The Leukemia Discovery publication validates Exscientia’s precision medicine platform to improve patient outcomes

The findings support deep learning for ex vivo drug screening using patient tissues as a promising tool for identifying effective individual therapies in advanced leukemia over conventional methods.

Custom deep learning algorithms and single-cell analysis of over 1 billion patient cells reveal additional potential to improve patient outcomes

Vienna and Oxford, England, September 20, 2022– (work wire) – Exscientia (Nasdaq: EXAI), ETH Zurich, the Medical University of Vienna and the Center for Molecular Medicine (CeMM) today announced New post in Detecting leukemiaJournal of the American Association for Cancer Research, titled “Deep learning for dispensing advances precision medicine by image-based drug testingFrom Professor Berend Snijder’s lab. This post-hoc analysis builds on the transformative work of the EXALT-1 experiment, Posted in Cancer Discoveryusing deep learning algorithms to classify complex cell morphology in patients’ cancer tissue samples into disease ‘morphotypes’.

EXALT-1 was the first prospective trial to show significantly improved outcomes for patients with late-stage leukemia using an AI-powered precision medicine platform to guide personalized treatment recommendations compared to physician selection of treatment. In EXALT-1, 40% of patients experienced exceptional responses that lasted at least three times longer than expected for their disease. Post analysis published today in Detecting leukemia It shows that the combination of technology used in EXALT-1 with new deep learning developments that take advantage of cell-specific features in high-content images revealed the potential to increase these patient outcomes.

“Following the results of the EXALT-1 study, these findings continue to validate that the AI-guided precision medicine platform has the potential to identify actionable clinical treatment recommendations for leukemia, deepen our insights and enhance the platform’s clinical predictive power for the purpose of clinical trials,” said Gregory Vladimir, Ph. Translational Research at Exscientia and co-inventor of the platform technology: “Help patients.” Cell morphology, or the evaluation of cell properties, is central to cancer diagnosis. Within this research, we have been able to leverage deep learning within the platform to improve our ability to identify personalized cancer treatments, leading to improved clinical outcomes for patients. At Exscientia, we are excited to expand the platform’s applications in order to bring personalized medicine to a larger population. “

“We believe that performing drug assays directly in tumor tissues of cancer patients represents a major step forward in understanding the complexity of tumor compared to traditional cell model systems. And the fact that we can now harness the power of deep learning to turn these terabytes of images into actionable insights, added Professor Berend Snijder, Principal Investigator at ETH’s Institute for Molecular Systems Biology in Zurich in Switzerland, it’s really very exciting.”

The effect of deep learning on the clinical predictive ability of outside of vivo Drug screening was evaluated in a post-hoc analysis of 66 patients over three years in a pooled dataset of 1.3 billion patient cells across 136 outside of vivo Drugs tested across hematological diagnoses including acute myeloid leukemia, T-cell lymphoma, diffuse large B-cell lymphoma, chronic lymphocytic leukemia and multiple myeloma. Patients receiving treatments recommended by platform immunofluorescence analysis or deep learning about cell shapes showed an increased rate of achieving exceptional clinical response, which was defined as a progression-free survival period that lasted three times longer than expected for each patient’s disease. Subsequent analyzes confirmed that clinical predictions became more accurate when also considering the drug’s toxicity on healthy cells within the tested patient sample.

Exscientia’s precision medicine platform uses custom deep learning and computer vision technologies to extract meaningful single-cell data from high-content images of individual patient tissue samples. This analysis generates clinically relevant insights into which treatments will most benefit the individual patient. Additional evaluation of individual patient outcomes with Exscientia’s genomics and transcription capabilities may help increase understanding of other patients who may benefit from similar treatments. The core technology was developed by Dr. Gregory Vladimir and Professor Bernd Snyder while working in the laboratory of Giulio Soberti Forga at the CMM Research Center for Molecular Medicine in Austria.

About Exscientia

Exscientia is an AI-powered pharmaceutical company committed to discovering, designing and developing the best possible drugs in the fastest and most effective way. Exscientia has developed the first ever functional micro-oncology platform to successfully guide treatment selection and improve patient outcomes in a future interventional clinical study, as well as to advance small molecules designed for artificial intelligence in the clinical setting. Our in-house pipeline focuses on leveraging our precision medicine platform in oncology, while our partner pipeline expands our approach to other therapeutic areas. By pioneering a new approach to medicine innovation, we believe the best scientific ideas can quickly become the best medicines for patients.

Exscientia is headquartered in Oxford (England, UK), with offices in Vienna (Austria), Dundee (Scotland, UK), Boston (Massachusetts, US), Miami (Florida, US), Cambridge (England, United Kingdom), and Osaka, Japan).

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forward-looking statements

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