Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making

A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against Streptococcus suis

Introduction: Streptococcus suis (S. suis) is a zoonotic pathogen threatening public health. Aditoprim (ADP), a novel veterinary medicine, exhibits an antibacterial effect against S. suis. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was used to determine the dosage regimens of ADP against S. suis and withdrawal intervals. Methods: The PBPK model of ADP injection can predict drug concentrations in plasma, liver, kidney, muscle, and fat. A semi-mechanistic pharmacodynamic (PD) model, including susceptible subpopulation and resistant subpopulation, is successfully developed by a nonlinear mixed-effect model to evaluate antibacterial effects. An integrated PBPK/PD model is conducted to predict the time-course of bacterial count change and resistance development under different ADP dosages. Results: ADP injection, administrated at 20 mg/kg with 12 intervals for 3 consecutive days, can exert an excellent antibacterial effect while avoiding resistance emergence. The withdrawal interval at the recommended dosage regimen is determined as 18 days to ensure food safety. Discussion: This study suggests that the PBPK/PD model can be applied as an effective tool for the antibacterial effect and safety evaluation of novel veterinary drugs.

Physiologically-based pharmacokinetic models to predict drug exposure during pregnancy

As pregnant women are constantly exposed to drugs during pregnancy, either to treat long-term conditions or acute illnesses, drug safety is a major concern for the fetus and the mother. Clinical trials are rarely made in this population due to strict regulation and ethical reasons. However, drug pharmacokinetic (PK) parameters vary during pregnancy with an increase in distribution volume, renal clearance and more. In addition, the fetal distribution should be evaluated with the importance of placental diffusion, both active and passive. Therefore, there is a recent interest in the use of physiologically-based pharmacokinetic (PBPK) modeling to characterize these changes and complete the sparse data available on drug PK during pregnancy. Indeed, PBPK models integrate drug physicochemical and physiological parameters corresponding to each compartment of the body to estimate drug concentrations. This review establishes an overview on the current use of PBPK models in drug dosage determination for the pregnant woman, fetal exposure and drug interactions in the fetal compartment.

Physiologically based radiopharmacokinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: studies of non-linearities, multi-bolus injections, and albumin binding

BACKGROUND

We aimed to develop a publicly shared computational physiologically based pharmacokinetic (PBPK) model to reliably simulate and analyze radiopharmaceutical therapies (RPTs), including probing of hot-cold ligand competitions as well as alternative injection scenarios and drug designs, towards optimal therapies.

RESULTS

To handle the complexity of PBPK models (over 150 differential equations), a scalable modeling notation called the "reaction graph" is introduced, enabling easy inclusion of various interactions. We refer to this as physiologically based radiopharmacokinetic (PBRPK) modeling, fine-tuned specifically for radiopharmaceuticals. As three important applications, we used our PBRPK model to (1) study the effect of competition between hot and cold species on delivered doses to tumors and organs at risk. In addition, (2) we evaluated an alternative paradigm of utilizing multi-bolus injections in RPTs instead of prevalent single injections. Finally, (3) we used PBRPK modeling to study the impact of varying albumin-binding affinities by ligands, and the implications for RPTs. We found that competition between labeled and unlabeled ligands can lead to non-linear relations between injected activity and the delivered dose to a particular organ, in the sense that doubling the injected activity does not necessarily result in a doubled dose delivered to a particular organ (a false intuition from external beam radiotherapy). In addition, we observed that fractionating injections can lead to a higher payload of dose delivery to organs, though not a differential dose delivery to the tumor. By contrast, we found out that increased albumin-binding affinities of the injected ligands can lead to such a differential effect in delivering more doses to tumors, and this can be attributed to several factors that PBRPK modeling allows us to probe.

CONCLUSIONS

Advanced computational PBRPK modeling enables simulation and analysis of a variety of intervention and drug design scenarios, towards more optimal delivery of RPTs.

Alternatives to animal testing in toxicity testing: Current status and future perspectives in food safety assessments

The development of alternative methods to animal testing has gained great momentum since Russel and Burch introduced the "3Rs" concept of Reduction, Refinement, and Replacement of animals in safety testing in 1959. Several alternatives to animal testing have since been introduced, including but not limited to in vitro and in chemico test systems, in silico models, and computational models (e.g., [quantitative] structural activity relationship models, high-throughput screens, organ-on-chip models, and genomics or bioinformatics) to predict chemical toxicity. Furthermore, several agencies have developed robust integrated testing strategies to determine chemical toxicity. The cosmetics sector is pioneering the adoption of alternative methodologies for safety evaluations, and other sectors are aiming to completely abandon animal testing by 2035. However, beyond the use of in vitro genetic testing, agencies regulating the food industry have been slow to implement alternative methodologies into safety evaluations compared with other sectors; setting health-based guidance values for food ingredients requires data from systemic toxicity, and to date, no standalone validated alternative models to assess systemic toxicity exist. The abovementioned models show promise for assessing systemic toxicity with further research. In this paper, we review the current alternatives and their applicability and limitations in food safety evaluations.

