Transcript HIV Drug Resistance

1
Rationale and Uses For a Public HIV Drug
Resistance Database
Bob Shafer, MD
Professor of Medicine and by Courtesy Pathology
(Infectious Diseases)
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Outline
• HIV drug therapy essentials
• HIVDB
• Application to drug-resistance surveillance
• Application to clinical drug-resistance
interpretation
HIV-1 Genome
HIV Replication and Targets of Therapy
5
HIV Genetic Variation
• Generation of variation
• High mutation rate
• Recombination
• Proviral DNA “archive”
• Selective evolutionary pressures
• Immunological
• Antiretroviral drugs (ARVs)
HIV Variability
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Antiretroviral Inhibitors (ARVs)
AZT
NVP
EFV
IDV
ABC
ddI
3TC
SQV
DLV
LPV
TDF
ddC
d4T
RTV
NFV
FPV
1990
Nucleoside
RT Inhibitors
1995
2000
ATV
EVG
FTC DRV RAL
T20
TPV MVC ETR RPV DTG
2005
Integrase
Inhibitors
Protease
Inhibitors
Nonnucleoside
RT inhibitors
2010
Fusion
Inhibitor
CCR5
Inhibitor
2015
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Genetic Barrier to Resistance
UNAIDS: 90-90-90 Targets
Target 1:
90% of HIV+
people
diagnosed
People (%)
100
80
36.9
million
90%
33.2
million
HIV Positive
People
Diagnosed
60
Target 2:
90% of
diagnosed
people on ART
81%
29.5
million
Target 3:
90% of people on
ART with HIV-1 RNA
suppression
73%
26.9
million
40
20
0
On ART
Viral
Suppression
Levi J, et al. IAS 2015. Abstract MOAD0102. Reproduced with permission.
UNAIDS: 90-90-90 Global Estimated Gaps
People (%)
100
80
36.9
million
53%
Breakpoint 1:
13.4 million
undiagnosed
60
40
19.8
million
20
0
HIV Positive Diagnosed
People
41%
Breakpoint 2:
14.9 million not
treated
15.0
million
On ART
32%
Breakpoint 3:
15.3 million not
virally
suppressed
11.6
million
Viral
Suppression*
*HIV-1 RNA < 1000 copies/mL.
Levi J, et al. IAS 2015. Abstract MOAD0102. Reproduced with permission.
Models Relating HIV Drug Resistance to Treatment
Response
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HIV-1 Evolution and Drug Resistance:
An Example
A
B
HIV-1 levels prior to TMB-202
HIV-1 levels during and following TMB-202
June
2009
2009
April 2010
Plasma HIV-1 RNA log copies / ml
1997
6.0
5.0
ENF
4.0
DRV + RAL
3.0
EFV
2.0
Below the level of quantification
Below the level of quantification
1.0
97
98
99
00
01
02
03
04
05
06
07
08
09
-4
0
4
8
12
16
20
24 28
32
36
40
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Ibalizumab Infusions
Accompanying antiretrovirals: etravirine + enfuvirtide
Fessel WJ, et al. The efficacy of an anti-CD4 monoclonal
antibody for HIV-1 treatment. Antivir Res 2011
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NNRTI Resistance Mutations
Active site
Etravirine
NNRTI resistance mutations
HIV-1 Protease Drug Resistance Mutations
Lopinavir
Major resistance mutations
Active site & substrate cleft
Minor resistance mutations
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Outline
• HIV drug therapy essentials
• HIVDB
• Application to drug-resistance surveillance
• Application to clinical drug-resistance
interpretation
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Evidence Underlying the Genotypic
Mechanisms of HIV Drug Resistance
• Genotype-treatment correlations
• Genotype-phenotype correlations
• Genotype-clinical outcome correlations
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Rationale for a Database
• Large amounts of drug resistance data from diverse
sources are required.
• Uniform representation of 3 main data correlations
facilitates meta-analyses.
• Consistent pipelines for analyzing sets of data
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Genetic Mechanisms of HIV Drug Resistance
Applications:

