Credit: David James Group
Oak Brook, IL – Just released is the April edition of SLAS Technology featuring cover article, “CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence,” by Agata Blasiak, Ph.D., Jeffrey Khong, Ph.D., and Theodore Kee, Ph.D., (University of Singapore and The N.1 Institute for Health). In this review, the authors explain an alternate approach to the current limited and suboptimal big data decision-making tools that are used to help medical teams determine a patient’s drug and dose recommendation.
In their review, Blasiak, Khong and Kee introduce CURATE.AI, an AI-derived mechanism-independent technology platform built to address the challenges in personalized dosing. CURATE.AI profiles are dynamically generated for an individual patient based on only that patient’s data, drug doses and phenotypic outputs. The profile is then used to recommend drug doses towards a desired response. Drug doses may be reduced, yet drug efficacy can increase with an accompanying drop in the toxicity levels.
Although different approaches aim to individualize drug selection, less focus has been given to personalizing the dose for the identified drug or treatment. When drugs are given at suboptimal doses, effectiveness can be impaired or absent. This also happens to be a major cause of clinical trial failure and poor response rates from patients. Additionally, the same patient’s medical state will be different from one day to the next, which means the original selected dose will require readjustments over time. “No two people are the same – an unavoidable reality that has complicated medical care throughout time. The treatment that works for one patient may fail for another,” says Blasiak. “With advances in engineering, the medical team is more and more equipped to tailor the treatment to an individual.”
Access to “CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence” is available at https:/
For more information about SLAS and its journals, visit http://www.
SLAS (Society for Laboratory Automation and Screening) is an international community of 16,000 professionals and students dedicated to life sciences discovery and technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.
SLAS Technology: Translating Life Sciences Innovation, 2018 Impact Factor 2.048. Editor-in-Chief Edward Kai-Hua Chow, Ph.D., National University of Singapore (Singapore).
SLAS Discovery: Advancing the Science of Drug Discovery, 2018 Impact Factor 2.192. Editor-in-Chief Robert M. Campbell, Ph.D., Eli Lilly and Company, Indianapolis, IN (USA).
Related Journal Article