Sage Journals: Discover world-class research

Abstract

Introduction

Doravirine (DOR) is a novel non-nucleoside reverse transcriptase inhibitor (NNRTI) that retains activity against common NNRTI resistance mutations. In this study, we aimed to investigate the prevalence of DOR resistance mutations compared with that of resistance mutations for other NNRTIs among HIV-1-infected treatment‐experienced and -naïve patients from Poland.

Methods

Resistance to DOR and other NNRTIs was assessed in two datasets: 1760 antiretroviral treatment-naïve HIV-1 patients and 200 treatment‐experienced patients. All 1960 sequences were derived from the patients using bulk sequencing. For resistance analyses, Stanford HIV drug resistance database scores were used.

Results

Overall, DOR resistance was present in 32 patients (1.62%), of whom 13 (0.74%) were naïve and 19 (9.50%) were treatment-experienced. The most common DOR resistance mutations observed among the naïve patients were A98G and K101E (0.2% each), and those among cART-experienced patients were L100I (2.0%), K101E, V108I, H221Y, and P225H (1.5% each). Furthermore, among the naïve patients, less common resistance to DOR (0.7%) compared with that to nevirapine (NVP) (2.1%; p = 0.0013) and rilpivirine (5.40%; p < 0.0001) was observed. For sequences obtained from treatment-experienced patients, the frequency of resistance to DOR (9.5%) was lower than that for efavirenz (25.5%; p < 0.0001) and NVP (26.0%; p < 0.0001).

Conclusions

The frequency of transmitted drug resistance to DOR is low, allowing for effective treatment of antiretroviral treatment-naïve patients and rapid treatment initiation. In cART-experienced patients, this agent remains an attractive NNRTI option with a higher genetic barrier to resistance.

Keywords

doravirine resistance doravirine mutations non-nucleoside reverse transcriptase inhibitor resistance human immunodeficiency virus antiretroviral

Introduction

Currently, human immunodeficiency virus (HIV) infections can be effectively controlled using an array of antiretroviral regimens [1]. Antiretroviral drugs effectively reduce the viral load in blood serum to an undetectable level, which decreases the risk of disease progression and prevents HIV transmission [2,3].

In 2018, 86% (72–92%) of people who received treatment across 60 countries were reported to have suppressed viral loads. However, globally, it is estimated that only 53% (42–63%) of all people with HIV infection achieve this success [4]. Virological success rates differ from >90% for West and North European countries to ∼73.1% in Southern Africa [5]. In Europe and Central Asia, the highest virologic efficacy antiretroviral treatment rates of almost 99% was recorded in Denmark, Monaco, Switzerland, and the United Kingdom, whereas the lowest of 40–60% was observed in Albania, Azerbaijan, Bulgaria, Kazakhstan, and Ukraine [6].

Therapeutic success can be hampered by viral drug resistance, which is both a cause and consequence of antiretroviral therapy failure. Studies have estimated the prevalence of acquired and transmitted drug resistance (TDR) in naïve patients [7–10] as approximately 10% in low- and middle-income countries (LMICs) (due to late introduction and low availability of cART-combined antiretroviral therapy) [11,12]. Increasing frequency of TDR to first-line agents is especially important in the context of non-nucleoside reverse transcriptase inhibitors (NNRTIs), which are commonly used for therapy initiation, especially in LMICs [11,13,14]. Additionally, resistance mutations develop in 70–80% of patients with virological failure [15], with NNRTI resistance frequently observed in 50–97% of patients failing this drug class [11,16,17].

Non-nucleoside reverse transcriptase inhibitor–based therapies have long been associated with low genetic barrier to antiretroviral drug resistance with cross-resistance between first-generation NNRTIs such as efavirenz (EFV) and nevirapine (NVP). In antiretroviral treatment guidelines, susceptibility testing is recommended prior to NNRTI treatment initiation [18–21]. The major primary mutations known to affect NNRTI susceptibility include K103N/S, V106A/M, Y181C/I/V, Y188L/C/H, and G190A/S/E [22–24]. Each of these mutations causes intermediate or high resistance to NVP and EFV [25]. Additionally, Y181C/I/V and Y188L/C/H are associated with a 5-fold reduction in susceptibility to rilpivirine (RPV) [26–28].

Accessory mutations such as L100I, K101P, P225H, and M230L usually appear in conjunction with one of the primary NNRTI resistance mutations [29]. Sequences with multiple mutations, including K103N/L100I and K103N/P225H, are frequently observed [30,31]. To increase the NNRTI genetic barrier against resistance, novel drugs of this group have been developed against common NNRTI-resistant viruses (with mutations K101E, E138K, and Y181V). These drugs are suitable for single-daily dosing with improved safety profiles [32–34].

