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Int. J. Mol. Sci. 2012, 13(4), 5207-5229; doi:10.3390/ijms13045207

Article

Poisson Parameters of Antimicrobial Activity: A Quantitative Structure-Activity Approach

Radu E. Sestraş ¹ , Lorentz Jäntschi ^1,2,*  and Sorana D. Bolboacă ³

¹ University of Agricultural Science and Veterinary Medicine Cluj-Napoca, 3-5 Mănăştur, Cluj-Napoca 400372, Romania ² Technical University of Cluj-Napoca, 28 Memorandumului, Cluj-Napoca 400114, Romania ³ Department of Medical Informatics and Biostatistics, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 6 Louis Pasteur, Cluj-Napoca 400349, Cluj, Romania

* Author to whom correspondence should be addressed.

Received: 22 March 2012; in revised form: 17 April 2012 / Accepted: 19 April 2012 / Published: 24 April 2012

(This article belongs to the Special Issue Advances in Biomolecular Simulation)

Download PDF Full-Text [519 KB, uploaded 24 April 2012 09:44 CEST]

Abstract: A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters.

Keywords: oils compounds; antimicrobial effect; bacteria and fungi species; probability distribution function; quantitative structure-activity relationship (QSAR); multiple linear regression (MLR)

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