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Background/Objectives: Etrasimod is a newly FDA-approved Sphingosine-1-Phosphate modulator indicated for moderate and severe ulcerative colitis. It is extensively metabolized in the liver via the cytochrome P450 system and may accumulate markedly in patients with hepatic dysfunction, exposing them to toxicity. The aim of the current study is to utilize a physiologically-based pharmacokinetic modeling approach to evaluate the impact of hepatic impairment on the pharmacokinetic behavior of etrasimod and to appropriately select dosage regimens for patients with chronic liver disease; Methods: PK-Sim was used to develop the etrasimod PBPK model, which was verified using clinical data from healthy subjects and subsequently adapted to reflect the physiological changes associated with varying degrees of hepatic dysfunction; Results: Simulations indicated that hepatic clearance of etrasimod is clearly reduced in patients with Child–Pugh B and C liver impairment. Based on these findings, dosing adjustments were proposed to achieve therapeutic exposures equivalent to those in individuals with normal liver function. In the Child–Pugh B and C population groups, 75% and 62.5%, respectively, of the standard dose were enough to have comparable exposure to the healthy population. These adjusted dosages aim to mitigate the risk of drug toxicity while maintaining efficacy; Conclusions: The PBPK model provides a robust framework for individualizing drug therapy in patients with hepatic impairment, ensuring safer and more effective treatment outcomes. Further clinical studies are warranted to verify these dosing recommendations and to refine the model for broader clinical applications. [ABSTRACT FROM AUTHOR]

Background: The development of long-acting products of a characterized drug substance is of great interest. It is possible to support the development of these products with available clinical data by matching the exposure to a predefined bracket of a minimal concentration for efficacy and a maximal concentration for safety. This bracketing approach would cut down on the time and cost of new long-acting contraceptive products progressing to market. The current study describes the assessment of the data available to support a bracketing approach to conclude comparable levels of efficacy and safety for a postulated novel long-acting reversible contraceptive (LARC) product of levonorgestrel. Methods: Literature evidence of levonorgestrel efficacy, as quantified by the Pearl Index, was utilized and modeled by incorporating three LARC products for the estimation of a minimal concentration required for efficacy. Further literature was reviewed to quantify the maximal concentration required to ensure product safety. Additionally, a review of the regulatory precedence for the approach was conducted using European and UK databases. Results: There was a reasonable definition of the minimal concentrations for efficacy where the target concentrations of levonorgestrel were in the range of 200–400 pg/mL. Maximum concentrations for safety were less well defined. Although regulatory guidance supports the bracketing approach, there is little precedence for licensing new products based on pharmacokinetic data only, despite much reduced clinical and non-clinical packages being evidenced. Conclusions: Understanding of the exposure response is not currently considered sufficient to support a bracketing approach for a new levonorgestrel product. If additional safety data are established, current regulations may allow for a reduced application package. Additional work is needed to support the approach, and this could utilize the wealth of information in real-world datasets combined with systems models. [ABSTRACT FROM AUTHOR]

A novel dual PI3K α/δ inhibitor, TQ‐B3525, has been developed for the targeted treatment of lymphoma and solid tumors. TQ‐B3525 is primarily metabolized by CYP3A4 and FOM3, while also serving as a substrate for the P‐glycoprotein transporter. The aim of this study was to anticipate the drug–drug interaction (DDI) of TQ‐B3525 and its two metabolites with CYP3A4 enzyme potent inducer (rifampicin) and CYP3A4/P‐gp inhibitor (itraconazole) utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Clinical data from healthy and cancer patient adults were employed to construct and evaluate the PBPK model for TQ‐B3525, M3, and M8‐3. Models involving rifampicin combined with midazolam, itraconazole combined with midazolam or digoxin were utilized to showcase the robustness of evaluating DDI effects. The simulated drug exposure of TQ‐B3525, M3, and M8‐3 in healthy and patient adults were consistent with clinical data, and the mean fold error values were within the acceptable ranges. The simulated results of positive substrates correspond to those reported in the literature. Co‐administration with rifampicin reduces Cmax and AUC of TQ‐B3525 to 76.1% and 46.0%, while increasing the levels of M3 and M8‐3. With itraconazole, Cmax and AUC of TQ‐B3525 rise to 131% and 204%, but decrease substantially for M3 and M8‐3. PBPK model simulation results showed that the systemic exposure of TQ‐B3525 was significantly affected when co‐administered with CYP3A4/P‐gp inducers and inhibitors. This indicates that the combination with strong inducers and inhibitors should be carefully avoided or adjust the dosage of TQ‐B3525 in clinic. [ABSTRACT FROM AUTHOR]

