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Citation: Novitsky, V.; Nyandiko, W.;
Vreeman, R.; DeLong, A.K.; Howison,
M.; Manne, A.; Aluoch, J.; Chory, A.;
Sang, F.; Ashimosi, C.; et al. Added
Value of Next Generation Sequencing
in Characterizing the Evolution of
HIV-1 Drug Resistance in Kenyan
Youth. Viruses 2023,15, 1416.
https://doi.org/10.3390/v15071416
Academic Editor: Luis
Menéndez-Arias
Received: 31 May 2023
Revised: 14 June 2023
Accepted: 20 June 2023
Published: 22 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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4.0/).
viruses
Article
Added Value of Next Generation Sequencing in Characterizing
the Evolution of HIV-1 Drug Resistance in Kenyan Youth
Vlad Novitsky 1, *, Winstone Nyandiko 2,3, Rachel Vreeman 2,4,5, Allison K. DeLong 6, Mark Howison 7,
Akarsh Manne 1, Josephine Aluoch 2, Ashley Chory 4, Festus Sang 2, Celestine Ashimosi 2, Eslyne Jepkemboi 2,
Millicent Orido 2, Joseph W. Hogan 2,6 and Rami Kantor 1,*
1Alpert Medical School, Brown University, Providence, RI 02912, USA; akarsh.manne@gmail.com
2Academic Model Providing Access to Healthcare (AMPATH), Eldoret 30100, Kenya;
nyandikom@yahoo.com (W.N.); rachel.vreeman@mssm.edu (R.V.); josteny@yahoo.com (J.A.);
festero85@gmail.com (F.S.); celehaw@gmail.com (C.A.); eslynejepkemboi@gmail.com (E.J.);
oridomillicent@yahoo.co.uk (M.O.); jwh@brown.edu (J.W.H.)
3College of Health Sciences, Moi University, Eldoret 30100, Kenya
4Department of Global Health and Health System Design, Icahn School of Medicine at Mount Sinai,
New York, NY 10029, USA; ashley.chory@mssm.edu
5Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
6School of Public Health, Brown University, Providence, RI 02912, USA; adelong@stat.brown.edu
7Research Improving People’s Lives, Providence, RI 02903, USA; mark@howison.org
*Correspondence: vnovitsky@lifespan.org (V.N.); rkantor@brown.edu (R.K.)
Abstract:
Drug resistance remains a global challenge in children and adolescents living with HIV
(CALWH). Characterizing resistance evolution, specifically using next generation sequencing (NGS)
can potentially inform care, but remains understudied, particularly in antiretroviral therapy (ART)-
experienced CALWH in resource-limited settings. We conducted reverse-transcriptase NGS and
investigated short-and long-term resistance evolution and its predicted impact in a well-characterized
cohort of Kenyan CALWH failing 1st-line ART and followed for up to ~8 years. Drug resistance
mutation (DRM) evolution types were determined by NGS frequency changes over time, defined as
evolving (up-trending and crossing the 20% NGS threshold), reverting (down-trending and crossing
the 20% threshold) or other. Exploratory analyses assessed potential impacts of minority resistance
variants on evolution. Evolution was detected in 93% of 42 participants, including 91% of 22 with
short-term follow-up, 100% of 7 with long-term follow-up without regimen change, and 95% of
19 with long-term follow-up with regimen change. Evolving DRMs were identified in 60% and
minority resistance variants evolved in 17%, with exploratory analysis suggesting greater rate of
evolution of minority resistance variants under drug selection pressure and higher predicted drug
resistance scores in the presence of minority DRMs. Despite high-level pre-existing resistance, NGS-
based longitudinal follow-up of this small but unique cohort of Kenyan CALWH demonstrated
continued DRM evolution, at times including low-level DRMs detected only by NGS, with predicted
impact on care. NGS can inform better understanding of DRM evolution and dynamics and possibly
improve care. The clinical significance of these findings should be further evaluated.
Keywords:
HIV-1 drug resistance; children and adolescents; evolution of drug resistance; minority
drug resistance variants; types of drug resistance evolution
1. Introduction
Global scale-up of antiretroviral therapy (ART), received by 75% of 38.4 million people
living with HIV/AIDS by the end of 2021, has saved millions of lives [
1
,
2
]. The emergence
of drug resistance compromises ART effectiveness and remains a barrier to sustainable
life-long ART [
3
–
5
]. Accumulation of drug resistance mutations (DRMs) over time can
impact ART susceptibility, increase cross-resistance, compromise current treatment options
Viruses 2023,15, 1416. https://doi.org/10.3390/v15071416 https://www.mdpi.com/journal/viruses
Viruses 2023,15, 1416 2 of 17
and limit future ones [
6
,
7
]. While minority drug resistance variants, detectable by next
generation sequencing (NGS), may be clinically relevant in certain circumstances, they are
still not reported in routine clinical care and their clinical relevance is an existing research
gap [8–12].
Children and adolescents living with HIV (CALWH) are more vulnerable than adults
to developing treatment failure and drug resistance, particularly when infected perinatally,
thus mandating life-long ART [
13
–
15
]. The vast majority (~90%) of CALWH failing ART in
resource-limited settings have drug resistance [
6
,
7
,
16
–
22
]. Despite some emerging NGS data
(e.g., Kemp et al., recently exploring drug resistance pathways in adults with HIV-1 subtype
C failing 2nd-line ART, demonstrating extensive intra-host viral dynamics [
23
]), data on
intra-host accumulation of DRMs over time, its impact, and the role of low-frequency
DRMs in treatment experienced by CALWH in low-resource settings with diverse HIV-1
subtypes are limited [6,17,21,24].
We have been following a cohort of perinatally infected Kenyan CALWH failing first-
line ART with extensive drug resistance and limited switch to second line ART, associated
with long-term failure and mortality [
25
,
26
]. Findings emphasize the urgent need for
interventions to sustain effective, life-long ART in this vulnerable population. Recently,
we assessed the potential added value of using NGS over Sanger sequencing in detecting
DRMs in this population. Despite good overall agreement between sequencing technologies
at high NGS thresholds, even in this resistance-saturated cohort, 12% of participants had
higher, potentially clinically relevant predicted resistance detected only by NGS, suggesting
potential benefits of the more sensitive NGS over existing technology [27].
