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Related Concept Videos

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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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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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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug

Erkhembayar Jadamba1, Miyoung Shin2

  • 1Bio-Intelligence & Data Mining Laboratory, Graduate School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sangyeok-dong, Buk-gu, Daegu 702-701, Republic of Korea.

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This study introduces a novel computational framework for drug repositioning, integrating genomic and pharmaceutical data to identify potential new uses for existing medications. The approach successfully identified promising drug candidates for breast cancer treatment.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Genomics

Background:

  • Drug repositioning accelerates the discovery of new therapeutic indications for existing drugs.
  • Current computational methods for drug repurposing often lack comprehensive integration of diverse biological and chemical data.
  • A need exists for efficient, systematic frameworks that leverage multiple data sources for drug repositioning.

Purpose of the Study:

  • To develop and evaluate a systematic computational framework for drug repositioning.
  • To integrate experimental genomic knowledge and pharmaceutical knowledge for identifying novel drug indications.
  • To discover promising drug candidates for specific diseases, exemplified by breast cancer.

Main Methods:

  • Constructed a pathway-drug network by integrating disease gene expression profiles and drug phenotype expression profiles.
  • Employed network propagation in a semisupervised manner, initializing node labels with disease pathways and phenotype-associated drugs.
  • Applied the framework to reposition 1309 drugs using four breast cancer datasets.

Main Results:

  • The proposed framework successfully identified promising drug candidates for breast cancer.
  • Experimental results demonstrated the utility of the systematic approach in drug repositioning.
  • A two-step validation procedure confirmed the efficacy of the identified candidate drugs.

Conclusions:

  • The developed systematic framework is a valuable tool for discovering potential drug repositioning candidates.
  • This integrated approach enhances the efficiency and comprehensiveness of computational drug repurposing.
  • The findings provide a foundation for further investigation of identified drugs for breast cancer therapy.