Physiologically Based Pharmacokinetic Model Research Articles

Using PBPK modeling to supplement clinical data and support the safe and effective use of dolutegravir in pregnant and lactating women.

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically-based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non-pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates. The model was then applied to predict the impact of UGT1A1 phenotypes in extensive (EM), poor (PM), and ultra-rapid metabolizers (UM). The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 PMs was 1.6-fold higher than in EMs. The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 UMs mothers was 1.3-fold lower than in EMs. The predicted mean systemic and umbilical vein concentrations were in excess of the dolutegravir IC90 at 17, 28, and 40 gestational weeks, regardless of UGT1A1 phenotype, indicating that the standard dose of dolutegravir (50 mg q.d., fed state) is generally appropriate in late pregnancy, across UGT1A1 phenotypes. Applying the model in breastfed infants, a 1.5-, 1.7-, and 2.2-fold higher exposure in 2-day-old neonates, 10-day-old neonates, and infants who were UGT1A1 PMs, respectively, compared with EMs of the same age. However, it should be noted that the exposure in breastfed infants who were UGT1A1 PMs was still an order of magnitude lower than maternal exposure with a relative infant daily dose of <2%, suggesting safe use of dolutegravir in breastfeeding women.

Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights.

Voriconazole (VRC), a broad-spectrum antifungal drug, exhibits nonlinear pharmacokinetics (PK) due to saturable metabolic processes, autoinhibition and metabolite-mediated inhibition on their own formation. VRC PK is also characterised by high inter- and intraindividual variability, primarily associated with cytochrome P450 (CYP)2C19 genetic polymorphism. Additionally, recent in vitro findings indicate that VRC main metabolites, voriconazole N-oxide (NO) and hydroxyvoriconazole (OHVRC), inhibit CYP enzymes responsible for VRC metabolism, adding to its PK variability. This variability poses a significant risk of therapeutic failure or adverse events, which are major challenges in VRC therapy. Understanding the underlying processes and sources of these variabilities is essentialfor safe and effective therapy. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) modelling framework that elucidates the complex metabolism of VRC and the impact of its metabolites, NO and OHVRC, on the PK of the parent, leveraging both in vitro and in vivo clinical data in a middle-out approach. A coupled parent-metabolite PBPK model for VRC, NO and OHVRC was developed in a stepwise manner using PK-Sim® and MoBi®. Based on available in vitro data, NO formation was assumed to be mediated by CYP2C19, CYP3A4, and CYP2C9, while OHVRC formation was attributed solely to CYP3A4. Both metabolites were assumed to be excreted via renal clearance, with hepatic elimination also considered for NO. Inhibition functions were implemented to describe the complex interaction network of VRC autoinhibition and metabolite-mediated inhibition on each CYP enzyme. Using a combined bottom-up and middle-out approach, incorporating data from multiple clinical studies and existing literature, the model accurately predicted plasma concentration-time profiles across various intravenous dosing regimens in healthy adults, of different CYP2C19 genotype-predicted phenotypes. All (100%) of the predicted area under the concentration-time curve (AUC) and 94% of maximum concentration (Cmax) values of VRC met the 1.25-fold acceptance criterion, with overall absolute average fold errors of 1.12 and 1.14, respectively. Furthermore, all predicted AUC and Cmax values of NO and OHVRC met the twofold acceptance criterion. This comprehensive parent-metabolite PBPK model of VRC quantitatively elucidated the complex metabolism of the drug and emphasised the substantial impact of the primary metabolites on VRC PK. The comprehensive approachcombining bottom-up and middle-out modelling, therebyaccounting for VRC autoinhibition, metabolite-mediated inhibition, and the impact of CYP2C19 genetic polymorphisms, enhances our understanding of VRC PK. Moreover, the model can be pivotal in designing further in vitro experiments, ultimately allowing for extrapolation to paediatric populations, enhance treatment individualisation and improve clinical outcomes.

Heterotropic Allosteric Modulation of CYP3A4 In Vitro by Progesterone: Evidence for Improvement in Prediction of Time Dependent Inhibition for Macrolides.

