Drug-drug Interactions in Patients with HIV and Cancer in Sub-Saharan Africa
Bibliographic record
Abstract
In Sub-Saharan Africa, the cancer burden is predicted to increase by > 85% by 2030, the largest increase worldwide. This region has a large HIV-positive population. Drug-drug interactions (DDIs) from concomitant use of multiple drugs increase the risk of drug toxicities, sub-optimal therapy, and drug resistance. With the increase in polypharmacy, involving antiretroviral (ARV), and anticancer drugs, there is a greater need for an appreciation of clinically relevant DDIs. Anticancer and ARV drugs studied in this review were from The World Health Organization's Model List of Essential Medicines 2017. We reviewed; drug package inserts, www.drugbank.ca and www.UpToDate.com, to evaluate pharmacokinetic interactions with cytochrome P450 (CYP450) and ABCB1. The DDIs between drugs were assessed using the University Of Liverpool, UK HIV Drug Interactions Checker, and the LexiComp Drug Interaction tool of www.UpToDate.com. About 70% of ARVs studied interact with CYP450, all involve CYP3A4, and 55% interact with ABCB1. About 65% of anticancer drugs interact with CYP450, 44% of which do so through CYP3A4. About 75% of anticancer drugs interact with ARV drugs, with nine absolute contraindications to concomitant therapy. There exist a substantial number of DDIs between ARV and anticancer drugs, primarily mediated through CYP450 enzymes. Dolutegravir based regimens offer the safest DDI profile for concurrent use with anticancer drugs. However, there are substantial gaps in our knowledge, and this study serves to highlight the need for additional research to better define these interactions and their effect on drug exposure, as attention to these DDIs is a relatively simple intervention that could lead to optimizing disease treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".