image: Harnessing Artificial Intelligence to Improve Drug Combination Design and Personalized Medicine view more
Credit: Zac Goh
A new auto-commentary published in SLAS Technology looks at how an emerging area of artificial intelligence, specifically the analysis of small systems-of-interest specific datasets, can be used to improve drug development and personalized medicine. The auto-commentary builds on a study recently published by the authors in Science Translational Medicine about an artificial intelligence (AI) platform, Quadratic Phenotypic Optimization Platform (QPOP), that substantially improves combination therapy in bortezomib-resistant multiple myeloma to identify the best drug combinations for individual multiple myeloma patients.
It is now evident that complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations as well as choosing the right drug combination for the right patient becomes more difficult.
Artificial intelligence is having a positive impact on drug development and personalized medicine. With the ability to efficiently analyze small datasets that focus on the specific disease of interest, QPOP and other small dataset-based AI platforms can rationally design optimal drug combinations that are effective and based on real experimental data and not mechanistic assumptions or predictive modeling. Furthermore, because of the efficiency of the platform, QPOP can also be applied towards precious patient samples to help optimize and personalize combination therapy.
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The SLAS Technology auto-commentary, entitled "Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine," can be accessed for free for a limited time at http://journals.sagepub.com/doi/full/10.1177/2472630318800774. For more information about SLAS and its journals, visit http://www.slas.org/journals.
A PDF of this article is available to credentialed media outlets upon request. Contact nhallock@slas.org.
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SLAS (Society for Laboratory Automation and Screening) is an international community of nearly 20,000 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 DISCOVERY: 2016 Impact Factor 2.355. Editor-in-Chief Robert M. Campbell, Ph.D., Eli Lilly and Company, Indianapolis, IN (USA). SLAS Discovery (Advancing Life Sciences R&D) was previously published (1996-2016) as the Journal of Biomolecular Screening (JBS).
SLAS TECHNOLOGY: 2016 Impact Factor 2.632. Editor-in-Chief Edward Kai-Hua Chow, Ph.D., National University of Singapore (Singapore). SLAS Technology (Translating Life Sciences Innovation) was previously published (1996-2016) as the Journal of Laboratory Automation (JALA).
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