Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network)

Rossetti, Barbara; Incardona, Francesca; Teodoro, Giulia Di; Mommo, Chiara; Saladini, Francesco; Kaiser, Rolf; Sönnerborg, Anders; Lengauer, Thomas; Zazzi, Maurizio

doi:10.3390/tropicalmed8050243

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…The detection of SDRMs in the present study was consistent with other recent studies published in Europe [ 6 , 7 , 8 ], making our findings one of the more up-to-date studies including the surveillance of resistance to INSTIs. The rate of SDRMs conferring resistance to NRTIs remained stable and similar to a previous study conducted in Portugal [ 7 ].…”

Section: Discussionsupporting

confidence: 93%

“…Upon identification of a DRM, ART regimens are usually modified accordingly, replacing ineffective drugs with those that are expected to effectively suppress the viral replication [ 5 ]. The European multi-center cohort study conducted in the past 10 years and the Portuguese HIV drug resistance (HIVDR) study conducted in 2021 showed that the prevalence of HIV resistance increased over time [ 6 , 7 , 8 ]. Based on this evidence, the World Health Organization (WHO) stated that further investigation on this subject is necessary in this era of a global scale-up of ART to achieve the 95-95-95 targets [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Applying Next-Generation Sequencing to Track HIV-1 Drug Resistance Mutations Circulating in Portugal

Pimentel,

Pingarilho,

Sebastião

et al. 2024

Viruses

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Background: The global scale-up of antiretroviral treatment (ART) offers significant health benefits by suppressing HIV-1 replication and increasing CD4 cell counts. However, incomplete viral suppression poses a potential threat for the emergence of drug resistance mutations (DRMs), limiting ART options, and increasing HIV transmission. Objective: We investigated the patterns of transmitted drug resistance (TDR) and acquired drug resistance (ADR) among HIV-1 patients in Portugal. Methods: Data were obtained from 1050 HIV-1 patient samples submitted for HIV drug resistance (HIVDR) testing from January 2022 to June 2023. Evaluation of DRM affecting viral susceptibility to nucleoside/tide reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTIs) was performed using an NGS technology, the Vela Diagnostics Sentosa SQ HIV-1 Genotyping Assay. Results: About 71% of patients were ART naïve and 29% were experienced. Overall, 20% presented with any DRM. The prevalence of TDR and ADR was 12.6% and 41.1%, respectively. M184V, T215S, and M41L mutations for NRTI, K103N for NNRTI, and M46I/L for PIs were frequent in naïve and treated patients. E138K and R263K mutations against INSTIs were more frequent in naïve than treated patients. TDR and ADR to INSTIs were 0.3% and 7%, respectively. Patients aged 50 or over (OR: 1.81, p = 0.015), originating from Portuguese-speaking African countries (PALOPs) (OR: 1.55, p = 0.050), HIV-1 subtype G (OR: 1.78, p = 0.010), and with CD4 < 200 cells/mm3 (OR: 1.70, p = 0.043) were more likely to present with DRMs, while the males (OR: 0.63, p = 0.003) with a viral load between 4.1 to 5.0 Log10 (OR: 0.55, p = 0.003) or greater than 5.0 Log10 (OR: 0.52, p < 0.001), had lower chances of presenting with DRMs. Conclusions: We present the first evidence on TDR and ADR to INSTI regimens in followed up patients presenting for healthcare in Portugal. We observed low levels of TDR to INSTIs among ART-naïve and moderate levels in ART-exposed patients. Regimens containing PIs could be an alternative second line in patients with intermediate or high-level drug resistance, especially against second-generation INSTIs (dolutegravir, bictegravir, and cabotegravir).

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Applying Next-Generation Sequencing to Track HIV-1 Drug Resistance Mutations Circulating in Portugal

Pimentel,

Pingarilho,

Sebastião

et al. 2024

Viruses

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“…The Euresist Integrated Database EIDB is one of the world’s largest databases regarding drug resistance in HIV-infected patients, both treatment-naïve and treated, undergoing clinical follow-up since 1998 ( Rossetti et al 2023 ). The data comprises nine national cohorts from Italy, Germany, Sweden, Portugal, Spain, Luxembourg, Belgium, Turkey, and Russia.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy’s outcome for HIV-1

Di Teodoro,

Pirkl,

Incardona

et al. 2024

Bioinformatics

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Motivation In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). Results The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. Supplementary information Supplementary material is available.

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Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

Paremskaia,

Rudik,

Filimonov

et al. 2023

Viruses

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Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR, for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

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Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network)

Cited by 4 publications

References 31 publications

Applying Next-Generation Sequencing to Track HIV-1 Drug Resistance Mutations Circulating in Portugal

Applying Next-Generation Sequencing to Track HIV-1 Drug Resistance Mutations Circulating in Portugal

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy’s outcome for HIV-1

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

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