A basic FcRn-regulated clearance mechanism is investigated using the method of matched asymptotic expansions. The broader aim of the work is to obtain further insight on the mechanism, thereby providing theoretical support for future pharmacologically-based pharmacokinetic modelling efforts. The corresponding governing equations are first non-dimensionalised and the order of magnitudes of the model parameters are assessed based on their values reported in the literature. Under the assumption of high FcRn-binding affinity, analytical approximations are derived that are valid over the characteristic phases of the problem. Additionally, relatively simple equations relating clearance and AUC to physiological model parameters are derived, which are valid over the longest characteristic time scale of the problem. For lower to moderate doses clearance is effectively linear, whereas for higher doses it is nonlinear. It is shown that for all doses sufficiently high the leading-order approximation for the IgG concentration in plasma, over the longest characteristic time scale, is independent of the initial dose. This is because IgG that is in 'excess' of FcRn is eliminated over a time scale much shorter than that of the terminal phase. In conclusion, analytical approximations of the basic FcRn mechanism have been derived using matched asymptotic expansions, leading to a simple equation relating clearance to FcRn binding affinity, the ratio of degradation and FcRn concentration, and the volumes of the system. [ABSTRACT FROM AUTHOR]

Introduction: All cosmetic ingredients must be evaluated for their safety to consumers. In the absence of in vivo data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients. We used a bottom-up approach to develop human PBPK models for genistein and daidzein for a read-across NGRA, whereby genistein was the source chemical for the target chemical, daidzein. Methods: An oral rat PBPK model for genistein was built using PK-Sim® and in vitro ADME input data. This formed the basis of the daidzein oral rat PBPK model, for which chemical-specific input parameters were used. Rat PBPK models were then converted to human models using human-specific physiological parameters and human in vitro ADME data. In vitro skin metabolism and penetration data were used to build the dermal module to represent the major route of exposure to cosmetics. Results: The initial oral rat model for genistein was qualified since it predicted values within 2-fold of measured in vivo PK values. This was used to predict plasma concentrations from the in vivo NOAEL for genistein to set test concentrations in bioassays. Intrinsic hepatic clearance and unbound fractions in plasma were identified as sensitive parameters impacting the predicted Cmax values. Sensitivity and uncertainty analyses indicated the developed PBPK models had a moderate level of confidence. An important aspect of the development of the dermal module was the implementation of first-pass metabolism, which was extensive for both chemicals. The final human PBPK model for daidzein was used to convert the in vitro PoD of 33 nM (from an estrogen receptor transactivation assay) to an external dose of 0.2% in a body lotion formulation. Conclusion: PBPK models for genistein and daidzein were developed as a central component of an NGRA read-across case study. This will help to gain regulatory confidence in the use of PBPK models, especially for cosmetic ingredients. [ABSTRACT FROM AUTHOR]

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all coauthors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each coauthor independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: (1) transporter structure and in vitro evaluation, (2) novel in vitro/ex vivo models, (3) endogenous biomarkers, and (4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fengjiao Bu,1,2,* Yong-Soon Cho,3,4,* Qingfeng He,1 Xiaowen Wang,1 Saurav Howlader,3,4 Dong-Hyun Kim,3,4 Mingshe Zhu,5 Jae Gook Shin,3,4 Xiaoqiang Xiang1,6 1Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China; 2Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, People’s Republic of China; 3Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; 4Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; 5Department of DMPK, MassDefect Technologies, Princeton, NJ, USA; 6Department of Preclinical Evaluation, Quzhou Fudan Institute, Quzhou, Zhejiang Province, 324002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jae Gook Shin; Xiaoqiang Xiang, Email phshinjg@gmail.com; xiangxq@fudan.edu.cnBackground: 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.Methods: 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.Results: 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.Conclusion: 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. Keywords: anlotinib, DDIs, CYP3A, CYP1A2, PBPK

Introduction. Deferasirox is a complexing drug and belongs to class II according to the biopharmaceutical classification system (BCS), has acidic properties and belongs to subclass “a” (acid). This class is characterized by high permeability and low solubility, which limits the absorption of the active substance into the blood. As a result, the development of drugs with an active substance that can be assigning BCS to this class is a difficult task, and for generic drugs it is also associated with a high risk of obtaining unproven equivalence during clinical trials. To minimize the above risks, a physiologically relevant test was carried out with further data processing and construction of putative pharmacokinetic profiles.Aim. The aim of the study is to conduct a physiologically relevant test (PRT) to predict in vitro pharmacokinetic profiles and compare them with in vivo data as part of a bioequivalence study of deferasirox.Materials and methods. The objects of the study are "Deferasirox, film-coated tablets, 360 mg" of a domestic manufacturer and "Jadenu®, film-coated tablets, 360 mg" (WTN22 series, expiration date until 10.2023, Novartis Pharma Stein AG, Switzerland). A physiologically relevant test was performed on the device SC PRT-6, Compliance. Quantitative analysis was carried out by HPLC-UV method.Pharmacokinetic profiles were modeled using PK-Sim® (Systems Biology Software Suite 11.2, Bayer Technology Services GmbH, Germany) based on data obtained from physiologically relevant test.Results and discussion. A physiologically relevant drug test for deferasirox was performed and release profiles were obtained, which formed the basis of a physiologically based pharmacokinetic model together with data on the physicochemical properties of the studied compound and literature data on the pharmacokinetics of deferasirox. The pharmacokinetic profiles obtained as part of the simulation on a virtual population were compared with data obtained during clinical trials.Conclusion. A physiologically relevant test for the drug deferasirox was carried out, quantitative determination in the samples was carried out by HPLC-UV. The test resulted in data that allowed prediction of pharmacokinetic profiles that reflected the same differences observed in the profiles of the test and reference drug in the comparative pharmacokinetics and bioequivalence study of deferasirox drugs.

