Wire Stories

Blood Cancer Discovery Publication Further Validates Exscientia’s AI Precision Medicine Platform for Improving Patient Outcomes

Findings support deep learning ex vivo drug screening with patient tissue as a promising tool to identify effective, individual treatments in advanced blood cancer over conventional methods

Custom deep learning algorithms and single-cell analysis of >1 billion patient cells reveals further potential for improved patient outcomes

VIENNA & OXFORD, England--(BUSINESS WIRE)--Exscientia (Nasdaq: EXAI), ETH Zurich, the Medical University of Vienna, and the Center for Molecular Medicine (CeMM) today announced a new publication in Blood Cancer Discovery, a journal of the American Association for Cancer Research, titled “Deep Morphology Learning Enhances Precision Medicine by Image-Based Ex Vivo Drug Testing” from the laboratory of Prof. Berend Snijder. This post-hoc analysis builds on the transformative work of the EXALT-1 trial, published in Cancer Discovery, by using deep learning algorithms to classify complex cell morphologies in patient cancer tissue samples into disease “morphotypes.”

EXALT-1 was the first prospective trial to demonstrate significantly improved outcomes for late-stage haematological cancer patients using an AI-supported precision medicine platform to guide personalised treatment recommendations as compared to physician’s choice of treatment. In EXALT-1, 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 Blood Cancer Discovery shows that combining the technology as used in EXALT-1 with new deep learning advancements that take advantage of cell-specific features in high-content images revealed a potential to further increase these patient outcomes.

“Following results of the EXALT-1 study, these findings continue to validate that our AI-guided precision medicine platform has the ability to identify highly actionable clinical treatment recommendations for blood cancers, deepening our insights and enhancing the clinical predictive power of the platform to help patients,” said Gregory Vladimer, Ph.D., VP Translational Research at Exscientia and co-inventor of the platform technology. “Cell morphology, or assessing the characteristics of cells, is fundamental to the diagnosis of cancer. Within this research, we were able to utilise deep learning within the platform to improve our ability to identify personalised cancer treatments, leading to improved clinical outcomes for patients. At Exscientia, we are excited to expand the platform’s applications in order to bring personalised medicine to broader populations.”

“We believe performing drug screens directly in tumour tissues of cancer patients is a great step forward in understanding tumour complexity compared to traditional cell model systems. The fact that we can now harness the power of deep learning to turn these terabytes of images into actionable insights is very exciting indeed,” added Prof. Berend Snijder, Principal Investigator at the Institute of Molecular Systems Biology of the ETH Zurich in Switzerland.

The impact of deep learning on the clinical predictive power of ex vivo drug screening was assessed in a post-hoc analysis of 66 patients over a period of three years in a combined data set of 1.3 billion patient cells across 136 ex vivo tested drugs across haematological diagnoses including acute myeloid leukaemia, T-cell lymphomas, diffuse large B-cell lymphomas, chronic lymphocytic leukaemia and multiple myeloma. Patients receiving treatments that were recommended by the platform’s immunofluorescence analysis or deep learning on cell morphologies showed an increased rate of achievement of exceptional clinical response, defined as a progression free survival period that lasted three times longer than expected for each patient’s respective disease. Post-hoc analyses confirmed that the clinical predictions became more accurate when also considering the drug toxicity on the healthy cells within the tested patient sample.

Exscientia’s precision medicine platform uses custom deep learning and computer vision techniques 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 deliver the most benefit to an individual patient. Further evaluation of individual patient results through Exscientia’s genomics and transcriptomics capabilities may help Exscientia further 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-driven pharmatech company committed to discovering, designing and developing the best possible drugs in the fastest and most effective manner. Exscientia 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 progress AI-designed small molecules into the clinical setting. Our internal pipeline is focused on leveraging our precision medicine platform in oncology, while our partnered pipeline broadens our approach to other therapeutic areas. By pioneering a new approach to medicine creation, we believe the best ideas of science can rapidly become the best medicines for patients.

Exscientia is headquartered in Oxford (England, U.K.), with offices in Vienna (Austria), Dundee (Scotland, U.K.), Boston (Mass., U.S.), Miami (Fla., U.S.), Cambridge (England, U.K.), and Osaka (Japan).

Visit us at https://www.exscientia.ai 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 with regard to Exscientia’s expectations with respect to the progress of development of candidate molecules, timing and progress of, and data reported from, preclinical studies and clinical trials of Exscientia’s product candidates, and Exscientia’s expectations regarding its precision medicine platform and AI-driven drug discovery platform. Words such as “anticipates,” "believes," “expects,” "intends," "projects," "anticipates," and "future" or similar expressions are intended to identify forward-looking statements. 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 Exscientia’s and its partners’ clinical trials and ramifications for the cost thereof; clinical, scientific, regulatory and technical developments; and those inherent in the process of discovering, developing and commercialising product candidates that are safe and effective for use as human therapeutics, and in the endeavor of building a business around such product candidates. Exscientia undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as may be required by law.

Contacts

Investors:
Sara Sherman

[email protected]

Media:
Amanda Galgay

[email protected]

To Top