The Influence of Protease Inhibitor Resistance Profiles on Selection of HIV Therapy in Treatment-Naive Patients
Bibliographic record
Abstract
Although protease inhibitors (PIs) have dramatically improved outcomes in HIV-infected patients, half still fail treatment with PI-based combination therapy. Genetic pressure from incomplete viral suppression rapidly selects for HIV variants with protease gene mutations that confer reduced susceptibility to PI drugs. A number of specific amino acid substitutions have been associated with PI resistance. However, high-level resistance to individual PIs requires the accumulation of several primary and secondary mutations, developing along drug-specific, step-wise pathways. HIV variants resistant to saquinavir and ritonavir usually contain L90M and V82A substitutions, respectively. Indinavir resistance may be linked to substitutions at positions 46 or 82. Resistance to nelfinavir is primarily associated with D30N but may alternatively be found with L90M. Resistance during exposure to amprenavir can follow development of I50V, which also may confer resistance to lopinavir. Failure during treatment with atazanavir is closely linked to 150L. The overlapping of these pathways can lead to multiple-PI resistance, limiting therapeutic options in antiretroviral-experienced patients. Reduced susceptibility to more than one PI is most likely to be associated with amino acid substitutions at six positions: 10, 46, 54, 82, 84 and 90. Other mutations (D30N, G48V, I50V or I50L) are relatively specific for particular PIs and are less likely to produce cross resistance. Certain resistance mutations selected by exposure to one PI may actually increase susceptibility to others. Patients newly diagnosed with HIV infection are increasingly found to harbour virus that is resistant to the more commonly used drugs. Newer PIs may select for mutations that result in less cross resistance with older agents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".