Learning from drug changes in antiretroviral therapy
Bibliographic record
Abstract
In middle 1990s, the availability of nucleoside analog and non-nucleoside analog inhibitors of the HIV reverse transcriptase (NRTI and NNRTI, respectively) and inhibitors of viral protease (PIs) allowed for the introduction of treatments combining different families of antiretrovirals. This approach was defined as HAART and is currently known as Combined Antiretroviral Therapy (cART). Initial cART combinations included three drugs (two NRTI and a PI or NNRTI) and achieved a durable reduction of HIV replication in HIV-infected patients, resulting in a sharp reduction in AIDS-related deaths and opening a new era in HIV treatment. With HIV replication under pharmacological control, new problems emerged. Resistance to cART resulting in virological failure was rapidly reported, mostly in suboptimally treated individuals [1]. An additional relevant fraction of patients showed short-term drug toxicity or a range of long-term side-effects associated with different drugs or combinations. This fact added complexity to the management of dangerous inflammatory sequels of viral replication [2]. Moreover, the requirement of a lifelong treatment uncovered an additional problem, the adherence to treatment, which was rapidly identified as a source for the appearance of resistances and increased risk of comorbidities. Several attempts to lessen these problems were explored, including proactive treatment switch [3], treatment simplifications [4], structured treatment interruptions (STIs) [5] and development of new drugs. Although the former strategies are still under debate, the use of STI was ruled out by the compelling data of the Strategies for Management of Anti-Retroviral Therapy study [5]. The availability of new drugs (more than 20 antiretrovirals are currently available in developed countries) has broaden drug choice and has improved the patient acceptance and satisfaction to treatment, as well as safety profile of new regimens. However, the definition of the best available starting cART is hampered by the lack of data on the exact impact of long-term durability of different regimens. Under the premise ‘never change a winning team’, the knowledge of how often a combination regimen is changed can be a good surrogate marker of its success. Moreover, the analysis of the reasons leading to drug change may point up the main limitations of cART regimens, and therefore may help to bring regimens closer to the best achievable setting. A study published by Abgrall et al. (pp. 803–813) in the current issue of AIDS addresses some of these issues and provides a wide picture of the stability of cART combinations over the period 2002–2009 in developed countries. The authors interrogate 18 cohorts (more than 20 000 patients) from seven European countries, Canada and the USA for the durability of first cART regimens. The results are clear and indicate a high rate of cART modifications that affects roughly one-third of patients after 1 year of treatment. This proportion increases over time and reaches 50 and 62% after 2 and 3 years of therapy, respectively. Although numbers are similar to those reported in smaller studies, the sample size strengthens the idea that we are (at least, we were in the last decade) far from an optimized cART treatment. Importantly, the work goes beyond the global cART modification rates and explores the nature of changes (death, treatment interruption or simplification, change between or within drug families) and their relationship with other factors such as (among others) age, CD4 T-cell counts at baseline, infection route, and importantly first PI/NNRTI or first NRTI background. This vast analysis shows several pieces of information that fit with major recurring issues of cART: when to start? And which regimen to start with? Two ‘usual suspects’ seem to be clearly correlated with increased rates of death and drug changes: advanced age and lower baseline CD4 T-cell counts. Indeed, early cART administration is not only associated with a better immune recovery [6] but also seems to allow for a more stable, and therefore easier treatment. However, the reported data also indicate that starting cART at younger age or with higher CD4 T-cell counts are risk factors for a poor adherence (that can be measured by the frequency of interruptions). Thus, we will probably need a better adherence follow-up for a completely successful early introduction of treatment. Also relevant is the analysis of stability of first cART regimens. Although differences are faint in some cases, one drug stands out in each family as the more stable first-line option: efavirenz among NNRTIs, atazanavir among PIs and emtricitabine/tenofovir among NRTI combinations. It cannot be a coincidence that these combinations are the drugs recommended some months ago by the ‘International Antiviral Society-USA panel’ for antiretroviral treatment of adult HIV infection [7]. The interpretation of these data is completed with an analysis of the reasons leading to cART changes. Although this analysis has been performed in a subset of 5000 patients, in which the potential bias is unknown, it is clear that the main force driving drug change is toxicity, whereas virological failure has a lower impact. This observation is consistent with other studies, and strongly suggests that individual responses to treatment are the main limitation to cART durability. Hypersensitivity to abacavir opened the door for an individualized approach to HAART; however, more complex approaches could be needed to predict the toxic effect of other antiretrovirals and its implementation could be compromised even in developed countries. Finally, the study of Abgrall et al. (pp. 803–813) cannot answer questions regarding recently approved antiretroviral drugs, but opens a methodological path to assess them in the near future. In addition, the reported data may help in the definition of strategies for cART implementation in developing countries, in which the analysis of drug availability, resistance development and toxicity has to drive the main decisions on regimen implementation. Acknowledgements Conflicts of interest J.B. has received research funding, consultancy fees or lecture sponsorships from Glaxo Smith Kline, ViiV and Merck. B.C. has served as a consultant on advisory boards or participated in speakers’ bureaus or conducted clinical trials with Boehringer-Ingelheim, Glaxo Smith Kline, Gilead, Janssen, Merck, Pfizer and ViiV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".