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Opening remarks Dr. Joseph Murungu
In memory of
Joep Lange and Jacqueline van Tongeren
and their contribution to the field of HIV
Scientific Agenda
Managing HIV in resource-limited settings: Growing the momentum
Viral load monitoring in RLS: Challenges and lessons; A South African perspective
Dr. Francesca Conradie South Africa
Acquired drug resistance in RLS: The causes, patterns and implications
Dr. Andrew Kambugu Uganda
Second-line therapies in RLS: The choices and evidence
Prof. Nick Paton Singapore
Closing remarks Dr. Joseph Murungu
Zimbabwe
AbbVie Commitment
• This scientific exchange symposium is sponsored by AbbVie. The presentations and content have been developed by the presenters with a medical education agency specifically for this symposium.
• During the course of today’s symposium, the presenters may be providing information and data on some uses of products that have not been approved by relevant health agencies. The presenters will indicate when a use we are discussing is not approved. AbbVie reviewed the slides and in no way intends to recommend or imply that the products should be used for such unapproved uses.
Disclosures
Speakers have acted as an advisor, received speaker fees/meeting expenses, or received research grants/drug support from:
• Dr. Joseph Murungu: AbbVie
• Dr. Francesca Conradie: AbbVie, GSK, Janssen, Merck
• Dr. Andrew Kambugu: AbbVie, MSD
• Prof. Nick Paton: AbbVie, GSK, Janssen, Merck, Roche
Viral Load Monitoring in RLS: Challenges and Lessons; A South African Perspective Dr. Francesca Conradie
Southern African HIV Clinicians Society
What does the WHO have to say about it?
• Viral suppression refers to the aim of antiretroviral therapy (ART) to maintain viral load below the level of detection of available assays, generally less than 50 copies/mL
– The current WHO virologic criterion for treatment failure is 1000 copies/mL or more
• Viral load (VL) testing is now recommended as the preferred approach to monitoring ART success and diagnosing treatment failure, complementing clinical and immunological monitoring of people receiving ART
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
Recommended and desirable laboratory tests at HIV diagnosis and monitoring on ART
Phase of HIV management
Recommended Desirable (if feasible)
HIV diagnosis HIV serology, CD4+ cell count TB screening
HBV (HBsAg) serology HCV serology Cryptococcus antigen if CD4+ cell count ≤100 cells/mm3
Screening for sexually transmitted infections Assessment for major non-communicable chronic diseases and comorbidities
Follow-up before ART
CD4+ cell count (every 6–12 months)
ART initiation CD4+ cell count Hemoglobin test for AZT Pregnancy test Blood pressure measurement Urine dipsticks for glycosuria and estimated glomerular filtration rate (eGFR) and serum creatinine for TDF Alanine aminotransferase for NVP
Receiving ART CD4+ cell count (every 6 months) HIV viral load (at 6 months after initiating ART and every 12 months thereafter)
Urine dipstick for glycosuria and serum creatinine for TDF
Treatment failure
CD4+ cell count HIV viral load
HBV (HBsAg) serology (before switching ARV regimen if this testing was not done or if the result was negative at baseline)
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
Targeted viral load monitoring (suspected clinical or
immunological failure)
Routine viral load monitoring (early detection of virological failure)
Test viral load
Viral load >1000 copies/mL
Evaluate for adherence concerns
Repeat viral load testing after 3–6 months
Viral load >1000 copies/mL Viral load ≤1000 copies/mL
Switch to second-line therapy Maintain first-line therapy
Why do we need viral loads?
• Monitoring of response to treatment
• Decisions on when to change therapy
• Choice of therapy
• Infant diagnosis
• (Prognosis)
• (Measure of infectiousness)
The South African experience
• National ART program began in April 2004
• CD4+ cell count entry 200 or AIDS defining
• VL tested at treatment initiation and every 6 months
• Failure defined as two VL above 5000 copies/mL
The South African experience
• Guideline updates 2008
– CD4+ cell count threshold increased to 350 cells/mm3 for pregnant women and TB
– Initiation VL dropped
– VL done every six months
• Guideline updates 2010
– CD4+ cell count threshold increased to 350 cells/mm3 for all
– VL tested at 6 months, one year and every year thereafter
– Failure defined as two VL above 1000 copies/mL
Numbers on National ART program
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
1 2 3 4 5 6 7 8
Number on treatment
Years since start of program
Nu
mb
er
on
tre
atm
ent
• Based on current guidelines, global viral load need is expected to grow as countries scale up treatment
• Current CHAI country estimates suggest 16 million by 2020, depending on algorithm may reach 40 million
Quantitating needs: Global VL market need: Donor perspective
CHAI, 2012, Trevor Peter personal cummunication
# tests
Other
Zimbabwe
Zambia
Uganda
Thailand
Tanzania
Swaziland
South Africa
Rwanda
Nigeria
Namibia
Mozambique
Malawi
Lesotho
Kenya
India 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
2
4
6
8
10
12
14
16
18
Viral Load (VL) Response
Note: Always check hepatitis B before stopping TDF. If patient has chronic hepatitis B, stopping TDF may lead to a fatal hepatitis flare. If hepatitis B positive, TDF should be continued as a 4th drug in the second-line regimen
<400 copies/mL • VL monitoring according to duration of ART and routine adherence support
• Continue routine VL monitoring as it may be 12 monthly depending on how long patient is on treatment
400–1000 copies/mL • Assess and manage adherence carefully • Repeat VL in 6 months and manage accordingly
>1000 copies/mL • Adherence assessment and intense adherence support • Repeat VL in 2 months and check HBV status and Hb, if not
already done • If <1000 copies/mL, repeat in 6 months and then reassess • If >1000 copies/mL and adherence issues addressed, switch
to second-line therapy after checking HBV status and Hb
Site Malawi
(Hosseinipour et al. 2009)
South Africa Cape Town
(Orrell et al. 2009)
South Africa Johannesburg
(Wallis et al. 2010)
South Africa Durban
(Marconi et al. 2008)
South Africa CIPRA-SA
Sample size Clinical sites
94 2
110 (2002–2007) 1
226 2
115 (2005–2006) 2
812 (83 failures) 2
Switch criteria
CD4+ decrease >30% or WHO staging
Viral load >5000 RNA c/mL
Viral load >5000 or 1000 RNA c/mL
Viral load >1000 RNA c/mL
Viral load >1000 RNA c/mL
Frequency of monitoring
Irregular 6 monthly-VL & CD4+ cell count
6 monthly-VL & CD4+ cell count
6 monthly-VL & CD4+ cell count
3 monthly-VL & CD4+ cell count
Median time on first-line (months)
36.5 (8–127) Unknown Unknown 10.8 (6.7–18.6) 15 (3–33)
% with failure & resistance
95% 85% 83% 83.5% 82%
Subtype C 100% 98% 96.5% 97.4% 100% M184V 81% 78% 72% 64.3% 67.2% NNRTI K103N V106M
93% 28% 6%
86% 55% 31%
78% 38% 17%
Unknown 51% 19%
75% 50% 14%
TAMs >3 K65R
56% 19%
23% 9%
11% 4.5%
13% 2.6%
1.5% 3%
Clinical case 1
• 45-year-old male started on AZT, 3TC and EFV
– CD4+ cell count = 178 cells/mm3
– VL = 134,000 copies/mL
• Within 8 weeks undetectable VL
• At Week 96, VL was 6800 copies/mL
• Repeated in 4 weeks 8900 copies/mL
Clinical case 1
• Transmitted resistance
• Acquired resistance
Clinical case 2
• 34-year-old female
– CD4+ cell count = 345 cells/mm3
– VL = 10,000 copies/mL
• 8 week VL 1200 copies/mL
• 16 week VL 6800 copies/mL
• Repeated in 4 weeks 8900 copies/mL
Clinical case 2
• Transmitted resistance
• Acquired resistance
HIV-1 drug resistance in antiretroviral-naïve individuals in sub-Saharan Africa after roll-out of antiretroviral therapy: A multicentre observational study
• Cross-sectional analysis of ARV-naïve individuals in 2007–2009 in 11 regions in Kenya (2), Uganda (3), Nigeria, South Africa (3), Zambia (3)
• 2436 (94%) of 2590, 57% women; CD4+ median: 133 cells/mm3; >18 years
• Sample weighted drug prevalence of resistance was: 5.6%: ranged from 1.1% in Pretoria (SA) to 12.3% in Kampala (Uganda)
• Pooled prevalence for 3 sites in Uganda was 11.6% compared to 3.5% for all other sites
• 2.5% NRTI, 3.3% NNRTIs, 1.3% for PIs and 1.1% for dual NRTI and NNRTI
• Odds ratio for drug resistance-associated with each additional year since ART roll-out was 1.3 (95% CI: 1.13–1.68)
Hamers RL, et al. Lancet 2011;11:750–9
Interpretation: The higher prevalence of primary drug resistance in Uganda than in other Africa countries is probably related to the earlier start of ART roll-out in Uganda Resistance surveillance and prevention should be prioritized in settings where ART programs are scaled up
Lab challenges
• 3-tiered laboratory infrastructure exists with only tertiary facilities and, to some extent, secondary laboratories able to implement
• Challenges
– High sample volumes
– Transportation logistics from remote sites
– Costs
– Phlebotomy in children
–National skills shortage
– Sample throughput of technology platforms
Laboratory type Assays
Tertiary reference NAT strategies (Roche Amplicor [COBAS and Taqman] and bDNA, NucliSENS EasyQ); Abbott RealTime in-house real-time PCR options; flow-based options*
Secondary (medium-tier) May do NAT; Exavir load assay; flow-based options*
Primary care POC technologies* or sample preparation for referral
NAT, nucleic acid testing; PCR, polymerase chain reaction; POC, point of care *Not available for implementation n at time of program initiation (in research and development)
Assays considered for viral load testing and the appropriate laboratory tier at the time of implementation of an antiretroviral therapy program
Stevens WS. and Marshall TM. J Infect Dis 2010;201:S78–S84
© 2010 by the Infectious Diseases Society of America
Clinical challenges
• No VL done at initiation
• Adherence to the guidelines
• What to do with a VL between 50 and 1000 copies/mL?
