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Related Concept Videos

Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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...
Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

Pharmacokinetics: Drug–Food and Drug–Viral Interactions

A drug interaction occurs when the concurrent use of another drug, food, or an external substance alters the pharmacological activity of a drug. This interaction can modify the action of the original drug, affecting its effectiveness and safety.Drug–food interactions are significant as they impact drug absorption, metabolism, and excretion. For example, grapefruit juice is a well-known disruptor of drug metabolism. It inhibits the cytochrome P450 3A4 enzyme, crucial for the metabolism of many...
Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

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.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...

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Related Experiment Video

Updated: Jul 9, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.

Carolina H Chung1, David C Chang1, Nicole M Rhoads2

  • 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Cell Reports Methods
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

We developed TACTIC, a machine learning framework to predict effective drug combinations for challenging bacterial infections. This approach overcomes data limitations for understudied pathogens, identifying novel synergistic therapies.

Keywords:
CP: microbiologyCP: systems biologyantibiotic resistancebroad-spectrum synergycombination therapiesdrug interactionsendophthalmitismachine learningnarrow-spectrum therapiesnon-tuberculous mycobacteria

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Related Experiment Videos

Last Updated: Jul 9, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Area of Science:

  • Microbiology
  • Computational Biology
  • Pharmacology

Background:

  • Drug resistance necessitates novel therapeutic strategies, particularly combination therapies.
  • Existing machine learning (ML) models struggle with limited data for emerging or understudied pathogens.
  • Efficiently screening vast drug combination spaces is computationally challenging.

Purpose of the Study:

  • To develop a novel computational framework, TACTIC (transfer-learning and cross-species training to infer combination therapies), for predicting synergistic drug combinations.
  • To address the limitations of ML models when applied to pathogens with scarce training data.
  • To identify and validate novel drug combinations against resistant bacterial pathogens.

Main Methods:

  • Developed TACTIC, a machine learning framework utilizing transfer learning and cross-species training.
  • Trained models on 2,965 drug interactions across 12 bacterial strains.
  • Analyzed approximately 600,000 predicted drug interactions across 18 bacterial strains.
  • Experimentally validated predicted synergistic drug combinations against clinical isolates.

Main Results:

  • TACTIC outperformed traditional ML models in predicting drug interactions for species with limited data.
  • Identified drug interactions selectively synergistic against Gram-negative and mycobacterial pathogens.
  • Validated synergistic combinations including clarithromycin, ampicillin, and mecillinam against *M. abscessus*.
  • Confirmed 11 synergistic combinations against *P. aeruginosa* and *S. aureus* causing endophthalmitis.

Conclusions:

  • TACTIC provides a robust computational approach for discovering synergistic drug combinations against challenging bacterial pathogens.
  • The framework enables effective ML model training even with limited pathogen-specific data.
  • Identified promising synergistic drug combinations for treating resistant infections like *M. abscessus*, *P. aeruginosa*, and *S. aureus*.