Stanford University HIV Drug Resistance Database - A curated public database designed to represent, store, and analyze the divergent forms of data underlying HIV drug resistance.

Drug Resistance Mutations Data Model

Last updated on 9/00
 

In early 2000, two expert panels recommended that resistance testing be incorporated into patient management [1,2]. However, the results of both genotypic and phenotypic tests are complex and require expert interpretation if they are to be used successfully to help guide physicians in their use of antiretroviral therapy. Unfortunately, there currently are no guidelines on precisely how resistance tests are to be interpreted in clinical situations. The first step towards correcting this situation is to take stock of what we know about drug resistance and where our knowledge comes from.

Many practicing physicians and clinical investigators may not be aware that most conventional wisdom about drug resistance derives from studies performed solely during the pre-clinical and early clinical development of a new drug. Information arising after a drug has been approved by the FDA and has been incorporated as a clinical tool is often not included on many of the "gene charts" that many physicians and reference labs rely on for interpreting genetic sequence data. Thousands of patients may have been treated inappropriately because sequence interpretations based on such oversimplified charts can be inaccurate. Additionally, complacent reliance on simple yet inaccurate data has masked the need for the additional research necessary to interpret genotypic tests in a clinically meaningful manner.

The interpretation of HIV genotypic assays in clinical settings is formidable because of the large numbers of drug resistance mutations and because these mutations interact with one another and emerge in complex patterns. Moreover, a disproportionate amount of current HIV drug resistance knowledge is derived from preclinical and early clinical drug development studies and there are major gaps in what is known about HIV drug resistance in patients receiving the complex drug combinations that represent the standard of care today.

HIV genotypic analysis consists of two distinct steps: the laboratory work to determine the nucleic acid sequence and the drug resistance interpretation which includes translating the nucleic acid sequence to amino acids, comparing the amino acid sequence to a reference consensus sequence to derive a list of mutations, and reporting the mutations together with their biological and clinical significance. Laboratories performing HIV genotypic analysis should provide physicians with the option of receiving the raw nucleic acid sequence data because online programs are available to assist in sequence interpretation.

There are four sources of data that form the basis for drug resistance knowledge:

  1. Genotypic-phenotypic correlations on laboratory isolates (often confirmed by site-directed mutagenesis studies);

  2. Genotypic-phenotypic correlations on clinical HIV-1 isolates;

  3. Correlations between HIV-1 genotype and the treatment history of patients from whom sequenced virus isolates are obtained;

  4. Correlations between HIV-1 genotype and the virologic response to a new treatment regimen.

(1) Genotypic-phenotypic correlations performed during early drug development: Drug-resistance mutations have traditionally been identified during the pre-clinical and initial clinical evaluation of a new antiretroviral drug. The pre-clinical evaluation involves culturing a wildtype HIV isolate in the presence of increasing concentrations of the drug being studied, and identifying mutations that allow the virus to continue to replicate. Site-directed mutagenesis experiments are done to confirm that the mutations arising during virus passage in the presence of the drug will confer drug resistance when introduced directly into a wildtype virus. Likewise, viruses that are cultured from patients during "phase I" or dose-finding studies of a new drug are sequenced ,tested for resistance, and confirmed by site-directed mutagenesis. Drug resistance mutations identified by this process acquire widespread acceptance as the predominant mutations responsible for resistance to the drug under evaluation, and are referred to as "canonical" resistance mutations.

While the process of characterizing resistance mutations by susceptibility testing and site-directed mutagenesis is the most rigorous means of demonstrating that a particular mutation confers drug resistance, there are several limitations with this approach. First, these data identify mutations that are sufficient to cause resistance to the drug under evaluation without proving that these mutations are in fact necessary for the development of drug resistance. Second, these data are generally derived from laboratory isolates containing only one or two drug-resistance mutations and rarely reflect the more complicated patterns of mutations observed in clinical isolates from patients receiving combination drug therapy.

(2) Genotypic-phenotypic correlations performed on clinical HIV-1 isolates: Clinical isolates are highly variable and the protease and RT sequences of clinical isolates may differ from the consensus B reference sequence at many positions. A portion of these differences may be well known drug-resistance mutations, but many are uncharacterized. Some of the uncharacterized mutations may be polymorphisms occurring commonly in isolates from untreated patients while others may contribute to drug resistance. Unlike laboratory isolates the sequences of clinical isolates are typical of those faced by physicians in clinical practice. However, the complexity of the sequences prevents direct correlation between resistance phenotypes and individual mutations, and also rules out site-directed mutagenesis, which would prove too costly and time consuming to perform for each possible combination of mutations.