Simultaneous toxicokinetic studies of aristolochic acid I and II and aristolactam I and II using a newly-developed microdialysis liquid chromatography-tandem mass spectrometry

Aristolochic acids (AAs) are naturally occurring genotoxic carcinogens linked to Balkan endemic nephropathy and aristolochic acid nephropathy. Aristolochic acid I and II (AA-I and AA-II) are the most abundant AAs, and AA-I has been reported to be more genotoxic and nephrotoxic than AA-II. The study aimed to explore metabolic differences underlying the differential toxicity. We developed a novel microdialysis sampling coupled with solid-phase extraction liquid chromatography-tandem mass spectrometry (MD-SPE-LC-MS/MS) to simultaneously study the toxicokinetics (TK) of AA-I and AA-II and their corresponding aristolactams (AL-I and AL-II) in the blood of Sprague Dawley rats co-treated with AA-1 and AA-II. Near real-time monitoring of these analytes in the blood of treated rats revealed that AA-I was absorbed, distributed, and eliminated more rapidly than AA-II. Moreover, the metabolism efficiency of AA-I to AL-I was higher compared to AA-II to AL-II. Only 0.58% of AA-I and 0.084% of AA-II was reduced to AL-I and AL-II, respectively. The findings are consistent with previous studies and support the contention that differences in the in vivo metabolism of AA-I and AA-II may be critical factors for their differential toxicities.

In-depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: PartI - Applications and rationale behind the use of open source-code software

PBPK applications published in the literature support a greater adoption of non-open source-code (NOSC) software as opposed to open source-code (OSC) alternatives. However, a significant number of PBPK modelers are still using OSC software, understanding the rationale for the use of this modality is important and may help those embarking on PBPK modeling. No previous analysis of PBPK modeling trends has included the rationale of the modeler. An in-depth analysis of PBPK applications of OSC software is warranted to determine the true impact of OSC software on the rise of PBPK. Publications focussing on PBPK modeling applications, which used OSC software, were identified by systematically searching the scientific literature for original articles. A total of 171 articles were extracted from the narrowed subset. The rise in the use of OSC software for PBPK applications was greater than the general discipline of pharmacokinetics (9 vs. 4), but less than the overall growth of the PBPK area (9 vs. 43). Our report demonstrates conclusively that the surge in PBPK usage is primarily attributable to the availability and implementations of NOSC software. Modelers preferred not to share the reasons for their selection of certain modeling software and no 'explicit' rationale was given to support the use of OSC analysed by this study. As the preference for NOSC versus OSC software tools in the PBPK area continues to be divided, initiatives to add the rationale in using one form over another to every future PBPK modeling report will be a welcomed and informative addition.

The plasma and tissue kinetics of sulfadiazine and its metabolite in Ictalurus punctatus after oral gavage at two temperatures

A plasma and tissue kinetic study of sulfadiazine (SDZ) and its metabolite, N₄ -acetyl sulfadiazine (ACT-SDZ), was characterized in channel catfish (Ictalurus punctatus) following a single oral dose of 50 mg/kg at 18 and 24°C. Samples were collected at predetermined time points and determined by ultra-performance liquid chromatography. The classical one-compartmental method was used to estimate the pharmacokinetic parameters. Results showed that the changing of temperature was markedly influential on the kinetics of SDZ and ACT-SDZ in plasma and tissues. When the temperature was increased from 18 to 24°C, the elimination half-life (K10_HF) of SDZ was decreased in gill, kidney, and muscle + skin, but increased in liver and plasma. The K10_HF of ACT-SDZ also had a decreased trend in gill, liver, and plasma but had comparable values in kidney and muscle + skin. The absorption half-life (K01_HF), time to peak concentration (T_max ), and area under concentration-time curve (AUC_0-∞ ) of SDZ and ACT-SDZ all exhibited declined tendencies in plasma and tissues. The apparent volume of distribution (V_F) of SDZ in plasma was increased from 0.53 to 1.48 L/kg, and the apparent systemic total body clearance (Cl_F) was increased from 0.028 to 0.060 L/h/kg. In a word, K01_HF, T_max , and AUC_0-∞ of SDZ and ACT-SDZ were decreased in plasma and tissues with the increase of temperature, whereas the V_F and Cl_F of SDZ were increased. Meanwhile, we calculated the percentage of time profile of SDZ concentration more than minimum inhibitory concentration to total time (%T > MIC) to guide clinical usage of SDZ. When the dosage interval was 24 h, the values of %T > MIC were all >90% in plasma and most tissues. Therefore, we recommend an oral dose of SDZ at 50 mg/kg once per 24 h at 18-24°C against the fish pathogens with an MIC value of ≤6.4 μg/mL.

