Preface to the special issue of Food and Chemical Toxicology on "New approach methodologies and machine learning in food safety and chemical risk assessment: Development of reproducible, open-source, and user-friendly tools for exposure, toxicokinetic, and toxicity assessments in the 21st century".

This Special Issue contains articles on applications of various new approach methodologies (NAMs) in the field of toxicology and risk assessment. These NAMs include in vitro high-throughput screening, quantitative structure-activity relationship (QSAR) modeling, physiologically based pharmacokinetic (PBPK) modeling, network toxicology analysis, molecular docking simulation, omics, machine learning, deep learning, and "template-and-anchor" multiscale computational modeling. These in vitro and in silico approaches complement each other and can be integrated together to support different applications of toxicology, including food safety assessment, dietary exposure assessment, chemical toxicity potency screening and ranking, chemical toxicity prediction, chemical toxicokinetic simulation, and to investigate the potential mechanisms of toxicities, as introduced further in selected articles in this Special Issue.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes.

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Translational two-pore PBPK model to characterize whole-body disposition of different-size endogenous and exogenous proteins.

Two-pore physiologically based pharmacokinetic (PBPK) modeling has demonstrated its potential in describing the pharmacokinetics (PK) of different-size proteins. However, all existing two-pore models lack either diverse proteins for validation or interspecies extrapolation. To fill the gap, here we have developed and optimized a translational two-pore PBPK model that can characterize plasma and tissue disposition of different-size proteins in mice, rats, monkeys, and humans. Datasets used for model development include more than 15 types of proteins: IgG (150 kDa), F(ab)2 (100 kDa), minibody (80 kDa), Fc-containing proteins (205, 200, 110, 105, 92, 84, 81, 65, or 60 kDa), albumin conjugate (85.7 kDa), albumin (67 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), dAb2 (23.5 kDa), proteins with an albumin-binding domain (26, 23.5, 22, 16, 14, or 13 kDa), nanobody (13 kDa), and other proteins (110, 65, or 60 kDa). The PBPK model incorporates: (i) molecular weight (MW)-dependent extravasation through large and small pores via diffusion and filtration, (ii) MW-dependent renal filtration, (iii) endosomal FcRn-mediated protection from catabolism for IgG and albumin-related modalities, and (iv) competition for FcRn binding from endogenous IgG and albumin. The finalized model can well characterize PK of most of these proteins, with area under the curve predicted within two-fold error. The model also provides insights into contribution of renal filtration and lysosomal degradation towards total elimination of proteins, and contribution of paracellular convection/diffusion and transcytosis towards extravasation. The PBPK model presented here represents a cross-modality, cross-species platform that can be used for development of novel biologics.

Whole-Body Disposition and Physiologically Based Pharmacokinetic Modeling of Adeno-Associated Viruses and the Transgene Product.

To facilitate model-informed drug development (MIDD) of adeno-associated virus (AAV) therapy, here we have developed a physiologically based pharmacokinetic (PBPK) model for AAVs following preclinical investigation in mice. After 2E11 Vg/mouse dose of AAV8 and AAV9 encoding a monoclonal antibody (mAb) gene, whole-body disposition of both the vector and the transgene mAb was evaluated over 3 weeks. At steady-state, the following tissue-to-blood (T/B) concentration ratios were found for AAV8/9: ∼50 for liver; ∼10 for heart and muscle; ∼2 for brain, lung, kidney, adipose, and spleen; ≤1 for bone, skin, and pancreas. T/B values for mAb were compared with the antibody biodistribution coefficients, and five different clusters of organs were identified based on their transgene expression profile. All the biodistribution data were used to develop a novel AAV PBPK model that incorporates: (i) whole-body distribution of the vector; (ii) binding, internalization, and intracellular processing of the vector; (iii) transgene expression and secretion; and (iv) whole-body disposition of the secreted transgene product. The model was able to capture systemic and tissue PK of the vector and the transgene-produced mAb reasonably well. Pathway analysis of the PBPK model suggested that liver, muscle, and heart are the main contributors for the secreted transgene mAb. Unprecedented PK data and the novel PBPK model developed here provide the foundation for quantitative systems pharmacology (QSP) investigations of AAV-mediated gene therapies. The PBPK model can also serve as a quantitative tool for preclinical study design and preclinical-to-clinical translation of AAV-based gene therapies.

