Didanosine, interferon-alfa and ribavirin: a highly synergistic combination with potential activity against HIV-1 and hepatitis C virus.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the antiviral triple combination didanosine (ddI), interferon-alfa (IFN-alpha), and ribavirin for potential synergy in inhibition of HIV-1 replication in vitro. METHODS: Phytohaemagglutinin-stimulated cord blood mononuclear cells were infected with HIV-1(IIIB) or the HXB2D molecular clone of HIV-1 then cultured with interleukin-2 with ddI, ribavirin or IFN-alpha, alone and in combination. Reverse transcriptase activity was measured after 7 days to determine the inhibitory concentration of 50% (IC(50)) for the various drugs in replicate assays. Analysis of combined effects was performed using both the median effect principle (CalcuSyn, Biosoft) and three-dimensional modelling (MacSynergy II). RESULTS: The triple combination was highly synergistic against HIV-1 in vitro with combination indices < 1. The mean IC(50) was reduced from 6.85 to 0.90 micromol/l (P < 0.001) for ddI and from 6.58 to 1.00 micromol/l (P < 0.001) for IFN-alpha. No increased cytotoxicity was observed. Results were similar with both viral strains and using both analyses. In the triple combination, increasing concentrations of IFN-alpha resulted only a slight enhancement of synergy: synergy volumes were 134 [95% confidence limit (CL), 77-191] with 5 U IFN-alpha and 214.92 (95% CL, 116-314) with 10 U. This supporting the observation that the majority of the synergistic activity was derived from the combination of ddI and ribavirin, with IFN-alpha providing additional additive suppression. CONCLUSIONS: This novel triple combination has the potential to provide simultaneous activity against both HIV and hepatitis C and deserves further study to determine if can be safely administered in the clinical setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".