Predictions of drug-drug interactions resulting from time-dependent inhibition (TDI) of CYP3A4 have consistently overestimated or mis-predicted (i.e. false positives) the interaction that is observed in vivo. Recent findings demonstrated that the presence of the allosteric modulator progesterone (PGS) in the in vitro assay could alter the in vitro kinetics of CYP3A4 TDI with inhibitors that interact with the heme moiety, such as metabolic-intermediate complex (MIC) forming inhibitors. The impact of the presence of 100 µM PGS on the TDI of molecules in the class of macrolides typically associated with MIC formation was investigated. Presence of PGS resulted in varied responses across the inhibitors tested. The TDI signal was eliminated for five inhibitors, and unaltered in the case of one, fidaxomicin. The remaining molecules erythromycin, clarithromycin, and troleandomycin, were observed to have a decrease in both potency and maximum inactivation rate ranging from 1.7-fold to 6.7-fold. These changes in TDI kinetics led to a >90% decrease in inactivation efficiency. In order to determine in vitro conditions that could reproduce in vivo inhibition, varied concentrations of PGS were incubated with clarithromycin and erythromycin. Resulting in vitro TDI kinetics were incorporated into dynamic physiologically-based pharmacokinetic (PBPK) models to predict clinically observed interactions. The results suggested that a concentration of ~45 µM PGS would result in TDI kinetic values that could reproduce in vivo observations and could potentially improve predictions for CYP3A4 TDI. Significance Statement The impact of the allosteric heterotropic modulator progesterone on the CYP3A4 time-dependent inhibition kinetics was quantified for a set of metabolic-intermediate complex forming mechanism-based inhibitors. We identify the in vitro conditions that optimally predict time-dependent inhibition for in vivo drug-drug interactions through dynamic physiologically-based pharmacokinetic modeling. The optimized assay conditions improve in vitro to in vivo translation and prediction of time-dependent inhibition.

Uncovering the Impact of COVID-19 Mediated Bidirectional Dysregulation of CYP3A4 on Systemic and Pulmonary Drug Concentrations Using Physiologically Based Pharmacokinetic Modeling.

Several clinical studies have shown that COVID-19 increases the systemic concentration of drugs in hospitalized COVID-19 patients. However, it is unclear how COVID-19-mediated bidirectional dysregulation of hepatic and pulmonary CYP3A4 impacts drug concentrations, especially in the lung tissue which is most affected by the disease. Herein, PBPK modeling was used to demonstrate the differences in systemic and pulmonary concentrations of four respiratory infectious disease drugs when CYP3A4 is concurrently downregulated in the liver and upregulated in the lung based on existing clinical data on COVID-19 - CYP3A4 interactions at varying severity levels including outpatients, non-ICU, and ICU patients. The study showed that hepatic metabolism is the primary determinant of both systemic and pulmonary drug concentrations despite the concurrent bidirectional dysregulation of liver and lung CYP3A4. ICU patients had the most systemic and pulmonary drug exposure with a percentage increase in AUCplasma of approximately 44%, 56%, 114%, and 196% for clarithromycin, nirmatrelvir, dexamethasone, and itraconazole, respectively, relative to the healthy group. Within the ICU cohort, clarithromycin exhibited its highest exposure in lung tissue mass with a fold change of 1189, while nirmatrelvir and dexamethasone showed their highest exposure in the plasma compartment, with fold changes of about 126 and 5, respectively, compared to the maximum therapeutic concentrations for their target pathogens. Itraconazole was significantly under-exposed in the lung fluid compartment potentially explaining its limited efficacy for the treatment of COVID-19. These findings underscore the importance of optimizing dosing regimens in at risk ICU patients to enhance both efficacy and safety profiles. Significance Statement This study investigated whether COVID-19-mediated concurrent hepatic downregulation and pulmonary upregulation of CYP3A4 leads to differences in the systemic and pulmonary concentrations of four respiratory medicines. The study demonstrated that intercompartmental differences in drug concentrations were driven by only hepatic CYP3A4 expression. This work suggests that ICU patients with significant COVID-19 - CYP3A4 interactions may be at risk of clinically relevant COVID-19-drug interactions, highlighting the need for optimizing dosing regimens in this patient group to improve safety and efficacy.

