DR_SEQAN: a PC/Windows-based software to evaluate drug resistance using human immunodeficiency virus type 1 genotypes

Application of Radial Basis Function Network Tool for Correlation of CD4+ Count with Plasma Viral Load in HIV-Seropositive Individuals

INTRODUCTION

Human Immunodeficiency Virus (HIV) infects and cripples the immune system of the body. The two important marker CD4+T cells and Plasma viral load are crucial not only in understanding the disease progression but also in starting the antiretroviral therapy. A lot of research is going on in understanding the dynamic nature of HIV.

AIM

To find the correlation between CD4+ count and Plasma Viral Load (PVL) measured by two different technologies; with the help of correlation technique in conjunction with the three dimensional HIV model with a purpose of establishing a mathematical model between the CD4+ cells and PVL using a sinusoidal function as well as Radial Basis Function (RBF) neural network.

MATERIALS AND METHODS

Plasma Viral Load were determined by two different methods viz Exavir Cavidi(TM) and Abbott Real time HIV-1 assay and then they were correlated with the CD4+ count with the help of computational intelligence in predicting viral load.

RESULTS

It was found that there exists a positive correlation between the CD4+ cells and viral loads. A correlation value of 0.4082 and 0.3652 was observed between CD4+ cells and viral measured using Exavir Cavidi(TM) and Abbott Real time HIV-1 assay respectively.

CONCLUSION

The existence of positive correlation had helped us to understand the nature and dynamic of the existence of HIV and how the CD4 + and PVL act.

HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation

Introduction

The science of information systems, management, and interpretation plays an important part in the continuity of care of patients. This is becoming more evident in the treatment of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS), the leading cause of death in sub-Saharan Africa. The high replication rates, selective pressure, and initial infection by resistant strains of HIV infer that drug resistance will inevitably become an important health care concern. This paper describes proposed research with the aim of developing a physician-administered, artificial intelligence-based decision support system tool to facilitate the management of patients on antiretroviral therapy.

Methods

This tool will consist of (1) an artificial intelligence computer program that will determine HIV drug resistance information from genomic analysis; (2) a machine-learning algorithm that can predict future CD₄ count information given a genomic sequence; and (3) the integration of these tools into an electronic medical record for storage and management.

Conclusion

The aim of the project is to create an electronic tool that assists clinicians in managing and interpreting patient information in order to determine the optimal therapy for drug-resistant HIV patients.

A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and—more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

A comparison of interpretation by three different HIV type 1 genotypic drug resistance algorithms using sequences from non-clade B HIV type 1 strains

The advent of affordable ART has benefited HIV-infected individuals. Prospective studies have shown that the availability of drug resistance reports for infected individuals has allowed more effective regimens to be prescribed as compared to a control group whose physicians had no access to drug resistance reports. There is a paucity of information on the performance of genotypic algorithms on non-clade B HIV-1 strains, especially clade C. In this study the results obtained on submission of HIV-1 RT and PR sequences of non-clade B strains to the Stanford University HIV drug resistance database (SHDB) were compared to the results obtained from Geno2Pheno (G2P) and DR_Seqan (DS). For the study, we took samples from 93 treatment-naive individuals and 21 samples from 19 infected individuals showing detectable viral load while on ART. There were discrepancies in the clade identification results obtained from the SHDB and G2P databases. This feature was not available in DS. The mean observed concordance between SHDB and G2P was 85.6% while between SHDB and DC it was 37%. When the level of concordance was determined based on exposure to ART, the G2P was found to have a better level of concordance (76.8%) to SHDB as compared to SHDB versus DS (36%). We do not have phenotypic data for the strains included in this study and hence we are not in a position to assign a particular algorithm as being superior. These results also show a possible need for a subtype-specific algorithm for interpretation of HIV-1 genotypic drug resistance.

Mechanisms of resistance to nucleoside analogue inhibitors of HIV-1 reverse transcriptase

Human immunodeficiency virus (HIV) reverse transcriptase (RT) inhibitors can be classified into nucleoside and nonnucleoside RT inhibitors. Nucleoside RT inhibitors are converted to active triphosphate analogues and incorporated into the DNA in RT-catalyzed reactions. They act as chain terminators blocking DNA synthesis, since they lack the 3'-OH group required for the phosphodiester bond formation. Unfortunately, available therapies do not completely suppress viral replication, and the emergence of drug-resistant HIV variants is facilitated by the high adaptation capacity of the virus. Mutations in the RT-coding region selected during treatment with nucleoside analogues confer resistance through different mechanisms: (i) altering discrimination between nucleoside RT inhibitors and natural substrates (dNTPs) (e.g. Q151M, M184V, etc.), or (ii) increasing the RT's phosphorolytic activity (e.g. M41L, T215Y and other thymidine analogue resistance mutations), which in the presence of a pyrophosphate donor (usually ATP) allow the removal of chain-terminating inhibitors from the 3' end of the primer. Both mechanisms are implicated in multi-drug resistance. The excision reaction can be modulated by mutations conferring resistance to nucleoside or nonnucleoside RT inhibitors, and by amino acid substitutions that interfere with the proper binding of the template-primer, including mutations that affect RNase H activity. New developments in the field should contribute towards improving the efficacy of current therapies.

Mutational patterns and correlated amino acid substitutions in the HIV-1 protease after virological failure to nelfinavir- and lopinavir/ritonavir-based treatments

Human immunodeficiency virus type 1 (HIV-1) antiviral drug resistance is a major consequence of therapy failure and compromises future therapeutic options. Nelfinavir and lopinavir/ritonavir-based therapies have been widely used in the treatment of HIV-infected patients, in combination with reverse transcriptase inhibitors. The aim of this observational study was the identification and characterization of mutations or combinations of mutations associated with resistance to nelfinavir and lopinavir/ritonavir in treated patients. Nucleotide sequences of 1,515 subtype B HIV-1 isolates from 1,313 persons with different treatment histories (including naïve and treated patients) were collected in 31 Spanish hospitals over the years 2002-2005. Chi-square contingency tests were performed to detect mutations associated with failure to protease inhibitor-based therapies, and correlated mutations were identified using statistical methods. Virological failure to nelfinavir was associated with two different mutational pathways. D30N and N88D appeared mostly in patients without previous exposure to protease inhibitors, while K20T was identified as a secondary resistance mutation in those patients. On the other hand, L90M together with L10I, I54V, A71V, G73S, and V82A were selected in protease inhibitor-experienced patients. A series of correlated mutations including L10I, M46I, I54V, A71V, G73S, and L90M appeared as a common cluster of amino acid substitutions, associated with failure to lopinavir/ritonavir-based treatments. Despite the relatively high genetic barrier of some protease inhibitors, a relatively small cluster of mutations, previously selected under drug pressure, can seriously compromise the efficiency of nelfinavir- and lopinavir/ritonavir-based therapies.