Unlocking the Power of Advanced Analytics in Life Sciences

While 2021 was another challenging year in many respects, the life science industry still managed some notable achievements. The second year of the COVID-19 pandemic was all about vaccines: how to make and distribute them and understanding how effective they are against emerging variants. Pfizer-BioNTech’s Comirnaty was the first to receive FDA approval and now Pfizer, Moderna, and others are working to develop boosters specifically targeting the highly contagious Omicron variant. 

2021 also saw the World Health Organization’s first approval of a malaria vaccine and promising early trial results for the University of Oxford’s R21, the first vaccine candidate to reach the WHO’s goal of 75% efficiency. The FDA approved 55 new biopharma drugs in 2021, the same as 2020 and even up from 48 approvals in 2019. 

With COVID vaccines and therapies taking priority it isn’t surprising that Deloitte’s annual estimate of the return on investment that a cohort of the top biopharma companies might expect from their late-stage pipelines has increased from 2.7% in 2020 to 7% in 2021. Even if you exclude COVID-related assets, however, the projected average internal rate of return (IRR) is still 3.2%, an uptick from the previous year that’s due in part to decreasing R&D costs and cycle times. Sonal Shah, senior manager for the Deloitte Center for Health Solutions, told Endpoint News, “This is a big reversal, after almost a decade-long decline in returns on innovation. So, the fact that that turnaround is happening despite COVID is really exciting.” 

As Deloitte noted in the report, continued adoption of digital solutions and collaborative data sharing will be key to sustaining this momentum. One of the most exciting developments in 2021 has been described without hyperbole as “the most important achievement in AI – ever”. This is, of course, AlphaFold, an artificial intelligence (AI) network developed by DeepMind (a Google AI offshoot) that has effectively solved the protein folding problem, that is, determining a protein’s three-dimensional shape from its amino acid sequence. Understanding protein structure is essential to understanding biological processes, and incorrect protein folding is the cause of degenerative diseases such as Alzheimer’s or Parkinson’s. 

The publication of the AlphaFold Protein Structure Database includes the complete human proteome and will be freely and openly available to the scientific community. The mind boggles with what this data and these kinds of predictive modeling tools could potentially lead to. Drug discovery is one area of enormous potential, and even complex post-translational modifications such as glycosylation in biologics can be predicted too. From a drug developer’s perspective, what if you could use in silico modeling to understand not just how the molecule works, but how it can be produced and purified to maximize productivity and extend shelf life? Doing more process optimization work through modeling and simulation, rather than time-consuming and resource-intensive wet lab work, can shorten the time and cost associated with development. Mechanistic modeling of purification steps such as chromatography, for example, is already being developed to help minimize product degradation and optimize product yield and purity.  

Before these exciting ideas can become a reality, however, there’s still a core issue the biopharma industry needs to solve. Inaccessible data silos are a problem at every stage of the biopharma lifecycle, both for developers and regulatory agencies. As former FDA commissioner and current nominee Dr. Robert Califf said recently, “Unless we fix this fundamental computing infrastructure, people will wonder why some things take a long time. It’s because it’s not being done in the most modern way.” Without a robust and well-maintained data backbone, the challenge of connecting disparate data sources and developing predictive models across development, clinical, and commercial manufacturing will continue to be a roadblock.

That’s why IDBS has created Polar, the world’s first biopharma life cycle management (BPLM) platform. 2021 was a significant year for IDBS as well with the acquisition of Skyland Analytics. By bringing together Polar and Skyland PIMS, IDBS is creating a uniquely differentiated offering that supports therapies from early development through commercial manufacturing. IDBS can help customers unlock the power of data to design and monitor more scalable, robust, and higher-yielding processes by streamlining technology transfer and creating a persistent, dynamic data backbone throughout the product lifecycle that improves process understanding,

If you’re interested in learning more, we have just the thing for you. In this webinar Henry Charlton, IDBS Commercial Director for Biologics, discusses how Polar now makes it possible to operationalize data governance throughout biopharma development, ensuring data integrity while also creating a more efficient path to process understanding. 

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