MedTech
Article | July 13, 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 12, 2022
Genomics is an interdisciplinary field of biology emphasizing the structure, editing, evolution, function, and mapping of genomes. It is creating deeper inroads across the precision health domain with the increasing introduction of advanced technologies such as quantum simulation, next-generation sequencing (NGS), and precise genome manipulation.
As precision health focuses on providing the proper intervention to the right patient at the right time, genomics increasingly finds applications in human and pathogen genome sequencing in clinical and research spaces.
Rising Hereditary Diseases Burden Paving the Way for Genomics in Precision Health
In the last few years, a significant surge in the prevalence of diseases and ailments such as diabetes, obesity, baldness, and others has been witnessed across the globe. A history of family members with chronic diseases, such as cancer, diabetes, high blood pressure, hearing issues, and heart disease, can sometimes continue into the next generation.
Hence, the study of genes is extensively being conducted for predicting health risks and early treatment of these diseases. It also finds use in CRISPR-based diagnostics and the preparation of precision medication for the individual.
In addition, ongoing advancements in genomics are making it possible to identify different genetic traits that persuade people to more widespread diseases and health problems.
The Emergence of Genomics Improves Disease Understanding
Genomics refers to the study of the complete genetic makeup of a cell or organism. Increasing scientific research in the area substantially contributes to increasing knowledge about the human genome and assists in improving the ability to understand disease etiology, risk, diagnosis, treatment, and prevention. On account of these improvements, innovative genomic technologies and tools are being developed to enable better precision health not only for the individual but for various regional populations as well.
The Way Forward
With growing preference for personalized medicine and an increasing need for more accurate pathogen detection and diagnostics, genomics is gaining huge popularity across the precision health domain.
Also, increasing research activities for developing novel high-precision therapeutics and rising importance of gene study in the prevention, diagnosis, and management of infectious and genetic diseases will further pave the way for genomics in the forthcoming years.
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Medical
Article | July 14, 2022
If you have ever visited the testing laboratory of a large biotechnology company, you will be aware that managing the laboratory's operations single-handedly is no easy task. The greater the size of a lab, the more research and testing activities it must accommodate.
A variety of diagnostic tests are prescribed for patients in order to detect various diseases. For example, it may include blood glucose testing for diabetics, lipid panel, or liver panel tests for evaluating cardiac risk and liver function, cultures for diagnosing infections, thyroid function tests, and others.
Laboratory management solutions such as laboratory information management systems (LIMS) and other software play a significant role in managing various operational data at biotech laboratories. It is one of the important types of software developed to address thedata management and regulatory challenges of laboratories. The software enhances the operational efficiency of biotech labs by streamlining workflows, proper record-keeping, and eradicating the need for manually maintaining data.
What Are the Benefits of Laboratory Information Management Software in Biotechnology?
As the trends of digitization and technology continue to create deeper inroads into the biotechnology sector, a significant rise in the adoption of innovative medical software solutions, such as LIMS, is being witnessed for managing research data, testing reports, and post-research results globally. Here are a few reasons that are encouraging biotech facilities to adopt LIMS solutions
Real-Time Data Collection and Tracking
Previously, collecting and transporting samples was a tedious and time-consuming task. However, the adoption of LIMS with innovative tracking modules has made the job easier. The real-time sample tracking feature of LIMS has made it possible for personnel to collect the research data in real-time and manage and control the workflow with a few mouse clicks on the screen.
Increase Revenue
LIMS makes it possible to test workflows while giving users complete control over the testing process. A laboratory is able to collect data, schedule equipment maintenance or upgrades, enhance operational efficiency, and maintain a lower overhead with the help of the LIMS, thereby increasing revenue.
Streamlined Workflow
With its completion monitoring, LIMS speeds up laboratory workflows and keeps track of information. It assigns tasks to the specialist along with keeping a real-time track of the status and completion of each task. LIMS is integrated into the laboratory using lab information, which ultimately speeds up internal processes and streamlines the workflow.
Automatic Data Exchange
LIMS solutions store data in a centralized database. Automated transfer of data between departments and organizations is one of the major features of LIMS. Through its automated information exchange feature, LIMS improves internal operations, decreases the reporting time for data sharing, and assists in faster decision-making.
Final Thoughts
As the healthcare sector continues to ride the wave of digital transformation, biotech laboratories are emphasizing adopting newer technologies to keep up with the changes.
