Physiologically Based Pharmacokinetic Research Articles

Erectile Dysfunction Therapy of Bariatric Patients: Tadalafil Biopharmaceutics and Pharmacokinetics Before vs. After Gastric Sleeve/Bypass.

Bariatric surgery introduces significant changes in the gastrointestinal tract, which may affect oral drug absorption/bioavailability. Here we investigate the phosphodiesterase-5 inhibitor (PDE5i) tadalafil for potentially impaired post-bariatric solubility/dissolution and absorption. Solubility was studied in vitro in different pHs, and ex vivoin gastric content aspirated from patients pre/post-surgery. Dissolution was studied in conditions mimicking pre/post-surgery stomach. Finally, the experimental data were used in physiologically-based pharmacokinetic (PBPK) model (GastroPlus®) to simulate pre- vs. post-surgery tadalafil PK. Tadalafil demonstrated low and pH-independent solubility, both in vitro and ex vivo. Tadalafil release from all drug products and under all gastric conditions was incomplete, with particularly poor dissolution (2%) of the highest dose under post-bariatric conditions. PBPK simulations revealed altered tadalafil PK after gastric bypass-but not after sleeve gastrectomy-compared to unoperated individuals, with 44-48% decreased Cmax, 35-56% decreased AUC and 44% shorter Tmax. This mechanistic analysis suggests that tadalafil may be as effective after sleeve gastrectomy as before the procedure; meanwhile, results after gastric bypass raise concerns regarding the bioperformance of the drug. In addition, the drug's duration of action may be much shorter after gastric bypass. Thus, the effectiveness of tadalafil, widely regarded as the 'weekend pill', may be shorter than expected among gastric bypass patients.

The Role of Simulation Science in Public Health at the Agency for Toxic Substances and Disease Registry: An Overview and Analysis of the Last Decade

Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure–Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR’s use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR’s work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers’ widespread appraisal and adaptation of the agency’s methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

Evaluation of Drug–Drug Interactions Between Clarithromycin and Direct Oral Anticoagulants Using Physiologically Based Pharmacokinetic Models

Objective: This study assessed the pharmacokinetic (PK) interactions between clarithromycin (a P-glycoprotein [P-gp] inhibitor) and four direct oral anticoagulants (DOACs) (P-gp substrates) using physiologically based PK (PBPK) models to elucidate the influence of P-gp in the interaction between them. Methods: PBPK models for clarithromycin, DABE–dabigatran (DAB), rivaroxaban, apixaban, and edoxaban were constructed using GastroPlus™ (version 9.9), based on physicochemical data and PK parameters from the literature. The models were optimized and validated in healthy subjects. We evaluated the predictive performance of the established model and further assessed the impact of P-gp on the PK of the four DOACs. Successfully validated models were then used to evaluate potential drug–drug interactions (DDIs) between clarithromycin and the DOACs. Results: The established PBPK models accurately described the PK of clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban. The predicted PK parameters (Cmax, Tmax, AUC0-t) were within 0.5–2 times the observed values. A sensitivity analysis of P-gp parameters indicated that an increase in P-gp expression was reduced by in vivo exposure to DOACs. The models demonstrated good predictive ability for DDIs between clarithromycin and the anticoagulants, and the ratio of the predicted values to the observed values of Cmax and the area under the curve (AUC) in the DDI state was within the range of 0.5–2. Conclusions: Comprehensive PBPK models for clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban were developed, which can effectively predict DDIs mediated by P-gp’s function. These models provide theoretical support for clinical dose adjustments and serve as a foundation for future PBPK model development for DOACs under specific pathological conditions.

