Article | July 11, 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.
Article | July 11, 2022
Gene therapy has historically been used to treat disorders with in-depth knowledge caused by a single genetic mutation. Thanks to the introduction of new generation technologies, the potential of gene therapy is expanding tAo treat diseases that were previously untreatable.
Evolution of Gene Therapy
One of the major success stories of the twenty-first century has been gene therapy. However, it has not been the same in the past. The field's journey to this point has been long and mostly difficult, with both tragedy and triumph along the way.
Initially, genetic disorders were thought to be untreatable and permanently carved into the genomes of individuals unfortunate enough to be born with them. But due to the constant technological advancement and research activities, gene therapy now has the potential to treat various genetic mutation-causing diseases with its ability to insert a new copy and replace faulty genes.
Gene Therapy is Finding New Roads in the Medical Sector
Gene therapy can help researchers treat a variety of conditions that fall under the general heading of epilepsy, instead of only focusing on a particular kind of disorder brought on by a genetic mutation. Following are some of the domains transformed by gene therapy.
Neurology – Gene therapy can be used for the treatment of seizures by directly injecting it into the area causing an uncontrolled electrical disturbance in the brain. Furthermore, by using DNA sequences known as promoters, gene therapy can be restricted to specific neurons within that area.
Ophthalmology – Genetic conditions such as blindness can be caused due to the mutation of any gene out of over 200 and resulting in progressive vision loss in children. With advanced gene therapies such as optogenetics, lost photoreceptor function can be transferred to the retinal cells, which are responsible for relaying visual information to the brain. This might give patients the ability to navigate in an unknown environment with a certain level of autonomy.
The Future of Gene Therapy
The news surrounding gene therapy has been largely favorable over the past few years, with treatment after treatment obtaining regulatory approvals, successful clinical trials, and garnering significant funds to begin development. With more than 1,000 clinical trials presently underway, the long-awaited gene therapy revolution might finally be here.
Article | September 22, 2022
The medical and life-science industries are experiencing a robust transformation with the increasing prevalence of various types of diseases, including infectious diseases, chronic disorders, and acute conditions around the world. As a result, a significant rise in demand for more effective therapeutic drugs and bionics is being witnessed, leading to a swift increase in the number of clinical trials.
For a successful trial, it is important for biotech companies to ensure the data submitted to regulatory bodies regarding clinical trials is accurate, reliable, and definitive from an ethical point of view. A reliable clinical trial management system plays a vital role in collecting, monitoring, and managing clinical data. The availability of high-quality clinical data also helps clinical research institutions make efficient treatment decisions and provide proper patient care.
Hence, a number of biotech companies and research organizations are focusing on leveraging innovative clinical trial management solutions to handle a large amount of data, particularly in multi-center trials, and generate reliable, high-quality, and statistically sound data from clinical trials. However, selecting the most appropriate and reliable clinical trial management system is vital for the clinical trial's success. Let's see some of the steps that will assist these firms in choosing the right CTMS.
Key Steps for Selecting Right Biotech Clinical Trial Management System
Prioritize Study Needs
Considering and prioritizing study needs is a crucial step in choosing the most reliable clinical trial management system for biotech companies. Prioritizing helps them to identify a solution that improves the study's quality and removes uncertainty for researchers when faced with difficult choices.
Hence, biotech and life-science organizations should choose a clinical trial system that is simple to use, well-organized, and suitably designed to minimize the number of clicks required to complete a task.
Select CTMS with Multiple Integrations
Integrated clinical trial management systems provide the best value for the companies’ funds as they guarantee the smooth functioning of research protocols. In addition, integrations are necessary to fully understand the importance and advantages of clinical trial management software for ensuring smooth transitions between site management and data collection.
Biotech and clinical research should look for CTMS platforms that can integrate with electronic medical record (EMR) platforms and clinical research process content (CRPC) billing grids. This will allow them to use the same billing designations and ensure compliance while minimizing the need for duplicate processes.
Ensure System Compliance and Security
Clinical research organizations need to adhere to a plethora of complex regulations in order to ensure compliance with one of the most challenging environments of principles, which is information security and privacy. Security and system compliance are vital aspects of choosing the right CTMS solutions for biotech firms as they assist in building trust and form a part of the system’s duties.
While selecting CTMS systems, it is essential for companies engaged in clinical research to ensure that these platforms are able to configure both, group and individual permissions, along with having a data backup and recovery plan for hosted systems. This will allow companies to assess the privacy and security implications of research and anticipate complications that may arise in each phase of the project.
Assess the Scalability
Choosing a scalable CTMS that can accommodate various types of fluctuations and expansions enables biotech and clinical firms to quickly adapt to fast-changing trends and demand spikes while reducing maintenance costs and enhancing user agility.
As scalability also means secure and expanded data storage, these businesses should instead use SaaS solutions than manually manage an ever-growing collection of hard drives. The right CTMS ensures accommodating the firm’s availability requirements without incurring the capital costs associated with expanding a physical infrastructure.
The Closing Thought
A well-executed and successful clinical trial involves multiple stages and processes. Several quality controls and stringent adherence to regulations are essential for the steps, along with efficient cross-departmental processes and procedures. Incorporating the right CTMS paves the way for paperless data collection, regulatory filing, and fiscal management tools for biotech researchers and administrative personnel.
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.