1. Rationale and Uses For a Public HIV Drug Resistance Database Bob Shafer, MD Professor of Medicine and by Courtesy Pathology (Infectious Diseases)

2. Outline HIV drug therapy essentials HIVDB
Application to drug-resistance surveillance
Application to clinical drug-resistance interpretation

3. HIV-1 Genome

4. HIV Replication and Targets of Therapy

5. Generation of variation5
HIV Genetic Variation
Generation of variation
High mutation rate
Recombination
Proviral DNA “archive”
Selective evolutionary pressures
Immunological
Antiretroviral drugs (ARVs) 5

6. HIV Variability

7. Antiretroviral Inhibitors (ARVs)7
Antiretroviral Inhibitors (ARVs)
EFV
NVP
ABC
LPV
ATV
IDV
3TC
TDF
FTC
DRV
RAL
EVG
ddI
SQV
DLV
AZT
MVC
ETR
RPV
DTG
ddC
d4T
RTV
NFV
FPV
T20
TPV
1990
1995
2000
2005
2010
2015
Nucleoside RT Inhibitors
Protease Inhibitors
Integrase Inhibitors
Nonnucleoside RT inhibitors
Fusion Inhibitor
CCR5 Inhibitor 7

8. Genetic Barrier to Resistance

9. UNAIDS: 90-90-90 Targets 100 90% 36.9 million 81% 33.2 million 80 73%
Target 1: 90% of HIV+ people diagnosed
Target 2: 90% of diagnosed people on ART
Target 3: 90% of people on ART with HIV-1 RNA suppression
100
90%
36.9 million
81%
33.2 million 80
73%
29.5 million
26.9 million
People (%) 60
ART, antiretroviral therapy. 40 20
HIV Positive People
Diagnosed
On ART
Viral Suppression
Levi J, et al. IAS Abstract MOAD0102. Reproduced with permission.

10. UNAIDS: 90-90-90 Global Estimated Gaps
100
53%
36.9 million
41%
Breakpoint 1: 13.4 million undiagnosed 80
32%
Breakpoint 2: 14.9 million not treated
Breakpoint 3: 15.3 million not virally suppressed 60
People (%)
19.8 million 40
15.0 million
11.6 million 20
ART, antiretroviral therapy.
HIV Positive People
Diagnosed
On ART
Viral Suppression*
*HIV-1 RNA < 1000 copies/mL.
Levi J, et al. IAS Abstract MOAD0102. Reproduced with permission.

11. Models Relating HIV Drug Resistance to Treatment Response

13. HIV-1 Evolution and Drug Resistance:
An Example
Fessel WJ, et al. The efficacy of an anti-CD4 monoclonal antibody for HIV-1 treatment. Antivir Res 2011

14. NNRTI Resistance Mutations
Active site
NNRTI resistance mutations
Etravirine

15. HIV-1 Protease Drug Resistance Mutations
Lopinavir
Major resistance mutations
Active site & substrate cleft
Minor resistance mutations

16. Outline HIV drug therapy essentials HIVDB
Application to drug-resistance surveillance
Application to clinical drug-resistance interpretation

18. Evidence Underlying the Genotypic Mechanisms of HIV Drug Resistance18
Evidence Underlying the Genotypic Mechanisms of HIV Drug Resistance
Genotype-treatment correlations
Genotype-phenotype correlations
Genotype-clinical outcome correlations 18

19. Rationale for a Database19
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 19

20. Genetic Mechanisms of HIV Drug Resistance20
Genetic Mechanisms of HIV Drug Resistance
Applications:
Interpreting genotypic resistance tests
Designing surveillance studies and public health decisions
Assisting drug development. 20

21. Outline HIV drug therapy essentials HIVDB
Application to drug-resistance surveillance
Application to clinical drug-resistance interpretation

22. Surveillance of Drug Resistance in ARV-Naive Patients
Assess extent of transmitted drug resistance (TDR).
Monitor the expected efficacy of first-line therapies.

23. 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).

24. 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.

25. Bennett DE et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug resistance:
2009 update. PLoS One 2009

26. Calibrated Population Resistance Analysis Tool
Applies SDRM list to a set of sequences
Standardized approach to handling missing data and poor sequence quality.
Surveillance is genotypic
Gifford, RJ et al. The calibrated population resistance tool: standardized
genotypic estimation of transmitted HIV-1 drug resistance. AIDS 2008

27. HIV-1 Resistance in ARV-Naïve Populations

28. Studies in HIVDB: ARV-Naïve Populations

29. HIV-1 Resistance in ARV-Naïve Populations: Prevalence by Region
No.
Studies
Persons
% Resistance
Median
IQR
North America 27
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
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
Obscures much detail, not weighted by size of study
Europe decrease over time
South America – Brazil dominates
SSEA excludes countries well-resourced ARVs available for a long time: Japan similar to Europe and S.A.
Rhee SY et al. Geographic and temporal trends in the molecular epidemiology and
genetic mechanisms of transmitted HIV-1 drug resistance. PLOS Medicine 2015

30. 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

31. 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

32. Sequence Relatedness of Viruses Sampled within a Study
An integer value was assigned to each sequence cluster starting from “1” and shown in front of each SequenceID.
All un-clustered sequences have “0” instead.
Nwobegahay et. al, 2011 (DI = 100%)
Yang et. al, 2002 (DI = 41%)

33. Sequence Relatedness of Viruses Sampled within a Study
BEAST analysis of sequences from Hattori et. al, 2010
A cluster of sequences from 16 patients with M46L alone
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

34. 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.

35. Outline HIV drug therapy essentials HIVDB
Application to drug-resistance surveillance
Application to clinical drug-resistance interpretation

36. 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

37. Genotypic HIV Resistance Testing
CCTCAGATCACTCTTTGGCAACGACCCATAGTCACAATAAAGATAGCGGGACAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAGAAGAAATGAATTTGCCAGGAAAATGGAAACCAAAAATAATAGTGGGAATTGGAGGGTTTACCAAAGTAAGACAGTATGATCATGTACAAATAGAAATCTGTGGACATAAAGTTATAGGTGCAGTATTAATAGGACCTACACCTGCCAATATAATTGGAAGAAATCTGTTGACTCAGCTTGGCTGTACTTTAAATTTT
PQITLWQRPIVTIKIAGQLKEALLDTGADDTVLEEMNLPGKWKPKIIVGIGGFTKVRQYDHVQIEICGHKVIGAVLIGPTPANIIGRNLLTQLGCTLNF
Differences from Consensus B:
L10I, G17R, K20I, E35D, N37S, M46I, I62V, L63P, A71I, G73S, I84V, L90M, I93L

38. Standard Sanger Sequencing Detects the Most Common Circulating HIV-1 Variants

39. HIVdb: Genotypic Resistance Interpretation

41. HIVdb Genotypic Resistance Program HTML Output

42. HIVdb Genotypic Resistance Program HTML Output

43. HIVdb Genotypic Resistance Program HTML Output

44. HIVdb Genotypic Resistance Program HTML Output

45. HIVDB as an Expert System45
HIVDB as an Expert System
Limitations of HIVDB interpretation program
Two main types of goals for expert systems:
Mimics an expert vs. gives makes the optimal decision
Two main types of approaches for expert systems:
Rules vs. machine learning 45

46. 46
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. 46

47. 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