Doravirine (DOR) is an NNRTI most recently approved by the United States Food and Drug Administration in August 2018 and by the European Medicines Agency in November 2018 [35,36]. Efficacy studies showed similar efficacy for DOR (DRIVE-AHEAD, 77.5%; DRIVE-FORWARD, 73.0%), darunavir/ritonavir (66.0%), and EFV (73.6%) based on three drug regimen combinations [37–40]. DOR can be used as a TDF/3TC/DOR single tablet or separately with available NNRTIs. Additionally, DOR has no pre-treatment HIV load limitations, no food restrictions, a reduced number of drug–drug interactions, and a favorable neuropsychiatric profile compared with EFV. Compared with other NNRTIs, DOR retains activity against common NNRTI resistance mutations, including K103N, E138K, Y181C, and G190A [23,41,42]. To date, only few studies have published data on the prevalence of resistance to DOR, which ranged from 0.52% to 1.92% in antiretroviral treatment-naïve patients [37,39,43] and 5.60% (based on the ANRS algorithm) to 16.0% (based on the Stanford database) in treatment-experienced patients [44].

To address the knowledge gap in the frequency of DOR-associated mutations, we aimed to analyze the prevalence of DOR-associated mutations (and those associated with other NNRTIs) in a large dataset of antiretroviral treatment-naïve and -experienced patients with HIV-1 infection living in Poland.

Methods

Study groups and sequencing

The study included a dataset of 1960 sequences, of which 1760 were obtained from newly diagnosed antiretroviral treatment-naïve HIV-1 patients, linked to care in the years 2015–2019 at eight Polish centers (Białystok, Bydgoszcz, Chorzów, Gdańsk, Kraków, Szczecin, Wrocław, and Zielona Góra), and the other 200 were from treatment‐experienced patients treated at the Department of Infectious, Tropical Diseases and Immune Deficiency in Szczecin, Poland. All the sequences were derived from Polish patients of Caucasian origin. Plasma samples from the treatment-naïve patients were collected at care entry (first visit at HIV-treatment center or during in-hospital stay) prior to introduction of antiretroviral treatment and shipped to the genotyping laboratory.

Human immunodeficiency virus RNA extraction and reverse transcriptase (RT) and protease (PR) genotyping were performed using a genotyping assay (Viroseq 2.8 or 2.9, Abbott Molecular, Abbott Park, Illinois, USA) according to the manufacturer’s protocol. Amplicons were obtained using nested PCR and sequenced utilizing the standard BigDye™ technology on the ABI 3500 platform (Applied Biosystems, Foster City, CA). Sequencing was performed in a quality-controlled clinical laboratory at the Department of Infectious, Tropical Diseases and Acquired Immune Deficiency, Pomeranian Medical University in Szczecin, Poland.

Subtyping was performed using genotyping software (REGA genotyping 3.0 tool; http://dbpartners.stanford.edu:8080/RegaSubtyping/stanford-hiv/typingtool) based on the obtained PR and RT sequences.

The analyzed data included sex, age at diagnosis (age at first positive-confirmation test), HIV load at care entry, CD4 lymphocyte count at diagnosis, and subtype.

Drug resistance interpretation

DOR resistance-associated mutations were divided into the following key resistance mutations: V106A/M, Y188FL, P225H, F227L, and Y318F (based on the Stanford HIV drug resistance database, http://hivdb.stanford.edu). A mutation with a score >10–59 was considered to indicate intermediate resistance, whereas a score >60 indicated complete resistance. Variants with scores ≤10 are not shown.

Additionally, the frequency of A98G, L100I, K101E, V108I, Y181I, and H221Y was also investigated owing to their low resistance to DOR. Furthermore, to compare with resistance to other NNRTIs, the following mutations were also assessed: K101H/P, K103N, E138A/G/K/Q, V179L, Y181C, Y188C, G190A, and K238T.

Statistical analysis

Statistical comparisons were performed using the chi² test for nominal variables. Continuous variables were analyzed using the Mann–Whitney U test for nonparametric statistics. Commercial software (Statistica™ 13.1, Statsoft, Warsaw, Poland) was used for the statistical calculations. Drug resistance in the treatment-experienced and -naïve groups was compared using Fisher’s exact test in R statistical platform, version 4.0.3.

Results

Baseline characteristics of patients

Treatment-naïve patients

Most of the cART-unexposed patients were male (n = 1541; 87.6%) with a median age of 34 (interquartile range [IQR], 28–40) years. A total of 1524 patients were infected with subtype B (86.6%) and 236 with non-B (13.4%) variants. The second most common subtype was A (n = 167, 9.5%), followed by subtypes C (n = 21, 1.2%), D (n = 14, 0.8%), and F (n = 4, 0.2%). Circulating recombinant forms CRF01_AE and CRF02_AG were noted in 10 (0.6%) and 17 (1.0%) cases, respectively, and CRF12_BF and CRF47_BF in one case (0.1%) each. One unique recombinant form (URF) of A/B was observed in the group. For all analyzed NNRTI resistance mutations, patients with high or intermediate resistance were notably older than individuals with complete susceptibility to these NNRTIs (Table 1). Additionally, drug resistance mutations (DRMs) for EFV, NVP, and RPV were more frequent among women than among men (4.1% vs. 1.1%, p = 0.0005; 4.6% vs. 1.6%, p = 0.005; and 8.2% vs. 5.0%, p = 0.05, respectively). Furthermore, the median CD4 lymphocyte count was lower among patients with RPV resistance (median, 344 [IQR, 77–524] cells/μL) than among patients without DRMs for RPV (median, 420.5 [IQR, 217.5–614] cells/μL).

