Researchers Explore Artificial Intelligence in Improving Drug Development and Personalized Medicine Therapy

Personalized Medicine

Researchers discussed the effectiveness of artificial intelligence in improving drug combination designing and personalized medicine in an auto-commentary issued in SLAS technology.

Based on a study published in Science Translational Medicine, the auto-commentary discusses an AI-based technology platform called quadratic phenotypic optimization platform (QPOP). It successfully creates a new system by using small experimental data sets to develop new combinations of drugs that act against drug-resistant multiple myeloma.

With growing complexity of diseases, including cancer, development of effective drug combination is necessary to deliver a significant therapeutic impact. Drugs for such combination therapies are specific to molecular targets and hence a high level of complexity is endured while designing effective drug combination and choosing the right combination that can be administered to the patient.

According to researchers, AI is having positive impact in transforming drug combination developments as well as personalized medicines. The emerging area of artificial intelligence in drug development enabled the researchers to efficiently analyze small datasets by using system-specific experimental data, without relying on previous assumptions of molecular mechanism of the disease. The AI-based QPOP substantially improves the combination therapy to identify the best drug combination for a specific disease model or patient.

The researchers added that it is very important for biotech labs and clinics to determine the specific drug combination of the targeted disease. Today, as drug development inclines more towards certain molecularly targeted therapeutics for complex diseases, identifying best drug combination for a new drug becomes vital to receive approval.

Identification of specific drug combination for each becomes more difficult with conventional methods. Researchers believe AI platform will solve the arising problems where large parameters can be obtained by using smaller number of tests. It maximizes the efficacy and safety of the drug combination without depending on pre-determined drug synergy or disease mechanism.

According to the authors, QPOP and other small dataset-based AI platforms can improve the designing of optimal drug combinations and personalized medicines. These platforms are able to analyze small datasets that specific disease of interest and improves the outcomes of clinical drug development. Further, they can be applied towards important patient samples to aid in optimize and personalize combination therapy.

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Ganesh Rajput

Ganesh Rajput

Ganesh’s extensive experienced in the field of market research reflects in the way his articles offer readers sharp insights on the latest developments across major industry verticals. His forte lies in churning out analytical commentaries on the evolving nature of various consumer-oriented industries.

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