Introduction. Sorafenib is an antineoplastic drug belonging to class IIc according to the biopharmaceutical classification system (BCS) due to the presence of both acidic and basic properties. In addition to low solubility, sorafenib is characterized by high variability during clinical trials, in particular bioequivalence studies (BE). To selecting batches that can be recommended for BE studies, the dissolution kinetics test is currently widely used, however, the results of this test are not always sufficient and additional tests, for example, a physiologically relevant test, are advisable. To minimize the risks of obtaining nonequivalent results during the BE study, a physiologically relevant test (PRT) was carried out with further data processing and interpretation of the results of physiologically based pharmacokinetic modeling (PBPK).Aim. The aim of the study is to conduct a physiologically relevant test (PRT) for the purpose of selecting a candidate batch for subsequent BE study of sorafenib drugs using the physiologically based pharmacokinetic model (PBPK).Materials and methods. The objects of the study are Nexavar®, film-coated tablets, 200 mg (Bayer AG, Germany) (one batch) and Sorafenib, film-coated tablets, 200 mg (two batches) (Russia). The physiologically relevant test was performed on the SC PRT-6 device (LLC "Scientific Compliance", Russia). Quantitative analysis was performed by HPLC-UV on the Chromatec-Crystal HPLC 2014 device (CJSC "Chromatec", Russia). The plasma concentration profiles were simulated using PK-Sim® software (Systems Biology Software Suite 11.2, Bayer Technology Services GmbH, Germany).Results and discussion. As part of the study, a method for the quantitative determination of sorafenib was developed and validated, a method for sample preparation was developed, and a method for conducting the PRT for sorafenib, as a representative of the IIc subclass of BCS, was developed. Based on the study results, release profiles were obtained that were used to select a candidate series for the BE study. The series were selected based on the PBPK analysis on a virtual population consisting of 36 healthy volunteers with activated enteropathic circulation, characteristic of sorafenib.Conclusion. The PRT was carried out for the drug sorafenib. Quantitative determination was carried out by HPLC-UV according to the developed and validated method. The test resulted in obtaining data that were subjected to PBPK analysis. It was shown that the studied batches have high risks of non-equivalence during the bioequivalence study.

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. [ABSTRACT FROM AUTHOR]

Protein therapeutics have revolutionized the treatment of a wide range of diseases. While they have distinct physicochemical characteristics that influence their absorption, distribution, metabolism, and excretion (ADME) properties, the relationship between the physicochemical properties and PK is still largely unknown. In this work we present a minimal physiologically-based pharmacokinetic (mPBPK) model that incorporates a multivariate quantitative relation between a therapeutic's physicochemical parameters and its corresponding ADME properties. The model's compound-specific input includes molecular weight, molecular size (Stoke's radius), molecular charge, binding affinity to FcRn, and specific antigen affinity. Through derived and fitted empirical relationships, the model demonstrates the effect of these compound-specific properties on antibody disposition in both plasma and peripheral tissues using observed PK data in mice and humans. The mPBPK model applies the two-pore hypothesis to predict size-based clearance and exposure of full-length antibodies (150 kDa) and antibody fragments (50–100 kDa) within a onefold error. We quantitatively relate antibody charge and PK parameters like uptake rate, non-specific binding affinity, and volume of distribution to capture the relatively faster clearance of positively charged mAb as compared to negatively charged mAb. The model predicts the terminal plasma clearance of slightly positively and negatively charged antibody in humans within a onefold error. The mPBPK model presented in this work can be used to predict the target-mediated disposition of a drug when compound-specific and target-specific properties are known. To our knowledge, a combined effect of antibody weight, size, charge, FcRn, and antigen has not been incorporated and studied in a single mPBPK model previously. By conclusively incorporating and relating a multitude of protein's physicochemical properties to observed PK, our mPBPK model aims to contribute as a platform approach in the early stages of drug development where many of these properties can be optimized to improve a molecule's PK and ultimately its efficacy. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease psychosis (PDP) is a common and distressing complication of Parkinson's disease (PD), characterized by hallucinations and delusions. This research aimed to assess the pharmacokinetics and safety of NH130, a selective serotonin 5-HT2A inverse agonist, as a potential PDP treatment in healthy individuals. Methods: We conducted clinical pharmacokinetic studies and safety evaluations for NH130, employing a physiologically based pharmacokinetic (PBPK) model to predict its behavior in human body. Results: In a single-dose escalation study, healthy volunteers received NH130 at varying doses (2 mg, 6 mg, 12 mg, 24 mg, 40 mg, 60 mg, and 90 mg) or a placebo. The drug demonstrated favorable pharmacokinetics, with no serious adverse events (AEs) reported. Clinical plasma concentrations correlated well with PBPK model predictions, validating the model's utility for guiding future clinical development. Conclusion: NH130 showed promising pharmacokinetic characteristics and safety profile, supporting its progression to multi-dose trials and suggesting its potential as a therapeutic agent for PDP. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) modelling is an important tool to predict drug disposition in the body. Rabbits play a pivotal role as a highly valued small animal model, particularly in the field of ocular therapeutics, where they serve as a crucial link between preclinical research and clinical applications. In this context, we have developed PBPK models designed specifically for rabbits, with a focus on accurately predicting the pharmacokinetic profiles of protein therapeutics following intravenous administration. Our goal was to comprehend the influence of key physiological factors on systemic disposition of antibodies and their functional derivatives. For the development of the systemic PBPK models, rabbit physiological factors such as gene expression, body weight, neonatal fragment crystallizable receptor (FcRn) binding, target binding, target concentrations, and target turnover rate were meticulously considered. Additionally, key protein parameters, encompassing hydrodynamic radius, binding kinetic constants (KD, koff), internal degradation of the proteintarget complex, and renal clearance, were represented in the models. Our final rabbit models demonstrated a robust correlation between predicted and observed serum concentration-time profiles after single intravenous administration in rabbits, covering IgG, Fab, F(ab)2, Fc, and Fc fusion proteins from various publications. These pharmacokinetic simulations offer a promising platform for translating preclinical findings to clinical settings. The presented rabbit intravenous PBPK models lay an important foundation for more specific applications of protein therapeutics in ocular drug development. [ABSTRACT FROM AUTHOR]

