How data has transformed the pharma industry

Every day a new drug remains in development is another day patients are waiting for the health benefits of the treatment — with often life-changing and even lifesaving results.

A Phase 1 program takes an average of 10.5 years to progress to regulatory approval and has only a 7.9 percent likelihood of approval. The entire process to bring a drug to market costs an average of $2.6 billion. 

BioPharma companies must move through the development process as efficiently as possible while ensuring their treatments are innovative, deliver results, ensure patient safety, and are affordable. Accomplishing all that is challenging at best, as compliance regulations continue to change and clinical trials become increasingly complex. Every challenge can quickly increase the development costs and extend the drug development process even further.

Organizations have come to realize that data holds the key to redesigning the drug development process. Instead of working through cumbersome and disjointed processes, scientists can now use the latest data insights to drive the process from drug discovery to deployment. Data has a wide range of uses for pharma companies, from drug discovery to detecting adverse drug effects.

New data types emerge seemingly every day. Additional external sources add to the complexity. BioPharma companies must act quickly and securely turn data insights into actionable steps that help them get treatments into patients’ hands as quickly as possible. To most effectively make data-driven decisions that improve patient health outcomes, organizations need the best practices and most effective technologies. The data retention and GXP policies for the industry are quite comprehensive, complex, and a very critical component of the industry.

As Shionogi & Co. moved to a data-driven approach, the Singapore-based pharmaceutical company discovered that its extensive amount of data was located across multiple platforms and storage locations. Because the data was not in a single location, it was impossible to analyze all the data together for meaningful and accurate insights. Although the company collected the data holding the key to drug innovations, it wasn’t able to use the data in a way that would shorten the development process. 

Company leaders realized that Shionogi needed a comprehensive data platform to quickly aggregate and analyze its data. The organization also needed to keep the data secure and comply with data governance policies. Dr. Yoshitake Kitanishi, vice president of the Data Science Department at Shionogi & Co., says that the Cloudera Data Platform (CDP) was the answer. 

“Its ability to aggregate and analyze a wide variety of internal and external data, structured or unstructured, lets it rapidly process large volumes of data,” says Kitanishi. 

Shionogi has now eliminated data silos and can analyze all related data in real time. By connecting to business intelligence tools and analysis systems, team members get the business insights they need, such as status of clinical trials. Additionally, leaders have confidence that the organization follows all privacy and compliance requirements.

To see the biggest impact on reducing the time to market and cost of drugs, pharma organizations continually look for new use cases of data insights throughout the drug development processes.

Organizations use data to predict drug target affinity, especially in terms of whether a drug-target pair has a strong interaction. Data scientists also use data mining and machine learning platforms to determine which drugs can be repurposed and repositioned, which speeds up drug development. Additionally, pharma organizations mine social media to analyze patients’ drug/treatment experiences, which can help them more effectively identify new symptoms and benefits that occur outside clinical trials. 

Complex clinical trials are often time-consuming and expensive. Pharma organizations are now using data analytics to create social media companies that attract patients who are willing to participate and complete clinical trials, which can help trials meet their time frame. Through wearables and sensors, pharma organizations can remotely collect patient data and determine risk factors for adverse events, which can improve intervention and drug success rates.

Creating a data-driven drug development process starts with selecting a data platform that provides flexible hybrid data management and analytics. However, simply purchasing and deploying the most effective tool on the market does not guarantee success. Pharma organizations must continually use the tools throughout the life cycle and focus on collaboration between life sciences leaders, functional heads, data scientists, and sales representatives.

In addition to the right technology, pharma organizations need to harness the people and processes to shorten the drug development process. By hiring data and analytics talent or partnering with an external analytics service provider, leaders can ensure that they can quickly exploit the power of the data and insights. Pharma leaders must also create a process roadmap that includes defined milestones and KPIs to measure performance. 

Moving to a data-driven approach is not a single project or an initiative. Pharma organizations that successfully use data to streamline the drug development process and improve patient outcomes are continually improving their processes through regular feedback. By starting with a use case and then expanding throughout the organization, pharma companies can successfully use data to help change patients’ lives. Learn how Cloudera is working with leading pharmaceutical companies.