Jeremy A. Leonard

A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways

Background Adverse outcome pathways (AOPs) link adverse effects in individuals or populations to a molecular initiating event (MIE) that can be quantified using in vitro methods. Practical application of AOPs in chemical-specific risk assessment requires incorporation of knowledge on exposure, along with absorption, distribution, metabolism, and excretion (ADME) properties of chemicals. Objectives We developed a conceptual workflow to examine exposure and ADME properties in relation to an MIE. The utility of this workflow was evaluated using a previously established AOP, acetylcholinesterase (AChE) inhibition. Methods Thirty chemicals found to inhibit human AChE in the ToxCast™ assay were examined with respect to their exposure, absorption potential, and ability to cross the blood–brain barrier (BBB). Structures of active chemicals were compared against structures of 1,029 inactive chemicals to detect possible parent compounds that might have active metabolites. Results Application of the workflow screened 10 “low-priority” chemicals of 30 active chemicals. Fifty-two of the 1,029 inactive chemicals exhibited a similarity threshold of ≥ 75% with their nearest active neighbors. Of these 52 compounds, 30 were excluded due to poor absorption or distribution. The remaining 22 compounds may inhibit AChE in vivo either directly or as a result of metabolic activation. Conclusions The incorporation of exposure and ADME properties into the conceptual workflow eliminated 10 “low-priority” chemicals that may otherwise have undergone additional, resource-consuming analyses. Our workflow also increased confidence in interpretation of in vitro results by identifying possible “false negatives.” Citation Phillips MB, Leonard JA, Grulke CM, Chang DT, Edwards SW, Brooks R, Goldsmith MR, El-Masri H, Tan YM. 2016. A workflow to investigate exposure and pharmacokinetic influences on high-throughput in vitro chemical screening based on adverse outcome pathways. Environ Health Perspect 124:53–60; http://dx.doi.org/10.1289/ehp.1409450

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Reconstructing exposures from biomarkers using exposure-pharmacokinetic modeling – A case study with carbaryl

Sources of uncertainty involved in exposure reconstruction for short half-life chemicals were characterized using computational models that link external exposures to biomarkers. Using carbaryl as an example, an exposure model, the Cumulative and Aggregate Risk Evaluation System (CARES), was used to generate time-concentration profiles for 500 virtual individuals exposed to carbaryl. These exposure profiles were used as inputs into a physiologically based pharmacokinetic (PBPK) model to predict urinary biomarker concentrations. These matching dietary intake levels and biomarker concentrations were used to (1) compare three reverse dosimetry approaches based on their ability to predict the central tendency of the intake dose distribution; and (2) identify parameters necessary for a more accurate exposure reconstruction. This study illustrates the trade-offs between using non-iterative reverse dosimetry methods that are fast, less precise and iterative methods that are slow, more precise. This study also intimates the necessity of including urine flow rate and elapsed time between last dose and urine sampling as part of the biomarker sampling collection for better interpretation of urinary biomarker data of short biological half-life chemicals. Resolution of these critical data gaps can allow exposure reconstruction methods to better predict population-level intake doses from large biomonitoring studies.

Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications

ABSTRACT Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over the last several decades, especially in conjunction with emerging alternative methods to animal testing, such as in vitro studies and data‐driven in silico quantitative‐structure‐activity‐relationship (QSAR) predictions. Experimental information derived from these new approach methods can be used as input for model parameters and allows for increased confidence in models for chemicals that did not have in vivo data for model calibration. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. Here, the results of a survey disseminated to the modelling community are presented in order to inform the frequency of use and applications of PBK models in science and regulatory submission. Additionally, the survey was designed to identify a network of investigators involved in PBK modelling and knowledgeable of GMP so that they might be contacted in the future for peer review of PBK models, especially in regards to vetting the models to such a degree as to gain a greater acceptance for regulatory purposes. Graphical abstract Figure. No caption available. HighlightsPhysiologically Based kinetic (PBK) models are used widely in academia, industry, and government.Good modelling practice (GMP) for model development and evaluation continues to expand.Further guidance for establishing GMP is called for.There remains some reluctance among regulatory agencies to use PBK models.The next generation of PBK models could be developed using only data from in vitro and in silico methods.

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.

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.