A science-based agenda for health-protective chemical assessments and decisions: overview and consensus statement

The manufacture and production of industrial chemicals continues to increase, with hundreds of thousands of chemicals and chemical mixtures used worldwide, leading to widespread population exposures and resultant health impacts. Low-wealth communities and communities of color often bear disproportionate burdens of exposure and impact; all compounded by regulatory delays to the detriment of public health. Multiple authoritative bodies and scientific consensus groups have called for actions to prevent harmful exposures via improved policy approaches. We worked across multiple disciplines to develop consensus recommendations for health-protective, scientific approaches to reduce harmful chemical exposures, which can be applied to current US policies governing industrial chemicals and environmental pollutants. This consensus identifies five principles and scientific recommendations for improving how agencies like the US Environmental Protection Agency (EPA) approach and conduct hazard and risk assessment and risk management analyses: (1) the financial burden of data generation for any given chemical on (or to be introduced to) the market should be on the chemical producers that benefit from their production and use; (2) lack of data does not equate to lack of hazard, exposure, or risk; (3) populations at greater risk, including those that are more susceptible or more highly exposed, must be better identified and protected to account for their real-world risks; (4) hazard and risk assessments should not assume existence of a "safe" or "no-risk" level of chemical exposure in the diverse general population; and (5) hazard and risk assessments must evaluate and account for financial conflicts of interest in the body of evidence. While many of these recommendations focus specifically on the EPA, they are general principles for environmental health that could be adopted by any agency or entity engaged in exposure, hazard, and risk assessment. We also detail recommendations for four priority areas in companion papers (exposure assessment methods, human variability assessment, methods for quantifying non-cancer health outcomes, and a framework for defining chemical classes). These recommendations constitute key steps for improved evidence-based environmental health decision-making and public health protection.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Insights into the mapping of green synthesis conditions for ZnO nanoparticles and their toxicokinetics

Research on ZnO nanoparticles (NPs) has broad medical applications. However, the green synthesis of ZnO NPs involves a wide range of properties requiring optimization. ZnO NPs show toxicity at lower doses. This toxicity is a function of NP properties and pharmacokinetics. Moreover, NP toxicity and pharmacokinetics are affected by the species type and age of the animals tested. Physiologically based pharmacokinetic (PBPK) modeling offers a mechanistic platform to scrutinize the colligative effect of the interplay between these factors, which reduces the need for in vivo studies. This review provides a guide to choosing green synthesis conditions that result in minimal toxicity using a mechanistic tool, namely PBPK modeling.

Machine learning and artificial intelligence in toxicological sciences

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.

Application of Physiologically Based Pharmacokinetic Modeling of Lamotrigine Using PK-Sim in Predicting the Impact of Drug Interactions and Dosage Adjustment

Background

Physiologically based pharmacokinetic (PBPK) models are helpful as mechanistic representations of pharmacokinetic parameters. There were no reports of lamotrigine (LTG) PBPK models developed in open source platforms like PK-Sim.

Objectives

The present work was aimed to build a LTG PBPK model and compare it to the clinical data from South Asian Indian patients and use this model to understand the drug interactions of LTG and explore the optimal doses.

Methods and Material

The PBPK model was developed using the PK-Sim software platform and qualified with LTG plasma concentration data from an Indian study. The European population database was chosen as the patient setting in the software. Physicochemical data of LTG and enzyme kinetic data were incorporated from the literature. Dosing protocols were as per the previous study. Interaction models for drug interactions with carbamazepine and valproate were also simulated.

Results

Most of the model predicted concentration-time profiles of LTG at steady-state were well within the observed concentrations. The developed models were suitably qualified. The drug interaction model was used to assess the impact of induction and inhibition of the pharmacokinetic profile of LTG.

Conclusions

The predicted plasma concentrations of the developed PBPK models using the European population database were very similar to the data from Indian patients. The developed LTG PBPK models are applicable in predicting the impact of drug interactions and can yield appropriate LTG doses to be administered.

Safety assessment of natural products in Malaysia: current practices, challenges, and new strategies

The belief that natural products are inherently safe is a primary reason for consumers to choose traditional medicines and herbal supplements for health maintenance and disease prevention. Unfortunately, some natural products on the market have been found to contain toxic compounds, such as heavy metals and microbes, as well as banned ingredients such as aristolochic acids. It shows that the existing regulatory system is inadequate and highlights the importance of thorough safety evaluations. In Malaysia, the National Pharmaceutical Regulatory Agency is responsible for the regulatory control of medicinal products and cosmetics, including natural products. For registration purpose, the safety of natural products is primarily determined through the review of documents, including monographs, research articles and scientific reports. One of the main factors hampering safety evaluations of natural products is the lack of toxicological data from animal studies. However, international regulatory agencies such as the European Food Safety Authority and the United States Food and Drug Administration are beginning to accept data obtained using alternative strategies such as non-animal predictive toxicological tools. Our paper discusses the use of state-of-the-art techniques, including chemometrics, in silico modelling and omics technologies and their applications to the safety assessments of natural products.

