Clinical Trial Intelligence
We find Responders in
Clinical trials Reducing
Failure Risk.
"Gold Standard" responder identification
Clinical Trial Intelligence
"Gold Standard" responder identification
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Products and SaaS for drug trial analysis, identifying responders and predicting responders & biomarker generation
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Exploration of pubilc data and novel compounds
Yes, we offer analysis of current clinical trials as well as broad backbones of public data on treatment. These fall into three categories:
1) Analysis of ongoing clinical trials using public data to assess success likelihood, possibility of respositioning, and potential biomarkers of response
2) Analysis of a trial sponsor's data, usually using gene-expression, to make detailed predictions of subgroups of response, biomarkers, and possible expanded markets. See our latest papers for details
3) SaaS products to run quick and more detailed analyses, both practical and hypothetical, into how different drugs much be positioned against various types of cancer.
Go to responder.bio to learn more!
We are currently focusing only on cancer for clinical trial prediction and subgrouping, as well as determining biomarkers of response and repositioning. The reasons for this are that our core computational platform is built on gene-expression data from public data. This allows us to utilize our geometrical learning technology, and extrapolate to cases where the same data is not available. Oncology research has the most vast public repositories of omics data, patient cell-line and treatment experiments, and this richness of data is difficult to duplicate in other types of disorders.
Nevertheless, we do plan to expand to autoimmune and inflammatory disorders, as well as neurological and neurodegenerative disorders in the future.
Yes! A core aspect of our technology is to use geometry to create backbones across multiple data types. We are able to impute new drugs based on SMILES/MOA similarities, even when no experimental data exists. This means that in principle, we can analyze any molecules possible.
Please note that the R2 for compounds without any experimental data is typically between 0.5-0.65, meaning that the predictions for these must take that into consideration.
However, a current limitation is in immunotherapy, for which we are working on methods to integrate into our current platform.
There are many startups using AI to try to do "AI drug discovery", with various methods. AI is great for problems where there is a lot of training data and an LLM or machine can learn those patterns. When data is limited, say a few hundred samples and 200,000 genes, other methods are needed.
Our geometrical learning technology transforms data into multiple geometries, while preserving and compressing relationships between the data. We then apply machine learning models to create unbiased estimates of prediction accuracy, and then find biomarkers if needed.
This allows us to find "gold standard" of responders in time to event analysis, and predict these with over 80% out of sample accuracy - this is not possible with other techniques!
See our latest paper for details: https://www.medrxiv.org/content/10.1101/2024.07.01.24309803v3
World leading technology to determine subgroups of response in clinical trials and treatment data.
Led by Professors with decades of research in mathematics, genetics, omics, machine learning and statistics & epidemiology.
Tools to improve chances of passing clinical trials, identify biomarkers of response (companion diagnostics), and getting the right drugs to the right patients.
Mail : [email protected]
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