Mathematical modelling response of Pseudomonas aeruginosa to meropenem
Bibliographic record
Abstract
OBJECTIVES: Widespread emergence of resistance to antimicrobial agents is a serious problem. The rate at which new agents are made available clinically is unlikely to keep up with these resistant pathogens, and there is an urgent need to accelerate antimicrobial agent development. We explored the use of mathematical modelling to guide selection of dosing regimens. METHODS: Using time-kill studies data of Pseudomonas aeruginosa over 24 h, we developed a mathematical model to capture the dynamic relationship between a heterogeneous microbial population and meropenem concentrations. The microbial behaviour in response to meropenem over 5 days was predicted via computer simulation and subsequently validated using an in vitro hollow fibre infection model. Three parallel differential equations were used, each characterizing the rate of change of drug concentration, microbial susceptibility and microbial burden of the surviving population over time, respectively. Several model structures were explored; they differed in the adaptation of the microbial population over time. Various fluctuating concentration-time profiles of meropenem were experimentally examined, mimicking human elimination and repeated dosing. RESULTS: Using limited experimental data as inputs, the mathematical model was reasonable in qualitatively predicting microbial response (sustained suppression or regrowth due to resistance emergence) to various pharmacokinetic profiles of meropenem. CONCLUSIONS: Our results suggest that mathematical modelling may be used to predict microbial response to a large number of antimicrobial agent dosing regimens efficiently, and have the potential to be used to guide highly targeted investigation of dosing regimens in pre-clinical studies and clinical trials. The in vivo relevance of the modelling approach warrants further investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".