MedTech
Article | July 20, 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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MedTech
Article | July 16, 2022
Genetic therapeutics such as genetic engineering and gene therapy are increasingly emerging as one of the most influential and transformed biotechnological solutions around the globe in recent times. These genetic solutions are being assessed across various medical domains, including cancer treatment, neurology, oncology, and ophthalmology. Citing the trend, the genetics industry is estimated to experience a tsunami of approvals, with over 1,000 cell and gene therapy clinical trials currently underway and over 900 companies worldwide focusing on these cutting-edge therapies.
Growing Cancer Encourages Advancements in Genetic Technologies
With the surging cases of cancers such as leukemias, carcinomas, lymphomas, and others, patients worldwide are increasing their spending on adopting novel therapeutic solutions for non-recurring treatment of the disease, such as gene therapy, genetic engineering, T-cell therapy, and gene editing.
As per a study by the Fight Cancer Organization, spending on the treatment of cancer increased to $200.7 billion, and the amount is anticipated to exceed $245 billion by the end of 2030.
Growing revenue prospects are encouraging biotechnology and biopharmaceutical companies to develop novel genetic solutions for cancer treatment. For instance, Bristol-Myers Squibb K.K., a Japanese pharmaceutical company, introduced a B-cell maturation antigen (BCMA)-directed chimeric antigen receptor (CAR) T cell immunotherapy, Abecma, for the treatment of relapsed or refractory (R/R) multiple myeloma in 2022.
Amid a New Market: Genetics Will Attract Massive Investments
Despite several developments and technological advancements, genetics is still considered to be in a nascent stage, providing significant prospects for growth to the companies that are already operating in the domain.
Genetics solutions such as gene therapies, gene editing, and T-cell immunotherapy are emerging as highly active treatments across various medical fields, resulting in increasing research and development activities across the domain, drawing significant attention from investors. Given the potential of genetic treatments and the focus on finding new ways to treat cancer and other related diseases, it's easy to understand why companies are investing in the domain.
For instance, Pfizer has recently announced an investment of around $800 million to construct development facilities supporting gene therapy manufacturing from initial preclinical research through final commercial-scale production.
Due to these advancements, cell and gene therapies are forecast to grow from $4 billion annually to more than $45 billion, exhibiting growth at a 63% CAGR.
The Future of Genetics
Though there is a significant rise in advancement in genetic technologies and developments, the number of approved genetic treatments remains extremely small. However, with gene transfer and CRISPR solutions emerging as new modalities for cancer treatment, the start-up companies will attract a growing amount and proportion of private and public investments.
This is expected present a tremendous opportunity for biopharma and biotechnology investors to help fund and benefit from the medical industry's shift from traditional treatments to cutting-edge genetic therapeutics in the coming years.
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Medical
Article | July 14, 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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Industrial Impact
Article | January 20, 2021
Artificial intelligence has grasped the foundation in biotech. It can have the most innovative impact on biotechnology. AI has already established its presence in our day-to-day life. AI has made the existence of self-driving cars possible. Likewise, the benefits and quality that it can contribute to biotech can also be felt.
With AI, bio technicians will be able to enhance virtual screening, overlook preliminary datasets from clinics, and decipher an enormous amount of information. It can also help in improving the medication process by gathering and analyzing every bit of information.
The Significance of AI in Biotechnology
In the past few years, the application of artificial intelligence in the biotechnology industry has shifted from being sci-fi to sci-fact. A vast number of biotech companies like Deep Genomics are adopting AI for making data-driven decisions and use analytics tools to work efficiently.
Unlike the AI robots in sci-fi that are ready to take over the world. AI designed for biotech has been designed to solve certain problems or complete a bunch of tasks by using automated algorithms.
The aim of AI technology for biotech is to collect insights along with hidden patterns from large amounts of data. All the different industries of biotech including agriculture, animal, medical, industrial, and bioinformatics are gradually being affected by artificial intelligence.
Moreover, the biotech industry is realizing that AI enables them some of the important strength to their business, including:
Expanding accessibility
Cost-effectiveness
Critical predictions
Efficient decision-making
Research centers like PwC have also estimated output of $15.7 trillion by 2030 solely with AI contribution in industries. A survey revealed that about 44% of life science experts are using AI for R&D activities, as well.
Use of AI in Biotechnology
Altering Biomedical and Clinical Data
So far the most developed use of AI is its ability to read voluminous data records and interpret them. It can prove to be a life-save for bio technicians who would have to examine that much data from research publications by themselves for the validation of their hypothesis.
With the help of AI, clinical studies of patients will also become easier as all the examination reports and prescriptions will be stored in one place for cross-reference. Furthermore, it will also help in blending and fetching data into usable formats for analysis.
Test Result Prediction
Through trial and error, AI along with machine learning can help in predicting the response of the patient to certain drugs to provide more effective outcomes.
Drug Design & Discovery
AI plays a vital role whether it’s designing a new molecule or identifying new biological targets. It helps in identifying and validating drugs. It reduces the cost and time spent on the entire drug trial process and reaches the market.
Personalized Medications for Rare Diseases
With the combination of body scan results, patients’ body and analytics, AI can also help in detecting dangerous diseases at an early stage.
Improving Process of Manufacturing
To improve the process of manufacturing in biotechnology, AI offers a wide range of opportunities. It controls quality, reduces wastage, improves useability, and minimizes the designing time.
Moving Towards AI-Enhanced Biotech Future
Ever since the concept of artificial intelligence has arrived, being curious by nature, humans have started working towards achieving this goal. It has been growing at a fast pace while showing unbelievable growth and achievements at times. In comparison to the traditional methods used in the biotechnology industry, AI-based methods seem more reliable and accurate. In the upcoming years, it will show its success by improving the quality of health people have. You can also develop your AI-based application or know more about it by taking IT consultations.
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