Discover

We combine cutting-edge machine learning and biology to advance drug discovery.

Our substantial increase in the understanding of cancer has not yet led to better treatments for patients.

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Challenges

How can we move from understanding the disease to discovering the treatment?

Disease heterogeneity

No two cancers are the same. From environmental factors to genetics, patients are all different, making it difficult to develop effective treatments.

Each cancer evolves differently

Cancer unfolds in a variety of ways across patients, calling for a personalized approach.

Under-utilization of knowledge

Researchers lack the powerful tools required to fully harness the vast potential of untapped scientific insights.

Disease heterogeneity

No two cancers are the same. From environmental factors to genetics, patients are all different, making it difficult to develop effective treatments.

Each cancer evolves differently

Cancer unfolds in a variety of ways across patients, calling for a personalized approach.

Under-utilization of knowledge

Researchers lack the powerful tools required to fully harness the vast potential of untapped scientific insights.

Owkin’s solution

We leverage our expert multimodal data access and deploy state-of-the-art, interpretable AI to refine our understanding of diseases to develop biomarkers and identify new drug targets. In addition, we build AI tools that match historical and emerging research to identify new drug combinations and opportunities for drug repurposing.

Biomarkers

Biomarkers

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We develop interpretable AI models to identify clinically relevant biomarkers from multimodal data, such as omics, imaging, histology and clinical data.

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Stratification (predictive) biomarkers

These biomarkers predict the best treatment for each patient by analyzing subgroups of good and bad responders. → To better predict the response to treatment, create surrogate markers and to steer research for future targets.

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Risk-score (prognostic) biomarkers

These biomarkers predict the course of the disease in a patient (outcome, overall survival, metastatic relapse). → To inform therapeutic decisions, accelerate clinical trial enrolment and design more accurate translational and early-stage trials.

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Screening (diagnostic) biomarkers

These biomarkers help us identify patients with a particular disease characteristic. → To define and accelerate clinical trial enrolment.

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Stratification (predictive) biomarkers

These biomarkers predict the best treatment for each patient by analyzing subgroups of good and bad responders. → To better predict the response to treatment, create surrogate markers and to steer research for future targets.

Find out more

Risk-score (prognostic) biomarkers

These biomarkers predict the course of the disease in a patient (outcome, overall survival, metastatic relapse). → To inform therapeutic decisions, accelerate clinical trial enrolment and design more accurate translational and early-stage trials.

Find out more

Screening (diagnostic) biomarkers

These biomarkers help us identify patients with a particular disease characteristic. → To define and accelerate clinical trial enrolment.

Find out more
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Advancing discovery

Drug target identification

We combine our expertise in AI and biology to fully interpret histogenomic biomarkers. This helps us discover and rank genes and proteins within the innovative drug target potential.

Drug target identification

Drug combination

Using real-world data (genomic data, clinical trial results and literature), we build AI models that analyze disease mechanisms to identify possible drug combinations. These are designed to improve the efficiency of a client's drug candidate.

Drug repurposing

We build AI models that analyze disease mechanisms to identify other possible diseases for existing drugs to target.

Drug target identification

We combine our expertise in AI and biology to fully interpret histogenomic biomarkers. This helps us discover and rank genes and proteins within the innovative drug target potential.

Drug target identification

Drug combination

Using real-world data (genomic data, clinical trial results and literature), we build AI models that analyze disease mechanisms to identify possible drug combinations. These are designed to improve the efficiency of a client's drug candidate.

Drug repurposing

We build AI models that analyze disease mechanisms to identify other possible diseases for existing drugs to target.

Testimonial

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“Precision medicine is based on the individualization of treatment strategies based on the actual features of the patient 's tumor. Historical approaches with precision medicine have been based on single biomarkers, mostly oncogene activation. Now with Owkin, we are moving to a more comprehensive and multimodal characterization, which is needed in the setting of innovative therapies such as immunotherapies. There is a need to integrate, beyond biomarkers, features such as tumor architecture, that reflects the interactions between the tumor and the microenvironment.”

Prof. Nicolas Girard, MD, PhD

Head of Thorax Institute Curie Montsouris

Prof. Nicolas Girard, MD, PhD