An Interactive Generic Physiologically Based Pharmacokinetic (igPBPK) Modeling Platform to Predict Drug Withdrawal Intervals in Cattle and Swine: A Case Study on Flunixin, Florfenicol and Penicillin G

Violative chemical residues in edible tissues from food-producing animals are of global public health concern. Great efforts have been made to develop physiologically based pharmacokinetic (PBPK) models for estimating withdrawal intervals (WDIs) for extralabel prescribed drugs in food animals. Existing models are insufficient to address the food safety concern as these models are either limited to 1 specific drug or difficult to be used by non-modelers. This study aimed to develop a user-friendly generic PBPK platform that can predict tissue residues and estimate WDIs for multiple drugs including flunixin, florfenicol, and penicillin G in cattle and swine. Mechanism-based in silico methods were used to predict tissue/plasma partition coefficients and the models were calibrated and evaluated with pharmacokinetic data from Food Animal Residue Avoidance Databank (FARAD). Results showed that model predictions were, in general, within a 2-fold factor of experimental data for all 3 drugs in both species. Following extralabel administration and respective U.S. FDA-approved tolerances, predicted WDIs for both cattle and swine were close to or slightly longer than FDA-approved label withdrawal times (eg, predicted 8, 28, and 7 days vs labeled 4, 28, and 4 days for flunixin, florfenicol, and penicillin G in cattle, respectively). The final model was converted to a web-based interactive generic PBPK platform. This PBPK platform serves as a user-friendly quantitative tool for real-time predictions of WDIs for flunixin, florfenicol, and penicillin G following FDA-approved label or extralabel use in both cattle and swine, and provides a basis for extrapolating to other drugs and species.

Comparative Pharmacokinetics of Sulfadiazine and Its Metabolite N4-Acetyl Sulfadiazine in Grass Carp (Ctenopharyngodon idella) at Different Temperatures after Oral Administration

In this study, the plasma pharmacokinetics and tissue disposition of sulfadiazine (SDZ) and its main metabolite, N₄-acetyl sulfadiazine (ACT-SDZ), were compared between 18 and 24 °C following a single oral administration of SDZ at 50 mg/kg in grass carp (Ctenopharyngodon idella). The plasma and tissues were sampled from 0.167 h up to 96 h and analyzed by ultra-performance liquid chromatography with an ultraviolet detector. The pharmacokinetic parameters were estimated using a one-compartmental approach. Results showed that pharmacokinetics of SDZ and ACT-SDZ in plasma and tissues were notably influenced by the increase of temperature. The increased temperature shortened the absorption half-life (K01_HL) of SDZ and ACT-SDZ in gill, kidney, and plasma, but increased in liver and muscle + skin. The elimination half-life (K10_HF) and the area under concentration-time curve (AUC_0–∞) of SDZ and ACT-SDZ all presented a declined trend. The apparent volume of distribution (V_F) of SDZ in plasma was increased from 0.93 to 1.64 L/kg, and the apparent systemic total body clearance (Cl_F) was also increased from 0.01 to 0.05 L/h/kg. Overall, the rise of temperature decreased K10_HF, AUC_0–∞ of SDZ, and ACT-SDZ in plasma and tissues, but increased V_F and Cl_F in the plasma for SDZ.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Background

Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.

Methods

This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.

Results

The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.

Conclusion

This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

Increasing application of pediatric physiologically based pharmacokinetic models across academic and industry organizations

There has been a significant increase in the use of physiologically based pharmacokinetic (PBPK) models during the past 20 years, especially for pediatrics. The aim of this study was to give a detailed overview of the growth and areas of application of pediatric PBPK (P‐PBPK) models. A total of 181 publications and publicly available regulatory reviews were identified and categorized according to year, author affiliation, platform, and primary application of the P‐PBPK model (in clinical settings, drug development or to advance pediatric model development in general). Secondary application areas, including dose selection, biologics, and drug interactions, were also assessed. The growth rate for P‐PBPK modeling increased 33‐fold between 2005 and 2020; this was mainly attributed to growth in clinical and drug development applications. For primary applications, 50% of articles were classified under clinical, 18% under drug development, and 33% under model development. The most common secondary applications were dose selection (75% drug development), pharmacokinetic prediction and covariate identification (47% clinical), and model parameter identification (68% model development), respectively. Although population PK modeling remains the mainstay of approaches supporting pediatric drug development, the data presented here demonstrate the widespread application of P‐PBPK models in both drug development and clinical settings. Although applications for pharmacokinetic and drug–drug interaction predictions in pediatrics is advocated, this approach remains underused in areas such as assessment of pediatric formulations, toxicology, and trial design. The increasing number of publications supporting the development and refinement of the pediatric model parameters can only serve to enhance optimal use of P‐PBPK models.

