3d- qsar of n- substituted imidazoles as antifungal agents
Journal name: World Journal of Pharmaceutical Research
Original article title: 3d- qsar of n- substituted imidazoles as antifungal agents
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Vithal M. Kulkarni, Sandeep S. Pathare and Kakasaheb R. Mahadik
World Journal of Pharmaceutical Research:
(An ISO 9001:2015 Certified International Journal)
Full text available for: 3d- qsar of n- substituted imidazoles as antifungal agents
Source type: An International Peer Reviewed Journal for Pharmaceutical and Medical and Scientific Research
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Summary of article contents:
Introduction
The study conducted by Vithal M. Kulkarni and colleagues focuses on the development of antifungal agents, particularly targeting N-substituted imidazole derivatives, through a method known as three-dimensional quantitative structure-activity relationship (3D-QSAR) using Comparative Molecular Field Analysis (CoMFA). This ensures the understanding of structural requirements that lead to antifungal activity against the major opportunistic fungal pathogen, Candida albicans. The research highlights an increasing demand for new, effective antifungal medications due to rising incidences of fungal infections and the emergence of drug resistance. The study employed a training set of 50 compounds to create the CoMFA model and validated it with a test set of 15 compounds.
CoMFA Analysis: Understanding Structure-Activity Relationships
The CoMFA method was pivotal in revealing the relationship between the molecular structure of the imidazole derivatives and their antifungal activity. The research showcased that the best CoMFA model was achieved using root-mean-square (rms) fitting, which provided a cross-validated r² value of 0.725, indicating good predictivity. This analysis emphasized the steric and electrostatic contributions of the compounds, with steric factors showing a significant impact on binding affinity. The results suggested that specific substituents and molecular orientations directly affect the antifungal efficacy of the tested compounds, providing essential insights for rational drug design.
Genetic Function Approximation (GFA) Methodology
In addition to CoMFA, the researchers employed Genetic Function Approximation (GFA) to further understand the structure-activity relationship. GFA, which utilizes a genetic algorithm and adaptive regression methods to evolve equations relating biological activity to physicochemical descriptors, yielded a promising model exhibiting a cross-validated r² of 0.608. This model highlighted crucial parameters influencing antifungal activity, such as the molecular shape and spatial arrangement, reinforcing the importance of steric factors in effectively interacting with the target enzyme.
Computational Studies and Descriptor Analysis
The computational part of the study involved meticulous molecular modeling techniques, including geometry optimization and conformational analysis using established software. The researchers calculated 34 different physicochemical descriptors to evaluate their effects on biological activity. This analysis revealed a clear link between specific descriptors—particularly those related to molecular size, shape, and charge distribution—and the antifungal efficacy of the compounds. Such descriptors were vital in determining the structural features required for optimal receptor binding.
Conclusion
The findings of this study contributed significantly to the understanding of antifungal agent development through the application of 3D-QSAR methodologies. The CoMFA models provided valuable insights into the steric and electrostatic interactions necessary for enhanced antifungal activity, while the GFA approach confirmed the relevance of molecular shape and other physicochemical factors in achieving desired biological effects. These collaborative results underscore a promising framework for future research aimed at developing new antifungal compounds with improved effectiveness against resistant fungal strains.
FAQ section (important questions/answers):
What is the focus of the 3D-QSAR study presented?
The study focuses on analyzing N-substituted imidazole derivatives to determine their structural requirements for antifungal activity using 3D-QSAR techniques, specifically comparative molecular field analysis (CoMFA).
How was the CoMFA model validated in this research?
The CoMFA model was validated using a test set of 15 compounds, evaluated against a training set of 50 compounds to ascertain its predictive ability and generalization in antifungal activity.
What were the main statistical results of the CoMFA model?
The best CoMFA model achieved a cross-validated r² of 0.725, a conventional r² of 0.939, and a predictive r² of 0.518, highlighting its reliability and predictive capability.
What does the CoMFA study reveal about the molecular interactions?
