Biological Response and Pathway-Based Toxicology

Biological Response and Pathway-Based Toxicology: Leverage New Approach Methods (NAMs)

We utilize New Approach Methods (NAMs) to provide mechanistic, human-relevant data that can be used to prioritize chemicals, fill data gaps, and support risk assessment in lieu of or alongside traditional animal studies.

 

Advanced Testing & Mechanistic Data

NAMs Strategy & Profiling

  • Selection and Laboratory placement (monitoring) of appropriate NAMs
  • In vitro high-throughput toxicity profiling to characterize biological activity and provide mechanistic insights
  • Interpretation of High-Throughput (HT) assay data for chemical prioritization and estimation of Margin of Exposure (MOE) relative to biological activity

Specialized and Guideline Assays

  • Specific in vitro testing for skin sensitization e.g. DRPA
  • OECD In vitro Assays (e.g., microcnucleus and estrogen/androgen receptor transactivation assays
  • In vitro inhalation lung toxicity testing, potentially using air-liquid interface cultures of human lung cells to stimulate lung exposure

Mechanistic Studies & Risk Quantification

  • Determine molecular points of departure (PODs) using Benchmark Dose (BMD) modelling
  • Derive transcriptomic reference values (TRVs) to inform risk assessments for data-poor chemicals (e.g., U.S. EPA’s Transcriptomic Assessment Product [ETAP])
  • Short-term mechanistic studies/assays paired with ‘omic techniques (e.g., transcriptomics, proteomics, metabolomics)

Validation & Ground Truthing

  • Use targeted in vivo models (when justified) for validation and ground-truthing of in vitro and in silico predictions
  • Fill data gaps where suitable in vitro models are currently unavailable

 

Computational & Regulatory Integration

Computational Toxicology (In Silico); Modelling & Prediction

  • Development and application of Quantitative Structure–activity relationship models ((Q)SARs)
  • Development and , application of data-driven and expert-driven read-across approaches e.g. genra-py
  • High throughput Toxicokinetic modeling to translate in vitro concentrations to in vivo exposure

Data Science & Cheminformatics

  • Leverage Machine Learning and Deep Learning approaches to visualize datasets and develop predictive models and disseminate them through interactive dashboards
  • Use of cheminformatic approaches to facilitate strategic NAM testing and categorization of inventories e.g. EPA New Chemical Categories
  • Incorporation of computational outputs into quantitative risk assessment frameworks

Regulatory Frameworks & Strategy; Systematic Integration

  • Implement Integrated Approaches to Testing and Assessment (IATA combining multiple NAM sources with weight-of-evidence evaluation
  • Apply Defined Approaches (DAs) for specific endpoints e.g. skin sensitization
  • Develop tier strategies to systematically incorporate HT, in vitro and in silico data

Adverse Outcome Pathway (AOP) Application

  • Organize mechanistic data into biologically plausible pathways of toxicity
  • Support read-across and chemical grouping strategies
  • Guide assay development, selection, and placement
  • Provide mechanistic anchoring for interpretation of HT and in vitro data