Analysis of mutational history of multidrug‐ resistant genotypes with a mutagenetic tree model

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life‐threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well‐known nucleoside reverse transcriptase (RT) inhibitor‐related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation‐specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.publishersversionpublishe

Correction: Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models

BACKGROUND: Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.METHODS AND FINDINGS: The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).CONCLUSIONS: Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.</p

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance.&nbsp;Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside&nbsp;reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance

An alternative methodology for the prediction of adherence to anti HIV treatment

BACKGROUND: Successful treatment of HIV-positive patients is fundamental to controlling the progression to AIDS. Causes of treatment failure are either related to drug resistance and/or insufficient drug levels in the blood. Severe side effects, coupled with the intense nature of many regimens, can lead to treatment fatigue and consequently to periodic or permanent non-adherence. Although non-adherence is a recognised problem in HIV treatment, it is still poorly detected in both clinical practice and research and often based on unreliable information such as self-reports, or in a research setting, Medication Events Monitoring System caps or prescription refill rates. To meet the need for having objective information on adherence, we propose a method using viral load and HIV genome sequence data to identify non-adherence amongst patients. PRESENTATION OF THE HYPOTHESIS: With non-adherence operationally defined as a sharp increase in viral load in the absence of mutation, it is hypothesised that periods of non-adherence can be identified retrospectively based on the observed relationship between changes in viral load and mutation. TESTING THE HYPOTHESIS: Spikes in the viral load (VL) can be identified from time periods over which VL rises above the undetectable level to a point at which the VL decreases by a threshold amount. The presence of mutations can be established by comparing each sequence to a reference sequence and by comparing sequences in pairs taken sequentially in time, in order to identify changes within the sequences at or around 'treatment change events'. Observed spikes in VL measurements without mutation in the corresponding sequence data then serve as a proxy indicator of non-adherence. IMPLICATIONS OF THE HYPOTHESIS: It is envisaged that the validation of the hypothesised approach will serve as a first step on the road to clinical practice. The information inferred from clinical data on adherence would be a crucially important feature of treatment prediction tools provided for practitioners to aid daily practice. In addition, distinct characteristics of biological markers routinely used to assess the state of the disease may be identified in the adherent and non-adherent groups. This latter approach would directly help clinicians to differentiate between non-responding and non-adherent patients

Spectrum of Atazanavir‐Selected Protease Inhibitor‐Resistance Mutations

Funding Information: Conflicts of Interest: A.S. received research grants from Gilead Sciences, and personal fees for ad‐ visory boards from Gilead Sciences, MSD, and ViiV Healthcare, all outside the present work. M.Z. received research grants from Gilead Sciences, MSD, Theratechnologies and ViiV Healthcare and personal fees for advisory boards from Gilead Sciences, Janssen‐Cilag, MSD, Theratechnologies and ViiV Healthcare, all outside the present work. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Funding Information: Funding: S.‐Y.R., A.J.B. and R.W.S. were supported in part by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health (NIH) (award number AI136618). The work of O.T. and D.K. was supported by the Russian Science Foundation Grant No. 19‐75‐10097. A.B.A. received funding from Fundação para a Ciência e Tecnologia through projects PTDC/SAU‐ INF/31990/2017 (INTEGRIV) and PTDC/SAU‐PUB/4018/2021 (MARVEL). G.D.T. was supported by EuResist Network GEIE. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Ritonavir‐boosted atazanavir is an option for second‐line therapy in low‐ and middle-income countries (LMICs). We analyzed publicly available HIV‐1 protease sequences from previ-ously PI‐naïve patients with virological failure (VF) following treatment with atazanavir. Overall, 1497 patient sequences were identified, including 740 reported in 27 published studies and 757 from datasets assembled for this analysis. A total of 63% of patients received boosted atazanavir. A total of 38% had non‐subtype B viruses. A total of 264 (18%) sequences had a PI drug‐resistance mutation (DRM) defined as having a Stanford HIV Drug Resistance Database mutation penalty score. Among sequences with a DRM, nine major DRMs had a prevalence >5%: I50L (34%), M46I (33%), V82A (22%), L90M (19%), I54V (16%), N88S (10%), M46L (8%), V32I (6%), and I84V (6%). Common accessory DRMs were L33F (21%), Q58E (16%), K20T (14%), G73S (12%), L10F (10%), F53L (10%), K43T (9%), and L24I (6%). A novel nonpolymorphic mutation, L89T occurred in 8.4% of non‐subtype B, but in only 0.4% of subtype B sequences. The 264 sequences included 3 (1.1%) interpreted as causing high‐level, 14 (5.3%) as causing intermediate, and 27 (10.2%) as causing low‐level darunavir re-sistance. Atazanavir selects for nine major and eight accessory DRMs, and one novel nonpolymor-phic mutation occurring primarily in non‐B sequences. Atazanavir‐selected mutations confer low-levels of darunavir cross resistance. Clinical studies, however, are required to determine the optimal boosted PI to use for second‐line and potentially later line therapy in LMICs.publishersversionpublishe