In this study, we characterize the evolution of drug resistance over time in this cohort
of ART-experienced CALWH with non-B HIV-1 subtypes in a resource-limited setting,
and further assess the potential value added by NGS in monitoring DRM evolution. By
characterizing short- and long-term changes in DRM profiles under specific regimen selec-
tion pressure and examining their potential clinical effects, we hypothesize that minority
resistance variants are more likely under drug selective pressure to evolve to levels that
may impact clinical outcomes, possibly justifying the consideration of using NGS to detect
such minority resistance variants early on.
2. Materials and Methods
2.1. Study Participants
CALWH were followed at AMPATH (Academic Model Providing Access to Healthcare)
clinics in western Kenya, which provide free ART and associated services for >160,000 persons
with HIV, including >10,000 youth [
28
,
29
], according to locally developed protocols per
WHO guidelines. Treatment monitoring before 2016 was with CD4 testing at diagnosis
and every 6 months thereafter, and viral load (VL) testing available on clinician suspicion
of failure. In 2016, the Kenya Ministry of Health and AMPATH implemented routine
VL testing and, since then, CALWH suspected as failing 1st-line regimens (consecutive
VL > 1000 copies/mL or immunologic/clinical failure) were switched to standard 2nd-line
protease inhibitor (PI)-based ART. Drug resistance data were not available at the time
of switch.
CALWH were enrolled at three timepoints (TPs): (1) in 2010–2013, as part of the
Comprehensive Adherence Measurement for Pediatrics (CAMP) study, if they were peri-
natally infected with HIV,
≤
14 years old, on or beginning 1st-line non-nucleoside reverse
transcriptase inhibitor (NNRTI)-based regimens and in care at one of 4 AMPATH clinics;
(2) in 2010–2013, as a ~3 month follow up; and (3) in 2016–2018, as part of the Resistance
in a Pediatric Cohort (RESPECT) study. Participants were included in this study if their
samples were successfully genotyped by NGS at least twice out of the three TPs.
The study was approved by The Miriam Hospital and Mount Sinai Human Subjects
Institutional Review Boards in the United States and the AMPATH Institutional Research
Ethics Committee in Kenya.
Viruses 2023,15, 1416 3 of 17
2.2. Specimen Collection and Laboratory Methods
Whole blood was collected, and plasma was separated and stored (
−
80
◦
C) at each TP.
CD4 and VL were tested routinely at sample collection and genotyping was attempted for
all detectable (VL > 40 copies/mL) samples.
CD4 count and percent were tested using FACSCalibur flow cytometry (BD Biosystems,
Erembodegem, Belgium). VL was tested using the Abbott Real Time HIV-1 assay on
the m2000 system (Abbott Molecular, Inc., Des Plaines, IL, USA; lower detection limit
40 copies/mL).
HIV-1 RNA was extracted from blood plasma by EZ1 Advanced XL (Qiagen, Ger-
mantown, MD) and using Qiagen EZ1 DSP Virus Kit (Qiagen GmbH, Hilden, Germany).
HIV-1 RNA was converted to cDNA using SuperScript III First-Strand Synthesis System
(Thermo Fisher Scientific, Waltham, MA, USA) and primer 3754 (5
0→
3
0
: CCAGGTG-
GCTTGCCAATACTCTGTCC, HXB2 nucleotide positions 3754–3779). Two rounds of PCR
amplification were performed by using Phusion High-Fidelity DNA Polymerase (New
England BioLabs, Ipswitch, MA, USA) and Platinum Taq DNA Polymerase High Fidelity
(Thermo Fisher Scientific, Waltham, MA, USA) in the 1st (primers 1849, 5
0→
3
0
: GAT-
GACAGCATGTCAGGGAG, HXB2 nucleotide positions 1827–1846 and 3754) and 2nd
round (primers PRO-1, 5
0→
3
0
: CAGAGCCAACAGCCCCACCA, HXB2 nucleotide posi-
tions 2147–2166 and RT-21, 5
0→
3
0
: CTGTATTTCTGCTATTAAGTCTTTTGATGGG, HXB2
nucleotide positions 3509–3539), respectively. Library preparation was performed using
the Nextera DNA Library Prep Kit (Illumina) followed by NGS using the Illumina MiSeq
platform. Generated sequences were quality controlled and processed using the bioinfor-
matics pipeline hivmmer including assessment of amino acid NGS frequencies [
30
]. All
samples in this study were processed using the same method for HIV-1 RNA extraction,
amplification, NGS and bioinformatics pipeline. Resulting sequences (HXB2 nucleotide po-
sitions 2550–3509) encompassed the HIV-1 region encoding the first 320 codons of Reverse
Transcriptase. Multiple sequence alignment was performed using mafft v7.450 [
31
]. HIV-1
subtyping was performed by using REGA v3 [
32
] with minor discrepancies resolved on a
case-by-case basis.
2.3. Drug Resistance Analyses
To estimate short- and long-term drug resistance evolution, the time interval between
visits ~3 months apart was defined as short-term, and between visits ~6 years apart as
long-term. The frequency of each DRM was determined by NGS at each TP. Minority drug
resistance variants were considered mutations when detected at NGS frequency 1–20%, not
typically detected by traditional Sanger sequencing. DRMs were considered established at
frequencies >20%, typically detected by Sanger sequencing. DRMs at NGS frequency <1%,
typically undistinguishable from noise, were considered undetected. Thus, the term ‘
≤
20%’
used throughout the text is referring to both DRMs identified at NGS frequency 1–20% and
undetected mutations, unless otherwise specified. A DRM was considered ‘detected’ if its
NGS frequency was ≥1% at least at one TP.
Types of DRM evolution were defined based on the change (or lack of change) in
NGS frequency of each mutation between two TPs (Table 1; Supplementary Figure S1).
An evolving DRM was defined as occurring at
≤
20% at an early TP and >20% at a late TP;
areverting DRM was defined as >20% at an early TP and
≤
20% at a late TP; DRMs with
increasing or decreasing frequency were defined as mutations with a >5% frequency change
within the 1–20% (minority resistance variants) or >20% (established DRMs) categories
between an early and late TPs; and a stable DRM was defined as having a
≤
5% frequency
change within either category (minority or established DRMs).
Viruses 2023,15, 1416 4 of 17
Table 1. Types of DRM evolution based on a change of NGS frequency over time.
Next Generation Sequencing Frequency Type of DRM Evolution
Earlier Time Point Later Time Point
≤20% >20% Evolving DRM
>20% ≤20% Reverting DRM
≤20% ≤20%; >5% increase Minority DRM, increasing frequency
≤20% ≤20%; >5% decrease Minority DRM, decreasing frequency
≤20% ≤20%; within 5% Minority DRM, stable
>20% >20%; >5% increase Established DRM, increasing frequency
>20% >20%; >5% decrease Established DRM, decreasing frequency
>20% >20%; within 5% Established DRM, stable
DRM, drug resistance mutation; NGS, next generation sequencing.
