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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.
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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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Understanding drugs, drug products, and their performance in pharmaceutical science is pivotal. Drugs, whether simple molecules or complex compounds, are designed to interact with the body's biological systems to diagnose, treat, or prevent diseases. Drug products include various delivery systems such as tablets, capsules, injections, and inhalers. The performance of these drug products is gauged by their ability to deliver the active ingredient to the desired site of action at the...
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Orally administered drugs primarily enter the systemic circulation via passive diffusion through the intestinal membranes. The drug's absorption is influenced by drug stability in the gastrointestinal GI tract, membrane permeability, the surface area available for absorption, luminal drug concentration, and residence time in the lumen. Drug permeability can be enhanced by adjusting the lipophilicity, polarity, or molecular size of the drug, promoting its passive transport across intestinal...
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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Updated: Sep 11, 2025

A Package of Established Analytical Tools to Investigate the Solid-State Alteration of Lipid-Based Excipients
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Ever-Increasing Role of Computational Tools in Solid-State Pharmaceutics: Advancing Drug Development with Enhanced

Abhishek Sharma1, Saurabh Shah1, Suraj Wagh1

  • 1Pharmaceutical Innovation and Translational Research Lab (PITRL), Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad 500037, India.

Molecular Pharmaceutics
|August 13, 2025
PubMed
Summary

Computational techniques accelerate drug development by optimizing solid-state properties of active pharmaceutical ingredients (APIs). These methods enhance understanding of molecular mechanisms, reducing time and resources for pharmaceutical solids.

Keywords:
amorphous formulationdensity functional theoryinclusion complexmachine learningmolecular dynamicssolid-state pharmaceutics

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Area of Science:

  • Solid-state pharmaceutics
  • Pharmaceutical sciences
  • Computational chemistry

Background:

  • Solid-state pharmaceutics investigates structural aspects of pharmaceutical solids to correlate structure with properties.
  • Physicochemical properties like solubility and dissolution influence drug efficacy.
  • Traditional development is time-consuming, often prolonging product time-to-market.

Purpose of the Study:

  • To highlight the revolutionary impact of computational techniques in solid-state pharmaceutics.
  • To demonstrate how computational methods accelerate drug development and reduce resource consumption.
  • To present a workflow for applying computational tools in API and formulation development.

Main Methods:

  • Application of Density Functional Theory, molecular dynamics, and artificial neural networks.
  • Crystal structure prediction for polymorph selection and excipient interaction prediction.
  • Utilizing computationally obtained prediction models validated against experimental results.

Main Results:

  • Computational techniques significantly cut down time and resources needed for drug development.
  • Successful implementation of prediction models for screening coformers, polymers, and amorphous solid dispersions (ASD).
  • Troubleshooting and addressing translational issues in solid-state pharmaceutics.

Conclusions:

  • Modern computational techniques are crucial for accelerating API, preformulation, and formulation development.
  • Case studies demonstrate the effective use of computational tools in solid-state pharmaceutics.
  • A presented workflow assists readers in applying specific computational methods for their needs.