A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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Startups


Marshall Pillay joins OKRA

   by Marina Gonzalo    169
Marshall Pillay joins OKRA

Cambridge, UK, October 5th 2020 - OKRA is thrilled to announce the appointment of Marshall Pillay as VP Commercial - Europe and International. Starting today, Marshall will join OKRA from AKTANA, where he was most recently GM of Europe, responsible for all business and operations in the continent.

OKRA raises ambitious target to 6 million predictions in Life Sciences by end of 2020

   by Marina Gonzalo    132
OKRA raises ambitious target to 6 million predictions in Life Sciences by end of 2020

Cambridge, UK, September 29th 2020 - Following on from June's breakthrough news that OKRA hit its target of 2 million validated predictions at >95% accuracy a full 6 months early, the company has set its sights on a record-breaking 6 million predictions completed by year end. This means that pharma companies have benefitted from an unparalleled level of support during the most uncertain time in recent memory, using predictions to guide their staff to take the right next step. This is 1 million more than the revised target set only in June.

[Interview] Expediting Drug Discovery Through Advanced Machine Learning

   by Andrii Buvailo    600
[Interview] Expediting Drug Discovery Through Advanced Machine Learning

The application of next-generation data analytics tools, powered by machine learning and artificial intelligence (AI) components, has become a long-term strategic priority for most companies in the pharmaceutical and biotech industries. However, such systems have to make sure the organisational data is findable, accessible, interoperable, and reusable across different sub-systems, applications, departments, teams, and even companies. 

Aigenpulse, a technology company at the forefront of data management and analytics in the Life Science industry, has built a portfolio of tools for working with organisational research data at scale and accelerating the discovery and development of better targets and candidates using advanced machine learning technologies.

Cytovia Therapeutics and NYSCF Annouce Filing of Provisional Patent for iPSC-derived NK Cells to Produce Unlimited On-demand NK and CAR-NK Cells for the Treatment of Cancer

   by Charlotte Tomic    156
Cytovia Therapeutics and NYSCF Annouce Filing of Provisional Patent for iPSC-derived NK Cells to Produce Unlimited On-demand NK and CAR-NK Cells for the Treatment of Cancer

(August 25, 2020)--New York, NY--Cytovia Therapeutics, an emerging biopharmaceutical company and the New York Stem Cell Foundation (NYSCF) Research Institute today announced the filing of a provisional patent application with the U.S. Patent & Trademark Office (USPTO) for the differentiation of Natural Killer (NK) cells from induced pluripotent stem cells (iPSCs). The NYSCF Research Institute is a pioneer and acknowledged leader in stem cell technology, having developed the NYSCF Global Stem Cell Array®, the premier automated robotic platform for reprogramming skin or blood into induced pluripotent stem cells (iPSCs) and differentiating them into disease-relevant cell types.

 

7 Notable AI Companies in Clinical Research to Watch in 2020

   by Irina Bilous    1794
7 Notable AI Companies in Clinical Research to Watch in 2020

The clinical trial is a critical stage of drug development workflow, with an estimated average success rate of about 11% for drug candidates moving from Phase 1 towards approval. Even if the drug candidate is safe and efficacious, clinical trials might fail due to the lack of financing, insufficient enrollment or poor study design. [Fogel DB. 2018].  

Artificial Intelligence (AI) is increasingly perceived as a source of opportunities to improve operational efficiency of clinical trials, and minimize clinical development costs.  Typically AI vendors offer their services and expertises in the three main areas. AI start-ups in the first area help to unlock information from disparate data sources, such as scientific papers, medical records, disease registries, and even medical claims by applying Natural Language Processing (NLP). This can support patient recruitment and stratification, site selection, improve clinical study design and understanding of diseases mechanisms. As an example, about 18 % of clinical studies fail due to insufficient recruitment, as 2015 study reported.