WELCOME TO The Biotechnology REPORT
Australian Researchers Use 3D Bioprinting Pen In Orthopaedic Surgery
| October 2, 2017
Cell Therapies P/L is a contract manufacturing organization (CMO) and cellular product distributor that both manufactures and deploys advanced cell based therapies.
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.
With everything that's going on with the COVID-19 pandemic, many healthcare companies have grabbed plenty of spotlight during these challenging times. At the same time, a number of otherwise promising businesses have slipped under the radar. That's especially true for small-cap biotech stocks that aren't actively involved in developing tests, vaccines or treatments for COVID-19. Vaccine developers, protective equipment producers, and healthcare service providers are all attracting plenty of attention during this pandemic, but there are just as many promising biotech stocks that aren't involved in these areas. Here are two such companies that you might have missed, but they deserve a spot on your watch list.
Whether it’s called a modern “Manhattan Project” or a medical moon shot, the concept of long-term economic recovery rests on how confident people are they won’t risk serious illness by venturing forth in public again. Wisconsin stands to be a significant part of such an undertaking, whatever it’s called. The shorter-term debate is well under way over the gradual lifting of COVID-19 emergency rules, such as the now-extended “safer-at-home” order in Wisconsin. At least a dozen states, including regional coalitions on the East and West coasts, are exploring next steps as they seek to balance responses to the virus with calls for reopening the economy, at least, in part. Wisconsin’s ability to shape longer-term responses will come from private and public resources, which range from companies engaged in production of diagnostics.
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.
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