Table 1.

Baseline characteristics for cART-unexposed patients based on NNRTI susceptibility.

	Doravirine				Efavirenz				Nevirapine				Etravirine				Rilpivirine
	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	Total, n (%)#
Age, m edian years (IQR)*	-	44 (35–46)	34 (28–40)	0.016	40 (29–43)	44 (41–47)	34 (28–40)	0.0005	42 (37–48)	35 (29–44)	34 (28–40)	0.007	45 (41–48)	36.5 (30–43.5)	34 (28–40)	0.043	44.5 (41–45)	35 (30–44)	34 (28–40)	0.006	34 (28–40)
Gender, n (%)
Male	-	10 (0.6)	1531 (99.4)	0.24	6 (0.4)	11 (0.7)	1524 (98.9)	0.0005	13 (0.8)	13 (0.8)	1515 (98.4)	0.005	3 (0.2)	19 (1.2)	1519 (98.6)	0.24	6 (0.4)	71 (4.6)	1464 (95.0)	0.05	1541 (87.6)
Female	-	3 (1.4)	216 (98.6)		5 (2.3)	4 (1.8)	210 (95.9)		6 (2.8)	4 (1.8)	209 (95.4)		0	1 (0.5)	218 (99.5)		0	18 (8.2)	201 (91.8)		219 (12.4)
Subtype*
Subtype B	-	11 (0.7)	1513 (99.3)	0.83	9 (0.6)	12 (0.8)	1503 (98.6)	0.67	15 (1.0)	16 (1.0)	1493 (98.0)	0.41	3 (0.2)	18 (1.2)	1503 (98.6)	0.71	6 (0.4)	74 (4.9)	1444 (94.7)	0.39	1524 (86.6)
Subtype non-B	-	2 (0.9)	234 (99.1)		2 (0.8)	3 (1.3)	231 (97.9)		4 (1.7)	1 (0.4)	231 (97.9)		0	2 (0.8)	234 (99.2)		0	15 (6.4)	221 (93.6)		236 (13.4)
Median HIV-RNA, log copies/mL (IQR)*	-	5.38 (3.8–4.8)	5.85 (4.1–5.2)	0.27	4.93 (4.27–5.20)	5.52 (3.78–5.55)	5.85 (4.13–5.24)	0.67	5.25 (4.27–5.36)	5.39 (4.36–5.49)	5.85 (4.13–5.24)	0.41	4.51 (3.78–4.77)	5.17 (4.73–5.46)	5.85 (4.13–5.24)	0.42	4.23 (3.27–4.51)	5.31 7(4.42–5.28)	5.86 (4.13–5.23)	0.27	5.85 (4.1–5.2)
HIV-RNA, ≥ 5-log copies/mL, n (%)	-	2 (0.4)	474 (99.6)	0.32	2 (0.4)	6 (1.3)	468 (98.3)	0.20	5 (1.0)	6 (1.3)	465 (97.7)	0.26	0	6 (1.3)	470 (98.7)	0.83	0	29 (6.1)	447 (93.9)	0.11	476 (34.9)
HIV-RNA, < 5-log copies/mL, n (%)	-	8 (0.9)	879 (99.1)		3 (0.3)	5 (0.6)	879 (99.1)		5 (0.6)	8 (0.9)	874 (98.5)		2 (0.2)	8 (0.9)	877 (98.9)		4 (0.5)	33 (3.7)	850 (95.8)		887 (65.1)
Median CD4 lymphocyte count at diagnosis, cells/μL (IQR)*	-	457.5 (233–709.5)	413 (211–611)	0.75	807 (807–807)	91 (31–830)	414 (212.5–610.5)	0.57	42 (30–807)	355 (140–589)	415 (213–611)	0.44	0	275 (76–527)	415 (213–612)	0.19	830 (830–830)	332 (77–470)	420.5 (217.5–614)	0.02	413 (211–611)

^* Total group size varies based on data availability. Viral load data for 1363 cases, CD4 lymphocyte counts for 955 cases, and age for 1736 cases.

Treatment-experienced patients

The mean patient age was 37 (IQR, 33–45) years, and most of them (n = 123; 61.5%) were male. A total of 151 patients were infected with subtype B (75.5%) and 49 with non-B (24.5%) variants. The second most common subtype was D (n = 31, 15.5%), followed by C (n = 4, 2.0%), A (n = 3, 1.5%), and G (n = 3, 1.5). The remaining strains displayed a recombinant forms and were classified as follows: CRF01_AE (n = 4, 2.0%), CRF02_AG (n = 2, 1.0%), and CRF12_BF (n = 1, 0.5%), with one A/G (n = 1, 0.5%) URF. Patients with high or intermediate resistance to etravirine (ETR) were notably older than individuals with complete susceptibility to this agent (p = 0.02). Treatment-experienced patients with viral load ≥ 5-log copies/mL were more resistant to ETR than those with HIV-RNA < 5-log copies/mL (p = 0.02) (Table 2). Median CD4 lymphocyte count was lower among patients with DOR, EFV, NVP, and ETR resistance mutations (median: 95.5 [IQR, 25–160], 119 [26–367], 119 [26–367), and 51 [23–121], respectively) than among patients without DRMs for these antiretrovirals. There were no other statistically significant differences in gender or subtype distribution for the resistance mutations.

