光动力疗法中光敏剂设计的量子计算。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Hope Zehr, Alberto Baiardi, Francesco Tacchino, Anthony Gandon, Laurin E Fischer, Yue Xu, Frank P DiFilippo, Leonardo Guidoni, Pi A B Haase, Walter N Talarico, Martina Stella, Fabio Tarocco, Anton Nykänen, Aaron Fitzpatrick, Aaron Miller, Leander Thiessen, Stefan Knecht, Elsi-Mari Borrelli, Sabrina Maniscalco, Fabijan Pavošević, Ivano Tavernelli, Edward Maytin, Vijay Krishna
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引用次数: 0

摘要

随着光动力疗法(PDT)用于治疗各种癌症的应用越来越多,光在医疗保健中的应用也在不断发展。PDT利用称为光敏剂(ps)的光激活分子产生活性氧(ROSs)来诱导肿瘤细胞凋亡和坏死。然而,PDT的使用受到PSs可用性的限制,这些PSs可以被深层组织穿透近红外光激活,具有低暗毒性,并且可以有效地产生ROSs。在此,我们回顾了目前在临床或临床前试验中使用的不同类别的PS,并强调了先进的计算方法的重要性,包括密度泛函和基于波函数的量子化学,以了解涉及PS激活的分子机制。尽管经典计算技术取得了进步,但高度相关分子系统中激发态动力学的复杂性需要创新的模拟方法,如量子计算。我们提出量子计算有望准确地模拟ps的激发态特性,以优化其设计并扩大临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum Computing for Photosensitizer Design in Photodynamic Therapy.

Use of light in healthcare is evolving with increasing applications of photodynamic therapy (PDT) for treating various cancers. PDT utilizes light-activated molecules called photosensitizers (PSs) that generate reactive oxygen species (ROSs) to induce tumor cell apoptosis and necrosis. However, the use of PDT is limited by the availability of PSs that can be activated by deep tissue-penetrating near-infrared light, exhibit low dark toxicity, and produce ROSs efficiently. Here we review the different categories of PS currently used in clinical or preclinical trials and highlight the significance of advanced computational methods, including density functional and wave function-based quantum chemistry, for understanding the molecular mechanisms involved in PS activation. Despite advancements in classical computational techniques, the complexities of excited state dynamics in highly correlated molecular systems demand innovative simulation approaches such as quantum computing. We propose that quantum computing holds promise for accurately modeling the excited-state properties of PSs to optimize their design and broaden clinical applications.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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