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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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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.
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In vitro dissolution and drug release tests assess how quickly and how much of a drug is released from its dosage form into an aqueous medium under standardized laboratory conditions. These tests are essential tools in pharmaceutical development and quality assurance, offering insight into the drug's performance before clinical use.During formulation development, dissolution testing identifies incomplete or inconsistent drug release issues. It also supports decisions on selecting the optimal...
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Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
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A Comprehensive Review on Homology Modelling in Drug Development: Methodologies, Tools, and Evaluation.

Priyanshu Nema1, Arpana Purohit1, Vandana Soni1

  • 1Integrated Drug Discovery Research Laboratory, Department of Pharmaceutical Sciences, Dr. Hari Singh Gour Central University, Sagar (MP), India.

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Homology modeling accurately predicts protein structures for drug design. This review details methods, tools, and AI integration for faster drug development, especially for difficult-to-crystallize proteins.

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Area of Science:

  • Bioinformatics
  • Computational Genomics
  • Structural Biology

Background:

  • Genomic and proteomic data necessitate accurate protein structure prediction for structure-based drug design.
  • Homology modeling offers a fast, cost-effective, and consistent approach to predicting three-dimensional protein structures.

Purpose of the Study:

  • To provide an in-depth and up-to-date review of homology modeling techniques and tools.
  • To explore the integration of artificial intelligence and machine learning in enhancing protein structure prediction.
  • To discuss the application of homology modeling in drug discovery and lead optimization.

Main Methods:

  • Review of key steps: template discovery, sequence alignment, model building, loop refinement, side chain modeling, and structure validation.
  • Examination of widely used tools: BLAST, MODELLER, Swiss-Model, SCWRL, PROCHECK, ProSA, and VERIFY3D.
  • Analysis of current challenges and focus areas: loop modeling, template selection, and validation metrics (RMSD, Z-score, Ramachandran plot).

Main Results:

  • Homology modeling integrates bioinformatics, computational genomics, and structural biology for efficient drug development.
  • AI and machine learning show potential to improve precision in homology modeling, especially with limited homology.
  • The review provides guidance for accelerating drug development for proteins challenging to crystallize.

Conclusions:

  • Homology modeling is a valuable tool for drug scientists, bridging traditional methods with modern computational technology.
  • The review highlights the importance of homology modeling in overcoming challenges in protein structure determination.
  • Effective application of homology modeling can significantly speed up the drug development pipeline.