FDA and EMA Oversight of Disruptive Science on Application of Finite Absorption Time (F.A.T.) Concept in Oral Drug Absorption: Time for Scientific and Regulatory Changes

Background: It has been demonstrated that the concept of infinite absorption time, associated with the absorption rate constant, which drives a drug’s gastrointestinal absorption rate, is not physiologically sound. The recent analysis of oral drug absorption data based on the finite absorption time (F.A.T.) concept and the relevant physiologically based finite-time pharmacokinetic (PBFTPK) models developed provided a better physiologically sound description of oral drug absorption. Methods: In this study, we re-analyzed, using PBFTPK models, seven data sets of ketoprofen, amplodipine, theophylline (three formulations), and two formulations (reference, test) from a levonorgestrel bioequivalence study. Equations for one-compartment-model drugs, for the estimation of fraction of dose absorbed or the bioavailable fraction exclusively from oral data, were developed. Results: In all cases, meaningful estimates for (i) the number of absorption stages, namely, one for ketoprofen and the levonorgestrel formulations, two for amlodipine, the immediate-release theophylline formulation, and the extended-release Theotrim formulation, and three for the extended-release Theodur formulation, (ii) the duration of each absorption stage and the corresponding drug input rate, and (iii) the total duration of drug absorption, which ranged from 0.75 h (ketoprofen) to 11.6 h for Theodur were derived. Estimates for the bioavailable fraction of ketoprofen and two theophylline formulations exhibiting one-compartment-model kinetics were derived. Conclusions: This study provides insights into the detailed characteristics of oral drug absorption. The use of PBFTPK models in drug absorption analysis can be leveraged as a computational framework to discontinue the perpetuation of the mathematical fallacy of classical pharmacokinetic analysis based on the absorption rate constant as well as in the physiologically based pharmacokinetic (PBPK) studies and pharmacometrics. The present study is an additional piece of evidence for the scientific and regulatory changes required to be implemented by the regulatory agencies in the not-too-distant future.

DDEL-11. PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING OF SPATIAL HETEROGENEITY OF DRUG PENETRATION AND EXPOSURE IN THE HUMAN BRAIN AND BRAIN TUMORS

Abstract BACKGROUD The difficulty in early, reliable prediction of drug penetration and exposure in the human brain and brain tumors makes development of new drugs and use of existing drugs for treating brain cancer a challenging and often unsuccessful task. We developed a physiologically based pharmacokinetic (PBPK) model platform for prediction of spatial pharmacokinetics in the central nervous system (CNS) following systemic drug administration. METHODS A 9-compartment CNS (9-CNS) PBPK model was developed to predict drug concentrations in brain blood, 2 brain parenchyma compartments (brain tissue adjacent to the CSF tract and deep brain parenchyma), 3 CSF compartments (ventricular CSF, cranial and spinal subarachnoid CSF), and 3 tumor compartments (tumor rim, bulk tumor, and tumor core). The pharmacokinetics in the CNS compartments were driven by plasma concentrations and governed by drug properties and CNS pathophysiology. The 9-CNS model was developed and validated with 4 drugs (abemaciclib, ribociclib, ceritinib, and temuterkib). RESULTS When considering tumor heterogeneity and inter-individual plasma pharmacokinetic variability following clinical standard dosing regimens, the model-predicted steady-state unbound drug tumor concentrations varied from 7.1 to 356 nM for abemaciclib, 74 to 4577 nM for ribociclib, 0.66 to 97 nM for ceritinib, and 0.8 to 288 nM for temuterkib, which were consistent with the heterogeneous drug tumor concentrations observed in glioblastoma patients. Drug penetration into the deep brain parenchyma was significantly less than that into tumors and CSF-adjacent brain tissue, particularly for strong ABCB1/ABCG2 substrate drugs. The ventricular and cranial subarachnoid CSF shared similar pharmacokinetics, while the spinal CSF pharmacokinetics was different. CONCLUSION A novel 9-CNS PBPK model was developed and validated for mechanistic prediction of spatial heterogeneity of drug penetration and exposure in the human brain, brain tumors, and CSF. It provides a valuable computational modeling tool to assist development and design of improved drugs or dosing regimens for brain cancer treatment.

Physiologically-Based Pharmacokinetic Modeling of Trofinetide in Moderate Renal Impairment for Phase 1 Clinical Study Dose Selection with Model Validation.

