Owkin

Owkin
IndustryBiotechnology
FoundedAugust 3, 2016 (2016-08-03)
FounderThomas Clozel, Gilles Wainrib
Headquarters
Paris
,
France
Area served
US, France, UK, Switzerland, Germany, Spain
ProductsMSIntuit CRC, RlapsRisk BC
ServicesAI Drug Discovery, AI Drug Development, AI Diagnostics
Number of employees
350 (2023)
Websiteowkin.com

Owkin is an AI biotech company that uses artificial intelligence to identify new treatments, optimize clinical trials and develop AI diagnostics.[1][2] The company uses federated learning, a type of privacy preserving technology, to access multimodal patient data from academic institutions and hospitals to train its AI models for drug discovery, development, and diagnostics. Owkin has collaborated with pharmaceutical companies around the world to improve their therapeutic programs.[3]

History

[edit]

Owkin was founded in 2016, by Thomas Clozel, a clinical research doctor and son of Jean-Paul and Martine Clozel founders of Swiss biotech Actelion, and Gilles Wainrib, a professor of Artificial Intelligence.[4]

Owkin has raised over $255 million and became a ‘unicorn’ – a startup valued at more than $1 billion – in November 2021 through a $180 million investment from French biopharma company Sanofi.[5]

Technologies

[edit]

Federated learning

[edit]

Owkin uses federated learning, a decentralized machine learning technique, to train machine learning models with multiple data providers.[6][7][8] Federated learning allows data providers to collaborate without moving or sharing their data.[8][7]

The MELLODDY project, an initiative that included Owkin, 10 pharmaceutical companies, and six other partners, applied federated learning to train AI on datasets without having to share proprietary data.[9][8][10] The aim was to improve drug discovery and they built a shared platform called MELLODDY (Machine Learning Ledger Orchestration for Drug Discovery).[9][10][8] The first results of the project were published in July 2022.[8]

Transfer learning

[edit]

Transfer learning is a machine learning technique that allows a model pre-trained on one task to be used on another related task.[11] Owkin uses transfer learning to work on very small datasets.[11] Owkin's model (CHOWDER) is able to understand high-level graphic patterns, such as tumors, that are themselves relying on very low-level visual patterns, in order to fully learn the tumor's visual pattern.[12]

Products and Services

[edit]

MSIntuit CRC

[edit]

MSIntuit CRC is an AI-powered digital pre-screening diagnostic tool to improve colorectal cancer diagnosis and treatment.[13] It screens patients for microsatellite instability (MSI), which is a key genomic biomarker in colorectal cancer.[13] MSIntuit CRC is approved for use across the European Union.[14] It underwent a blind validation in 2023, made possibly partly by its availability within Medipath, the largest pathology lab network in France.[1]

Dx RlapsRisk BC

[edit]

Dx RlapsRisk BC uses AI to predict if breast cancer patients will relapse within a few years of initial treatment.[14] It is used by pathologists and oncologists to help determine the right treatment pathway for breast cancer patients.[14]

Partnerships

[edit]

Amgen

[edit]

Owkin collaborated with Amgen to test the ability of AI to improve cardiovascular prediction.[3]

Sanofi

[edit]

In November 2021 Owkin entered a strategic alliance with Sanofi.[15] The alliance included a $180 million equity investment, and a $90 million discovery and development partnership focused on Sanofi’s oncology efforts in four different cancers.[16] Sanofi used Owkin’s technology to find new biomarkers and therapeutic targets, build prognostic models, and predict response to treatment.[17]

Bristol-Myers Squibb

[edit]

In June 2022, Owkin entered a strategic alliance with Bristol-Myers Squibb to help them design potentially more precise and efficient clinical trials.[17] The collaboration initially focused on cardiovascular disease, and has the potential to expand into projects in other therapeutic areas.[18]

MSD

[edit]

In December 2023, Owkin entered a strategic alliance with MSD to develop and commercialize AI-powered digital pathology diagnostics for the EU market that could be used to identify patients suitable for immunotherapies.[19]

Servier

[edit]

In October 2023, Owkin and Servier started a multi-year partnership focused on developing “better-targeted therapies” in oncology and other disease areas.[20] The partnership’s first two projects were in translational medicine and digital pathology.[20]

MOSAIC

[edit]

MOSAIC (Multi Omic Spatial Atlas in Cancer) was formed by Owkin, Nanostring Technologies, the University of Pittsburgh, Gustave Roussy, Lausanne University Hospital, Uniklinikum Erlangen/Friedrich-Alexander-Universität Erlangen-Nürnberg, and Charité-Universitätsmedizin Berlin.[21][22] It uses spatial omics, multimodal patient data, and artificial intelligence, and aims to “offer unprecedented information on the structure of tumors” and guide new treatments.[22][21]

Publications

[edit]

Owkin’s research on AI/ML has led to a number of publications that focus on machine learning methodologies and the development of predictive models for different disease areas, mainly oncology.

