. In individuals without impaired clearance or when PG exposure is through formulations that contain excipients with possible interaction with PG, a total daily dose of 100-200 mg/kg/day in adults and 25-50 mg/kg/day in neonates is unlikely to result in toxic PG levels or PG clearance saturation.

Key Factors for Improving Predictive Accuracy and Avoiding Overparameterization of the PBPK Absorption Model in Food Effect Studies of Weakly Basic Water-Insoluble Compounds in Immediate Release Formulations.

Background/Objectives: Physiologically based pharmacokinetic (PBPK) absorption models are instrumental for assessing drug absorption prior to clinical food effect studies, though discrepancies in predictive and actual outcomes are observed. This study focused on immediate release formulations of weakly basic water-insoluble compounds, namely rivaroxaban, ticagrelor, and PB-201, to investigate factors that could improve the predictive accuracy of PBPK models regarding food effects. Methods: Comprehensive in vitro experimental results provided the basis for the development of mechanistic absorption models, which were then combined with mechanistic disposition models to predict the systemic exposure of the model drugs in both fasted and fed states. Results: The developed PBPK models showed moderate to high predictive accuracy for food effects in Caucasian populations. For the Chinese population, the ticagrelor model's initial overestimation of fed-state absorption was addressed by updating the permeability parameters from Caco-2 cell assays to those derived from parallel artificial membrane permeability assays in FaSSIF and FeSSIF media. This refinement was also applied to the rivaroxaban and ticagrelor models, leading to a more accurate representation of absorption in Caucasians. Conclusions: This study highlights the importance of apparent permeability in enhancing the predictive accuracy of PBPK absorption models for weakly basic water-insoluble compounds. Furthermore, the precipitation of PB-201 in the two-stage transfer experiments suggests that precipitation may not be a universal phenomenon for such compounds in vivo. Consequently, the precipitation rate constant, a theoretically essential parameter, should be determined based on experimental evidence to avoid overparameterization and ensure robust predictive accuracy of PBPK models.

Development of a Physiologically Based Pharmacokinetic (PBPK) Model for F-53B in Pregnant Mice and Its Extrapolation to Humans.

Chlorinated polyfluorinated ether sulfonic acid (F-53B), a commonly utilized alternative for perfluorooctane sulfonate, was detected in pregnant women and cord blood recently. However, the lack of detailed toxicokinetic information poses a significant challenge in assessing the human risk assessment for F-53B exposure. Our study aimed to develop a physiologically based pharmacokinetic (PBPK) model for pregnant mice, based on toxicokinetic experiments, and extrapolating it to humans. Pregnant mice were administered 80 μg/kg F-53B orally and intravenously on gestational day 13. F-53B concentrations in biological samples were analyzed via ultraperformance liquid chromatography-mass spectrometry. Results showed the highest F-53B accumulation in the brain, followed by the placenta, amniotic fluid, and liver in fetal mice. These toxicokinetic data were applied to F-53B PBPK model development and evaluation, and Monte Carlo simulations were used to characterize the variability and uncertainty in the human population. Most of the predictive values were within a 2-fold range of experimental data (>72%) and had a coefficient of determination (R2) greater than 0.68. The developed mouse model was then extrapolated to the human and evaluated with human biomonitoring data. Our study provides an important step toward improving the understanding of toxicokinetics of F-53B and enhancing the quantitative risk assessments in sensitive populations, particularly in pregnant women and fetuses.

Physiologically based pharmacokinetic modeling of long-acting extended-release naltrexone in pregnant women with opioid use disorder.

Opioid use disorders (OUD) are a major issue in the U.S. Current treatments for pregnant women, like methadone and buprenorphine require daily dosing and have adverse effects. Monthly injectable naltrexone (XR-NTX) mitigates these adverse effects but is not recommended during pregnancy due to limited pharmacokinetic and safety data. This study developed a physiologically based pharmacokinetic (PBPK) model to describe XR-NTX pharmacokinetics during pregnancy, and to predict dosing recommendations. Model predictions were successfully validated with observed data. Maternal plasma XR-NTX profiles were simulated for 400 non-pregnant virtual females at the approved dose of 380 mg, then randomized to continue with either 380, 285, 190, or 95 mg during pregnancy. The non-pregnant virtual females had a mean predicted Cmax, AUC0-7days, and AUC0-28days of 23.3 ng/mL, 142 ng·d/mL, and 148 ng·d/mL, respectively. Maternal XR-NTX exposure (AUC0-28days) were predicted to increase by 1.37, 1.43, and 1.72 times during the first, second, and third trimester of pregnancy. However, the fetal-to-maternal exposure (AUC0-28days) was lower in the first (15%), second (7%), and third (9%) trimesters. A dose of 285 mg of XR-NTX in pregnancy during the first/second trimester and dose of 190 mg in the third trimester were predicted to provide maternal exposures that were comparable to non-pregnant levels at the standard dose. This study provides crucial insights into XR-NTX pharmacokinetics and proposes a dosing strategy during pregnancy, potentially aiding further clinical investigations and decision making regarding XR-NTX use during pregnancy.

