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

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Biopharma Insights


[Interview] Expediting Drug Discovery Through Advanced Machine Learning

   by Andrii Buvailo    189
[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.

Clinical Research, Artificial Intelligence, and COVID-19

   by Raj Indupuri    334
Clinical Research, Artificial Intelligence, and COVID-19

How life sciences companies are reimagining trials during the biggest health crisis in a century

Whatever the world was like on March 15, it’s not like that now — and it probably won’t be for months or years. Everything from buying groceries to renewing a driver’s license is completely different from the way we did things just a few months ago. Clinical trials, like most medical activities, have been significantly affected as governments and pharmaceutical companies have pivoted to a single focus: combatting the novel coronavirus. At the same time, the rush to develop cures and vaccines for COVID-19 is condensing the review and approval process from years to months. This is where artificial intelligence (AI) can play a vital role in changing how clinical trials are conducted and how therapies are evaluated and tested.

A Leader in COVID-19 Vaccine Race, and the Bright Future for mRNA

   by Ivan Kondratov    444
A Leader in COVID-19 Vaccine Race, and the Bright Future for mRNA

Recently my favorite website ENDPOINTSNEWS published a rating of key players in the ongoing COVID-19 vaccine race. Compared to many vaccine developers with announced initiatives in this space, all the 28 organizations in the ENDPOINTSNEWS list stated at least preliminary positive results of their vaccine programs, and are firmly advancing them towards later stages of development. 

By the way, a notorious “Russian vaccine” by The Gamaleya Research Institute of Epidemiology and Microbiology also made its way to this list under the number 6, but I prefer not to touch upon this development. A lot has been said already about this story - to the point, and not so much. 

In this post I would rather talk about the number one in the list, a development undeservedly underrated by the media, which however might be the first to actually enter the global SARS-CoV2 vaccine market…

7 Notable AI Companies in Clinical Research to Watch in 2020

   by Irina Bilous    1269
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.

[Interview] Shaping European AI Regulation To Secure Global Leadership In Healthcare

   by Andrii Buvailo    583
[Interview] Shaping European AI Regulation To Secure Global Leadership In Healthcare

While the history of Artificial Intelligence (AI) field began in the distant 1950s, its practical value had largely remained limited all the way until the emergence of powerful hardware (GPUs) in the late 1990s. Other complementary technologies played an important role in the AI progress too: new data storage capabilities, cheap cloud infrastructures, advanced deep learning algorithms -- all these things became reality only in the 21st century, effectively setting AI field for exponential development trajectory and commercial practical utility. 

Today AI tech has matured to an extent it has become a strategic factor, a competitive differentiator, not only for individual companies but for the whole industries and countries. Needless to say, healthcare -- one of the major industries -- is an important end-user of AI technologies. Countries that care to adopt AI in their healthcare strategies today will have major competitive benefits for public health and safety tomorrow.