Research Roundup: Alzheimer’s, Schizophrenia, Diabetes and More

It’s well known that the accumulation of amyloid beta proteins in the brain are significantly involved in the development of Alzheimer’s disease, even if there’s some debate as to exactly what the role is. Monomers of amyloid beta, in other words, the proteins by themselves, have specific jobs they do. But when they accumulate, that’s where the problems start. At least, that’s been the thinking. Now, researchers at the University of Washington have found evidencethat smaller aggregates of amyloid beta are the toxic components of the disease. They published their research in the Proceedings of the National Academy of Sciences.

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Igenomix

IGENOMIX is a company that provides advanced services in reproductive genetics. Our broad experience and qualifications make us one of the global leaders in this field. Our constant efforts in R&D, led by Prof. Dr. Carlos Simón (2011 Jaime I award-winner for Clinical Research), enable us to create and develop specific tools to support professionals in the reproductive medicine field.

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Medical

Immunology: A New Frontier in Medical Science

Article | August 16, 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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MedTech

Making Predictions by Digitizing Bioprocessing

Article | October 7, 2022

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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MedTech

Top 3 Biotech Clinical Data Management Trends to Watch in 2022

Article | July 16, 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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Medical

Advancement in Genomics Accelerating its Penetration into Precision Health

Article | June 22, 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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Spotlight

Igenomix

IGENOMIX is a company that provides advanced services in reproductive genetics. Our broad experience and qualifications make us one of the global leaders in this field. Our constant efforts in R&D, led by Prof. Dr. Carlos Simón (2011 Jaime I award-winner for Clinical Research), enable us to create and develop specific tools to support professionals in the reproductive medicine field.

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New machine learning approach could accelerate bioengineering

Phys.org | May 30, 2018

Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel.Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.

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Bioengineering team's 'circuit' work may benefit gene therapy

phys.org | March 06, 2018

Tyler Quarton, a bioengineering graduate student, and Dr. Leonidas Bleris, associate professor of bioengineering in the Erik Jonsson School of Engineering and Computer Science, said they hope their work, published in Systems Biology and Applications, has a big impact on synthetic biology and gene therapy. Every living cell contains a compilation of genes, which serves as the blueprint for all the biological activity within a cell. Bleris explained this system by comparing genes to musicians. Their collective expression creates a genetic symphony that can invoke a multitude of cellular emotions, calming or exciting the cell when appropriate. Stretching this analogy, the conductor of this symphony, equipped with a waving baton, can quiet an individual or whole section if they begin to play too loudly.

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New machine learning approach could accelerate bioengineering

Phys.org | May 30, 2018

Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel.Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.

Read More

Bioengineering team's 'circuit' work may benefit gene therapy

phys.org | March 06, 2018

Tyler Quarton, a bioengineering graduate student, and Dr. Leonidas Bleris, associate professor of bioengineering in the Erik Jonsson School of Engineering and Computer Science, said they hope their work, published in Systems Biology and Applications, has a big impact on synthetic biology and gene therapy. Every living cell contains a compilation of genes, which serves as the blueprint for all the biological activity within a cell. Bleris explained this system by comparing genes to musicians. Their collective expression creates a genetic symphony that can invoke a multitude of cellular emotions, calming or exciting the cell when appropriate. Stretching this analogy, the conductor of this symphony, equipped with a waving baton, can quiet an individual or whole section if they begin to play too loudly.

Read More

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