A Literature Review of Changes in Phase II Drug-Metabolizing Enzyme and Drug Transporter Expression during Pregnancy.

The purpose of this literature review is to comprehensively summarize changes in the expression of phase II drug-metabolizing enzymes and drug transporters in both the pregnant woman and the placenta. Using PubMed®, a systematic search was conducted to identify literature relevant to drug metabolism and transport in pregnancy. PubMed was searched with pre-specified terms during the period of 26 May 2023 to 10 July 2023. The final dataset of 142 manuscripts was evaluated for evidence regarding the effect of gestational age and hormonal regulation on the expression of phase II enzymes (n = 16) and drug transporters (n = 38) in the pregnant woman and in the placenta. This comprehensive review exposes gaps in current knowledge of phase II enzyme and drug transporter localization, expression, and regulation during pregnancy, which emphasizes the need for further research. Moreover, the information collected in this review regarding phase II drug-metabolizing enzyme and drug transporter changes will aid in optimizing pregnancy physiologically based pharmacokinetic (PBPK) models to inform dose selection in the pregnant population.

Toxicity and risk priority ranking of polybrominated diphenyl ethers (PBDEs): A relative receptor-bound concentration approach.

Toxicity and risk priority ranking of chemicals are crucial to management and decision-making. In this work, we develop a new mechanistic ranking approach of toxicity and risk priority ranking for polybrominated diphenyl ethers (PBDEs) based on receptor-bound concentration (RBC). Based on the binding affinity constant predicted using molecular docking, internal concentration converted from human biomonitoring data via PBPK model, and the receptor concentration derived from the national center for biotechnology information (NCBI) database, the RBC of 49 PBDEs binding to 24 nuclear receptors were calculated. 1176 RBC results were successfully obtained and analyzed. High brominated PBDEs, including BDE-201, BDE-205, BDE-203, BDE-196, BDE-183, BDE-206, BDE-207, BDE-153, BDE-208, BDE-204, BDE-197, and BDE-209, exerted more potent than low brominated congeners (BDE-028, BDE-047, BDE-099, and BDE-100) at the same daily intake dose in terms of toxicity ranking. For risk ranking, with human biomonitoring serum data, the relative RBC of BDE-209 was significantly greater than that of any others. For receptor prioritization, constitutive androstane receptor (CAR), retinoid X receptor alpha (RXRA), and liver X receptor alpha (LXRA) may be the sensitive targets for PBDEs to trigger effects in the liver. In summary, high brominated PBDEs are more potent than low brominated congeners, thus, besides BDE-047 and BDE-099, BDE-209 should be priority controlled. In conclusion, this study provides a new approach for toxicity and risk ranking of groups of chemicals, which can readily be used for others.

Development and Validation of Physiologically Based Pharmacokinetic Model of Levetiracetam to Predict Exposure and Dose Optimization in Pediatrics.

Levetiracetam (Lev) is an antiepileptic drug that has been increasingly used in the epilepsy pediatric population in recent years, but its pharmacokinetic behavior in pediatric population needs to be characterized clearly. Clinical trials for the pediatric drug remain difficult to conduct due to ethical and practical factors. The purpose of this study was to use the physiologically based pharmacokinetic (PBPK) model to predict changes in plasma exposure of Lev in pediatric patients and to provide recommendations for dose adjustment. A PBPK model of Lev in adults was developed using PK-Sim® software and extrapolated to the entire age range of the pediatric population. The model was evaluated using clinical pharmacokinetic data. The results showed the good fit between predictions and observations of the adult and pediatric models. The recommended doses for neonates, infants and children are 0.78, 1.67 and 1.22 times that of adults, respectively. Moreover, at the same dose, plasma exposure in adolescents was similar to that of adults. The PBPK models of Lev for adults and pediatrics were successfully developed and validated to provide a reference for the rational administration of drugs in the pediatric population.

Addressing maternal medication use during breastfeeding using clinical resources and a novel physiologically based pharmacokinetic model-derived metric: A qualitative study.

