1. Synergy evaluation of non‐normalizable dose–response data: Generalization of combination index for the linear effect of drugs
- Author
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Milan Nagy, Elena Kurin, and Branislav Novotný
- Subjects
Pharmacology ,Statistics and Probability ,Dose-Response Relationship, Drug ,Generalization ,Drug Synergism ,Combination index ,Sigmoid function ,Function (mathematics) ,01 natural sciences ,Upper and lower bounds ,Drug Combinations ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Data Interpretation, Statistical ,Bounded function ,Humans ,Applied mathematics ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,Dose response data ,Mathematics - Abstract
The study of drug synergy plays a prominent role in the search for drug combinations with beneficial interactions. Firstly, in this process, the drug-effect response of individual parts and the mixture needs to be derived. This function is usually well described by Hill (or other logistic or sigmoid) curve. Due to its boundedness, it allows the measured data to be normalized. The normalized data can then be processed by interaction analysis using the Loewe, Bliss, or other models to evaluate possible synergy or antagonism of two or more drugs. However, sometimes, the drug-effect responses observed in pharmaceutical research do not appear to be bounded. Theoretically, the drug-effect curve cannot grow to infinity, but it may be impossible to determine its upper bound within the observed region. In this case, standard models cannot be used, since they assume that data are normalized. The approach of this article bypasses the need to normalize the data, allowing its broader application and usefulness in finding potential synergies in pharmaceutical research.
- Published
- 2021
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