Rapid, accurate, and reproducible <i>de novo</i> prediction of resistance to antituberculars
Bibliographic record
Abstract
As one of the deadliest infectious diseases in the world, tuberculosis is responsible for millions of new cases and deaths reported annually. The rise of drug-resistant tuberculosis, particularly resistance to first-line treatments like rifampicin, presents a critical challenge for global health, which complicates the treatment strategies and calls for effective diagnostic and predictive tools. In this study, we apply an ensemble-based molecular dynamics computer simulation method, TIES_PM, to estimate the binding affinity through free energy calculations and predict rifampicin resistance in RNA polymerase. By analyzing 61 mutations, including those in the rifampicin resistance-determining region, TIES_PM produces reliable results in good agreement with clinical reference and identifies abnormal data points indicating alternative mechanisms of resistance. In the future, TIES_PM is capable of identifying and selecting leads with a lower risk of resistance evolution and, for smaller proteins, it may systematically predict antibiotic resistance by analyzing all possible codon permutations. Moreover, its flexibility allows for extending predictions to other first-line drugs and drug-resistant diseases. TIES_PM provides a rapid, accurate, low-cost, and scalable supplement to current diagnostic pipelines, particularly for drug resistance screening in both research and clinical domains.IMPORTANCEAntimicrobial resistance (AMR), a global threat, challenges early diagnosis and treatment of tuberculosis (TB). This study employs TIES_PM, a free-energy calculation method, to efficiently predict AMR by quantifying how mutations in bacterial RNA polymerase (RNAP) affect rifampicin (RIF) binding. On simulating 61 clinically observed mutations, the results align with WHO classifications and reveal ambiguous cases, suggesting alternative resistance mechanisms. Each mutation requires ~5 h, offering rapid, cost-effective predictions. An ensemble approach ensures statistical robustness. TIES_PM can be extended to smaller proteins for systematic codon permutation analysis, enabling comprehensive antibiotic resistance prediction, or adapted to identify low-resistance-risk drug leads. It also applies to other TB drugs and resistant pathogens, supporting personalized therapy and global AMR surveillance. This work provides novel tools to refine resistance mutation databases and phenotypic classification standards, enhancing early diagnosis while advancing translational research and infectious disease control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".