Drug resistance evolution was assessed within study groups, which included short-
and long-term time periods, and—within the latter—with and without ART regimen
change. For each DRM evolution type, per-participant rate of evolution was computed as
the proportion of individuals having the detected DRM.
Drug resistance was assessed for nucleoside reverse transcriptase inhibitors (NRTIs)
and NNRTIs. Predicted drug resistance scores and levels were assessed according to the
penalty scores from the Stanford University HIV Drug Resistance Database HIVdb Program,
ver. 8.9 [
33
,
34
]. The impact of evolving DRMs was evaluated on the basis of whether it
affected the predicted resistance scores and/or changed predicted resistance levels. This
analysis was performed both for medications taken at the time of sampling, and for
potential future medications that were not previously taken—the NRTIs zidovudine (AZT)
and tenofovir disoproxil fumarate (TDF), and the NNRTIs etravirine (ETR), rilpivirine
(RPV) and doravirine (DOR).
2.4. Assessment of Potential Impact of Minority Resistance Variants on Mutation Evolution
In exploratory analyses, we examined associations between evolution of minority drug
resistance variants and their potential clinical relevance. First, to address the hypothesis
that established, minority or undetected drug resistance variants are more likely to evolve
if the DRM has a positive penalty score towards the current regimen, we use regression
analysis to quantify the association between DRM positive penalty scores and evolution in
NGS frequency from early to later TPs.
Second, we address the hypothesis that participants with minority drug resistance
variants and positive penalty scores for the current regimen will have a lower predicted
resistance score compared to those without minority drug resistance variants or with DRMs
that have zero or negative penalty scores for current regimens. To accomplish this, we
fit a regression model where change in the predicted resistance score is the dependent
variable, and having a minority resistance variant with a non-zero penalty score is the
independent variable.
Associations are represented in terms of regression model coefficients, with bootstrap
resampling used to generate confidence intervals. All analyses were implemented using R
version 4.2.3 [35]. Full details are in the Supplementary Materials.
3. Results
3.1. Study Participants
A total of 42 virologically unsuppressed CALWH had NGS sequences at ≥2 TPs and
were included in the analyses. There were 22 short-term cases (mean interval between
visits 2.8 months; IQR 2.8–2.9; range 2.6–3.1) and 26 long-term ones (mean interval between
visits 5.6 years; IQR 4.6–6.6; range 3.9–7.8). Six participants had genotypes available at
3 TPs and were included in both short- and long-term evaluations. Over the short-term
period, all participants remained on the same ART regimen. Over the long-term period,
7 participants remained on the same regimen, while 19 participants had a change of their
Viruses 2023,15, 1416 5 of 17
ART regimen including 32% (6/19) staying on a NNRTI-based regimen and 68% (13/19)
switching to a protease inhibitor (PI)-based regimen.
The study cohort was predominantly (64%) male, and at enrollment had a mean age of
9 years (range 2–15 years), CD4 percent of 21, and VL of 41,547 copies/mL. ART regimens at
TP1 included abacavir (ABC) or AZT or stavudine (D4T), lamivudine (3TC), and efavirenz
(EFV) or nevirapine (NVP), taken for an average of 2.7 years. Participants harbored diverse
HIV-1 subtypes, predominantly A1 and D (see Table 2for further details and breakdown
per study group).
Table 2.
Cohort characteristics at enrollment according to time of follow up and ART regimen change.
Variable Study Participants,
Total, n= 42
Study Groups
Short-Term Evolution,
n= 22
Long-Term Evolution
without Regimen
Change, n= 7
Long-Term Evolution
with Regimen
Change, n= 19
Gender (% females) 36 36 57 21
Age, mean years (range) 9 (2–15) 9 (6–15) 8 (2–13) 10 (5–13)
Mean CD4 count, cells/mm3(range) 565 (42–2748) 423 (42–1062) 702 (394–1225) 684 (78–2748)
Mean CD4 percent (range) 21 (2–42) 18 (2–33) 25 (17–31) 23 (3–42)
Mean viral load, copies/mL (range) 41,547 (460–676,730) 67,342 (700–676,730) 7936 (1280–21,090) 41,725 (460–482,200)
Treatment failure, n(%) 39 (93%) 20 (91%) 7 (100%) 18 (95%)
Regimens
ABC, 3TC, EFV/NVP; n(%) 22 (52%) 9 (41%) 5 (71%) 12 (63%)
AZT, 3TC, NVP; n(%) 13 (31%) 6 (27%) 2 (29%) 5 (26%)
D4T, 3TC, EFV/NVP; n(%) 7 (17%) 7 (32%) 2 (11%)
Mean time on ART, years (range) 2.7 (0.1–6.8) 2.5 (0.1–6) 2 (0.7–5.7) 2.9 (0.1–6.8)
HIV-1 subtype/recombinant
A1; n(%) 26 (62%) 10 (45%) 4 (57%) 15 (79%)
A1, C; n(%) 2 (5%) 2 (9%) 1 (14%) 1 (5%)
A1, D; n(%) 3 (7%) 1 (5%) 2 (11%)
C; n(%) 3 (7%) 3 (14%)
D; n(%) 8 (19%) 6 (27%) 2 (29%) 1 (5%)
DRMs per participant
NRTI, mean (range) 2.5 (0–8) 2.9 (0–8) 1.9 (1–4) 2.3 (0–5)
NNRTI, mean (range) 2.2 (0–4) 2.1 (0–4) 2.3 (1–3) 2.2 (0–4)
NRTI and NNRTI, mean (range) 4.8 (0–12) 5 (0–12) 4.1 (2–6) 4.5 (0–7)
Abbreviations: 3TC, lamivudine; ABC, abacavir ART, antiretroviral therapy; AZT, zidovudine; D4T, stavudine;
DRM, drug resistance mutation; EFV, efavirenz; NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI,
nucleoside reverse transcriptase inhibitor; NVP, nevirapine.
3.2. Evolution of Drug Resistance
Any evolution of DRMs between early and late TPs (i.e., any type of evolution ex-
amined other than stable mutations) was identified in 39/42 (93%) participants including
91% (20/22) in the short-term group, 100% (7/7) in the long-term group without regimen
change, and 95% (18/19) in the long-term group with regimen change. Evolving DRMs
(from
≤
20% to >20%) were seen overall in 60% (25/42) of participants (10/22, 45% in the
short-term group; 5/7, 71% in the long-term group without regimen change; and 14/19,
74% in the long-term group with regimen change; see Table 3for details). Evolving DRMs
from 1–20% to >20% occurred in 17% (7/42) of participants (5/22, 23% in the short-term
group; 2/7, 29% in the long-term group without regimen change; and 3/19, 16% in the
long-term group with regimen change).
