Biology subjects

Aranzana-Climent, V.

Publications and source records attributed to Aranzana-Climent, V..

5 recordsLinked to original sources

bioRxiv2026

Replacing In Vivo Experiments for PK/PD Target Determination Through In Vitro Time-Kill Experiments and PK/PD Modelling Incorporating Inter-strain Variability: Application to Meropenem Against Pseudomonas aeruginosa

Background. Optimal antibiotic dosing regimens depend on the pharmacokinetic/pharmacodynamic (PK/PD) index that best predicts antibacterial efficacy. PK/PD targets are traditionally determined using murine infection models based on a limited number of bacterial isolates. Objective. This study aimed to investigate whether animal experiments could be replaced by in vitro time-kill experiments performed on a large collection of clinical isolates and analyzed using a modelling approach accounting for inter-strain variability. The proposed framework was evaluated using meropenem against Pseudomonas aeruginosa. Materials and Methods. In vitro time-kill experiments were performed on 66 clinical isolates of P. aeruginosa. A population pharmacodynamic model was developed from experimental data. A murine pharmacokinetic model was reproduced from literature and combined with the pharmacodynamic model to simulate in vivo bacterial burden over time. The relationships between simulated bacterial counts at 24 h and the three main PK/PD indices (fCmax/MIC, fAUC/MIC and %fT>MIC) were characterized using nonlinear mixed-effects Imax models. Results. The PK/PD index showing the strongest correlation with meropenem efficacy at 24 h was %fT>MIC (R2 = 0.989), compared with fAUC/MIC (R2 = 0.373) and fCmax/MIC (R2 = 0.284). These findings are consistent with previous studies using murine thigh infection models. The %fT>MIC target required to achieve a 2-log CFU reduction was estimated at 44%, with substantial inter-strain variability (10th and 90th percentiles: 27% and 71%, respectively). Conclusions. Using meropenem against P. aeruginosa as a proof of concept, we demonstrate that in vitro time-kill experiments combined with pharmacometric modelling can identify the same PK/PD efficacy targets as animal infection models. Moreover, performing experiments on a large panel of clinical isolates enables the quantification of inter-strain variability in PK/PD targets, providing information that may improve their translation to clinical dosing optimization.

pharmacology and toxicology↗

bioRxiv2026

Model-based evaluation of Targeted-Antibacterial-Plasmids (TAPs) transfer kinetics and resensitization of pOXA-48 carbapenem-resistant Escherichia coli

Background Targeted-Antibacterial Plasmids (TAPs) are engineered mobile genetic elements that use bacterial conjugation to deliver selective CRISPR/Cas9 antibacterial activity against a specific target strain. Yet, the efficiency of TAPs is typically evaluated at a single time point, whereas the success of TAP-mediated resensitization critically depends on the dynamics of plasmid transfer and the complex interactions between bacterial subpopulations. This is the first study to evaluate the efficiency of a conjugation-based antibacterial approach at the subpopulation level, using an analytical framework analogous to that used for conventional antibiotics. Here, we investigate which process limits resensitization by TAPF-dCas9-OXA48: plasmid delivery, dCas9 activity, or the emergence of refractory and escape populations. Methods We fitted a mechanistic model of five interacting subpopulations (donors, recipients, transconjugants, escapers, and recusants) to 44 longitudinal conjugation experiments and used the fitted model to explore a range of biologically relevant scenarios. Results Using longitudinal conjugation data spanning 24 h, we show that up to 24% of recipients become recusants within 24h, refractory to further conjugation via entry exclusion, while secondary transconjugant emergence stays below 0.01%. Overall resensitization efficiency reaches up to 80%. Conclusion Plasmid transfer, rather than dCas9 repression, therefore appears to be the main bottleneck limiting the efficiency of TAPF-dCas9-OXA48 efficiency. These results identify plasmid delivery as a key engineering target for improving the performance of future TAPs.

bioinformatics↗

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↗

bioRxiv2025

Optimizing Antimicrobial Susceptibility Testing: Cost and Environmental Benefits of MIC Volume Reduction

The determination of Minimum Inhibitory Concentrations (MICs) is essential for evaluating antimicrobial efficacy, guiding both clinical treatment decisions and drug development. The standard broth microdilution method is widely used but requires significant reagent volumes, which can be limiting when working with novel or expensive antimicrobials. This study assesses the feasibility of reducing assay volumes without compromising MIC accuracy. We compared the MIC values obtained in standard 96-well plates (200 {micro}L) to those in 384-well plates with reduced volumes (30-50 {micro}L) for a range of Gram-negative and Gram-positive bacteria, as well as yeast species. Our results demonstrate that except for micafungin, MIC values obtained with reduced volumes remained within the acceptable variability ranges defined by EUCAST and CLSI. Evaporation, a potential source of bias in smaller volumes, was mitigated by conducting experiments in a water-saturated atmosphere. Furthermore, reduced assay volumes significantly lowered material costs and antimicrobial consumption, particularly for expensive drugs such as cefiderocol. This miniaturization approach offers a cost-effective, high-throughput alternative for antimicrobial susceptibility testing while maintaining accuracy and reproducibility.

microbiology↗

bioRxiv2024

A freely accessible, adaptable hollow-fiber setup to reproduce first-order absorption: illustration with linezolid cerebrospinal fluid pharmacokinetic data

The main objective of this study was to validate an algorithm and experimental setup to simulate first-order absorption pharmacokinetic profiles without altering the standard in vitro hollow fiber infection model (HFIM). For that, clinical cerebrospinal fluid (CSF) linezolid concentrations after 30-minute infusions at dosing regimens 600 mg q12 h, 900 mg q12 h, and 900 mg q8 h were reproduced in the HFIM over 4 days. To approximate the apparent first-order absorption observed on CSF pharmacokinetic profiles, we split the dosing interval into a series of sub-intervals during which continuous infusions were delivered to the system. During each sub-interval, the same amount of linezolid was delivered but the sub-intervals had different durations and flow rates which were computed by a newly developed algorithm. In addition, we independently reproduced plasma concentrations to validate our system. Samples were collected from the central reservoir and the extracapillary space (ECS) of the cartridge of the HFIM and assayed by liquid chromatography-tandem mass spectrometry. Observed pharmacokinetic parameters and concentrations in the ECS were compared with the target clinical pharmacokinetic parameters and concentrations. Observed pharmacokinetic parameters were within 20 % of target pharmacokinetic parameters for all experiments, thus validating the ability of our experimental setup to reproduce plasma and CSF linezolid pharmacokinetic profiles. The algorithm and setup are available in the open-source web application https://varacli.shinyapps.io/hollow_fiber_app/ to easily design other HFIM experiments.

pharmacology and toxicology↗