Exscientia to Highlight Precision Medicine Platform and Pipeline Data at the American Association of Cancer Research Annual Meeting 2022

Rhea-AI Summary

Exscientia (NASDAQ: EXAI) has announced the acceptance of three abstracts for presentation at the upcoming AACR Annual Meeting 2022 in New Orleans, showcasing the potential of its AI-driven drug discovery platform.

The abstracts focus on innovations such as targeting adenosine antagonist patient responses and profiling GTAEXS-617, a potent inhibitor of CDK7. These advances aim to improve patient outcomes in oncology by identifying responsive patient groups and enhancing drug design.

Positive

• Accepted three abstracts for presentation at AACR Annual Meeting 2022, indicating strong research output.
• Demonstrated potential benefits of AI-driven drug discovery platform in targeting specific patient populations.
• GTAEXS-617 shows promising preclinical results in anti-cancer activity.

Negative

• Dependence on preclinical data to translate findings into clinical success remains a challenge.
• Risks related to the effective implementation of AI models in patient selection and treatment outcomes.

AI-driven discovery platform enables new way to find novel pathways as well as progress towards identifying patients who may better respond to drugs in the clinic

Preclinical data highlights the potential benefits of Exscientia's AI-driven design to rapidly discover molecules with optimised properties, including GTAEXS-617

OXFORD, England--(BUSINESS WIRE)-- Exscientia (Nasdaq: EXAI) today announced the acceptance of three abstracts for poster presentation at the upcoming American Association for Cancer Research (AACR) Annual Meeting 2022, being held April 8-13, 2022, at the Ernest N. Morial Convention Center in New Orleans, LA.

“The abstracts highlighted at AACR demonstrate the potential of our functional precision oncology tools to improve clinical and patient outcomes by guiding treatment and patient selection using a combination of AI and disease-relevant models,” said Andrew Hopkins, DPhil., CEO and founder of Exscientia. "We believe these data demonstrate the exciting potential of our pipeline and further validate our translational research capabilities and AI-driven drug discovery platform as we continue in our efforts to deliver better molecules and identify promising therapeutic assets that have the best chance of clinical success."

Abstracts Accepted for Poster Presentation:

Title: Enriching for adenosine antagonist patient responses through deep learning
Session Title: Immunomodulatory Agents and Interventions
Abstract Number: #4150
Date/Time: Wednesday, April 13 / 9:00 AM - 12:30 PM CT

Translation of preclinical data to the clinical setting has been a persistent gap in successful drug discovery. In this study, researchers leveraged Exscientia’s AI-driven platform to develop patient gene signatures that could guide and better inform clinical study of new medicine candidates. By using deep learning driven image analysis, researchers are working towards identifying an adenosine-induced, tumour protective, immunosuppression biomarker to potentially improve the likelihood of clinical success for A2aR targeted therapies. Further, by leveraging patient material as well as baseline and treatment condition transcriptomics, Exscientia was able to model and functionally validate patient gene signatures to map the association of anti-cancer immune activity with the inhibition of adenosine signaling by EXS-21546, Exscientia's clinical stage A2a antagonist, in development for the treatment of high adenosine signature cancers. These encouraging data suggest that stratification of patient gene signatures could be implemented in future studies of EXS-21546 to identify patients that may respond optimally to A2aR targeted therapies.

Title: AI-driven discovery and profiling of GTAEXS-617, a selective and highly potent inhibitor of CDK7
Session Title: Emerging New Anticancer Agents
Abstract Number: #3930
Date/Time: Wednesday, April 13 / 9:00 AM - 12:30 PM CT

Historically, CDK7 inhibition, a validated target that has been shown to severely limit the ability of cancer cells to proliferate in vitro and in vivo, has been challenging to address due to side effect profiles from development candidates, possibly due to covalent binding mechanism of action or poor oral absorption. By leveraging AI models and active learning, Exscientia was able to design an orally bioavailable, highly potent and selective small-molecule antagonist of CDK7, GTAEXS-617, currently in IND-enabling studies as a potential treatment for transcriptionally addicted cancers, including ovarian and breast cancer. Preclinical data show that ‘617 has potent anti-proliferative activity in in vitro models of high-grade serous ovarian cancer (HGSOC) and triple negative breast cancer (TNBC), and potent anti-tumour activity in HGSOC and TNBC xenograft tumour-bearing mice, resulting in complete tumour regression. By leveraging Exscientia's precision oncology platform, researchers examined the impact of '617 on primary patient ovarian cancer samples. On the platform, two response groups of the patient samples began to form, and researchers are continuing to investigate this phenomenon with the aim to define a patient selection biomarker to enrich patients more likely to respond to CDK7 inhibition. The AI-driven platform was able to improve upon historic design concerns with CDK7 inhibitors, including efflux and GI tract toxicity.

Title: Deep learning supported high content analysis of primary patient samples identifies ALK inhibition as a novel mechanism of action in a subset of ovarian cancers
Session Title: New Technologies for Drug Discovery
Abstract Number: #1893
Date/Time: Monday, April 11 / 1:30 PM - 5:00 PM CT

Targeted therapies are needed for patients suffering from a myriad of diseases, but preclinical drug discovery is often performed in murine models which are not human disease relevant and lack the microenvironment and heterogeneity of human biology. This study highlights the potential of Exscientia's precision medicine platform to identify novel targets and targetable pathways using human disease relevant patient tissue models, which could have the potential to improve patient outcomes by uncovering clinical relevance at the target discovery stage. By evaluating malignant pleural effusion and ascites from 20 patients with ovarian cancer against greater than 80 small molecules using high content microscopy, researchers were able to identify a pathway containing anaplastic lymphoma kinase (ALK) as a potential novel target in a subset of ovarian cancer patient samples. These encouraging data support further research of patient-focused drug development using human disease relevant models and deep learning to better understand the target landscape for ovarian cancer and the potential for the development of novel therapeutic approaches.

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 pipeline of internal and partnered programmes demonstrates our ability to rapidly translate scientific concepts into precision-designed therapeutic candidates, with more than 25 projects underway. By designing better drugs, faster, we believe the best ideas of science can rapidly become the best medicines for patients.

Exscientia has offices in Oxford, Vienna, Dundee, Boston, Miami, Cambridge (UK) and Osaka. For more information visit us on 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. No assurance can be given that the AI-supported precision medicine platform discussed above will be successful in proposing which treatment would be most effective for individual patients, including late-stage haematological cancer patients. The success of the platform to match targeted therapies to individual patients is subject to numerous factors, many of which are beyond the control of Exscientia, including, without limitation, the ability of healthcare providers to collect viable cells and each patient’s ability to respond due to pretreatments. 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.

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Investors:

Sara Sherman

investors@exscientia.ai

Media:

Amanda Galgay

media@exscientia.ai

Source: Exscientia

FAQ

What abstracts did Exscientia present at AACR 2022?

Exscientia presented three abstracts focusing on patient selection for adenosine antagonists and the profiling of GTAEXS-617, a CDK7 inhibitor.

How does Exscientia use AI in drug discovery?

Exscientia employs AI to optimize drug design and identify patient groups that may benefit from targeted therapies.

What is the significance of GTAEXS-617?

GTAEXS-617 is a CDK7 inhibitor showing potent anti-cancer activity in preclinical studies, aimed at treating certain cancers.

When is the AACR Annual Meeting 2022?

The AACR Annual Meeting 2022 takes place from April 8-13, 2022, in New Orleans, LA.

What challenges does Exscientia face in drug development?

Exscientia faces challenges in translating preclinical data to successful clinical outcomes and ensuring effective patient treatment matching.