Informing the risk assessment related to lactation and drug exposure: A physiologically based pharmacokinetic lactation model for pregabalin.

Breastfeeding is important in childhood development, and medications are often necessary for lactating individuals, yet information on the potential risk of infant drug exposure through human milk is limited. Establishing a lactation modeling framework can advance our understanding of this topic and potentiate clinical decision making. We expanded the modeling framework previously developed for sotalol using pregabalin as a second prototypical probe compound with similar absorption, distribution, metabolism, and elimination (ADME) properties. Adult oral models were developed in PK-Sim® and used to build a lactation model in MoBi® to simulate drug transfer into human milk. The adult model was applied to breastfeeding pediatrics (ages 1 to 23 months) and subsequently integrated with the lactation model to simulate infant drug exposure according to age, size, and breastfeeding frequency. Physiologically based pharmacokinetic (PBPK) model simulations captured the data used for verification both in adults and pediatrics. Lactation simulations captured observed milk and plasma data corresponding to doses of 150 mg administered twice daily to lactating individuals, and estimated a relative infant dose (RID) of approximately 7% of the maternal dose. The infant drug exposure simulations showed peak plasma concentrations of 0.44 μg/mL occurring within the first 2 weeks of life, followed by gradual decline with age after week four. The modeling framework performs well for this second prototypical drug and warrants expansion to other drugs for further validation. PBPK modeling and simulation approaches together with clinical lactation data could ultimately help inform infant drug exposure risk assessments to guide clinical decision making.

Establishing a physiologically based pharmacokinetic framework for aldehyde oxidase and dual aldehyde oxidase-CYP substrates.

Aldehyde oxidase (AO) contributes to the clearance of many approved and investigational small molecule drugs, which are often dual substrates of AO and drug-metabolizing enzymes such as cytochrome P450s (CYPs). As such, the lack of established framework for quantitative translation of the clinical pharmacologic correlates of AO-mediated clearance represents an unmet need. This study aimed to evaluate the utility of physiologically based pharmacokinetic (PBPK) modeling in the development of AO and dual AO-CYP substrates. PBPK models were developed for capmatinib, idelalisib, lenvatinib, zaleplon, ziprasidone, and zoniporide, incorporating invitro functional data from human liver subcellular fractions and human hepatocytes. Prediction of metabolic elimination with/without the additional empirical scaling factors (ESFs) was assessed. Clinical pharmacokinetics, human mass balance, and drug-drug interaction (DDI) studies with CYP3A4 modulators, where available, were used to refine/verify the models. Due to the lack of clinically significant AO-DDIs with known AO inhibitors, the fraction metabolized by AO (fmAO) was verified indirectly. Clearance predictions were improved by using ESFs (GMFE ≤1.4-fold versus up to fivefold with physiologically-based scaling only). Observed fmi from mass balance studies were crucial for model verification/refinement, as illustrated by capmatinib, where the fmAO (40%) was otherwise underpredicted up to fourfold. Subsequently, independent DDI studies with ketoconazole, itraconazole, rifampicin, and carbamazepine verified the fmCYP3A4, with predicted ratios of the area under the concentration-time curve (AUCR) within 1.5-fold of the observations. In conclusion, this study provides a novel PBPK-based framework for predicting AO-mediated pharmacokinetics and quantitative assessment of clinical DDI risks for dual AO-CYP substrates within a totality-of-evidence approach.

