{"title":"通过拓扑指数、多图建模和多标准决策建立抗癌药物药代动力学特征的结构-属性模型","authors":"Ugasini Preetha Pandi, Sakander Hayat, Suresh Marimuthu, Julietraja Konsalraj","doi":"10.1002/qua.27428","DOIUrl":null,"url":null,"abstract":"<p>This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as <span></span><math>\n <semantics>\n <mrow>\n <mi>r</mi>\n <mo>=</mo>\n <mn>0</mn>\n <mo>.</mo>\n <mn>926</mn>\n </mrow>\n <annotation>$$ r=0.926 $$</annotation>\n </semantics></math> for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's <span></span><math>\n <semantics>\n <mrow>\n <mi>r</mi>\n <mo>=</mo>\n <mn>0</mn>\n <mo>.</mo>\n <mn>68</mn>\n </mrow>\n <annotation>$$ r=0.68 $$</annotation>\n </semantics></math> with the <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mrow>\n <mi>M</mi>\n </mrow>\n <mrow>\n <mn>2</mn>\n </mrow>\n </msub>\n </mrow>\n <annotation>$$ {M}_2 $$</annotation>\n </semantics></math>-ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts.</p>","PeriodicalId":182,"journal":{"name":"International Journal of Quantum Chemistry","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structure-property modeling of pharmacokinetic characteristics of anticancer drugs via topological indices, multigraph modeling and multi-criteria decision making\",\"authors\":\"Ugasini Preetha Pandi, Sakander Hayat, Suresh Marimuthu, Julietraja Konsalraj\",\"doi\":\"10.1002/qua.27428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>r</mi>\\n <mo>=</mo>\\n <mn>0</mn>\\n <mo>.</mo>\\n <mn>926</mn>\\n </mrow>\\n <annotation>$$ r=0.926 $$</annotation>\\n </semantics></math> for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>r</mi>\\n <mo>=</mo>\\n <mn>0</mn>\\n <mo>.</mo>\\n <mn>68</mn>\\n </mrow>\\n <annotation>$$ r=0.68 $$</annotation>\\n </semantics></math> with the <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mrow>\\n <mi>M</mi>\\n </mrow>\\n <mrow>\\n <mn>2</mn>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation>$$ {M}_2 $$</annotation>\\n </semantics></math>-ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts.</p>\",\"PeriodicalId\":182,\"journal\":{\"name\":\"International Journal of Quantum Chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Quantum Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/qua.27428\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Quantum Chemistry","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/qua.27428","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Structure-property modeling of pharmacokinetic characteristics of anticancer drugs via topological indices, multigraph modeling and multi-criteria decision making
This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's with the -ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts.
期刊介绍:
Since its first formulation quantum chemistry has provided the conceptual and terminological framework necessary to understand atoms, molecules and the condensed matter. Over the past decades synergistic advances in the methodological developments, software and hardware have transformed quantum chemistry in a truly interdisciplinary science that has expanded beyond its traditional core of molecular sciences to fields as diverse as chemistry and catalysis, biophysics, nanotechnology and material science.