- author = "Salvatore Rampone and Caterina Pagliarulo and Chiara Marena and Antonello Orsillo and Margherita Iannaccone and Carmela Trionfo and Daniela Sateriale and Marina Paolucci",
- title = "In silico analysis of the antimicrobial activity of phytochemicals: towards a technological breakthrough",
- journal = "Computer Methods and Programs in Biomedicine",
- volume = "200",
- pages = "105820",
- year = "2021",
- ISSN = "0169-2607",
-
DOI = "
doi:10.1016/j.cmpb.2020.105820",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S0169260720316539",
- keywords = "genetic algorithms, genetic programming, Antimicrobial susceptibility, Phytobiotics, analysis, Artificial neural networks",
-
abstract = "Background
The complications associated with infections from pathogens increasingly resistant to traditional drugs lead to a constant increase in the mortality rate among those affected. In such cases the fundamental purpose of the microbiology laboratory is to determine the sensitivity profile of pathogens to antimicrobial agents. This is an intense and complex work often not facilitated by the test's characteristics. Despite the evolution of the Antimicrobial Susceptibility Testing (AST) technologies, the technological breakthrough that could guide and facilitate the search for new antimicrobial agents is still missing.
Methods
In this work, we propose the experimental use of in silico instruments, particularly feedforward Multi-Layer Perceptron (MLP) Artificial Neural Network, and Genetic Programming (GP), to verify, but also to predict, the effectiveness of natural and experimental mixtures of polyphenols against several microbial strains.
Results
We value the results in predicting the antimicrobial sensitivity profile from the mixture data. Trained MLP shows very high correlations coefficients (0,93 and 0,97) and mean absolute errors (110,70 and 56,60) in determining the Minimum Inhibitory Concentration and Minimum Microbicidal Concentration, respectively, while GP not only evidences very high correlation coefficients (0,89 and 0,96) and low mean absolute errors (6,99 and 5,60) in the same tasks, but also gives an explicit representation of the acquired knowledge about the polyphenol mixtures.
Conclusions
In silico tools can help to predict phytobiotics antimicrobial efficacy, providing an useful strategy to innovate and speed up the extant classic microbiological techniques.",