Interpreting genotypic resistance tests

Designing surveillance studies and public health decisions

Assisting drug development.
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Outline
• HIV drug therapy essentials
• HIVDB
• Application to drug-resistance surveillance
• Application to clinical drug-resistance
interpretation
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Surveillance of Drug Resistance in ARV-Naive
Patients
• Assess extent of transmitted drug resistance (TDR).
• Monitor the expected efficacy of first-line therapies.
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Challenges to ARV-Resistance Surveillance
• There is no perfect definition of genotypic resistance.
•
There are many different drug-resistance mutations (DRMs).
•
Drug resistance mutations occasionally occur in the absence
of selective drug pressure. Therefore, not all drug-resistance
mutations are evidence for transmitted drug resistance
(TDR).
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Surveillance Drug Resistance Mutations (SDRMs)
• Drug-resistance mutations with a high sensitivity and specificity
for detecting selective ARV pressure.
• Nonpolymorphic.
•
Applicable to all HIV-1 subtypes.
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Bennett DE et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug resistance:
2009 update. PLoS One 2009
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Calibrated Population Resistance Analysis Tool
• Applies SDRM list to a
set of sequences
•
Standardized approach
to handling missing data
and poor sequence
quality.
Gifford, RJ et al. The calibrated population resistance tool: standardized
genotypic estimation of transmitted HIV-1 drug resistance. AIDS 2008
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HIV-1 Resistance in ARV-Naïve Populations
http://hivdb.stanford.edu/surveillance/map/
Studies in HIVDB: ARV-Naïve Populations
HIV-1 Resistance in ARV-Naïve Populations:
Prevalence by Region
Region
No.
Studies
No.
Persons
% Resistance
Median
% Resistance
IQR
North America
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9,283
11.5
8.3 – 14.6
Europe
42
11,802
9.4
6.1 – 15.1
Latin America
38
5,628
7.6
3.9– 10.2
High-income Asia
12
3,190
5.6
3.5 – 9.0
Former Soviet Union
12
1,124
4.0
0.0 – 6.4
South/Southeast Asia
56
4,181
2.9
1.8 – 5.3
Sub-Saharan Africa
95
9,904
2.8
1.3 – 5.6
287
51,220
Rhee SY et al. Geographic and temporal trends in the molecular epidemiology and
genetic mechanisms of transmitted HIV-1 drug resistance. PLOS Medicine 2015
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Temporal Trends in Sub-Saharan Africa
Rhee SY et al. Geographic and temporal trends in the molecular epidemiology and
genetic mechanisms of transmitted HIV-1 drug resistance. PLOS Medicine 2015
Relationship between Observed Mutations in
Naïve Patients and Treated Patients
Rhee SY et al. Geographic and temporal trends in the molecular epidemiology and
genetic mechanisms of transmitted HIV-1 drug resistance. PLOS Medicine 2015
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Sequence Relatedness of Viruses Sampled
within a Study
Nwobegahay et. al, 2011 (DI = 100%)
Yang et. al, 2002 (DI = 41%)
Sequence Relatedness of Viruses Sampled
within a Study
Hattori et. al, 2010
Fujisaki et. al, 2009
Rhee SY et al. Geographic and temporal trends in the molecular epidemiology and
genetic mechanisms of transmitted HIV-1 drug resistance. PLOS Medicine 2015
Surveillance of Drug Resistance in ARV-Treated
Patients
• In resource-limited regions, ~20% of patients receiving
first-line ART develop virological failure within 1 year.
• Drug-resistance mutations are detected in 50% to 90%
of patients with virological failure.
• Patients in resource-limited countries are monitored
infrequently and second-line therapy is chosen without
genotypic resistance testing.
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Outline
• HIV drug therapy essentials
• HIVDB
• Application to drug-resistance surveillance
• Application to clinical drug-resistance
interpretation
HIV-1 Genotypic Resistance Testing: Online Interpretation
Meaningful Results
(1) Quality control
(2) Sequence Interpretation
(3) Literature references
(4) Clinical education / advice
Shafer RW et al. HIV-1 RT and Protease Search Engine for Queries. Nat Med 2000
Genotypic HIV Resistance Testing
CCTCAGATCACTCTTTGGCAACGACCCATAGTCACAATAAAGATAGCGGGACAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACA
GTATTAGAAGAAATGAATTTGCCAGGAAAATGGAAACCAAAAATAATAGTGGGAATTGGAGGGTTTACCAAAGTAAGACAGTATGATCATGTAC
AAATAGAAATCTGTGGACATAAAGTTATAGGTGCAGTATTAATAGGACCTACACCTGCCAATATAATTGGAAGAAATCTGTTGACTCAGCTTGGC
TGTACTTTAAATTTT
PQITLWQRPIVTIKIAGQLKEALLDTGADDTVLEEMNLPGKWKPKIIVGIGGFTKVRQYDHVQIEICGHKVIGAVLIGPTPANIIGR
NLLTQLGCTLNF
Differences from Consensus B:
L10I, G17R, K20I, E35D, N37S, M46I, I62V, L63P, A71I, G73S, I84V, L90M, I93L
Standard Sanger Sequencing Detects the Most
Common Circulating HIV-1 Variants
HIVdb: Genotypic Resistance Interpretation
http://hivdb.stanford.edu
HIVdb Genotypic Resistance Program HTML Output
HIVdb Genotypic Resistance Program HTML Output
HIVdb Genotypic Resistance Program HTML Output
HIVdb Genotypic Resistance Program HTML Output
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HIVDB as an Expert System
• Limitations of HIVDB interpretation program
• Two main types of goals for expert systems:
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Mimics an expert vs. gives makes the optimal decision
• Two main types of approaches for expert systems:
•
Rules vs. machine learning
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Conclusions
• Drug resistance knowledge is important for interpreting genotypic
resistance tests, designing surveillance studies, and drug
development.
• Large amounts of drug resistance data from diverse sources are
important for generating drug-resistance knowledge.
• HIV drug resistance knowledge is a tool that facilitates
the analysis of newly acquired data.
Acknowledgements
• Funding: NIH / NIAID / Division of AIDS
• People:
→ Soo-Yon Rhee, Ph.D.
→ Tommy Liu (now with Sirona Genomics)
→ Vici Varghese, Ph.D.
• Contributors and collaborators
• Disclosures:
→ Research funding: Gilead Sciences; Bristol-Myers Squibb, Merck
→ Research gifts: Celera, Siemens-Health Care, Roche Molecular