FDA  plans to replace companion diagnostics in cancer treatment through one test – one drug approach. 

In order to address the varying aggressiveness of cancer in each patient and reduce the need for multiple tests to determine an accurate medication regimen, the FDA has initiated a pilot project aimed at eliminating the necessity for companion diagnostics.

While exploring the field of cancer treatment, we encountered two startups that are developing inventions aligned with the idea proposed by the FDA.

OncXerna Therapeutics (a US-based startup) has developed a method to categorize cancer using a classifier TME Panel-1 (a machine learning-based classifier). This classifies cancer on the basis of the tumor microenvironment and suggests treatment based on the presence or absence of a specific TME phenotype class.

Similary, Precomb Therapeutics (a Swiss startup) has developed a method for evidence-based therapy decisions in cancer. The technology uses a fresh sample of a patient’s tumor and generates representative 3D tumor twins to analyze their susceptibility or resistance to various cancer drugs and combinations.

Advanced methods, such as machine learning models or an automated drug screening model for decision-making, will expedite the development of a personalized cancer treatment process. By collaborating with such innovators, the expansion of personalized cancer treatment in the markets can be accelerated.