Finding new ways to repurpose or combine existing drugs has proved to be a powerful tool to treat complex diseases. Drugs used to treat one type of cancer, for instance, have effectively strengthened treatments for other cancer cells. Complex malignant tumors often require a combination of drugs, or "drug cocktails," to formulate a concerted attack on multiple cell types. Drug cocktails can not only help stave off drug resistance but also minimize harmful side effects. But finding an effective combination of existing drugs at the right dose is extremely challenging, partly because there are near-infinite possibilities.
Facebook claims that its new artificial intelligence can predict the way drugs interact with each other inside cells quicker than existing methods, enabling speedier discovery of new drug combinations to treat illnesses like cancer, but some researchers say it may not translate into results that will be useful in humans. The system, developed by Facebook AI Research and the Helmholtz Centre in Munich, Germany, is claimed to be the first easy-to-use AI model able to estimate how different drugs will work in the body. It could speed up our ability to uncover new treatments for diseases like cancer. "Drug research often takes half a decade to develop a compound," says Fabian Theis at the Helmholtz Centre, one of the authors of the work. The model works by measuring how individual cells change in response to treatment from a particular set of drugs and recording those responses.
A team of UCLA bioengineers has demonstrated that its technology may go a long way toward overcoming the challenges of treatment for acute lymphoblastic leukemia, among the most common types of cancer in children, and has the potential to help doctors personalize drug doses. The five-year survival rate for individuals with pediatric acute lymphoblastic leukemia is about 85 percent, however those who experience a recurrence generally have a poor prognosis and a bone marrow transplant is their only option for a permanent cure. Conventional treatment for this leukemia includes a combination of drugs, which come with short- and long-term side effects. Two of these drugs, 6-mercaptopurine and methotrexate, can cause liver disease and other life-threatening infections. During the maintenance phase of treatment, which aims to keep individuals in remission, dosing for these two drugs is frequently adjusted through a system of trial and error, which is not always accurate.
You are free to share this article under the Attribution 4.0 International license. To more quickly identify drug combinations, such as those that might treat COVID-19, researchers have come up with an artificial intelligence platform called IDentif.AI. Traditionally, when dangerous new bacterial and viral infections emerge, the response is to develop a treatment that combines several different drugs. However, this process is laborious and time-consuming, with drug combinations chosen sub-optimally, and selection of doses a matter of trial and error. This costly and inefficient way of developing a treatment presents problems when a rapid response becomes crucial to tackle a global pandemic and resources need to be conserved.
The spread of tuberculosis (TB) has diminished in the developed world, but it is still prevalent in the developing parts of the world such as in Asia and Africa. The rise of HIV in the 1980's also saw an increase in TB infections due to the weakened immune systems of patients with HIV. Currently about 1.6 million people die from TB each year, and 10 million people develop active TB infections, which is also contagious. Tuberculosis is caused by Mycobacterium tuberculosis bacteria and it generally affects the lungs. Individuals can harbor the TB bacteria but show no symptoms.