By unlocking valuable insights from the wealth of data generated during drug discovery and development, biopharma companies can develop innovative and effective therapies even faster. At AstraZeneca (AZ), when Pascal Soirot took over as CEO in 2012, the company had an impending crisis. Their exclusivity for the blockbuster drug Arimidex, a hormone-based therapy used to treat breast cancer, had expired in the US and the EU. In the coming years, AZ patents for cardiovascular and neuroscience therapies were also soon to expire. Something had to be done.
As the patent cliff approached, Soirot made several strategic decisions, including the “5R strategy” to enable data-driven decisions on the right target, right tissue, right patient, right safety, and right commercial potential. Employing a 5R strategy meant creating feedback loops across the drug development process so that data from different stages could inform better decisions regarding which candidates to progress with. Before the 5R strategy was implemented, AZ clinical trial success rates were below the industry average but between 2012 and 2016, their cumulative success rate increased fourfold to 19%. The success rates of each phase (Preclinical to Phase III) also increased above the industry average between 2012 and 2016. Today, AZ continually strives to improve its success rates by improving collaboration, embracing precision medicine and genomics, and emerging technologies to unlock value from the large amounts of data generated using high-throughput technologies.
Other large pharmaceutical companies have deployed similar initiatives to maximize the value of big data by digitalizing their R&D processes. One of these examples is the data42 program at Novartis. Such large-scale projects require significant investment which may seem out of scope for emerging clinical-stage biopharma and biotechnology companies. However, there are lessons that biotechs of all sizes can learn from this approach.
With many contract research organizations (CROs) offering services to generate data, growing biopharma can externalize research activities as needed. However, the internalization of the data produced is vital to realize its full potential. Multi-omic, phenotypic, imaging, and clinical data are among the wide range of data types that can be leveraged from pre-clinical and clinical workflows to unlock insights on therapy mechanism of action, predict toxicology, patient response to treatment, and resistance. Researchers can then prioritize candidates or even repurpose existing treatments for new indications.
By following the FAIR principles for data management, biopharma companies can execute a data strategy and ensure the success of candidates across clinical trial phases. Here are a few ways biotech and biopharma companies can better unlock value from their data to advance clinical programs:
Enable Easy Access to Data across Teams By democratizing data access, data becomes more findable for data consumers within the company, increasing the productivity of day-to-day tasks. Yet, to maintain data security, it is crucial to control this access and the data-related activities they can perform according to specific user roles. Biopharma and biotech companies require a single point of truth where they can access the right version of the data they need at any given moment.
Automate Processing and Analysis to Save Time Bioinformaticians and data scientists are not easy to find. By automating routine data processing and democratizing access to analytical tools, teams can scale their activities and improve reproducibility without having to invest in additional FTEs.
Visualize Data for Accelerated Go/No-Go Decision-Making By enabling users to easily use their preferred analytics tool, they can adopt a variety of analytical approaches and generate actionable scientific insights to advance current and future projects. An advanced analytics environment that allows to perform advanced statistics and develop artificial intelligence methods is highly useful in allowing to uncover hidden trends and make data-driven predictions. Interactive visualizations based on the most recent data would drive efficient and effective communication of project results and progress to internal stakeholders as well as partners and investors.
Genedata Profiler® is the leading data integration and analytics platform that was built with the FAIR principles (Findability, Accessibility, Interoperability, and Reproducibility) as a guiding framework. The software provides innovative, growing biotech and biopharma companies with a secure data warehouse that is validation-ready for GxP compliance.
Adopting Genedata Profiler means successfully implementing the three key tenets mentioned above with the software’s Data Portal which increases the accessibility of data through simplified searchability, discoverability, and exploration. While data is made easy to find, the software has stringent access and handling controls depending on a specific user’s role. Built for automation and scalability, the ready-to-use customizable workflows of Genedata Profiler automate data processing, quality control (QC), and harmonization making tasks more efficient and reproducible. The additional time gained by users can be used to focus on making scientific deductions or designing future experiments.
Genedata Profiler facilitates the use of a wide range of expert data analysis and visualization tools which are further enhanced thanks to its interoperability with business intelligence tools such as Power BI and Tableau. This allows insights to be generated at a rapid rate and enables effective communication of results between geographically dispersed teams, internal or external stakeholders, and partners.
Implementing a data strategy that improves the success rate of clinical trials can greatly increase drug candidate net present value (rNPV) and return-on-investment (ROI). By clicking below, you can visualize this financial impact.