PO.CH01.07 · 化学
VINI：一个用于在KRAS突变型胰腺癌中发现合理药物联合的多模态计算机模拟平台
VINI: A multimodal in silico platform for discovering rational drug combinations in KRAS-mutant pancreatic cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
对于诸如胰腺导管腺癌（PDAC）等侵袭性KRAS突变型癌症，迫切需要合理的联合治疗方案，此类癌症的5年生存率低于10%。VINI是一个多模态计算机模拟（in silico）平台，整合了人工智能、量子信息算法、通路建模和结构数据，以加速有效药物联合的发现。其基础建立在KEGG癌症通路之上，采用来自癌症细胞系百科全书（CCLE）的基因表达和突变数据、来自PubChem和DrugBank的分子结构、来自RCSB PDB或AlphaFold预测的三维蛋白结构，以及来自UniProt和DrugBank（针对单克隆抗体）的蛋白序列，从而捕获疾病特异性生物学特征。
VINI利用人工智能以及半经验量子信息虚拟筛选（计划在即将开展的Horizon项目中实施）来预测细胞内药物疗效及多药协同作用。VINI使用经典计算化学工具，包括Rosetta、AutoDock Vina、UCSF Chimera和Scripps MGLTools，以生成高质量预测，并借助高性能计算实现快速评估。
概念验证研究证明了VINI能够预测针对激素敏感型前列腺癌中ALK、BCL-2、mTOR、DNA修复和雄激素通路的新型三药联合，在16种癌症类型中与临床结局的一致性达到79.3%，在DU-145和PC3细胞系中达到100%。VINI还已应用于SARS-CoV-2的药物联合，展示了其广泛的适用性。
在即将开展的欧盟Horizon项目中，VINI将在鉴定针对KRAS突变型PDAC的有效三药联合方面发挥重要作用：（1）多伦多大学开发的新型KRAS抑制剂，（2）RBI发现的DNMT1抑制剂，以及（3）同样由RBI使用VINI鉴定的针对PDAC标志性特征的靶向单克隆抗体。通过这个跨国联盟——包括多伦多大学、隆德大学、弗劳恩霍夫研究所、EPFL以及其他七家领先机构——VINI将整合人工智能、结构建模和计算化学，为耐药性癌症提供可付诸行动的见解，并指导未来的临床前验证。
查看英文原文 English abstract
Rational combination therapies are urgently needed for aggressive KRAS-mutant cancers such as pancreatic ductal adenocarcinoma (PDAC), which has a 5-year survival rate below 10%. VINI is a multimodal in silico platform that integrates AI, quantum-informed algorithms, pathway modeling, and structural data to accelerate the discovery of effective drug combinations. Its foundation is based on KEGG cancer pathways, with gene expression and mutation data from the Cancer Cell Line Encyclopedia (CCLE), molecular structures from PubChem and DrugBank, three-dimensional protein structures from RCSB PDB or AlphaFold predictions, and protein sequences from UniProt and DrugBank (for monoclonal antibodies), capturing disease-specific biology.
VINI uses AI and semi-empirical quantum-informed virtual screening (planned for implementation in the upcoming Horizon project) to predict intracellular drug efficacy and multi-drug synergy. Classical computational chemistry tools, including Rosetta, AutoDock Vina, UCSF Chimera, and Scripps MGLTools, are used by VINI to generate high-quality predictions, with high-performance computing enabling rapid evaluation.
Proof-of-concept studies demonstrated VINI's ability to predict novel triple-drug combinations targeting ALK, BCL-2, mTOR, DNA repair, and androgen pathways in hormone-sensitive prostate cancer, achieving 79.3% agreement with clinical outcomes across 16 cancer types and 100% for DU-145 and PC3 lines. VINI has also been applied to SARS-CoV-2 drug combinations, illustrating its broad applicability.
In the upcoming EU Horizon project, VINI will be instrumental in identifying effective three-drug combinations against KRAS-mutant PDAC: (1) novel KRAS inhibitors developed at the University of Toronto, (2) DNMT1 inhibitors discovered at RBI, and (3) targeted monoclonal antibodies against PDAC hallmarks, also identified at RBI using VINI. Through this multinational consortium - including the University of Toronto, Lund University, Fraunhofer, EPFL, and seven other leading institutions - VINI will integrate AI, structural modeling, and computational chemistry to provide actionable insights for treatment-resistant cancers and guide future preclinical validation.
利益披露 Disclosure
D. Tomic, None.