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Developmental toxicity testing is essential to identify substances that may harm embryonic development. This study aimed to establish a protocol for evaluating developmental toxicity using human induced pluripotent stem cells (iPSCs) by analyzing cellular activity and gene expression changes. Two ICH S5(R3) positive substances, valproic acid (VPA), which is a substance previously detected as positive by other test methods, and thalidomide (Thalido), were examined during early trichoderm differentiation without fetal bovine serum. RNA-seq analysis identified seven candidate genes, including TP63, associated with altered expression following exposure to VPA or Thalido. These genes were implicated in pathways related to tissue development, cell growth, and molecular interactions. While the assay effectively detected VPA and Thalido, its limitations include testing only soluble substances and focusing on early differentiation stages. Nevertheless, the protocol demonstrates potential for the classification and evaluation of emerging modality drugs based on physical properties such as solubility, polarity, and pH. Integration with AI analysis may enhance its capacity to uncover genetic variations and evaluate previously uncharacterized substances. This study provides a foundation for alternative developmental toxicity testing methods, with further refinements in the culture method expected to improve accuracy and applicability in regulatory toxicology. [ABSTRACT FROM AUTHOR]

Combination therapy with anti-angiogenic drugs and immune checkpoint inhibitors has shown enhanced clinical activity and has been approved for the treatment of multiple tumor types. Despite extensive research, predictive biomarkers for combination therapy remain poorly understood. Microvessel density (MVD), a surrogate marker for aberrant angiogenesis measured by immunohistochemistry (IHC), has been associated with response to monotherapy with anti-angiogenesis inhibitors. However, obtaining tumor tissue with a sufficient mass for IHC analysis is not always practical, and IHC-based MVD measurements are unavailable in large public datasets. In this study, we developed an MVD gene score based on RNA-sequencing data that reflects MVD by using RNA-seq and MVD measured by IHC in 12 mouse syngeneic tumor models. We explored the relationship between the MVD gene score and a gene signature predicting the response to anti-PD-1 therapy in mouse and human tumor datasets. The MVD gene score correlated with the antitumor activity of lenvatinib, a multiple tyrosine kinase inhibitor mainly targeting VEGFRs and FGFRs, in mouse tumor models and MVD measured by IHC in commercially available human formalin-fixed, paraffin-embedded tumor samples. Tumor types in The Cancer Genome Atlas were classified into four subgroups based on the MVD gene score and T-cell inflamed gene expression profile (TcellinfGEP), which were correlated with clinical indications for treatment. In conclusion, the newly developed MVD gene score enables the estimation of MVD in large public datasets where IHC data are unavailable and has potential clinical utility together with the TcellinfGEP to characterize patients' tumors for precision medicine.