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Summary of the Most Commonly Used Antiretrovi- rals (ARVs) for Salvage Therapy ARV class and regimen
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Interpreting human immunodeficiency virus type 1 (HIV-1) genotypic drug-resistance test results is challenging for clinicians treating HIV-1-infected patients. Multiple drug-resistance interpretation algorithms have been developed, but their predictive value has rarely been evaluated using contemporary clinical data sets.
We examined the predictive...
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The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswe...
Citations
... Such switch, called Treatment Change Episodes (TCEs), were proven to be foreseeable by predictors based on indicators such as Individualized Genetic Barriers (IGBs) 9-11 , Genotypic Susceptibility Score (GSS) [12][13][14] or CEPAC Dynamic Model (CDM) 15 , and numerous predictive models have therefore been developed [16][17][18][19][20] . However, clinical HIV response is evolving fast and most of these indicators are based on old sets of drugs and thus outdated and unsuitable for immediate use due to the emergence of new ARVs on the market since their development. ...
... Our best trained model (XGBoost tuned with a TreeParzen Estimator) reached an accuracy of 75.9%, performing 20.76% better than ground clinical decisions (which has a success rate evaluated as 62.85%, see Methods). It outperforms most previously published models 9,[12][13][14]17,20 and is slightly below the others 9,18 based on AUC scores, which suits better than the other metrics to compare models built on differently balanced datasets 38 . Models performing better are using more advanced features and indicators such as IGBs, GSSs, genotype-phenotype drug resistance or CDMs and the respective studies mention how their performances are mainly relying on those indicators. ...
A wide variety of antiretroviral drugs has been developed in the last decades to help patients fight AIDS, with most current therapy cares comprised of several antiretroviral drugs combined. Those therapies actively suppress viral replication by targeting different stages of the viral life cycle simultaneously. However, evolutionary escape dynamics of HIV complicate the task, with appearance of drug resistance subsequently. Nowadays, finding an effective treatment for each patient remains challenging: therapies which need to be personalized due to the accumulation of mutations conferring resistance to drugs, complex drug interactions between antiretroviral, uncertain therapy adherence and various side effects. Manual consideration of all these factors by doctors is infeasible hence precision medicine steps in, aiming to assist clinical decision based on statistical models. However, available clinical HIV dataset are sparse and unbalanced with limited observations available for most of drug combinations due to the diversity of antiretroviral and rapid pace of mutation appearance. With continuous improvements in machine and deep learning techniques, scientists develop models with potential applications to support medical care of HIV-infected patients, helped by the emergence of large cohort studies and extensive databases. Here we propose approaches to predict the chance of a therapy switch to be successful, design of optimal treatments as well as forecast occurrence of new mutational events. Our model to predict treatment response address challenge of treatment diversity and demonstrate promising performances with limited features. Thus, with the aid of additional indicators, we believe on its potential to significantly improve the interpretation of genotypic drug resistance tests and the clinical decision to prescribe suitable treatments.
... The best BLASTX hit was chosen for each read for the amino acid counting, which was performed by in-house script. The resistance was interpreted as per the mutation lists provided in the Stanford HIVDB, accessed on 6 January 2019 [39]. The complete script is available in github: https://github.com/neogilab/MiDRMPol_ ...
Background
HIV-1C has been shown to have a greater risk of virological failure and reduced susceptibility towards boosted protease inhibitors (bPIs), a component of second-line combination antiretroviral therapy (cART) in South Africa. This study entailed an evaluation of HIV-1 drug resistance-associated mutations (RAMs) among minor viral populations through high-throughput sequencing genotypic resistance testing (HTS-GRT) in patients on the South African national second-line cART regimen receiving bPIs.
Methods
During 2017 and 2018, 67 patient samples were sequenced using high-throughput sequencing (HTS), of which 56 samples were included in the final analysis because the patient’s treatment regimen was available at the time of sampling. All patients were receiving bPIs as part of their cART. Viral RNA was extracted, and complete pol genes were amplified and sequenced using Illumina HiSeq2500, followed by bioinformatics analysis to quantify the RAMs according to the Stanford HIV Drug Resistance Database.
Results
Statistically significantly higher PI RAMs were observed in minor viral quasispecies (25%; 14/56) compared to non-nucleoside reverse transcriptase inhibitors (9%; 5/56; p = 0.042) and integrase inhibitor RAM (4%; 2/56; p = 0.002). The majority of the drug resistance mutations in the minor viral quasispecies were observed in the V82A mutation ( n = 13) in protease and K65R ( n = 5), K103N ( n = 7) and M184V (n = 5) in reverse transcriptase.
Conclusions
HTS-GRT improved the identification of PI and reverse transcriptase inhibitor (RTI) RAMs in second-line cART patients from South Africa compared to the conventional GRT with ≥20% used in Sanger-based sequencing. Several RTI RAMs, such as K65R, M184V or K103N and PI RAM V82A, were identified in < 20% of the population. Deep sequencing could be of greater value in detecting acquired resistance mutations early.