Table 2.

Baseline characteristics for cART-exposed patients based on NNRTI susceptibility.

	Doravirine				Efavirenz				Nevirapine				Etravirine				Rilpivirine				Total, n (%)#
	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value	High level	Intermediate	Susceptible	p value
Age, median years (IQR)*	31 (30–34)	35.5 (32.5–44.5)	37 (33–45)	0.19	36 (32–38)	49 (38–52)	37 (33–45)	0.11	37 (32–40)	50 (50–50)	37 (33–45)	0.07	32 (28–36.5)	38.5 (33–50)	37 (33–45)	0.02	32 (30–37)	37.5 (31–50)	37 (33–45)	0.44	37 (33–45)
Gender, n (%)
Male	5 (4.1)	10 (8.1)	108 (87.8)	0.10	24 (19.5)	7 (5.7)	92 (74.8)	0.90	30 (24.4)	2 (1.6)	91 (74.0)	0.99	5 (4.0)	13 (10.6)	105 (85.4)	0.15	11 (8.9)	13 (10.6)	99 (80.5)	0.81	123 (61.5)
Female	1 (1.3)	3 (3.9)	73 (94.8)	0.10	15 (19.5)	5 (6.5)	57 (74.0)		20 (26.0)	0	57 (74.0)		0	6 (7.8)	71 (92.2)		1 (1.3)	13 (16.9)	63 (81.8)		77 (38.5)
Subtype
Subtype B	5 (3.3)	8 (5.3)	138 (91.4)	0.45	27 (17.9)	9 (5.9)	115 (76.2)	0.34	36 (23.8)	0	115 (76.2)	0.22	5 (3.3)	14 (9.3)	132 (87.4)	0.66	11 (7.3)	18 (11.9)	122 (80.8)	0.90	151 (75.5)
Subtype non-B	1 (2.0)	5 (10.2)	43 (87.8)	0.45	12 (24.5)	3 (6.1)	34 (69.4)		14 (28.6)	2 (4.1)	33 (67.3)		0	5 (10.2)	44 (89.8)		1 (2.1)	8 (16.3)	40 (81.6)		49 (24.5)
Median HIV-RNA, log copies/mL (IQR)*	4.94 (4.1–5.0)	4.68 (3.1–5.1)	4.91 (3.3–4.8)	0.42	4.81 (3.37–4.93)	4.89 (3.57–5.03)	4.92 (3.16–4.84)	0.54	4.83 (3.54–4.99)	3.32 (3.32–3.32)	4.92 (3.16–4.84)	0.65	4.62 (3.95–4.87)	4.93 (3.54–5.14)	4.90 (3.26–4.77)	0.30	4.82 (3.10–5.00)	4.77 (3.70–4.80)	4.92 (3.16–4.82)	0.29	4.90 (3.29–4.85)
HIV-RNA, ≥ 5-log copies/mL, n (%)	2 (7.1)	3 (10.7)	23 (82.2)	0.09	6 (21.4)	3 (10.7)	19 (67.9)	0.36	9 (32.1)	0	19 (67.9)	0.41	0	7 (25.0)	21 (75.0)	0.02	3 (10.7)	5 (17.9)	20 (71.4)	0.18	28 (16.5)
HIV-RNA, < 5-log copies/mL, n (%)	3 (2.1)	8 (5.6)	131 (92.3)	0.09	26 (18.3)	8 (5.6)	108 (76.1)		34 (23.9)	1 (0.7)	107 (75.4)		2 (1.4)	11 (7.7)	129 (90.9)		6 (4.2)	19 (13.4)	117 (82.4)		142 (83.5)
Median CD4 lymphocyte count at diagnosis, cells/μL (IQR)*	95.5 (17–121)	86 (27.5–274.5)	262 (51–497)	0.019	121 (30–350)	60.5 (24–367)	275 (65–524.5)	0.03	119 (26–367)	0	275 (65–524.5)	0.03	23 (15–84)	61 (24.5–151)	266 (59–508)	0.01	67.5 (20–128)	264 (42–390)	245.5 (55–497)	0.36	196 (49–485)

^* Total group size varies based on data availability. Viral load data for 170 cases, CD4 lymphocyte counts for 167 cases, and age for 160 cases.

Prevalence of NNRTI DRMs, including DOR

Overall, resistance to any NNRTI was detected in 165 patients (8.42%), of whom 105 (5.97%) were treatment-naïve and 60 (30.0%), treatment-experienced. DOR resistance (at least one DOR DRM) in the total dataset was detected in 32 (1.62%) individuals, of whom 13 (0.74%) were treatment-naïve and 19 (9.50%) treatment-experienced.

The number of DOR resistance sequences with one, two, three, or four mutations among the treatment-naïve patients was four (0.23%), six (0.34%), two (0.11%), and one (0.06%), respectively (Supplementary Table 1). For cART-experienced patients, the numbers were one (0.50%), five (2.50%), nine (4.50%), and three (1.50%), respectively. Notably, one (0.50%) treatment-experienced patient had five mutations (Supplementary Table 2).

