Not more than twenty years ago, only four or five blood cancers could be defined; today, hundreds of different types of lymphoma, leukemia, and myeloma cancers have been characterized—each one with a unique molecular structure and genome. As a result, an enormous amount of data has become available in the area of research and development for cancer drugs. According to Panna Sharma, CEO of Lantern Pharma, there is no better place to apply the tools of machine learning and artificial intelligence (AI). “There could be more permutations of chemical compounds than there are galaxies, so it’s a perfect problem area for AI,” says Sharma. He goes on to explain why some drugs—despite being extremely effective for some people—don’t stay on the market. In addition to depriving patients access to a drug which could truly help them, this also represents a significant financial loss, considering that hundreds of millions of dollars can go into the development of a single cancer drug. The technology being employed by Lantern Pharma has the ability to identify which drugs have worked for some people in the past, why they worked, and how to use this information as a predictive measure of what may (or may not) work for any given patient with a particular type of cancer. Sharma is a wealth of knowledge, and discusses the details of all this and more, including: How many cancer drugs are already built in to the Lantern Pharma model How many gene expression profiles there are within a given tumor The importance of understanding a tumor’s response to a given drug and how Lantern Pharma is trying to elucidate this in prostate cancer Learn more at https://www.lanternpharma.com/.