... The best BLASTX hit was chosen for each read for the amino acid counting, which was performed by in-house script. The resistance was interpreted as per the mutation lists provided in the Stanford HIVDB, accessed on 6 January 2019 [19]. The complete script is available in github: https://github.com/neogilab/MiDRMPol_SouthAfrica. ...
Background: HIV-1C has been shown to have a greater risk of virological failure and reduced susceptibility towards boosted protease inhibitors (bPIs), a component of second-line combination antiretroviral therapy (cART) in South Africa. This study entailed an evaluation of HIV-1 drug resistance-associated mutations (RAMs) among minor viral populations through high-throughput sequencing genotypic resistance testing (HTS-GRT) in patients suspected of failing on the South African national second-line cART regimen with bPIs.
Methods: During 2017 and 2018, 67 patient samples were selected, of which 56 samples were successfully analyzed. All patients were receiving bPIs as part of their cART. Viral RNA was extracted, and complete pol genes were amplified and sequenced using Illumina HiSeq2500, followed by bioinformatics analysis to quantify the RAMs according to the Stanford HIV Drug Resistance Database.
Results: Statistically significantly (p<0.001) higher PI RAMs were observed in minor viral quasispecies (25%; 14/56) compared to nucleoside reverse transcriptase inhibitors (11%; 6/56), non-nucleoside reverse transcriptase inhibitors (9%; 5/56) and integrase inhibitor RAM (4%; 2/56). The majority of the drug resistance mutations in the minor viral quasispecies were observed in the V82A mutation (n=13) in protease and K65R (n=5), K103N (n=7) and M184V (n=5) in reverse transcriptase.
Conclusions: HTS-GRT improved the identification of PI and reverse transcriptase inhibitor (RTI) RAMs in second-line cART patients from South Africa compared to the conventional GRT with ≥20% used in Sanger-based sequencing. Several RTI RAMs, such as K65R, M184V or K103N and PI RAM V82A, were identified in <20% of the population. Deep sequencing could be of greater value in detecting acquired resistance mutations early.
... Genotypic susceptibility score (GSS) of the antiretroviral regimen built in accordance to Stanford HIV database genotypic resistance interpretation system could be a helpful tool to assess the activity of the single antiretroviral drugs composing an antiretroviral regimen 10 . Moreover, GSS has been demonstrated to predict the virological outcome of antiretroviral regimens 11,12 . ...
To assess the impact of genotypic susceptibility score (GSS) on combined antiretroviral therapy (cART) outcomes during primary HIV infection (PHI) we retrospectively enrolled patients with PHI diagnosed between 2008 and 2015 at 9/24 Italian Network ACuTe HIV InfectiON (INACTION) centers. One hundred‐seventy‐six patients were enrolled. Fifty‐five (32.9%) patients started with more than 3 drugs and 11 (7.2%) started with a GSS <3. Regimen's GSS (per 1 point increase) [aOR 4.82 (95%CI 1.62‐14.28); p=0.005] and baseline HIV‐RNA (per 1 log10 increase) [aOR 2.02 (95%CI 1.09‐3.73); p=0.025] resulted associated with early cART initiation. In conclusion, regimen's GSS resulted to be associated to the time to cART initiation during PHI.
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... Rule-based systems are more commonly used for interpretation, because they consider diverse forms of data and incorporate expert opinions [80,[88][89][90]. These systems are reproducible, transparent, and educational, but subjective. ...
... Well-described rule-based systems include those from the French National Agency for Research on AIDS and Viral Hepatitis, Rega, HIV Genotypic Resistance-Algorithm Deutschland, and the Stanford HIV Drug Resistance Database [80,91,92]. Although these systems may produce somewhat different estimates of drug resistance for the same drug, their predictive ability generally has been similar [88,89,93]. An online system for interpreting HIV-2 sequences has also been developed [94]. ...
Background:
Contemporary antiretroviral therapies (ART) and management strategies have diminished both human immunodeficiency virus (HIV) treatment failure and the acquired resistance to drugs in resource-rich regions, but transmission of drug-resistant viruses has not similarly decreased. In low- and middle-income regions, ART roll-out has improved outcomes, but has resulted in increasing acquired and transmitted resistances. Our objective was to review resistance to ART drugs and methods to detect it, and to provide updated recommendations for testing and monitoring for drug resistance in HIV-infected individuals.
Methods:
A volunteer panel of experts appointed by the International Antiviral (formerly AIDS) Society-USA reviewed relevant peer-reviewed data that were published or presented at scientific conferences. Recommendations were rated according to the strength of the recommendation and quality of the evidence, and reached by full panel consensus.