In silico molecular targets, docking, dynamics simulation and physiologically based pharmacokinetics modeling of oritavancin

IntroductionOritavancin is a semi-synthetic lipoglycopeptide antibiotic primarily used to treat serious infections caused by Gram-positive bacteria. The aim of this study was to elucidate possible molecular targets of oritavancin in human and microbes in relevance to its mechanism of action and model its pharmacokinetics for optimal dose selection in clinical practice.MethodsComputational methods were used in this study which include target prediction, molecular docking, molecular dynamics simulation, pharmacokinetics prediction, and physiological-based pharmacokinetics (PBPK) modeling.ResultsOritavancin was moderately soluble in water and did not permeate the blood-brain barrier. Seven molecular targets were identified in humans. Molecular docking results showed highest binding affinity of oritavancin with PI3-kinase p110-gamma subunit (−10.34 kcal/mol), followed by Acyl-CoA desaturase (−10.07 kcal/mol) and Cytochrome P450 2C19 (−8.384 kcal/mol). Oritavancin PBPK modelling in adult human showed that infusion has lower peak concentrations (Cmax) compared to bolus administration, with 1200 mg dose yielded Cmax of 16.559 mg/L, 800 mg dose yielded Cmax of 11.258 mg/L, and 200 mg over 3 days dose yielded Cmax of 7.526 mg/L. Notably, infusion gave extended half-life (t1/2) for all doses and slightly higher clearance rates compared to bolus, particularly for the 1200 mg and 800 mg doses. The results corroborated existing clinical pharmacokinetic data, and confirmed the model’s accuracy and predictive capability.ConclusionThis comprehensive computational study has provided invaluable insights into the pharmacological profile of Oritavancin, aiding its further development and optimization for clinical use.

Absolute Membrane Protein Abundance of P-gp, BCRP and MRPs in Term Human Placenta Tissue and Commonly Used Cell Systems: Application in PBPK Modeling of Placental Drug Disposition.

The placenta acts as a barrier, excluding noxious substances whilst actively transferring nutrients to the fetus, mediated by various transporters. This study quantified the expression of key placental transporters in term human placenta (n=5) and BeWo, BeWo b30, and JEG-3 placenta cell lines. Combining these results with pregnancy physiologically-based pharmacokinetic (PBPK) modeling, we demonstrate the utility of proteomic analysis for predicting placental drug disposition and fetal exposure. Using targeted proteomics with QconCAT standards, we found significant expression of P-gp, BCRP, MRP2, MRP4, and MRP6 in the human placenta (0.05 - 0.25 pmol/mg membrane protein) with only regional differences observed for P-gp. Unexpectedly, both P-gp and BCRP were below the limit of quantification in the regularly used BeWo cells, indicating that this cell line may not be suitable for the study of placental P-gp and BCRP-mediated transport. In cellular and vesicular overexpression systems, P-gp and BCRP were detectable as expected. Vesicle batches showed consistent P-gp expression correlating with functional activity (N-methyl-quinidine (NMQ) transport). However, BCRP activity (Estrone 3-sulfate (E1S) transport) did not consistently align with expression levels. Incorporating in vitro transporter kinetic data, along with placental transporter abundance, into a PBPK model enabled the evaluation of fetal exposure. Simulation with a hypothetical drug indicated that estimating fetal exposure relies on the intrinsic clearances of relevant transporters. To minimize interlaboratory discrepancies, expression data was generated using consistent proteomic methodologies in the same lab. Integration of this data in pregnancy-PBPK modeling offers a promising tool to investigate maternal, placental and fetal drug exposure. Significance Statement This study quantified the expression of key transporters in human placenta and various placental cell lines, revealing significant expression variations. By integrating these data with PBPK modeling, the study highlights the importance of transporter abundance data in understanding and predicting placental drug disposition.

