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Related Concept Videos

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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Factors Affecting Drug Response: Overview01:21

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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Factors Affecting Protein-Drug Binding: Drug Interactions01:23

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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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Factors Affecting Drug Distribution: Miscellaneous Factors01:19

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Drug distribution in the human body is a complex process influenced by various individual factors, including age, pregnancy, obesity, diet, body water composition, pH levels, and specific disease conditions.
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Factors Affecting Protein-Drug Binding: Drug-Related Factors01:18

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Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization.

Ron Nafshi1, Timothy R Lezon1

  • 1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States.

Frontiers in Bioinformatics
|October 28, 2022
PubMed
Summary

Predicting synergistic drug combinations using Probabilistic Matrix Factorization (PMF) can accelerate cancer treatment development. This approach accurately identifies effective drug pairs from limited data, reducing the need for extensive testing.

Keywords:
active learningcombination therapiesdrug discoveryexperimental designmatrix factorizationphenotypic screening

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Area of Science:

  • Computational biology
  • Pharmacology
  • Drug discovery

Background:

  • Drug development is expensive and slow, necessitating efficient strategies for novel therapeutics.
  • Drug combinations offer potential for reduced dosages, fewer side effects, and overcoming resistance.
  • Testing all possible drug combinations is infeasible due to time and resource constraints.

Purpose of the Study:

  • To investigate the efficacy of recommender algorithms, specifically Probabilistic Matrix Factorization (PMF), in predicting synergistic drug combinations.
  • To identify promising drug combinations from a limited dataset, accelerating therapeutic development.
  • To propose a novel experimental design for detecting synergistic combinations efficiently.

Main Methods:

  • Utilized the NCI ALMANAC dataset, containing pairwise combinations of 104 anticancer drugs against 60 cancer cell lines.
  • Applied the low-rank matrix completion algorithm, Probabilistic Matrix Factorization (PMF), to predict drug combination efficacy.
  • Developed a PMF-guided experimental design for efficient identification of synergistic combinations.

Main Results:

  • PMF accurately predicted the efficacy of two-drug combinations using partial interaction data.
  • The model demonstrated robustness to variations in training data.
  • The proposed experimental design allows for the detection of all synergistic combinations without exhaustive testing.

Conclusions:

  • Probabilistic Matrix Factorization is a powerful tool for predicting synergistic drug combinations in cancer therapy.
  • This computational approach can significantly accelerate the identification of effective drug combinations.
  • PMF-guided experimental design offers a resource-efficient strategy for drug discovery.