The SMAC Mimetic AZD5582 is a Potent HIV Latency Reversing Agent
Bibliographic record
Abstract
Abstract The leading strategy towards eradication of human immunodeficiency virus (HIV) infection is the depletion of viral reservoirs through reversal of viral latency, followed by clearance of persistently infected cells. To date, a latency reversing agent (LRA) that reactivates a majority of the quiescent provirus population, without significant off-target effects, has not been identified. We show here that molecules mimicking the active N-terminal tetrapeptide of the second mitochondrial-derived activator of caspases (SMACm) potently reverse HIV latency in vitro and ex vivo without the pleotropic cellular effects seen with other LRA. We verified that SMACm facilitate latency reversal through activation of the non-canonical NFkB pathway as exemplified by rapid degradation of cellular inhibitor of apoptosis protein 1 (cIAP1), followed by a slower conversion of the inactive p100 form of NFκB2 into the active p52 transcription factor. A potent representative of this class, AZD5582, increases cell-associated HIV RNA expression in resting CD4+ T cells from ART-suppressed, HIV-infected donors while altering the expression of a restricted number of human genes. These findings represent the first demonstration that SMACm have single agent latency reversal activity in patient-derived cells and support evaluation of this class of molecules in preclinical animal models.
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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 | 0.000 |
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".