A critical review of the current state-of-the-science for the physiologically based pharmacokinetic (PBPK) modeling of metal nanoparticles and their application to human health risk assessment for inhalation exposures was conducted. A systematic literature search was used to identify four model groups (defined as a primary publication along with multiple supplementary publications) subject to review. Using a recent guideline document from the Organization for Economic Cooperation and Development (OECD) for PBPK model evaluation, these model groups were critically peer-reviewed by an independent panel of experts to identify those to be considered for modeling and simulation application. Based upon the expert panel input, model confidence scores for the four model groups ranged from 30 to 41 (out of a maximum score of 50). The three highest-scoring model groups were then applied to compare predictions to a different metal nanoparticle (i.e. not specifically used to parameterize the original models) using a recently published data set for tissue burdens in rats, as well as predicting human tissue burdens expected for corresponding occupational exposures. Overall, the rat models performed reasonably well in predicting the lung but tended to overestimate systemic tissue burdens. Data needs for improving the state-of-the-science, including quantitative particle characterization in tissues, nanoparticle-corona data, long-term exposure data, interspecies extrapolation methods, and human biomonitoring/toxicokinetic data are discussed. [ABSTRACT FROM AUTHOR]

The article discusses a meeting report on DMPK optimization strategies and quantitative translational PKPD frameworks for predicting human PK and efficacious dose of targeted protein degraders. Targeted protein degradation (TPD) is a novel therapeutic modality that has expanded the druggable proteome for cancer treatment. The meeting brought together experts to discuss challenges and opportunities in DMPK and PKPD optimization for TPDs, with presentations covering physicochemical attributes, DMPK screening optimization, PBPK modeling, quantitative PKPD modeling, and in vitro-in vivo extrapolation of PROTAC clearance. The attendees appreciated the content of the presentations and the interactive Q&A session, leading to plans for a second meeting in 2026 to further advance the field of DMPK/PD science of TPDs. [Extracted from the article]

Aims: Early clinical studies have indicated that the pharmacokinetics of Atuliflapon (AZD5718) are time and dose dependent. The reason(s) for these findings is(are) not fully understood, but pre‐clinical profiling suggests that time‐dependent CYP3A4 inhibition cannot be excluded. In clinical practice, Atuliflapon will be co‐administered with CYP3A4 substrates; thus, it is important to determine the impact of Atuliflapon on the pharmacokinetics (PK) of CYP3A4 substrates. The aim of this study was to evaluate the effect of Atuliflapon on the pharmacokinetics of a sensitive CYP3A4 substrate, midazolam, and to explore if the time‐/dose‐dependent effect seen after repeated dosing could be an effect of change in CYP3A4 activity. Methods: Open‐label, fixed‐sequence study in healthy volunteers to assess the PK of midazolam alone and in combination with Atuliflapon. Fourteen healthy male subjects received single oral dose of midazolam 2 mg on days 1 and 7 and single oral doses of Atuliflapon (125 mg) from days 2 to 7. A physiologically based pharmacokinetic (PBPK) model was developed to assess this drug–drug interaction. Results: Mean midazolam values of maximum plasma concentration (Cmax) and area under the curve (AUC) to infinity were increased by 39% and 56%, respectively, when co‐administered with Atuliflapon vs. midazolam alone. The PBPK model predicted a 27% and 44% increase in AUC and a 23% and 35% increase in Cmax of midazolam following its co‐administrations with two predicted therapeutically relevant doses of Atuliflapon. Conclusions: Atuliflapon is a weak inhibitor of CYP3A4; this was confirmed by the validated PBPK model. This weak inhibition is predicted to have a minor PK effect on CYP3A4 metabolized drugs. [ABSTRACT FROM AUTHOR]