Simultaneous toxicokinetics characterization of acrylamide and its primary metabolites using a novel microdialysis isotope-dilution liquid chromatography mass spectrometry method

Acrylamide (AA) is a toxicant in high-temperature processed foods and an animal carcinogen. Upon absorption, AA is metabolized to glycidamide (GA) or conjugates with glutathione (AA-GSH). Important advantages of microdialysis coupled with liquid chromatography-tandem mass spectrometry (MD-LC-MS/MS) include its minimization of potential losses during sample collection, storage and preparation, as well as an improvement in temporal resolution for toxicokinetics (TKs). We aimed to simultaneously study the TKs of AA and products of its primary metabolism using an isotope-dilution (ID) MD-LC-MS/MS method. MD probes implanted into the jugular vein/right atrium of anesthetized Sprague Dawley rats were connected to the ID-LC-MS/MS for continuous monitoring of AA, GA and AA-GSH in the blood every 15 min over 8 h following intraperitoneal AA administration (0.1 mg/kg or 5 mg/kg). AA, GA, and AA-GSH TKs followed linear kinetics: GA AUC/AA AUC = 0.11 and AA-GSH AUC/AA AUC = 0.011 at 5 mg/kg. Elimination half-life (T_e1/2) values were 2.44 ± 0.70, 4.93 ± 2.37 and 3.47 ± 1.47 h for AA, GA and AA-GSH, respectively. GA TKs reached a plateau at 3-6 h, suggesting that metabolic saturation of AA and T_e1/2 values of the analytes were prolonged with AA at 5 mg/kg. Our results demonstrate that oxidation of AA to GA overwhelmed the conjugation of AA with GSH. Our innovative MD-ID-LC-MS/MS method facilitates the simultaneous characterization of multiple TKs associated with toxicants and their active metabolites with excellent temporal resolution to capture metabolic saturation of AA to GA.

A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage

Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel^® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.

Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks

This study describes the development and intercomparison of generic physiologically-based toxicokinetic (PBTK) models for humans comprised of internally consistent one-compartment (1Co-) and multi-compartment (MCo-) implementations (G-PBTK). The G-PBTK models were parameterized for an adult male (70 kg) using common physiological parameters and in vitro biotransformation rate estimates and subsequently evaluated using independent concentration versus time data (n = 6) and total elimination half-lives (n = 15) for diverse organic chemicals. The model performance is acceptable considering the inherent uncertainty in the biotransformation rate data and the absence of model calibration. The G-PBTK model was then applied using hypothetical neutral organics, acidic ionizable organics and basic ionizable organics (IOCs) to identify combinations of partitioning properties and biotransformation rates leading to substantial discrepancies between 1Co- and MCo-PBTK calculations for whole body concentrations and half-lives. The 1Co- and MCo-PBTK model calculations for key toxicokinetic parameters are broadly consistent unless biotransformation is rapid (e.g., half-life less than five days). When half-lives are relatively short, discrepancies are greatest for the neutral organics and least for the acidic IOCs which follows from the estimated volumes of distribution (e.g., VD_SS = 9.6-15.4 L/kg vs 0.3-1.6 L/kg for the neutral and acidic compounds respectively) and the related approach to internal chemical equilibrium. The model intercomparisons demonstrate that 1Co-PBTK models can be applied with confidence to many exposure scenarios, particularly those focused on chronic or repeat exposures and for prioritization and screening-level decision contexts. However, MCo-PBTK models may be necessary in certain contexts, particularly for intermittent, short-term and highly variable exposures. A key recommendation to guide model selection and the development of tiered PBTK modeling frameworks that emerges from this study is the need to harmonize models with respect to parameterization and process descriptions to the greatest extent possible when proceeding from the application of simpler to more complex modeling tools as part of chemical assessment activities.

In vitro metabolism of lidocaine in subcellular post-mitochondrial fractions and precision cut slices from cattle liver

In vitro models are widely used to study the biotransformation of xenobiotics and to provide input parameters to physiologically based kinetic models required to predict the kinetic behavior in vivo. For farm animals this is not common practice yet. The use of slaughterhouse-derived tissue material may provide opportunities to study biotransformation reactions in farm animals. The goal of the present study was to explore the potential of slaughterhouse-derived bovine liver S9 (S9) and precision cut liver slices (PCLSs) to capture observed biotransformation reactions of lidocaine in cows. The in vitro data obtained with both S9 and PCLSs confirm in vivo findings that 2,6-dimethylaniline (DMA) is an important metabolite of lidocaine in cows, being for both PCLSs and S9 the end-product. In case of S9, also conversion of lidocaine to lidocaine-N-oxide and monoethylglycinexylidine (MEXG) was observed. MEGX is considered as intermediate for DMA formation, given that this metabolite was metabolized to DMA by both PLCSs and S9. In contrast to in vivo, no in vitro conversion of DMA to 4-OH-DMA was observed. Further work is needed to explain this lack of conversion and to further evaluate the use of slaughterhouse-derived tissue materials to predict the biotransformation of xenobiotics in farm animals.

