Many biopharmaceutical companies are still in the digital dark age when it comes to data collection and analysis. They use tools such as Microsoft Excel to process the data. Although this tool has many features, it is not tailored for biopharmaceuticals. Companies implementing digital transformation often install fragmented software tools that generate data in silos and require significant human resources to integrate, format, and graph the data. This is a labor-intensive process that involves manually collating and assimilating data from disparate systems.
As the amount of data generated by the biopharmaceutical industry increases exponentially, this piecemeal approach is no longer adequate. Imagine a room full of bioreactors that generate process monitoring data every minute, cell culture sampling performed many times a day, and you want to compare the performance and efficiency of these bioreactors. please. That alone generates hundreds of thousands of data points. A growing number of biopharmaceutical companies are now able to continuously and automatically ingest data from a vast network of machines in their labs, enabling them to innovate with available, reliable data. By doing so, we are considering implementing advanced digital technologies to accelerate our digitalization efforts. as soon as you can. One application that is gaining increasing interest is the “digital twin.” A digital twin ingests data from multiple sensors and systems, models and analyzes processes in a computer, and provides feedback that scientists can use to optimize processes on the fly.
It’s easy to see how biopharmaceutical companies can benefit by establishing a “digital data backbone.” A digital data backbone is designed to help organizations collect, structure, and organize all data from all operational activities and facilitate timely, intelligent analysis within a single platform. A fully optimized digital backbone can automatically capture data from a diverse set of instruments and contextualize it with experimental and scientific metadata for analysis. All without human intervention. It can be implemented across all stages of drug development and facilitates smooth handover of process and product data. For example, the tedious task of creating cell line history reports across teams, systems, scientists, experiments, etc. can be streamlined by the availability, accessibility, and context of all relevant data from within the same platform. there is.
The rapid development of cell and gene therapies is further increasing the value of digital backbones. According to the American Society for Gene and Cell Therapy, approximately 3,000 cell and gene therapies are currently in development. Some of these advanced treatments, especially those tailored to individual patients, can be developed and launched in about a month. This development process alone can generate millions of data points very quickly. Cell and gene therapy manufacturers who value data transfer accuracy, high-risk material touchpoints, and development speed need a platform that can centralize data and provide seamless, automated information transfer. This is what older information management and analysis systems have. It simply cannot be provided.
The rise of automation raises several questions about how the role of scientists will evolve. There’s no doubt that with data becoming more readily available, scientists won’t have to run from machine to machine collecting data and figuring out how to compile it all into a spreadsheet. We believe that the dataset complies with data integrity rules such as the ALCOA+ principles, and while having all relevant data readily available, we also maintain the complete dataset, including failed and terminated experiments. . Capturing failures along with successes provides a more complete picture of all experiments, allowing researchers to track the causes of poor performance trends and normalize the true successes of experimental work. Ultimately, scientists will be able to use these more precisely calibrated data models to leverage artificial intelligence tools that can help predict trends and optimize processes.
This means scientists can spend more time on cutting-edge science. A digital backbone gives you the power to achieve that goal. Unleash the full potential of advanced analytical tools and generate stronger insights faster by bringing all your properly constructed and contextualized metadata, product and process data together in one place. It will be.
How can businesses switch to a digital backbone? This can’t be driven by IT departments alone. It must be supported and led by scientists and their leaders. Taken individually, IT professionals may not fully understand the company’s therapeutic goals and have difficulty envisioning how digital technologies can best drive necessary scientific and business outcomes. It has become. True digital transformation efforts require IT and scientists to work together to drive optimal outcomes and be championed across the enterprise. You cannot take this on as a side project. A coordinated, harmonized, and global effort is required to assess the current digital environment and implement tools in a way that positions organizations at the forefront of scientific and digital progress.
This is an exciting time for patients, as genomic discoveries combine with advances in AI and automation to accelerate the discovery and development of new treatments. Leverage your digital backbone to help the biopharmaceutical industry make the most of this opportunity.
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