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

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Pharma 4.0 (Digital Tech and Data Analytics)

Section: Biopharma Insights     View all sections


[Interview] A New Way To Work With Data In Life Sciences

   by Andrii Buvailo    406
[Interview] A New Way To Work With Data In Life Sciences

Founded by renowned database researcher, Turing Award laureate MIT Professor Michael Stonebraker, Paradigm4 is not just any data analytics company in the Life Sciences. The organization is built on the decades of pioneering research in database design and possesses unique technological know-how in scientific data management and scalable computation. 

The firm has recently launched its REVEAL™: Single Cell app to offer biopharmaceutical developers the ability to break through the data wrangling and programming challenges associated with the analysis of large-scale, single-cell datasets. 

[Interview] Expediting Drug Discovery Through Advanced Machine Learning

   by Andrii Buvailo    437
[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.

[Interview] Shaping European Life Sciences with AI

   by Andrii Buvailo    743
[Interview] Shaping European Life Sciences with AI

There is a great deal of hype and a lot of misconceptions among life science experts as to how AI can or can’t be applied in pharmaceutical research and business. Judging by the rapidly increasing number of AI-involved deals and partnerships tapped by big pharma recently, it becomes obvious that life sciences decision-makers are eager to understand what this new and disruptive technology can bring to the table, and how it can be adopted efficiently with tangible ROI.

In order to get valuable first-hand insight and new ideas about the technology and its emerging role in the life sciences industry, I have asked several questions to Dr. Loubna Bouarfa, Founder and CEO at ​OKRA Technologies ― a leading AI company for healthcare, which builds a sophisticated AI-driven engine specialized in supporting faster and more accurate decisions for life science executives and field teams. Loubna is also a member of the European Union AI High-Level Expert Group (HLEG) and the winner of several prestigious awards, such as MIT Innovator Under 35 and Forbes Top 50 European Women in Technology. Last year, OKRA was named the Best Female-Led Startup at the StartUp Europe Awards.

Paving A Pathway To Pharma 4.0

   by Cathal Strain    753
Paving A Pathway To Pharma 4.0

Pharmaceutical manufacturers find themselves at a critical juncture. In the past 30 years, pharma has seen some significant shifts. Consider the expanding range of drug therapies, once confined almost exclusively to small molecule drugs. Biologics now comprise a substantial share of the market, and novel treatments such as cell and gene therapies are rapidly gaining traction. Another major trend is the rise of outsourced manufacturing, primarily for small molecule drugs.

Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

   by Tim Sandle    1539
Is Pharma Ready For Serialization? The Answer Lies In Digital Technology

New legislation requiring pharmaceutical companies to implement 'serialization' is now coming into force. This means that no counterfeit product should enter the supply chain and no legitimate product is diverted from its intended destination. To work effectively, serialization requires a comprehensive system to track and trace the passage of prescription drugs through the entire supply chain. The application of track and trace principles can help to avoid counterfeit medicines from entering the supply chain. To be effective, digital technologies such as blockchain and RFID-enabled tag and trace systems need to be embraced.