Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal, Brazil.
PLoS One. 2022 Dec 12;17(12):e0279039. doi: 10.1371/journal.pone.0279039. eCollection 2022.
Obesity is a significant risk factor for several chronic non-communicable diseases, being closely related to Diabetes Mellitus. Computer modeling techniques favor the understanding of interaction mechanisms between specific targets and substances of interest, optimizing drug development. In this article, the protocol of two protocols of systematic reviews are described for identifying therapeutic targets and models for treating obesity or diabetes mellitus investigated in silico. The protocol is by the guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes Protocols (PRISMA-P) and was published in the International Prospective Register of Systematic Reviews database (PROSPERO: CRD42022353808). Search strategies will be developed based on the combination of descriptors and executed in the following databases: PubMed; ScienceDirect; Scopus; Web of Science; Virtual Health Library; EMBASE. Only original in silico studies with molecular dynamics, molecular docking, or both will be inserted. Two trained researchers will independently select the articles, extract the data, and assess the risk of bias. The quality will be assessed through an adapted version of the Strengthening the Reporting of Empirical Simulation Studies (STRESS) and the risk of bias using a checklist obtained from separate literature sources. The implementation of this protocol will result in the elaboration of two systematic reviews identifying the therapeutic targets for treating obesity (review 1) or diabetes mellitus (review 2) used in computer simulation studies and their models. The systematization of knowledge about these treatment targets and their in silico structures is fundamental, primarily because computer simulation contributes to more accurate planning of future either in vitro or in vivo studies. Therefore, the reviews developed from this protocol will guide decision-making regarding the choice of targets/models in future research focused on therapeutics of obesity or Diabetes Mellitus contributing to mitigate of factors such as costs, time, and necessity of in vitro and/or in vivo assays.
肥胖是几种慢性非传染性疾病的重要危险因素，与糖尿病密切相关。计算机建模技术有利于理解特定靶点和感兴趣物质之间的相互作用机制，从而优化药物开发。本文描述了两种系统评价方案的方案，旨在确定治疗肥胖或糖尿病的治疗靶点和模型，这些模型是通过计算机模拟方法研究的。该方案符合系统评价和荟萃分析报告的首选项目（PRISMA-P）指南，并在国际前瞻性注册系统评价数据库（PROSPERO：CRD42022353808）中发表。检索策略将根据描述符的组合制定，并在以下数据库中执行：PubMed；ScienceDirect；Scopus；Web of Science；Virtual Health Library；EMBASE。只有具有分子动力学、分子对接或两者结合的原始计算机模拟研究才会被纳入。两名经过培训的研究人员将独立选择文章、提取数据并评估偏倚风险。将通过改编版的加强模拟研究报告（STRESS）和从其他文献来源获得的检查表评估质量。该方案的实施将产生两项系统评价，确定用于计算机模拟研究的治疗肥胖（综述 1）或糖尿病（综述 2）的治疗靶点及其模型。对这些治疗靶点及其计算机模拟结构的知识进行系统化是至关重要的，主要是因为计算机模拟有助于更准确地规划未来的体外或体内研究。因此，从该方案中开发的综述将为未来专注于肥胖或糖尿病治疗学的研究中的目标/模型选择提供决策指导，有助于减轻成本、时间和体外和/或体内检测的必要性等因素。
