Drug transporter mRNA expression and genital inflammation in South African women on oral pre-exposure prophylaxis (PrEP)
Bibliographic record
Abstract
Globally HIV remains a major public health problem. In sub-Saharan Africa most new HIV infections occur in adolescent girls and young women. Previously tested antiretroviral drugs as different pre-exposure prophylaxis (PrEP) formulations have shown inconsistent levels of protection against HIV in African women. Besides adherence, biological factors such as drug transporter proteins are increasingly recognized as key modulators of PrEP levels. Drug transporter mRNA expression levels has been significantly correlated to altered PrEP levels in-vitro in different tissues, with inflammation identified as a further modifier of drug transporters mRNA expression and thus PrEP levels. We therefore, aimed to determine possible concordance between drug transporter mRNA expression in the female genital tract (FGT) and blood of N = 45 South African women taking oral PrEP-Truvada® [TDF/FTC)] over 6 months for HIV prevention. Additionally, we determined associations between drug transporter mRNA expression, genital inflammation, and blood-tenofovir diphosphate (TFV-DP). mRNA-expression of four efflux P-gp; MATE-1; MRP-2; MRP-4 and two influx OAT-1 and OAT-3 drug transporters were determined by qRT-PCR. Multiplexed technology was used to measure 27 cytokines to define genital inflammation. Significant positive correlations of mRNA expression for P-gp, MATE-1, MRP-2, and MRP-4 were observed between the FGT and blood at 3- and 6-months post-PrEP initiation (p < 0.05). For OAT-1 however, significant positive correlations were observed pre- and post-PrEP exposure (p < 0.05). Linear-mixed models showed moderate associations between FGT cytokines and drug transporter mRNA expression, with a direct relationship observed between MIP-1β concentration and MATE-1 mRNA expression. Similarly, PLS-DA showed that in women with genital inflammation, consistently higher mRNA expression of MATE-1 was observed compared to women without genital inflammation. No significant associations were observed between drug transporter mRNA expression and blood TFV-DP. Our results suggest that drug transporters may be similarly expressed in the FGT and blood. Furthermore, genital inflammation may modify PrEP levels by altering drug transporter mRNA expression. Collectively, our data may be used to better understand biological factors that may affect PrEP efficacy in African women who remain vulnerable to HIV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".