• Emphasis in the program on CD4+ cell count
• Lack of urgency around detectable VLs including “myths”
Decisions on when to change therapy
First-line: Fixed dose combination with EFV
– Simplicity
– TB friendly
– Low but predictable barrier to resistance
Second-line: Appropriate NRTIs and a boosted PI
– Higher barrier to resistance
–Unpredictable barrier to resistance
– Twice a day dosage
Third-line: Combination of DRV/r ETR and RAL based on genotype test with appropriate NRTIs
1 3 2 5 4
“Low Genetic Barrier” for ARV resistance (3TC or FTC, NNRTIs)
Number of mutations
Re
lati
ve r
esi
stan
ce
1 3 2 5 4
Re
lati
ve r
esi
stan
ce
1 3 2 5 4
“High Genetic Barrier” for ARV resistance (TDF, AZT, d4T, boosted PI)
Number of mutations
Time to repeat VL
• From an NNRTI regimen: 2–3 months
• For a PI regimen: 6 months
Choice of therapy
• In most cases the starting VL does not make a difference
• Two exceptions to this
Rilpivirine
Rilpivirine
• Novel NNRTI
• Single daily dosage
• Co-formulated with TDF and FTC as FTC/RVP/TDF
• Should only be used when the VL is less than 100,000 copies/mL
• Randomized, double-blind phase III trials
*THRIVE only †Selected by investigator from ABC/3TC, TDF/FTC, ZDV/3TC
ECHO, THRIVE: Rilpivirine vs. EFV in treatment-naïve patients
Cohen C, et al. AIDS 2010. Abstract THLBB206
Rilpivirine 25 mg QD + TDF/FTC 300/200 mg QD
(n=346)
EFV 600 mg QD + TDF/FTC 300/200 mg QD
(n=344)
Stratification by BL HIV-1 RNA <100,000
vs. ≥100,000 copies/mL, NRTI use*
Week 96 final analysis
Week 48 primary analysis
Rilpivirine 25 mg QD + 2 NRTIs†
(n=340)
EFV 600 mg QD + 2 NRTIs†
(n=338)
ECHO (N=690)
THRIVE (N=678)
Treatment-naive, HIV-1 RNA ≥5000 copies/mL
no NNRTI RAMs, susceptible to NRTIs
ECHO, THRIVE: Rilpivirine vs. EFV in treatment-naïve patients
Cohen C, et al. AIDS 2010. Abstract THLBB206. Graphics used with permission
*P<0.0001 for noninferiority at -12% margin
Rilpivirine
EFV
HIV-1 RNA <50 copies/mL (ITT-TLOVR) at Wk 48
40
0
100
20
80 82.3 84.3
60
682 686 n =
ECHO THRIVE Pooled
Pat
ien
ts (
%)
82.8 82.9 85.6
81.7
338 340 344 346
-3.6 (-9.8 to +2.5)
>100,000 copies/mL
125/ 165
121/ 153
246/ 318
149/ 181
136/ 171
285/ 352
77 81 79 80 76 82
Pat
ien
ts (
%)
40
0
100
20
80
60
Pooled THRIVE ECHO
HIV-1 RNA <50 copies/mL at Wk 48 by BL VL
6.6 (1.6 to 11.5)
≤100,000 copies/mL
162/ 181
170/ 187
332/ 368
136/ 163
140/ 167
276/ 330
90 83
91 84
90 84
Pat
ien
ts (
%)
40
0
100
20
80
60
ECHO THRIVE Pooled
ECHO, THRIVE: Treatment failure, resistance, and adverse events
Cohen C, et al. AIDS 2010. Abstract THLBB206. Table used with permission.
Wk 48 outcome Rilpivirine
(n=686) Efavirenz (n=682)
VF with resistance data, n 62 28
No NNRTI or NRTI RAMs,% 29 43
1 Emergent NNRTI RAM,% 63 54
Most frequent NNRTI RAM E138K K103N
1 Emergent NRTI RAMs, % 68 32
Most frequent NRTI RAM M184I M184V
Adverse events and discontinuation
Resistance at virologic failure
Wk 48 outcome, % Rilpivirine (n=686)
Efavirenz (n=682)
P Value
DC for AE 3 8 0.0005
Most common AEs of interest, %
Any neurologic AE 17 38 <0.0001
Any psychiatric AE 15 23 0.0002
Any rash 3 14 <0.0001
EFV
686
6
0
3
9
Treatment failure in ECHO and THRIVE 15
12
4.8
n =
VF
9.0
682 686
6.7
AE
2.0
682
Pat
ien
ts (
%)
Rilpivirine
Triple nucleosides
• ABC, 3TC and AZT
• Or any other combination
• Should only be used when the VL is less than 100,000 copies/mL
Early infant diagnosis (EID)
• At ~6 weeks of age
• 270,000 HIV-exposed infants are born annually in SA
• Bottleneck was addressed during 2005 by the introduction of dried blood spot sampling (DBS)
• By 2006, approximately half of the public health facilities submitted HIV PCR tests
• 2012 virtually all 4000 public sector facilities submitted PCR tests
Early vertical transmission in infants ≤2 months of age in South Africa
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
23.2%
20.9%
17.0% 16.4%
13.6%
9.7%
6.4%
4.4%
2.8% 2.4%
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
HIV
DN
A P
CR
tes
ts, n
Po
sitivity, %
25
0
20
15
10
5
≤2 months: positive tests All ages: total tests ≤2 months: total tests ≤2 months: % positivity
Illustration of the rapid scale-up of PCR testing nationally with marked reduction in early vertical transmission of HIV over a decade
(KwaZulu-Natal data missing from 2003 to 2005)
Measure of infectiousness
Attia S, et al. AIDS 2009;23:1397–404
Summary of HIV transmission rates according to use of ART and plasma viral load
Attia S, et al. AIDS 2009;23:1397–404
All studies
0 0.01 0.2 0.1 0.5 1 2 5 10 20
≥50,000
10,000–49,999
3500–9999
400–3499
<400
All studies
≥400
<400
HIV-1 RNA copies/mL On ART
Not on ART
No. of studies
No. of events
No. of persons
years
Rate (95% CI)
10
5
6
8
6
6
5
1
2
456
48
47
18
7
1
5
0
0
9998
534
668
456
457
631
1098
52
291
5.64 (3.28–9.70)
9.03 (3.87–21.09)
8.12 (2.78–23.77)
4.17 (0.84–20.65)
2.06 (0.57–7.47)
0.16 (002–1.13)
0.46 (0.19–1.09)
0 (0–5.79)
0 (0–1.27)
Rate per 100 person-years
Acquired Drug Resistance: Causes, Patterns and Implications Dr. Andrew D. Kambugu, FRCP
Infectious Diseases Institute
Makerere University College of Health Sciences
Outline
• Epidemiology of acquired drug resistance (ADR) in Africa (First-line antiretroviral therapy [ART])
– East Africa
– South Africa
• Consequences of ADR in first-line ART
– Treatment failure
– Transmitted drug resistance (TDR)
• Drivers/associations of ADR
– Patient factors
– Programmatic factors
• Strategies to minimize ADR in resource-limited settings (RLS)
Definitions
• Acquired Drug Resistance (ADR): A mechanism of HIV drug resistance (HDR) driven by drug selection pressure occurring in patients on ART
• Transmitted Drug Resistance (TDR): A mechanism of HDR resulting from infection by a resistant strain
• HIV Drug Resistance Mutations (DRMs)/Resistance-Associated Mutations (RAMs): Changes in the HIV genome that result in reduced susceptibility to ART
Common HDR mutation sites
Nucleosides (NRTIs) Non-nucleosides (NNRTIs)
The epidemiology of ADR in Africa
• ADR rates lower in African ART cohorts compared with the West
• ADR rates increase with duration on ART
– ADR emergence mirrors the peculiar characteristics of HIV (high mutation rates driven by high replication/ replication errors)
ADR extract of World Health Organization (WHO) HDR report
• 9.4% of patients on ART experience treatment failure at 12 months
• 60% of these are associated with HDR mutations
• 5.1% of patients on ART for 12 months had ADR
WHO HIV Drug Resistance Report 2012. Available at: http://www.who.int/hiv/pub/drugresistance/report2012/en/
• 40 surveys: 2006–2010 • 12 countries (Africa & SE Asia)
• 29 survey subsets with 12 ART follow-up data
