Biology subjects

Magreault, S.

Publications and source records attributed to Magreault, S..

2 recordsLinked to original sources

bioRxiv2026

Collective protection drives human gut microbiota response to amoxicillin treatment.

The gut microbiota is central to human health, contributing to nutrient processing, metabolite production and protection against pathogens. Yet it is also an unintended target of antibiotic treatments, particularly after oral administration. Antibiotic exposure can therefore disrupt community structure, leading to dysbiosis and promoting the selection of resistant bacteria. Although studies in patients and animal models have shown that these effects vary markedly between individuals, host-related factors have made it difficult to isolate the specific contribution of the microbiota itself. Here, we used a controlled in vitro gut model (MBRA) to examine how 16 human gut microbiotas from the NutriNet-Sante cohort respond to the widely prescribed {beta}-lactam amoxicillin (AMX). By combining dense temporal sampling, 16S amplicon sequencing and mass spectrometry, we observed highly heterogeneous response trajectories, ranging from near-stable communities to strong but reversible disruptions. These differences were not only reflected in the final magnitude of perturbation, but also in the timing, pace and recovery of microbiota change during treatment. Initial community composition partly structured these responses, as Lachnospiraceae/Bacteroidaceae ratio strongly correlated with perturbation. Dynamic quantification of AMX further showed that microbiotas differed in their capacity to deplete the drug over time, thereby altering the duration of exposure above critical concentration thresholds. Supplementation with clavulanic acid that inhibits {beta}-lactamases confirmed that this process was largely mediated by {beta}-lactamase activity. Finally, microbiotas displaying rapid AMX depletion showed reduced selection of resistant Enterobacteriaceae. Together, our results indicate that both initial community composition and the temporal dynamics of antibiotic inactivation jointly determine microbiota resilience and resistance selection.

microbiology↗

bioRxiv2026

Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care

A priori model informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patients covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Model-development cohorts were reproduced, and steady-state trough concentrations were simulated using cohort-specific dosing regimens. As reference methods, weighted model ensembling (WME) and classification tree (CT)-informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)-informed ensembling, using RT to predict the log individual prediction/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine, k-nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single-model PopPK dosing, standard dosing, and a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (30-42 % correct predictions) and ML (36-39 %) exceeded single-model approaches (14-32 %). RT-informed and FAMD ensembling improved performance by 6-10 % over uninformed ensembling on clinical data. In clinical patients receiving continuous infusion, ensembling further improved performance, with FAMD achieving 49 % correct predictions. ML-based ensembling eliminates the need for model selection and increase target attainment.

pharmacology and toxicology↗