The number of sequences with DRMs for other NNRTIs is presented in the next section.

Considering all the mutations for both low and high resistance to DOR, the most prevalent among the antiretroviral-naïve patients were A98G and K101E (n = 3; 0.2% each), and among the antiretroviral-experienced patients, they were L100I (n = 4; 2.0%), K101E, V108I, H221Y, and P225H (n = 3; 1.5% each) (Figure 1).

Figure 1.

Frequency of doravirine resistance-associated mutations per codon position.

The most common mutations associated with resistance to other NNRTIs in both the treatment-naïve and -experienced patients were E138A/G/K/Q (83 [4.7%] and 15 [7.5%] sequences, respectively), K103N (10 [0.6%] and 36 [18.0%] sequences, respectively), and Y181C/I (5 [0.3%] and 13 [6.5%] sequences, respectively). DRM frequencies for the individual NNRTIs, among antiretroviral-naïve and -experienced individuals, are shown in Supplementary Figures 1–4.

Comparison between number of patients with intermediate and high resistance to DOR and other NNRTIs

Among the antiretroviral-naïve patients, DRM to DOR was less common (n = 13; 0.7%) compared with mutations to NVP (n = 36; 2.1%; p = 0.0013), RPV (n = 95; 5.4%; p < 0.0001), and EFV (p = 0.052) (Figure 2). Additionally, frequency of RPV DRM in treatment-naïve patients was higher compared to EFV (n = 26; 1.5%; p < 0.0001), ETR (n = 23; 1.3%; p < 0.0001), and NVP (p < 0.0001). Among the treatment-experienced patients, the frequency of resistance mutations to DOR (n = 19; 9.5%) was lower than that for EFV (n = 51; 25.5%; p < 0.0001) and NVP (n = 52; 26.0%; p < 0.0001). In addition, resistance mutations for ETR (n = 24; 12.0%) were less frequent compared with those for EFV (p = 0.0001) and NVP (p < 0.0001), and higher frequency of resistance to NVP than to RPV (n = 38; 19.0%; p < 0.0001) was observed.

Figure 2.

Comparison between number of patients with intermediate and high-level resistance to doravirine and other non-nucleoside reverse transcriptase inhibitors. * p < 0.0001, ** p = 0.001.

Discussion

The data presented in this paper outline the frequency of DRMs associated with DOR compared with those for other NNRTIs among both treatment-naïve and -experienced individuals from Poland, in the years 2015–2019. This is the first Polish study on the frequency of DOR resistance, with the inclusion of a large dataset of sequences. Analyses were based on the Stanford HIV resistance database, as DOR resistance mutations were not included in the surveillance algorithm [45] and confirmed that the frequency of mutations reducing DOR susceptibility was exceptionally low (<2%), especially among treatment-naïve patients (0.74%), being 9.5% for treatment-experienced patients. This favorable resistance profile observed from real-world data confirms the possibility of rapidly introducing DOR as the initial antiretroviral regimen and eliminating the necessity of baseline genotyping prior to cART.

In treated patients, the occurrence of resistance to DOR is infrequent. In the DRIVE-FORWARD clinical trial that included 382 naïve patients, only two (0.52%) patients failed treatment owing to the emergence of NNRTI resistance mutations, with selection of the V106I/H221Y/F227C and V106A/P225Y/H variants [46]. Moreover, in the DRIVE-AHEAD study, among 364 patients, seven patients (1.92%) with protocol-defined virological failure had DOR resistance mutations (Y188L, Y318F, V106I+F227C, V106I+H221Y+F227C, F227C, V106A+P225H+Y318F, and V106M/T+F227C/R) [39]. Similar to the data shown in our study, these DOR resistance mutations were irregularly present with the most common V106A mutation in the DRIVE trials, present in only one (0.06%; V106A) treatment-experienced patient (the V106I mutation was present in 71 naïve [4.03%] and five cART-experienced patients [2.50%], but owing to its low scoring in the Stanford database, it was not analyzed in the current study).

Moreover, Soulie et al. (2018) reported that DOR resistance was present in 1.4% (n = 142) of 9764 treatment-naïve European patients (according to the 2017 ANRS algorithm; no data based on the Stanford database); this frequency is higher than that in our study (0.7%) [43]. The most common mutations reducing DOR susceptibility are also more frequent than those in our study (V108I, 0.6% vs 0.1%; Y188L and Y318F, 0.2% each vs no mutations in our study; and H221Y, 0.2% versus 0.1%). Comparing the resistance to individual NNRTIs, in our study, the treatment-naïve patients had higher frequency of resistance to RPV (5.4%) than to other NNRTIs owing to the presence of the E138A mutation, which reduces sensitivity to RPV by approximately 2-fold [47]. In the study by Soulie et al. [43], resistance to RPV was present in 9.9% of the patients.

