Selventa, a biomarker discovery company that enables personalized healthcare through the stratification of patients based on disease-driving mechanisms, recently announced a US patent that relates methods and techniques that facilitate discovery of biomarkers, thus aiding in the development of predictive and prognostic diagnostic tests for therapeutics targeting complex multi-factorial diseases.
The Massachusetts-based company uses molecular patient data to identify key disease-driving mechanisms. This information allows for acceleration of the development process to clarify therapeutic and diagnostic decisions through identification and development of biomarkers for patient stratification.
Selventa’s patent, U.S. Patent No 8,802,109, titled “Computer-aided Discovery of Biomarker Profiles in Complex Biological Systems,” was issued on December 20, 2011. The patent describes a method of biomarker discovery using a model representative of one or more causative biophysical or biochemical relationships underlying a biological state in the biological system of interest. A candidate set of biological data is then compared against the model to distinguish a candidate biomarker for the biological state. A sample received from a patient can then be assayed for the presence of the candidate biomarkers to discern a biological state of the patient.
With the Rise of AI, What IP Disputes in Healthcare Are Likely to Emerge?
Munck Wilson Mandala Partner Greg Howison shared his perspective on some of the legal ramifications around AI, IP, connected devices and the data they generate, in response to emailed questions.
According to David de Graaf, Ph.D., President and CEO of Selventa:
Using the technique, biomarkers may be developed to predict efficacy or toxic effects of a drug. This may permit a researcher or physician to know, in advance, whether the presenting patient will benefit from or be harmed by administration of the drug. In addition, the patented technique facilitates the development of biomarkers as diagnostics for a disease, as a means of making treatment decisions or as a means of stratifying patients in a clinical trial. This will likely improve the success of clinical trials by screening for subjects that are more likely to benefit from the drug and avoiding those who are unlikely to respond.
Source: Selventa
A Personalized Approach to Medication Nonadherence
At the Abarca Forward conference earlier this year, George Van Antwerp, managing director at Deloitte, discussed how social determinants of health and a personalized member experience can improve medication adherence and health outcomes.
Dr. Walter J. Jessen works in the biopharma industry as a computational biologist. He curates knowledge on biomarkers and personalized medicine at BiomarkerCommons.org.