Trofinetide, the first approved treatment for Rett syndrome (RTT), is primarily excreted unchanged in the urine; therefore, it is important to assess the extent to which the exposure is affected in patients with renal impairment. Pharmacokinetic modeling overcomes the challenge of dose finding in phase 1 studies that include special populations where there is the potential for increased exposure to study drug. The objectives of this phase 1 study were to evaluate trofinetide pharmacokinetics, safety, and tolerability in a population with moderate renal impairment and normal renal function. The observed pharmacokinetic profiles were used to validate the dosing adjustments in moderate renal impairment that were previously predicted using a physiologically-based pharmacokinetic (PBPK) model. The PBPK model was first used to predict dose adjustments that are necessary to achieve similar exposure in the four stages of renal impairment (mild, moderate, severe, end stage renal disease) as in healthy controls. The predicted dose adjustment from 12 to 6 g for the moderate renal impairment category was then applied to the phase 1 clinical study. Subsequent validation of the PBPK model was achieved by comparing the model-predicted and clinically observed exposures in subjects with moderate renal impairment. In a phase 1, open-label study, trofinetide exposure was assessed in healthy (n = 10) and moderate renal impairment (n = 10) participants receiving single oral doses of 12 g or 6 g, respectively. Observed exposures [area under the blood concentration-time curve from time 0 to infinity (AUCinf) and maximum concentration (Cmax)] were compared with predicted exposures from simulations in virtual healthy and moderate renal impairment populations (n = 100) to validate a PBPK model of renal impairment that had previously predicted doses across renal impairment categories. Dose-normalized geometric mean ratios for Cmax were comparable [1.02 (90% CI 0.69-1.50)] while AUCinf was approximately two-fold higher [1.81 (90% CI 1.31-2.50)] in moderate renal impairment participants compared with healthy controls. These observed values closely aligned with predicted distributions. Treatment-emergent adverse events were reported in two (20.0%) participants with moderate renal impairment and four healthy participants (40.0%). PBPK modeling of trofinetide in a virtual population with moderate renal impairment predicted a 50% dose reduction compared to individuals with normal renal function. Comparison of observed pharmacokinetic results from a phase 1 study in subjects with moderate renal impairment and matched healthy participants to the model-predicted exposures validated this dose reduction. No new safety concerns for trofinetide emerged.

Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as Cmax and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two-way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratiosin drug-drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

Background/Objectives: The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations. Methods: We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA. Results: Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results. Conclusions: This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Development of a Physiologically Based Pharmacokinetic Model for Flunixin in Cattle and Swine Following Dermal Exposure.

Flunixin meglumine is a nonsteroidal anti-inflammatory drug (NSAID). Banamine® Transdermal is a pour-on formulation of flunixin approved for pain control in beef and dairy cattle, but not for calves and some classes of dairy cattle or swine. Violative flunixin residues in edible tissues in cattle and swine have been reported and are usually attributed to non-compliant drug use or failure to observe an appropriate withdrawal time. This project aimed to develop a physiologically based pharmacokinetic (PBPK) model for flunixin in cattle and swine to predict withdrawal intervals (WDI) after exposures to different therapeutic regimens of Banamine® Transdermal. Due to the lack of comprehensive skin physiological data in cattle, the model was initially developed for swine and then adapted for cattle. Monte Carlo simulation was employed for population variability analysis. The model predicted WDIs were rounded to 1 and 2 days for liver and muscle in cattle, respectively, under FDA tolerance levels, while under EU maximum residue limits (MRLs), the WDIs were rounded to 1, 3, 2, and 2 days for liver, kidney, muscle, and fat, respectively, following a labeled single transdermal 3.3 mg/kg dose in cattle. The model was converted into a user-friendly interactive PBPK (iPBPK) interface. This study reports the first transdermal absorption model for drugs in cattle. This iPBPK model provides a scientifically based tool for the prediction of WDIs in cattle and swine administered with flunixin in an extra-label manner, especially by the transdermal route.

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.

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.

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.

Changes in Plasma Clearance of CYP450 Probe Drugs May Not be Specific for Altered In Vivo Enzyme Activity Under (Patho)Physiological Conditions: How to Interpret Findings of Probe Cocktail Studies.