Citing this trend, laboratory information management systems are becoming crucial for biotech and medical organizations for maintaining research data, instant reporting, and managing confidential, inventory, and financial data with centralized data storage.
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MedTech
Article | September 22, 2022
Introduction
The administration of medical records and data has advanced significantly during the past few decades. Clinical data management, which was once only a small subset of biotech research organizations, has now developed into a mission-critical, specialized unit.
In the late 1990s, electronic data capture (EDC) began to alter the traditional function of clinical data management. After that, the data configuration and management of data queries for the EDC system fell under the purview of clinical data management services.
Today, clinical data management is not only responsible for managing the clinical data configuration and data queries but also developing and implementing data administration plans, ensuring data accuracy and completeness, and maintaining optimum data security.
In recent years, as digital technologies have gained acceptance around the globe, data has become a vital aspect in decision-making across numerous industries, and the life sciences and biotechnology sectors are no exception. Using data has provided granular insights to biotech organizations, assisting them in creating breakthroughs in drug development and medical research and signifying the importance of clinical trial management systems in these medical verticals.
The Biggest Biotech Clinical Data Management Trends to Know About Today
The future of clinical data management is contingent upon the implementation of systems and regulations. It is imperative for all organizations participating in a medical or life science trial to have transparent rules in place for sharing and retaining patient data. Also, there is a need to have a standardized format for maintaining these records and documents related to trials. This assists biotech organizations in reducing the chances of ambiguity regarding who owns what kind of data or paperwork at any given time.
Over the past couple of years, the focus of the life science and biotechnology industries has shifted towards developing more effective medications and therapies, implementing personalized treatment, and finding cures for diseases such as cancer and AIDS. In response to this, a substantial rise in the number of clinical trials is being witnessed globally.
As the number of clinical trials continues to accelerate, the spending on these trials rises as well. In response to this, the worldwide cost of conducting clinical trials is anticipated to reach US$ 49.80 billion in 2022.
With the transition of the world from traditional to digital, medical professionals and biotech businesses are increasingly shifting towards adopting high-tech and reliable clinical trial management systems for various applications, starting from diagnosis and clinical trials to patient data documentation. But, what are the future trends in biotechnology clinical data management? Let’s discuss.
Cloud-Based Clinical Metadata Repositories
Automation is emerging as a new frontier in the biotech clinical data management domain, along with other innovative technologies such as artificial intelligence and machine learning. Because of this, life science establishments are witnessing a huge shift from paper-based documentation toward data-based documentation, which is creating mountains of research, compliance, and clinical data.
The growing demand for new and more effective medications and drugs is augmenting the need to expedite clinical trials. This is resulting in an increased number of initiatives aimed at optimizing clinical trial processes to prepare and launch successful trials. However, pharmaceutical and biotechnology laboratories are encountering several challenges in collecting, managing, and analyzing metadata due to its complexities.
So, what is the best solution to this problem? The answer to this is cloud-based clinical metadata repositories.
Clinical research facilities are leveraging advanced, all-in-one, cloud-based clinical metadata repositories to assist them in centralizing and managing metadata; increasing metadata quality, consistency, and accuracy; and speeding up clinical trial management, documentation, and compliance processes.
Shift Towards Digital Solutions
Electronic Case Report Form
Adequate research and accurate data are crucial for a clinical trial to succeed. Whether developing new drugs, medication, or therapies; conducting life science research; or studying the latest clinical trial systems, it is best to use electronic solutions as it reduces the room for mistakes during the transition of clinical data from paper-based format.
Realizing this, biotech organizations are shifting towards using electronic case report forms to speed up record retrieval, improve record security, and cut down on operational costs associated with running clinical trials. The electronic case report form assists in lowering the failure rate of the clinical trial, enhancing efficiency, and optimizing security along with improving clinical trial documentation and productivity, further driving its adoption in the medical space.
Electronic Clinical Outcome Assessment
Electronic clinical outcome assessment is surfacing as one of the fast-growing future trends in biotechnology. It allows clinical trial facilities to automate data entry and improve the reliability of the collected information. The technology enables clinical trial institutions to automatically record patient-provided information about side effects, symptoms, drug timing, and other aspects during the clinical trial for increased precision.
It also helps these institutions analyze the results of medication or therapy in clinical trials and lets clinical researchers use medical technologies like biosensor-enabled devices, self-service applications, and medical wearables for evaluation.
Hence, biotech clinical facilities are increasingly deploying advanced electronic clinical outcome assessment systems to ensure adherence to protocols and regulations.
