POTENTIAL SCREENING METHOD TO IDENTIFY ANTIMICROBIAL DRUGS FOR SUCCESSFUL TREATMENT OF NAEGLERIA FOWLERI INFECTION

This study presents a methodology to identify potential antimicrobial drugs for the treatment of Naegleria fowleri (N. fowleri) infection, which is an amoebic protozoan infection, and in which survival after infection is extremely unlikely. Also known as the “brain-eating amoeba”, this microbe causes primary amebic meningoencephalitis (PAM). This study focuses on a group of antimicrobials that have been found to be effective for the clinical treatment of the deadly N. fowleri infection. These drugs are Amphotericin B, miltefosine, fluconazole voriconazole, azithromycin, rifampin, and posaconazole. The molecular properties of these agents was determined and run through extensive statistical analysis to establish a criteria for screening other antimicrobials for the treatment of N. fowleri infection. Speed of treatment is vital for surviving N. fowleri infection, and this screening methodology is anticipated to increase the efficiency of selecting antimicrobials for successful clinical treatment. The molecular properties of the seven agents that are successful for clinical treatment have been determined and multiple regression analysis of molecular properties of these seven drugs successful and effective in treatment, produced an equation useful for design of similar compounds, based on molecular properties. The 95% confidence interval for the molecular mass of these seven effective agents is 378.3 to 839.1, for Log P is 1.130 to 5.147, for polar surface area is 65.29 A2 to 238.23 A2, etc. In addition, pattern recognition methodologies Discriminant Analysis, Convex Hulls, and 95% Ellipses are applied to interpret relationships within these multivariate data sets. This methodology has the potential to assist the rapid identification of potentially successful antimicrobials. for the treatment of PAM and specifically N. fowleri infections.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-01
DOI
https://doi.org/10.5281/zenodo.18428414
Primary Topic
Legionella and Acanthamoeba research
Type
article
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article

POTENTIAL SCREENING METHOD TO IDENTIFY ANTIMICROBIAL DRUGS FOR SUCCESSFUL TREATMENT OF NAEGLERIA FOWLERI INFECTION

*Ronald Bartzatt
Zenodo (CERN European Organization for Nuclear Research)
Legionella and Acanthamoeba research
article

POTENTIAL SCREENING METHOD TO IDENTIFY ANTIMICROBIAL DRUGS FOR SUCCESSFUL TREATMENT OF NAEGLERIA FOWLERI INFECTION

*Ronald Bartzatt
article en

Abstract

This study presents a methodology to identify potential antimicrobial drugs for the treatment of Naegleria fowleri (N. fowleri) infection, which is an amoebic protozoan infection, and in which survival after infection is extremely unlikely. Also known as the “brain-eating amoeba”, this microbe causes primary amebic meningoencephalitis (PAM). This study focuses on a group of antimicrobials that have been found to be effective for the clinical treatment of the deadly N. fowleri infection. These drugs are Amphotericin B, miltefosine, fluconazole voriconazole, azithromycin, rifampin, and posaconazole. The molecular properties of these agents was determined and run through extensive statistical analysis to establish a criteria for screening other antimicrobials for the treatment of N. fowleri infection. Speed of treatment is vital for surviving N. fowleri infection, and this screening methodology is anticipated to increase the efficiency of selecting antimicrobials for successful clinical treatment. The molecular properties of the seven agents that are successful for clinical treatment have been determined and multiple regression analysis of molecular properties of these seven drugs successful and effective in treatment, produced an equation useful for design of similar compounds, based on molecular properties. The 95% confidence interval for the molecular mass of these seven effective agents is 378.3 to 839.1, for Log P is 1.130 to 5.147, for polar surface area is 65.29 A2 to 238.23 A2, etc. In addition, pattern recognition methodologies Discriminant Analysis, Convex Hulls, and 95% Ellipses are applied to interpret relationships within these multivariate data sets. This methodology has the potential to assist the rapid identification of potentially successful antimicrobials. for the treatment of PAM and specifically N. fowleri infections.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Openalex Percentile: Top 68%
Legionella and Acanthamoeba research
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