Geographical distribution of 12 countries reporting results from WHO surveys of acquired HIV drug resistance, 2006–2010
Country reporting results from WHO surveys of acquired drug resistance, 2006–2010 No data available or not participating in the surveys
Not applicable
WHO Report ADR extract: Prevalence of RAMs at 12 months of ART
WHO HIV Drug Resistance Report 2012. Available at: http://www.who.int/hiv/pub/drugresistance/report2012/en/
Mutations were defined using the 2009 WHO surveillance drug resistance mutations list
% o
f en
dp
oin
t ge
no
typ
es 80%
70%
60%
50%
40%
30%
20%
10%
0%
Endpoint characteristics at 12 months in 3 sentinel ART cohorts: Uganda
Characteristics Masaka n (%) Mbale n (%) Nsambya n (%) Total N (%)
Status at endpoint
Dead 8 (5.7) 7 (4.9) 5 (3.5) 20 (4.7)
Lost to follow-up 23 (16.3) 23 (16.1) 20 (14.2) 66 (15.3)
Still on first-line 103 (73.0) 105 (73.4) 105 (74.5) 313 (73.7)
Stop 0 (0) 0 (0.0) 1 (0.7) 1 (0.2)
Switch 2 (1.4) 2 (1.4) 4 (2.8) 8 (1.9)
Transfer – out 5 (3.6) 6 (4.2) 6 (4.3) 17 (4.0)
Drug pick-up
≥90% on-time 35 (27.3) 18 (13.8) 3 (2.3) 56 (14.4)
RNA viral load (copies/mL)
<1000 83 (79.0) 95 (88.2) 94 (86.2) 272 (84.7)
≥1000 22 (21.0) 12 (11.2) 15 (13.8) 49 (15.3)
Potential HIV drug resistance 29 (22.6) 26 (20.0) 26 (20.0) 81 (20.9)
HIV drug resistance 16 (12.5) 9 (6.9) 10 (7.7) 35 (9.0)
HIV DRMs at endpoint
NRTI only 0 (0) 3 (2.3) 1 (0.8) 4 (1.0)
NNRTI only 1 (0.8) 1 (0.8) 1 (0.8) 3 (0.8)
Both NRTI & NNRTI 15 (11.7) 5 (3.8) 8 (6.2) 28 (7.2) Kapaata et al: Manuscript in preparation
HDR mutations at endpoints in 3 cohorts
Kapaata et al: Manuscript in preparation
2 3
17
7
1
27
2 5
13
4 5 1
6 3
21
0
10
20
30
40
50
Freq
ue
ncy
(%
)
NRTI NNRTI Mutations
ART resistance among 38 individuals with ART failure in Rakai
n (%) of individuals Baseline (n=31) First-line failure (n=36) Second-line failure (n=6) NRTI mutations: Any TAMs 1 (3%) 8 (22%) 3 (50%)
1 TAM 1 (3%) 5 (14%) 2 (33%) 2 TAMs 0 (0%) 3 (8%) 0 (0%) 3+ TAMs 0 (0%) 0 (0%) 1 (17%) 41L 0 (0%) 2 (6%) 1 (17%) 65R 0 (0%) 2 (6%) 0 (0%) 67N 0 (0%) 1 (3%) 1 (17%) 70R 0 (0%) 2 (6%) 0 (0%) 184I/V 1 (3%) 28 (78%) 5 (83%) 210W 0 (0%) 1 (3%) 1 (17%) 215F/Y 1 (3%) 5 (14%) 2 (33%)
NNRTI mutations: Any NNRTI mutation 4 (13%) 31 (86%) 5 (83%) 103N 1 (3%) 9 (25%) 2 (33%) 108I 0 (0%) 3 (8%) 1 (17%) 181C/I 3 (10%) 14 (39%) 1 (17%) 188L/H 0 (0%) 2 (6%) 0 (0%) 190A/S 1 (3%) 10 (28%) 2 (33%) 225H 0 (0%) 3 (8%) 3 (50%)
PI mutations: Any PI mutation 2 (6%) 2 (6%) 0 (0%) 46L 1 (3%) 1 (3%) 0 (0%) 50V 1 (3%) 1 (3%) 0 (0%)
Reynolds SJ, et al. AIDS Res Hum Retroviruses 2012;28:1739–44 TAMs, thymidine analogue mutations
Cross-sectional study on virologic failure and acquired ARV resistance (on ART >6/12) in PEPFAR-supported clinics in Uganda and Nigeria
• Uganda 7% (23/325) – Viral load (VL)
• 2012–1,833,084 – No drug resistance-associated
mutations (DRAMs)
• 14.8% – TAMs
• <1 year: 12% • >1 year: 50%
– 3TC resistance (M184V)
• All
– NNRTI mutations
• All patients with DRAMs – Subtype
• A 43.6%, D 38%, C 14.3%
• Nigeria 13.8% (45/325) – VL
• 1120–405,000 – No DRAMs
• 14.3% – TAMs
• 60% – 3TC resistance (M184V)
• 90% – NNRTI mutations
• All patients with DRAMs except one – Subtype
• CRF02_AG 62.8%, G 34.2%, A1 2.8%
Crawford KW, et al. AIDS Res Hum Retroviruses 2014;30:796–9
PEPFAR: President’s emergency plan for AIDS relief
ADR epidemiology: Southern Africa
Manasa J, et al. Plos One 2013;8:e72152
90 80
70
60
50
40
30
20
10
0
Freq
uen
cy (
%)
100
80
70
60
50
40
30
20
10
0
90
ADR studies in South Africa: A summary
Manasa J, et al, Plos One 2013;8:e72152
Dates Criteria N ART duration (months)
≥1 DRM NNRTI M184V TAM TAM ≥3 K65R Q151M %
Limpopo (rural clinic) - 1xVL>1000 21 9.0 90.5 85.8 52.4 0.0 0.0 0.0 0.0 Durban (2 urban hospitals)
Jun 05 1xVL>1000 115 10.8 83.5 78.3 64.3 32.2 13.0 2.6 0.9
Cape Town (8 urban clinics)
Jul 02 1xVL>1000 110 8.9 88.2 88.2 78.2 22.7 NR 9.1 0.0
Johannesburg (urban workplace clinic)
Aug 02 1xVL>1000 68 - 66.2 61.8 36.8 5.9 NR 0.0 0.0
Johannesburg (urban hospitals)
- 2xVL>1000 or 2xVL>5000
226 - 83.0 77.9 72.1 31.0 12.0 3.5 2.2
Soweto (urban hospital)
2008 ART>12M & VL>400
94 - 80.8 80.8 61.7 16.0 NR 1.1 0.0
Johannesburg (urban hospital)
Sept 06 2xVL>5000 43 22.0 88.4 86.1 74.4 53.5 16.3 7.0 2.3
Western Cape (urban hospital & community health centre)
Oct 07 1xVL>400 167 13.5 83.0 82.0 60.5 12.0 2.4 4.2 0.0
Johannesburg & Cape Town (urban clinical trial)
- 2xVL>1000 83 8.5 73.0 71.0 57.0 1.0 NR 3.0 0.0
Soweto (urban hospital) 2008 ART>12M & VL>400
38 45 81.6 81.6 65.8 21.0 10.5 2.6 0.0
Durban (urban hospital) - 1xVL>5000 43 29 95.0 95.0 87.0 55.0 NR NR NR Hlabisa (rural clinics) Dec 10 1xVL>1000 222 42 86.0 83.0 78.0 40.0 18.0 6.0 1.0
Summary of acquired drug resistance studies in adults treated with first-line ARV therapy in South Africa
Resistance in children failing first-line ART at the Joint Clinical Research Centre
• Of 109 genotypic resistance profiles analysed, the commonest
–NNRTI RAMs were: • K103N: 59 (54%)
• Y181C: 36 (27%)
• G190A: 26 (24%)
–NRTI RAMs were: • M184V: 89 (81%)
• T215Y: 36 (24%)
• M41L: 23 (21%)
• K70R: 22 (20%)
• D67N: 15 (14%)
• K219Q/E: 14 (13%)
• L210W: (9%)
Sebunya R, et al. AIDS Res Ther 2013;10:25
NNRTI-based regimen (n=73)
PI-based regimen (n=16)
NNRTI mutations N % N %
Any NNRTI DRM 60 82.2 4 25
L100I 5 6.9 0 0
K101EP 6 8.2 0 0
K103NRS 46 63.0 2 12.5
V106M 23 31.5 2 12.5
V108I 7 9.6 1 6.3
Y181C 2 2.7 0 0
Y188HCL 7 9.6 1 6.3
G190AS 9 12.3 1 6.3
P225H 14 19.2 0 0
M230L 3 4.1 0 0
NNRTI-based regimen (n=73)
PI-based regimen (n=16)
NRTI mutations N % N % Any mutation 63 86.3 10 62.5 M41L 7 9.6 0 0 K65NR 4 5.5 1 6.3 D67NG 8 11.0 0 0 K70ER 7 9.6 0 0 L74VI 4 5.5 1 6.3 Y115F 3 4.1 2 12.5 M184VI 60 82.2 10 62.5 L210W 1 1.4 0 0 T215FYI 9 12.3 0 0 K219QREN 5 6.9 0 0 Any TAMs 22 30.1 0 0 1 TAM 11 15.1 0 0 2 TAMs 7 9.6 0 0 ≥3 TAMs 4 5.5 0 0
ADR mutations pediatrics cohort: South Africa
Pillay S, et al. AIDS Res Ther 2014;11:3
Frequency of major drug resistance mutations and resistance complexes associated with PIs, NRTIs and NNRTIs of the 89 genotyped patients
Drivers of ADR: Patient factors
• Suboptimal drug adherence leads to resistance
– Suboptimal adherences associated with emergence of ADR Odds Ratio of 4.9 in Namibia
Decreased risk
NNRTI resistance
80–99% adherence
0–79% adherence
0.1
Increased risk
10 1
PI resistance
80–99% adherence
0–79% adherence NRTI resistance
80–99% adherence
0–79% adherence Any resistance
80–99% adherence
0–79% adherence
NNRTI strategy
Decreased risk
0.1
Increased risk
10 1
PI strategy
Risk of virologic failure with resistance 100
60
80
40
20
Pro
po
rtio
n w
ith
re
sist
ance
at
failu
re
0 0–79% 80–99% 100%