Despite these difficulties, the systematic study of genotypic and phenotypic data on clinical isolates from large numbers of patients can often reveal previously unsuspected patterns of mutations. Comparing these mutation patterns will help elucidate how known and uncharacterized drug resistance mutations interact with one another in vivo to contribute to drug resistance. The potential importance of genotypic-phenotypic correlations on clinical isolates is underscored by the fact that at least one company (Virco, Mechelen, Belgium) has created a proprietary database linking genotypic data to phenotypic drug susceptibility results [3].

(3) Correlations between HIV-1 genotype and the treatment history: Correlations between certain drug treatments and the corresponding mutations in HIV strains isolated from patients receiving the treatment provides additional knowledge about drug resistance. First, such correlations show which mutations and which mutation patterns occur in patients (rather than just in vitro). Second, such correlations are essential for elucidating the genetic mechanisms of resistance to drugs that are difficult to test in vitro. This is the case for the nucleoside RT inhibitors ddI, ddC, and d4T. For example, the canonical d4T-resistance mutation is V75T. However, this mutation is extremely rare clinically. In contrast, patients receiving d4T commonly exhibit the classical AZT-resistance mutations. This has provided a strong clue to the mechanism of d4T resistance even though phenotypic assays using that drug have difficulty detecting drug resistance.

(4) Correlation between genotype and clinical outcome. Evidence suggesting the clinical utility of drug resistance testing has come from both retrospective and prospective intervention-based studies. The retrospective studies have been invaluable in allowing physicians to infer how patients with a particular mutation will respond to a new anti-HIV treatment regimen. Recent examples include two studies from Stanford University Medical Center that have examined the genotypic correlates of response to salvage therapy with ritonavir/saquinavir and efavirenz containing regimens [4,5].

In conclusion, there is much more to know about drug resistance than the canonical mutations identified during pre-clinical testing of a new drug. The additional types of knowledge described here are likely to be clinically extremely relevant. However, it is often not feasible for practicing physicians to keep track of all sources of knowledge in this field. Therefore, it is incumbent upon providers of resistance assays to keep track of drug resistance data and to incorporate this knowledge into their drug resistance reports. This database currently focuses on the first three types of correlations. However, the schema of the database is being expanded to include the fourth type of correlation.



References:

1) Panel on Clinical Practices for Treatment of HIV Infection. Guidelines for the use of antiretroviral agents in HIV-infected adults adolescents [on-line]. www.hivatis.org/trtgdlns.html Department of Health and Human Services, (2000).

2) Hirsch MS, Brun-Vezinet F, D'Aquila RT, Hammer SM, Johnson VA, Kuritzkes DR, Loveday C, Mellors JW, Clotet B, Conway B, Demeter LM, Vella S, Jacobsen DM, Richman DD. Antiretroviral drug resistance testing in adult HIV-1 infection: recommendations of an International AIDS Society-USA Panel. JAMA. 2000 May 10;282(18):2417-26.

3) Larder B, De Vroey V, Dhertogh P, Kemp S, Bloor S, Hertogs K. Predicting HIV-1 phenotypic resistance from genotype using a large phenotype-genotype relational database [abstract 59]. Antiviral Therapy 1999;4(Supplement 1):41.

4) Zolopa AR, Shafer RW, Warford A, Montoya JG, Hsu P, Katzenstein D, Merigan TC, Efron B. HIV-1 genotypic resistance patterns predict response to saquinavir-ritonavir therapy in patients in whom previous protease inhibitor therapy had failed.Ann Intern Med. 1999;131:813-21.

5) Shulman NS, Zolopa AR, Passaro DJ, Murlidharan U, Israelski DM, Brosgart CL, Miller MD, Van Doren S, Shafer RW, Katzenstein DA. Rapid Communication: Efavirenz- and Adefovir Dipivoxil---Based Salvage Therapy in Highly Treatment-Experienced Patients: Clinical and Genotypic Predictors of Virologic Response. JAIDS 2000;23:221-6.

Last update: 9/12/00