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.

embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules

“…To evaluate the performance of our final model embryoTox, we compared it with other available state-of-the-art ap-proaches. While recently Jiang et al 39 and Jaklin and colleagues 40 published supervised machine learning approaches, built using human teratogenicity data, to assess chemical reproductive toxicity, unfortunately neither provided their source code or model making direct comparison challenging. Our final model, embryoTox, outperformed both approaches (Table 3).…”

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

“…[ 119 ]. By 2022, Manuela Jaklin had developed the TeraTox system, a machine-learning algorithm with the ability to examine concentration-dependent cytotoxicity and changes in genetic expression induced by pharmaceutical compounds in hiPSC-derived embryoid bodies [ 100 ]. Particularly, during the COVID-19 pandemic in 2020, the DeepNEU was deployed for modelling lung aiPSC infection with SARS-CoV-2, facilitating the rapidly identification of antiviral therapeutic targets and opportunities for drug repurposing [ 61 ].…”

Establishment of a Human Cell-Based in Vitro Battery to Assess Developmental Neurotoxicity Hazard of Chemicals

“…CNS: central nervous system; hiPSC: human induced pluripotent stem cells; NEP: neuroepithelial precursor; NPC: neural progenitor cell; MEA: microelectrode array; PNS: peripheral nervous system; RoFA: rosette formation assay. Jaklin et al, 2022;Spreng et al, 2022) now allow sufficient throughput for screening applications and it is likely that such assays will add additional information to the IVB in the future.…”