Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays

Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effective interdependent doses, durations, and sequences of multidrug administration regimens is a time- and labor-intensive task. Here, we integrated machine-learning, automation, and large microfluidic arrays of cancer spheroids or patient-derived organoids formed in a tissue-mimetic hydrogel to achieve drastic acceleration of the discovery of effective multidrug administration regimens. For the clinically approved drug combination, we discovered a sequential administration regimen leading to a substantial reduction in the total drug dose, in comparison with concurrent drug supply, both at comparable drug efficacy. For the drugs that are currently under clinical development, we found a synergistic effect of concurrently administered drugs and showed that the synergy diminishes for the sequential drug supply. The developed strategy holds promise for the discovery of effective combination ther..., , , # Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays [https://doi.org/10.5061/dryad.0vt4b8h8x](https://doi.org/10.5061/dryad.0vt4b8h8x) ## Overview This dataset supports the study of optimizing multidrug administration regimens for organoid arrays using machine learning (ML)-guided strategies. The data include processed fluorescence imaging results representing cell viability in response to different drug combinations, administration sequences, and timing strategies. The primary goal of the study is to identify optimal drug sequences that improve therapeutic efficacy using organoid-based models and Bayesian optimization (BO). ## Description of the data and file structure The data was collected from the fluorescence images of spheroids and organoids in the microarrays. ## Data collection and processing Fluorescence images of spheroids and organoids cultured in microfluidic arrays were collected to quantify live/dead cells. These im...,联合疗法可提升癌症治疗的疗效，但确定多药给药方案中有效且相互匹配的剂量、持续时长与给药顺序，却是一项耗时耗力的工作。本研究整合机器学习（machine learning）、自动化技术（automation），以及在组织模拟水凝胶（tissue-mimetic hydrogel）中构建的大规模癌球体（cancer spheroids）或患者来源类器官（patient-derived organoids）微流控阵列（microfluidic arrays），实现了有效多药给药方案发现的大幅加速。针对已获临床批准的药物联合方案，本研究发现了一种序贯给药方案：在药效相当的前提下，相较于同时给药策略，该方案可显著降低总给药剂量。针对当前处于临床研发阶段的药物，本研究发现同时给药具有协同效应，且该协同效应会在序贯给药策略中减弱。本研究开发的策略有望助力有效联合治疗方案的发现[原文内容截断]，# 机器学习辅助的类器官阵列多药给药方案探索 https://doi.org/10.5061/dryad.0vt4b8h8x ## 概述 本数据集支持针对类器官阵列开展的、基于机器学习（ML）引导策略的多药给药方案优化研究。数据集包含经过预处理的荧光成像结果，这些结果反映了不同药物联合方案、给药顺序与时序策略下的细胞活力。 本研究的核心目标是利用基于类器官的模型与贝叶斯优化（Bayesian optimization, BO）技术，筛选出可提升治疗疗效的最优给药序列。 ## 数据与文件结构说明 数据来源于微阵列中癌球体与类器官的荧光成像结果。 ## 数据采集与处理 为量化活细胞与死细胞的比例，本研究采集了在微流控阵列中培养的癌球体与类器官的荧光成像结果。后续内容[原文截断]