Drug-drug interactions between letermovir and tacrolimus in Japanese renal transplant recipients simulated using a physiologically based pharmacokinetic model.

Letermovir (LET) is a novel antiviral agent recently approved for cytomegalovirus (CMV) prophylaxis of renal transplant patients in Japan. However, its interactions with tacrolimus (TAC), an important immunosuppressant, remain ambiguous, warranting careful evaluation considering the unique genetic and physiological characteristics of Japanese patients. Therefore, in this study, we aimed to investigate the drug-drug interactions between LET and extended-release TAC (ER-TAC) in Japanese renal transplant patients via physiologically based pharmacokinetic (PBPK) modeling. We developed PBPK models for LET and TAC, including a new model for ER-TAC, using the Simcyp simulator. We also created a virtual Japanese post-transplant population by incorporating physiological parameters specific to Japanese patients, including CYP3A5 genotypes. Our model accurately predicted the pharmacokinetics of both immediate-release and ER-TAC co-administered with LET. In the Japanese population, LET significantly increased ER-TAC exposure, with the effect varying by CYP3A5 genotype. For CYP3A5*1 carrier, the area under the curve ratio ranged from 2.33 to 2.53, while for CYP3A5*3/*3 carriers, it ranged from 2.82 to 2.86. The maximum concentration ratio was approximately 1.50 across all groups. Our findings suggest reducing the ER-TAC dose by approximately 57-60% for CYP3A5*1 carrier and 65% for CYP3A5*3/*3 carriers when co-administered with LET for Japanese renal transplant patients. Moreover, the developed model incorporating population-specific factors, such as hematocrit values and CYP3A5 genotype frequencies, is a valuable tool to evaluate complex drug interactions and guide the dosing strategies for LET and TAC in Japanese patients. Overall, this study expands the application of PBPK modeling in transplant pharmacology, contributing to the development of effective immunosuppressive strategies for Japanese renal transplant patients.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward.

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made. Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus. Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective. We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Prediction of tissue exposures of polymyxin-B, amikacin and sulbactam using physiologically-based pharmacokinetic modeling.

The combination antimicrobial therapy consisting of amikacin, polymyxin-B, and sulbactam demonstrated in vitro synergy against multi-drug resistant Acinetobacter baumannii. The objectives were to predict drug disposition and extrapolate their efficacy in the blood, lung, heart, muscle and skin tissues using a physiologically-based pharmacokinetic (PBPK) modeling approach and to evaluate achievement of target pharmacodynamic (PD) indices against A. baumannii. A PBPK model was initially developed for amikacin, polymyxin-B, and sulbactam in adult subjects, and then scaled to pediatrics, accounting for both renal and non-renal clearances. The simulated plasma and tissue drug exposures were compared to the observed data from humans and rats. Efficacy was inferred using joint probability of target attainment of target PD indices. The simulated plasma drug exposures in adults and pediatrics were within the 0.5 to 2 boundary of the mean fold error for the ratio between simulated and observed means. Simulated drug exposures in blood, skin, lung, and heart were consistent with reported penetration ratio between tissue and plasma drug exposure. In a virtual pediatric population from 2 to <18 years of age using pediatric dosing regimens, the interpretive breakpoints were achieved in 85-90% of the population. The utility of PBPK to predict and simulate the amount of antibacterial drug exposure in tissue is a practical approach to overcome the difficulty of obtaining tissue drug concentrations in pediatric population. As combination therapy, amikacin/polymyxin-B/sulbactam drug concentrations in the tissues exhibited sufficient penetration to combat extremely drug resistant A. baumannii clinical isolates.

Physiologically based pharmacokinetic modelling to predict potential drug-drug interactions of dersimelagon (MT-7117).

