PDF(Pubmed)
Abstract：
Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for screening psychoactive drugs with psychedelic properties.
摘要：
psilocybin,氯胺酮,MDMA和MDMA是精神活性化合物，具有可区分但重叠的特征。对使用这些化合物作为治疗剂的日益增长的兴趣需要能够准确筛选迷幻剂和相关类似物的临床前测定。我们认为，一种有希望的方法可能是测量药物对天然脑组织中神经可塑性标志物的作用。因此，我们开发了一种用于药物分类的管道，使用在细胞分辨率下立即早期基因表达的光片荧光显微镜，然后进行机器学习。我们用一组药物测试了雄性和雌性小鼠，包括psilocybin,氯胺酮,5-MeO-DMT,6-氟-DET,MDMA,急性氟西汀,慢性氟西汀，和车辆。在一对一对休息分类中，准确的药物以66%的准确率被识别，显著高于12.5%的机会水平。在一对一分类中，psilocybin与5-MeO-DMT区分，氯胺酮,MDMA,或急性氟西汀，准确度>95%。我们使用Shapley加性解释来确定驱动机器学习预测的大脑区域。我们的结果支持一种筛选具有迷幻特性的精神活性药物的新方法。