  1. Courtiol, Pierre et al. “Deep learning-based classification of mesothelioma improves prediction of patient outcome”, Nat Med 25, 1519–1525 (2019)[23]
  2. Schmauch, Benoît et al. “A deep learning model to predict RNA-Seq expression of tumours from whole slide images”, Nature Communications volume 11, Article number: 3877 (2020)[24]
  3. Jean Ogier du Terrail et al. “Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer" Nat Med (2023). 10.1038/s41591-022-02155[25]
  4. Saiilard et al., “Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma” Nat Commun 14, 3459 (2023)[26]
  5. Saillard et al., “Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides” Nature Communications 14, 6695 (2023)[27]
  6. Saillard et al., "Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides" Hepatology 72 (2020)[28]

Awards

[edit]
  • 2019 AI For Health challenge[29]
  • 2020 Galien Foundation Best Digital Health Product Nominee[30]
  • 2021 Tech For Good Awards - “Health” category[31]
  • 2021 Member Recognition Awards from the French American Chamber of Commerce - Technology, Startups & Entrepreneurs Committee Awards[32]

References

[edit]
  1. ^ a b "AI Steps Up to Streamline MSI Testing in Colorectal Cancer". AZoRobotics.com. 2023-11-07. Retrieved 2023-12-14.
  2. ^ Alston, Fiona (2023-03-31). "French AI biotech unicorn Owkin has launched a €33 million AI-powered precision medicine project for cancer diagnosis and treatment". www.tech.eu. Retrieved 2023-11-01.
  3. ^ a b "Amgen, Owkin Use AI to Improve Cardiovascular Risk Prediction". Contract Pharma. Retrieved 2021-12-29.
  4. ^ "Owkin heads to Basel". Basel Area Business & Innovation. Retrieved 2021-12-29.
  5. ^ Rosemain, Mathieu (2021-11-18). "Drugmaker Sanofi invests $180 mln in French AI startup Owkin". Reuters. Retrieved 2021-12-29.
  6. ^ Vinluan, Frank (2022-10-04). "Sanofi exec jumps to Owkin to ramp up the AI biotech's pharma partnership plans". MedCity News. Retrieved 2023-12-19.
  7. ^ a b Nanalyze (2020-01-27). "Federated Learning Explained Simply - Nanalyze". www.nanalyze.com. Retrieved 2023-12-19.
  8. ^ a b c d e "L'apprentissage fédéré, le futur de la médecine basée sur les données - mind Health". www.mind.eu.com. Retrieved 2023-12-19.
  9. ^ a b Wiggers, Kyle (2020-09-17). "Major pharma companies, including Novartis and Merck, build federated learning platform for drug discovery". VentureBeat. Retrieved 2024-01-11.
  10. ^ a b "Federated Learning Can Protect Patients' Data In Hospitals". The Medical Futurist. 2021-04-13. Retrieved 2024-01-02.
  11. ^ a b outsourcing-pharma.com (2018-02-15). "OWKIN secures $11m to scale AI-driven drug discovery platform". outsourcing-pharma.com. Retrieved 2024-01-02.
  12. ^ Saillard, Charlie; Dubois, Rémy; Tchita, Oussama; Loiseau, Nicolas; Garcia, Thierry; Adriansen, Aurélie; Carpentier, Séverine; Reyre, Joelle; Enea, Diana; von Loga, Katharina; Kamoun, Aurélie; Rossat, Stéphane; Wiscart, Corentin; Sefta, Meriem; Auffret, Michaël (2023-11-06). "Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides". Nature Communications. 14 (1): 6695. doi:10.1038/s41467-023-42453-6. ISSN 2041-1723. PMC 10628260.
  13. ^ a b Thomas, Uduak (2023-11-10). "Owkin's AI Diagnostic for Colorectal Cancer Takes Center Stage with Promising Validation Results". GEN - Genetic Engineering and Biotechnology News. Retrieved 2023-12-20.
  14. ^ a b c "Owkin AI for identifying breast, colorectal cancer types score EU approval". Fierce Biotech.
  15. ^ "Sanofi inks $270M cancer AI deal with R&D platform developer Owkin". Fierce Biotech.
  16. ^ "Drugmaker Sanofi invests $180 mln in French AI startup Owkin". Reuters.
  17. ^ a b Burroughs, Tasmin Lockwood, Callum. "Exclusive: Medical AI startup Owkin just secured $80 million as it gears up to enhance drug trials with the pharmaceutical giant Bristol Myers Squibb". Business Insider. Retrieved 2024-01-04.{{cite web}}: CS1 maint: multiple names: authors list (link)
