Challenges and opportunities for overcoming dog use in agrochemical evaluation and registration.

Progress in developing new tools, assays, and approaches to assess human hazard and health risk provides an opportunity to re-evaluate the necessity of dog studies for the safety evaluation of agrochemicals. A workshop was held where partic­ipants discussed the strengths and limitations of past use of dogs for pesticide evaluations and registrations. Opportunities were identified to support alternative approaches to answer human safety questions without performing the required 90-day dog study. Development of a decision tree for determining when the dog study might not be necessary to inform pesticide safety and risk assessment was proposed. Such a process will require global regulatory authority participation to lead to its acceptance. The identification of unique effects in dogs that are not identified in rodents will need further evaluation and determination of their relevance to humans. The establishment of in vitro and in silico approaches that can provide critical data on relative species sensitivity and human relevance will be an important tool to advance the decision process. Promising novel tools including in vitro comparative metabolism studies, in silico models, and high-throughput assays able to identify metabolites and mechanisms of action leading to development of adverse outcome pathways will need further development. To replace or eliminate the 90-day dog study, a collaborative, multidisciplinary, international effort that transcends organi­zations and regulatory agencies will be needed in order to develop guidance on when the study would not be necessary for human safety and risk assessment.

A systematic evidence map for the evaluation of noncancer health effects and exposures to polychlorinated biphenyl mixtures.

Assessing health outcomes associated with exposure to polychlorinated biphenyls (PCBs) is important given their persistent and ubiquitous nature. PCBs are classified as a Group 1 carcinogen, but the full range of potential noncancer health effects from exposure to PCBs has not been systematically summarized and evaluated. We used systematic review methods to identify and screen the literature using combined manual review and machine learning approaches. A protocol was developed that describes the literature search strategy and Populations, Exposures, Comparators, and Outcomes (PECO) criteria used to facilitate subsequent screening and categorization of literature into a systematic evidence map of PCB exposure and noncancer health endpoints across 15 organs/systems. A comprehensive literature search yielded 62,599 records. After electronic prioritization steps, 17,037 studies were manually screened at the title and abstract level. An additional 900 studies identified by experts or supplemental searches were also included. After full-text screening of 3889 references, 1586 studies met the PECO criteria. Relevant study details such as the endpoints assessed, exposure duration, and species were extracted into literature summary tables. This review compiles and organizes the human and mammalian studies from these tables into an evidence map for noncancer health endpoints and PCB mixture exposure to identify areas of robust research as well as areas of uncertainty that would benefit from future investigation. Summary data are available online as interactive visuals with downloadable metadata. Sufficient research is available to inform PCB hazard assessments for most organs/systems, but the amount of data to inform associations with specific endpoints differs. Furthermore, despite many years of research, sparse data exist for inhalation and dermal exposures, which are highly relevant human exposure routes. This evidence map provides a foundation for future systematic reviews and noncancer hazard assessments of PCB mixtures and for strategic planning of research to inform areas of greater uncertainty.

Systematic evidence map (SEM) template: Report format and methods used for the US EPA Integrated Risk Information System (IRIS) program, Provisional Peer Reviewed Toxicity Value (PPRTV) program, and other "fit for purpose" literature-based human health analyses.

BACKGROUND: Systematic evidence maps (SEMs) are gaining visibility in environmental health for their utility to serve as problem formulation tools and assist in decision-making, especially for priority setting. SEMs are now routinely prepared as part of the assessment development process for the US Environmental Protection Agency (EPA) Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) assessments. SEMs can also be prepared to explore the available literature for an individual chemical or groups of chemicals of emerging interest. OBJECTIVES: This document describes the typical methods used to produce SEMs for the IRIS and PPRTV Programs, as well as "fit for purpose" applications using a variety of examples drawn from existing analyses. It is intended to serve as an example base template that can be adapted as needed for the specific SEM. The presented methods include workflows intended to facilitate rapid production. The Populations, Exposures, Comparators and Outcomes (PECO) criteria are typically kept broad to identify mammalian animal bioassay and epidemiological studies that could be informative for human hazard identification. In addition, a variety of supplemental content is tracked, e.g., studies presenting information on in vitro model systems, non-mammalian model systems, exposure-level-only studies in humans, pharmacokinetic models, and absorption, distribution, metabolism, and excretion (ADME). The availability of New Approach Methods (NAMs) evidence is also tracked (e.g., high throughput, transcriptomic, in silico, etc.). Genotoxicity studies may be considered as PECO relevant or supplemental material, depending on the topic and context of the review. Standard systematic review practices (e.g., two independent reviewers per record) and specialized software applications are used to search and screen the literature and may include the use of machine learning software. Mammalian bioassay and epidemiological studies that meet the PECO criteria after full-text review are briefly summarized using structured web-based extraction forms with respect to study design and health system(s) assessed. Extracted data is available in interactive visual formats and can be downloaded in open access formats. Methods for conducting study evaluation are also presented which is conducted on a case-by-case basis, depending on the usage of the SEM.

Predicting nonlinear relationships between external and internal concentrations with physiologically based pharmacokinetic modeling.