Out of a total of 419 detected DRMs across all participant groups, evolving DRMs
accounted for 12%, reverting DRMs 5%, established DRMs 44% (33% stable, 6% increasing
and 4% decreasing), and minority DRMs 39% (30% stable, 4% increasing and 6% decreasing).
Further details on DRM evolution types according to study groups and specific DRMs
are provided in Table 4, Supplementary Table S1 and Figure S2 for NRTIs, and Table 5,
Supplementary Table S2 and Figure S3 for NNRTIs.
Viruses 2023,15, 1416 6 of 17
Table 3. DRM evolution type according to participants and study groups.
Type of DRM Evolution
Number of Participants with DRMs, n(%)
Total, n= 42 Short Term, n= 22
Long Term with
No Regimen
Change, n= 7
Long Term
with Regimen
Change, n= 19
Evolving DRM 25 (60%) 10 (45%) 5 (71%) 14 (74%)
Reverting DRM 12 (29%) 4 (18%) 3 (43%) 9 (47%)
Minority DRM
Increasing 9 (21%) 3 (14%) 1 (14%) 6 (32%)
Decreasing 17 (40%) 8 (36%) 3 (43%) 10 (53%)
Stable 41 (98%) 22 (100%) 6 (86%) 19 (100%)
Minority total 41 (98%) 22 (100%) 6 (86%) 19 (100%)
Established DRM
Increasing 18 (43%) 10 (45%) 4 (57%) 6 (32%)
Decreasing 10 (24%) 5 (23%) 1 (14%) 7 (37%)
Stable 37 (88%) 20 (91%) 6 (86%) 14 (74%)
Established total 40 (95%) 21 (95%) 7 (100%) 18 (95%)
Any type except stable DRM 39 (93%) 20 (91%) 7 (100%) 18 (95%)
Abbreviations: DRM, drug resistance mutation.
Out of 190 detected NRTI mutations, evolving DRMs accounted for 15% (range 6–21%
by study group), reverting 5% (range 3–9%), established 52% (range 38–70%; mostly stable),
and minority resistance variants 28% (range 21–33%; mostly stable). Evolving DRMs
from 1–20% to >20% occurred in 2/5 DRMs in the short-term group; 0/5 in the long-term
group without regimen change; and 1/19 in the long-term group with regimen change.
Breakdowns according to specific study groups and mutations are provided in Figure 1,
Supplementary Table S1 and Figure S2.
Table 4. NRTI DRM evolution types according to mutations and study groups.
DRM Evolution
Types
Study Groups
Short-Term Evolution, n= 22;
~3 Months
Long-Term Evolution, n= 26; ~6 Years
No Regimen Change, n= 7 Regimen Change, n= 19
Total per
Group
Per
Person % * Total per
Group
Per
Person % * Total per
Group
Per
Person % *
Evolving DRM 5 0.23 5.6 5 0.71 20 19 1 20.7
Reverting DRM 3 0.14 3.4 1 0.14 4 8 0.42 8.7
Minority
variant
DRM
Increasing
1 0.05 1.1 0 0 0 7 0.37 7.6
Decreasing
2 0.09 2.2 0 0 0 3 0.16 3.3
Stable 16 0.73 18 7 1 28 20 1.05 21.7
Total 19 0.87 21.3 7 1 28 30 1.58 32.6
Established
DRM
Increasing
10 0.45 11.2 2 0.29 8 3 0.16 3.3
Decreasing
3 0.14 3.4 0 0 0 9 0.47 9.8
Stable 49 2.23 55.1 10 1.43 40 23 1.21 25
Total 62 2.82 69.7 12 1.72 48 35 1.84 38.1
Total NRTI DRMs 89 4.06 25 3.57 92 4.84
* Percent of the total number of identified DRMs per group. Abbreviations: DRM, drug resistance mutation; NRTI,
nucleoside reverse transcriptase inhibitor.
Viruses 2023,15, 1416 7 of 17
Table 5. NNRTI DRM evolution types according to mutations and study groups.
DRM Evolution Types
Study Groups
Short-Term Evolution,
n= 22; ~3 Months
Long-Term Evolution, n= 26; ~6 Years
No Regimen Change, n= 7 Regimen Change, n= 19
Total per
Group
Per
Person % * Total per
Group
Per
Person % * Total per
Group
Per
Person % *
Evolving DRM 8 0.36 7.1 4 0.57 10.3 14 0.74 13.2
Reverting DRM 2 0.09 1.8 2 0.29 5.1 9 0.47 8.5
Minority
variant
DRM
Increasing 2 0.09 1.8 1 0.14 2.6 4 0.21 3.8
Decreasing 11 0.5 9.7 4 0.57 10.3 10 0.53 9.4
Stable 46 2.09 40.7 14 2 35.9 34 1.79 32.1
Total minority
variant DRM 59 2.68 52.2 19 2.71 48.8 48 2.53 45.3
Established
DRM
Increasing 6 0.27 5.3 4 0.57 10.3 5 0.26 4.7
Decreasing 4 0.18 3.5 1 0.14 2.6 4 0.21 3.8
Stable 34 1.55 30.1 9 1.29 23.1 26 1.37 24.5
Total established
DRM 44 2 38.9 14 2 36 35 1.84 33
Total NNRTI DRMs 113 5.13 39 5.57 106 5.58
* Percent of the total number of identified DRMs per group. Abbreviations: DRM, drug resistance mutation;
NNRTI, non-nucleoside reverse transcriptase inhibitor.
Out of 229 detected NNRTI mutations, evolving DRMs accounted for 10% (range
7–13% by study group), reverting for 4% (2–9%), established for 37% (range 33–39%; mostly
stable), and minority resistance variants for 49% (45–52%; mostly stable). Evolving DRMs
from 1–20% to >20% occurred in 3/8 DRMs in the short-term group; 3/4 in the long-term
group without regimen change; and 3/14 in the long-term group with regimen change.
Breakdowns according to specific study groups and mutations are provided in Figure 2,
Supplementary Table S2 and Figure S3.
3.3. Effect of Evolving DRMs on Predicted Resistance
Of the 25/42 participants with evolving DRMs, predicted resistance scores increased in
92% (23/25) and an escalation of predicted drug resistance level was found in 80% (20/25)
of the participants, respectively.
