Of the tens of thousands of chemicals in commerce or on various manufacturing inventories, only hundreds have comprehensive toxicological data or have undergone some form of human health risk assessment (Guyton et al. 2009
; Judson et al. 2009
). Most assessments that support risk-based environmental health decision making rely on epidemiologic data, experimental animal data, or both, to quantitatively estimate risk and develop toxicity values or exposure thresholds. Such assessments, including the U.S. Environmental Protection Agency’s (EPA’s) Integrated Science Assessments and Integrated Risk Information System (IRIS) toxicological reviews, are highly data-, time-, and resource-intensive. However, a need exists for reliable approaches to systematically estimate human health risks for all, rather than for just a few, chemicals. Accordingly, the National Academies recommended the “development of default approaches to support risk estimation for the large number of chemicals lacking chemical-specific information” (NRC 2009
There are a number of examples of how experimental data from short-term studies in animals or nonmammalian systems, such as the lethal dose 50% (
) or mutagenicity determinations, can be used to develop chronic toxicity estimates. For example, Zeise et al. (1984
) developed an approach to estimate the cancer potency of a chemical when only the
of that chemical is known. That study showed that a quantitative estimate of chronic cancer hazard from acute lethality information can be used to provide a conservative estimate of a “worst-case scenario” to guide decision making based on the assumption that the initial stages of chemical absorption and distribution would be comparable for both lethality and carcinogenicity (Zeise et al. 1984
). Follow-up work extended this approach to estimate cancer risk based on data from short-term mutation and toxicity tests (Travis et al. 1990
). Such approaches are sensible because
information is available for thousands of chemicals (https://chem.nlm.nih.gov/chemidplus
/), but only a few hundred of these have been tested in a 90-d toxicity study or in a 2-y cancer bioassay.
In parallel to the efforts in high-throughput toxicity testing, quantitative structure–activity relationship (QSAR) tools for predicting chemical metabolism (Maltarollo et al. 2015
; Pinto et al. 2016
) or for generating categorical predictions, such as classifying cancer and noncancer hazards (Jolly et al. 2015
; Low et al. 2014
; Mansouri et al. 2016
; Rusyn et al. 2012
), are already widely used in various decision contexts. However, there are few computational tools to generate quantitative (e.g., dose–response) estimates, information that is highly relevant for decision making and risk management. Specifically, there is a need for predictions of the continuous values used in decision making above and beyond the prediction of a chemical as a “hazard” versus a “nonhazard.” Therefore, this study aimed to address this significant gap through the development of the Conditional Toxicity Value (CTV) Predictor. The CTV Predictor is a compendium of QSAR models and a web portal that predicts, based on the chemical’s structure, an array of toxicity values that are often used in risk management decisions. The term “conditional” is used to distinguish the QSAR-based prediction of a toxicity value from toxicity values derived using human, animal, and other traditional data streams. The CTV-predicted values include the reference dose (RfD) and concentration (RfC), the oral slope factor (OSF), the inhalation unit risk (IUR), the cancer potency value [CPV; a California EPA (CalEPA)-specific OSF], and various estimates of the point-of-departure (POD). CTV predictions rely on a comprehensive database of existing toxicity values and experimental data and incorporate Organisation for Economic Co-operation and Development (OECD) principles for model building and external cross-validation. All of the data, models, and results are made publicly available for use by interested stakeholders.
One of the gaps addressed by this study and the accompanying web tool is that QSAR methodologies can be applied to chemicals of interest, regardless of the data available. Thus, this study’s output provides a means to derive a “conditional” toxicity value for a chemical when one does not already exist. Additionally, experimental data and regression methodologies can be used to supplement QSAR-based predictions when available.
CTV fills a critical gap not currently covered by existing chemical structure–based approaches. For example, most available QSAR tools, such as TOPKAT, ToxTree, OECD QSAR toolbox, RepDose, and others (Bhatia et al. 2015
), are designed to identify hazards by placing compounds in categories (e.g., nontoxic vs. toxic) and do not provide quantitative outputs (e.g., PODs) that can be used for risk characterization. One exception is the threshold of toxicological concern (TTC), which assigns chemicals to structural classes, each of which is associated with a TTC representing a “conservative” POD (Patlewicz et al. 2014
). Another approach that can provide PODs is read-across, in which chemicals with PODs are used to fill data gaps for chemicals without PODs (Cote et al. 2016
; Judson et al. 2011
). CTV can be thought of as a hybrid between the TTC approach and read-across because molecular descriptors are used to generate a “custom category” for each chemical via machine learning, and toxicity values are derived accordingly. Similar to how a TTC is commonly defined as the lower fifth percentile of the values within the structural class, the lower confidence bound for the CTV-derived toxicity value can then be thought of as a chemical-specific TTC.
