Our Research

Since the AIDS pandemic was first recognized in the early 1980s, HIV has killed more than 30 million individuals. The threat that this virus posed to both individuals and entire populations galvanized the scientific and public health communities to unprecedented discovery and accomplishment. HIV research ushered in a golden age of antiviral drug development. National HIV treatment programs saved millions of lives, prevented millions of infections, and restored hope to the populations hardest hit by the pandemic. The success of the global response to HIV also united public health officials, political leaders, and global funders to combat other pandemic infections.

Highly effective antiretroviral (ARV) drugs that inhibit HIV replication have been developed and made available at cost in many parts of the world. However, one of the main challenges in treating HIV it mutates at high rates and develops drug resistance. This poses an omnipresent threat of acquired HIV drug resistance (HIVDR) in persons receiving treatment and the potential for transmission of HIVDR strains to previously untreated individuals. Both acquired (ADR) and transmitted drug resistance (TDR) increase the risk of treatment failure, HIV disease progression, and the need for costlier second- and third-line treatment regimens.

The Stanford HIV Drug Resistance Database (HIVDB) is an essential resource for public health officials monitoring ADR and TDR, for scientists developing new ARV drugs, and for HIV care providers managing patients with HIVDR. Researchers studying HIVDR also use HIVDB to place their findings in the context of previous studies and to perform meta-analyses that require data from large numbers of studies, making it possible to discover new knowledge that cannot be discovered from a single study.

Epidemiology of Naturally Occurring HIV Variability and Acquired and Transmitted Drug Resistance

We collect, annotate, and analyze genotypic drug resistance data from previously untreated (ARV-naïve) and previously treated (ARV-experienced) persons and systematically aggregate data from many published studies to analyze temporal global trends in the drug-resistance mutations responsible for ADR and TDR, thus creating a resource for clinical and molecular epidemiologists. Our work is made challenging by the extensive genetic sequence heterogeneity of HIV, including the many different HIV subtypes and recombinant forms. Nonetheless, by collecting well-characterized HIV sequences from more than 150,000 individuals, we have been able to distinguish naturally occurring HIV mutations from those that are selected by ARV therapy and contribute to HIVDR.

We are collaborating with researchers from around the world to track global trends in ADR and TDR and to characterize the genetic mechanisms of HIVDR to the ARVs for which more data are needed including those belonging to the nucleoside RT inhibitor (NRTI), nonnucleoside RT inhibitor (NNRTI), protease inhibitor (PI), and integrase strand transfer inhibitor (INSTI) class of drugs.

Drug Resistance Interpretation

The presence of HIVDR prior to the start of an ARV-treatment regimen is a strong predictor of the success or failure of that regimen. Genotypic resistance testing using DNA sequencing is performed routinely in upper-income countries before patients start ARV therapy and in patients who develop virological failure during treatment. In low- and middle income countries (LMICs) it is used sparingly but with increasing frequency. Interpreting HIV genotypic resistance tests is difficult because there are many different DRMs, which occur in complex patterns and which have diverse effects on the ARVs within each drug class.

HIVDB contains a free publicly available genotypic resistance interpretation system that can be accessed via an HTML or an automated web service. The interpretation system has been integrated into the workflows of the World Health Organization’s HIV drug resistance network, the NIH-funded AIDS Clinical Trials Group, and several hundred research and clinical laboratories worldwide. Several thousand clinicians also use the program’s HTML interface each month to help choose the most appropriate treatment for their patients. The interpretation system is accompanied by detailed mutation penalties and comments and HIVDR notes for each of the ARV classes as outlined in the system’s Release Notes and Primer.

Ongoing research is devoted to extending the interpretation to HIV-1 sequences obtained using next-generation sequencing (NGS) technologies; investigational ARVs, and HIV-2.

Point-of-care testing

The increasing prevalence of HIVDR is a threat to HIV treatment and prevention in the LMICs hardest hit by the HIV pandemic. An inexpensive point-of-care genotypic resistance test would enable HIV care providers to make informed treatment decisions for patients starting therapy and for patients developing virological failure while on therapy. Through the delivery of immediately actionable information upon the detection of virological failure, a point-of-care genotypic resistance test would benefit treatment programs by reducing treatment delivery costs and would benefit patients by reducing travel costs and work absenteeism and by increasing the likelihood that treated patients achieve and maintain suppressed virus levels. The development of a point-of-care genotypic resistance test would be the first step in addressing the broader challenge of detecting mutations in rapidly evolving epidemic viruses whether they are drug-resistance mutations or critical vaccine-escape or gain-of-function mutations.