Cumulative adherence
NNRTI resistance on NNRTI
PI resistance on non-boosted PI
PI resistance on boosted PI
60
80
40
20
Pro
po
rtio
n w
ith
re
sist
ance
at
failu
re
100
0 0–79% 80–99% 100%
Cumulative adherence
NRTI resistance on NNRTI
NRTI resistance on non-boosted PI
NRTI resistance on boosted PI
Gardner EM, et al. AIDS 2009;23:1035–46 Hong SY, et al. J Acquir Immune Defic Syndr 2015;68:463–71
Drivers of ADR: Programmatic factors
• Drug stock-outs leading to unstructured treatment interruption
• 10–28% of treatment
discontinuation due to stock-outs • Stock-outs increased the odds of
interruptions in care or death by 2.8
Drivers of ADR: Programmatic factors
Gupta RK, et al. Lancet Infect Dis 2009;9:409–17
ART monitoring strategy and the emergence of resistance
Gupta et al, Lancet Infect Dis 2009;9: 409-17
Any thymidine analogue mutations NNRTI clinical trials
Cohort (frequent monitoring) Cohort (infrequent or no monitoring)
M184V/I mutation NNRTI clinical trials
Cohort (frequent monitoring) Cohort (infrequent or no monitoring)
Major NNRTI NNRTI clinical trials
Cohort (frequent monitoring) Cohort (infrequent or no monitoring)
K65R mutation NNRTI clinical trials
Cohort (frequent monitoring) Cohort (infrequent or no monitoring)
0% 1% 2% 3% 4% 5% 6% 7% 8%
% of all patients starting HAART with resistance at 48 weeks (95% CI)
Consequences of ADR: 1
• The emergence of DRMs is associated with treatment failure
• Virologic failure: Immunologic failure: Clinical Failure
• DRMs acquired cumulatively
– Specific patterns of emergence
0 5 10 15 20 25 30 35
0.00
0.02
0.04
0.06
0.08
0.10
Time since virologic failure (months)
Pro
bab
ility
of
dea
th
Immediate switch Switch 6th month Switch 12th month Switch 24th month No switch
0 5 10 15 20 25 30 35
0.00
0.02
0.04
0.06
0.08
0.10 All patients with virologic failure: Immediate switch All patients with virologic failure: No switch Patients without immunologic failure: immediate switch Patients without immunologic failure: No switch
Delayed switch of antiretroviral therapy after virologic failure associated with elevated mortality among HIV-infected adults in Africa
Petersen ML, et al. AIDS 2014;28:2097–107
Counterfactual mortality curves following confirmed virologic failure on first-line NNRTI-based ART
First-line ART failure associated with poor neurocognitive performance which improves on second-line: The EARNEST study
Kambugu A, et al. CROI 2015: Abstract 57
Global GEE P=0.35 PI/RAL vs. PI/NRTI GEE P=0.20 Pl-mono vs. PI/NRTI GEE P=0.42
PI/NRTI PI/RAL PI-mono
0
-3
48 96
-2.5
-2
-1.5
Weeks from randomisation
Ove
rall
z-sc
ore
Initiating second-line ART may be associated with IRIS events
• 40 year old male
• Failing first-line with CD4+ count of 46 cells/mm3
• Started on second-line ART in study
• Week 12 presented with headache and left sided weakness
Consequences of ADR: 2
Gupta RK, et al.
WEST & CENTRAL AFRICA
EAST AFRICA
LATIN AMERICA & CARIBBEAN
SOUTHERN AFRICA
Prevalence of TDR (NNRTI) with years since roll out of public program in Africa
0 2 4 6 8
10 12 14 16 18 20
2 4 6 8 0
0
5
10
15
20
25
30
35
2 4 6 8 0 10 12 14 16
% o
f p
arti
cip
ants
wit
h d
rug
resi
stan
ce
Years since rollout
0 2 4 6 8
10 12 14 16 18 20
2 4 6 0
0 2 4 6 8
10 12 14 16 18 20
2 4 6 8 0 10
Intervention component
Number of studies
% with positive results for ≥1
outcome measure
% with positive results for ≥1 positive effect each for a biological and a subjective
or objective adherence outcome
CBT 60 67 20
Education 28 79 21
Treatment supporter 26 62 19
DOT 20 85 30
ARD 20 75 25
Structural 10 70 10
Counselling 8 88 63
Nutrition-support 7 71 43
PRD 5 60 0
Financial incentives 5 60 0
Drug use treatment 5 80 40
Depression treatment 1 0 0
ARD, active reminder device; CBT, cognitive-behavioural therapy; DOT, directly observe therapy; PRD, passive reminder device
Strategies to minimize ADR
• Optimizing adherence: Systematic review
Chaiyachati KH, et al. AIDS 2014;28:S187–S204
Summary of effects of adherence-enhancing interventions
Mobile phone text messages improve ART adherence
Horvath T, et al. Cochrane Database Syst Rev 2012;3:CD009756
0.01 0. 1 1 10 100 Weekly messages Daily messages
ART adherence at 48 weeks: Weekly vs. daily messages (overall)
ART adherence at 48 weeks: Short vs. long messages (overall)
2 Mobile phone text messages (intervals and durations), outcome: 2.2
2 Mobile phone text messages (intervals and durations), outcome: 2.3
Risk ratio (non-event)
0.01 0. 1 1 10 100 Short messages Long messages
Risk ratio (non-event)
Strategies to minimize ADR
• Ensuring consistent ART drug supplies:
– Coordinated (donor/country) forecasting
– Secured multi-year funding commitments
– Multi-year procurement arrangements
– Prompt payment by national programs
– Access accurate consumption information
Lalvani P, et al. Center for Global Development. Available at: http://www.cgdev.org/doc/HIVAIDSMonitor/ARV_Background-FINAL1.pdf
Medicines supply chain for Kenya
Conclusions
• 5.4% of patients have ADR mutations within 12 months of first-line ART
• Common mutations include: M184V, K103N, Y181C and K65R (if TDF is used in first-line ART)
• Above mutations are observed across the different regions of Africa and in both adult and pediatric populations
• ADR is driven by both patient and programmatic factors
• Emergence of ADR to first-line leads to poor patient outcomes in the absence of timely switch to second-line
• TDR is rising with increasing ART experience, especially in East Africa
• Optimization of adherence and public ART supply chain is needed to prevent ADR with first-line ART
Acknowledgements
• Joint Clinical Research Centre (JCRC)
– F. Ssali
– C. Kityo
– V. Musiime
• Uganda Virus Research Institute (UVRI)
– P. Kaleebu
– C. Waters
– A. Kapaata
• Francois Venter (WRHI, SA)
Second-line treatment options in RLS: The choices and evidence Prof. Nicholas Paton, MD FRCP
Professor of Medicine
National University of Singapore
Second-line treatment options in resource-limited settings (RLS)
• World Health Organization (WHO) recommendations
• Second-line randomized controlled trials (RCTs)
• Summary of evidence-based choices
• Generalizing beyond the evidence of RCTs
• Conclusions
Second-line treatment options in RLS
• WHO recommendations
• Second-line RCTs
• Summary of evidence-based choices
• Generalizing beyond the evidence of RCTs
• Conclusions
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
Second-line treatment options in RLS
• WHO recommendations
• Second-line RCTs
• Summary of evidence-based choices
• Generalizing beyond the evidence of RCTs
• Conclusions
Second-line RCTs
N (total) PI/RAL
vs. PI/NRTIs
PI-mono vs.