Dynamic in vitro absorption systems and mechanistic absorption modeling via PBPK have both shown promise in predicting human oral absorption, although these efforts have been largely separate; this work aimed to integrate knowledge from these approaches to investigate the oral absorption of a RET inhibitor, pralsetinib, with BCS Class II properties. Tiny-TIM (TIM B.V., Weteringbrug​, The Netherlands) is a dynamic in vitro model with close simulation of the successive physiological conditions of the human stomach and small intestine. Tiny-TIM runs with pralsetinib were performed at doses of 200 mg and 400 mg under fasting conditions. Mechanistic modeling of absorption was performed in Simcyp V21 (Certara, Manchester, UK). Pralsetinib fasted bioaccessibility in the Tiny-TIM system was 63% at 200 mg and 53% at 400 mg; a 16% reduction at 400 mg was observed under elevated gastric pH. Maximum pralsetinib solubility from the small intestinal compartment in Tiny-TIM directly informed the supersaturation/precipitation model parameters. The PBPK model predicted a similar fraction absorbed at 200 mg and 400 mg, consistent with the dose proportional increases in observed pralsetinib exposure. Integrating dynamic in vitro systems with mechanistic absorption modeling provides a promising approach for understanding and predicting human absorption with challenging low solubility compounds. [ABSTRACT FROM AUTHOR]

Granulation is the critical process for the pharmaceutical development of poorly water-soluble drug products. Poorly formulated products have challenges in dissolution and bioequivalence studies. Rivaroxaban (RXB) is a poorly soluble drug and has 66% fasting bioavailability at a high strength of 20 mg. Establishing the bioequivalence between test and reference products for high strength requires comparative dissolution profiles and bioequivalence. Improper granulation products and the rest of the batches failed in virtual bioequivalence. The present study provided insight into the optimization of the wet granulation process for manufacturing RXB generic immediate-release tablets using PBPK modeling and simulations. Furthermore, PBPK models are not only useful for formulation optimization but also for process optimization during pharmaceutical product development. [ABSTRACT FROM AUTHOR]

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM. [ABSTRACT FROM AUTHOR]

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field. [ABSTRACT FROM AUTHOR]

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure–activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects. Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. This study used computational models, including quantitative structure–activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of 103 botanical constituents in 13 botanicals. Over half constituents had high permeability and absorption at oral doses no greater than 100 mg per day. However, most glycosides showed low bioavailability after oral administration due to low absorption. [ABSTRACT FROM AUTHOR]

Tyrosine kinase inhibitors (TKIs) have demonstrated significant efficacy against various types of cancers through molecular targeting mechanisms. Over the past 22 years, more than 100 TKIs have been approved for the treatment of various types of cancer indicating the significant progress achieved in this research area. Despite having significant efficacy and ability to target multiple pathways, TKIs administration is associated with challenges. There are reported inconsistencies between observed food effect and labeling administration, challenges of concomitant administration with acid-reducing agents (ARA), pill burden and dosing frequency. In this context, the objective of present review is to visit administration challenges of TKIs and effective ways to tackle them. We have gathered data of 94 TKIs approved in between 2000 and 2022 with respect to food effect, ARA impact, administration schemes (food and PPI restrictions), number of pills per day and administration frequency. Further, trend analysis has been performed to identify inconsistencies in the labeling with respect to observed food effect, molecules exhibiting ARA impact, in order to identify solutions to remove these restrictions through novel formulation approaches. Additionally, opportunities to reduce number of pills per day and dosing frequency for better patient compliance were suggested using innovative formulation interventions. Finally, utility of physiologically based pharmacokinetic modeling (PBPK) for rationale formulation development was discussed with literature reported examples. Overall, this review can act as a ready-to-use-guide for the formulation, biopharmaceutics scientists and medical oncologists to identify opportunities for innovation for TKIs. [ABSTRACT FROM AUTHOR]

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region’s healthcare.

Purpose: Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data. Methods: This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy. Results: Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy. Conclusion: The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development. [ABSTRACT FROM AUTHOR]

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools , in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions Cefoperazone is commonly used off‐label in the treatment of bacterial meningitis and sepsis in children, and the pharmacokinetic (PK) data are limited in this vulnerable population. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict pediatric cefoperazone exposure for rational dosing recommendations.A cefoperazone PBPK model for adults was first constructed using Simcyp V22 simulator. Subsequently, the model was extended to children based on the built in age‐dependent physiological parameters, while the drug characteristics remained unchanged. The verified pediatric PBPK model was then utilized to assess the rationality of the common dosing regimens for children at different age groups.Cefoperazone PBPK model included elimination via biliary excretion, glomerular filtration, and organic anion transporter 3 (OAT3)‐mediated tubular secretion. 95.2% of the observed mean concentrations and 100% of the area under the plasma drug concentration–time curve (AUC) and peak concentration (Cmax) in adults were within a twofold range of model mean predictions. Good predictive accuracy was also observed in children, including neonates. 50 mg/kg q12h cefoperazone demonstrated effective target attainment in virtual term neonates (<1 month) when the MIC was ≤1 mg/L, adhering to the stringent PK/PD target of 75% fT > MIC. 37.5 mg/kg q12h cefoperazone achieved the common 50% fT > MIC target for an MIC ≤ 0. 25 mg/L in virtual pediatric patients ranging from 1 month to 18 years of age.A pediatric PBPK model was developed for cefoperazone, and it could serve as the basis for deriving rational dosing regimens in children. [ABSTRACT FROM AUTHOR]

