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Analyzing Complex Resistance Patterns of Protease Inhibitors with Bio-informatics Resistance Determination, a Novel Approach Employing Synthetic Viral Genes Carrying Clinically Relevant Patterns of PI-resistance Mutations
Herwig Van Marck*1, G Meersseman1, I Dierynck1, L Borozdina2, G Kraus1, M P De Béthune1, S Hallenberger1, and K Hertogs1
1Tibotec, Mechelen, Belgium and 2Centocor / EGEA, San Diego, CA, US
Background: The increasing
incidence of HIV-1 protease inhibitor (PI) cross-resistance necessitates the
better understanding of the molecular basis of resistance development to
currently marketed, as well as investigational PI. The aim of this study is to
investigate the differential impact of multi-drug-resistance-associated
mutations involved in resistance to such drugs.
Methods: A novel bio-informatics resistance determination (BIRD)
approach was established. An analysis of our in-house database of matching
phenotypic and genotypic profiles of HIV-1 clinical isolates was performed,
yielding a defined set of multi-PI-resistant viruses (sources) and a
corresponding set of genetically closely related PI susceptible viruses
(targets). For each source we constructed a set of synthetic protease genes
carrying all combinations of resistance mutations that differ between source
and target, representing multiple genetic pathways. Those synthetic gene
products were used for recombinant protein or virus production to build a
multidimensional dataset, that helps to delineate the genetic pathways involved
in HIV-1 resistance to PI.
Results: In a
proof-of-concept study, we selected 2 source viruses to construct a set
comprising 40 synthetic protease gene variants carrying the primary PI
mutations L33F, M46I, I50V, I84V, and L90M in a background of
resistance-associated mutations—L10I, K20I, M36I, I47V, I54L, A71V, and V77I—in
different combinations. Removal of the mutations L33F and A71V from their
sources had a significant effect on viral growth resulting in an impaired
viability. Subsequent phenotypic characterization (21 of 40 viruses) showed
that L33F, I47V, and I54L in this PI resistance genetic background played a
crucial role in reduced susceptibility to marketed PI (e.g., amprenavir and
lopinavir). In addition we observed
that the binding affinity (Surface Plasmon Resonance) between such PI
and multi-drug-resistance proteases was often reduced, mainly as a result of
increased dissociation rates.
Conclusions: The BIRD
approach elaborates on genetic profiles observed in the clinic and allows the
assessment of the influence of all combinations of mutations in complex,
PI-resistant genetic background. Future work includes measurement of binding
kinetics between mutant proteases and PI. This novel approach will guide our
further understanding of the molecular basis for resistance development to
current and investigational PI (e.g., TMC114).
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