PI/NRTIs
Which PI and
NRTIs?
EARNEST 1277 √ √
SECOND-LINE 541 √
SELECT 600 (max) √
STAR 200 √
2LADY 454 √
A pragmatic randomized controlled strategy trial of three second-line treatment options for use in public health
rollout program settings:
The Europe-Africa Research Network for Evaluation of Second-line Therapy (EARNEST) Trial
NRTI
NRTI
The EARNEST question
“moderate quality evidence” for second-line regimen
Standardized first-line Standardized second-line ≈ 3% fail/year
NNRTI NRTI
NRTI PI ?
The EARNEST question
PI RAL
NRTI
NRTI
Standardized first-line Standardized second-line
NNRTI NRTI
NRTI PI ?
≈ 3% fail/year
The EARNEST question
PI RAL
NRTI
NRTI
Standardized first-line Standardized second-line
NNRTI NRTI
NRTI PI ?
PI
≈ 3% fail/year
EARNEST aims
• EARNEST hypotheses: 1) PI/r + RAL superior efficacy (↓ toxicity? ↑cost)
2) PI/r mono. non-inferior efficacy (↓toxicity ↓complexity ↓cost )
• EARNEST aims:
– Compare these 3 options for second-line therapy
– A pragmatic trial that replicates typical public health approach settings (i.e. without all the monitoring)
HIV-positive adolescents / adults (n=1200) First-line NNRTI-based regimen >12 months;
Failure by WHO (2010) clinical, CD4+ cell count (VL-confirmed) or VL criteria
RANDOMIZE
PI + 2–3 NRTIs (NRTIs according to
local standard of care)
PI + RAL (12 week induction)
PI (monotherapy)
FOLLOW-UP FOR 144 WEEKS
Primary outcome at Week 96: Good HIV disease control – defined as all of: • Alive and no new WHO4 events from 0–96 weeks AND • CD4+ cell count >250 cells/mm3 at 96 weeks AND • VL <10,000 c/mL OR >10,000 c/mL without PI resistance mutations at 96 weeks
PI + RAL
Trial design (1)
Paton NI, et al. N Engl J Med 2014;371:234–47
Trial design (2)
• Visits
– Weeks 0–24: 4-weekly
– Weeks 24–48: 6-weekly
– Weeks 48–144: 8-weekly
• Adherence
– Assessed each visit by structured questions
– Intensive counseling at baseline and follow-up
Paton NI, et al. N Engl J Med 2014;371:234–47
Trial design (3)
• Monitoring
– Clinical: FBC, Cr, ALT at Week 12 and annually; local lab; open
– CD4+ cell count: every 12–16 weeks; local lab; open
– VL: annual visits; batched analysis in central lab (JCRC Kampala, Abbott m2000rt assay); blinded with DMC review
– Resistance: annual visits (all VL >1000 c/mL); batched analysis in central lab (Janssen Diagnostics); blinded with DMC review
Paton NI, et al. N Engl J Med 2014;371:234–47
Sites and recruitment
April 2010–April 2011: 1277 patients randomized
Paton NI, et al. N Engl J Med 2014;371:234–47
Sites Initial Added
Uganda 6 3
Zimbabwe 1
Malawi 1 1
Kenya 1
Zambia 1
Baseline characteristics (at randomization/switch to second-line)
PI/NRTI PI/RAL PI-mono Total
Randomized 426 433 418 1277
Female 264 (62%) 263 (61%) 215 (51%) 742 (58%)
Age (years) 37 (31–43) 37 (30–43) 38 (32–44) 37 (31–44)
Years since started ART 4.0
(2.8–5.4) 4.0
(2.9–5.5) 3.9
(2.7–5.4) 4.0
(2.8–5.4)
CD4+ (cells/mm3) 72 (29–143) 70 (27–142) 70 (33–149) 71 (30–146)
Pre-ART CD4+ 62 (23–144) 63 (23–135) 63 (22–152) 62 (23–145)
VL (c/mL) 67,515 (23,065–175,800)
74,500 (25,004–205,000)
70,874 (21,584–210,000)
69,782 (23,183–194,690)
VL ≥100,000 c/mL 168 (40%) 181 (41%) 181 (43%) 530 (42%)
Note: n (%) or median (interquartile range [IQR])
Paton NI, et al. N Engl J Med 2014;371:234–47
Initial EARNEST NRTIs
PI/NRTI PI/RAL PI-mono Total
Randomized 426 433 418 1277
NRTIs
TDF + 3TC/FTC (+ ZDV*) 336 (79%)
ABC + ddI/3TC 67 (16%)
ZDV + ddI/3TC 20 (8%)
Other 3 (<1%)
PI
LPV 426 (100%) 433 (100%) 418 (100%) 1277 (100%)
*Malawi National Guidelines suggested 3 NRTIs This changed to 2 in August 2011
Adherence to ART and follow-up
PI/NRTI PI/RAL PI-mono Total
Randomized 426 433 418 1277
Regimen compatible with strategy (% time)
99.5% 97.1% 97.4% 98.0%
Visits with complete ART adherence*
87% 89% 88% 88%
Protocol-mandated visits attended¶ 98% 98% 98% 98%
LTFU / withdrawn 4 (0.9%) 7 (1.6%) 7 (1.7%) 18 (1.3%)
*Complete adherence defined as report of no pills missed in the last month ¶19,448 mandated protocol visits
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60%
Note: using multiple imputation for missing CD4+ cell count (10%), VL (10%) and resistance (11% with VL >1000 c/mL) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4>250
VL<10,000 or no PIresistant mutations
%
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60% PI/RAL: 64%
• Risk diff (95% CI): PI/RAL – PI/NRTI: +4.2% (-2.4%, +10.8%; P=0.21)
Note: using multiple imputation for missing CD4+ cell count (10%), VL (10%) and resistance (11% with VL >1000 c/mL) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4>250
VL<10,000 or no PIresistant mutations
%
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60% PI/RAL: 64% PI-mono: 56%
• Risk diff (95% CI): PI/RAL – PI/NRTI: +4.2% (-2.4%, +10.8%; P=0.21)
• Risk diff (95% CI): PI-mono – PI/NRTI: -4.1% (-10.8%, +2.6%; P=0.23)
Note: using multiple imputation for missing CD4+ cell count (10%), VL (10%) and resistance (11% with VL >1000 c/mL) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4>250
VL<10,000 or no PIresistant mutations
%
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60% PI/RAL: 64% PI-mono: 56%
• Risk diff (95% CI): PI/RAL – PI/NRTI: +4.2% (-2.4%, +10.8%; P=0.21)
• Risk diff (95% CI): PI-mono – PI/NRTI: -4.1% (-10.8%, +2.6%; P=0.23)
Note: using multiple imputation for missing CD4+ cell count (10%), VL (10%) and resistance (11% with VL >1000 c/mL) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4>250
VL<10,000 or no PIresistant mutations
%
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60% PI/RAL: 64% PI-mono: 56%
• Risk diff (95% CI): PI/RAL – PI/NRTI: +4.2% (-2.4%, +10.8%; P=0.21)
• Risk diff (95% CI): PI-mono – PI/NRTI: -4.1% (-10.8%, +2.6%; P=0.23)
Note: using multiple imputation for missing CD4+ cell count (10%), VL (10%) and resistance (11% with VL >1000 c/mL) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4+ >250