Effectiveness of integrase strand transfer inhibitors in HIV-infected treatment-experienced individuals across Europe

Funding Information: The INTEGRATE project received an unconditioned grant from Gilead Sciences Europe Ltd. This study was also funded by the Swedish Research Council (2016‐01675, to AS), the European Union by the CARE H2020 project (under grant agreement no. 825673) and Stockholm County Council (ALF 20190451 and CIMED 20200645; to AS), and the Fundação para a Ciência e Tecnologia, Portugal (INTEGRIV Project PTDC/SAU‐INF/31990/20170 and GHTM‐UID/Multi/04413/2013; to AA). Funding Information: The INTEGRATE project received an unconditioned grant from Gilead Sciences Europe Ltd. This study was also funded by the Swedish Research Council (2016-01675, to AS), the European Union by the CARE H2020 project (under grant agreement no. 825673) and Stockholm County Council (ALF 20190451 and CIMED 20200645; to AS), and the Fundação para a Ciência e Tecnologia, Portugal (INTEGRIV Project PTDC/SAU-INF/31990/20170 and GHTM-UID/Multi/04413/2013; to AA). The INTEGRATE study group: A. Abecasis, Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical - Universidade Nova de Lisboa, Lisbon, Portugal M. Bobkova, Gamaleya Federal Center for Epidemiology and Microbiology of Russia C. Seguin-Devaux, Department of Infection and Immunity, Luxembourg Institute of Health, Luxembourg M. Fabbiani, University Hospital of Siena, Siena, Italy F. Garcia, Hospital Universitario San Cecilio, Granada, Spain A. M. Geretti, University of Liverpool, UK P. Gomes, Laboratório de Biologia Molecular (LMCBM, SPC, CHLO-HEM), Lisbon, Portugal and Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Universitário Egas Moniz, Caparica, Portugal F. Incardona, EuResist Network, Roma, Italy – IPRO, Roma, Italy R. Kaiser, University of Cologne, Cologne, Germany R. Paredes, Irsicaixa, Spain B. Rossetti, University Hospital of Siena, Siena, Italy M. Sayan, Kocaeli University, Medical Faculty, Turkey A. Sönnerborg, Karolinska Institutet, Stockholm, Sweden A. M. Vandamme, REGA Institut KU Leuven, Belgium M. Zazzi, University of Siena, Siena, Italy. Publisher Copyright: © 2022 British HIV Association.Objectives: To explore the effectiveness and durability of integrase strand transfer inhibitor (INSTI)-based regimens in pre-treated subjects. Methods: Treatment-experienced individuals starting an INSTI-based regimen during 2012–2019 were selected from the INTEGRATE collaborative study. The time to virological failure [VF: one measurement of viral load (VL) ≥ 1000 copies/mL or two ≥ 50 copies/ml or one VL measurement ≥ 50 copies/mL followed by treatment change] and to INSTI discontinuation were evaluated. Results: Of 13 560 treatments analysed, 4284 were from INSTI-naïve, non-viraemic (IN-NV) individuals, 1465 were from INSTI-naïve, viraemic (IN-V) individuals, 6016 were from INSTI-experienced, non-viraemic (IE-NV) individuals and 1795 were from INSTI-experienced, viraemic (IE-V) individuals. Major INSTI drug resistance mutations (DRMs) were previously detected in 4/519 (0.8%) IN-NV, 3/394 (0.8%) IN-V, 7/1510 (0.5%) IE-NV and 25/935 (2.7%) IE-V individuals. The 1-year estimated probabilities of VF were 3.1% [95% confidence interval (CI): 2.5–3.8] in IN-NV, 18.4% (95% CI: 15.8–21.2) in IN-V, 4.2% (95% CI: 3.6–4.9) in IE-NV and 23.9% (95% CI: 20.9–26.9) in IE-V subjects. The 1-year estimated probabilities of INSTI discontinuation were 12.1% (95% CI: 11.1–13.0) in IN-NV, 19.6% (95% CI: 17.5–21.6) in IN-V, 10.8% (95% CI: 10.0–11.6) in IE-NV and 21.7% (95% CI: 19.7–23.5) in IE-V subjects. Conclusions: Both VF and INSTI discontinuation occur at substantial rates in viraemic subjects. Detection of DRMs in a proportion of INSTI-experienced individuals makes INSTI resistance testing mandatory after failure.publishersversionpublishe