The main challenge in developing a point-of-care genotypic resistance test is the genetic variability at and surrounding each HIVDR mutation position. We are collaborating with the biotechnology company InSilixa Inc. (Sunnyvale, CA) to detect the most clinically relevant HIVDR mutations using a multiplex solid-phase melt-curve analysis platform.

Novel HIVDR mutations

The many genetic mechanisms of HIVDR present challenges to researchers who require representative resistant viruses for in vitro mechanistic studies and for developing new ARVs against the most clinically relevant drug-resistant viruses. During the past 15 years, we have developed four panels of prototypical recombinant molecular infectious HIV clones resistant to NRTIs, NNRTIs, PIs, and INSTIs and have contributed them to the NIH AIDS Reagent Program to enable drug developers to evaluate novel compounds using representative highly resistant HIV clones.

We are also used standard sequencing and NGS technologies to identify and/or characterize novel HIVDR mutations.

Biomedical Informatics

In contrast to most other sequence databases, the data in HIVDB are not confined to passively acquired publicly available data or to data provided by a consortium of research groups required to submit data to a central repository. The majority of data in HIVDB are obtained from GenBank, peer-reviewed publications, and often from the authors of these publications. The continued improvement and long-term sustainability of HIVDB requires novel and transformative approaches such as the creation of consensus data sharing guidelines specific to antiviral drug resistance studies and automated tools to benchmark adherence to data sharing guidelines. This will require collaboration with experts in antiviral drug resistance, journal editors, funding agencies, and stakeholders in public health, academia, and industry.

Several of the tools we have developed or are developing for HIVDB will be useful for piloting databases for drug therapy targets in other viruses of biomedical importance. The development of these databases requires standardized approaches to sequence alignment, post-alignment processing, and quality control. High quality alignments that leverage knowledge from published studies will make it possible to ascertain the prevalence of each amino acid according to virus genotype or subtype and to identify positions under selective immune or drug pressure. Such alignments are particularly important as an increasing proportion of sequence data is likely be generated by high-throughput NGS platforms. Finally, as the genetic sequencing of other viruses becomes more common, expert systems for interpreting these sequences will become increasingly necessary to optimize surveillance, clinical research, and clinical practice efforts.

Data Sharing

Data is the most important commodity in science and its management is of critical importance to knowledge discovery and patient care. Publicly available databases are integral to life sciences research. In certain fields there is a strong culture of data sharing. For example, genomics researchers traditionally submit the raw data described in their studies to public databases at the time their studies are published. However, data sharing is uncommon in medical fields where researchers often treat data as a private preserve. The dearth of databases linking biological and clinical data is a major obstacle to medical progress. Many expert panels and funding agencies have published principles and guidelines underscoring that the raw data described in a published paper should be made publicly available so that other researchers can validate the published findings and re-use the data to promote discovery. However, there is much uncertainty on how to promote adherence to these data sharing principles and guidelines.

Most published HIV drug resistance studies are not initially amenable for re-use or individual patient-level meta-analyses. Although the genetic sequences described in HIV drug resistance studies may occasionally be submitted to GenBank, these sequences must also be linked to patient data, such as the treatment history of the patient from whom the sequenced virus was obtained, or to laboratory data, such as the results of drug susceptibility testing of the sequenced virus. We systematically review published HIVDR studies and GenBank submissions to identify the datasets to add to HIVDB. Following this review, we contact study authors and request that they contribute the relevant raw data to HIVDB. We often must also provide authors incentives to make their data available, including co-authorship on meta-analyses that use the data from their original publication. The experience of recruiting data for HIVDB has provided us with an unparalleled understanding of the obstacles to data sharing and the means to overcome them.

HIVDB is a high-profile example of how data sharing can accelerate research and improve patient care. It has shown that aggregating raw data from many studies generates new knowledge that cannot be obtained from individual studies. As the use of genetic sequencing tests in clinical medicine increases, expert systems for interpreting these sequences have become increasingly necessary. The public accessibility and transparency of HIVDB and its genotypic drug-resistance interpretation programs have improved patient management and streamlined HIV treatment research. Ensuring the long-term sustainability of HIVDB and its data sharing tradition is critical to maintaining this resource for future decades and for demonstrating the viability of data sharing to researchers in other fields.