Introduction: Breastfeeding has major benefits to the maternal-infant dyad and yet healthcare providers have expressed uncertainty about advocating breastfeeding when mothers are taking medications. The tendency for some providers to be more cautious in their advising approach is likely a consequence of limited, unfamiliar, and unreliable existing information on medication use during lactation. A novel risk metric termed the Upper Area Under the Curve Ratio (UAR) was developed to overcome existing resource shortcomings. However, the perception and use of the UAR in practice by providers is not known. The aim of this study was to understand existing resource use and potential UAR use in practice, their advantages and disadvantages, and areas of improvement for the UAR. Methods: Healthcare providers mainly practicing in California with experience advising on medication use during lactation were recruited. One-on-one semi-structured interviews that included questions on current practices when advising medication use during breastfeeding, and approaches to a given a scenario with and without information about the UAR were conducted. The Framework Method was applied for data analysis to construct themes and codes. Results: Twenty-eight providers representing multiple professions and disciplines were interviewed. Six main themes emerged: (1) Current Practice Approaches, (2) Advantages of Existing Resources, (3) Disadvantages of Existing Resources, (4) Advantages of the UAR, (5) Disadvantages of the UAR, and (6) Strategies to Improve the UAR. Overall, 108 codes were identified that illustrated theme topics ranging from a general lack of metric use to the realities of advising. A workflow describing current practice approaches connected all other themes. Almost all disadvantages of existing resources could be overcome by advantages of other resources and the UAR. Several improvements to the UAR were identified to address its shortcomings. Conclusion: Through interviews with providers who use resources to advise on medication use during breastfeeding, an improved understanding of current practice approaches and accessed resources was ascertained. Ultimately, it was found that the UAR would confer multiple benefits over existing resources, and improvements of the UAR were identified. Future work should focus on implementing the suggested recommendations to ensure optimal uptake of the UAR to improve advising practices.

Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies.

Motivated by a series of work demonstrating the effect of molecular charge on antibody pharmacokinetics (PK), physiological-based pharmacokinetic (PBPK) models are emerging that relate in silico calculated charge or in vitro measures of polyspecificity to antibody PK parameters. However, only plasma data has been used for model development in these studies, leading to unvalidated assumptions. Here, we present an extended platform PBPK model for antibodies that incorporate charge-dependent endothelial cell pinocytosis rate and nonspecific off-target binding in the interstitial space and on circulating blood cells, to simultaneously characterize whole-body disposition of three antibody charge variants. Predictive potential of various charge metrics was also explored, and the difference between positive charge patches and negative charge patches (i.e., PPC-PNC) was used as the charge parameter to establish quantitative relationships with nonspecific binding affinities and endothelial cell uptake rate. Whole-body disposition of these charge variants was captured well by the model, with less than 2-fold predictive error in area under the curve of most plasma and tissue PK data. The model also predicted that with greater positive charge, nonspecific binding was more substantial, and pinocytosis rate increased especially in brain, heart, kidney, liver, lung, and spleen, but remained unchanged in adipose, bone, muscle, and skin. The presented PBPK model contributes to our understanding of the mechanisms governing the disposition of charged antibodies and can be used as a platform to guide charge engineering based on desired plasma and tissue exposures.

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

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

Human health risk assessment of 6:2 Cl-PFESA through quantitative in vitro to in vivo extrapolation by integrating cell-based assays, an epigenetic key event, and physiologically based pharmacokinetic modeling.

Human health risk assessment of chemicals is essential but often relies on time-consuming and animal and labor-extensive procedures. Here, we develop a population-based, quantitative in vitro to in vivo extrapolation (QIVIVE) approach which depended on cellular effects monitored by in vitro assays, considered chemical internal concentration determined by LC-MS/MS, extrapolated into in vivo target tissue concentration through physiologically based pharmacokinetic (PBPK) modelling, and assessed populational health risk using in silico modelling. By applying this QIVIVE approach to 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), as a representative of the emerging pollutants, we find that 6:2 Cl-PFESA disturbed lipid homeostasis in HepG2 cells through enhancement of lipid accumulation and fatty acid ß-oxidation, during which miR-93-5p served as a key event towards toxicity and thus, could serve as an efficient toxicity marker for risk assessment; further, the disruption potency of lipid homeostasis of 6:2 Cl-PFESA for the most of studied populations in China might be of moderate concern. Together, our approach improved the reliability of QIVIVE during human health risk assessment, which can readily be used for other chemicals.

