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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.
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Antipsychotic drugs primarily block dopamine and serotonin receptors and cholinergic, adrenergic, and histaminergic receptors, thereby reducing hallucinations and delusions in conditions like schizophrenia. However, they can trigger unwanted extrapyramidal effects such as dystonias, Parkinson-like symptoms, and tardive dyskinesia.
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DIGREM: an integrated web-based platform for detecting effective multi-drug combinations.

Minzhe Zhang1, Sangin Lee2, Bo Yao1

  • 1Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Bioinformatics (Oxford, England)
|October 9, 2018
PubMed
Summary
This summary is machine-generated.

Predicting synergistic drug combinations computationally is crucial for complex diseases. DIGREM is a new toolkit that integrates multiple models to effectively predict effective multi-drug combinations in silico, reducing costly in vivo screening.

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Area of Science:

  • Pharmacogenomics
  • Computational Biology
  • Drug Discovery

Background:

  • Synergistic drug combinations offer multi-target therapeutic benefits for complex diseases.
  • In vivo screening of numerous drug combinations is prohibitively expensive and time-consuming.
  • Developing robust in silico models is essential to predict drug synergy efficiently.

Purpose of the Study:

  • To develop and present DIGREM, an online toolkit for predicting synergistic drug combinations.
  • To facilitate the identification of effective multi-drug combinations through computational analysis.
  • To reduce the need for extensive and costly in vivo drug screening.

Main Methods:

  • DIGREM integrates multiple predictive models: DIGRE, IUPUI_CCBB, gene set-based, and correlation-based approaches.
  • The toolkit utilizes dose-response data and drug-treated gene expression profiles for synergy prediction.
  • Computational models are employed to simulate and forecast synergistic drug interactions.

Main Results:

  • DIGREM effectively predicts synergistic drug combinations using integrated computational models.
  • The toolkit provides a user-friendly platform for analyzing drug synergy based on genomic response.
  • Successful identification of potential synergistic drug combinations is demonstrated.

Conclusions:

  • DIGREM offers a valuable computational tool for accelerating drug discovery and development.
  • The toolkit can significantly streamline the process of identifying effective multi-drug combinations for complex diseases.
  • DIGREM facilitates cost-effective in silico screening, paving the way for targeted therapeutic strategies.