A Scoping Review of Technologies and Their Applicability for Exposome-Based Risk Assessment in the Oil and Gas Industry

INTRODUCTION

Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry.

METHOD

A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019.

RESULTS

In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector.

DISCUSSION AND CONCLUSION

New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Application of physiologically based pharmacokinetic models to promote the development of veterinary drugs with high efficacy and safety

Physiologically based pharmacokinetic (PBPK) models have become important tools for the development of novel human drugs. Food-producing animals and pets comprise an important part of human life, and the development of veterinary drugs (VDs) has greatly impacted human health. Owing to increased affordability of and demand for drug development, VD manufacturing companies should have more PBPK models required to reduce drug production costs. So far, little attention has been paid on applying PBPK models for the development of VDs. This review begins with the development processes of VDs; then summarizes case studies of PBPK models in human or VD development; and analyzes the application, potential, and advantages of PBPK in VD development, including candidate screening, formulation optimization, food effects, target-species safety, and dosing optimization. Then, the challenges of applying the PBPK model to VD development are discussed. Finally, future opportunities of PBPK models in designing dosing regimens for intracellular pathogenic infections and for efficient oral absorption of VDs are further forecasted. This review will be relevant to readers who are interested in using a PBPK model to develop new VDs.

Modeling Pharmacokinetic Natural Product-Drug Interactions for Decision-Making: A NaPDI Center Recommended Approach

The popularity of botanical and other purported medicinal natural products (NPs) continues to grow, especially among patients with chronic illnesses and patients managed on complex prescription drug regimens. With few exceptions, the risk of a given NP to precipitate a clinically significant pharmacokinetic NP-drug interaction (NPDI) remains understudied or unknown. Application of static or dynamic mathematical models to predict and/or simulate NPDIs can provide critical information about the potential clinical significance of these complex interactions. However, methods used to conduct such predictions or simulations are highly variable. Additionally, published reports using mathematical models to interrogate NPDIs are not always sufficiently detailed to ensure reproducibility. Consequently, guidelines are needed to inform the conduct and reporting of these modeling efforts. This recommended approach from the Center of Excellence for Natural Product Drug Interaction Research describes a systematic method for using mathematical models to interpret the interaction risk of NPs as precipitants of potential clinically significant pharmacokinetic NPDIs. A framework for developing and applying pharmacokinetic NPDI models is presented with the aim of promoting accuracy, reproducibility, and generalizability in the literature. SIGNIFICANCE STATEMENT: Many natural products (NPs) contain phytoconstituents that can increase or decrease systemic or tissue exposure to, and potentially the efficacy of, a pharmaceutical drug; however, no regulatory agency guidelines exist to assist in predicting the risk of these complex interactions. This recommended approach from a multi-institutional consortium designated by National Institutes of Health as the Center of Excellence for Natural Product Drug Interaction Research provides a framework for modeling pharmacokinetic NP-drug interactions.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

Physiological parameter values for physiologically based pharmacokinetic models in food-producing animals. Part III: Sheep and goat

This report is the third in a series of studies that aimed to compile physiological parameters related to develop physiologically based pharmacokinetic (PBPK) models for drugs and environmental chemicals in food‐producing animals including swine and cattle (Part I), chickens and turkeys (Part II), and finally sheep and goats (the focus of this manuscript). Literature searches were conducted in multiple databases (PubMed, Google Scholar, ScienceDirect, and ProQuest), with data on relevant parameters including body weight, relative organ weight (% of body weight), cardiac output, relative organ blood flow (% of cardiac output), residual blood volume (% of organ weight), and hematocrit reviewed and statistically summarized. The mean and standard deviation of each parameter are presented in tables. Equations describing the growth curves of sheep and goats are presented in figures. When data are sufficient, parameter values are reported for different ages or production classes of sheep, including fetal sheep, lambs, and market‐age sheep (mature sheep). These data provide a reference database for developing standardized PBPK models to predict drug withdrawal intervals in sheep and goats, and also provide a basis for extrapolating PBPK models from major species such as cattle to minor species such as sheep and goats.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

PBPK model reporting template for chemical risk assessment applications

Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.