Dersimelagon is a novel, investigational, orally administered, selective agonist of the melanocortin-1 receptor that has demonstrated efficacy at increasing symptom-free light exposure and an acceptable safety profile in patients with protoporphyria. A phase 1 drug-drug interaction (DDI) study demonstrated that dersimelagon 300 mg has the potential for clinically relevant DDIs with drugs that are substrates for breast cancer resistance protein, such as atorvastatin and rosuvastatin. This study uses physiologically based pharmacokinetic (PBPK) modelling to further investigate the DDI effects at lower doses of dersimelagon with substrate drugs. The data from in silico, in vitro and in vivo studies were used to construct a PBPK model for dersimelagon to assess the DDI potential between dersimelagon and substrate drugs for cytochrome P450 3A, P-glycoprotein, organic anion transporting polypeptide 1B1/1B3, organic anion transporter 3 and breast cancer resistance protein, including atorvastatin and rosuvastatin. The systemic exposure of atorvastatin based on the maximum plasma concentration and area under the plasma concentration-time curve was predicted to increase 1.21-fold and 1.25-fold, respectively, if coadministered with dersimelagon 100 mg, and 1.42-fold and 1.45-fold with dersimelagon 200 mg. The systemic exposure of rosuvastatin followed trends similar to atorvastatin (1.67-fold and 1.34-fold increase in maximum plasma concentration and area under the plasma concentration-time curve, respectively, with dersimelagon 100 mg, and 2.40-fold and 1.69-fold with dersimelagon 200 mg). Overall, PBPK modelling results indicate that the simulated changes in plasma exposure of atorvastatin and rosuvastatin following coadministration with dersimelagon 100 or 200 mg are not clinically significant, but caution and appropriate clinical monitoring should be recommended.

Development and application of a physiologically-based pharmacokinetic model for ractopamine in goats.

Physiologically Based Pharmacokinetic (PBPK) models can provide forecasts of the drug residues within the organism. Ractopamine (RAC) is a typical β-agonist. In this study, we developed a PBPK model for RAC in goats. The goal was to predict the distribution of the drug after multiple oral administrations. The preliminary PBPK model for RAC in goats performed well in predicting the drug's distribution in most tissues. In our sensitivity analysis, we found that the parameter of Qclu (Blood Flow Volume through Lungs) had the greatest impact on the RAC concentrations in plasma, liver, and kidney and was the most sensitive parameter. Furthermore, our study aimed to assess the withdrawal time (WT) of RAC in different tissues after RAC long-term exposure in goats. We found that the WT of RAC in the kidney was the longest, lasting for 13 days. Overall, the insights gained from this study have important implications for optimizing drug administration in goats and ensuring appropriate withdrawal times to prevent any potential risks.

Bioaccumulation and toxicological effects of dietborne arsenic exposure on the apple snail (Pomacea canaliculata)

An eight-compartment physiologically based pharmacokinetic (PBPK) model was used to simulate the bioaccumulation and distribution of arsenic (As) within the apple snail (Pomacea canaliculata) following the ingestion of As-contaminated lettuce. The bioaccumulation results revealed that the shell contained the majority (67.21 %) of the total As content, with the liver and the head-foot containing approximately 11.14 % and 10.45 % of the total As content in the snail, respectively. Modeling quantified the process of intestine-stomach absorption of dietborne As and revealed its crucial role in the subsequent distribution of As within the body. The liver is the primary metabolic site, whereas the shell is the primary storage site. Exposure to dietborne As leads to pronounced physiological and biochemical alterations in apple snails. Total protein levels decreased by 24.06 %, superoxide dismutase (SOD) activity decreased by 24.43 %, malondialdehyde (MDA) content increased by 47.51 %, glutathione (GSH) content decreased by 46.99 %, and glutathione S-transferase (GST) activity decreased by 42.22 %. Furthermore, the subcellular-level results indicated that dietborne As exposure altered subcellular distribution in the liver. Additionally, dietborne As exposure significantly reduced the abundance of gut microbiota in apple snails.

PBPK modeling: What is the role of CYP3A4 expression in the gastrointestinal tract to accurately predict first-pass metabolism?

