Clinical Development…In a Perfect World

Life sciences organizations run a challenging gauntlet when it comes to clinical development-balancing rising clinical costs, relentless pressure to speed time to market, increasingly stringent regulatory requirements, and complex safety considerations. Ultimately, the answer to these challenges comes down to collecting, managing, and analyzing data-a process that is complicated by the rapidly increasing volume, velocity, variety, and value of data that is outstripping organizations’ capacity to make effective use of it.

The result? Life sciences organizations continue to grapple with data in a number of ways, including:

• Obtaining meaningful insight and analysis from clinical data-when, increasingly, there’s just “too much data”

• Eliminating siloes-internal and external

• Incorporating and enabling the use of real-world data, including electronic medical records and electronic patient-reported outcomes

• Managing and assessing structured and unstructured data

• Effectively aggregating all relevant data-not just clinical but genomic, financial, project management, enterprise resource planning (ERP), and more

The need for better and more universally adopted standards is another persistent issue. Companies continue to struggle with creating a standard data model that can accommodate the many types and sources of data that they require to drive discovery and innovation.
 

Seeking Perfection

The promise and challenges of data-big and small-were the focus of a lively discussion at a recent Oracle customer advisory forum, where we posed this question to life sciences executives in the room:

If you could have perfection in clinical development, solving your operational and data challenges and providing ideal outcomes, what would it look like? What role would technology play?

The group identified three core qualities of perfection within clinical development:

• Automation:  Clinical development leaders look to reduce manual intervention with greater automation and machine intelligence/expert systems-increasing data quality, reducing trial risk and cost, and increasing time to market
   

• Process Optimization:  Development processes are optimized and technology-enabled-for example, transforming a fragmented and inefficient study startup process to compress cycle-times by months or years; utilizing a data-driven approach to protocol design to drive greater efficiency, safety, and compliance; or implementing analytics to set, track, and assess key performance indicators and performance in real time to drive real operational improvement
   

• Removing Silos:  As data grows in volume and complexity, life sciences organizations seek transparent and seamless data, with a goal of fully “liquid data.” What does that mean? It means data that is dynamic and standards-based in nature, composed of data from multiple sources, and giving people-from sponsors and contract research organizations to investigators and regulatory authorities-the ability to tap in from anywhere at any time to access, visualize, and analyze the data

To achieve the objectives above requires an agile and dynamic technology infrastructure that is standards-based and can readily accommodate multiple types of data-structured and unstructured-from many different internal and external sources. Increasingly, this includes clinical, genomic, patient record, operational, financial, payer, enterprise resource planning, industry benchmark, outcome, social media data, and more.  

The platform must also incorporate automated feedback loops to continuously update and refresh the data from all sources. In addition, it must be highly secure to protect from growing threats, scalable to easily handle massive amounts of data, and extensible to remain compliant with global and local regulatory guidelines and mandates.

Blue Skies Ahead

Life sciences organizations are optimistic about the future as clinical development processes and platforms continue to advance. They see several important benefits on the near horizon:

• Better collaboration with strategic partners through the ability to process and analyze data in one place available for all partners. This will drive additional efficiency gains by enabling strategic development partners to use their internal systems and processes to capture both operational and clinical data.
   

• Transformation of the classic sequential and randomized trial model paradigm to better support adaptive approaches, implement fast and short pilot trials, conduct site-less trials, and more. With an integrated view of aggregated data, health sciences organizations would be ideally positioned to effectively explore therapeutic efficacy and safety and demonstrations in cohorts or sub-populations and make more informed and timely decisions to optimize study and program outcomes.
   

• Patient self-enrollment/self-service that allows patients to access their own data to improve enrollment, retention, adherence, safety, and outcomes-and reduces operational costs.
   

• Personalized medicine proliferation through the integrated use and analysis of molecular data.
   

• Data-driven pipeline decision-making with centralized data, advanced analytics that enable better insight into trial progress, and key milestones across numerous dimensions, including the complete book of work or by therapeutic area. These capabilities support faster identification of promising trends and early warnings of underperforming trials that should be terminated early.
   

• Accelerated regulatory review and improved approval rates through pre-submission collaboration and data transparency. The ability to successfully manage data in a single platform would enable life sciences organizations to quickly and accurately acquire, clean, aggregate, and prepare clinical trial data for regulatory submissions.
   

• Rapid, accurate product defense and audit response. Centralized data facilitates analysis, pooling, and reporting on trends that either support claims related to efficacy or safety or refute similar claims made by competitors-especially regarding post-marketing (Phase IV) trials. Clean data stored in one place also enables faster response to audit questions without having to spend resources and effort on programming and reproducing the situation that led to the audit
   

• Resource Optimization through process reuse and automation, including the reuse of objects, such as data models, transformations, and queries-freeing valuable resources to focus on high-value tasks rather than manual (custom) development activities

While perfection in clinical development is certainly a lofty idea, the industry is uniquely poised to make significant and measureable gains in the year ahead when it comes to aggregating, managing, analyzing and, most important, using their data to advance discovery and drive innovation. Let the journey begin!

Mukhtar Ahmed is Global Vice President, Life Sciences Strategy, Oracle Health Sciences