In a different study by Soulie et al. [44] conducted on a dataset of 9199 sequences, among treatment-experienced patients, the prevalence of sequences associated with DOR resistance was 16.0% (n = 1468; based on the Stanford algorithm), which is also higher than that in our study (9.5%). Among the treatment-experienced patients in our study, the mutation most commonly responsible for resistance to NVP (26.0%) and EFV (25.5%) was K103N, which causes high-level reduction in NVP and EFV susceptibility and does not affect responses to other NNRTIs [24,48,49]. However, studies have shown that up to 80% of patients who have failed EFV or NVP therapy develop NNRTI DRMs, which may impact the success of second-generation NNRTIs [50]. Moreover, the prevalence of resistance in people who restarted NVP and EFV after interrupting initial treatment was much higher (21%) than that in those who used them for the first time (8%) [11].

To conclude, DOR resistance mutations associated with virological failure were infrequent among antiretroviral treatment-naïve (99.3% of patients with complete DOR susceptibility) and -experienced (90.5% of sequences with DOR susceptibility) patients. Both groups also reported lower frequency of resistance to DOR than to other NNRTI drugs, which justifies the use of DOR in treatment-naïve groups and represents the attractive NNRTI option related to the higher genetic barrier to resistance in cART-experienced patients.

The limitation of this study is related to the fact that analysis of the DOR resistance mutations was based only on the Stanford HIV drug resistance database; as a new agent, it was not included in the resistance interpretation algorithms; also for the treatment-experienced group, we did not collect the full history of antiretroviral drug use—such analysis is in progress.

Supplemental Material

sj-pdf-1-avt-10.1177_13596535211043044 – Supplemental Material for Low prevalence of doravirine-associated resistance mutations among polish human immunodeficiency-1 (HIV-1)–infected patients

Supplemental Material, sj-pdf-1-avt-10.1177_13596535211043044 for Low prevalence of doravirine-associated resistance mutations among polish human immunodeficiency-1 (HIV-1)–infected patients by Kaja Scheibe, Anna Urbańska, Paweł Jakubowski, Maria Hlebowicz, Monika Bociąga-Jasik, Aleksandra Raczyńska, Aleksandra Szymczak, Bartosz Szetela, Władysław Łojewski and Miłosz Parczewski in Antiviral Therapy

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the National Science Centre project UMO-2018/30/E/NZ6/00696

ORCID iD

Miłosz Parczewski

Supplemental Material

Supplemental material for this article is available online.

References

Parczewski

Siwak

Leszczyszyn-Pynka

, et al. Meeting the WHO 90% target: antiretroviral treatment efficacy in Poland is associated with baseline clinical patient characteristics. J Int AIDS Soc 2017; 20(1):21847. doi: 10.7448/IAS.20.1.21847.

INSIGHT START Study Group Lundgren

Babiker

, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med 2015; 373(9):795–807. doi:10.1056/NEJMoa1506816N.

Cohen

Chen

McCauley

, et al. Antiretroviral therapy for the prevention of HIV-1 transmission. N Engl J Med 2016; 375(9):830–839. doi:10.1056/NEJMoa1600693.

Marsh

Eaton

Mahy

, et al. Global, regional and country-level 90-90-90 estimates for 2018: assessing progress towards the 2020 target. AIDS 2019; 33(Suppl 3):S213–S226. doi:10.1097/QAD.0000000000002355

Hermans

Carmona

Nijhuis

, et al. Virological suppression and clinical management in response to viremia in South African HIV treatment program: a multicenter cohort study. PLoS Med 2020; 17(2):e1003037. doi:10.1371/journal.pmed.1003037.

European Centre for Disease Prevention and Control . Continuum of HIV care. Monitoring Implementation of the Dublin Declaration on Partnership to Fight HIV/AIDS in Europe and Central Asia: 2018 Progress Report. Stockholm: ECDC; 2018.

Kassaye

Grossman

Balamane

, et al. Transmitted HIV drug resistance is high and longstanding in metropolitan Washington, DC. Clin Infect Dis 2016; 63(6):836–843. doi:10.1093/cid/ciw382.

Castor

Low

Evering

, et al. Transmitted drug resistance and phylogenetic relationships among acute and early HIV-1-infected individuals in New York City. J Acquir Immune Defic Syndr 2012; 61(1):1–8. doi:10.1097/QAI.0b013e31825a289b.

Schmidt

Kollan

Fätkenheuer

, et al. Estimating trends in the proportion of transmitted and acquired HIV drug resistance in a long term observational cohort in Germany. PLoS One 2014; 9(8):e104474. doi:10.1371/journal.pone.0104474.

10.

Baxter

Dunn

White

, et al. Global HIV-1 transmitted drug resistance in the INSIGHT strategic timing of antiretroviral treatment (START) trial. HIV Med 2015; 16(Suppl 1):77–87. doi:10.1111/hiv.12236.

11.

WHO

CDC

, The Global Fund. HIV Drug Resistance Report 2019. https://www.who.int/hiv/pub/drugresistance/hivdr-report-2019/en/. Accessed 2020 Nov 25.

12.

Hamers

Rinke de Wit

Holmes

. HIV drug resistance in low-income and middle-income countries. Lancet HIV 2018; 5(10):e588-e596. doi: 10.1016/S2352-3018(18)30173-5.

13.