Spectrum of Atazanavir-Selected Protease Inhibitor-Resistance Mutations

Ritonavir-boosted atazanavir is an option for second-line therapy in low- and middle-income countries (LMICs). We analyzed publicly available HIV-1 protease sequences from previously PI-na&iuml;ve patients with virological failure (VF) following treatment with atazanavir. Overall, 1497 patient sequences were identified, including 740 reported in 27 published studies and 757 from datasets assembled for this analysis. A total of 63% of patients received boosted atazanavir. A total of 38% had non-subtype B viruses. A total of 264 (18%) sequences had a PI drug-resistance mutation (DRM) defined as having a Stanford HIV Drug Resistance Database mutation penalty score. Among sequences with a DRM, nine major DRMs had a prevalence &gt;5%: I50L (34%), M46I (33%), V82A (22%), L90M (19%), I54V (16%), N88S (10%), M46L (8%), V32I (6%), and I84V (6%). Common accessory DRMs were L33F (21%), Q58E (16%), K20T (14%), G73S (12%), L10F (10%), F53L (10%), K43T (9%), and L24I (6%). A novel nonpolymorphic mutation, L89T occurred in 8.4% of non-subtype B, but in only 0.4% of subtype B sequences. The 264 sequences included 3 (1.1%) interpreted as causing high-level, 14 (5.3%) as causing intermediate, and 27 (10.2%) as causing low-level darunavir resistance. Atazanavir selects for nine major and eight accessory DRMs, and one novel nonpolymorphic mutation occurring primarily in non-B sequences. Atazanavir-selected mutations confer low-levels of darunavir cross resistance. Clinical studies, however, are required to determine the optimal boosted PI to use for second-line and potentially later line therapy in LMICs

Low-level HIV viraemia during antiretroviral therapy : Longitudinal patterns and predictors of viral suppression

OBJECTIVES: Our objective was to characterize longitudinal patterns of viraemia and factors associated with viral suppression in people with HIV and low-level viraemia (LLV) during antiretroviral therapy (ART).METHODS: We included people with HIV in the EuResist Integrated Database with LLV following ART initiation after 2005. LLV was defined as two or more consecutive viral load (VL) measurements of 51-199 copies/mL 30-365 days apart after >12 months of ART. Viraemia patterns were analyzed over 24 months. Factors associated with viral suppression at 12 months after LLV episodes were identified using univariable and multivariable logistic regression.RESULTS: Of 25 113 people with HIV, 2474 (9.9%) had LLV. Among 1387 participants with 24 months of follow-up after LLV, 406 (29%) had persistent suppression, 669 (48%) had transient viraemic episodes, 29 (2%) had persistent LLV, and 283 (20%) had virological failure. Following LLV episodes, the proportion with detectable viraemia declined (p for trend <0.001 and 0.034, in the first and second year, respectively). At 12 months, 68% had undetectable VL, which was associated with suppression before LLV (adjusted odds ratio [aOR] 1.7; 95% confidence interval [CI] 1.2-2.4) and ART modification after LLV (aOR 1.6; 95% CI 1.0-2.4). The following factors were negatively associated with undetectable VL at 12 months: higher VL during LLV (aOR 0.57 per log 10 copies/mL; 95% CI 0.37-0.89), injecting drug use (aOR 0.67; 95% CI 0.47-0.96), and regimens with protease inhibitors (aOR 0.65; 95% CI 0.49-0.87) or combined anchor drugs (aOR 0.52; 95% CI 0.32-0.85). CONCLUSION: Most people with LLV did not experience sustained viral suppression during 24-month follow-up, supporting the association between LLV and inferior treatment outcome