The CoMFA study indicates the importance of steric and electrostatic interactions between the N-substituted imidazole compounds and their biological targets, emphasizing specific structural features for enhanced antifungal activity.
What computational methods were used for QSAR analysis?
The study utilized comparative molecular field analysis (CoMFA) and genetic function approximation (GFA) to establish a relationship between molecular descriptors and antifungal activity, enhancing model predictions.
What role do N-substituted groups play in antifungal activity?
N-substituted groups in imidazole derivatives significantly influence their antifungal activity by affecting both steric and electronic properties, which are crucial for binding interactions with fungal enzymes.
Glossary definitions and references:
Scientific and Ayurvedic Glossary list for “3d- qsar of n- substituted imidazoles as antifungal agents”. This list explains important keywords that occur in this article and links it to the glossary for a better understanding of that concept in the context of Ayurveda and other topics.
1) Activity:
In pharmacology, 'activity' refers to the degree to which a compound elicits a biological effect, particularly in relation to its desired therapeutic outcomes. In this study, antifungal activity is quantified by the minimum inhibitory concentration (MIC), a standard measure for evaluating how effectively a compound can inhibit fungal growth, thereby emphasizing the importance of structure-activity relationships in drug design.
2) Table:
'Table' denotes a systematic arrangement of data, typically in rows and columns, used to present information clearly. In this context, tables are crucial for organizing experimental data, including chemical structures and biological activities of compounds, facilitating comparison and interpretation of results essential for quantitative structure-activity relationship (QSAR) analyses.
3) Field:
The term 'field' in CoMFA refers to the specific area of study within pharmacology, focusing on the interactions between molecular structures and their biological activities. It encompasses fields of sterics and electrostatics, which are evaluated to understand how molecular characteristics influence antifungal activity, informing the design of more effective drugs.
4) Training:
'Training' refers to the dataset used in QSAR studies to develop predictive models for biological activity based on chemical structures. The training set comprises compounds selected for their structural diversity and range of activities, which inform the development of models that predict the efficacy of untested compounds.
5) Study (Studying):
'Study' signifies the detailed investigation and analysis of a specific subject or phenomenon. In this research, the study focuses on the 3D-QSAR of N-substituted imidazoles as antifungal agents, examining the relationship between chemical structure and biological activity to guide the design of new antifungal drugs.
6) Drug:
'Drug' refers to a substance used for medical treatment to prevent, diagnose, or cure diseases. In this research, the focus is on antifungal drugs, particularly azole derivatives, which are evaluated for their efficacy against pathogens like Candida albicans, highlighting the development of new therapeutic agents to combat fungal infections.
7) Calculation:
'Calculation' relates to the mathematical processes involved in analyzing data from experiments. In QSAR studies, calculations include determining various molecular descriptors, predicting biological activity, and validating models, which are essential for understanding the relationship between chemical properties and pharmacological effects, leading to more effective drug design.
8) Bharati:
'Bharati' refers to Bharati Vidyapeeth University, an educational institution in India where this research was conducted. The university provides resources and facilities for advanced studies in pharmaceutical sciences, contributing to the academic and practical advancements in drug design and related areas within the field of pharmaceutical chemistry.
9) India:
'India' is the country where the research takes place, representing the geographical and cultural context of the study. The nation has a growing pharmaceutical industry and research base, contributing to global efforts to develop novel drugs, especially in combating rising issues like antifungal resistance in healthcare.
10) Pune:
'Pune' is a city in India, specifically in Maharashtra, where the research institution conducting the study, Poona College of Pharmacy, is located. It is a hub for education and research in pharmaceutical sciences, playing a significant role in training professionals and advancing pharmaceutical research and development.
11) Maharashtra (Maharastra, Maha-rashtra):
This appears to be a typographical error for 'Maharashtra', which is the state in India referenced in the study. The correct term reflects the importance of the location in fostering pharmaceutical research and educational initiatives that contribute to advancements in drug development and therapeutic innovation in the region.