Modeling digestion, absorption, and ketogenesis after administration of tricaprilin formulations to humans.

At present, tricaprilin is used as a ketogenic source for the management of mild to moderate Alzheimer's disease. After administration of the medium-chain triglyceride, tricaprilin is hydrolyzed to octanoic acid and further metabolized to ketones, acting as an alternative energy substrate for the brain. In this investigation, we developed a physiologically-based biopharmaceutics model simulating in vivo processes following the peroral administration of tricaprilin. The model includes multiple data sources to establish a partially verified framework for the simulation of plasma profiles. The input parameters were identified based on existing literature data and in vitro digestion studies. Model validation was conducted using the data from a phase I clinical trial. A partial parameter sensitivity analysis elucidated various influences on the plasma ketone levels that are mainly responsible for the therapeutic effects of tricaprilin. Based on our findings, we concluded that dispersibility and lipolysis of tricaprilin together with the gastric emptying patterns are limiting ketogenesis, while other steps such as the conversion of octanoic acid to ketone bodies play a minor role only.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

A Physiologically-Based Pharmacokinetic Model of Ruxolitinib and Posaconazole to Predict CYP3A4-Mediated Drug-Drug Interaction Frequently Observed in Graft versus Host Disease Patients.

Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug-drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim® Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (Cmax) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

Utility of Physiologically Based Biopharmaceutics Modeling (PBBM) in Regulatory Perspective: Application to Supersede f2, Enabling Biowaivers & Creation of Dissolution Safe Space.

Product DRL is a generic IR tablet formulation with BCS Class-III API, available in two strengths: 50mg & 100mg. The reference and test formulations have salt-A & salt-B of API but both products were bioequivalent based on the in vivo bioequivalence study conducted for higher strength 100mg. While leveraging the generic product to different market, the reference product from other market showed slower release than generic formulation resulting in f2<50 in pH 6.8 for both 50mg and 100mg, because of which waiver for BE study couldn't be granted. To support f2 mismatch at 100mg, 50mg and to facilitate biowaiver of 50mg, a Gastroplus® PBBM model was developed & validated. Virtual bioequivalence trials were performed using the slower dissolution profile of other market reference. It was demonstrated that despite slower dissolution, bioequivalence was achieved for test product against other market reference for 50mg & 100mg strengths. Additionally, dissolution safe space was created using virtual dissolution profiles, which indicated that when >85% released up to 60 min there is no impact on bioequivalence. Overall, for molecules with permeability controlled absorption (i.e. BCS-III), very rapid dissolution criteria can be relaxed by defining dissolution safe space thereby enabling more waivers in future.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone.

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug-gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration-time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound's exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

A web-based interactive physiologically based pharmacokinetic (iPBPK) model for meloxicam in broiler chickens and laying hens.

Meloxicam is a non-steroidal anti-inflammatory drug (NSAID) commonly used in food-producing animals, including chickens in an extralabel manner. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model for meloxicam in broiler chickens and laying hens to facilitate withdrawal interval (WDI) estimations. The model structure for broiler chickens contained six compartments including plasma, muscle, liver, kidney, fat and rest of body, while an additional compartment of ovary was included for laying hens. The model adequately simulated available pharmacokinetic data of meloxicam in plasma of broiler chickens as well as tissue and egg data of laying hens. The model was converted to a web-based interface and used to predict WDIs following extralabel administrations. The results showed that the estimated WDIs were 50, 44, 11, 3, 3, 22 and 4 days for liver, kidney, muscle, fat, ovary, yolk and white, respectively in laying hens after 14 repeated oral administrations of meloxicam (1 mg/kg) at 24-h intervals. This model provides a useful and flexible tool for risk assessment and management of residues for meat and eggs from chickens treated with meloxicam and will serve as a basis for extrapolation to other NSAID drugs and other poultry species to aid animal-derived food safety assessment.