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Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Combined Effects of Drugs: Antagonism01:30

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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
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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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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning.

Xing Chen1, Biao Ren2,3, Ming Chen2

  • 1School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China.

Plos Computational Biology
|July 15, 2016
PubMed
Summary

This study introduces a novel computational method, NLLSS, to predict synergistic antifungal drug combinations. This approach effectively identifies potent drug pairings to combat drug-resistant fungal infections.

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Area of Science:

  • Mycology
  • Computational Biology
  • Pharmacology

Background:

  • Fungal infections are a major cause of hospital-acquired infections with high mortality.
  • Drug resistance in fungi necessitates novel treatment strategies.
  • Synergistic drug combinations offer increased efficacy and reduced toxicity.

Purpose of the Study:

  • To develop a computational method for predicting synergistic antifungal drug combinations.
  • To address the challenge of drug resistance in fungal infections.

Main Methods:

  • Proposed a principle of similar drug combinations for synergistic effects.
  • Developed the Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) algorithm.
  • Integrated known synergistic drug combinations, drug-target interactions, and chemical structures.

Main Results:

  • NLLSS demonstrated excellent performance in cross-validation and independent prediction of antifungal synergistic drug combinations.
  • Biological experiments confirmed 7 out of 13 predicted combinations against Candida albicans.
  • The algorithm provides an efficient strategy for identifying potential synergistic antifungal combinations.

Conclusions:

  • NLLSS is an effective tool for predicting synergistic antifungal drug combinations.
  • This computational approach can aid in overcoming drug resistance in fungal infections.
  • The findings support the use of synergistic drug combinations for enhanced antifungal therapy.