Results:
Resistance testing remains a cornerstone of ART. It is recommended in newly-diagnosed individuals and in patients in whom ART has failed. Testing for transmitted integrase strand-transfer inhibitor resistance is currently not recommended, but this may change as more resistance emerges with widespread use. Sanger-based and next-generation sequencing approaches are each suited for genotypic testing. Testing for minority variants harboring drug resistance may only be considered if treatments depend on a first-generation nonnucleoside analogue reverse transcriptase inhibitor. Different HIV-1 subtypes do not need special considerations regarding resistance testing.
Conclusions:
Testing for HIV drug resistance in drug-naive individuals and in patients in whom antiretroviral drugs are failing, and the appreciation of the role of testing, are crucial to the prevention and management of failure of ART.
... As more individuals are started on ART, treatment goals will require more robust and durable first-line regimens. Drug resistance testing programs can identify pre-existing and acquired resistance mutations, customizing and optimizing treatment options and clinical management over time [17][18][19][20][21]. Drug resistance testing is recommended at diagnosis, before ART initiation and at the time of virological failure, where possible. ...
HIV continues to spread among vulnerable heterosexual (HET), Men-having-Sex with Men (MSM) and intravenous drug user (IDU) populations, influenced by a complex array of biological, behavioral and societal factors. Phylogenetics analyses of large sequence datasets from national drug resistance testing programs reveal the evolutionary interrelationships of viral strains implicated in the dynamic spread of HIV in different regional settings. Viral phylogenetics can be combined with demographic and behavioral information to gain insights on epidemiological processes shaping transmission networks at the population-level. Drug resistance testing programs also reveal emergent mutational pathways leading to resistance to the 23 antiretroviral drugs used in HIV-1 management in low-, middle- and high-income settings. This article describes how genotypic and phylogenetic information from Quebec and elsewhere provide critical information on HIV transmission and resistance, Cumulative findings can be used to optimize public health strategies to tackle the challenges of HIV in “real-world” settings.
... There are several machine-learning systems for HIV-1 GRT interpretation that use proprie- tary datasets containing either large numbers of correlations between viral genotype and phe- notype [16][17][18][19][20] or between genotype and the virological response to a new treatment regimen [21][22][23][24][25]. However, rule-based systems have been used more commonly than machine-learning systems for HIV-1 GRT interpretation due to their transparency, ability to take into account diverse forms of data, and ability to represent expert opinion [26][27][28][29][30][31]. ...
Introduction:
HIV-1 genotypic resistance test (GRT) interpretation systems (IS) require updates as new studies on HIV-1 drug resistance are published and as treatment guidelines evolve.
Methods:
An expert panel was created to provide recommendations for the update of the Stanford HIV Drug Resistance Database (HIVDB) GRT-IS. The panel was polled on the ARVs to be included in a GRT report, and the drug-resistance interpretations associated with 160 drug-resistance mutation (DRM) pattern-ARV combinations. The DRM pattern-ARV combinations included 52 nucleoside RT inhibitor (NRTI) DRM pattern-ARV combinations (13 patterns x 4 NRTIs), 27 nonnucleoside RT inhibitor (NNRTI) DRM pattern-ARV combinations (9 patterns x 3 NNRTIs), 39 protease inhibitor (PI) DRM pattern-ARV combinations (13 patterns x 3 PIs) and 42 integrase strand transfer inhibitor (INSTI) DRM pattern-ARV combinations (14 patterns x 3 INSTIs).
Results:
There was universal agreement that a GRT report should include the NRTIs lamivudine, abacavir, zidovudine, emtricitabine, and tenofovir disoproxil fumarate; the NNRTIs efavirenz, etravirine, nevirapine, and rilpivirine; the PIs atazanavir/r, darunavir/r, and lopinavir/r (with "/r" indicating pharmacological boosting with ritonavir or cobicistat); and the INSTIs dolutegravir, elvitegravir, and raltegravir. There was a range of opinion as to whether the NRTIs stavudine and didanosine and the PIs nelfinavir, indinavir/r, saquinavir/r, fosamprenavir/r, and tipranavir/r should be included. The expert panel members provided highly concordant DRM pattern-ARV interpretations with only 6% of NRTI, 6% of NNRTI, 5% of PI, and 3% of INSTI individual expert interpretations differing from the expert panel median by more than one resistance level. The expert panel median differed from the HIVDB 7.0 GRT-IS for 20 (12.5%) of the 160 DRM pattern-ARV combinations including 12 NRTI, two NNRTI, and six INSTI pattern-ARV combinations. Eighteen of these differences were updated in HIVDB 8.1 GRT-IS to reflect the expert panel median. Additionally, HIVDB users are now provided with the option to exclude those ARVs not considered to be universally required.