Gastrointestinal first-pass metabolism plays an important role in bioavailability and in drug-drug interactions. Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to integrate these processes mechanistically. However, a correct bottom-up prediction of GI first-pass metabolism is challenging and depends on various model parameters like the level of enzyme expression and the basolateral intestinal mucosa permeability (Pmucosa). This work aimed to investigate if cytochrome P450 (CYP) 3A4 expression could help predict the first-pass effect using PBPK modeling or whether additional factors like Pmucosa do play additional roles using PBPK modeling. To this end, a systematic review of the absolute CYP3A expression in the human gastrointestinal tract and liver was conducted. The resulting CYP3A4 expression profile and two previously published profiles were applied to PBPK models of seven CYP3A4 substrates (alfentanil, alprazolam, felodipine, midazolam, sildenafil, triazolam, and verapamil) built-in PK-Sim®. For each compound, it was assessed whether first-pass metabolism could be adequately predicted based on the integrated CYP3A4 expression profile alone or whether an optimization of Pmucosa was required. Evaluation criteria were the precision of the predicted interstudy bioavailabilities and area under the concentration-time curves. It was found that none of the expression profiles provided upfront an adequate description of the extent of GI metabolism and that optimization of Pmucosa as a compound-specific parameter improved the prediction of most models. Our findings indicate that a pure bottom-up prediction of gastrointestinal first-pass metabolism is currently not possible and that compound-specific features like Pmucosa must be considered as well.

Elucidation of DPP-4 involvement in systemic distribution and renal reabsorption of linagliptin by PBPK modeling with a cluster Gauss-Newton method.

The dipeptidyl peptidase-4 (DPP-4) inhibitor linagliptin (LNG) exhibits target-mediated drug disposition (TMDD) in clinical settings, characterized by saturable binding to plasma soluble DPP-4 (sDPP-4) and tissue transmembrane DPP-4 (tDPP-4). Previous studies have indicated that saturable renal reabsorption of LNG contributes to its nonlinear urinary excretion observed in humans and wild-type mice, but not in Dpp-4 knockout mice. To elucidate the mechanisms underlying these complex phenomena, including DPP-4-related renal reabsorption of LNG, we employed physiologically-based pharmacokinetic (PBPK) modeling combined with a cluster Gauss-Newton method (CGNM). The CGNM facilitated the exploration of parameters in rat and human PBPK models for LNG and the determination of parameter identifiability. Through PBPK-CGNM analysis using reported autoradiography data ([14C]-LNG) in wild-type and Dpp-4-deficient rats, DPP-4-specific distributions of LNG in various tissues were clearly differentiated from nonspecific parts. By fitting to human plasma concentrations and urinary and fecal excretions of LNG after intravenous and oral administrations, multiple unknown PBPK parameters were simultaneously estimated by the CGNM. Notably, the amount of tDPP-4 and the reabsorption clearance for LNG-DPP-4 complexes were identifiable, indicating their critical role in explaining the complex nonlinear pharmacokinetics of LNG. Compared with previous PBPK analyses, the CGNM allowed us to incorporate greater model complexity (e.g., consideration of tDPP-4 expressions and invitro binding kinetics), ultimately resulting in a more accurate reproduction of LNG's TMDD. In conclusion, by considering LNG as a high-affinity probe for DPP-4, comprehensive PBPK-CGNM analyses suggested a dynamic whole-body distribution of DPP-4, including its involvement in the renal reabsorption of LNG.

Prediction of Pharmacokinetic Drug-Drug Interactions Involving Anlotinib as a Victim by Using Physiologically Based Pharmacokinetic Modeling.

Anlotinib was approved as a third line therapy for advanced non-small cell lung cancer in China. However, the impact of concurrent administration of various clinical drugs on the drug-drug interaction (DDI) potential of anlotinib remains undetermined. As such, this study aims to evaluate the DDI of anlotinib as a victim by establishing a physiologically based pharmacokinetic (PBPK) model. The PBPK model of anlotinib as a victim drug was constructed and validated in the Simcyp® incorporating parameters derived from in vitro studies, pre-clinical investigations, and clinical research encompassing patients with cancer. Subsequently, plasma exposure of anlotinib in cancer patients was predicted for single- and multi-dose co-administration with typical perpetrators mentioned in Food and Drug Administration (FDA) industrial guidance. Based on predictions, the CYP3A potent inhibitor ketoconazole demonstrated the most significant DDI with anlotinib, regardless of whether anlotinib is administered as a single dose or multiple doses. Ketoconazole increased the area under the concentration-time curve (AUC) and maximum concentration (Cmax) of single-dose anlotinib to 1.41-fold and 1.08-fold, respectively. In contrast, rifampicin, a potent inducer of CYP3A enzymes, exhibited a relatively higher level of DDI, with AUCR and CmaxR values of 0.44 and 0.79, respectively. Based on the PBPK modeling, there is a low risk of DDI between anlotinib and potent CYP3A/1A2 inhibitors, but caution and enhanced monitoring for adverse reactions are advised. To mitigate the risk of anti-tumor treatment failure, it is recommended to avoid concurrent use of strong CYP3A inducers. In conclusion, our study enhances understanding of anlotinib's interaction with medications, aiding scientists, prescribers, and drug labels in gauging the expected impact of CYP3A/1A2 modulators on anlotinib's pharmacokinetics.

