Determination of phage load and administration time in simulated occurrences of antibacterial treatments

Abstract

The use of phages as antibacterials is becoming more and more common in Western countries. However, a successful phage-derived antibacterial treatment needs to account for additional features such as the loss of infective virions and the multiplication of the hosts. The parameters critical inoculation size (VF) and failure threshold time (TF) have been introduced to assure that the viral dose (Vϕ) and administration time (Tϕ) would lead to the extinction of the targeted bacteria. The problem with the definition of VF and TF is that they are non-linear equations with two unknowns; thus, obtaining their explicit values is cumbersome and not unique. The current study used machine learning to determine VF and TF for an effective antibacterial treatment. Within these ranges, a Pareto optimal solution of a multi-criterial optimization problem (MCOP) provided a pair of Vϕ and Tϕ to facilitate the user’s work. The algorithm was tested on a series of in silico microbial consortia that described the outgrowth of a species at high cell density by another species initially present at low concentration. The results demonstrated that the MCOP-derived pairs of Vϕ and Tϕ could effectively wipe out the bacterial target within the context of the simulation. The present study also introduced the concept of mediated phage therapy, where targeting booster bacteria might decrease the virulence of a pathogen immune to phagial infection and highlighted the importance of microbial competition in attaining a successful antibacterial treatment. In summary, the present work developed a novel method for investigating phage/bacteria interactions that can help increase the effectiveness of the application of phages as antibacterials and ease the work of microbiologists.

Determination of Optimal Phage Load and Administration Time for Antibacterial Treatment

“…Windows users can execute the procedure by converting the shell commands provided in the protocols below to their DOS equivalents. The life history parameters used to describe the phages and their host were previously described (Plunder et al, 2022).…”

“…The biosafety level (BSL) for actual experiments depends mainly on the type of bacterium employed; the most common level would be BSL‐2. The work presented herein is derived from a previous publication (Plunder et al., 2022) that reanalyzed with an in silico approach actual experiments carried out on Escherichia coli (BSL‐2), Pseudomonas aeruginosa (BSL‐2), Azotobacter vinelandii (BSL‐1), Streptococcus mutans (BSL‐2), Limosilactobacillus reuteri (BSL‐1; previously Lactobacillus reuteri ), and the protist Candida albicans (BSL‐2). It is possible to determine the BSL category for bacteria from the website of the German Collection of Microorganisms and Cell Culture (https://www.dsmz.de).…”

“…Unfortunately, there is a paucity of values for these parameters in the literature; the user is encouraged to determine them experimentally. The values used herein were obtained from our previous procedure definition (Plunder et al., 2022), but they should be considered only for exemplificative purposes.…”

“…Suboptimal amounts of viruses or time of administration might lead to ineffective treatments, explaining the high failure rate of several actual phage therapies (Payne & Jansen, 2001). To help identify the best pair of phage load and administrator time, we have developed a machine learning approach based on a decision tree algorithm (Plunder et al., 2022). The model computes the amount of target bacteria at the end of the simulation and classifies the treatment as either “active,” “delayed,” “passive,” or “failed.” Active and delayed treatments are obtained when there is an increase in the number of phages over the initial administered amount either immediately or after a temporal lag.…”