Conclusions:
The HIVDB GRT-IS was updated through a collaborative process to reflect changes in HIV drug resistance knowledge, treatment guidelines, and expert opinion. Such a process broadens consensus among experts and identifies areas requiring further study.
... Cuando se dispone de estudios genotípicos previos, deben valorarse todas las MR acumuladas en los sucesivos FV. Para algunos IP/r y ETR se ha elaborado un índice ponderado de resistencia genotípica que cuantifica el peso de cada MR y establece un índice de respuesta al fármaco 127 . Los test ultrasensibles de secuenciación genotípica detectan poblaciones virales resistentes minoritarias y en algunos casos podrían mejorar el resultado del TAR de rescate 128 . ...
In this update, antiretroviral therapy (ART) is recommended for all patients infected by type 1 human immunodeficiency virus (HIV-1). The strength and grade of the recommendation vary depending on the CD4+ T-lymphocyte count, the presence of opportunistic infections or comorbid conditions, age, and the efforts to prevent the transmission of HIV. The objective of ART is to achieve an undetectable plasma viral load (PVL). Initial ART should comprise three drugs, namely, two nucleoside reverse transcriptase inhibitors (NRTI) and one drug from another family. Three of the recommended regimens, all of which have an integrase strand transfer inhibitor (INSTI) as the third drug, are considered a preferred regimen; a further seven regimens, which are based on an INSTI, an non-nucleoside reverse transcriptase inhibitor (NNRTI), or a protease inhibitor boosted with ritonavir (PI/r), are considered alternatives. The reasons and criteria for switching ART are presented both for patients with an undetectable PVL and for patients who experience virological failure, in which case the rescue regimen should include three (or at least two) drugs that are fully active against HIV. The specific criteria for ART in special situations (acute infection, HIV-2 infection, pregnancy) and comorbid conditions (tuberculosis and other opportunistic infections, kidney disease, liver disease, and cancer) are updated.
... Although well established treatment guidelines are available for initial therapy in drug naïve individuals harboring wild-type virus (BMH, 2014) (see Appendix A), the optimal management of HAART in heavily treatment-experienced HIV-infected patients remains a challenge (Imaz et al., 2009). Prospective studies have confirmed that patients whose clinicians have access to genotypic drug resistance data respond better to therapy than control patients whose clinicians do not have access to the same data (LANL, 2013; Rhee et al., 2009; Tural et al., 2002). However, difficulties facing anyone, no matter how expert, in the interpretation of genotypic information are considerable. ...
... The antiviral effect of ARVs is not explicitily considered in our study. As shown by Rhee et al. (2009), by incorporating ARV potency into a genotypic drug-resistance interpretation algorithm, such as HIVdb, one can improve the virologic response prediction. However, wheighting drug-specific GSSs by ARV potency is not a trivial task, and further research is required to explore ARV-specific weightings that would faithfully account for the potencies of individual ARVs and ARV combinations (Rhee et al., 2009). ...
... As shown by Rhee et al. (2009), by incorporating ARV potency into a genotypic drug-resistance interpretation algorithm, such as HIVdb, one can improve the virologic response prediction. However, wheighting drug-specific GSSs by ARV potency is not a trivial task, and further research is required to explore ARV-specific weightings that would faithfully account for the potencies of individual ARVs and ARV combinations (Rhee et al., 2009). In fact, such sophistication could explain why the combination ABC + 3TC, which was prescribed by some clinicians for patients F and G (Table 7), is highly effective under most circumstances in vivo despite the fact that M184 V decreases susceptibility to both NRTIs (Shafer and Schapiro, 2008). ...
The dramatic reduction in morbidity and mortality associated with the use of highly active antiretroviral therapy has created new challenges for clinicians: AIDS has become a chronic, potentially life-threatening, condition in many clinical instances. In this paper, a novel system engineering approach based on mixed-integer linear programming (MILP) is presented to support HIV/AIDS clinicians when formulating real-world therapeutic plans for heavily treatment-experienced patients under variable settings. Our results suggest that, while current practices (standard protocols and/or subjective recommendations based on the clinician's experience) can generally provide satisfactory management of drug resistance in the short-term, optimization-based therapy planning has a far greater potential to achieve this goal over expanded time horizons thereby changing paradigms and rethinking best practices in the HIV/AIDS clinical arena. Moreover, the ability of this methodology to address other viral pathologies (e.g., hepatitis B and C virus) can make this work appeal to a broader audience.
... Cuando se dispone de estudios genotípicos previos, deben valorarse todas las MR acumuladas en los sucesivos FV. Para algunos IP/r y ETR se ha elaborado un índice ponderado de resistencia genotípica que cuantifica el peso de cada MR y establece un índice de respuesta al fármaco 127 . Los test ultrasensibles de secuenciación genotípica detectan poblaciones virales resistentes minoritarias y en algunos casos podrían mejorar el resultado del TAR de rescate 128 . ...
