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Topological modelling of analgesia
K C Mathur1, Sunita Gupta, P V Khadikar
1Department of Chemistry, A.P.S. University, Rewa 486003, India.
Bioorganic & Medicinal Chemistry
|March 28, 2003
Summary
This study modeled analgesic activity using topological indices, finding that the Wiener index best predicts pain relief efficacy across different drug families. Splitting analgesics into categories improved modeling accuracy.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Pharmacology
Background:
- Analgesic activity is crucial for pain management.
- Quantitative Structure-Activity Relationships (QSAR) are vital for drug discovery.
- Topological indices offer a way to represent molecular structure numerically.
Purpose of the Study:
- To model the analgesic activity (log IC) of 97 analgesics using topological indices.
- To investigate inter-familial correlations in analgesic activity.
- To identify the most effective topological indices for predicting analgesic properties.
Main Methods:
- Utilized a series of distance-based topological indices to model analgesic activity.
- Employed regression analyses to correlate topological indices with analgesic activity.
- Categorized 97 analgesics into five groups to account for inter-familial correlations.
Main Results:
- Analgesic activity (log IC) shows inter-familial correlations, necessitating categorization for accurate modeling.
- Topological indices including Wiener (W), Branching (B), and first-order connectivity (chi) were effective predictors.
- The Wiener (W) index demonstrated excellent performance in modeling analgesic activity.
Conclusions:
- Topological indices, particularly the Wiener index, are valuable tools for predicting analgesic activity.
- Molecular structure, as represented by topological indices, significantly influences drug efficacy.
- Categorizing drugs based on familial relationships enhances the accuracy of QSAR models for analgesic activity.