Saif Khan , Saheem Ahmad , Mahvish Khan , Farrukh Aqil , Mohd Yasir Khan , Mohd Sajid Khan
{"title":"Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs","authors":"Saif Khan , Saheem Ahmad , Mahvish Khan , Farrukh Aqil , Mohd Yasir Khan , Mohd Sajid Khan","doi":"10.1016/j.compbiomed.2024.108848","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524009338","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.