Blood Cancer Discovery Releases Analysis Supporting Validation of Exscientia’s AI-Based Precision Medicine Platform to Improve Patient Outcomes

Results confirm that ex vivo deep learning drug screening from patient tissue is a promising tool for identifying effective individual treatments for advanced blood cancer, compared to conventional methods

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

Exscientia (Nasdaq: EXAI), ETH Zurich, Medical University of Vienna and the CeMM Research Center for Molecular Medicine today announce a new publication in Discovery of blood cancera journal of the American Association for Cancer Research, entitled “Deep morphology learning improves precision medicine with image-based ex vivo drug testingcarried out by the laboratory of Professor Berend Snijder. This post-hoc analysis builds on the groundbreaking work of the EXALT-1 trial, published in Cancer Discovery, using deep learning algorithms to classify complex cell morphologies into disease “morphotypes” in samples from cancerous tissues of patients.

EXALT-1 was the first prospective trial to demonstrate significantly improved outcomes for patients with advanced hematological cancer, using an AI-based precision medicine platform to guide personalized treatment recommendations, compared the doctor’s choice of treatment. In the EXALT-1 trial, 40% of patients experienced exceptional responses lasting at least three times longer than expected for their respective disease. The post-hoc analysis published today in Discovery of blood cancer reveals that combining technology as used in EXALT-1 with new advances in deep learning that take advantage of cell-specific features in high-content images has revealed the potential to further improve these outcomes in patients .

“Following the results of the EXALT-1 trial, these observations continue to validate that our AI-based precision medicine platform has the ability to identify highly actionable clinical treatment recommendations for blood cancers, our knowledge and improving the clinical predictive power of the platform to help patients,” said Gregory Vladimer, Ph.D., vice president of translational research at Exscientia and co-investor in the technology of the platform. “Cell morphology, or the assessment of cell characteristics, is fundamental to the diagnosis of cancer. As part of this research, we were able to use deep learning in the platform to improve our ability to identify personalized cancer treatments, which leads to better clinical outcomes in patients. At Exscientia, we are excited to expand the applications of the platform to bring personalized medicine to wider populations. »

“We believe that performing drug screens directly in the tumor tissues of cancer patients is a big step forward in understanding the complexity of tumors compared to traditional cell model systems. The fact that we can now harness the power of deep learning to turn those terabytes of images into actionable information is really very encouraging,” added Professor Berend Snijder, Principal Investigator at the Institute for Systems Molecular Biology in ETH Zurich, Switzerland.

The impact of deep learning on the clinical predictive power of screening ex-vivo of drugs was evaluated in a post-hoc analysis of 66 patients over a three-year period in a combined dataset of 1.3 billion patient cells for 136 drugs tested ex-vivo in hematological diagnoses including acute myeloid leukemia, T-cell lymphomas, diffuse large B-cell lymphomas, chronic lymphocytic leukemia and multiple myeloma. Patients receiving treatments recommended by the platform’s immunofluorescence analysis or deep learning on cell morphologies showed an increased rate of achieving an exceptional clinical response, defined as a period of progression-free survival that has lasted three times longer than expected for each patient’s respective illness. Post-hoc analyzes confirmed that clinical predictions became more accurate when the drug’s toxicity to healthy cells in the patient sample tested was also taken into account.

Exscientia’s precision medicine platform uses custom deep learning and computer vision techniques to extract meaningful single-cell data from high-content images of each patient’s tissue samples. This analysis generates clinically relevant information about which treatments will be most beneficial for an individual patient. Further evaluation of individual patient outcomes using Exscientia’s genomic and transcriptomic capabilities can help Exscientia better understand which other patients may benefit from similar treatments. The underlying technology was developed by Dr Gregory Vladimer and Prof. Berend Snijder while working in the laboratory of Giulio Superti-Furga at the CeMM Research Center for Molecular Medicine in Austria.

About Exscientia

Exscientia is an AI-powered pharmaceutical technology company committed to discovering, designing and developing the best possible medicines in the fastest and most efficient way. Exscientia has developed the first-ever functional precision oncology platform to successfully guide treatment selection and improve patient outcomes in a prospective interventional clinical study, as well as to advance small molecules designed with to AI. Our portfolio of internal projects builds on our precision medicine platform in oncology, while our portfolio of partnered projects extends our approach to other therapeutic areas. As pioneers of a new approach to drug creation, we believe that the best scientific ideas can very quickly become the best medicines for patients.

Based in Oxford (England, United Kingdom), Exscientia has offices in Vienna (Austria), Dundee (Scotland, United Kingdom), Boston (Massachusetts, United States), Miami (Florida, United States), Cambridge ( England, United Kingdom) and Osaka (Japan).

For more information, please visit or follow us on Twitter @exscientiaAI.

Forward-looking statements

This press release contains certain forward-looking statements within the meaning of the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, including statements relating to Exscientia’s expectations regarding the progress of the development of candidate molecules. , the timing and progress of preclinical studies and clinical trials of Exscientia’s product candidates, and Exscientia’s expectations regarding its precision medicine platform and AI-powered drug discovery platform. Words such as “plans”, “believes”, “expects”, “intends”, “plans”, “anticipates” and future tense or similar expressions are intended to identify statements forward-looking. These forward-looking statements are subject to the uncertainties inherent in predicting future results and conditions, including the scope, progress and expansion of Exscientia’s product development efforts; the initiation, scope and progress of clinical trials by Exscientia and its partners and the cost implications thereof; clinical, scientific, regulatory and technical developments; and those inherent in the process of discovering, developing and commercializing product candidates that are safe and effective for use in human therapeutics, and in the effort to develop businesses around these product candidates . Exscientia undertakes no obligation to publicly update or revise any forward-looking statements, as a result of new information, future events or other factors, except as required by law.

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