12) Relative:
'Relative' refers to the comparison between different entities, often used in statistical analyses to determine how variables influence each other. In this study, the relative biological activity of compounds is assessed to establish structure-activity relationships, tying together molecular features with their efficacy as antifungal agents.
13) Poona:
'Poona' is the historical name of Pune, the city where the Poona College of Pharmacy is situated. The reference emphasizes the institution's heritage and its longstanding commitment to pharmaceutical education and research, impacting drug development and enhancing healthcare through innovative studies like the one discussed.
14) Rules:
'Rules' refer to the established guidelines or methodologies followed during scientific research to ensure accuracy and reliability. In the context of QSAR studies, alignment rules are crucial for appropriately positioning molecular models in CoMFA, directly affecting the model's predictive power and the subsequent evaluation of drug efficacy.
15) Rich (Rch):
'Rich' often relates to the abundance of certain characteristics or features. When discussing molecular properties in drug design, 'rich' descriptors indicate a variety of chemical features that contribute to the drug's activity. In the context of this article, a rich diversity in compound structures is essential for developing effective antifungal therapies.
16) Discussion:
'Discussion' is a critical component of scientific research where findings are interpreted and implications for further studies or applications are considered. In this paper, the discussion section evaluates the results of CoMFA and GFA models, providing insights on how the research informs the development of new antifungal agents.
17) Similarity:
'Similarity' pertains to the extent to which two entities share common characteristics. In QSAR studies, structural similarity among compounds is vital for developing predictive models, as similar compounds often exhibit comparable biological activities, allowing for extrapolations about untested compounds' potential efficacy based on existing data.
18) Account:
'Account' refers to a detailed report or explanation of findings. In research, accounts summarize methodologies, results, and their significance, ensuring that studies can be reproduced and verified. This term underscores the importance of documenting findings in the paper for transparency and future reference in scientific inquiry.
19) Surface:
'Surface' often describes the outer boundary of a molecular structure, which can influence interactions with biological targets. In pharmaceutical research, understanding the molecular surface properties, such as area and hydrophobicity, is crucial for predicting how compounds will interact with receptors or enzymes, impacting drug efficacy.
20) Kadam:
'Kadam' refers to Dr. Shivajirao S. Kadam, the Vice Chancellor of Bharati Vidyapeeth University, who played a key role in facilitating the research. His leadership and academic support are vital in promoting high-quality research and fostering an environment conducive to innovation and the advancement of pharmaceutical sciences.
21) Water:
'Water' refers to the solvent used in many biochemical processes and experiments. It plays a crucial role in the solubility and stability of compounds during drug formulation and interaction studies, affecting the bioavailability of drugs. In the context of this research, water pertains to physiological relevance in biological systems.
22) Noise:
'Noise' represents irrelevant or extraneous data that can obscure important signals in research. In QSAR studies, reducing noise through careful design and analysis is vital for enhancing the reliability of model predictions, ensuring that the findings accurately reflect the underlying structure-activity relationships of the compounds studied.
23) Life:
'Life' encompasses the biological context within which the study is situated. In the framework of drug development, it refers to the living organisms affected by diseases, such as fungal infections treated in this research. The goal is to improve human health outcomes through effective antifungal therapy against pathogens like Candida albicans.
Other Science Concepts:
Discover the significance of concepts within the article: ‘3d- qsar of n- substituted imidazoles as antifungal agents’. Further sources in the context of Science might help you critically compare this page with similair documents:
Cross validation, Minimum inhibitory concentration, Biological Activity, Multidisciplinary approach, Drug resistance, Fungal infection, Bioavailability issues, Statistical method, Quantitative structure-activity relationship, Three dimensional quantitative structure activity relationship, Cytochrome P450, Department of Pharmaceutical Chemistry, Training set, Pharmacophore modeling, Azole antifungals, Root mean square deviation, Bootstrapping analysis, QSAR models, Electron withdrawing group, Pharmacophore model, Electrostatic interaction, Aromatic ring, Molecular model, Steric interaction, Spatial orientation.