Drug resistance is a persistent and worldwide problem that has emerged as a grave and significant threat to public health, reducing the effectiveness of therapies that treat bacteria, parasites, viruses, fungi and cancer. Examining how genetic variation associated with resistance to therapeutic treatments manifests at the molecular level and how it affects organism fitness is vital to the development of new therapeutics. Moreover, there is potential to use such molecular level insights to identify future variations more prone to leading to resistance and answer specific questions around the modes of resistance in individual infectious diseases.
Identifying drug resistance mutations
Within protein coding regions resistance can be confirmed through a number of mechanisms including modulation of the drug compound’s target or, as many infectious disease drugs are delivered as pro-drugs, modulation of the proteins that convert the pro-drug to its active form. Mutations resulting in changing the profile of compounds that can be transported by efflux pumps is another means by which resistance can be introduced. Alternative routes to resistance that do not involve protein coding mutations include: the protection of drug targets by post-translational modifications such as methylation; the presence and increased expression of proteins that bind to targets preventing drug interactions; or limiting the physical entry of the drug compound to the cell or increasing the efficiency of drug efflux through increasing the type or number of drug efflux pumps. The project has so far focused on analysing those mutations in protein coding regions.
The aim of this project is to develop computational tools for automatically analysing the molecular consequences of mutations linked with therapeutic resistance and to use this wide-ranging analysis to build a predictive model to identify future mutations that could lead to therapeutic failure. By generating a few broad rules, we have been able to identify resistance mutations in whole genome Tuberculosis sequences, chemotherapeutic and HIV inhibitor resistance mutations, and antibody escape mutations in HIV and influenza. Using this to predict and anticipate how and when resistance is likely to arise will allow implementation of better drug use management to prolong drug efficacy. This is being developed as part of a platform to rapidly identify drug resistance in the clinic, guiding public health policy and patient treatment.
Designing resistance resistant treatments
If we can identify likely resistance mutations before they arise, it could be used to guide drug and biotherapeutic development in order to avoid sites identified as likely resistance hot-spots. This could reduce and/or delay the incidence of resistance. The ultimate goal is to integrate this analysis along with our pharmacokinetic and toxicity predictions into an integrated system where all the parameters can be explored and optimised in real time to aid the development of resistance resistant therapies.