The development of generic ophthalmic drug products with complex formulations is challenging due to the complexity of the ocular system and a lack of sensitive testing to evaluate the interplay of its physiology with ophthalmic drugs. New methods are needed to facilitate the development of ophthalmic generic drug products. Ocular physiologically based pharmacokinetic (O-PBPK) models can provide insight into drug partitioning in eye tissues that are usually not accessible and/or are challenging to sample in humans. This study aims to demonstrate the utility of an ocular PBPK model to predict human exposure following the administration of ophthalmic suspension. Besifloxacin (Bes) suspension is presented as a case study. The O-PBPK model for Bes ophthalmic suspension (Besivance® 0.6%) accounts for nasolacrimal drainage, suspended particle dissolution in the tears, ocular absorption, and distribution in the rabbit eye. A topical controlled release formulation was used to integrate the effect of Durasite® on Bes ocular retention. The model was subsequently used to predict Bes exposure after its topical administration in humans. Drug-specific parameters were used as validated for rabbits. The physiological parameters were adjusted to match human ocular physiology. Simulated human ocular pharmacokinetic profiles were compared with the observed ocular tissue concentration data to assess the OCAT models' ability to predict human ocular exposure. The O-PBPK model simulations adequately described the observed concentrations in the eye tissues following the topical administration of Bes suspension in rabbits. After adjustment of physiological parameters to represent the human eye, the extrapolation of clinical ocular exposure following a single ocular administration of Bes suspension was successful. [ABSTRACT FROM AUTHOR]

Introduction: We performed an exposure-based Next Generation Risk Assessment case read-across study using New Approach Methodologies (NAMs) to determine the highest safe concentration of daidzein in a body lotion, based on its similarities with its structural analogue, genistein. Two assumptions were: (1) daidzein is a new chemical and its dietary intake omitted; (2) only in vitro data were used for daidzein, while in vitro and legacy in vivo data for genistein were considered. Methods: The 10-step tiered approach evaluating systemic toxicity included toxicokinetics NAMs: PBPK models and in vitro biokinetics measurements in cells used for toxicogenomics and toxicodynamic NAMs: pharmacology profiling (i.e., interaction with molecular targets), toxicogenomics and EATS assays (endocrine disruption endpoints). Whole body rat and human PBPK models were used to convert external doses of genistein to plasma concentrations and in vitro Points of Departure (PoD) to external doses. The PBPK human dermal module was refined using in vitro human skin metabolism and penetration data. Results: The most relevant endpoint for daidzein was from the ERa assay (Lowest Observed Effective Concentration was 100 ± 0.0 nM), which was converted to an in vitro PoD of 33 nM. After application of a safety factor of 3.3 for intra-individual variability, the safe concentration of daidzein was estimated to be 10 nM. This was extrapolated to an external dose of 0.5 µg/cm2 for a body lotion and face cream, equating to a concentration of 0.1%. Discussion: When in vitro PoD of 33 nM for daidzein was converted to an external oral dose in rats, the value correlated with the in vivo NOAEL. This increased confidence that the rat oral PBPK model provided accurate estimates of internal and external exposure and that the in vitro PoD was relevant in the safety assessment of both chemicals. When plasma concentrations estimated from applications of 0.1% and 0.02% daidzein were used to calculate bioactivity exposure ratios, values were >1, indicating a good margin between exposure and concentrations causing adverse effects. In conclusion, this case study highlights the use of NAMs in a 10-step tiered workflow to conclude that the highest safe concentration of daidzein in a body lotion is 0.1%. [ABSTRACT FROM AUTHOR]

The United States Food and Drug Administration guidelines for the bioequivalence (BE) testing of the generic drug products suggests that there should be an equal proportion of male and female population in the BE study. Despite this requirement, many generic drug companies do not maintain the suggested proportion of female population in their studies. Several socio‐economic and cultural factors lead to lower participation of the females in the BE studies. More recently, the regulatory agencies across the globe are requesting the generic drug companies to demonstrate the performance of their drug products in the under‐represented sex via additional studies. In this work, we describe the case of Dextromethorphan modified release tablets where the gender effect on the product performance was evaluated by physiologically based pharmacokinetic (PBPK) modeling approach. We have compared the drug product's performance by population simulations considering four different scenarios. The data from all‐male population (from in house Pharmacokinetic [PK] BE studies) was considered as a reference and other scenarios were compared against the all‐male population data. In the first scenario, we made a comparison between all‐male (100% male) vs all‐female (100% female) population. Second scenario was as per agency's requirements—equal proportion of male and female in the BE study. As an extreme scenario, 100% male vs 30:70 male:female was considered (higher females than males in the BE studies). Finally, as a more realistic scenario, 100% male versus 70:30 male:female was considered (lower females than males in the BE studies). Population PK followed by virtual BE was employed to demonstrate the similarity/differences in the drug product performance between the sexes. This approach can be potentially utilized to seek BE study waivers thus saving cost and accelerating the entry of the generic products to the market. [ABSTRACT FROM AUTHOR]