Rhee

S-Y

Blanco

Jordan

, et al. Geographic and temporal trends in the molecular epidemiology and genetic mechanisms of transmitted HIV-1 drug resistance: an individual-patient- and sequence-level meta-analysis. PLoS Med 2015; 12:e1001810.

14.

Gupta

Gregson

Parkin

, et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect Dis. 2018; 18:346–355.

15.

Clutter

Jordan

Bertagnolio

, et al. HIV-1 drug resistance and resistance testing. Infect Genet Evol 2016; 46:292–307. doi:10.1016/j.meegid.2016.08.031.

16.

TenoRes Study Group . Global epidemiology of drug resistance after failure of WHO recommended first-line regimens for adult HIV-1 infection: a multicentre retrospective cohort study [published correction appears in Lancet Infect Dis 2016; 16(6):636. doi:10.1016/S1473-3099(15)00536-8.

17.

Tien

Pho

Hong

, et al. Antiretroviral drug resistance mutations among patients failing first-line treatment in Hanoi, Vietnam. Infect Drug Resist 2019; 12:1237–1242. Published 2019 May 10. doi:10.2147/IDR.S196448.

18.

Panel on Antiretroviral Guidelines for Adults and Adolescents . Guidelines for the use of antiretroviral agents in adults and adolescents with HIV. Department of health and human services. Available at https://clinicalinfo.hiv.gov/sites/default/files/inline-files/AdultandAdolescentGL.pdf. Accessed 2020 Nov 25.

19.

Snedecor

Khachatryan

Nedrow

, et al. The prevalence of transmitted resistance to first-generation non-nucleoside reverse transcriptase inhibitors and its potential economic impact in HIV-infected patients. PLoS One Guidel Use Antiretroviral Agents Adults Adolescents HIV G-55 2013; 8(8):e72784. Available at: http://www ncbi nlm nih.gov/pubmed/23991151.

20.

Cohen

Molina

J-M

Cahn

, et al. Efficacy and safety of rilpivirine (TMC278) versus efavirenz at 48 weeks in treatment-naive HIV-1-infected patients: pooled results from the phase 3 double-blind randomized ECHO and THRIVE Trials. J Acquir Immune Defic Syndr 2012; 60(1):33–42. Available at: http://www ncbi nlm nih.gov/pubmed/22343174.

21.

EACS . Guidelines V 10.1 [Internet] 2020. Available at: https://www.eacsociety.org/files/guidelines-10.1_5.pdf. Accessed 2020 Nov 25.

22.

Sluis-Cremer

. The emerging profile of cross-resistance among the nonnucleoside HIV-1 reverse transcriptase inhibitors. Viruses 2014; 6(8):2960–2973.

23.

Wensing

Calvez

Ceccherini-Silberstein

, et al. 2019 update of the drug resistance mutations in HIV-1. Top Antivir Med 2019; 27(3):111–121.

24.

Melikian

Rhee

S-Y

Varghese

, et al. Non-nucleoside reverse transcriptase inhibitor (NNRTI) cross-resistance: implications for preclinical evaluation of novel NNRTIs and clinical genotypic resistance testing. J Antimicrob Chemother 2014; 69(1):12–20.

25.

Stanford HIV Drug Resistance Database . https://hivdb.stanford.edu/dr-summary/resistance-notes/NNRTI/. Accessed 2020 Nov 25.

26.

Haddad

Napolitano

Paquet

, et al. Mutation Y188L of HIV-1 Reverse Transciptase Is Strongly Associated with Reduced Susceptibility to rilpivirine. Conference on Retroviruses and Opportunistic Infections, 2012.

27.

Johnson

Pauly

Rai

, et al. A comparison of the ability of rilpivirine (TMC278) and selected analogues to inhibit clinically relevant HIV-1 reverse transcriptase mutants. Retrovirology, 2012.

28.

Rimsky

Vingerhoets

Van Eygen

, et al. Genotypic and phenotypic characterization of HIV-1 isolates obtained from patients on rilpivirine therapy experiencing virologic failure in the phase 3 ECHO and THRIVE studies: 48-week analysis. J Acquir Immune Defic Syndr, 2012.

29.

Stanford HIV Drug Resistance Database . https://hivdb.stanford.edu/pages/documentPage/NNRTI_mutationClassification.html. Accessed 2020 Nov 25.

30.

Koval

Dykes

Wang

, et al. Relative replication fitness of efavirenz-resistant mutants of HIV-1: correlation with frequency during clinical therapy and evidence of compensation for the reduced fitness of K103N + L100I by the nucleoside resistance mutation L74V. Virology 2006; 353(1):184–192. doi:10.1016/j.virol.2006.05.021.

31.

Alcaro

Alteri

Artese

, et al. Docking analysis and resistance evaluation of clinically relevant mutations associated with the HIV-1 non-nucleoside reverse transcriptase inhibitors nevirapine, efavirenz and etravirine. ChemMedChem 2011; 6(12):2203.

32.

Khalilieh

Yee

Sanchez

, et al. Clinical pharmacokinetics of the novel HIV-1 non-nucleoside reverse transcriptase inhibitor doravirine: an assessment of the effect of patient characteristics and drug-drug interactions. Clin Drug Investig 2020; 40(10):927–946. doi:10.1007/s40261-020-00934-2.

