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拓扑指数在预测阿尔茨海默病药物疗效中的回归分析。

Regression analysis of topological indices for predicting efficacy of Alzheimer's drugs.

机构信息

Department of Mathematics, Government College University, Faisalabad, Pakistan.

Nanotechnology Center of Excellence, Addis Ababa Science and Technology University, Addis Adaba, Ethopia.

出版信息

PLoS One. 2024 Nov 1;19(11):e0309477. doi: 10.1371/journal.pone.0309477. eCollection 2024.

DOI:10.1371/journal.pone.0309477
PMID:39485746
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11530053/
Abstract

Alzheimer's Disease(AD) is the most common type of dementia. It is a progressive disease beginning with mild memory loss and possibly leading to loss of the ability to carry on a conversation and respond to the environment. This study investigates the relationship between the chemical structure of potential AD drugs and their therapeutic efficacy using Multi-Criteria Decision-Making (MCDM) techniques including The approach for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) method. A comprehensive dataset comprising molecular descriptors and corresponding pharmacological properties, i.e., melting point, boiling point, molecular weight and density of AD drugs was compiled from diverse sources. Topological indices were calculated to capture the structural characteristics of these compounds. Application of TOPSIS and SAW through Entropy method helps obtain optimal drugs for curing AD. Quantitative Structure Property Relationships (QSPR) analysis has been done between properties and topological indices of AD's drug structures. Results revealed significant relations between specific topological indices and drug efficacy, providing insights into the structural features crucial for AD treatment efficacy. This approach offers a promising avenue for rational drug design and optimization in the quest for novel AD therapeutics.

摘要

阿尔茨海默病（AD）是最常见的痴呆症类型。它是一种进行性疾病，最初表现为轻度记忆丧失，最终可能导致丧失进行对话和对环境做出反应的能力。本研究使用多准则决策（MCDM）技术，包括逼近理想解的排序方法（TOPSIS）和简单加权法（SAW），研究潜在 AD 药物的化学结构与其治疗效果之间的关系。综合数据集包括从不同来源编译的 AD 药物的分子描述符和相应的药理学特性，如熔点、沸点、分子量和密度。拓扑指数用于捕捉这些化合物的结构特征。通过熵方法应用 TOPSIS 和 SAW 有助于获得治疗 AD 的最佳药物。已经对 AD 药物结构的性质和拓扑指数之间进行了定量构效关系（QSPR）分析。结果表明，特定拓扑指数与药物疗效之间存在显著关系，为 AD 治疗效果的结构特征提供了深入了解。这种方法为合理药物设计和优化提供了有前途的途径，以寻找新型 AD 治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/210b/11530053/332ad698e143/pone.0309477.g013.jpg
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