Progress in data interoperability to support computational toxicology and chemical safety evaluation

Abstract

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21^st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Keywords: Applications; Bioinformatics; Computational Toxicology; Data Interoperability; Databases.

Figure 1:. A historical view of the evolving infrastructure to support modern chemical safety evaluation

Pictured is an abstract representation of the changing infrastructure that supports USEPA’s computational toxicology efforts, presented as an example of data interoperability needs as they have evolved. (A) Initially, information across relevant domains in toxicology were aggregated from external databases to a single database accessed through a single web application called ACToR. (B) With continued success in data generation projects like ToxCast, multiple products were developed. The dashed arrows represent indirect access to the needed information. Indirect access means that the underlying information was duplicated because each web application is supported by a separate database, which is consistent with silo-ing and reinforcing data inconsistency. Note that the ToxCast Dashboard and the EDSP21 Dashboard are being sunset in 2019, with their functionality merged into the CompTox Chemicals Dashboard. CPDat = Consumer Product Database; DSSTox = distributed structure-searchable toxicity database; EDSP21 = Endocrine Disruptor Screening Program for the 21^st century; httk = high-throughput toxicokinetics; tcpl= ToxCast data pipeline.

Figure 2:. Integrating stressor and biological information into an Adverse Outcome Pathway workflow

Each key event (KE) in an AOP has information about a specific biological target and subsequent biological measurement, dependent on testing methodology and the assay principle, associated with a stressor. Key event relationships (KERs) provide evidence to support linkage of KEs. Combined, all components can be organized into a series of steps that begin with a molecular initiating event (MIE) and ending with an adverse outcome (AO).

Figure 3:. Workflow for literature based chemical assessments based on systematic review methodology

Iterations of literature-based chemical-centric systematic review methods ensure rigor and transparency while making use of the best available scientific information, requiring data interoperability to maximize unbiased data recall.