Development of PK- and PBPK-based modeling tools for derivation of biomonitoring guidance values

Lead Exposure Estimation Through a Physiologically Based Toxicokinetic Model Using Human Biomonitoring Data and Comparison with Scenario-Based Exposure Assessment: A Case Study in Korean Adults

Pb toxicity is linked to cardiovascular and nephrotoxicity issues. Exposure to this heavy metal can occur through food and drinking water. Therefore, this study aimed to evaluate Pb exposure and assess health risks in Korean adults using a physiologically based toxicokinetic (PBTK) model. Human blood Pb concentrations were monitored using the Korean National Environmental Health Survey (KoNEHS) Cycle 4. The average Pb exposure in Korean adults was 0.520 μg/kg bw/day. The PBTK results were compared with scenario-based results from the 2021 risk assessment report of five heavy metals, including Pb, conducted by the MFDS. Exposure determined through reverse dosimetry was approximately two times higher than scenario-based exposure (0.264 μg/kg bw/day). The higher exposure levels obtained during PBTK analysis may be attributed to sustained exposure within historically more contaminated living environments and the long half-life of Pb. These findings suggest that the PBTK-based method can quantify aggregated exposure levels in the body over time, potentially serving as a complementary tool to address the constraints of scenario-based assessment methods for integrated risk assessment. Moreover, this model is convenient and cost-effective compared with scenario-based exposure estimation. These findings can facilitate the application of model for tracking continuous national changes in hazardous substance levels.

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

Physiologically Based Pharmacokinetic Modeling of Salivary Concentrations for Noninvasive Biomonitoring of 2,4-Dichlorophenoxyacetic Acid (2,4-D)

Saliva has become a favorable sample matrix for biomonitoring due to its noninvasive attributes and overall flexibility in collection. To ensure measured salivary concentrations reflect the exposure, a solid understanding of the salivary transport mechanism and relationships between salivary concentrations and other monitored matrices (ie, blood, urine) is needed. Salivary transport of a commonly applied herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D), was observed in vitro and in vivo and a physiologically based pharmacokinetic (PBPK) model was developed to translate observations from the cell culture model to those in animal models and further evaluate 2,4-D kinetics in humans. Although apparent differences in experimental in vitro and in vivo saliva:plasma ratios (0.034 and 0.0079) were observed, simulations with the PBPK model demonstrated dynamic time and dose-dependent saliva:plasma ratios, elucidating key mechanisms affecting salivary transport. The model suggested that 2,4-D exhibited diffusion-limited transport to saliva and was additionally impacted by protein binding saturation and permeability across the salivary gland. Consideration of sampling times post-exposure and potential saturation of transport mechanisms are then critical aspects for interpreting salivary 2,4-D biomonitoring observations. This work utilized PBPK modeling in in vitro to in vivo translation to explore benefits and limitations of salivary analysis for occupational biomonitoring.

Plasma protein binding, metabolism, reaction phenotyping and toxicokinetic studies of fenarimol after oral and intravenous administration in rats

Fenarimol (FNL), an organic chlorinated fungicide, is widely used in agriculture for protection from fungal spores and fungi. Despite being an endocrine disruptor, no toxicokinetic data is reported for this fungicide. In the present work, we determined the plasma protein binding, metabolic pathways and toxicokinetics of FNL in rats. In vitro binding of FNL to rat and human plasma proteins was ∼90%, suggesting that FNL is a highly protein bound fungicide. The predicted in vivo hepatic clearance of FNL in rats and humans was estimated to be 36.71 and 14.39 mL/min/kg, respectively, indicating it to be an intermediate clearance compound. Reaction phenotyping assay showed that CYP3A4 mainly contributed to the overall metabolism of FNL. The oral toxicokinetic study of FNL in rats at no observed adverse effect level dose (1 mg/kg) showed maximum plasma concentration (C _max) of 33.97 ± 4.45 ng/mL at 1 h (T _max). The AUC_0-∞ obtained was 180.18 ± 17.76 h*ng/mL, whereas, the t _1/2 was ∼4.74 h. Following intravenous administration, FNL displayed a clearance of 42.48 mL/min/kg which was close to the predicted in vivo hepatic clearance. The absolute oral bioavailability of FNL at 1 mg/kg dose in rats was 45.25%. FNL at 10 mg/kg oral dose exhibited non-linear toxicokinetics with greater than dose-proportional increase in the systemic exposure (AUC_0-∞ 8270.53 ± 1798.59 h*ng/mL).

