AI-gestuurd ontdekkingsplatform maakt nieuwe manieren mogelijk om nieuwe paden te vinden en vooruitgang te boeken bij het identificeren van patiënten die mogelijk beter reageren op medicijnen in de kliniek

Preklinische gegevens benadrukken de potentiële voordelen van Exscientia’s AI-gestuurde ontwerp om snel moleculen te ontdekken met geoptimaliseerde eigenschappen, waaronder GTAEXS-617

OXFORD, Engeland-(BUSINESS WIRE)- Exscientia (Nasdaq: EXAI) heeft vandaag aangekondigd dat drie abstracts zijn aanvaard voor posterpresentatie tijdens de komende American Association voor Cancer Research (AACR) Annual Meeting 2022, die van 8 tot 13 april 2022 wordt gehouden in het Ernest N. Morial Convention Center in New Orleans, LA.

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

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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Contacts

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Media:
Amanda Galgay

media@exscientia.ai