Towards the understanding of the IVPT results variability—Development, verification and validation of the PBPK model of caffeine in vitro human skin permeation

In the context of evaluating the safety and efficacy of dermal products, pharmacokinetic (PK) studies face considerable challenges, particularly concerning topically applied formulations. This underscores the necessity for alternative methods, such as in vitro permeation tests (IVPT) and physiologically based pharmacokinetic (PBPK) modelling, to better understand the dermal pharmacokinetics of a product. The purpose of this study was to modify, verify, and validate the PBPK model of caffeine permeation through human skin previously developed by Patel et al. (2022), and compare simulation results with experimental data from IVPT studies. Moreover, the study aimed to analyse the IVPT data variability and explore the potential of using the PBPK model to understand the influence of biological and drug-related factors on the IVPT results. In total, eight manuscripts describing nine experiments were included. The overall shapes of the permeation curves were considered acceptable based on visual checks for all analysed experiments. Five out of nine experiments met the predefined standard 2-fold difference criterion for comparison of the cumulative amount of caffeine in the receptor solution.. Our investigation highlights challenges in validating PBPK models for IVPT experiments, as the quality and consistency of experimental results pose significant hurdles. Despite access to data on caffeine permeation in scientific literature, reliable model validation is currently infeasible. Inter-laboratory variation suggests that alternative validation methods may be needed. Further studies should focus on issues with other compounds, especially lipophilic ones.

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.

Predicting Food–Drug Interactions between Piperine and CYP3A4 Substrate Drugs Using PBPK Modeling

Predicting Food–Drug Interactions between Piperine and CYP3A4 Substrate Drugs Using PBPK Modeling

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.

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.

The development of drug resistance in metastatic tumours under chemotherapy: An evolutionary perspective

We present a mathematical model of the evolutionary dynamics of a metastatic tumour under chemotherapy, comprising non-local partial differential equations for the phenotype-structured cell populations in the primary tumour and its metastasis. These equations are coupled with a physiologically-based pharmacokinetic model of drug administration and distribution, implementing a realistic delivery schedule. The model is carefully calibrated from the literature, focusing on BRAF-mutated melanoma treated with Dabrafenib as a case study. By means of long-time asymptotic and global sensitivity analyses, as well as numerical simulations, we explore the impact of cell migration from the primary to the metastatic site, physiological aspects of the tumour tissues and drug dose on the development of chemoresistance and treatment efficacy. Our findings provide a possible explanation for empirical evidence indicating that chemotherapy may foster metastatic spread and that metastases may be less impacted by the chemotherapeutic agent.

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.

Physiology-based pharmacokinetic model with relative transcriptomics to evaluate tissue distribution and receptor occupancy of anifrolumab.

Type I interferons contribute to the pathogenesis of several autoimmune disorders, including systemic lupus erythematosus (SLE), systemic sclerosis, cutaneous lupus erythematosus, and myositis. Anifrolumab is a monoclonal antibody that binds to subunit 1 of the type I interferon receptor (IFNAR1). Results of phase IIb and phase III trials led to the approval of intravenous anifrolumab 300 mg every 4 weeks (Q4W) alongside standard therapy in patients with moderate-to-severe SLE. Here, we built a population physiology-based pharmacokinetic (PBPK) model of anifrolumab by utilizing the physiochemical properties of anifrolumab, binding kinetics to the Fc gamma neonatal receptor, and target-mediated drug disposition properties. A novel relative transcriptomics approach was employed to determine IFNAR1 expression in tissues (blood, skin, gastrointestinal tract, lungs, and muscle) using mRNA abundances from bioinformatic databases. The IFNAR1 expression and PBPK model were validated by testing their ability to predict clinical pharmacokinetics over a large dose range from different clinical scenarios after subcutaneous and intravenous anifrolumab dosing. The validated PBPK model predicted high unbound local concentrations of anifrolumab in blood, skin, gastrointestinal tract, lungs, and muscle, which exceeded its IFNAR1 dissociation equilibrium constant values. The model also predicted high IFNAR1 occupancy with subcutaneous and intravenous anifrolumab dosing. The model predicted more sustained IFNAR1 occupancy ≥90% with subcutaneous anifrolumab 120 mg once-weekly dosing vs. intravenous 300 mg Q4W dosing. The results informed the dosing of phase III studies of anifrolumab in new indications and present a novel approach to PBPK modeling coupled with relative transcriptomics in simulating pharmacokinetics of therapeutic monoclonal antibodies.

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.