Tiwari Pragya, Katyal Ashish, Khan Mohd F, Ashraf Ghulam Md, Ahmad Khurshid
Department of Biotechnology, MG Institute of Management and Technology, Lucknow-Kanpur Road, Lucknow, India.
Department of Biotechnology, Meerut Institute of Engineering and Technology, Meerut, India.
Endocr Metab Immune Disord Drug Targets. 2019;19(6):754-774. doi: 10.2174/1871530319666190304121826.
Diabetes, defined as a chronic metabolic syndrome, exhibits global prevalence and phenomenal rise worldwide. The rising incidence accounts for a global health crisis, demonstrating a profound effect on low and middle-income countries, particularly people with limited healthcare facilities.
Highlighting the prevalence of diabetes and its socio-economic implications on the population across the globe, the article aimed to address the emerging significance of computational biology in drug designing and development, pertaining to identification and validation of lead molecules for diabetes treatment.
The drug discovery programs have shifted the focus on in silico prediction strategies minimizing prolonged clinical trials and expenses. Despite technological advances and effective drug therapies, the fight against life-threatening, disabling disease has witnessed multiple challenges. The lead optimization resources in computational biology have transformed the research on the identification and optimization of anti-diabetic lead molecules in drug discovery studies. The QSAR approaches and ADMET/Toxicity parameters provide significant evaluation of prospective "drug-like" molecules from natural sources.
The science of computational biology has facilitated the drug discovery and development studies and the available data may be utilized in a rational construction of a drug 'blueprint' for a particular individual based on the genetic organization. The identification of natural products possessing bioactive properties as well as their scientific validation is an emerging prospective approach in antidiabetic drug discovery.
糖尿病被定义为一种慢性代谢综合征，在全球范围内普遍存在且发病率呈显著上升趋势。发病率的上升构成了一场全球健康危机，对低收入和中等收入国家，尤其是医疗设施有限的人群产生了深远影响。
本文强调了糖尿病的患病率及其对全球人口的社会经济影响，旨在探讨计算生物学在药物设计与开发中日益凸显的重要性，涉及糖尿病治疗先导分子的识别与验证。
药物研发项目已将重点转向计算机模拟预测策略，以尽量减少冗长的临床试验和费用。尽管有技术进步和有效的药物治疗方法，但对抗这种危及生命、使人致残的疾病仍面临诸多挑战。计算生物学中的先导优化资源改变了药物研发研究中抗糖尿病先导分子识别与优化的研究。定量构效关系（QSAR）方法和药物代谢及毒性（ADMET）参数为评估来自天然来源的潜在“类药物”分子提供了重要依据。
计算生物学促进了药物研发研究，现有数据可用于基于基因组成合理构建针对特定个体的药物“蓝图”。识别具有生物活性的天然产物并对其进行科学验证是抗糖尿病药物研发中一种新兴的前瞻性方法。
