Current Methods for Quantifying Drug Synergism.

Bliss Combination Index Drug combinations Loewe Synergy

Journal

Proteomics & bioinformatics : current research
ISSN: 2641-7561
Titre abrégé: Proteom Bioinform
Pays: United States
ID NLM: 101757996

Informations de publication

Date de publication:
Jul 2019
Historique:
entrez: 12 2 2020
pubmed: 12 2 2020
medline: 12 2 2020
Statut: ppublish

Résumé

The effectiveness of drug combinations for treatment of a variety of complex diseases is well established. "Drug cocktail" treatments are often prescribed to improve the overall efficacy, decrease toxicity, alter pharmacodynamics, etc in an overall treatment strategy. Specifically, if when combined, drugs interact in some way that causes the total effect to be greater than that predicted by their individual potencies, then drugs are considered synergistic. While there are established ways to quantify the impact of drug combinations clinically, it is an open challenge to quantitatively summarize a synergistic interaction. In this paper, we discuss an overview of the current statistical and mathematical methods for the study of drug combination effects, especially drug synergy quantification (where the interaction effect is not just detected, but quantified according to its magnitude). We first introduce two popular reference models for testing to null hypothesis of non-interaction for a combination, including the Bliss independence model and the Loewe additivity model. Then we discuss several methods for quantifying drug synergism. The advantages and disadvantages with these methods are also provided, and finally, we discuss important next directions in this area.

Identifiants

pubmed: 32043089
pmc: PMC7010330
mid: NIHMS1058718

Types de publication

Journal Article

Langues

eng

Pagination

43-48

Subventions

Organisme : Intramural NIH HHS
ID : Z99 ES999999
Pays : United States

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Auteurs

Jun Ma (J)

Bioinformatics Research Center, North Carolina State University.

Alison Motsinger-Reif (A)

Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences.

Classifications MeSH