Automating lab workflows to remove manual data management will spearhead a new era of therapeutic development
Bioprocessing is important for discovering effective biologics that can be leveraged in the development of treatments and therapeutics. In the first half of 2019, 36% of the new molecular entities (NMEs) approved by the FDA were biologics, up from 29% in 2018 and 26% in 2017. This is a trend that is expected to continue.
However, it is a challenging and expensive problem to figure out. Since 2014, biologic drugs account for nearly all of the growth in net drug spending: 93 percent of it, in fact. The methods, workflows, and APIs (Active Pharmaceutical Ingredients) used by biopharma in the discovery of biologics, and subsequent development and implementation of therapeutics, need to be streamlined for maximum efficiency. This means bioprocessing workflows and data need to be fully automated from data collection to analysis of the data. By automating and connecting equipment and systems, manual tasks and associated human error are eliminated which keep the total cost of ownership (TCO) low.
Anywhere between 20% - 30% of time spent by researchers is wasted on manual data transcription and tedious manual data integration.
In addition to manual data management, bioprocessing data is typically in heterogeneous formats. The data is produced by different lab equipment and systems throughout multiple stages of the workflow. This leads to data silos that are difficult to integrate. These challenges - manual tasks, human error, heterogeneous formats - slow down bioprocess development significantly.
The new era of bioprocessing, known as Bioprocessing 4.0, focuses on the integration and connectivity of lab equipment and systems. This will give scientists access to higher quality data and allow for deeper insights.
Connecting bioprocessing data with disparate life sciences R&D data for further analysis
Unifying BIO-API (or bioprocessing) data with other R&D data produced in the development process will provide scientists with deeper insights that have the potential to accelerate the drug discovery process. AGU’s Sm@rtLine Data Cockpit (SDC) enables the use of sensors and analyzers for the collection, review, and approval of trial results in laboratories. AGU and SDC have over ten years of experience serving top global pharmas in bioprocessing (BIO-API). In that time, they have improved quality, reduced significant costs, and shortened the length of discovery time for both biopharmaceutical companies and the BIO-API industry. The cloud-native and enterprise-scale Tetra Data Platform powers the centralization and harmonization of all R&D data, preparing it for advanced analytics and data science. The partnership not only lowers the total cost of ownership (TCO) and is significantly quicker to deploy than building internally, but will speed up the rate of discovery and development of therapeutics and treatments.
IMAGE: SDC + TetraScience example architecture diagram
Bioprocess data collected through the SDC-TetraScience integration is immediately ready for data science.
Higher data integrity and deeper insights power development
Life sciences, biotech, and biopharmaceutical organizations will reap massive benefits through adopting the concept of Bioprocessing 4.0 in their workflows. The foundation of Bioprocessing 4.0 - the automatic collection, centralization, harmonization, and unification of bioprocessing, BIO-API, and R&D data in a central cloud-native platform - is not easy to achieve. Once fully adopted, Bioprocessing 4.0 will allow scientists to stop wasting time with tedious manual data management. It will reduce human error, keeping data integrity high. Additionally, data silos will be eliminated, allowing scientists to query large volumes of bioprocessing data and providing the ability to reach actionable insights efficiently.
The partnership between SDC and TetraScience expands the network of connectors and integrations that automates data collection - the first step towards Bioprocessing 4.0. Scientists have the ability to leverage bioprocessing data collected by SDC. The data is uploaded into the cloud, where it is centralized, harmonized, and prepared for advanced analytics and data science, making R&D data in the cloud truly accessible and actionable.
Learn more about the TetraScience and AGU partnership and how bioprocessing data is automatically harmonized and centralized, connecting disparate data silos to activate the flow of data across the R&D and Bioprocessing ecosystem.
Our recent blog post - Data Science Use Cases for the Digital Lab - highlights how scientists are leveraging our network of connections and integrations to truly harness the power of life sciences R&D data. The blog post features seven data sciences use cases crowdsourced from our partners and customers.