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

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

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

HIV Variability

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

Genetic Barrier to Resistance

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 2015. Abstract MOAD0102. Reproduced with permission.

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 2015. Abstract MOAD0102. Reproduced with permission.

Models Relating HIV Drug Resistance to Treatment Response

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

NNRTI Resistance Mutations Active site NNRTI resistance mutations Etravirine

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

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

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

Rationale for a Database 19 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

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

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

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

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).

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.

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

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

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 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

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

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%)

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

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.

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 CCTCAGATCACTCTTTGGCAACGACCCATAGTCACAATAAAGATAGCGGGACAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAGAAGAAATGAATTTGCCAGGAAAATGGAAACCAAAAATAATAGTGGGAATTGGAGGGTTTACCAAAGTAAGACAGTATGATCATGTACAAATAGAAATCTGTGGACATAAAGTTATAGGTGCAGTATTAATAGGACCTACACCTGCCAATATAATTGGAAGAAATCTGTTGACTCAGCTTGGCTGTACTTTAAATTTT PQITLWQRPIVTIKIAGQLKEALLDTGADDTVLEEMNLPGKWKPKIIVGIGGFTKVRQYDHVQIEICGHKVIGAVLIGPTPANIIGRNLLTQLGCTLNF 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

HIVDB as an Expert System 45 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 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

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