Combining transcriptomics and PBPK modeling indicates a primary role of hypoxia and altered circadian signaling in dichloromethane carcinogenicity in mouse lung and liver

Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs

Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.

Principles of dose-setting in toxicology studies: the importance of kinetics for ensuring human safety

Regulatory toxicology seeks to ensure that exposures to chemicals encountered in the environment, in the workplace, or in products pose no significant hazards and produce no harm to humans or other organisms, i.e., that chemicals are used safely. The most practical and direct means of ensuring that hazards and harms are avoided is to identify the doses and conditions under which chemical toxicity does not occur so that chemical concentrations and exposures can be appropriately limited. Modern advancements in pharmacology and toxicology have revealed that the rates and mechanisms by which organisms absorb, distribute, metabolize and eliminate chemicals-i.e., the field of kinetics-often determine the doses and conditions under which hazard, and harm, are absent, i.e., the safe dose range. Since kinetics, like chemical hazard and toxicity, are extensive properties that depend on the amount of the chemical encountered, it is possible to identify the maximum dose under which organisms can efficiently metabolize and eliminate the chemicals to which they are exposed, a dose that has been referred to as the kinetic maximum dose, or KMD. This review explains the rationale that compels regulatory toxicology to embrace the advancements made possible by kinetics, why understanding the kinetic relationship between the blood level produced and the administered dose of a chemical is essential for identifying the safe dose range, and why dose-setting in regulatory toxicology studies should be informed by estimates of the KMD rather than rely on the flawed concept of maximum-tolerated toxic dose, or MTD.

Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology

Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

Environmental chemicals affect circadian rhythms: An underexplored effect influencing health and fitness in animals and humans

Circadian rhythms control the life of virtually all organisms. They regulate numerous aspects ranging from cellular processes to reproduction and behavior. Besides the light-dark cycle, there are additional environmental factors that regulate the circadian rhythms in animals as well as humans. Here, we outline the circadian rhythm system and considers zebrafish (Danio rerio) as a representative vertebrate organism. We characterize multiple physiological processes, which are affected by circadian rhythm disrupting compounds (circadian disrupters). We focus on and summarize 40 natural and anthropogenic environmental circadian disrupters in fish. They can be divided into six major categories: steroid hormones, metals, pesticides and biocides, polychlorinated biphenyls, neuroactive drugs and other compounds such as cyanobacterial toxins and bisphenol A. Steroid hormones as well as metals are most studied. Especially for progestins and glucocorticoids, circadian dysregulation was demonstrated in zebrafish on the molecular and physiological level, which comprise mainly behavioral alterations. Our review summarizes the current state of knowledge on circadian disrupters, highlights their risks to fish and identifies knowledge gaps in animals and humans. While most studies focus on transcriptional and behavioral alterations, additional effects and consequences are underexplored. Forthcoming studies should explore, which additional environmental circadian disrupters exist. They should clarify the underlying molecular mechanisms and aim to better understand the consequences for physiological processes.

Evaluation of the carcinogenicity of dichloromethane in rats, mice, hamsters and humans

Dichloromethane (DCM) is a high production volume chemical (>1000 t/a) mainly used as an industrial solvent. Carcinogenicity studies in rats, mice and hamsters have demonstrated a malignant tumor inducing potential of DCM only in the mouse (lung and liver) at 1000-4000 ppm whereas human data do not support a conclusion of cancer risk. Based on this, DCM has been classified as a cat. 2 carcinogen. Dose-dependent toxicokinetics of DCM suggest that DCM is a threshold carcinogen in mice, initiating carcinogenicity via the low affinity/high capacity GSTT1 pathway; a biotransformation pathway that becomes relevant only at high exposure concentrations. Rats and hamsters have very low activities of this DCM-metabolizing GST and humans have even lower activities of this enzyme. Based on the induction of specific tumors selectively in the mouse, the dose- and species-specific toxicokinetics in this species, and the absence of a malignant tumor response by DCM in rats and hamsters having a closer relationship to DCM toxicokinetics in humans and thus being a more relevant animal model, the current classification of DCM as human carcinogen cat. 2 remains appropriate.

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

The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has thus far occurred for only a few environmental chemicals. In order to encourage decision-makers to embrace the critical role of PBPK modeling in risk assessment, several important challenges require immediate attention from the modeling community. The objective of this contemporary review is to highlight 3 of these challenges, including: (1) difficulties in recruiting peer reviewers with appropriate modeling expertise and experience; (2) lack of confidence in PBPK models for which no tissue/plasma concentration data exist for model evaluation; and (3) lack of transferability across modeling platforms. Several recommendations for addressing these 3 issues are provided to initiate dialog among members of the PBPK modeling community, as these issues must be overcome for the field of PBPK modeling to advance and for PBPK models to be more routinely applied in support of public health decision-making.

A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

Redox regulation of circadian molecular clock in chronic airway diseases

At the cellular level, circadian timing is maintained by the molecular clock, a family of interacting clock gene transcription factors, nuclear receptors and kinases called clock genes. Daily rhythms in pulmonary function are dictated by the circadian timing system, including rhythmic susceptibility to the harmful effects of airborne pollutants, exacerbations in patients with chronic airway disease and the immune-inflammatory response to infection. Further, evidence strongly suggests that the circadian molecular clock has a robust reciprocal interaction with redox signaling and plays a considerable role in the response to oxidative/carbonyl stress. Disruption of the circadian timing system, particularly in airway cells, impairs pulmonary rhythms and lung function, enhances oxidative stress due to airway inhaled pollutants like cigarette smoke and airborne particulate matter and leads to enhanced inflammosenescence, inflammasome activation, DNA damage and fibrosis. Herein, we briefly review recent evidence supporting the role of the lung molecular clock and redox signaling in regulating inflammation, oxidative stress, and DNA damage responses in lung diseases and their exacerbations. We further describe the potential for clock genes as novel biomarkers and therapeutic targets for the treatment of chronic lung diseases.

Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.