Doravirine responses to HIV-1 viruses bearing mutations to NRTIs and NNRTIs under <i>in vitro</i> selective drug pressure
Bibliographic record
Abstract
OBJECTIVES: The NNRTI doravirine has been recently approved for the first-line treatment of HIV-infected patients, eliciting favourable responses against viruses bearing the K103N, Y181C and G190A mutations. This study used in vitro drug selections to elaborate the breadth of doravirine responses against viruses bearing NNRTI and NRTI resistance-associated mutations (RAMs). METHODS: WT clinical isolates (n = 6) and viruses harbouring common NRTI and NNRTI RAMs (n = 6) were serially passaged in escalating concentrations of doravirine, doravirine/islatravir, doravirine/lamivudine and rilpivirine over 24 weeks. Genotypic analysis ascertained the appearance and accumulation of NNRTI RAMs. Phenotypic drug susceptibility assays assessed resistance conferred by acquired NNRTI RAMs. RESULTS: For WT viruses, doravirine pressure led to the appearance of V108I or V106A/I/M RAMs after 8 weeks, conferring low-level (∼2-fold) resistance. After 24 weeks, the accumulation of three to six secondary RAMs, including F227L, M230L, L234I and/or Y318, resulted in high-level (>100-fold) resistance to doravirine. Notably, viruses with these doravirine RAMs remained susceptible to rilpivirine and efavirenz. This contrasted with rilpivirine where acquisition of E138K, L100I and/or K101E resulted in >50-fold cross-resistance to all NNRTIs. Doravirine selection of viruses bearing common NRTI and NNRTI RAMs showed delayed acquisition of RAMs compared with WT virus. Pairing doravirine with islatravir or lamivudine attenuated the development of NNRTI RAMs. CONCLUSIONS: Doravirine showed favourable resistance profiles against viruses harbouring NRTI and NNRTI RAMs. The high barrier to resistance to doravirine coupled with the long intracellular half-life of islatravir may provide the opportunity for long-acting treatment options.
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 | 0.000 |
| 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 | 0.000 |
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".