Of the 16/42 participants with evolving NRTI DRMs, evolution impacting a predicted
score increase was seen in 88% (14/16) of them, including 75% (3/4) in the short-term
group, 100% (3/3) in the long-term group without ART change, and 90% (9/10) in the
long-term group with ART change (Figure 1). These changes resulted in an escalation of
predicted drug resistance level in 69% (11/16) of participants, including 50% (2/4) in the
short-term group, 67% (2/3) in the long-term group without ART change, and 80% (8/10)
in the long-term group with ART change. The main evolving DRMs in participants without
regimen change were related to ABC (e.g., L74V and Y155F, with an already-existing high
level resistance to XTC at the earlier TP).
Of the 20/42 participants with evolving NNRTI DRMs, evolution impacting a pre-
dicted score increase was seen in 85% (17/20) of cases, including 86% (6/7) in the short-term
group, 100% (2/2) in the long-term group without ART change, and 83% (10/12) in the
long-term group with ART change (Figure 2). These changes resulted in an escalation of
predicted drug resistance level in 70% (14/20) of participants, including 86% (6/7) in the
short-term group, 100% (2/2) in the long-term group without ART change, and 58% (7/12)
in the long-term group with ART change. The main evolving DRM in participants without
regimen change was the NVP-related Y181C.
Viruses 2023,15, 1416 8 of 17
Viruses 2023, 15, x FOR PEER REVIEW 9 of 19
Figure 1. Participants with evolving and reverting NRTI DRMs. This figure presents NRTI DRM
evolution and its predicted impact in study participants with short-term evolution (panel A.), long-
term evolution without regimen change (panel B.), and long-term evolution with regimen change
(panel C.). Each row represents one study participant and includes regimens at earlier (TP1) and
later (TP2) time points (first column); identified cumulative DRMs at TP1 and TP2 (second column);
and predicted resistance scores (numbers) and levels (colors) at TP1 and TP2, depicted in blocks in
the third column on the right, to NRTI drugs abacavir (ABC), zidovudine (AZT), lamivudine and
emtricitabine (XTC) and tenofovir disoproxil fumarate (TDF). Identified DRMs are color-coded by
evolutionary type according to the legend at the top left, including evolving DRMs in red; reverting
Figure 1.
Participants with evolving and reverting NRTI DRMs. This figure presents NRTI DRM
evolution and its predicted impact in study participants with short-term evolution (panel (
A
)), long-
term evolution without regimen change (panel (
B
)), and long-term evolution with regimen change
(panel (
C
)). Each row represents one study participant and includes regimens at earlier (TP1) and
later (TP2) time points (first column); identified cumulative DRMs at TP1 and TP2 (second column);
and predicted resistance scores (numbers) and levels (colors) at TP1 and TP2, depicted in blocks in
the third column on the right, to NRTI drugs abacavir (ABC), zidovudine (AZT), lamivudine and
emtricitabine (XTC) and tenofovir disoproxil fumarate (TDF). Identified DRMs are color-coded by
evolutionary type according to the legend at the top left, including evolving DRMs in red; reverting
Viruses 2023,15, 1416 9 of 17
DRMs in blue; and all other DRMs (including stable, and increasing/decreasing established/minority
DRMs) in black. NGS mutation frequencies for the evolving and reverting DRMs are indicated by
the number following the underscores (<1 = undetectable). Predicted resistance levels for each drug
are presented as color-coded blocks according to the legend at the top right from green (susceptible)
to high-level predicted resistance (red). In panel (
C
), the first column regimens are at TP1 and the
second column regimens are at TP2.
Viruses 2023, 15, x FOR PEER REVIEW 10 of 19
DRMs in blue; and all other DRMs (including stable, and increasing/decreasing established/minor-
ity DRMs) in black. NGS mutation frequencies for the evolving and reverting DRMs are indicated
by the number following the underscores (<1 = undetectable). Predicted resistance levels for each
drug are presented as color-coded blocks according to the legend at the top right from green (sus-
ceptible) to high-level predicted resistance (red). In panel C., the first column regimens are at TP1
and the second column regimens are at TP2.
Figure 2. Participants with evolving and reverting NNRTI DRMs. This figure presents NNRTI DRM
evolution and its predicted impact in study participants with short-term evolution (panel A.), long-
term evolution without regimen change (panel B.), and long-term evolution with regimen change
(panel C.). For details and color-coding, see legend to Figure 1.
Figure 2.
Participants with evolving and reverting NNRTI DRMs. This figure presents NNRTI
DRM evolution and its predicted impact in study participants with short-term evolution (panel (
A
)),
long-term evolution without regimen change (panel (
B
)), and long-term evolution with regimen
change (panel (C)). For details and color-coding, see legend to Figure 1.
Viruses 2023,15, 1416 10 of 17
3.4. Analyses of Minority DRM Evolution
In the exploratory analyses to examine the evolution of minority drug resistance
variants under a regimen that has positive penalty scores, we identified two NRTI (L74V
and Y115F) and six NNRTI (A98G, G190A, H221Y, K103N, V108I, and Y181C) mutations
in at least three participants with established DRMs at the later TP, and at least two
participants with no penalty score for these DRMs at the later TP, who were candidates for
further analysis.
For NRTIs, participants taking at least one drug with an associated positive penalty
score demonstrated higher increase in prevalence of both L74V and Y115F (compared
to those without penalty score for those two DRMs), indicating more extensive minority
variant DRM evolution due to the presence of a penalty score (Figure 3A). For both mu-
tations, the impact of a penalty score on DRM prevalence at the later TP was larger in
individuals with either minority resistance variants or established DRMs at TP1, but not
among participants with undetected DRM at TP1.
For NNRTIs, participants taking at least one drug with an associated positive penalty
score demonstrated higher increase in prevalence of Y181C if they had a minority (15%
prevalence) or established (80% prevalence) DRM at TP1 (Figure 3B). Some minority drug
resistance variants showed an empirical association with negative differences for the
prevalence of K103N and G190A at higher TP1 prevalence (15 or 80%). However, the
wide confidence intervals associated with these findings preclude drawing any defini-
tive conclusion.
Interestingly, we found 19 DRMs among 9/42 (21%) participants, all with ART regimen
change, with emerging or evolving DRMs between TP1 and TP3, despite no apparent
relevant drug pressure based on their ART regimen (Table 6). Of these 19 DRMs, 14 were
evolving (7 from none to >20%; 3 from 1–20% to >20%), and 5 were established increasing
(all with >5 percentage point increases).
