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

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Biopharma Insights


Contract Research Organizations Tap Into AI To Increase Value Proposition

   by Andrii Buvailo    446
Contract Research Organizations Tap Into AI To Increase Value Proposition

A few decades ago, pharmaceutical giants did most of the discovery work in-house, along with every other work necessary to get a drug or medical device to market. Nowadays, nearly any type of R&D or regulatory filing work that a drug maker or a medical-device producer needs to do -- from pre-clinical development to running clinical trials and go-to-market activities -- can be outsourced to CROs, and often it is the case.

According to a report by Kalorama Information, a market-research publisher in Rockville, Maryland, more than one-third of all global drug-discovery research will be outsourced to CROs by 2021.

In the wake of "the artificial intelligence (AI)-revolution" in the pharmaceutical and healthcare industries, CROs are tapping into various AI technologies to further cement their position in the global pharmaceutical R&D market -- in some instances competing for expertise and talent even with the leading drug makers.

Findings published by Deep Pharma Intelligence, a pharmaceutical industry think-tank in London, United Kingdom, in their report "AI for Drug Discovery, Biomarker Development and Advanced R&D Landscape Overview 2020" reveal that some of the leading CROs are actively adopting various artificial intelligence (AI) technologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP) in their research workflows -- via building in-house platforms, or partnering with AI-vendors and AI-focused biotech startups. 

Since the overwhelming majority of CROs are involved in clinical-stage projects, most AI-involving projects are primarily dealing with such use cases as patient recruitment for clinical trials, clinical trial management and result modeling, and patient stratification. However, notable areas of AI application include early drug discovery, preclinical development, and pharmacovigilance.   

The AI Productivity Game in Pharma

   by Amandeep Singh    818
The AI Productivity Game in Pharma

The pharmaceutical business is one of the riskiest industries to venture into. Drug discovery is an artisanal process where a carefully designed drug takes about 10 years and approximately 2.5 billion dollars to be approved and launched into the market. The complexity of biological systems places the odds at a ridiculous failure rate of 90%. In recent years, the declining efficiency of the R&D efforts has put the pharma industry on its toes. 

In the past decade, Artificial Intelligence (AI) has already revolutionized several industries, including automotive, entertainment and fintech. AI dictates routes and ETA on google maps, executes multiple stock exchange transactions, enables facial recognition, and powers the voice assistants Siri and Alexa. However, the adoption of AI in pharma has been restricted due to limited data available about what works (the successful 10%) and the innate complexity of the process of drug discovery.

[Interview] A Company on the Cutting Edge of COVID-19 Vaccine Development

   by Andrii Buvailo    1505
[Interview] A Company on the Cutting Edge of COVID-19 Vaccine Development

Recently, COVAXX announced it would soon enter human trials as the company is focused on rapidly developing a Multitope Peptide-based Vaccine (MPV) against SARS-CoV-2. While the biotech industry is now on a race to develop vaccines to curb COVID-19 pandemics, with several dozen players competing for the future market, COVAXX is a special case that can not be taken lightly.

Not only COVAXX’s new vaccine candidate is constructed off a commercially proven peptide-based vaccine platform by United Biomedical (UBI), a leader with a 30-year history in creating vaccines with over 500 million doses sold annually and 5 billion -- cumulatively in animal health indications for infectious disease that has demonstrated safety and efficacy. But it is also co-founded by two biotech “stars” of modern-day: Peter H. Diamandis, M.D., founder and executive chairman of XPRIZE, executive founder of Singularity University, and a dozen other tech companies, a popular science author; and Mei Mei Hu, co-founder of United Neuroscience, a member of Fortune “40 Under 40” and TIME “100 Next List”.

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

   by Andrii Buvailo    614
[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    650
[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.