I worked with DaVaughn Lauderdale on this project. Our research topic was: Nineteen antiretroviral drugs have been approved for the treatment of HIV-1 infection: one nucleotide and seven nucleoside reverse transcriptase inhibitors (NRTIs), seven protease inhibitors (PIs), three nonnucleoside RT inhibitors (NNRTIs), and one fusion inhibitor. In previously untreated individuals with drug-susceptible HIV-1 strains, combinations of three or more drugs from two drug classes can lead to prolonged virus suppression and immunologic reconstitution.
There have been several studies on this theory since its age. Most recently researchers have been trying to identify drug resistance mutations in HIV-1 protease this has been going on for the last 20 years as well. The drugs target the protein in the HIV virus. How it works is when it the drugs attack the protein it mutates and then the drug becomes less effective, hence the fact that there is no cure for the virus. HIV is constantly evolving new drug resistance mutations, often within weeks of introduction of a new drug. Thus, a completely automated approach for identifying drug resistance mutations would be valuable, yet very expensive. According to Stanford Univ there are three fundamental types of correlations form the basis of drug resistance knowledge: (i) Correlations between genotypic data with the treatments of persons from whom sequenced HIV-1 isolates have been obtained (genotype-treatment); (ii) Correlations between genotype and in vitro drug susceptibility (genotype-phenotype); and (iii) Correlations between genotype and the clinical response to a new treatment regimen (genotype-outcome). The following table summarizes the results of published studies linking genotype and the clinical response to a new PI-containing regimen.
· Send Amino acid sequences to Quickphyre
· Make graphs from info received
· Quantitatively analyze the graphs |
