Tosh Christopher, Tec Mauricio, White Jessica B, Quinn Jeffrey F, Ibanez Sanchez Glorymar, Calder Paul, Kung Andrew L, Dela Cruz Filemon S, Tansey Wesley
Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Nat Commun. 2025 Jan 2;16(1):156. doi: 10.1038/s41467-024-55287-7.
Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. On retrospective experiments from previous large-scale screens, BATCHIE designs rapidly discover highly effective and synergistic combinations. In a prospective combination screen of a library of 206 drugs on a collection of pediatric cancer cell lines, the BATCHIE model accurately predicts unseen combinations and detects synergies after exploring only 4% of the 1.4M possible experiments. Further, the model identifies a panel of top combinations for Ewing sarcomas, which follow-up validation experiments confirm to be effective, including the rational and translatable top hit of PARP plus topoisomerase I inhibition. These results demonstrate that adaptive experiments can enable large-scale unbiased combination drug screens with a relatively small number of experiments. BATCHIE is open source and publicly available ( https://github.com/tansey-lab/batchie ).
由于可能的组合数量巨大，大规模联合药物筛选通常被认为难以处理。现有方法采用临时固定实验设计，然后训练机器学习模型来估算未观察到的组合。在此，我们提出了BATCHIE，一种以批次动态进行实验的正交方法。BATCHIE利用信息论和概率建模，根据先前实验的结果，将每个批次设计为具有最大信息量。在先前大规模筛选的回顾性实验中，BATCHIE设计能迅速发现高效和协同的组合。在一项对206种药物库针对一组儿科癌细胞系的前瞻性联合筛选中，BATCHIE模型仅探索了140万个可能实验中的4%，就能准确预测未观察到的组合并检测到协同作用。此外，该模型为尤因肉瘤确定了一组顶级组合，后续验证实验证实这些组合是有效的，包括PARP加拓扑异构酶I抑制这一合理且可转化的顶级命中组合。这些结果表明，适应性实验能够通过相对较少的实验实现大规模无偏倚联合药物筛选。BATCHIE是开源且公开可用的（https://github.com/tansey-lab/batchie）。
