Optimization of the TeraTox assay for preclinical teratogenicity assessment

Abstract

Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives. To improve the sensitivity of human teratogenicity prediction, we characterized the TeraTox test, a newly developed multi-lineage differentiation assay using 3D human induced pluripotent stem cells. TeraTox produces as primary output concentration-dependent cytotoxicity and altered gene expression induced by each test compound. These data are fed into an interpretable machine-learning model to perform prediction, which relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Comparing TeraTox predictions with known human or animal toxicity, we report an accuracy of 69% (specificity: 53%, sensitivity: 79%). TeraTox performed better than two quantitative structure-activity relationship models, and had a higher sensitivity than the murine embryonic stem cell test (accuracy: 58%, specificity: 76%, sensitivity: 46%) run in the same laboratory. The overall prediction accuracy could be further improved by combining TeraTox and mEST results. Furthermore, patterns of altered gene expression revealed by TeraTox may help grouping toxicologically similar compounds and possibly deducing common modes of action. The TeraTox assay and the dataset described here therefore represent a new tool and a valuable resource for drug teratogenicity assessment.