Systematic determination of in vitro phenotypic resistance to HIV-1 integrase strand transfer inhibitors from clinical samples
Bibliographic record
Abstract
ABSTRACT Phenotypic resistance data is relatively sparse for the newest HIV-1 integrase strand transfer inhibitors (INSTIs), dolutegravir (DTG), bictegravir (BIC), and cabotegravir (CAB). In this study, we report the phenotypic susceptibility of a large panel of oligo-clonal patient-derived HIV-1 integrase viruses. Representative clinical samples (N=141) were selected from a large database (N=17,197) of clinically-derived HIV integrase sequences, based on the presence of permutations of substitutions at 27 pre-defined positions in integrase (N=288). HIV-1 RNA was extracted from patient samples and diluted to approximately 500 HIV RNA copies/mL. Using an “oligo-clonal” amplification approach to achieve single-copy amplification, these dilutions were subjected to 12 parallel RT-PCR reactions to amplify integrase. Confirmed clonal amplicons were co-transfected with linearized pNL4.3∆int into CEM-GXR cells. In total, 162 HIV-1 viruses that carried no mixtures and had a unique sequence were harvested, and phenotyped in MT4-LTR-EGFP cells subsequently. Variants with the highest fold change (FC) had G140S and Q148R/H and resistant to all five drugs; R263K was the only single variant conferring >3-FC to DTG, BIC and CAB. There was extensive cross-resistance between DTG, BIC, and CAB and phenotypic resistance values for all the three INSTIs were almost collinear. The greatest exceptions were variants with N155H/G163E or L74I/T97M/F121C/V151I/E157Q/G163K, where both had >70-FC for CAB, while <3-FC for DTG and BIC. While site-directed mutagenesis is invaluable; the systematic selection of representative mutational patterns observed in vivo provides an efficient way to identify clinically relevant drug resistance.
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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 | 0.001 | 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) | 0.001 | 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".