VL<10,000 or no PIresistant mutations
%
Primary endpoint at 96 weeks
• Good disease control: PI/NRTI: 60% PI/RAL: 64% PI-mono: 56%
• Risk diff (95% CI): PI/RAL – PI/NRTI: +4.2% (-2.4%, +10.8%; P=0.21)
• Risk diff (95% CI): PI-mono – PI/NRTI: -4.1% (-10.8%, +2.6%; P=0.23)
Note: using multiple imputation for missing CD4 (10%), VL (10%) and resistance (11% with VL >1000 c/ml ) at Week 96
0%
20%
40%
60%
80%
100%
PI/NRTI PI/RAL PI-mono
%
Good disease control
Alive & no new WHO4
CD4+ >250
VL <10,000 or no PIresistant mutations
P<0.0001
%
VL suppression at 96 weeks
91% 88% 86%
74%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<10,000 c/mL <1000 c/mL <400 c/mL <50 c/mL
% s
up
pre
sse
d
PI/NRTI PI/RAL PImono+
VL suppression at 96 weeks
91% 88% 86%
74%
93% 87% 86%
73%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<10,000 c/mL <1000 c/mL <400 c/mL <50 c/mL
% s
up
pre
sse
d
PI/NRTI PI/RAL PImono+
PI/RAL vs. PI/NRTI P=0.36 P=0.87 P=0.97 P=0.88
VL suppression at 96 weeks
91% 88% 86%
74%
93% 87% 86%
73%
83%
67% 61%
44%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<10,000 c/mL <1000 c/mL <400 c/mL <50 c/mL
% s
up
pre
sse
d
PI/NRTI PI/RAL PI-mono+
PI/RAL vs. PI/NRTI P=0.36 P=0.87 P=0.97 P=0.88 PI-mono+ vs. PI/NRTI P=0.002 P<0.0001 P<0.0001 P<0.0001
Resistance at 96 weeks (predicted in whole population)
4
0 0
2 2
1
18
0
2
4
6
8
10
12
14
16
18
20
PI/NRTIs PI/RAL PI-mono
% o
f ra
nd
om
ize
d p
atie
nts
wit
h
inte
rme
dia
te/h
igh
leve
l re
sist
ance
TDF/ZDV/ABC/ddI RAL LPV
Note: assuming susceptible if VL<1000 c/mL at week 96; and using inverse probability weighting for VL>1000 c/mL with missing genotype at week 96 based on those with observed genotypes
*One patient in PI/RAL with intermediate/high level resistance to TDF moved to 3TC TDF LPV/r at week 4 due to rash
X X *
Mean CD4+ cell count through 96 weeks
PI/RAL vs. PI/NRTI Week 96 P=0.05 Global P=0.05 PI-mono vs. PI/NRTI Week 96 P=0.99 Global P=0.19
100
200
300
400
Me
an a
bso
lute
CD
4+
cell
cou
nt
(95
% C
I)
0 12 24 36 48 64 80 96
Weeks from randomization (switch to second-line)
PI-mono PI/NRTI PI/RAL
HR (PI/RAL: PI/NRTI) = 1.08 (0.81, 1.43) HR (PI-mono: PI/NRTI) = 1.09 (0.82, 1.45)
Global P=0.54
Grade 3/4 adverse events
PI/NRTI PI/RAL PI-mono Total
Total participants 426 433 418 1277
Gr3/4 AEs (participants) 94 (22%) 100 (23%) 100 (24%) 294 (23%)
Rate per 100 PY 14.4 15.1 13.8 14.5
Pro
bab
ility
of
rem
ain
ing
gra
de
3/4
ad
vers
e e
ven
t-fr
ee
0.00
0.25
0.50
0.75
1.00
0 24 48 72 96 Weeks from randomization
PI-mono PI/NRTI PI/RAL
Mean eGFR through 96 weeks
eGFR, estimated glomerular filtration rate
PI/RAL vs. PI/NRTI Week 96 P=0.02 Global P=0.03 PI-mono vs. PI/NRTI Week 96 P=0.06 Global P=0.14
-15
-10
-5
0
Me
an c
han
ge e
GFR
mL/
min
/1.7
3m
2
95
% C
I)
Weeks from randomization
PI-mono PI/NRTI PI/RAL
0 12 48 96
EARNEST conclusions (1)
PI/NRTI
• Excellent clinical outcome: – 90% WHO4 event-free survival
– 86% VL suppression <400 c/mL at 96 weeks, even in advanced first-line failure
• Well-tolerated and safe
• Given with laboratory monitoring approach widely attainable in this setting: – Modest safety monitoring
– Clinical and CD4+ monitoring
– No real-time VL monitoring
– No resistance testing - NRTIs selected by clinical algorithm
• PI/NRTI merits its place as standardized regimen in second-line therapy in public health approach
EARNEST conclusions (2)
• PI/RAL was not superior to PI/NRTI (“good disease control” and VL suppression)
• With cost differential, PI/RAL not suited for a standardized second-line regimen for large-scale use in WHO public health approach
• But PI/RAL non-inferior across range of outcomes and safe/ well-tolerated
• May represent an alternative regimen for some patients in resource-rich settings where individualized therapy is possible
EARNEST conclusions (3)
PI-mono
• Inferior to PI/NRTI: Lower VL suppression, more resistance
• Unsuitable for public health approach
EARNEST conclusions (4) – the marvel of “recycled” NRTIs
• NRTIs retain substantial virologic activity in second-line
• Even in patient population with advanced first-line failure expected to have extensive cross-resistance
• Low incidence of NRTI-attributable toxicity (e.g. renal failure)
Other second-line trials: PI/RAL vs. PI/2NRTIs
SECOND-LINE trial: Design
(n=270)
(n=271)
Setting: Middle/high income countries Entry criteria: On first-line 2NRTI + NNRTI for ≥24 weeks Virologic failure (VL >500 c/mL) x 2 LPV/r 400/100 mg BID + RAL
96 weeks 48 0
LPV/r 400/100 mg BID + 2–3 NRTIs
Primary endpoint: HIV RNA <200 c/mL at Week 48
Hypothesis: PI/RAL non-inferior to PI/NRTI, margin 12%
Lab monitoring: CD4+ cell count + VL at each visit (12-weekly) Resistance testing allowed for NRTI selection
Boyd MA, et al. Lancet 2013;381:2091–99
SECOND-LINE trial: VL suppression at Week 48
Boyd MA, et al. Lancet 2013;381:2091–99
<200 c/mL <50 c/mL
0
30
70
100
%, m
od
ifie
d IT
T p
op
ula
tio
n
10
40
80
20
50
90 80.8% 82.6%
LPV/r + NRTIs
LPV/r + RAL
LPV/r + RAL
LPV/r + NRTIs
70.5% 71.1%
SECOND-LINE trial: Bone mineral density (BMD) change
• Bone loss: LPV/r + NRTIs > LPV/r + RAL at 48 weeks
• No difference in osteopenia (7%) or osteoporosis (2%) between groups Martin A, et al. AIDS 2013;27:2403–11
LPV/r + 2–3 NRTI LPV/r + RAL
Proximal femur Lumbar spine
-6
-3
0
Me
an %
ch
ange
in
BM
D f
rom
We
ek
0 t
o 4
8
-5
-2
-4
-1
P=0.0001
P=0.0006
Week 144 EARNEST results: VL suppression
Hakim et al. CROI 2015
0
100
80
60
40
20
0 4 12 24 36 48 64 80 96 144
Weeks from randomization
% w
ith
VL
<40
0 c
op
ies/
mL
PI-mono PI/NRTI PI/RAL
Week 144 EARNEST results: CD4+ change
Hakim et al. CROI 2015
300
100
0
0 4 12 24 36 48 64 80 96 144
200
112 128
Me
an c
han
ge C
D4
+ ce
lls/m
m3 (
95
% C
I)
PI-mono PI/NRTI PI/RAL
Weeks from randomization
Does the choice of NRTI matter? – for PI/2NRTI option
Does the choice of PI matter?