CYP450 (CYP) phenotyping involves quantifying an individual's plasma clearance of CYP-specific probe drugs, as a proxy for in vivo CYP enzyme activity. It is increasingly applied to study alterations in CYP enzyme activity under various (patho)physiological conditions, such as inflammation, obesity, or pregnancy. The phenotyping approach assumes that changes in plasma clearance of probe drugs are driven by changes in CYP enzyme activity. However, plasma clearance is also influenced by protein binding, blood-to-plasma ratio, and hepatic blood flow, all of which may change under (patho)physiological conditions. Using a physiologically based pharmacokinetic (PBPK) workflow, we aimed to evaluate whether the plasma clearance of commonly used CYP probe drugs is indeed directly proportional to alterations in CYP enzyme activity (sensitivity), and to what extent alterations in protein binding, blood-to-plasma ratio, and hepatic blood flow observed under (patho)physiological conditions impact plasma clearance (specificity). Plasma clearance of CYP probe drugs is sensitive to alterations in CYP enzyme activity, since alterations in intrinsic clearance between - 90% and + 150% resulted in near-proportional changes in plasma clearance, except for midazolam in the case of > 50% CYP3A4 induction. However, plasma clearance also changed near-proportionally with alterations in the unbound drug fraction, diminishing probe specificity. This was particularly relevant for high protein-bound probe drugs, as alterations in plasma protein binding resulted in larger relative changes in the unbound drug fraction. Alterations in the blood-to-plasma ratio and hepatic blood flow of ± 50% resulted in plasma clearance changes of less than ± 16%, meaning they limitedly impacted plasma clearance of CYP probe drugs, except for midazolam. In order to correct for the impact of non-metabolic determinants on probe drug plasma clearance, an R script was developed to calculate how much the CYP enzyme activity is actually altered under (patho)physiological conditions, when alterations in the unbound drug fraction, blood-to-plasma ratio, and/or hepatic blood flow also impact probe drug plasma clearance. As plasma protein binding can change under (patho)physiological conditions, alterations in unbound drug fraction should be accounted for when using CYP probe drug plasma clearance as a proxy for CYP enzyme activity in patient populations. The tool developed in this study can support researchers in determining alterations in CYP enzyme activity in patients with (patho)physiological conditions.

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.

An IQ Industry Perspective on Informing Dosing Recommendations in Patients With Renal Impairment.

The IQ CPLG Organ Impairment WG conducted a survey to understand how various IQ member companies develop dosing recommendations for patients with renal impairment. Eighteen IQ member companies participated in the survey. The survey results were summarized by the working group in light of the regulatory renal impairment guidance documents as well as recent publications from the pharmaceutical industry and nephrology community. There were two important learnings from the survey: (i) pharmaceutical companies do not use a consistent methodology to assess renal function in their drug development programs. (ii) there has been significant improvement in predicting how kidney function affects drug pharmacokinetics (PK) and thus dose recommendations. Applying model-based methods such as population PK, physiologically-based PK (PBPK), and virtual controls has enabled earlier prediction of how kidney function influences PK, leading to the participation of more patients with impaired kidney function in Phase 2 and 3 trials.

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.

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.

Mechanistic Model for Drug Release from PLGA-Based Biodegradable Implants for In Vitro Release Testing: Development and Validation.

Several factors can affect drug release from polylactide coglycolide (PLGA)-based formulations, including polymer and drug properties, formulation components, manufacturing processes, and environmental in vitro or in vivo conditions. To achieve optimal release profiles for specific drug delivery applications, it is crucial to understand the mechanistic processes that determine drug release from PLGA-based formulations. In the current study, we developed a mechanistic model for the in vitro drug release of PLGA-based solid implants. The model accounts for all known critical quality attributes (CQAs) and considers the most important release rate processes, including water or dissolution medium influx into the porous structure of the implant, initial noncatalytic hydrolysis of PLGA, autocatalytic hydrolysis, dissolution of oligomers and monomers into the aqueous medium, the liberation of the trapped solid drug from the polymer matrix, dissolution of the solid drug into the wetted pore network, diffusion of the dissolved drug out of the implant, and distribution of the dissolved drug into the dissolution medium. The model has been validated using in vitro release data obtained from implants of four drugs (buserelin, afamelanotide, brimonidine, and nafarelin). The model presented in this manuscript provides valuable insights into the kinetics and mechanism of drug release from PLGA-based solid implants and has demonstrated the potential for optimizing formulation design. The in vitro release model, coupled with physiologically based pharmacokinetic (PBPK) modeling, can predict the in vivo performance of implants and can be used to support bioequivalence studies in a drug development program.

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.