How data, AI and machine learning can open the doors to drug discovery and advance personalized medicine.
Developing personalized and precision medicine is the next breakthrough for drug developers – particularly for cancer treatments. A recent review paper, from researchers at Rutgers University Department of Biomedicine Engineering, highlights its potentially transformative impact on cancer treatments.
It is easy to see how.
Personalized medicine does away with the traditional approach of using medicine to treat a broad population, and instead focuses on the individual case. This means the individual’s disease pattern, constitution, gender and the resulting implications for the therapies and medicines are all considered. The result is more effective treatment for the patient.
Using data to your advantage
But there are numerous challenges and complexities facing the practice. For the analysis and diagnosis of cancers, highly sensitive flow cytometers and cell sorters are required to detect circulating tumor cells. Once identified, characterization is achieved by going down to the molecular level to sequence the DNA of a single tumor cell. Organ-on-a-chip technologies are then used to grow three-dimensional cell cultures, reducing the need for animal tests in R&D while advancing personalized medicine development.
But with so much data being generated at every step, the problem becomes how to make sense of it. The answer lies in harnessing emerging technologies to take the burden of data analysis.
AI and machine learning can create significant benefits here – streamlining the data analysis process to ensure scientists are focused on development, not analysis. A collaboration between GSK and 23andMe is harvesting massive amounts of data and applying AI to advance drug discovery. The data analysis from such a project can have major implications in creating personalized medicine at speed.
If getting data right is essential, then ensuring it is recorded accurately and information is easily accessible and shareable is just as important. Building the correct data infrastructure and harnessing AI and machine learning are all essential steps in being able to develop advanced personalized medicine.