Although external concentrations are more readily quantified and often used as the metric for regulating and mitigating exposures to environmental chemicals, the toxicological response to an environmental chemical is more directly related to its internal concentrations than the external concentration. The processes of absorption, distribution, metabolism, and excretion (ADME) determine the quantitative relationship between the external and internal concentrations, and these processes are often susceptible to saturation at high concentrations, which can lead to nonlinear changes in internal concentrations that deviate from proportionality. Using generic physiologically-based pharmacokinetic (PBPK) models, we explored how saturable absorption or clearance influence the shape of the internal to external concentration (IEC) relationship. We used the models for hypothetical chemicals to show how differences in kinetic parameters can impact the shape of an IEC relationship; and models for styrene and caffeine to explore how exposure route, frequency, and duration impact the IEC relationships in rat and human exposures. We also analyzed available plasma concentration data for 2,4-dichlorophenoxyacetic acid to demonstrate how a PBPK modeling approach can be an alternative to common statistical methods for analyzing dose proportionality. A PBPK modeling approach can be a valuable tool used in the early stages of a chemical safety assessment program to optimize the design of longer-term animal toxicity studies or to interpret study results.

Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies.

Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.

Correction to Designing QSARs for Parameters of High Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and fraction of chemical unbound in plasma (fup) serve as important parameters for high throughput toxicokinetic models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on Bioactivity: Exposure Ratios (BER), in which a BER < 1 indicates exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6631 chemicals) we found that the proportion of chemicals with BER < 1 was similar using either in silico (1337/6631; 20.16%) or in vitro (151/850; 17.76%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER < 1 or >1 using either in silico or in vitro parameters (776/850, 91.30%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment.

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Genetic Mutations in the S-loop of Human Glutathione Synthetase: Links Between Substrate Binding, Active Site Structure and Allostery.

The second step in the biosynthesis of the cellular antioxidant glutathione (GSH) is catalyzed by human glutathione synthetase (hGS), a negatively cooperative homodimer. Patients with mutations in hGS have been reported to exhibit a range of symptoms from hemolytic anemia and metabolic acidosis to neurological disorders and premature death. Several patient mutations occur in the S-loop of hGS, a series of residues near the negatively cooperative γ-GC substrate binding site. Experimental point mutations and molecular dynamic simulations show the S-loop not only binds γ-GC through a salt bridge and multiple hydrogen bonds, but the residues also modulate allosteric communication in hGS. By elucidating the role of S-loop residues in active site structure, substrate binding, and allostery, the atomic level sequence of events that leads to the detrimental effects of hGS mutations in patients are more fully understood.

The acetaminophen metabolite N-acetyl-p-benzoquinone imine (NAPQI) inhibits glutathione synthetase in vitro; a clue to the mechanism of 5-oxoprolinuric acidosis?

1. Metabolic acidosis due to accumulation of l-5-oxoproline is a rare, poorly understood, disorder associated with acetaminophen treatment in malnourished patients with chronic morbidity. l-5-Oxoprolinuria signals abnormal functioning of the γ-glutamyl cycle, which recycles and synthesises glutathione. Inhibition of glutathione synthetase (GS) by N-acetyl-p-benzoquinone imine (NAPQI) could contribute to 5-oxoprolinuric acidosis in such patients. We investigated the interaction of NAPQI with GS in vitro. 2. Peptide mapping of co-incubated NAPQI and GS using mass spectrometry demonstrated binding of NAPQI with cysteine-422 of GS, which is known to be essential for GS activity. Computational docking shows that NAPQI is properly positioned for covalent bonding with cysteine-422 via Michael addition and hence supports adduct formation. 3. Co-incubation of 0.77 µM of GS with NAPQI (25-400 µM) decreased enzyme activity by 16-89%. Inhibition correlated strongly with the concentration of NAPQI and was irreversible. 4. NAPQI binds covalently to GS causing irreversible enzyme inhibition in vitro. This is an important novel biochemical observation. It is the first indication that NAPQI may inhibit glutathione synthesis, which is pivotal in NAPQI detoxification. Further studies are required to investigate its biological significance and its role in 5-oxoprolinuric acidosis.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for Fub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

The role of strong electrostatic interactions at the dimer interface of human glutathione synthetase.

The obligate homodimer human glutathione synthetase (hGS) provides an ideal system for exploring the role of protein-protein interactions in the structural stability, activity and allostery of enzymes. The two active sites of hGS, which are 40 Šapart, display allosteric modulation by the substrate γ-glutamylcysteine (γ-GC) during the synthesis of glutathione, a key cellular antioxidant. The two subunits interact at a relatively small dimer interface dominated by electrostatic interactions between S42, R221, and D24. Alanine scans of these sites result in enzymes with decreased activity, altered γ-GC affinity, and decreased thermal stability. Molecular dynamics simulations indicate these mutations disrupt interchain bonding and impact the tertiary structure of hGS. While the ionic hydrogen bonds and salt bridges between S42, R221, and D24 do not mediate allosteric communication in hGS, these interactions have a dramatic impact on the activity and structural stability of the enzyme.