Clinical Trial Customization
The success of a new drug is determined by numerous factors other than its effectiveness, safety, and creativity of its developers, such as a successful clinical trial. Each clinical trial involves a number of decision-making points, and one wrong choice in any of these aspects can jeopardize the success of the entire endeavor. A crucial component of making well-informed decisions is data management, which is a part of clinical study as a whole.
Clinical trial customization is emerging as one of the most prominent biotech clinical trial management trends. Every clinical trial is unique and needs a tailored approach to be successful. With the emergence of the trend of personalized treatment around the globe, biotech and pharmaceutical organizations are adopting innovative customized clinical trial management solutions to accelerate the pace of clinical trials and approvals. This is giving clinical researchers innovative ways to come up with new medicines for patients and streamline the clinical data as per the requirements for faster approvals.
What Are the Key Clinical Data Management Challenges Faced by Biotech Companies?
Groundbreaking medical interventions are of no use without reliable, accurate, and extensive clinical trial data. Without the data, biotech and pharmaceutical companies will not be able to provide the assurance of safety and efficacy required to bring the medication to market.
Regulatory bodies such as the Food and Drug Administration (FDA), the Medicines and Healthcare Products Regulatory Agency (MHRA), and others are putting stricter rules in place to ensure the quality of clinical data. In addition, the fast-changing clinical development environment is creating more obstacles for biotech and medical spaces to ensure the accuracy, standard, and completeness of the clinical trial data. Hence, clinical teams are spending valuable time cleaning up data instead of analyzing it.
Time spent trying to figure out issues with clinical trial data is detrimental and expensive but also mission-critical. This is because a small issue in the data can lead to numerous consequences, from small delays to calamitous setbacks, making it necessary to rerun clinical trials. This problem will only get more challenging to address as the volume of data and the types of data sources continue to grow. Here are some of the major clinical data management challenges that biotech firms encounter
Standardization of Clinical Metadata
Stringent Regulatory Compliance
Increased Clinical Trial Complexity
Mid-Study Changes
Why Are Clinical Data Management Systems Garnering Popularity in the Biotech Industry?
With the changing regulatory and clinical landscape, biotech and pharmaceutical companies are facing several obstacles in the management of clinical data and clinical trials. In addition, regulatory agencies are moving toward integrated electronic systems, which is making it more and more important for clinical laboratories to change the format of their submissions.
Because of this, several biotech clinical labs are focusing on adopting innovative laboratory solutions, such as biotech clinical data management systems, to meet the need for standardized data inputs and replace all manual ways of working with electronic systems.
A clinical data management system establishes the framework for error-free data collection and high-quality data submission, resulting in speedier drug discovery and shorter time-to-market. These solutions are gaining huge traction among biotech and pharmaceutical companies, owing to their ability to effectively manage clinical data, accelerate clinical trials, and ensure compliance.
Let’s see some of the features of biotech clinical data management software that are most sought after by life-science companies
Controlled, standardized data repository.
Centralized data analysis and administration.
Reduced operational expenditures for clinical data processes.
Enhanced process effectiveness.
Superior submission quality
Compliance with predefined standards.
Clinical Data Management Systems: The Future
The role of clinical data management systems is evolving at a rapid pace as the life science and medical industries continue to incorporate digital solutions for diverse operations. These systems are being used in a variety of biotech clinical settings, ranging from clinical data compliance to data science and analytics, to help them analyze large and growing volumes of clinical data.
Hence, a number of high-tech medical companies are aiming at integrating innovative technologies, such as artificial intelligence and machine learning, into clinical data management software to automate clinical data management tasks, improve clinical data submission, and enhance data quality.
These new biotech clinical management technologies are anticipated to help life science laboratories gain a better understanding of diseases and speed up clinical trials in the coming years.
FAQ
What is a clinical data management system?
A clinical data management system (CDMS) is a tool used in clinical research to track, record, and manage clinical trial data across medical establishments such as biotech laboratories.
What are the key functions of the biotech clinical data management system?
Some of the key functions of biotech clinical data management system are
Documentation of Protocols and Regulations
Patient Recruitment
Real-time Clinical Study Analytics
Reporting
Investigator Relationship Management
Electronic Visit Report
Why is a clinical data management system needed for clinical trials today?
A clinical data management system helps shorten the time from drug development to marketing by assisting in the collection of high-quality, statistically sound, and accurate data from clinical trials.
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