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

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

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-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 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