Table 6. Long-term DRM evolution unrelated to drug selective pressure.
Patient
ID DRM ARV Class TP1 Regimen TP2 Regimen
TP1
Penalty
Score *
TP2
Penalty
Score **
TP1
NGS,
%
TP2
NGS,
%
1 V108I NNRTI AZT, 3TC, NVP ABC, 3TC, LPV/r 15 0 none 87
1 G190A NNRTI AZT, 3TC, NVP ABC, 3TC, LPV/r 60 0 none 5
1 K103N NNRTI AZT, 3TC, NVP ABC, 3TC, LPV/r 60 0 65 99
2 P225H NNRTI ABC, 3TC, NVP AZT, 3TC, ATV/r 45 0 none 11
2 Y318F NNRTI AZT, 3TC, NVP ABC, 3TC, LPV/r 30 0 14 88
2 K103N NNRTI ABC, 3TC, NVP AZT, 3TC, ATV/r 60 0 none 89
3 T69D NRTI AZT, 3TC, NVP TDF, 3TC, NVP 0 0 none 98
4 T69D NRTI ABC, 3TC, EFV AZT, 3TC, LPV/r 10 0 79 99
5 A98G NNRTI AZT, 3TC, NVP ABC, 3TC, LPV/r 30 0 none 100
5 Y318F NNRTI ABC, 3TC, NVP AZT, 3TC, ATV/r 30 0 none 7
6 Y181C NNRTI D4T, 3TC, NVP TDF, 3TC, ATV/r 60 0 15 92
6 K103N NNRTI D4T, 3TC, NVP TDF, 3TC, ATV/r 60 0 57 75
6 H221Y NNRTI ABC, 3TC, NVP TDF, 3TC, ATV/r 15 0 none 87
6 K103S NNRTI D4T, 3TC, NVP TDF, 3TC, ATV/r 60 0 none 19
7 H221Y NNRTI D4T, 3TC, NVP TDF, 3TC, ATV/r 15 0 12 88
8 K101E NNRTI ABC, 3TC, NVP TDF, 3TC, ATV/r 30 0 none 99
8 A98G NNRTI ABC, 3TC, NVP TDF, 3TC, ATV/r 30 0 none 35
8 G190A NNRTI ABC, 3TC, NVP TDF, 3TC, ATV/r 60 0 66 99
9 K101H NNRTI ABC, 3TC, NVP TDF, 3TC, ATV/r 60 0 70 100
* for any drug in the TP1 regimen; ** to any drug in the TP2 regimen. Abbreviations: 3TC, lamivudine; ABC,
abacavir; ATV, atazanavir; AZT, zidovudine; D4T, stavudine; DRM, drug resistance mutation; EFV, efavirenz;
LPV, lopinavir; NGS, next generation sequencing; NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI,
nucleoside reverse transcriptase inhibitor; NVP, nevirapine; RTV, ritonavir.
Viruses 2023,15, 1416 11 of 17
Viruses 2023, 15, x FOR PEER REVIEW 13 of 19
Figure 3. Difference in DRM prevalence at TP2 among participants taking a drug with penalty score
in the short- and long-term studies (Exploratory Analysis 1). The Y-axis shows the estimated differ-
ence in TP2 DRM prevalence for participants taking at least one antiretroviral with a positive pen-
alty score versus not taking an antiretroviral with a positive penalty score at TP2. Differences are
presented for participants with TP1 DRM prevalence of 0, 15 or 80%. A: NRTI DRMs at short- and
long-term study. B: NNRTI DRMs at long-term study.
Figure 3.
Difference in DRM prevalence at TP2 among participants taking a drug with penalty
score in the short- and long-term studies (Exploratory Analysis 1). The Y-axis shows the estimated
difference in TP2 DRM prevalence for participants taking at least one antiretroviral with a positive
penalty score versus not taking an antiretroviral with a positive penalty score at TP2. Differences are
presented for participants with TP1 DRM prevalence of 0, 15 or 80%. (
A
) NRTI DRMs at short- and
long-term study (see details in Supplementary Table S3). (
B
) NNRTI DRMs at long-term study (see
details in Supplementary Table S4).
In the second exploratory analysis, participants with at least one minority drug resis-
tance variant with a positive penalty score for at least one drug in the current regimen in
the early TP (compared to participants without minority drug resistance variants) had an
elevated predicted resistance score for that drug in the later TP (Figure 4). In the short-term
group, all analyzed antiretrovirals had larger positive changes in the resistance score at TP3
Viruses 2023,15, 1416 12 of 17
in the presence of at least one minority resistance variant at TP1. The largest differences
were found for the two NNRTIs—EFV and NVP. In the long-term group, all but one an-
tiretroviral (ABC) had larger positive changes in the resistance score at TP3 in the presence
of at least one minority drug resistance variant at TP1. The antiretroviral for which minority
drug resistance variants had the largest impact on the resistance score was AZT.
Viruses 2023, 15, x FOR PEER REVIEW 14 of 19
Figure 4. Difference in predicted resistance score at TP2 among participants with a minor drug re-
sistance variant at TP1 with penalty score in the short- (left panel) and long-term (right panel) study
(Exploratory Analysis 2). The X-axis shows the estimated difference at TP2 in resistance score for
participants with at least one minority drug resistance variant at TP1 with positive penalty score
versus those without a minority drug resistance variant with a positive penalty score. Contrasts are
presented for each drug. The drugs are sorted by the median difference.
DRM profiles of the six participants who had genotypes available at all three TPs
demonstrated multiple paerns of DRM evolution, following known drug-specific re-
sistance pathways (Table 7). Evolving DRMs were detected in five of the six participants,
NRTI DRMs in three and NNRTI DRMs in four, all at >20% frequencies, thus detectable
by Sanger (highlighted in red in Table 7). Two of these participants had evolving DRMs to
both antiretroviral classes. Some evolving DRMs (NRTI Y115F and NNRTI Y181C and
H221Y) in three participants demonstrated long term sustainability evident from evolving
at the second TP and maintaining presence over years until the third TP, again with >20%
frequencies (highlighted by bold red in Table 7). Other evolving mutations were not sus-
tainable and after appearing at the second TP were not detected at the third TP (e.g., NRTI
L74V and NNRTI K103N in participant #2). Lastly, in three participants, minority drug
resistance variants NRTI-Y115F and NNRTI- Y181C and K103N (not detectable by Sanger)
evolved to established mutations within a few months, two of which were sustained for
many years (highlighted in green in Table 7).
