Mon, 23 March 2020
Robert M. Campbell, Ph.D., is Senior Research Advisor at Eli Lilly and Company in Indianapolis, IN, where he heads the Cancer Cell Growth and Epigenetics Group. Dr. Campbell has served as Editor-in-Chief of SLAS Discovery for the past 12 years, where he has been a vital leader to grow the journal and share its important content. In this podcast, Dr. Campbell talks with our SLAS Discovery Podcast Editor Rob Howes, Ph.D. (AstraZeneca Gothenburg, Sweden) to discuss his work with the journal, as well as a little history about SLAS and the journal, and to make a call to authors and readers about upcoming content and his interest to work with listeners to publish and share their work. For more about Dr. Bob Campbell, read his ELN member profile article, Drum Roll Please! For more information about the journal, visit the SLAS Discovery website or contact Dr. Campbell directly at email@example.com.
Mon, 2 March 2020
SLAS Technology Authors Talk Tech: CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence
The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.
Link to journal article: https://journals.sagepub.com/doi/full/10.1177/2472630319890316
Link to issue: http://journals.sagepub.com/toc/jlad/25/2