Drug-Combination Nanoparticles (DcNP) are a novel drug delivery system designed for synchronized delivery of multiple drugs in a single, long-acting, and targeted dose. Unlike depot formulations, slowly releasing drug at the injection site into the blood, DcNP allows multiple-drug-in-combination to collectively distribute from the injection site into the lymphatic system. Two distinct classes of long-acting injectables products are proposed based on pharmacokinetic mechanisms. Class I involves sustained release at the injection site. Class II involves a drug-carrier complex composed of lopinavir, ritonavir, and tenofovir uptake and retention in the lymphatic system before systemic access as a part of the PBPK model validation. For clinical development, Class II long-acting drug-combination products, we leverage data from 3 nonhuman primate studies consisting of nine PK datasets: Study 1, varying fixed-dose ratios; Study 2, short multiple dosing with kinetic tails; Study 3, long multiple dosing (chronic). PBPK validation criteria were established to validate each scenario for all drugs. The models passed validation in 8 of 9 cases, specifically to predict Study 1 and 2, including PK tails, with ritonavir and tenofovir, fully passing Study 3 as well. PBPK model for lopinavir in Study 3 did not pass the validation due to an observable time-varying and delayed drug accumulation, which likely was due to ritonavir's CYP3A inhibitory effect building up during multiple dosing that triggered a mechanism-based drug-drug interaction (DDI). Subsequently, the final model enables us to account for this DDI scenario. [ABSTRACT FROM AUTHOR]

Following up on the first PBPK model for an oral vaccine built for alpha-tocopherol, three peptides are explored in this article to verify if they could support an oral vaccine formulation as adjuvants using the same PBPK modeling approach. A literature review was conducted to verify what peptides have been used as adjuvants in the last decades, and it was noticed that MDP derivatives have been used, with one of them even being commercially approved and used as an adjuvant when administered intravenously in oncology. The aim of this study was to build optimized models for three MDP peptides (MDP itself, MTP-PE, and murabutide) and to verify if they could act as adjuvants for an oral vaccine. Challenges faced by peptides in an oral delivery system are taken into consideration, and improvements to the formulations to achieve better results are described in a step-wise approach to reach the most-optimized model. Once simulations are performed, results are compared to determine what would be the best peptide to support as an oral adjuvant. According to our results, MTP-PE, the currently approved and commercialized peptide, could have potential to be incorporated into an oral formulation. It would be interesting to proceed with further in vivo experiments to determine the behavior of this peptide when administered orally with a proper formulation to overcome the challenges of oral delivery systems. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) modelling in pregnancy is a relatively new approach that is increasingly being used to assess drug systemic exposure in pregnant women to potentially inform dosing adjustments. Physiological changes throughout pregnancy are incorporated into mathematical models to simulate drug disposition in the maternal and fetal compartments as well as the transfer of drugs across the placenta. This mini‐review gathers currently available pregnancy PBPK models for drugs commonly used during pregnancy. In addition, information about the main PBPK modelling platforms used, metabolism pathways, drug transporters, data availability and drug labels were collected. The aim of this mini‐review is to provide a concise overview, demonstrate trends in the field, highlight understudied areas and identify current gaps of PBPK modelling in pregnancy. Possible future applications of this PBPK approach are discussed from a clinical, regulatory and industry perspective. [ABSTRACT FROM AUTHOR]

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process. [ABSTRACT FROM AUTHOR]

Tenofovir alafenamide (TAF) is a BCS Class III compound and an oral pro-drug of Tenofovir (TFV) with limited oral bioavailability. The bioavailability of the oral intake increases with food as a result of the low stability of the active substance in the stomach. The reference drug is "Vemlidy® 25 mg Film Tablet", which contains 25 mg of TAF in "hemifumarate" form, is under patent protection until 15.08.2032 by Gilead, and so the "monofumarate" form was used in the present study. At first, a pilot study was conducted involving 12 subjects under fed conditions. The results of the pilot study revealed the test and reference products were not bioequivalent, as a result of insufficient statistical power and high inter-subject variability. Secondly, a physiologically based pharmacokinetic (PBPK) simulation was performed based on the pilot study results and literature data. Finally, the power of the design was increased and the pivotal study design was optimized into a four-period, full-replicated, cross-over study with 34 subjects under fed conditions and it was concluded that the test and reference products were bioequivalent. In conclusion, the present study proved the importance of a correct study design with higher statistical power for a BCS Class III compound with high variability, to present the pharmacokinetics. [ABSTRACT FROM AUTHOR]

Nifedipine is used for treating mild to severe hypertension and preventing preterm labor in pregnant women. Nevertheless, concerns about nifedipine fetal exposure and safety are always raised. The aim of this study was to develop and validate a maternal‐placental‐fetal nifedipine physiologically based pharmacokinetic (PBPK) model and apply the model to predict maternal, placental, and fetal exposure to nifedipine at different pregnancy stages. A nifedipine PBPK model was verified with nonpregnant data and extended to the pregnant population after the inclusion of the fetoplacental multicompartment model that accounts for the placental tissue and different fetal organs within the Simcyp Simulator version 22. Model parametrization involved scaling nifedipine transplacental clearance based on Caco‐2 permeability, and fetal hepatic clearance was obtained from in vitro to in vivo extrapolation encompassing cytochrome P450 3A7 and 3A4 activities. Predicted concentration profiles were compared with in vivo observations and the transplacental transfer results were evaluated using 2‐fold criteria. The PBPK model predicted a mean cord‐to‐maternal plasma ratio of 0.98 (range, 0.86‐1.06) at term, which agrees with experimental observations of 0.78 (range, 0.59‐0.93). Predicted nifedipine exposure was 1.4‐, 2.0‐, and 3.0‐fold lower at 15, 27, and 39 weeks of gestation when compared with nonpregnant exposure, respectively. This innovative PBPK model can be applied to support maternal and fetal safety assessment for nifedipine at various stages of pregnancy. [ABSTRACT FROM AUTHOR]