Figure 4.
Difference in predicted resistance score at TP2 among participants with a minor drug
resistance variant at TP1 with penalty score in the short- (left panel) and long-term (right panel) study
(Exploratory Analysis 2). The X-axis shows the estimated difference at TP2 in resistance score for
participants with at least one minority drug resistance variant at TP1 with positive penalty score
versus those without a minority drug resistance variant with a positive penalty score. Contrasts are
presented for each drug. The drugs are sorted by the median difference.
DRM profiles of the six participants who had genotypes available at all three TPs
demonstrated multiple patterns of DRM evolution, following known drug-specific resis-
tance pathways (Table 7). Evolving DRMs were detected in five of the six participants,
NRTI DRMs in three and NNRTI DRMs in four, all at >20% frequencies, thus detectable
by Sanger (highlighted in red in Table 7). Two of these participants had evolving DRMs
to both antiretroviral classes. Some evolving DRMs (NRTI Y115F and NNRTI Y181C and
H221Y) in three participants demonstrated long term sustainability evident from evolving
at the second TP and maintaining presence over years until the third TP, again with >20%
frequencies (highlighted by bold red in Table 7). Other evolving mutations were not sus-
tainable and after appearing at the second TP were not detected at the third TP (e.g., NRTI
L74V and NNRTI K103N in participant #2). Lastly, in three participants, minority drug
resistance variants NRTI-Y115F and NNRTI- Y181C and K103N (not detectable by Sanger)
evolved to established mutations within a few months, two of which were sustained for
many years (highlighted in green in Table 7).
Viruses 2023,15, 1416 13 of 17
Table 7. DRM profiles of six participants with available genotypes at three TPs.
ID TP Time ART Regimen NRTI DRM * NNRTI DRM *
1
1 0 ABC, 3TC, NVP L74V_35 M184V_93
A98G_7 K101E_32 V179D_1_L_4
Y181C_10_F_5 Y188F_6 G190S_99
H221Y_7 Y318F_9
2 2.8 months ABC, 3TC, NVP L74V_99 M184V_95 V179L_4 Y181C_96_F_4 Y188F_4
G190S_99
3 7 years ABC, 3TC, NVP K70N_1 L74V_100 Y115F_99
M184V_96 K219Q_81
Y181C_99 Y188F_4 G190S_100
H221Y_99
2
1 0 ABC, 3TC, NVP M184I_98 K103N_9 Y181C_97_S_1 Y188F_1
Y318F_24
2 3 months ABC, 3TC, NVP L74V_56_I_13 V75A_2 Y115F_2
M184V_83_I_16 K219N_2
K101E_1 K103N_70 Y181C_98_S_1
H221Y_41 Y318F_25
3 7.6 years TDF, 3TC, ATV/r K219Q_88 Y181C_84_S_2 H221Y_87
3
1 0 ABC, 3TC, NVP M184V_99 Y181C_100
2 2.6 months ABC, 3TC, NVP M184V_87 Y181C_88 H221Y_87
3 7.2 years ABC, 3TC, EFV None E138K_1 Y181C_38 H221Y_39
4
1 0 ABC, 3TC, NVP L74V_82 Y115F_6 M184V_98 V179L_2 Y181C_97_S_2 H221Y_98
M230I_1
2 2.9 months ABC, 3TC, NVP L74V_97 Y115F_92 F116Y_3
M184_V_98 V179L_2 Y181C_97_S_1 H221Y_99
3 7.1 years TDF, 3TC, EFV L74I7_V_44 Y115F_50 M184V_86 K103N_2 Y181C_97 H221Y_95
5
1 0 D4T, 3TC, NVP V75S_2 M184V_59 T215F_43_I_11 K103N_57 Y181C_15 H221Y_12
M230L_48 Y318F_12
2 3.1 months D4T, 3TC, NVP V75S_3 M184V_51 L210W_1
T215F_48
A98G_15 K103N_47 M230L_56
Y318F_5
3 6.8 years TDF, 3TC, ATV/r V75S_1 M184V_95 T215F_93 K103N_75_S_19 Y181C_92
H221Y_88 M230L_5
6
1 0 D4T, 3TC, NVP K65R_99 V75I_99 F116Y_99
Q151M_99 M184I_99
K103N_95_S_5 Y181C_98 Y318F_17
2 2.8 months D4T, 3TC, NVP K65R_100 V75I_100 F116Y_99
Q151M_99 M184I_99 K103N_100 Y181C_98 Y318F_10
3 5.7 years ABC, 3TC, LPV/r K65R_100 V75I_100 F116Y_98
Q151M_99 M184I_99 K103N_100 Y181C_97_S_1
* Number after underscore indicates % NGS frequency of the drug resistant amino acid at the specified amino
acid position. Some positions have two drug resistance amino acids. Evolving mutations are highlighted in
red; mutations evolving at the second TP and also identified at the third TP above 20%, are highlighted in
bold red; minority resistance variants detected at 1–20% at an earlier TP and evolving to >20% at a later TP
are highlighted in green. Abbreviations: 3TC, lamivudine; ABC, abacavir; ATV, atazanavir; D4T, stavudine;
DRM, drug resistance mutation; EFV, efavirenz; LPV, lopinavir; NGS, next generation sequencing; NNRTI, non-
nucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; NVP, nevirapine; /r,
ritonavir; TP, timepoint.
4. Discussion
In a unique ART-experienced cohort of CALWH with diverse HIV-1 subtypes in Kenya,
longitudinally followed for up to ~8 years, we characterized drug resistance evolution and
estimated the potential impact of minority drug resistance variants detected by NGS on
clinical outcomes. Evolution of drug resistance was detected in almost every one of the
42 study participants, including evolution from minority to established resistance variants
in some, even in this cohort with an already extensive drug resistance. This evolution led
to a significant escalation of predicted drug resistance levels to both NRTIs and NNRTIs.
Longitudinal follow-up and comprehensive analysis enabled identification of distinct types
of DRM evolution and exploration of the impact of detecting minority drug resistance
variants early on, which highlighted the potential added value of such an analysis. The new
knowledge thus generated provides better and deeper understanding of drug resistance
evolution, which might impact clinical care and deserves further study.
