HIV-1 drug resistance genotyping. A review of clinical and economic issues

Identifying representative drug resistant mutants of HIV

BACKGROUND

Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs.

RESULTS

In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants.

CONCLUSION

This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.

Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors

The drug resistance of various clinically available HIV-1 protease inhibitors has been studied using a new computational protocol, that is, computational mutation scanning (CMS), leading to valuable insights into the resistance mechanisms and structure-resistance correction of the HIV-1 protease inhibitors associated with a variety of active site and nonactive site mutations. By using the CMS method, the calculated mutation-caused shifts of the binding free energies linearly correlate very well with those derived from the corresponding experimental data, suggesting that the CMS protocol may be used as a generalized approach to predict drug resistance associated with amino acid mutations. Because it is essentially important for understanding the structure-resistance correlation and for structure-based drug design to develop an effective computational protocol for drug resistance prediction, the reasonable and computationally efficient CMS protocol for drug resistance prediction should be valuable for future structure-based design and discovery of antiresistance drugs in various therapeutic areas.

Antiretroviral resistance in individuals presenting therapeutic failure and subtypes of the human immunodeficiency virus type 1 in the Northeast Region of Brazil

This study aimed to analyze human immunodeficiency virus (HIV) mutation profiles related to antiretroviral resistance following therapeutic failure, and the distribution of hiv subtypes in the Northeast Region of Brazil. A total of 576 blood samples from AIDS patients presenting therapeutic failure between 2002 and 2004 were analyzed. The genotyping kit viroSeq was used to perform viral amplification in order to identify mutations related to hiv pol gene resistance. An index of 91.1% of the patients presented mutations for nucleoside reverse transcriptase inhibitors (nrti), 58.7% for non-nucleoside reverse transcriptase inhibitors (nnrti), and 94.8% for protease inhibitors (pi). The most prevalent mutations were 184V and 215E for nrti, 103N and 190A for nnrti. Most mutations associated with PIs were secondary, but significant frequencies were observed in codons 90 (25.2%), 82 (21.1%), and 30 (16.2%). The resistance index to one class of antiretrovirals was 14%, to two classes of antiretrovirals 61%, and to three classes 18.9%. Subtype B was the most prevalent (82.4%) followed by subtype F (11.8%). The prevalence of mutations related to nrti and nnrti was the same in the two subtypes, but codon analysis related to PI showed a higher frequency of mutations in codon 63 in subtype B and in codon 36 in subtype F. The present study showed that there was a high frequency of primary mutations, which offered resistance to nrti and nnrti. Monitoring patients with treatment failure is an important tool for aiding physicians in rescue therapy.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Retrospective analysis of antiretroviral HIV treatment success based on medical history or guided by the reverse hybridisation LiPA HIV genotyping system

The changes in viral load and CD4(+) count at 3 and 6 months in a group of 166 HIV-infected patients was evaluated. The new therapy was chosen based on the medical history procedures for 70 patients, and in 96 patients it was guided by the partial or complete result of the line probe assay (LiPA) HIV RT and Protease resistance tests. The absolute difference from the baseline of the log viral load at 3 and 6 months was significantly different between the two groups when adjusted for baseline viral load (P < 0.0001) and stayed significant when intention-to-treat analysis was carried out (P < 0.001). The absolute difference of the CD4(+) count was not significantly different when adjusted for baseline CD4(+) (P = 0.854, 3 months; P = 0.06, 6 months). The proportion of patients with a viral load </=200 cp/ml in the medical history group (14.5%, 3 months; 15.2%, 6 months) was significantly different from the proportion of responders in the LiPA group (28.7%, 3 months, P = 0.03; 34.7%, 6 months, P = 0.008). In the intention-to-treat population, the difference between the two groups remained significant (P = 0.01). There was no statistical difference between the two groups in terms of adherence (P = 0.88), number of drug failures (P = 0.12) and for the time since starting treatment (in years) (P = 0.48), but there was a significant difference for the number of new drugs in the new regimen (P < 0.0001) and for the pill burden of the treatment (P = 0.0006). A higher antiretroviral HIV treatment success guided by the LiPA HIV genotyping system than that based on the medical history only is reported. Of note, this study reached the same conclusions as previous studies, which all used sequencing.

Impact of Genotypic Resistance Testing on Selection of Salvage Regimen in Clinical Practice

Objective

To determine whether genotypic resistance testing leads to selection of more potent drug regimens when compared to regimens based on treatment history only.

Design

Prospective, tertiary care centre-based study. Patients: One-hundred-and-forty-five HIV-infected adults on stable antiretroviral therapy (ART) for >6 months experiencing virological failure.

Methods

The physicians’ decision-making process when choosing a salvage regimen was prospectively documented: at time of virological failure, on ‘failing ART’, genotyping was performed and a hypothetical ‘clinical expert ART’ based upon patient's drug history was documented. Subsequently, data on resistance mutations, rating by a decision support software and drug history were used to define ‘genotyping ART’. After discussion with the patient, final treatment, ‘new personalized ART’ was chosen and prescribed. To compare the relative potency of the four ART regimens in a standardized manner, a resistance score ranging from 1 (best) to 8 (worst) based on drug ranking by decision support software was attributed to each ART regimen. Virological and immunological outcomes were analysed based on the magnitude of the resistance score.

Results

Median follow-up was 1.5 years. In all 145 patients, median resistance scores for the stepwise selected ART regimens were: ‘failing ART’: 4.5, ‘clinical expert ART’: 1.8, ‘genotyping ART’: 1.5 and ‘new personalized ART’: 2. The latter was 1.5 in patients who effectively switched to ‘new personalized ART’ ( n=89). Lower resistance scores translated into significantly improved virological response after initiation of ‘new personalized ART’. In multivariable analysis, lower resistance scores, lower baseline HIV RNA levels and use of novel antiretroviral drugs were associated with the probability of reducing plasma viraemia to <50 copies/ml. Conclusions: This study suggests that treatment choices including genotype and decision support software were virologically superior to those based on drug history only.

Molecular diagnostics as a predictive tool: genetics of drug efficacy and toxicity

There is a rapidly growing body of evidence linking genetic polymorphisms with functional changes in proteins that are responsible for the metabolism and disposition of many medications. Likewise, polymorphisms in genes encoding the targets of medications (e.g. receptors) can alter the pharmacodynamics of the drug response by changing receptor sensitivity. As a result, the inherited basis of drug effects is often polygenic in nature, and thus more challenging to define. However, technological advances, coupled with new insights into the molecular pharmacology of medications and the functional consequences of polymorphisms in the human genome, are providing the tools needed to elucidate genetic determinants of drug response, and translate functional genomics into personalized medicine.

Humans as the world's greatest evolutionary force

In addition to altering global ecology, technology and human population growth also affect evolutionary trajectories, dramatically accelerating evolutionary change in other species, especially in commercially important, pest, and disease organisms. Such changes are apparent in antibiotic and human immunodeficiency virus (HIV) resistance to drugs, plant and insect resistance to pesticides, rapid changes in invasive species, life-history change in commercial fisheries, and pest adaptation to biological engineering products. This accelerated evolution costs at least $33 billion to $50 billion a year in the United States. Slowing and controlling arms races in disease and pest management have been successful in diverse ecological and economic systems, illustrating how applied evolutionary principles can help reduce the impact of humankind on evolution.