Blog

Advancing Oncology Drug Development with Precision Medicine

Traditionally a disease has been defined by its clinical presentation and observable characteristics, not by the underlying molecular mechanisms, pathways and biological processes specific to a particular patient.

This can lead to classifying what are often highly heterogeneous medical conditions as a single disease.

In recent years, the healthcare industry has made significant steps in further understanding and targeting the underlying molecular cause of disease, however there is much more to be done. Consequently, patients with the same disease diagnosis tend to receive the same treatment. This kind of one-size-fits-all treatment strategy inadequately accounts for inter-individual variability between patients or the stage of their disease course. The result is that a large number of patients fail to respond to the treatments they are prescribed for.

One obvious example is cancer, which is a heterogeneous disease, with both between-patient variability and differences in the characteristics of disease within a given patient, making developing long-term cures challenging in many cancer types. Over the past decades, there have been major advances in developing cancer therapeutics; patients who receive targeted therapies have benefitted from improved survival [1,2,3]. However, there is still a huge unmet need for patients who either do not respond to existing treatments or develop resistance to them.

Our Precision Medicine team is working to change this by starting drug discovery from endotypes - groups of patients with the same underlying cause of disease.  By stratifying patients, we believe we have a better chance at identifying responder patients, designing more effective clinical trials and developing new therapies for complex and heterogeneous diseases like cancer.

Our team takes a multi-disciplinary approach to leverage the vast collection of patient-level data produced by recent developments in technology. We apply machine learning, bioinformatics, biological knowledge, and translational medicine to advance our drug discovery programs with a data driven approach. We use unsupervised machine learning techniques to capture patient heterogeneity and uncover different underlying biological mechanisms within the disease. We then incorporate these new insights into our core biomedical knowledge graph for drug target identification.

Our approach has been used in multiple discovery programs in disease areas such as neurology and oncology. Recently we announced our collaboration with Novartis Global Drug Development, where we are investigating new indications and responders for Novartis oncology medicines currently in clinical development. With this initiative, we combine expertise from both parties to further expedite the process of delivering the next generation of cancer therapies to the clinic.  

Our focus on precision medicine is important because we are translating scientific discoveries into real-world practice more efficiently, to more precisely target medicines for the patients who need them most.


Pijika Watcharapichat, Senior Research Scientist, Health Informatics

More Posts

You Might Also Like

News
BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial
NIAID trial shows baricitinib in combination with remdesivir reduces the recovery time in patients hospitalised with COVID-19, validating BenevolentAI’s AI-derived hypothesis.
Sep 14, 2020
Blog
Building the next generation of leaders with Circl
BenevolentAI Director of Talent Acquisition and Development, Yasmina Rahiman, explores the power of coaching for leadership development in technology.
Aug 21, 2020
News
Clinical data validates BenevolentAI's AI predicted hypothesis for baricitinib as a potential treatment for COVID-19
Research published in EMBO Molecular Medicine confirms AI predictions for anti-viral and anti-cytokine signalling effects of baricitinib in critically hospitalised COVID-19 patients
Jul 1, 2020
News
COVID-19 and AI: An editorial review in EMBO from Michael B. Schultz, Daniel Vera, David A. Sinclair
David Sinclair and colleagues review our recent publication in the EMBO Molecular Medicine Journal in support of our AI-derived hypothesis for a potential treatment of COVID-19.
Jul 1, 2020
Video
How do we get the next 10 years right? w/ Joanna Shields, CogX
“We need leaders with empathy who care about their fellow citizens and are prepared to work to end injustice and create opportunities for all". Our CEO Joanna Shields opens CogX 2020 sharing inspiration for the next decade.
Jun 8, 2020
Blog
Kindness matters: mental health and wellbeing through COVID-19 and beyond
To conclude Mental Health awareness week, we share some thoughts about the power of kindness and how building a culture of openness will help shape the future of business.
May 27, 2020