Viruses 2023,15, 1416 14 of 17
The potential added value of this unique longitudinal analysis using NGS to detect
evolution of minority drug resistance variants is demonstrated in several ways in this
manuscript. First, in addition to the 60% of participants who had any type of DRM
evolution, evolution from minority to established drug resistance variants was detected
in 17%, suggesting that earlier consideration of minority DRMs might impact regimen
selection even in this cohort with already high levels of drug resistance. Second, the changes
in DRM frequencies that were detected by NGS were apparent in both the short and long-
term studied groups, and in those with, but also perhaps surprisingly, in those without
regimen changes, suggesting active replication and DRM selection in all circumstances,
that should perhaps be monitored. This was also seen in the small but distinctive group
of six participants with both short- and long-term follow-up opportunities, allowing the
observation of a few evolving minority resistance variants that were sustained for many
years. Third, exploratory analyses (limited by sample size) alluded to the possibility of
higher evolution of minority resistance variants to both classes with existing relevant
penalty scores, speculatively suggesting that the detection of minority resistance variants
may indeed be relevant, as opposed to ‘noise’. Lastly, analyses enabled detection of
evolution of DRMs despite no selective drug pressure, possibly due to three dimensional
allosteric and/or functional compensation and covariation of mutations (not explored
here), but also due to some still unknown mechanisms of drug resistance. Such exploration
emphasizes the potential of NGS as a critical tool to gain new knowledge and identify
avenues for further study of drug resistance dynamics. Overall, while recognizing the
limitations of these data as outlined below, they suggest that incorporating individual and
longitudinal NGS to monitor HIV drug resistance evolution in CALWH, and perhaps other
populations, may be beneficial to support clinical care, and warrant further studies in larger
cohorts and with more current antiretroviral medications.
A comprehensive analysis in this study demonstrated continued, complex, extensive
and diverse evolution of NRTI and NNRTI DRMs over time, even in a small sample size
of 42 participants with heterogenous ART experience and already pre-existing high drug
resistance. Participants experienced continuous evolution of drug resistance that was
evident through dynamic changes of numerous DRMs spanning the entire range of NGS
frequencies. Some DRM evolution was detected in 93% of participants, including 91% in
the short-term group with no regimen change. Evolving DRMs were identified in 60% of
participants when considering evolution from
≤
20% to >20%, and in 17% when considering
evolution from 1–20% to >20%. These findings highlight the need to closely monitor this
vulnerable population using regular VL and drug resistance testing, and also suggest that
early detection of DRMs at low NGS frequency might help improve care and facilitate
rational regimen design, even in resource-limited settings with fewer treatment options.
Though our main focus was on development of mutations, we also detected reverting
DRMs in 29% of participants (5% of all identified DRMs). Though little is known about
reversion of DRMs in CALWH, this phenomenon may occur due to weakening drug
selection pressure (e.g., sub-optimal adherence, treatment interruption or regimen change),
or fitness costs [
36
–
39
]; however, its clinical significance is uncertain. In fact, most DRMs
defined as reverting in this study had little or no effect on predicted resistance, likely due
to other co-occurring DRMs.
This study has several limitations. First, a small sample size limits analyses and renders
some only exploratory in nature, though the analyses do investigate resistance evolution in a
unique and vulnerable population. Second, the number of available follow-up timepoints is
limited to two or three, possibly over-simplifying viral evolution. Third, the study uses only
one (Stanford) resistance interpretation algorithm and also lacks functional testing, leading
to only a prediction of clinical relevance, which requires further study. Fourth, use of older,
mostly non-current ART regimens (though some with current specific medications) only
provides a proof of concept, but mandates further investigation with more current regimens,
different drug resistance barriers and resistance dynamics. Fifth, the impact of individual
DRM viral fitness, VL, treatment interruptions or non-adherence were not available or
Viruses 2023,15, 1416 15 of 17
considered. Lastly, NGS data were used without precise quantification of minority DRMs
at low frequencies, such as in the primer ID approach [
40
,
41
], possibly resulting in some
inaccurate mutation frequencies. Though considering relative comparisons (rather than
absolute values) of NGS frequencies of the same DRMs in the same participants could
alleviate this limitation, at least partially, the reported frequencies of <20% should be
interpreted cautiously.
5. Conclusions
In a small but unique cohort of Kenyan CALWH with pre-existing extensive HIV-1
drug resistance, we identified ongoing DRM evolution suggesting continued selection of
minority and established viral variants under drug selection pressure. NGS and longitudi-
nal follow-up of evolution of DRM frequencies can be informative for better understanding
of the mechanisms and dynamics of HIV drug resistance evolution, and could play an
important role in HIV monitoring and ART regimen selection. The clinical significance
of the identified types of drug resistance evolution and whether they differ according to
HIV-1 subtype remain to be evaluated in future studies.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/v15071416/s1, Details of the statistical analyses. Figure S1:
Concept of DRM Evolution; Figure S2: Dynamics of NRTI DRMs; Figure S3: Dynamics of NNRTI
DRMs. Table S1: Evolving NRTI DRMs; Table S2: Evolving NNRTI DRMs; Table S3: Changes in NRTI
DRM prevalence over time; Table S4: Changes in NNRTI DRM prevalence over time.
Author Contributions:
Conceptualization, V.N. and R.K.; funding acquisition, W.N., R.V. and R.K.;
methodology, V.N., A.K.D., M.H., A.M., E.J., M.O. and J.W.H.; project administration, J.A. and A.C.;
resources, V.N., W.N., R.V., A.K.D., M.H., A.M., J.A., A.C., F.S., C.A., E.J., M.O., J.W.H. and R.K.;
writing—original draft, V.N. and R.K.; writing—review and editing, V.N., A.K.D., J.W.H. and R.K. All
authors have read and agreed to the published version of the manuscript.
Funding:
National Institutes of Allergy and Infectious Diseases at the National Institutes of Health
[grant numbers R01 AI120792, K24 AI134359, and P30 AI042853].
Institutional Review Board Statement:
The study was approved by the Miriam Hospital and Mount
Sinai Human Subjects Institutional Review Boards in the United States and the AMPATH Institutional
Research Ethics Committee in Kenya.
Informed Consent Statement:
The informed consent was obtained from parents and/or caregivers
and assent for those ≥10 years old. A $5 transport support was provided to study participants.
Data Availability Statement:
Data collected for this study contains protected health information
(PHI) of a vulnerable population of Kenyan children and adolescents, and cannot be made publicly
available. Please contact Rami Kantor (rkantor@brown.edu) for data-related inquiries.
Acknowledgments:
The authors acknowledge and thank the children and adolescents who partici-
pated in this research as well as their caregivers, care providers and all current and past members of
our research teams.
Conflicts of Interest:
M.H. is currently Sr. Data Scientist at Amazon.com, Inc., but conducted this
research prior to starting that role. Other authors declared no conflict of interest.
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