Cell culture selections reveal favourable drug resistance profiles for doravirine and islatravir
Bibliographic record
Abstract
BACKGROUND: The newer generation NNRTIs, including doravirine and rilpivirine, were designed to show high potency and overcome K103N, Y181C and G190A resistance. OBJECTIVES: To assess emergent resistance to doravirine and rilpivirine, alone and paired with lamivudine or islatravir through in vitro drug selections. METHODS: Subtype B (n = 3), non-B subtype (n = 3), and pNL4.3 viral isolates were passaged in cord blood mononuclear cells with progressively increasing concentrations of drug(s). Genotypic analysis compared the acquisition and accumulation of drug resistance mutations at weeks 8 and 24 following drug pressure. Cell-based phenotypic assays assessed cross-resistance patterns to NNRTIs by acquired resistance mutations. RESULTS: Doravirine pressure resulted in the acquisition of V108I (6/7) and V106A/I/M (5/7) mutations at weeks 8, followed by F227L (4/7), Y318F (4/7), M230L (2/7) or L234I (2/7) by weeks 24. In contrast, rilpivirine resulted in E138K (5/7) followed by L100I (3/7), K101E (1/7), or M230L (1/7). Doravirine resistance pathways retained susceptibility to rilpivirine, whereas rilpivirine resistance conferred intermediate resistance (12-152-fold) to doravirine. Dual selections with islatravir or lamivudine delayed and diminished emergent resistance to doravirine, resulting in V108I (9/15) with fewer or no other changes at weeks 24. There was a lesser delay in emergent resistance to rilpivirine when combined with islatravir or lamivudine. The M184V mutation did not arise in dual selections with islatravir or lamivudine. CONCLUSIONS: Doravirine showed a more robust resistance profile compared with other NNRTIs. The long intracellular half-life of islatravir and delayed acquisition of resistance in dual selections provide an opportunity for long-acting treatment options.
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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 | 0.000 |
| 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 | 0.001 |
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".