ArticleInvestigational new drugs2026
Class-level DILI saturation and hERG-driven risk stratification among 2024-2025 FDA-approved kinase inhibitors: an in silico multi-platform safety analysis.
Article in Investigational new drugs, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Newly approved kinase inhibitors (2024-2025) have limited post-approval safety data. We developed a weighted ToxPi framework to prioritize multi-endpoint liabilities spanning hERG cardiotoxicity, DILI, Ames genotoxicity, and CYP3A4-mediated drug-drug interaction potential. Thirteen compounds (12 KIs approved in 2024-2025 plus imatinib as a historical reference anchor) were profiled using ADMETlab 3.0, vNN-ADMET, and SwissADME (formula inputs) and integrated into a weighted composite (wToxPi = 0.30 × hERG + 0.30 × DILI + 0.20 × Ames + 0.20 × CYP3A4). Robustness was assessed across weighting schemes and sensitivity analyses addressing fusion-recoding scenarios, CYP3A4 platform asymmetry, and combination-regimen confounding. DILI signal saturation dominated the cohort: 12 of 13 compounds showed DILIcon ≥ 0.80, limiting hepatic discrimination and shifting differentiation to hERG and Ames variability. Sevabertinib (0.860), Zongertinib (0.840), and Lazertinib (0.775) ranked highest, whereas Mirdametinib (0.080) was the structural outlier. Ranking remained stable: ADMETlab-only and thresholded majority-vote alternatives preserved the principal structure (Spearman ρ = 0.780 and 0.812), whereas soft-label pseudo-probability mapping showed near-identity concordance (ρ = 0.978); a broader sensitivity grid (p = 0.60-0.90) confirmed structural stability (ρ = 0.929-0.995). Exploratory FAERS findings provided supportive post-marketing context. This framework provides transparent, reproducible, hypothesis-generating prioritization of newly approved KIs. Class-level DILI saturation reinforces the need for close hepatic surveillance in pharmacovigilance interpretation regardless of composite rank; hERG and Ames provide the main inter-compound discriminatory signal. Sevabertinib, Zongertinib, and Lazertinib ranked highest, whereas Mirdametinib was the structural outlier; for Inavolisib and Tovorafenib, endpoint-specific hepatic surveillance remains warranted irrespective of composite rank. The model should support pharmacovigilance prioritization rather than replace clinical monitoring or individualized risk assessment.
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