PBPK-based translation from preclinical species to humans for the full-size IgG therapeutic efalizumab.

Animal models play a vital role in pharmaceutical research and development by supporting the planning and design of later clinical studies. To improve confidence and reliability of first in human dose estimates it is essential to assess the comparability of animal studies with the human situation. In the context of large molecules, it is particularly important to evaluate the cross-species-translatability of parameters related to neonatal fragment crystallizable receptor (FcRn) binding and target mediated drug disposition (TMDD), as they greatly influence distribution and disposition of proteins in the body of an organism. Plasma pharmacokinetic data of the therapeutic protein efalizumab were obtained from literature. Physiologically based pharmacokinetic (PBPK) models were built for three different species (rabbit, non-human primate (NHP), human). Target binding was included in the NHP and human models. The assumption of similar target turnover and target-binding in NHP and human was explored, to gain insights into how these parameters might be translated between species. Efalizumab PBPK models were successfully developed for three species and concentration-time-profiles could be described appropriately across different intravenously administered doses. The final NHP and human models feature a common set of parameters for target turnover and drug-target-complex internalization, as well as comparable target-binding parameters. Our analyses show that different parameter values for FcRn affinity are crucial to accurately describe the concentration-time profiles. Based on the available data in rabbits, NHP and humans, parameters for FcRn affinity cannot be translated between species, but parameters related to target mediated drug disposition can be translated from NHP to human. The inclusion of additional pharmacokinetic (PK) data including different efalizumab doses would further support and confirm our findings on identifying TMDD and, thus, binding kinetics of efalizumab in NHPs. Furthermore, we suggest that information on target expression and internalization rates could make it possible to develop comprehensive human PBPK models with minimal animal testing. In this project, we compared the pharmacokinetics of a therapeutic protein in rabbit, NHP and human using an open PBPK modeling platform (Open Systems Pharmacology Suite, http://www.open-systems-pharmacology.org). Our findings could support similar translatory studies for first in human dose predictions in the future.

Applications of the Cholesterol Metabolite, 4β-Hydroxycholesterol, as a Sensitive Endogenous Biomarker for Hepatic CYP3A Activity Evaluated within a PBPK Framework

Background/Objectives: Plasma levels of 4β-hydroxycholesterol (4β-OHC), a CYP3A-specific metabolite of cholesterol, are elevated after administration of CYP3A inducers like rifampicin and carbamazepine. To simulate such plasma 4β-OHC increase, we developed a physiologically based pharmacokinetic (PBPK) model of cholesterol and 4β-OHC in the Simcyp PBPK Simulator (Version 23, Certara UK Ltd.) using a middle-out approach. Methods: Relevant physicochemical properties and metabolic pathway data for CYP3A and CYP27A1 was incorporated in the model. Results: The PBPK model recovered the observed baseline plasma 4β-OHC levels in Caucasian, Japanese, and Korean populations. The model also captured the higher baseline 4β-OHC levels in females compared to males, indicative of sex-specific differences in CYP3A abundance. More importantly, the model recapitulated the increased 4β-OHC plasma levels after multiple-dose rifampicin treatment in six independent studies, indicative of hepatic CYP3A induction. The verified model also captured the altered 4β-OHC levels in CYP3A4/5 polymorphic populations and with other CYP3A inducers. The model is limited by scant data on relative contributions of CYP3A and CYP27A1 pathways and does not account for regulatory mechanisms that control plasma cholesterol and 4β-OHC levels. Conclusion: This study provides a quantitative fit-for-purpose and framed-for-future modelling framework for an endogenous biomarker to evaluate the DDI risk with hepatic CYP3A induction.

Ideal Time to Conduct a Pharmacokinetic Investigation After Delivery to Fully Capture the Effect of Pregnancy on Drug Exposure.