– for PI/2NRTI or PI/RAL option
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
NRTI resistance at baseline
• Baseline sequences obtained in 92% of those randomized to PI/NRTI arm
• Figure shows resistance data from 792 randomized patients
Kityo et al. CROI 2015; Poster 595
0
20
40
60
80
100
%
3TC FTC ABC TDF ZDV ddI D4T
Susceptible
Potential low
Low
Intermediate
High
Predicted activity of NRTIs in regimens
• Number of predicted “active” NRTIs in prescribed second-line therapy*: 0 230 (59%) 1 128 (33%) ≥2 33 (8%)
• GSS for NRTIs in prescribed second-line therapy: 0 114 (29%) 0.25–0.75 177 (45%) 1–1.75 73 (19%) ≥2 27 (7%)
*NRTI predicted “active” if no int./high level resistance by Stanford
Paton NI, et al. CROI 2015; Abstract 119
VL response by number of active NRTIs in the regimen
Paton NI, et al. CROI 2015; Abstract 119
PI + RAL (N>280) PI-mono (N>374)
0
20
40
60
80
100
0 4 12 24 36 48 64 80 96 144
81%
61%
% w
ith
VL
<40
0 c
op
ies/
mL
Weeks from switch to second-line
VL response by number of active NRTIs in the regimen
Paton NI, et al. CROI 2015; Abstract 119
PI/NRTI(0) (N>149)
PI + RAL (N>280) PI-mono (N>374)
0
20
40
60
80
100
0 4 12 24 36 48 64 80 96 144
81%
88%
61%
% w
ith
VL
<40
0 c
op
ies/
mL
Weeks from switch to second-line Global P<0.0001 NRTI() = number of active (susceptible-low resistance) NRTIs
VL response by number of active NRTIs in the regimen
PI/NRTI(0) (N>149)
PI/NRTI(1) (N>86) PI + RAL (N>280) PI-mono (N>374)
0
20
40
60
80
100
0 4 12 24 36 48 64 80 96 144
81%
88%
61%
Weeks from switch to second-line
85%
% w
ith
VL
<40
0 c
op
ies/
mL
Global P<0.0001 NRTI() = number of active (susceptible-low resistance) NRTIs
Paton NI, et al. CROI 2015; Abstract 119
VL response by number of active NRTIs in the regimen
Paton NI, et al. CROI 2015; Abstract 119
77%
PI/NRTI(0) (N>149)
PI/NRTI(1) (N>86) PI/NRTI(2–3) (N>17) PI + RAL (N>280) PI-mono (N>374)
0
20
40
60
80
100
0 4 12 24 36 48 64 80 96 144
81%
88%
61%
Weeks from switch to second-line
85%
% w
ith
VL
<40
0 c
op
ies/
mL
Global P<0.0001 NRTI() = number of active (susceptible-low resistance) NRTIs
VL response by GSS of NRTIs in the regimen
Paton NI, et al. CROI 2015; Abstract 119
83%
PI + 0 GSS (N>86) PI + 0.25–0.75 GSS (N>140) PI + 1–1.75 GSS (N>59) PI + 2 + GSS (N>21) PI + RAL (N>280) PI-mono (N>374)
0
20
40
60
80
100
0 4 12 24 36 48 64 80 96 144
81%
89%, 89%
73%
61%
Weeks from switch to second-line
% w
ith
VL
<40
0 c
op
ies/
mL
Global P<0.0001 NRTI() = number of active (susceptible-low resistance) NRTIs
Factors associated with VL <400 c/mL in PI/NRTI
Unadjusted
Odds ratio (95% CI)
P
value
Adjusted
Odds ratio (95% CI)
P
value
GSS of second-line regimen
0
0.25–0.75
1–1.75
2–3
1
0.59 (0.26, 1.33)
0.60 (0.23, 1.61)
0.28 (0.09, 0.89)
0.19
(trend
0.08)
1
0.46 (0.19, 1.09)
0.39 (0.13, 1.19)
0.23 (0.06, 0.88)
0.12
(trend
0.03)
Viral load at baseline (per doubling) 0.70 (0.60, 0.83) <0.001 0.66 (0.55, 0.80) <0.001
Proportion non-adherent visits (per 5% higher)* 0.89 (0.82, 0.96) 0.003 0.89 (0.81, 0.98) 0.01
Unemployed at baseline 0.51 (0.28, 0.94) 0.03 0.48 (0.24, 0.98) 0.04
Age (per 10 years older) 1.45 (1.10, 1.92) 0.008 1.60 (1.15, 2.22) 0.005
Note: Multivariable regression modeling for VL suppression at week 96. N=346, excluding those with missing week 96 VL, baseline genotype or baseline employment status. Factors with P>0.1 sex, center, baseline CD4+ cell count, diabetes, cardiovascular disease, prior tuberculosis, smoking, alcohol consumption, hours worked per week, household income, food availability, presence of M184V in the baseline genotype, years on first-line, eGFR, hemoglobin, and glucose, previous CNS disease; viral subtype
*Non-adherent visit defined as missed, more than 7 days late, or reported any missing ART in the last month
Paton NI, et al. CROI 2015; Abstract 119
Conclusions
• Paradoxical relationship between resistance and VL suppression
– Confounding by adherence (although persists after adjustment)
– Also consistent with fitness/fidelity effect
–May be be better to base NRTI choice on tolerability/convenience than predicted activity?
• Algorithmic NRTI drug selection and attention to adherence can achieve excellent outcomes from second-line therapy in public health approach
– Resistance testing to select NRTIs is of little added value
Does the choice of NRTI matter? – for PI/2NRTI option
Does the choice of PI matter?
– for PI/2NRTI or PI/RAL option
WHO, 2013 ART guidelines. Available at: http://apps.who.int/iris/bitstream/10665/85321/1/9789241505727_eng.pdf
Second-line studies with PI/r + 2NRTI
Ajose O, et al. AIDS 2012;26:929–38
Characteristics of included studies
2LADY Trial
• Arm A: emtricitabine/tenofovir + lopinavir/ritonavir
• Arm B: didanosine + abacavir + lopinavir/ritonavir (+ lamivudine 150 mg if HBsAg positive)
• Arm C: emtricitabine/tenofovir + darunavir/ritonavir
Ciaffi et al, 7th EDCTP forum
2LADY Trial
Ciaffi et al. 7th EDCTP forum
ITT: Proportion in each arm of patients with VL <50 copies/mL with 95% CI
65.2%
0
20
40
60
80
100
-2 4 12 24 36 48
Time (weeks)
C: FTC + TDF + DRV/r (n=154)
A: FTC + TDF + LPV/r (n=152) B: ABC + ddl + LPV/r (n=145)
%
2LADY Trial
Ciaffi et al, 7th EDCTP forum
A: TDF/FTC + LPV/r B: ddI/ABC + LPV/r C: TDF/FTC + DRV/r
Primary endpoint Difference in proportion of patients with VL <50 copies/mL
across arms at Week 48 with IC 95%
-15 -10 -5 0 5 10 15 20
ITT A-B
ITT A-C
PP A-B
PP A-C
5.6% (-5.1; 16.4)
6.1% (-4.5; 16.7)
2.3% (-8.4; 13.1)
4.9% (-5.7; 15.5)
Evidence for ATV/r vs. LPV/r in second-line
• No RCTs in second-line!
• Circumstantial evidence from other settings….
• CASTLE: treatment-naïve
• BMS-045: salvage
CASTLE trial
Molina JM, et al. J Acquir Immune Defic Syndr 2010;53:323–32
ATV/RTV LPV/RTV
HIV RNA <100,000 copies/mL HIV RNA ≥100,000 copies/mL
0
30
70
100
Re
spo
nd
er (
%)
<5
0 c
op
ies/
mL
10
40
80
20
50
90
75% 70%
74%
66%
Proportion of patients with HIV RNA <50 c/mL at Week 96 (ITT; CVR, NC=F analysis), by qualifying HIV-1 RNA
n= 217 218 223 225
CASTLE trial (2)
Molina JM, et al. J Acquir Immune Defic Syndr 2010;53:323–32
Treatment-emergent resistance through Week 96 in isolates from patients with virologic failure
ATV/RTV n=438
LPV/RTV n=443
Virologic failure (HIV RNA ≥400 c/mL) 28 (6%) 29 (7%)
Paired genotypes 26 26
Major PI substitution 1 0
Minor PI substitution 1 1
PI polymorphisms (without major or minor PI substitutions) 11 14
Wild type 14 11
M184I/V 5 7
K65R 1 0
TAM (M41I, D67N, K70R, L210W, T215FY, K219EQ) 1 3
Paired phenotypes 25 23
PI phenotypic resistance ATV/RTV >5.2 1 0
LPV/RTV FC >9 0 1
Other boosted PIs 2 4
RTI phenotypic resistance FTC FC >3.5 or 3TC FC >3.5 5 5
TDF FC >1.4 0 2
Other NRTIs 3 5
BMS-045
Patients who had failed ≥2 previous regimens (with NRTI, NNRTI and PI) 72% had resistance to 3TC at baseline
Johnson M, et al. AIDS 2006;20:711–8
48 44
36
0
20
40
60
80
100
BL 12 24 48 72 96
Re
spo
nd
ers
(%)
Weeks
ATV/SQV (n=115)
LPV/RTV (n=123) ATV/RTV (n=120)
BMS-045
Naeger LK, Struble KA. AIDS 2006;20:847–53
ATV/RTV (n=110) LPV/RTV (n=112)
Baseline phenotype
0–2 0
30
70
100
Pro
po
rtio
n o
f re
spo
nd
ers
(<4
00
co
pie
s/m
L)
10
40
80
20
50
90
>2–5 >5–10 >10
PI/NRTI (N=426)
PI/RAL (N=433)
PI-mono (N=418)
Global P-value
PI/NRTI vs. RAL PI/NRTI vs. PI-mono Risk difference
(& hazard ratio)* (95% CI)
P value Risk difference (& hazard ratio)*
(95% CI)
P value
Week 48 Alive with no WHO stage 4‡, n (%)
395 (93%) 400 (92%) 390 (93%) 0.86 -0.4 (-4.6, +3.8) HR=0.96 (0.59, 1.56)
0.86 +0.7 (-3.4, +4.8) HR=1.10 (0.66, 1.83)
0.72
Alive, n (%) 405 (95%) 409 (94%) 401 (96%) 0.60 -0.7 (-4.2, +2.7) HR=0.89 (0.49, 1.59)
0.69 +1.0 (-2.2, +4.2) HR=1.22 (0.64, 2.31)
0.55
CD4+ >250 cells/mm3,
n/total with CD4+, n (%) CD4+, mean change (SE)
197/389 (51%)
168 (6)
210/395 (53%)
189 (8)
202/393 (51%)
179 (7)
0.77
0.13
+2.5% (-4.5%, +9.5%)
+21 (+1, +40)
0.48
0.04
+0.8% (-6.3, +7.8%)
+11 (-8, +30)
0.83
0.27 Viral load
Available <50 <400 <1000 <10,000
395
298 (75%) 336 (85%) 350 (89%) 364 (92%)
400
313 (78%) 365 (91%) 372 (93%) 379 (95%)
389
214 (55%) 293 (75%) 317 (81%) 352 (90%)
<0.0001 <0.0001 <0.0001
0.07
+2.8% (-3.1%, +8.7%) +6.2% (+1.7%,
+10.7%) +4.4% (+0.4%, +8.4%) +2.6% (-0.8%, +6.0%)
0.35 0.007 0.03 0.14
-20.4% (-26.9%, -13.9%) -9.7% (-15.3%, -4.2%) -7.1% (-12.1%, -2.1%) -1.7% (-5.6%, +2.3%)
<0.0001 0.0006 0.005 0.41
Any major or minor PI resistance mutation
6 (2%) 0 18 (6%) <0.0001 Not estimable†
+4.1% (+1.0%, +7.1%) 0.009
Intermediate/high level LPV/r resistance
6 (2%) 0 (0%) 13 (4%) 0.0006 Not estimable† +2.4% (-0.3%, +5.1%) 0.09
Week 48 data on LPV in EARNEST PI-mono
Primary and key secondary efficacy outcomes at 48 weeks
*Absolute risk difference and difference in rate per 100 person years for binary and time-to-event outcomes (WHO stage 4/death, death) respectively. Hazard ratio from Cox proportional hazards model also provided for time-to-event outcomes. ‡Including oesophageal candidiasis and mucosal herpes simplex virus infections. †Not estimable using weighted Poisson regression to account for missing genotypes in those with VL >1000 c/mL
Does PI choice matter with PI/RAL option?
