OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
Falciparum malaria, a devastating parasitic disease affecting millions of people each year, is treated using fixed dose combination treatments, the Artemisinin Combination Therapies (ACTs) over 3 days of dosing. The constant threat of resistance and the need to deliver alternative options to treat patients in the event of all ACTs failing, as well as the need for improved drugs against other human-infecting Plasmodium parasites, has led to Medicines for Malaria Venture (MMV) and its partners, in collaboration, to build up a portfolio of projects and compounds focused on the treatment and prevention of malaria. MMV’s mission is to reduce the burden of malaria in disease-endemic countries by discovering, developing and facilitating delivery of new, effective and affordable antimalarial drugs in collaboration with international partners.
MMV manages a significant antimalarial pipeline, and this has been strengthened in recent years with the delivery of new products, new clinical candidates and early-stage discovery projects. The talk will outline the progress made, against the ever present challenge of resistance emergence to frontline therapies, and the complexities required to deliver impactful products for control and elimination. In particular, our focus is on delivering differentiated candidate drugs with long human half-lives and duration of cover either from oral or intra-muscular dosing. In this context, new thinking and modes of working, such as optimizing specifically to reduce the resistance risk, utilizing unbound pharmacokinetic and pharmacodynamic parameters to better design and estimate dose and machine learning opportunities will be highlighted along with specific candidate drug case studies.