New findings in HCV genotype distribution in selected West European, Russian and Israeli regions

Background: HCV affects 185 million people worldwide and leads to death and morbidities. HCV has a high genetic diversity and is classified into seven genotypes and 67 subtypes. Novel anti-HCV drugs (Direct-Acting-Antivirals) eligibility, resistance and cure rates depend on HCV geno/subtype (GT). Objectives: Analysis of epidemiological information and viral GT from patients undergoing viral genotyping in 2011–2015. Study design: Anonymized information from 52 centers was analyzed retrospectively. Results: 37,839 samples were included in the study. We show that the GT distribution is similar throughout Western European countries, with some local differences. Here GTs 1 and 2 prevalences are lower and of GT4 higher than in all previous reports. Israel has a unique GT pattern and in South Russia the GT proportions are more similar to Asia. GTs 5 and 6 were detected in very low proportions. Three cases of the recombinant genotype P were reported in Munich (Germany). In addition, we observed that GT proportion was dependant on patients’ gender, age and transmission route: GTs 1b and 2 were significantly more common in female, older, nosocomially-infected patients, while GTs 1a, 3 and 4 were more frequent in male, younger patients infected by tattooing, drug consume, and/or sexual practices. In infections acquired by drug consume, GTs 1a (35.0%) and 3 (28.1%) prevailed. In infections related to sexual practices lower proportion of GT3 (14.0%) and higher of GT4 (20.2%) were detected. GT4 was mostly abundant in MSM (29.6%). HIV coinfection was significantly associated with higher proportions GTs 1a and 4 (42.5% and 19.3%, respectively). Conclusion: Genotype prevalence evolves and correlates to epidemiological factors. Continuous surveillance is necessary to better assess hepatitis C infection in Europe and to take appropriate actions.status: publishe

Effectiveness of integrase strand transfer inhibitor-based regimens in HIV-infected treatment-naive individuals: results from a European multi-cohort study

Background: INSTIs have become a pillar of first-line ART. Real-world data are needed to assess their effectiveness in routine care. Objectives: We analysed ART-naive patients who started INSTI-based regimens in 2012-19 whose data were collected by INTEGRATE, a European collaborative study including seven national cohorts. Methods: Kaplan-Meier analyses assessed time to virological failure (VF), defined as one viral Load (VL) >= 1000 copies/mL, two consecutive VLs >= 50 copies/mL, or one VL >= 50 copies/mL followed by treatment change after >= 24 weeks of follow-up, and time to INSTIs discontinuation (INSTI-DC) for any reason. Factors associated with VF and INSTI-DC were explored by Logistic regression analysis. Results: Of 2976 regimens started, 1901 (63.9%) contained dolutegravir, 631 (21.2%) elvitegravir and 444 (14.9%) raltegravir. The 1 year estimated probabilities of VF and INSTI-DC were 5.6% (95% CI 4.5-6.7) and 16.2% (95% CI 14.9-17.6), respectively, and were higher for raltegravir versus both elvitegravir and dolutegravir. A baseline VL >= 100 000 copies/mL [adjusted HR (aHR) 2.17, 95% CI 1.55-3.04, P= 200 cells/mm(3) reduced the risk (aHR 0.52, 95% CI 0.37-0.74, P3 drugs versus 3 drugs (aHR 2.73, 95% CI 1.55-4.79, P< 0.001) and starting ART following availability of dolutegravir (aHR 0.64, 95% CI 0.48-0.83, P= 0.001). Major INSTI mutations indicative of transmitted drug resistance occurred in 2/1114 (0.2%) individuals. Conclusions: This large multi-cohort study indicates high effectiveness of elvitegravir- or dolutegravir-based first-line ART in routine practice across Europe