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Computational Embryology and Predictive Toxicology ACT Nov 2023)
Citation:
Knudsen, T. Computational Embryology and Predictive Toxicology ACT Nov 2023). American College of Toxicology (ACT) 44th Annual Conference, Orlando, FL, November 12 - 15, 2023. https://doi.org/10.23645/epacomptox.24592035
Impact/Purpose:
This is an invited lecture at the American College of Toxicology 44th Annual meeting November 2023, in a session entitled "In Silico Models: When and How Will We Be Animal Free".
Description:
Scientific and practical needs exist for a different way to predict human developmental toxicity with less reliance on animal testing. The term New Approach Methods (NAMs) refers to any technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment to avoid the use of intact animals. NAMs that accurately predict the potential for human developmental toxicity in lieu of pregnant animal tests are highly desirable but constrained by the lack of physical and multicellular patterning of embryonic tissues undergoing morphogenesis. Embryo-inspired computational (in silico) models with emergent, self-organizing capacity can simulate critical phase transitions during developmental processes and toxicities. Toward a fully computable ‘virtual embryo’, cellular agent-based model (ABM) can be built to biological specification to uniquely simulate a complex morphogenetic series of events. Nature-inspired agents (cells) and rules (behaviors) are set into motion as a self-organizing virtual system, using an open-source modeling environment (CompuCell3d.org). ABM systems ranging from gastrulation in the epiblast to neovascularization of the neural tube to morphogenetic fusion of urogenital folds cover rudimentary targets of teratogenesis. Soft computing uses fuzzy logic to simulate forces or properties governing cell activity where rules are inexact or knowledge incomplete (computational intelligence). These systems change course in response to a particular situation or stimulus, such as genetic errors or molecular lesions fed into the model from real world data (sensitivity analysis). As such, they are amenable to mechanistic titration with NAMs data in space and time with probabilistic renderings of where, when, and how a particular lesion or condition might lead to an adverse developmental outcome (cybermorph). By running countless perturbation scenarios, the ABMs can be evaluated individually and in combination for mechanistic evaluation (perturbation matrices). The range of ABMs provide a novel in silico platform to support predictive toxicology by putting chemical effects data into motion (toxicodynamics). This abstract does not necessarily reflect Agency policy.
URLs/Downloads:
DOI: Computational Embryology and Predictive Toxicology ACT Nov 2023)
ACT_KNUDSEN_FINAL_V3.PDF (PDF, NA pp, 7348.936 KB, about PDF)