Silver Diamine Fluoride in Children Using Physiologically Based PK Modeling

Silver diamine fluoride (SDF) is used topically to prevent or arrest dental caries and has been tested clinically in toddlers to elderly adults. Following SDF application, small quantities of silver can be swallowed and absorbed. To monitor silver concentrations, pharmacokinetic studies can be performed. However, pharmacokinetic studies are time-consuming, resource intensive, and challenging to perform in young children. The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict silver disposition in children. The PBPK model for silver was developed using Simcyp software (version 17.0) based on information obtained from literature sources. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data from published rat and human data following intravenous or oral silver administration. The predicted silver concentrations were within 2-fold of observed blood and tissue silver concentrations in rats and within the 95% confidence interval of observed plasma silver concentrations in healthy human adults. The PBPK model was applied to the pediatric population by accounting for developmental physiological changes. For a given SDF dose, the simulated peak silver concentrations were 5.2-, 4.3-, 2.7-, and 1.3-fold higher in children aged 1 to 2, 2 to 4, 5 to 10, and 12 to 17 y, respectively, compared to adults. As silver is reportedly excreted in the bile, the half-life of silver was comparable in all ages and plasma and tissue silver concentrations were predicted to return to baseline levels within 2 wk after SDF application. The simulation in children suggests that conventional SDF application to teeth to prevent or arrest dental caries results in plasma and tissue silver concentrations lower than toxic concentrations. PBPK modeling offers a novel approach to studying dental exposures in younger children, where pharmacokinetic studies would be difficult to conduct.

Impact of stressors in the aviation environment on xenobiotic dosimetry in humans: physiologically based prediction of the effect of barometric pressure or altitude

Standard health risks from volatile organic compounds (VOCs) are generally interpreted at ambient environmental conditions. The aim of this study was to develop a strategy for using physiologically based pharmacokinetic (PBPK) modeling to compare known risks in the general population to calculated risks in pilots experiencing pressure-based stressors. PBPK models facilitate these comparisons by prediction of how target-tissue specific doses are altered when a stressor, such as high altitude, produces changes in physiological parameters. Cardiac output, regional blood flow, and alveolar ventilation rate following acute exposure to altitude ranging from moderate to extremely high were estimated from published data from 52 groups of human subjects. Scenarios where pilots might inhale toluene, 1,2,4-trimethylbenzene (1,2,4-TMB), or cyclohexane during routine military flight training were simulated. At the recommended Threshold Limit Values (TLV), arterial blood concentrations were predicted to be higher for exposure at 15000 ft (4572 m) than at sea level. The differences were greater for toluene and TMB, which have higher blood: air and fat: blood partition coefficients than less lipophilic cyclohexane. In summary, quantitative approaches to internal dosimetry prediction that take advantage of existing knowledge of physiological changes induced by occupational stressors possess potential as tools in performing a human health risk assessment.

Physiological parameter values for physiologically based pharmacokinetic models in food-producing animals. Part I: Cattle and swine

Physiologically based pharmacokinetic (PBPK) models for chemicals in food animals are a useful tool in estimating chemical tissue residues and withdrawal intervals. Physiological parameters such as organ weights and blood flows are an important component of a PBPK model. The objective of this study was to compile PBPK‐related physiological parameter data in food animals, including cattle and swine. Comprehensive literature searches were performed in PubMed, Google Scholar, ScienceDirect, and ProQuest. Relevant literature was reviewed and tables of relevant parameters such as relative organ weights (% of body weight) and relative blood flows (% of cardiac output) were compiled for different production classes of cattle and swine. The mean and standard deviation of each parameter were calculated to characterize their variability and uncertainty and to allow investigators to conduct population PBPK analysis via Monte Carlo simulations. Regression equations using weight or age were created for parameters having sufficient data. These compiled data provide a comprehensive physiological parameter database for developing PBPK models of chemicals in cattle and swine to support animal‐derived food safety assessment. This work also provides a basis to compile data in other food animal species, including goats, sheep, chickens, and turkeys.

Methods for evaluating variability in human health dose-response characterization

The Reference Dose (RfD) and Reference Concentration (RfC) are human health reference values (RfVs) representing exposure concentrations at or below which there is presumed to be little risk of adverse effects in the general human population. The 2009 National Research Council report Science and Decisions recommended redefining RfVs as "a risk-specific dose (for example, the dose associated with a 1 in 100,000 risk of a particular end point)." Distributions representing variability in human response to environmental contaminant exposures are critical for deriving risk-specific doses. Existing distributions estimating the extent of human toxicokinetic and toxicodynamic variability are based largely on controlled human exposure studies of pharmaceuticals. New data and methods have been developed that are designed to improve estimation of the quantitative variability in human response to environmental chemical exposures. Categories of research with potential to provide new database useful for developing updated human variability distributions include controlled human experiments, human epidemiology, animal models of genetic variability, in vitro estimates of toxicodynamic variability, and in vitro-based models of toxicokinetic variability. In vitro approaches, with further development including studies of different cell types and endpoints, and approaches to incorporate non-genetic sources of variability, appear to provide the greatest opportunity for substantial near-term advances.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.