33.

Feng

Sachs

, et al. Doravirine suppresses common nonnucleoside reverse transcriptase inhibitor-associated mutants at clinically relevant concentrations. Antimicrob Agents Chemother 2016; 60:2241–2247.

34.

Feng

Wang

Grobler

, et al. In vitro resistance selection with doravirine (MK-1439), a novel nonnucleoside reverse transcriptase inhibitor with distinct mutation development pathways. Antimicrob Agents Chemother 2015; 59:590–598.

35.

FDA . Integrated Review for Pifeltro and Delstrigo Approval. 2018. [Accessed November 20, 2020]. https://www.fda.gov/media/128270/download.

36.

EMA . Assessment Report for Delstrigo. 2018. EMA/874672/2018. https://www.ema.europa.eu/en/documents/assessment-report/delstrigo-epar-public-assessment-report_en.pdf. Accessed January 24, 2021.

37.

Molina

Squires

Sax

, et al. Doravirine versus ritonavir-boosted darunavir in antiretroviral-naive adults with HIV-1 (DRIVE-FORWARD): 48-week results of a randomised, double-blind, phase 3, non-inferiority trial. Lancet HIV 2018; 5(5):e211-e220. doi: 10.1016/S2352-3018(18)30021-3.

38.

Molina

Squires

Sax

, et al. DRIVE-FORWARD trial group. Doravirine versus ritonavir-boosted darunavir in antiretroviral-naive adults with HIV-1 (DRIVE-FORWARD): 96-week results of a randomised, double-blind, non-inferiority, phase 3 trial. Lancet HIV 2020; 7(1):e16-e26. doi: 10.1016/S2352-3018(19)30336-4.

39.

Orkin

Squires

Molina

J-M

, et al. DRIVE-AHEAD study group. doravirine/lamivudine/tenofovir disoproxil fumarate is non-inferior to efavirenz/emtricitabine/tenofovir disoproxil fumarate in treatment-naive adults with human immunodeficiency virus-1 infection: week 48 results of the DRIVE-AHEAD trial. Clin Infect Dis 2019; 68(4):535–544. doi: 10.1093/cid/ciy540.

40.

Orkin

Squires

Molina

, et al. 2018. Doravirine/lamivudine/tenofovir DF continues to be non-inferior to efavirenz/emtricitabine/tenofovir DF in treatment-naïve adults with HIV-1 infection: week 96 results of the DRIVE-AHEAD trial.

41.

Lai

Munshi

, et al. Mechanistic study of common non-nucleoside reverse transcriptase inhibitor-resistant mutations with K103N and Y181C substitutions. Viruses 2016; 8(10):263.

42.

Lai

M-T

Feng

Falgueyret

J-P

, et al. In vitro characterization of MK-1439, a novel HIV-1 nonnucleoside reverse transcriptase inhibitor. Antimicrob Agents Chemother 2014; 58(3):1652–1663.

43.

Soulie

Santoro

Charpentier

, et al. Rare occurrence of doravirine resistance-associated mutations in HIV-1-infected treatment-naive patients. J Antimicrob Chemother 2019; 74(3):614–617. doi: 10.1093/jac/dky464. PMID: 30476106.

44.

Soulie

Santoro

Storto

, et al. Prevalence of doravirine-associated resistance mutations in HIV-1-infected antiretroviral-experienced patients from two large databases in France and Italy. J Antimicrob Chemother 2020; 75(4):1026–1030. doi: 10.1093/jac/dkz553. PMID: 31976534.

45.

Bennett

Camacho

Otelea

, et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS One 2009; 4(3):e4724. doi:10.1371/journal.pone.0004724.

46.

Molina

Squires

Sax

, et al. Doravirine (DOR) ver-sus ritonavir-boosted darunavir (DRV+r): 96-week results of the randomized, double-blind, phase 3 DRIVE-FORWARD Noninferiority trial. In: Paper presented at: 22nd international AIDS conference, Amsterdam, The Netherlands, July 23–27 2018.

47.

Haddad

Napolitano

Paquet

, et al. Impact of HIV-1 reverse transcriptase E138 mutations on rilpivirine drug susceptibility. [abstract 10.]. Antivir Ther. 2011;16(Suppl 1):A18.

48.

Reuman

Rhee

Holmes

, et al. Constrained patterns of covariation and clustering of HIV-1 non-nucleoside reverse transcriptase inhibitor resistance mutations. J Antimicrob Chemother 2010; 65(7):1477

49.

Zhang

Koh

, et al. A novel nonnucleoside analogue that inhibits human immunodeficiency virus type 1 isolates resistant to current nonnucleoside reverse transcriptase inhibitors. Antimicrob Agents Chemother 2007.

50.

Theys

Camacho

Gomes

, et al. Predicted residual activity of rilpivirine in HIV-1 infected patients failing therapy including NNRTIs efavirenz or nevirapine. Clin Microbiol Infect 2015; 21(6):607. doi: 10.1016/j.cmi.2015.02.011.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.38 MB