  18. ^ "BMS Enlists Owkin's AI/ML Tech to Improve Clinical Trials". BioSpace. Retrieved 2024-01-04.
  19. ^ "Owkin and MSD join forces on AI-powered digital pathology". pharmaphorum. Retrieved 2024-03-01.
  20. ^ a b "Owkin signs up another pharma partner for its AI platform". pharmaphorum. Retrieved 2024-03-14.
  21. ^ a b "ASCO: AI-powered MOSAIC will build 3D atlas for cancer". pharmaphorum. Retrieved 2024-03-06.
  22. ^ a b outsourcing-pharma.com (2023-06-08). "Owkin invests $50M in spatial omics project that will 'revolutionize cancer research'". outsourcing-pharma.com. Retrieved 2024-03-06.
  23. ^ Courtiol, Pierre; Maussion, Charles; Moarii, Matahi; Pronier, Elodie; Pilcer, Samuel; Sefta, Meriem; Manceron, Pierre; Toldo, Sylvain; Zaslavskiy, Mikhail; Le Stang, Nolwenn; Girard, Nicolas; Elemento, Olivier; Nicholson, Andrew G.; Blay, Jean-Yves; Galateau-Sallé, Françoise (October 2019). "Deep learning-based classification of mesothelioma improves prediction of patient outcome". Nature Medicine. 25 (10): 1519–1525. doi:10.1038/s41591-019-0583-3. ISSN 1078-8956.
  24. ^ Schmauch, Benoît; Romagnoni, Alberto; Pronier, Elodie; Saillard, Charlie; Maillé, Pascale; Calderaro, Julien; Kamoun, Aurélie; Sefta, Meriem; Toldo, Sylvain; Zaslavskiy, Mikhail; Clozel, Thomas; Moarii, Matahi; Courtiol, Pierre; Wainrib, Gilles (2020-08-03). "A deep learning model to predict RNA-Seq expression of tumours from whole slide images". Nature Communications. 11 (1): 3877. doi:10.1038/s41467-020-17678-4. ISSN 2041-1723. PMC 7400514.
  25. ^ Ogier du Terrail, Jean; Leopold, Armand; Joly, Clément; Béguier, Constance; Andreux, Mathieu; Maussion, Charles; Schmauch, Benoît; Tramel, Eric W.; Bendjebbar, Etienne; Zaslavskiy, Mikhail; Wainrib, Gilles; Milder, Maud; Gervasoni, Julie; Guerin, Julien; Durand, Thierry (January 2023). "Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer". Nature Medicine. 29 (1): 135–146. doi:10.1038/s41591-022-02155-w. ISSN 1546-170X.
  26. ^ Saillard, Charlie; Delecourt, Flore; Schmauch, Benoit; Moindrot, Olivier; Svrcek, Magali; Bardier-Dupas, Armelle; Emile, Jean Francois; Ayadi, Mira; Rebours, Vinciane; de Mestier, Louis; Hammel, Pascal; Neuzillet, Cindy; Bachet, Jean Baptiste; Iovanna, Juan; Dusetti, Nelson (2023-06-13). "Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma". Nature Communications. 14 (1): 3459. doi:10.1038/s41467-023-39026-y. ISSN 2041-1723. PMC 10264377.
  27. ^ Saillard, Charlie; Dubois, Rémy; Tchita, Oussama; Loiseau, Nicolas; Garcia, Thierry; Adriansen, Aurélie; Carpentier, Séverine; Reyre, Joelle; Enea, Diana; von Loga, Katharina; Kamoun, Aurélie; Rossat, Stéphane; Wiscart, Corentin; Sefta, Meriem; Auffret, Michaël (2023-11-06). "Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides". Nature Communications. 14 (1): 6695. doi:10.1038/s41467-023-42453-6. ISSN 2041-1723. PMC 10628260.
  28. ^ Saillard, Charlie; Schmauch, Benoit; Laifa, Oumeima; Moarii, Matahi; Toldo, Sylvain; Zaslavskiy, Mikhail; Pronier, Elodie; Laurent, Alexis; Amaddeo, Giuliana; Regnault, Hélène; Sommacale, Daniele; Ziol, Marianne; Pawlotsky, Jean-Michel; Mulé, Sébastien; Luciani, Alain (December 2020). "Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides". Hepatology. 72 (6): 2000. doi:10.1002/hep.31207. ISSN 0270-9139.
  29. ^ Maignan, Iris (2019-09-13). "3 informations pour bien commencer la journée : Chaire Good In Tech, les Rebondisseurs, et Owkin". Maddyness - Le média pour comprendre l'économie de demain (in French). Retrieved 2024-01-30.
  30. ^ Hamilton-Basich, Melanie (2020-10-05). "Prix Galien Nominees for Best Digital Health Product Announced". 24x7. Retrieved 2024-01-30.
  31. ^ "Tech For Good Awards: découvrez les gagnants de l'édition 2021". BFM BUSINESS (in French). Retrieved 2024-01-26.
  32. ^ "2021 Member Recognition Awards Recognize Impactful Initiatives in the FACC-NY Network". www.faccnyc.org. Retrieved 2024-01-26.
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