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LPIXEL, the University of Dundee, and Medicines for Malaria Venture (MMV) are partnering, with financial support from the Global Health Innovative Technology Fund, to develop a platform that uses advanced image analysis and machine learning to accelerate the discovery of novel antimalarials. Specifically, the platform will use images of stained parasite cells to better understand a compound's biological impact on the malaria parasite, providing key insights into its mode of action, a process that is usually months-long. As the emergence of parasite resistance to antimalarials poses a new threat to malaria control and elimination efforts, this new platform could help pave the way for new and effective therapies. MMV will also make the platform open access, enabling researchers around the world to use the technology to improve drug discovery efforts across a range of diseases.
Results from a multi-country Phase 3 clinical trial published last week found that a shorter all-oral drug regimen was effective in some patients with pre-extensively drug-resistant tuberculosis (TB) but with mixed results. The trial tested the efficacy of 6- and 9-month regimens compared to the World Health Organization-recommended 18-month regimen, finding that while the shorter regimens had a high success rate, they did not meet the criteria to be considered at least as effective as the standard, in part because patients with extensive disease did not respond as well as those with limited disease. The trial provides important insights to guide future research into shorter drug regimens for those with drug-resistant TB.
A team of researchers from Austria and Ghana recently published the results of a study testing a new pathogen detection test, finding that it was highly effective and suitable for low-resource settings. The test demonstrated high sensitivity and specificity, maintained its efficacy while being transported long distances at room temperature, and is extremely affordable. The researchers have made the test protocol open source, allowing it to be used to address critical unmet needs in low- and middle-income countries and other low-resource settings—it is already been used to develop an HIV test and a test for influenza and the virus that causes COVID-19. This research effort stemmed from challenges with implementing large-scale testing during the COVID-19 pandemic.