The use of in vitro
data such as HTS assays combined with IVIVE using reverse toxicokinetics represents another major effort to address the lack of toxicity values for the majority of chemicals to which humans are exposed in the environment (Wetmore 2015
). However, the chemical space to which HTS assays can be applied is limited (e.g., restrictions related to molecular weight, solubility, volatility). Moreover, conversion of in vitro
activity to exposure estimates requires toxicokinetic modeling, further limiting the applicability to the few chemicals for which relevant data on toxicokinetics are available or can be generated (Holman et al. 2017a
). Thus, CTV, given its broad domain of applicability, can provide a complementary approach to HTS-based methods. Additionally, our direct comparison between CTV- and HTS assay–based toxicity values suggests that, given the present state of HTS-based risk assessment, CTV might currently provide more accurate and precise estimates for use in risk assessment when benchmarked against peer-reviewed toxicity values developed by federal or state agencies.
It is also worth noting that there is a limit to how accurately a toxicity value can be predicted given the extensive scientific judgment involved in their development. Indeed, risk estimates can vary widely across regulatory settings even for the same chemical and based on the same underlying observational data (NRC 2009
). For example, for the same chemical, differences across agencies in the RfDs from our database are typically approximately 0.6
units. This value is only slightly smaller than the typical absolute error estimate for CTV’s RfD predictions of
units, suggesting that our models are close to the accuracy/precision limit imposed by inherent heterogeneity in the underlying toxicity values. Part of this heterogeneity is likely because the RfDs are not precisely defined and, as described by the NRC (2009
), do not take into account the probability of harm. Probabilistic approaches to calculating toxicity values that provide quantitative risk estimates for noncancer effects are available (Chiu and Slob 2015
; WHO/IPCS 2014
) and have recently been applied to recalculate RfDs for approximately 600 chemicals and replace them with “
” values that reflect human dose (HD) estimates for a specific magnitude of effect M at a specific population incidence level I (Chiu et al. In Press
). Thus, development of future QSAR models based on probabilistic toxicity values may be beneficial.
An additional limitation is that there were inadequate data to develop models for organ-specific toxicity values. Organ-specific information was not available for many chemicals (e.g., a NOAEL does not always specify a particular target organ), so developing QSAR-based organ-specific toxicity value predictions would reduce the size of the data set to preclude confident modeling (Tropsha 2010
). However, there are many reasons why end point–specific values would be beneficial, such as addressing cumulative risk. Moreover, it would be interesting to ascertain whether QSAR models perform better for certain organ systems or end points. Therefore, as health assessment programs such as the U.S. EPA’s IRIS move toward developing “end point–specific” reference values, and as HTS assays, perhaps through adverse outcome pathways, become more closely associated with target organ toxicities, future QSARs may be able to address this limitation.
We anticipate a number of refinements to CTV in the future. First, additional models can be developed for specific needs, such as for additional state- or agency-specific toxicity values or for probabilistic toxicity values as they become available. At the same time, as new toxicity values are developed by federal and state agencies, it will be necessary to update the CTV databases and models periodically. Additionally, we plan to solicit user feedback to improve the presentation and visualization of our web portal to better communicate the modeling results and their uncertainties. Furthermore, “ensemble” models based on combining multiple molecular descriptor sets and multiple machine learning approaches can be evaluated regarding their value for increasing predictive accuracy and precision. Finally, it may be possible to incorporate biological data, such as HTS assay results, into the predictive models. However, a key question will be the extent to which adding such data improves model performance, which can be evaluated in a value-of-information approach.
The need for a publicly accessible computational tool, such as CTV, to predict quantitative toxicity values within an order of magnitude of traditionally derived toxicity values has been emphasized in multiple scientific and risk assessment venues. In 2009, the National Academies explicitly suggested that the U.S. EPA “perform quantitative structure activity relationship (QSAR) analyses … for developing distributions of toxicity parameter values derived from data on representative data-rich chemicals” (NRC 2009
). Creation of a companion web-based portal to enable wide accessibility to the predictive quantitative QSAR models, based on transparently reported data and methods, enables stakeholders to make predictions on chemicals of interest and directly meets the challenges faced by decision makers at all levels of government.
Specifically, the predicted values derived from CTV can be applied in a variety of risk assessment settings, including in ranking and prioritization of compounds for additional study and evaluation, as well as when decisions about chemical safety and risk management are needed. Accordingly, we anticipate that groups across state and federal governments who are mandated to make decisions about chemical safety and the need for remediation efforts, such as CalEPA or the U.S. EPA Office of Land and Emergency Management, would be able to use CTV to inform those risk assessment decisions when no other data are available. Additionally, as shown by our example with 4-methylcyclohexanemethanol, CTV could be very useful in deriving screening levels for application in an emergency situation such as a chemical spill or a natural disaster, where rapid decisions are necessary to ensure protection of public health and the environment. Although rigorous health assessments of chemicals that are of concern to state and federal governments remain important and will continue, this project will facilitate the work of health assessors at multiple levels when decisions are needed quickly, or when a chemical of concern has not yet been tested or reviewed. Thus, the outputs of this study fill a gap in the current risk assessment paradigm that requires extensive experimental/human data, time-consuming systematic review, and rigorous peer review (Mansouri et al. 2016