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In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ new approach methodologies, including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (predictor of sensitization for medical devices). To enable model development, we (1) collected, curated, and integrated the largest publicly available dataset for GPMT results; (2) succeeded in developing externally predictive (balanced accuracy of 70%–74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) quantitative structure-activity relationships (QSAR) models for GPMT using machine learning algorithms, including deep learning; and (3) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.

Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points.We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets.In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays.We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to

Exogenous metal particles and ions from implant devices are known to cause severe toxic events with symptoms ranging from adverse local tissue reactions to systemic toxicities, potentially leading to the development of cancers, heart conditions, and neurological disorders. Toxicity mechanisms, also known as Adverse Outcome Pathways (AOPs), that explain these metal-induced toxicities are severely understudied. Therefore, we deployed in silico structure- and knowledge-based approaches to identify proteome-level perturbations caused by metals and pathways that link these events to human diseases. We captured 177 structure-based, 347 knowledge-based, and 402 imputed metal-gene/protein relationships for chromium, cobalt, molybdenum, nickel, and titanium. We prioritized 72 proteins hypothesized to directly contact implant surfaces and contribute to adverse outcomes. Results of this exploratory analysis were formalized as structured AOPs. We considered three case studies reflecting the following possible situations: (i) the metal-protein-disease relationship was previously known; (ii) the metal-protein, protein-disease, and metal-disease relationships were individually known but were not linked (as a unified AOP); and (iii) one of three relationships was unknown and was imputed by our methods. These situations were illustrated by case studies on nickel-induced allergy/hypersensitivity, cobalt-induced heart failure, and titanium-induced periprosthetic osteolysis, respectively. All workflows, data, and results are freely available in https://github.com/DnlRKorn/Knowledge_Based_Metallomics/. An interactive view of select data is available at the ROBOKOP Neo4j Browser at http://robokopkg.renci.org/browser/.