AI drug positioning
Matching the right drug and patient for better responses
We explore new drug uses by combining existing knowledge and multimodal patient data
Question
How do we match the right patient to the right drug?
By better understanding the drug mechanism of action in patient subpopulations through more complete knowledge of the tumor microenvironment and of clinical heterogeneity.
Owkin’s solution
Drug positioning engine
For a given drug, our engine identifies novel disease indications and subgroups for development, by aggregating causal evidence from prior knowledge and multimodal patient data.
Input: multimodal data plus known target of candidate drug
Methodology
Click
1
to screen all diseases impacted by the target(s) in question.
Knowledge graph of target.
Step 1
Build knowledge graphs
To screen all diseases
Click
2
to analyze knowledge graphs to select a short list of diseases impacted by the relevant mechanistic pathway of interest.
Ranked list of top disease indications impacted by the target(s) in question.
Step 2
Apply interpretable AI
To select impacted diseases
Click
3
(Optional - indication specific)
To understand biological traits that impact the treatment response.
To understand biological traits that impact the treatment response.
Key +/- regulators of treatment response, subgroups and/or potential combos.
Step 3
Deep dive into indications
To understand biological drivers
Subgroup discovery
Multimodal AI-powered biomarkers
We combine cutting-edge machine learning and biology to identify biomarkers.
TargetMATCH
A new approach to target discovery
A suite of end-to-end tools that uses multimodal patient data as the input, and outputs the top candidate targets and paired patient subgroups that would most benefit from therapeutic intervention on these targets.