The field of pharmaceutical development and therapeutic application both face substantial challenges. Therapeutic domain calls for more treatment alternatives while numerous promising pre-clinical drugs fail in clinical trails. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stage of drug development. Although in-silico CRE models offer a solution to this problem, existing methodologies are either limited to early development stages or lack the capacity for a comprehensive CRE analysis. Herein, we introduce a novel computational model named DeepCRE and present the potential of DeepCRE in advancing therapeutic discovery and development. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7\% in patient-level CRE, and a 5-fold increase in indication-level CRE. Furthermore, DeepCRE has identified six drug candidates that show significantly greater effectiveness than a comparator set of two approved drug in 5/8 colorectal cancer (CRC) organoids. This highlights DeepCRE's ability to identify a collection of drug candidates with superior therapeutic effects, underscoring its potential to revolutionize the field of therapeutic development.

翻译：药物开发与治疗应用领域均面临重大挑战。治疗领域需要更多替代方案，而大量有前景的临床前药物在临床试验中失败。原因之一在于药物研发后期阶段"跨药物反应评估"的不足。尽管计算机模拟的CRE模型为此问题提供了解决方案，但现有方法要么局限于早期开发阶段，要么缺乏全面CRE分析的能力。本文提出了一种名为DeepCRE的新型计算模型，并展示了其在推进治疗发现与开发中的潜力。DeepCRE在患者层面CRE评估中实现平均性能提升17.7%，在适应症层面CRE评估中实现5倍提升，显著优于现有最优模型。此外，DeepCRE成功识别出6种候选药物，在5/8的结直肠癌类器官中对两种已获批药物展现出显著更优的疗效。这凸显了DeepCRE识别具有卓越治疗效果候选药物的能力，彰显其革新治疗开发领域的潜力。