In the context of international development, the ‘best’ often becomes the enemy of the ‘good’. This JLI project takes the opposite approach: ‘how can the good turn into the best’? Such an approach is typical for one of the aspects of the overall strategy of JLI, which is to seed-fund daring global health projects that may be controversial and less eligible for funding by established funding institutions. Such JLI seed-funding is meant to generate preliminary results, that can convince more conventional funders to take over and invest in the next phase(s) of the pertinent project. Criteria for such JLI funding include that the project should address a compelling global health problem, the solution should contain a substantial component of digital technology, the results of the project should in principle be more universally applicable.
This project is about a relatively simple and at first glance quite ‘sloppy’ biomedical assay and subsequent big data analytics that could make a true difference in the area of combating HIV. The project is performed in collaboration with renowned researchers from the Harvard School of Public Health, USA.The HIV virus is developing resistance to the current drugs. This research looks for faster and cheaper methods to determine drug-resistance among HIV+ populations
HIV treatment in resource-poor settings is one of the most successful interventions in modern medical history. However, this comes at a price: HIV is developing resistance to the drugs being used. Population-wide HIV drug resistance is growing worldwide, particularly also in Eastern and Southern African countries. Countries need insights in the level of HIV drug resistance in their HIV+ populations in order for the policy makers to make the right decisions for purchasing HIV drugs. Today, such information is obtained through expensive and laborious national HIV drug resistance monitoring and surveillance approaches along the guidelines of the World Health Organisation (WHO).
WHO monitoring and surveillance for HIV drug resistance currently involves laborious and expensive DNA sequencing work on hundreds of individual patient samples that are carefully collected all over the country. Typically, such surveys cost in the order of 100,000 euro and take a year to perform. In the current project, a radical simplification is proposed: instead of working on individual samples of patients, we mix these samples into one single ‘soup’. Subsequently we use very modern DNA deep-sequencing techniques and powerful statistics to draw conclusions on HIV drug resistance for the pertinent mix. With continuously decreasing prices of DNA sequencing and statistical analytics, the price for such an approach will be substantially lower (in the range of 10x) and results can be obtained in 1-2 months.
If proven of sufficient sensitivity and specificity, the ‘pooled HIV drug resistance monitoring approach’ might become the new standard worldwide for HIV drug resistance monitoring.