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions. [ABSTRACT FROM AUTHOR]

Background: Evaluating drug transplacental clearance is vital for forecasting fetal drug exposure. Ex vivo human placenta perfusion experiments are the most suitable approach for this assessment. Various in silico methods are also proposed. This study aims to compare these prediction methods for drug transplacental clearance, focusing on the large molecular weight drug vancomycin (1449.3 g/mol), using maternal–fetal physiologically based pharmacokinetic (m-f PBPK) modeling. Methods: Ex vivo human placenta perfusion experiments, in silico approaches using intestinal permeability as a substitute (quantitative structure property relationship (QSPR) model and Caco-2 permeability in vitro-in vivo correlation model) and midazolam calibration model with Caco-2 scaling were assessed for determining the transplacental clearance (CLPD) of vancomycin. The m-f PBPK model was developed stepwise using Simcyp, incorporating the determined CLPD values as a crucial input parameter for transplacental kinetics. Results: The developed PBPK model of vancomycin for non-pregnant adults demonstrated excellent predictive performance. By incorporating the CLPD parameterization derived from ex vivo human placenta perfusion experiments, the extrapolated m-f PBPK model consistently predicted maternal and fetal concentrations of vancomycin across diverse doses and distinct gestational ages. However, when the CLPD parameter was derived from alternative prediction methods, none of the extrapolated maternal–fetal PBPK models produced fetal predictions in line with the observed data. Conclusion: Our study showcased that combination of ex vivo human placenta perfusion experiments and m-f PBPK model has the capability to predict fetal exposure for the large molecular weight drug vancomycin, whereas other in silico approaches failed to achieve the same level of accuracy. [ABSTRACT FROM AUTHOR]

IntroductionAll cosmetic ingredients must be evaluated for their safety to consumers. In the absence of in vivo data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients. We used a bottom-up approach to develop human PBPK models for genistein and daidzein for a read-across NGRA, whereby genistein was the source chemical for the target chemical, daidzein.MethodsAn oral rat PBPK model for genistein was built using PK-Sim® and in vitro ADME input data. This formed the basis of the daidzein oral rat PBPK model, for which chemical-specific input parameters were used. Rat PBPK models were then converted to human models using human-specific physiological parameters and human in vitro ADME data. In vitro skin metabolism and penetration data were used to build the dermal module to represent the major route of exposure to cosmetics.ResultsThe initial oral rat model for genistein was qualified since it predicted values within 2-fold of measured in vivo PK values. This was used to predict plasma concentrations from the in vivo NOAEL for genistein to set test concentrations in bioassays. Intrinsic hepatic clearance and unbound fractions in plasma were identified as sensitive parameters impacting the predicted Cmax values. Sensitivity and uncertainty analyses indicated the developed PBPK models had a moderate level of confidence. An important aspect of the development of the dermal module was the implementation of first-pass metabolism, which was extensive for both chemicals. The final human PBPK model for daidzein was used to convert the in vitro PoD of 33 nM (from an estrogen receptor transactivation assay) to an external dose of 0.2% in a body lotion formulation.ConclusionPBPK models for genistein and daidzein were developed as a central component of an NGRA read-across case study. This will help to gain regulatory confidence in the use of PBPK models, especially for cosmetic ingredients.

Physiologically based pharmacokinetic (PBPK) modelling is an important tool to predict drug disposition in the body. Rabbits play a pivotal role as a highly valued small animal model, particularly in the field of ocular therapeutics, where they serve as a crucial link between preclinical research and clinical applications. In this context, we have developed PBPK models designed specifically for rabbits, with a focus on accurately predicting the pharmacokinetic profiles of protein therapeutics following intravenous administration. Our goal was to comprehend the influence of key physiological factors on systemic disposition of antibodies and their functional derivatives. For the development of the systemic PBPK models, rabbit physiological factors such as gene expression, body weight, neonatal fragment crystallizable receptor (FcRn) binding, target binding, target concentrations, and target turnover rate were meticulously considered. Additionally, key protein parameters, encompassing hydrodynamic radius, binding kinetic constants (KD, koff), internal degradation of the protein-target complex, and renal clearance, were represented in the models. Our final rabbit models demonstrated a robust correlation between predicted and observed serum concentration-time profiles after single intravenous administration in rabbits, covering IgG, Fab, F(ab)2, Fc, and Fc fusion proteins from various publications. These pharmacokinetic simulations offer a promising platform for translating preclinical findings to clinical settings. The presented rabbit intravenous PBPK models lay an important foundation for more specific applications of protein therapeutics in ocular drug development.

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.

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.