Biochemical mechanism of clinical resistance to rilpivirine
Bibliographic record
Abstract
The introduction of HAART has significantly prolonged the life span of HIV-infected patients. However, the error-prone nature of HIV-1 reverse transcriptase (HIV-1 RT) results in the emergence of drug-resistant viruses and threatens the effectiveness of HAART. HIV-1 RT is a primary target of two classes of drugs: nucleoside reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). Recent phase III clinical trials have shown that two HIV-1 RT mutations, E138K and M184I, were the most frequent mutations found in patients that experienced virological failure during therapy that included rilpivirine (RPV), emtricitabine (FTC), and tenofovir (TDF). To investigate the mechanistic basis for resistance caused by the E138K and M184I mutations we used transient kinetics to characterize the enzymatic properties and drug susceptibility of RTs with these mutations and determined the biochemical mechanism of resistance to RPV. Specifically, we compared wild-type (WT) RT to RTs mutated in one or both of the enzymatic subunits: p66M184I/p51M184I, p66E138K/p51E138K, p66E138K/p51M184I, p66E138K/p51WT, p66WT/p51138K. Our results show that M184I reduces the catalytic efficiency of RT by more than two-fold (p66M184I/p51M184Ihas more than 2-fold reduced kpol/Kd.dNTP with respect to WT). This defect is compensated by mutation E138K either in both subunits or only in p51 subunit (p66WT/p51138K). None of the mutations affected the template-primer binding affinity of RT. As expected, M184I does not reduce the susceptibility to RPV. Instead, RPV resistance is achieved by reduction in the binding affinity of the drug to RT because of the E138K mutation in the p51 subunit.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".