• In second-line, no comparative RCTs of PI/RAL options
• Circumstantial evidence from first-line RCTs: – PROGRESS: PI(LPV)/RAL non-inferior to PI/NRTI (but not superior)
– NEAT: PI(DRV)/RAL inferior to PI/NRTI (in CD4+ cell count <200, VL >100K)
– SPARTAN: PI(ATV)/RAL inferior to PI/NRTI
• HARNESS (switch): PI(ATV)/RAL inferior to PI/NRTI
• Uncontrolled naïve studies
– ACTG5262: PI(DRV)/RAL, high VL failure rate at Week 48 (26%)
Conclusions: Options for second-line therapy in resource-limited settings
• Good evidence for LPV/r + 2NRTIs providing excellent outcomes in second-line using public health approach
• Good evidence for LPV/r + RAL being non-inferior to standard of care in second-line – may be attractive in some settings for individualizing therapy
• Choice of NRTIs in second-line may not matter (and probably don’t need resistance testing)
• Choice of PI may matter with the PI/2NRTIs combination (limited evidence)
• Choice of PI probably does matter with PI/RAL combination for second-line
Acknowledgments
Uganda: JCRC Kampala (African trial co-ordinating centre; 231) E Agweng, P Awio, G Bakeinyaga, C Isabirye, U Kabuga, S Kasuswa, M Katuramu, C Kityo, F Kiweewa, H Kyomugisha, E Lutalo, P Mugyenyi, D Mulima, H Musana, G Musitwa, V Musiime, M Ndigendawan, H Namata, J Nkalubo, P Ocitti Labejja, P Okello, P Olal, G Pimundu, P Segonga, F Ssali, Z Tamale, D Tumukunde, W Namala, R Byaruhanga, J Kayiwa, J Tukamushaba. IDI, Kampala (216): G Bihabwa, E Buluma, P Easterbrook, A Elbireer, A Kambugu, D Kamya, M Katwere, R Kiggundu, C Komujuni, E Laker, E Lubwama, I Mambule, J Matovu, A Nakajubi, J Nakku, R Nalumenya, L Namuyimbwa, F Semitala, B Wandera, J Wanyama; JCRC, Mbarara (97): H Mugerwa, A Lugemwa, E Ninsiima, T Ssenkindu, S Mwebe, L Atwine, H William, C Katemba, S Abunyang, M Acaku, P Ssebutinde, H Kitizo, J Kukundakwe, M Naluguza, K Ssegawa, Namayanja, F Nsibuka, P Tuhirirwe, M Fortunate; JCRC Fort Portal (66): J Acen, J Achidri, A Amone, M. Chamai, J Ditai, M Kemigisa, M Kiconco, C Matama, D Mbanza, F Nambaziira, M Owor Odoi, A Rweyora, G. Tumwebaze. San Raphael of St Francis Hospital, Nsambya (48): H Kalanzi, J Katabaazi , A Kiyingi, M Mbidde, M. Mugenyi, R Mwebaze, P Okong, I Senoga. JCRC Mbale (47): M Abwola, D Baliruno, J Bwomezi, A Kasede, M Mudoola, R Namisi, F Ssennono, S Tuhirwe.
JCRC Gulu (43): G Abongomera, G Amone, J Abach, I Aciro, B Arach, P Kidega, J Omongin, E Ocung, W Odong, A Philliam. JCRC Kabale (33): H Alima, B Ahimbisibwe, E Atuhaire, F Atukunda, G Bekusike, A Bulegyeya, D. Kahatano, S Kamukama, J Kyoshabire, A Nassali, A Mbonye, T M Naturinda, Ndukukire, A Nshabohurira, H. Ntawiha, A Rogers, M Tibyasa; JCRC Kakira (31): S. Kiirya, D. Atwongeire, A. Nankya, C. Draleku, D. Nakiboneka, D. Odoch, L. Lakidi, R. Ruganda, R. Abiriga, M. Mulindwa, F. Balmoi, S. Kafuma, E. Moriku
Zimbabwe: University of Zimbabwe Clinical Research Centre, Harare (265): J Hakim, A Reid, E Chidziva, G Musoro, C Warambwa, G Tinago, S Mutsai, M Phiri, S Mudzingwa, T Bafana, V Masore, C Moyo, R Nhema, S Chitongo
Malawi: College of Medicine, University of Malawi, Blanytre (92): Rob Heyderman, Lucky Kabanga, Symon Kaunda, Aubrey Kudzala, Linly Lifa, Jane Mallewa, Mike Moore, Chrissie Mtali, George Musowa, Grace Mwimaniwa, Rosemary Sikwese, Joep van Oosterhout, Milton Ziwoya. Mzuzu Central Hospital, Mzuzu (19): H Chimbaka. B Chitete, S Kamanga, T Kayinga E Makwakwa, R Mbiya, M Mlenga, T Mphande, C Mtika, G Mushani, O Ndhlovu, M Ngonga, I Nkhana, R Nyirenda
Kenya: Moi Teaching and Referral Hospital (52): P Cheruiyot, C Kwobah, W Lokitala Ekiru, M Mokaya, A Mudogo, A Nzioka, A Siika, M Tanui, S Wachira, K Wools-Kaloustian
Zambia: University Teaching Hospital (37): P Alipalli, E Chikatula, J Kipaila, I Kunda, S Lakhi, J Malama, W Mufwambi, L Mulenga, P Mwaba, E Mwamba, A Mweemba, M Namfukwe
The Aids Support Organisation (TASO), Uganda: E Kerukadho, B Ngwatu, J Birungi
MRC Clinical Trials Unit: N Paton, J Boles, A Burke, L Castle, S Ghuman, L Kendall, A Hoppe, S Tebbs, M Thomason, J Thompson, S Walker, J Whittle, H Wilkes, N Young
Monitors: C Kapuya, F Kyomuhendo, D Kyakundi, N Mkandawire, S Mulambo, S Senyonjo
Clinical Expert Review Committee: B Angus, A Arenas-Pinto, A Palfreeman, F Post, D Ishola
European Collaborators: J Arribas, B Colebunders, M Floridia, M Giuliano, P Mallon, P Walsh, M De Rosa, E Rinaldi
Trial Steering Committee: I Weller (Chair), C Gilks, J Hakim, A Kangewende, S Lakhi, E Luyirika, F Miiro, P Mwamba, P Mugyenyi, S Ojoo, N Paton, S Phiri, J van Oosterhout, A Siika, S Walker, A Wapakabulo,
Data Monitoring Committee: T Peto (Chair), N French, J Matenga
Pharmaceutical companies: J van Wyk, M Norton, S Lehrman, P Lamba, K Malik, J Rooney, W Snowden, J Villacian, G Cloherty
Funding and in-kind support: Funded by the European and Developing Countries Clinical Trials Partnership (EDCTP) with contributions from the Medical Research Council, UK, Institito de Salud Carlos III, Spain, Irish Aid, Ireland, Swedish International Development Cooperation Agency (SIDA), Sweden, Instituto Superiore di Sanita (ISS), Italy and Merck, USA. Substantive in-kind contributions were made by the Medical Research Council Clinical Trials Unit, UK, CINECA, Bologna, Italy, Janssen Diagnostics, Mechelen, Belgium; GSK, UK; Abbott Laboratories, USA. Trial medication was donated by AbbVie, Merck, Pfizer, GSK and Gilead
Thanks to all the participants ….