Tiered Approaches to Incorporate the Adverse Outcome Pathway Framework into Chemical-Specific Risk-Based Decision Making

The concept of Adverse Outcome Pathways (AOPs) arose as a means of addressing the challenges associated with establishing relationships between high-throughout (HT) in vitro dose response data and in vivo biological outcomes. However, AOP development has also been met with challenges of its own, such as the time, effort, and expertise necessary to achieve a scientifically sound construct able to support ecotoxicology and human health risk assessment. Thus, a staged development process has been developed to match the information content of an AOP with the decision context in which it will be used. This approach allows effort to be spent on detailed evidence evaluation and quantitative assessment of the dose-response characteristics for those AOPs where this level of confidence and precision is needed. In addition, through advances in computational analytical methodologies that integrate HT data (e.g., transcriptomic data) with traditional toxicology information spanning a broad chemical and biological space, computationally predicted AOPs can be rapidly generated to help accelerate the curation of AOPs. AOPs are chemical agnostic thereby allowing a single AOP to be coupled with in vitro dose-response information from a variety of chemicals. To predict an in vivo outcome, however, exposure and pharmacokinetic characteristics (i.e., absorption, metabolism, distribution, and elimination) must be considered. As with the staged development process for AOPs, it is possible to develop ADME predictions in a tiered manner such that lower tiers provide qualitative or semi-quantitative predictions when data is lacking, and higher tiers provide quantitative predictions with increasing confidence when data is abundant. Tiered approaches to AOP development and ADME predictions provide a mechanism for using AOPs, with chemical-specific exposure and pharmacokinetic considerations, for risk assessment both in data poor and data rich scenarios. They also provide a natural mechanism for identifying areas of research that would have the highest impact on risk-based decision making by highlighting AOPs and/or ADME predictions that are insufficient to address the decision context in which they could be used.

Aggregate exposure pathways in support of risk assessment

Over time, risk assessment has shifted from establishing relationships between exposure to a single chemical and a resulting adverse health outcome, to evaluation of multiple chemicals and disease outcomes simultaneously. As a result, there is an increasing need to better understand the complex mechanisms that influence risk of chemical and non-chemical stressors, beginning at their source and ending at a biological endpoint relevant to human or ecosystem health risk assessment. Just as the Adverse Outcome Pathway (AOP) framework has emerged as a means of providing insight into mechanism-based toxicity, the exposure science community has seen the recent introduction of the Aggregate Exposure Pathway (AEP) framework. AEPs aid in making exposure data applicable to the FAIR (i.e., findable, accessible, interoperable, and reusable) principle, especially by (1) organizing continuous flow of disjointed exposure information;(2) identifying data gaps, to focus resources on acquiring the most relevant data; (3) optimizing use and repurposing of existing exposure data; and (4) facilitating interoperability among predictive models. Herein, we discuss integration of the AOP and AEP frameworks and how such integration can improve confidence in both traditional and cumulative risk assessment approaches.

CHAPTER 4:Linking Environmental Exposure to Toxicity

As the number of chemicals and environmental toxicants in commerce continue to increase, so does the need to understand the links between exposure to these stressors and any potential toxic reactions. Assessing the impact of these stressors on public health as well as our environment requires an understanding of the underlying mechanistic processes connecting their introduction into the environment to the associated adverse outcomes.Traditional in vivo methods of toxicity testing have become too costly and inefficient. In recent times, in vitro high-throughput toxicity screening methods have been introduced to reduce the burden of in vivo testing and keep pace with the ever increasing number of required tests. The adverse outcome pathway (AOP) concept has been adopted by many in the toxicology community as a framework for linking the biological events that occur from the point of contact with these stressors and the resulting adverse outcome. This provides a mechanistic framework for understanding the potential impacts of perturbations that are measured via in vitro testing strategies. The aggregate exposure pathway (AEP) has been proposed as a companion framework to the AOP. The goal of the AEP is to describe the path the introduction of the stressor into the environment at its source to a target site within an individual that is comparable with the concentrations in the in vitro toxicity tests. Together, these frameworks provide a comprehensive view of the source to adverse outcome continuum.Standardizing our representation of the mechanistic information in this way allows for increased interoperability for computational models describing different parts of the system. It also aids in translating new research in exposure science and toxicology for risk assessors and decision makers when assessing the impact of specific stressors on endpoints of regulatory significance.

A workflow for identifying metabolically active chemicals to complement in vitro toxicity screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.