Drug-Penetration Gradients Associated with Acquired Drug Resistance in Patients with Tuberculosis
Bibliographic record
Abstract
RATIONALE: Acquired resistance is an important driver of multidrug-resistant tuberculosis (TB), even with good treatment adherence. However, exactly what initiates the resistance and how it arises remain poorly understood. OBJECTIVES: To identify the relationship between drug concentrations and drug susceptibility readouts (minimum inhibitory concentrations [MICs]) in the TB cavity. METHODS: We recruited patients with medically incurable TB who were undergoing therapeutic lung resection while on treatment with a cocktail of second-line anti-TB drugs. On the day of surgery, antibiotic concentrations were measured in the blood and at seven prespecified biopsy sites within each cavity. Mycobacterium tuberculosis was grown from each biopsy site, MICs of each drug identified, and whole-genome sequencing performed. Spearman correlation coefficients between drug concentration and MIC were calculated. MEASUREMENTS AND MAIN RESULTS: Fourteen patients treated for a median of 13 months (range, 5-31 mo) were recruited. MICs and drug resistance-associated single-nucleotide variants differed between the different geospatial locations within each cavity, and with pretreatment and serial sputum isolates, consistent with ongoing acquisition of resistance. However, pretreatment sputum MIC had an accuracy of only 49.48% in predicting cavitary MICs. There were large concentration-distance gradients for each antibiotic. The location-specific concentrations inversely correlated with MICs (P < 0.05) and therefore acquired resistance. Moreover, pharmacokinetic/pharmacodynamic exposures known to amplify drug-resistant subpopulations were encountered in all positions. CONCLUSIONS: These data inform interventional strategies relevant to drug delivery, dosing, and diagnostics to prevent the development of acquired resistance. The role of high intracavitary penetration as a biomarker of antibiotic efficacy, when assessing new regimens, requires clarification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".