Medical
Article | August 16, 2022
Biotechnology is a vast discipline of biology that employs diverse biological systems to create solutions that can significantly alter the ways in which they operate across various domains. That said, biotechnology is not a new notion. It has existed for millennia, with ancient civilizations using its earliest incarnations to cultivate crops and create alcoholic beverages.
Today, the biotechnology industry has developed by leaps and bounds and has amassed a vast quantity of scientific data through study and research. Given the importance of data in the biotechnology business, it is not difficult to understand why biotech companies utilize data analytics.
Modern data analytics tools have made it possible for researchers in the biotech industry to build predictive analytics models and gain knowledge about the most efficient approaches to accomplish their desired goals and objectives. Data analytics is increasingly being adopted by biotech businesses to better understand their industry and foresee any problems down the road.
How is Data Analytics Revolutionizing Fields in Biotechnology?
Today's business and scientific fields greatly benefit from data. Without the analysis of vast information libraries that provide new insights and enable new innovations, no industry can really advance. Being highly reliant on big data analytics, biotech is not an exception in this regard.
With the tools and methods that help scientists systematize their findings and speed up their research for better and safer results, data analytics is making deeper inroads into the biotechnology industry. It is emerging as a crucial link between knowledge and information and is extensively being used for purposes other than just examining the information that is already available. The following are a few of the cutting-edge biotechnology applications of data analytics
Genomics and Disease Treatment
Pharmaceutical Drug Discovery
Drug Recycling and Safety
Agriculture and Agri-products
Environmental Damage Mitigation
Data Analytics Possibilities in Biotechnology
With data analytics becoming an integral part of how biotech businesses operate, biotechnologists and related stakeholders need to understand its emergence and crucial role.
Data analytics has opened new frontiers in the realm of biotechnology. Thanks to developments in data analytics, research and development activities that once took years may now be accomplished in a matter of months. Also, now scientists have access to biological, social, and environmental insights that can be exploited to create more effective and sustainable products.
By understanding the importance of data-related tools and techniques applications, biotech companies are aiming to invest in the popularizing technology to stay updated in the fast-paced biotechnology industry.
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MedTech
Article | July 13, 2022
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.
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MedTech
Article | July 20, 2022
Introduction
Recent developments in the bioengineering of monoclonal antibodies (mAbs) have revolutionized the treatment of numerous rheumatic and immunological disorders.
Currently, several immunological disorders are successfully being targeted and treated using innovative medical techniques such as immunotherapy. Leading companies are increasingly investing in research activities to expand the usage and application of immunology for the treatment of various infectious diseases, including multiple sclerosis, inflammatory bowel disorders, lupus, and psoriasis, leading companies are increasingly investing in research activities.
Today, the efforts of researchers in immunology, with a long history of study and research, have borne fruit, as bioengineered mAbs are now being employed in clinical practices.
Accelerating Investments: Paving the Way for Immunology
The increasing prevalence of infectious diseases, cancer, and immune-mediated inflammatory disorders (IMIDs) is raising the need for more precise classification and an in-depth understanding of the pathology underlying these ailments.
Numerous leaders in the biotechnology domain are thus focusing on undertaking numerous strategies, such as new facility launches and collaborations, to address the need by finding deeper inroads into immunology and its use in disease treatments.
For instance, in 2022, the University of Texas MD Anderson Cancer Center announced the launch of a visionary research and innovation hub, the James P. Allison Institute, to find new roads in immunotherapy, develop new treatments, and foster groundbreaking science.
These developments will result in better diagnosis through the use of selective biomarkers, and early detection of fatal diseases and their treatment, which will prevent complications from happening. Also, the identification of high-risk populations through a deeper understanding of genetic and environmental factors can assist in the prevention of disease through immunotherapy.
The Way Forward
Immunology has led to the development of biotechnology, making it possible to develop novel drugs and vaccines, as well as diagnostic tests, that can be used to prevent, diagnose, and treat a wide range of autoimmune, infectious, and cancerous diseases.
With the rapid advancement in technology and the integration of artificial intelligence, immunology is finding its way into an array of domains and industries, encompassing several research areas including medicine, pharmaceuticals, agriculture, and space.
Today, not only researchers but also leading biotech and pharmaceutical companies have recognized that conventional therapies with pharmaceutical and chemical products are being replaced by products derived from immunology. This is because they work well for health problems, are environmentally friendly, and are also emerging as a wealth-generating business in the medical field.
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Diagnostics
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.
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