Grant Support

  • Campbell Foundation: A Combined Drug Resistance & Virus Load Point of Care Assay (2018-2019)
  • NIH/NIAID: HIV Drug Resistance Database R24 AI136618 (2018-2023)
  • NIH/NIAID: HIV Resistance Testing Using Point-of-Care Dynamic Solid-Phase Melt Analysis R21 AI131918 (2018-2020)
  • NSF: NSFDEB-BSF: Collaborative Research: The Fitness Cost of Every Single Mutation in the HIV Genome (2017-2020)
  • Stanford Center for Innovation in Global Health: Building HIV Drug Resistance Testing Capacity in LMICs - A Multidisciplinary Approach (2017)
  • Vela Diagnostics: Clinical Plasma Samples with Representative HIV-1 RT, Protease, and Integrase Drug Resistance mutations & Enhancing the HIVDB Genotypic Resistance Testing Interpretation Software (2016-2019).
  • NIH/NIAID: Public HIV Drug Resistance Database 1R01AI68581 (2006 - 2018)
  • Kaiser-Permanente: A Retrospective Analysis of Emergent Drug Resistance in Persons Experiencing Virological Failure in the Kaiser Permanente Medical Care Program - Northern California between 2010 and 2017. (2017-2018)
  • Gilead Sciences: HIV Drug Resistance Database (2015 - 2016)
  • Bristol Myers Squibb Transmitted Antiretroviral Resistance 2003 to 2012. Effect of Deep Sequencing on Prevalence of Drug Resistance Mutations and Response to First-Line Therapy (2014 - 2015)
  • Merck Transmitted Antiretroviral Resistance and Low-Abundance Drug Resistance Mutations: Implications for Raltegravir plus Truvada Therapy (2014 - 2015)
  • Bill & Melinda Gates Foundation: Consensus HIV-1 Drug Resistance Mutations for Point-Of-Care (POC) Genotypic Resistance Tests (2013 - 2014)
  • NIH/NIGMS: Targeting Ensembles of Drug Resistant Protease Variants Project: Conformational Flexibility of HIV-1 Protease in vivo. PO1 (2002 - 2011)
  • Gilead Sciences: Hepatitis B Minority Variants in Patients Treated with Lamivudine and/or Interferon (2009 - 2011)
  • Merck: Raltegravir: Clinical Use, Virological Response, and Drug Resistance in a Large Clinic-Based Population (2009 - 2010)
  • Hoffman La Roche Ultra-Deep Sequencing to Detect Resistance to Small Molecule Hepatitis C Inhibitors (2009 - 2011)
  • California HIV Research Program: Clinical Significance of Minority HIV-1 Variants (2009 - 2011)
  • NIH/Blood Systems Research Institute (R01 subcontract): Metagenomic Analysis of the Human Virome (2007 - 2011)
  • NIH/NIAID 1R01 AI46148-01: Identification of Multidrug-Resistant HIV-1 Isolates (1998 - 2008)
  • California University-Wide AIDS Research Program: Optimizing Clinical HIV Genotypic Resistance Interpretation (2004 - 2006)

Unrestricted Gifts

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Pharmaceutical Companies

  • Abbott Laboratories: 2000, 2002, 2004
  • Agouron Pfizer: 1998, 2000
  • Boehringer-Ingelheim: 2002, 2004
  • Bristol Myers Squibb: 2001, 2003, 2004, 2006, 2008
  • Dupont: 2001
  • Gilead Sciences: 1998, 2001, 2004, 2006, 2009
  • GlaxoSmithKline: 1999, 2000, 2002, 2003, 2006
  • Hoffman LaRoche: 1999, 2002, 2003, 2004, 2005, 2006
  • Idenix: 2004
  • Merck: 1998, 2000
  • Sequoia: 2006
  • Triangle: 2002

Diagnostic Companies

  • InSilixa: 2018
  • Applied Biosystems: 1998, 2000, 2001
  • Celera: 2003, 2005-2007, 2009, 2011, 2012, 2014
  • LabCorps: 2003
  • Quest Diagnostics: 1999
  • Roche Molecular: 1999, 2003, 2004, 2012
  • Siemens Health Care: 2009, 2012, 2014
  • Specialty Laboratories: 2000
  • SRA Life Sciences: 2000
  • Visible Genetics: 1999

Medical Education Companies

  • Academy of Continuing Education Programs: 2000
  • Clinical Care Options: 2005
  • Virology Networks: 1999, 2004

Non-Profit Organizations

  • CNYL Foundation: 2014
  • JM Kaplan Foundation: 2000