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Ionic liquids (ILs), a class of materials with unique physicochemical properties, have been used extensively in the fields of chemical engineering, biotechnology, material sciences, pharmaceutics, and many others. Because ILs are very polar by nature, they can migrate into the environment with the possibility of inclusion in the food chain and bioaccumulation in living organisms. However, the chemical natures of ILs are not quintessentially biocompatible. Therefore, the practical uses of ILs must be preceded by suitable toxicological assessments. Among different methods, the use of microorganisms to evaluate IL toxicity provides many advantages including short generation time, rapid growth, and environmental and industrial relevance. This article reviews the recent research progress on the toxicological properties of ILs toward microorganisms and highlights the computational prediction of various toxicity models.

Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.

Development of a physiologically based pharmacokinetic model for assessment of human exposure to bisphenol A

A previously developed physiologically based pharmacokinetic (PBPK) model for bisphenol A (BPA) in adult rhesus monkeys was modified to characterize the pharmacokinetics of BPA and its phase II conjugates in adult humans following oral ingestion. Coupled with in vitro studies on BPA metabolism in the liver and the small intestine, the PBPK model was parameterized using oral pharmacokinetic data with deuterated-BPA (d6-BPA) delivered in cookies to adult humans after overnight fasting. The availability of the serum concentration time course of unconjugated d6-BPA offered direct empirical evidence for the calibration of BPA model parameters. The recalibrated PBPK adult human model for BPA was then evaluated against published human pharmacokinetic studies with BPA. A hypothesis of decreased oral uptake was needed to account for the reduced peak levels observed in adult humans, where d6-BPA was delivered in soup and food was provided prior to BPA ingestion, suggesting the potential impact of dosing vehicles and/or fasting on BPA disposition. With the incorporation of Monte Carlo analysis, the recalibrated adult human model was used to address the inter-individual variability in the internal dose metrics of BPA for the U.S. general population. Model-predicted peak BPA serum levels were in the range of pM, with 95% of human variability falling within an order of magnitude. This recalibrated PBPK model for BPA in adult humans provides a scientific basis for assessing human exposure to BPA that can serve to minimize uncertainties incurred during extrapolations across doses and species.

Comprehension of drug toxicity: software and databases

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods.

Development of screening tools for the interpretation of chemical biomonitoring data

Evaluation of a larger number of chemicals in commerce from the perspective of potential human health risk has become a focus of attention in North America and Europe. Screening-level chemical risk assessment evaluations consider both exposure and hazard. Exposures are increasingly being evaluated through biomonitoring studies in humans. Interpreting human biomonitoring results requires comparison to toxicity guidance values. However, conventional chemical-specific risk assessments result in identification of toxicity-based exposure guidance values such as tolerable daily intakes (TDIs) as applied doses that cannot directly be used to evaluate exposure information provided by biomonitoring data in a health risk context. This paper describes a variety of approaches for development of screening-level exposure guidance values with translation from an external dose to a biomarker concentration framework for interpreting biomonitoring data in a risk context. Applications of tools and concepts including biomonitoring equivalents (BEs), the threshold of toxicologic concern (TTC), and generic toxicokinetic and physiologically based toxicokinetic models are described. These approaches employ varying levels of existing chemical-specific data, chemical class-specific assessments, and generic modeling tools in response to varying levels of available data in order to allow assessment and prioritization of chemical exposures for refined assessment in a risk management context.