Abstract：
Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are concentration dependent cross-interacting drugs as they may have a synergistic as well as antagonistic effect(s) on HOMA indicators when administered concurrently. We have employed a blend of machine learning: Artificial Neural Network (ANN), and evolutionary optimization: multiobjective Genetic Algorithms (GA) to discover the optimum regime of the nanoparticle-metformin combination. We demonstrated how to successfully employ a tested and validated ANN to classify the exposed drug regimen into categories of interest based on gradient information. This study also prescribed standard categories of interest for the exposure of multiple diabetic drug regimen. The application of categorization greatly reduces the time and effort involved in reaching the optimum combination of multiple drug regimen based on the category of interest. Exposure of optimum AuNP, AgNP and Metformin to Diabetic rats significantly improved HOMA β functionality (∼63 %), Insulin resistance (HOMA IR) of Diabetic animals was also reduced significantly (∼54 %). The methods explained in the study are versatile and are not limited to only diabetic drugs.
摘要：
胰岛素抵抗的稳态模型评估（HOMA-IR）和β细胞功能的稳态模型评估（HOMA-β）的改善显着降低了致残糖尿病的风险。纳米颗粒(AuNP-AgNP)-二甲双胍是浓度依赖性交叉相互作用药物,因为当同时施用时,它们可能对HOMA指标具有协同和拮抗作用。我们采用了机器学习的混合方法：人工神经网络(ANN)，和进化优化：多目标遗传算法（GA），以发现纳米颗粒-二甲双胍组合的最佳方案。我们展示了如何成功地使用经过测试和验证的ANN来根据梯度信息将暴露的药物方案分类为感兴趣的类别。这项研究还规定了多种糖尿病药物方案暴露的标准类别。分类的应用极大地减少了基于感兴趣的类别达到多种药物方案的最佳组合所涉及的时间和精力。最佳AuNP的曝光，AgNP和二甲双胍对糖尿病大鼠的作用显着改善HOMAβ功能（~63%），糖尿病动物的胰岛素抵抗(HOMAIR)也显著降低(~54%)。研究中解释的方法是通用的，不仅限于糖尿病药物。