The World Health Organization is pushing to accelerate the study of new human immunodeficiency virus drugs in pregnant women. However, regulatory guidelines do not specify when to conduct pharmacokinetic studies in postpartum women. This knowledge gap carries the potential to jeopardize the outcomes and conclusions of clinical trials aiming to study the effect of pregnancy on drug exposure. We used physiologically based pharmacokinetic (PBPK) modeling along with clinical data to determine the time needed after delivery for drug exposure to return to prepregnancy levels. A literature review was conducted to collect physiological parameters of pregnant and postpartum women. Regression analyses were performed to derive equations describing the parameters trajectory throughout pregnancy and post partum to inform our PBPK model. Published pharmacokinetic data in pregnant and postpartum women were used for the model verification. The PBPK model was subsequently applied to investigate pharmacokinetic changes throughout pregnancy and post partum. In agreement with the clinical data the PBPK model was able to describe the different effects of pregnancy on drug exposure, with bictegravir showing the largest reduction in exposure (approximately 50%) during the third trimester while ritonavir and raltegravir showing the lowest (approximately 30%). The successfully verified PBPK model predicted that all evaluated antiretrovirals mostly return to prepregnancy exposure 4 weeks after delivery. Pharmacokinetic investigations on hepatically cleared drugs should not be conducted before the fifth week after delivery to fully characterize the effect of pregnancy on drug exposure. Because physiological changes remain after delivery, early measurements can underestimate the pregnancy effect on pharmacokinetics, leading to suboptimal dosing recommendations during pregnancy.

Making PBPK models more reproducible in practice.

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

Physiologically-Based Pharmacokinetic Modeling of Total and Unbound Valproic Acid to Evaluate Dosing in Children With and Without Hypoalbuminemia.

Valproic acid (VPA) demonstrates nonlinear pharmacokinetics (PK) due to a capacity-limited protein binding, which has potential implications on its total and unbound plasma concentrations, especially during hypoalbuminemia. A physiologically based pharmacokinetic (PBPK) model was developed to assess the nonlinear dose-exposure relationship of VPA with special emphasis on pediatric patients with hypoalbuminemia. A PBPK model was first developed and evaluated in adults using PK-Sim® and MoBi® (v.11) and the scaled to children 1 year and older. The capacity-limited protein binding was characterized by second-order kinetics between VPA and albumin with a 2:1 molar ratio. All drug-specific parameters were informed by literature and optimized using published PK data of VPA. PK simulations were performed in virtual populations with normal and low albumin levels. The reported concentration-time profiles of total and unbound VPA were adequately predicted by the PBPK model across the age and dose range (3-120 mg/kg). The model was able to characterize the nonlinear PK, as the concentration-dependent fraction unbound (fu) and the related dose-dependent clearance values were well predicted. Simulated steady-state trough concentrations of total VPA were less than dose-proportional and were within the therapeutic drug monitoring range of 50-100 mg/L for doses between 30 and 45 mg/kg per day in children with normal albumin concentrations. However, virtual children with hypoalbuminemia largely failed to achieve the target exposure. The PBPK model helped assess the nonlinear dose-exposure relationship of VPA and the impact of albumin concentrations on the achievement of target exposure.

A tutorial on physiologically based pharmacokinetic approaches in lactation research.

In breastfeeding mothers, managing medical conditions presents unique challenges, particularly concerning medication use and breastfeeding practices. The transfer of drugs into breast milk and subsequent exposure to nursing infants raises important considerations for drug safety and efficacy. Modeling approaches are increasingly employed to predict infant exposure levels, crucial for assessing drug safety during breastfeeding. Physiologically-based pharmacokinetic (PBPK) modeling provides a valuable tool for predicting drug exposure in lactating individuals and their infants. This tutorial offers an overview of PBPK modeling in lactation research, covering key concepts, prediction approaches, and best practices for model development and application. We delve into milk composition dynamics and its influence on drug transfer into breast milk, addressing modeling considerations, knowledge gaps, and future research directions. Practical examples and case studies illustrate PBPK modeling application in lactation studies. We demonstrate how prediction algorithms for Milk-to-Plasma (M/P) ratios within a PBPK framework can support scenarios lacking clinical lactation data or extend the utility of available lactation clinical data to support further untested clinical scenarios. This tutorial aims to assist researchers and clinicians in understanding and applying PBPK modeling to understand and support clinical scenarios in breastfeeding mothers. Advances in PBPK modeling techniques, along with ongoing research on lactation physiology and drug disposition, promise further insights into drug transfer during lactation.