Antiviral Activity of Bictegravir and Cabotegravir against Integrase Inhibitor-Resistant SIVmac239 and HIV-1
Bibliographic record
Abstract
ABSTRACT Animal models are essential to study novel antiretroviral drugs, resistance-associated mutations (RAMs), and treatment strategies. Bictegravir (BIC) is a novel potent integrase strand transfer inhibitor (INSTI) that has shown promising results against HIV-1 infection in vitro and in vivo and against clinical isolates with resistance against INSTIs. BIC has a higher genetic barrier to the development of resistance than two clinically approved INSTIs, termed raltegravir and elvitegravir. Another clinically approved INSTI, dolutegravir (DTG) also possesses a high genetic barrier to resistance, while a fourth compound, termed cabotegravir (CAB), is currently in late phases of clinical development. Here we report the susceptibilities of simian immunodeficiency virus (SIV) and HIV-1 integrase (IN) mutants containing various RAMs to BIC, CAB, and DTG. BIC potently inhibited SIV and HIV-1 in single cycle infection with 50% effective concentrations (EC 50 s) in the low nM range. In single cycle SIV infections, none of the E92Q, T97A, Y143R, or N155H substitutions had a significant effect on susceptibility to BIC (≤4-fold increase in EC 50 ), whereas G118R and R263K conferred ∼14-fold and ∼6-fold increases in EC 50 , respectively. In both single and multiple rounds of HIV-1 infections, BIC remained active against the Y143R, N155H, R263K, R263K/M50I, and R263K/E138K mutants (≤4-fold increase in EC 50 ). In multiple rounds of infection, the G140S/Q148H combination of substitutions decreased HIV-1 susceptibility to BIC 4.8-fold compared to 16.8- and 7.4-fold for CAB and DTG, respectively. BIC possesses an excellent resistance profile in regard to HIV and SIV and could be useful in nonhuman primate models of HIV infection.
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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 | 0.000 |
| 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".