AI Insight
Researchers tested whether human induced pluripotent stem cell-derived 3D brain organoids could predict drug-induced seizure risk by measuring spontaneous calcium network activity. A panel of 66 small-molecule drugs with known clinical profiles was tested across concentrations calibrated to human exposure levels, and the resulting calcium signals were combined with chemical structure data in a machine-learning model. The model achieved an AUROC of 0.872 with 83.3% sensitivity and 88.9% specificity, outperforming previously published in vitro and animal-based seizure-liability models.
Why it matters
Drug-induced seizures are a significant cause of drug development failures and patient harm, and current preclinical models poorly predict human risk. If validated, this organoid-based platform could provide a more accurate, human-relevant screening tool to flag seizure liability earlier in the drug development pipeline, potentially reducing both clinical risk and attrition costs.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Drug-induced seizures remain a major safety concern in drug development, yet human seizure liability is difficult to predict using conventional preclinical models. Here, we evaluated whether spontaneous calcium network activity in human induced pluripotent stem cell-derived CNS-3D Brain Organoids could predict clinically observed seizure risk across a pharmacokinetically anchored drug set. CNS-3D organoids contained neuronal and astrocytic populations, expressed neuroactive receptor and ion-channel gene programs that aligned with human cortical tissue, and exhibited reproducible spontaneous calcium oscillations across production batches. A retrospective drug panel of 66 small-molecule drugs was assembled from human clinical evidence, including 30 seizure-associated drugs and 36 comparator drugs without documented clinical seizure liability. Drugs were tested across concentration ranges anchored to reported clinical Cmax, and calcium time-series responses were integrated with chemical structure features using a machine-learning workflow. The final model predicted clinical seizure liability with an AUROC of 0.872, achieving 83.3% sensitivity and 88.9% specificity in drug-level cross-validation. Model scores also stratified seizure-associated drugs by clinical context and prevalence, suggesting that CNS-3D activity profiles capture clinically meaningful differences in seizure risk. Compared with published in vitro and preclinical seizure-liability models, CNS-3D organoid-based predictions showed improved balanced sensitivity and specificity. These findings support high-throughput calcium profiling in human CNS-3D organoids as a scalable, exposure-aware platform for predicting human seizure liability and contributing functional human data to neuro-safety assessment.
Source: Human CNS-3D organoids predict clinical seizure liability from calcium network activity