Integrated Population Exposure–Response of Dolutegravir in HIV‐1 Supports Bridging of Clinical Response Influenced by Relevant Intrinsic and Extrinsic Patient Characteristics
Bibliographic record
Abstract
Dolutegravir (DTG) is a human immunodeficiency virus type 1 (HIV‐1) integrase strand transfer inhibitor indicated in combination with other antiretroviral agents for the treatment of HIV‐1 infection in adults and pediatric subjects aged at least 4 weeks. The present work aimed to characterize the viral response based on a pooled analysis of exposure–response (E–R) from five studies in treatment‐experienced and integrase‐resistant (INI‐r) patients infected with HIV‐1. Importantly, model‐based simulations of the E–R relationships with DTG provided insight into the clinical relevance of known intrinsic (e.g., sub‐population with Q148‐driven integrase mutation) and extrinsic (food, enzyme inducers, and metal cation‐containing products) factors expected to influence the DTG E–R relationship. Model‐based post hoc exposure metrics (C min and C avg) were incorporated into a mechanistic population viral dynamic model describing the short‐term effect of DTG on log10 HIV‐1 RNA viral load over 8 or 10 days. In addition, the impact of DTG in combination with background ARTs on the 24‐week HIV RNA response was also assessed using logistic regression. There was good concordance between model‐based predictions and observed virologic response on day 10 and week 24. The E–R model‐based simulations exploring the potential impact of a higher dose (100 mg b.i.d.) of DTG in subpopulations experiencing exposure changes due to covariates did not show clinically relevant changes in virological response compared with the approved 50 mg b.i.d. clinical dose. Overall, our study confirmed the current recommendation of dolutegravir 50 mg b.i.d. in the integrase inhibitor‐resistant (INI‐r) population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".