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Artificial intelligence to improve drug combination design and personalized medicine

Date:
September 25, 2018
Source:
SLAS (Society for Laboratory Automation and Screening)
Summary:
A new auto-commentary 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.
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FULL STORY

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.


Story Source:

Materials provided by SLAS (Society for Laboratory Automation and Screening). Note: Content may be edited for style and length.


Journal Reference:

  1. Masturah Bte Mohd Abdul Rashid, Edward Kai-Hua Chow. Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 2018; 247263031880077 DOI: 10.1177/2472630318800774

Cite This Page:

SLAS (Society for Laboratory Automation and Screening). "Artificial intelligence to improve drug combination design and personalized medicine." ScienceDaily. ScienceDaily, 25 September 2018. <www.sciencedaily.com/releases/2018/09/180925075124.htm>.
SLAS (Society for Laboratory Automation and Screening). (2018, September 25). Artificial intelligence to improve drug combination design and personalized medicine. ScienceDaily. Retrieved April 22, 2024 from www.sciencedaily.com/releases/2018/09/180925075124.htm
SLAS (Society for Laboratory Automation and Screening). "Artificial intelligence to improve drug combination design and personalized medicine." ScienceDaily. www.sciencedaily.com/releases/2018/09/180925075124.htm (accessed April 22, 2024).

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