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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Quantitative Aspects of Drug-Receptor Interaction01:30

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Therapeutic Index01:13

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The therapeutic index of a drug is a key parameter in pharmacology that quantifies the relative safety of a drug by calculating the ratio between the dose that causes toxicity in half the population (50%) to the dose that proves to be effective for half the population (50%). It provides a spectrum of doses for a particular drug ranging from effective to potentially toxic. To illustrate, consider an anticoagulant agent like warfarin. It possesses a narrow window within its therapeutic index to...
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Agonism and Antagonism: Quantification01:14

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When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective.

Ranita Pal1, Shanti Gopal Patra2, Pratim Kumar Chattaraj2

  • 1Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.

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|November 10, 2022
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Summary

Quantitative structure-activity relationship (QSAR) modeling aids drug discovery by predicting compound activity. This study explores electrophilicity and hydrophobicity descriptors for toxicity and disease-curing activity predictions.

Keywords:
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Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Drug Discovery

Background:

  • Preclinical drug discovery involves extensive, costly experiments.
  • Quantitative Structure-Activity Relationship (QSAR) modeling and machine learning have improved efficiency.
  • QSAR utilizes experimental data to predict biological activity of novel compounds.

Purpose of the Study:

  • To review Multiple Linear Regression (MLR)-based QSAR studies.
  • To assess compound toxicity towards Pimephales promelas and Tetrahymena pyriformis using CDFT-electrophilicity index (ω).
  • To evaluate Human African Trypanosomiasis (HAT) curing activity of pyridyl benzamide derivatives.

Main Methods:

  • Utilized global conceptual density functional theory (CDFT)-based electrophilicity index (ω) as a descriptor.
  • Compared electrophilicity index (ω) with hydrophobicity parameter (logP).
  • Employed Multiple Linear Regression (MLR) for QSAR modeling.

Main Results:

  • Electrophilicity index (ω) was used to predict toxicity and HAT activity.
  • QSAR models incorporated electrophilicity (ω, ω²) and hydrophobicity (logP, (logP)²) parameters.
  • The study highlights the utility of CDFT descriptors in drug discovery.

Conclusions:

  • QSAR modeling, particularly with CDFT-derived descriptors like electrophilicity, offers a more efficient approach to predict compound activity and toxicity.
  • Electrophilicity index (ω) provides a computationally accessible alternative to logP for QSAR studies.
  • This review consolidates findings on QSAR applications in predicting environmental toxicity and therapeutic efficacy.