Artificial intelligence (AI) holds great promise in transforming drug development and personalized medicines.
It is now evident that complex diseases, such as cancer, often require effective drug combinations to make significant therapeutic impact. As the drugs in such 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.
In a study published in Science Translational Medicine, an Artificial intelligence based technology platform, called quadratic phenotypic optimization platform (QPOP) has successfully create a new system that uses a small experimental data sets to develop new drug combinations against drug-resistant multiple myeloma.
With Artificial intelligence, the researchers were efficiently analyzed small datasets that does not rely on previous assumptions of molecular mechanisms of disease, but rather uses a system-specific experimental data to determine the best drug combinations for a specific disease model or from a specific patient sample.
According to the study, it is very important for both the clinic and the laboratory to identify optimal drug combinations for a specific disease. As drug development trends toward more specific molecularly targeted therapeutics, identifying the most effective drug combination for a new drug will become increasingly vital to their success in getting clinical approval.
Furthermore, as more drug combinations become approved for clinical use, identifying the best drug combination for each patient will prove more and more difficult by conventional methods.
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Artificial intelligence appears to be able to address both of these problems through the ability to use a small number of tests from a large-parameters to maximize drug combination efficacy and safety independent of disease mechanism or predetermined drug synergy.
Artificial intelligence platforms such as QPOP, which can maximize the use of small datasets directly from patient samples or patients themselves, will ultimately improve the efficacy and outcomes of clinical drug combination development and personalized medicine.
For more information please read the complete study on this link.
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