WELCOME TO The Biotechnology REPORT
Can Investors Stay the Course? Nasdaq Biotechnology Ishares ETF (IBB) Mass Index Update
| April 4, 2019
In 2003, the founders of Nexcelom introduced the CP2, a convenient tool to assist with manual cell counting. According to Dr. Jean Qiu, founder and CTO at Nexcelom,
Article | April 20, 2021
With advances in data analytics and machine learning, the move from descriptive and diagnostic analytics to predictive and prescriptive analytics and controls—allowing us to better forecast and understand what will happen and thus optimize process outcomes—is not only feasible but inevitable, according to Bonnie Shum, principal engineer, pharma technical innovation, technology & manufacturing sciences and technology at Genentech.
“Well-trained artificial intelligence systems can help drive better decision making and how data is analyzed from drug discovery to process development and to manufacturing processes,” she says.
Those advances, though, only really matter when they improve the lives of patients. That’s exactly what Shum expects.
“The convergence of digital transformation and operational/processing changes will be critical for the facilities of the future and meeting the needs of our patients,” she continues. “Digital solutions may one day provide fully automated bioprocessing, eliminating manual intervention and enabling us to anticipate potential process deviations to prevent process failures, leading to real-time release and thus faster access for patients.”
To turn Bioprocessing 4.0 into a production line for precision healthcare, real-time release and quickly manufacturing personalized medicines will be critical. Adding digitization and advanced analytics wherever possible will drive those improvements. In fact, many of these improvements, especially moving from descriptive to predictive bioprocessing, depend on more digitization.
Article | February 18, 2020
The Bioprocessing 4.0 concept seeks to apply automation and technology to the digital transformation of biologics manufacturing. As the paradigm moves forward, it faces barriers to its adoption, according to Eric Langer, president of BioPlan Associates. “Perhaps the greatest challenges involve unsecured links and adapting the applications to areas where automation is critically needed today,” says Langer. “Unresolved security issues could seriously affect a company’s data in a regulated environment, so they will need to have iron-clad anti-hacking protection in place. Unfortunately, cyber security is not yet a top focus for the industry.”
Article | April 3, 2020
The integration of artificial intelligence within life sciences is making drug discovery and development more innovative, less labor intensive and more cost-effective, says Deloitte’s annual global outlook. According to Deloitte’s 2020 Global Life Sciences Outlook, the biotech sector is at an inflection point. To prepare for the future and remain relevant in the ever-evolving business landscape, biopharma and medtech organizations will be looking for new ways to create value and new metrics to make sense of today’s wealth of data, the report overview says. As data-driven technologies provide biopharma and medtech organizations with treasure troves of information, and automation takes over some mundane tasks, new talent models are emerging based on purpose and meaning. The integration of artificial intelligence (AI) and machine learning approaches within life sciences is making drug discovery and development more innovative, time-effective and cost-effective, the Deloitte report states.
Article | March 18, 2020
Scientists at the Perelman School of Medicine at the University of Pennsylvania discovered early on in each cell, FoxA2 simultaneously binds to both the chromosomal proteins and the DNA, opening the flood gates for gene activation. The discovery, “Gene network transitions in embryos depend upon interactions between a pioneer transcription factor and core histones,” published in Nature Genetics, helps untangle mysteries of how embryonic stem cells develop into organs, according to the researchers. “Gene network transitions in embryos and other fate-changing contexts involve combinations of transcription factors. A subset of fate-changing transcription factors act as pioneers; they scan and target nucleosomal DNA and initiate cooperative events that can open the local chromatin. However, a gap has remained in understanding how molecular interactions with the nucleosome contribute to the chromatin-opening phenomenon,” write the investigators.
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