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FBA03-TRCA-Model-CandMet-CABL-Wt-Rxn.gms is used to estimate a set of candidate metabolites. from Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer

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The Royal Society Figshare2022-10-26 更新2026-04-17 收录
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https://rs.figshare.com/articles/dataset/FBA03-TRCA-Model-CandMet-CABL-Wt-Rxn_gms_is_used_to_estimate_a_set_of_candidate_metabolites_from_Fuzzy_optimization_for_identifying_anti-cancer_targets_with_few_side_effects_in_constraint-based_models_of_head_and_neck_cancer/21401100/1
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资源简介:
Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.

计算机辅助方法可用于筛选潜在药物候选靶点,同时缩短药物研发周期、降低研发成本。此类方法大多以合成致死(synthetic lethality)作为治疗判定标准来筛选药物靶点,但在靶点识别阶段均未考量治疗相关副作用。本研究开发了一种用于抗癌靶点筛选的模糊多目标优化(fuzzy multi-objective optimization)方法,该方法可同时评估癌细胞死亡率并最小化治疗带来的副作用。本研究针对头颈部癌(head and neck cancer, HNC)筛选出了潜在的抗癌酶类与抗代谢物。本次筛选得到的单靶点与双靶点酶类主要参与六大核心通路,涵盖嘌呤代谢、嘧啶代谢与磷酸戊糖途径。多数已筛选出的靶点可被获批药物调控,因此这些药物可作为头颈部癌治疗的潜在药物重定位(drug repurposing)候选方案。此外,本研究还筛选出了与基因中心法(gene-centric approach)所识别通路相似的抗代谢物。另外,研究发现敲低HMGCR无法阻断头颈部癌细胞的增殖;但UMPS与HMGCR、CAD与HMGCR这两组双靶点组合,可在不降低细胞活力等级的前提下,使癌细胞死亡率提升超过22%,同时改善代谢偏差等级。
提供机构:
Chen, Pei-Rong; Chen, Ting-Yu; Zhang, Hao-Xiang; Wang, Feng-Sheng
创建时间:
2022-10-26
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