Analysis of HIV-1 resistance to the CCR5 antagonist maraviroc

Lead Research Organisation: University of Manchester
Department Name: Life Sciences


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Technical Summary

In terms of the importance of the project, the HIV/AIDS pandemic continues to remain a large burden on human health and global economies, and the success of anti-viral drugs - the only viable treatment available to patients - is directly related to the probability of resistance emerging. In the case of maraviroc, testing for the presence of CXCR4-using virus must additionally be performed prior to the drug being used. For this reason it is imperative to study drug resistance and understand its emergence. Additionally, HIV‘s ability to use the drug-bound CCR5 receptor, exemplifies the plasticity of the virus; its ability to change itself and maintain an infection.

The unique selling points of this Exchange are (i) access for the Fellow to Pfizer data‘s from clinical trials with freedom to share knowledge and results with the wider scientific community, (ii) the virology and complementary bioinformatics expertise in both the Industrial and Academic organisations and (iii) our demonstrated (and already productive) working collaboration between the Industrial-Academic partners.

In the Exchange our research objective is to identify sequence and structural characteristics that can be used to predict probable resistance to the CCR5-antagonist maraviroc. This knowledge will be used to determine the feasibility of a predictive algorithm using a combination of structural and genotypic methods, which can be tested in the phenotypic assays performed by Pfizer. The subsequent implementation of the algorithm would enable susceptibility to maraviroc (CXCR4-use and R5 resistance) to be predicted at screening by envelope V3 loop genotyping.

To achieve this, genotypic data from HIV-1‘s envelope region will be studied to determine key residues in the sequence that are associated with different methods of binding to the CCR5 receptor using structural analysis. These sites will then be investigated at Pfizer in the phenotypic maraviroc susceptibility assay to te the genotypic analysis. The genotypic analysis will be combined with the structural information to develop a computational method for the prediction of resistance that can be tested in data generated by the 1095 trial. The resulting data will be used to develop a novel sequence based algorithm for predicting response to maraviroc, e.g., using position specific scoring matrix (PSSM), support vector machines (SVMs) and hidden markov models (HMM).

While we are clear on this analysis framework and the importance of the problem, the time and input required to perform this work will require the full-time RA requested, and this is the justification for the type of Exchange requested.

To conclude, Pfizer‘s interest in (and knowledge of) maraviroc resistance, combined with the Fellow‘s highly specific research expertise in HIV-1 evolution and diversity make this a perfect partnership. Our approach will combine expertise in sequence analysis, molecular phylogenetics and computational structural biology. In terms of putting the analysis into the context of protein structure, DR has an active collaboration with Dr Simon Lovell at the Univ. of Manchester. They have co-authored a number of important research articles including articles in high impact journals such as MBE PNAS and Nature Biotech (see annex 1 for citations).
Description WT ISSF award
Amount £10,000 (GBP)
Organisation University of Manchester 
Sector Academic/University
Country United Kingdom
Start 01/2012 
End 10/2013
Title Software 
Description Software for predicting HIV-1 tropism 
Type Of Material Model of mechanisms or symptoms - human 
Year Produced 2013 
Provided To Others? Yes  
Impact Research article in preparation arising from use of tool by collaborators. 
Description Genotypic prediction of maraviroc sensitivity in a clinical setting 
Organisation Royal Free Hospital
Department Centre for Virology
Country United Kingdom 
Sector Hospitals 
PI Contribution Results and methods arising from MRC project on HIV-1 drug resistance to be investigated in a clinical setting.
Collaborator Contribution Daniel Webster at the Royal Free and I have submitted a joint research proposal to ViiV Healthcare for funding.
Impact Multi-disciplinary: computational biology/bioinformatics and virology. Collaboration has resulted in some conference presentations and we have an article in preparation on analysis of Royal Free data using software arising from this project.
Start Year 2012
Title Software for HIV-1 tropism prediction 
Description Trogen is a novel genotypic tropism prediction algorithm. Based on the V3 loop sequence of HIV-1's envelope gene and its structure, it classifies viral sequences as CCR5- or CXCR4-using, based on transitions between 35 residues in close structural proximity within the V3 loop. 
Type Of Technology Software 
Year Produced 2014 
Open Source License? Yes  
Impact Novel software for predicting HIV-1 drug resistance/tropism in the context of entry-inhibitor drugs.