A Petri Net Approach to Physiologically Based Toxicokinetic Modeling

Physiologically based toxicokinetic (PBTK) modeling enables researchers to predict internal tissue concentrations for various species exposed to exogenous compounds through different routes at varying concentrations without having to run in vivo experiments for each scenario. Parameters for the models may be gathered from in vivo or in vitro measurements, cross-species or cross-chemical extrapolations, literature reviews, or other models. The PBTK models, described using ordinary differential equations (ODEs), are then simulated using these parameters for a given compound/exposure/species scenario. Although they are potentially useful for regulatory toxicology, the complexity of ODE programming and simulation remains a barrier for many would-be researchers. Petri nets, a graphical modeling framework, offers a more intuitive approach to PBTK modeling. To demonstrate their utility and ease of use, we present a model of waterborne fluoranthene exposure to rainbow trout (Oncorhynchus mykiss) written and simulated in Snoopy, a graphical Petri net development and simulation software package. We converted an existing ODE PBTK model and evaluated the Petri net model against the ODE model results. The simulated tissue concentrations of the Petri net model closely mirrored the simulated concentrations of the ODE model. To convert the ODE model to a Petri net model, we introduced a new parameter, blood volume (V _BLOOD ). Sensitivity analysis found V _BLOOD to be very robust when varied over an order of magnitude. The resulting Petri net PBTK model has a number of advantages over ODE models, while maintaining equivalent predictive functionality. Environ Toxicol Chem 2019;00:1-10. © 2019 SETAC.

Integration of Food Animal Residue Avoidance Databank (FARAD) empirical methods for drug withdrawal interval determination with a mechanistic population-based interactive physiologically based pharmacokinetic (iPBPK) modeling platform: example for flunixin meglumine administration

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

Physiologically based mathematical models of nanomaterials for regulatory toxicology: A review

The development of physiologically based (PB) models to support safety assessments in the field of nanotechnology has grown steadily during the last decade. This review reports on the availability of PB models for toxicokinetic (TK) and toxicodynamic (TD) processes, including in vitro and in vivo dosimetry models applied to manufactured nanomaterials (MNs). In addition to reporting on the state-of-the-art in the scientific literature concerning the availability of physiologically based kinetic (PBK) models, we evaluate their relevance for regulatory applications, mainly considering the EU REACH regulation. First, we performed a literature search to identify all available PBK models. Then, we systematically reported the content of the identified papers in a tailored template to build a consistent inventory, thereby supporting model comparison. We also described model availability for physiologically based dynamic (PBD) and in vitro and in vivo dosimetry models according to the same template. For completeness, a number of classical toxicokinetic (CTK) models were also included in the inventory. The review describes the PBK model landscape applied to MNs on the basis of the type of MNs covered by the models, their stated applicability domain, the type of (nano-specific) inputs required, and the type of outputs generated. We identify the main assumptions made during model development that may influence the uncertainty in the final assessment, and we assess the REACH relevance of the available models within each model category. Finally, we compare the state of PB model acceptance for chemicals and for MNs. In general, PB model acceptance is limited by the absence of standardised reporting formats, psychological factors such as the complexity of the models, and technical considerations such as lack of blood:tissue partitioning data for model calibration/validation.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

PKQuest: PBPK modeling of highly lipid soluble and extracellular solutes

One of the primary objectives of physiologically based pharmacokinetics (PBPK) is the prediction of a drug’s pharmacokinetics just from knowledge of its physicochemical structure. Unfortunately, at present, the accuracy of this prediction is limited for most drugs because of uncertainty about the drug’s organ/blood partition coefficient (K). However, there are two classes of solutes which are exceptions to this: 1) the highly lipid soluble (HLS) solutes, and 2) the extracellular (ECS) solutes. Since the HLS drugs (eg, volatile anesthetics, propofol, cannabinol) have lipid/water partition coefficients (P_L/W) of 100 or greater, their K is dominated by the tissue fat fraction and one can accurately predict K just from in vitro measurements of P_L/W along with prior anatomic measurements of the fat fraction of the organs in the PBPK model. Since the ECS drugs, such as most antibiotics, cannot penetrate cells, they are not subject to the intracellular binding that complicates the prediction of K for the weak bases and acids. The ECS K is determined primarily by plasma and interstitial albumin binding and can be predicted from in vitro measurements of plasma albumin binding along with prior measurements of interstitial tissue volume and albumin concentrations. This review provides an in depth discussion of the PBPK modeling of these two drug classes along with many specific clinical examples illustrating the good PBPK predictions possible with just zero (volatile anesthetics) or 1 (the clearance) adjustable parameter. The PBPK analysis uses PKQuest, a freely distributed, general purpose pharmacokinetic program. PKQuest is designed so that application to the HLS and ECS solute classes is especially easy. The user only needs to enter the specific parameters that are required to characterize the drug (eg, P_L/W for HLS or plasma albumin binding for ECS) with all the other PBPK parameters (organ blood flow, fat fraction, extracellular volumes, etc.) are set by default.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.