Intelligent Data Application and Management Will Define Pharma’s Future

The next decade of drug development will be based upon new and more effective means of data mining and data analytics

By Bikash Chatterjee, president and chief science officer, Pharmatech Associates

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The pharma landscape is evolving as rapidly as the speed of technology. The industry that former FDA Commissioner Dr. McClellan admonished in 2003 with, “You need to improve!”1 has taken those words to heart to embrace innovation and technology as never before. Whether motivated by the FDA’s shift toward a scientific, data-driven definition for quality or a need to innovate to survive and be competitive in the new world marketplace, there is no doubt the push for greater understanding has resulted in a renewed emphasis on the ability to acquire, verify and leverage the power of data.

Every market sector needs a push sometimes to adapt and evolve. The regulated life sciences are one of the most conservative sectors when it comes to technology innovation. However, several developments have elevated the need for pharma to make the management and security of data a primary strategy.

First, vertical drug manufacturing in the United States is no longer a leading strategy. While some companies continue to build internal manufacturing capability, this is largely the exception. The emerging markets have matured over the last decade and most large pharma and biotechs have established a distributed supply chain leveraging regional contract manufacturing organizations for internal development and manufacturing capability.

Second, supply segmentation has become a core strategy for large complex supply chains, creating multiple virtual supply chains within the construct of a single physical supply chain. Achieving this has reemphasized the importance of visibility and access to key information within each virtual supply chain.

Third, the maturity of mobile clinical platforms has resulted in a significant reduction in clinical trial costs on a per-patient basis, allowing virtual startups to move further into the clinical development process. Today, the rise of virtual biotech organizations has become a major factor in driving and growing the outsourcing movement. Compounding this is the growth of combination products. For many biotech products, the ability to utilize a pre-filled syringe or auto-injector dramatically broadens the potential patient market. However, the complexity of establishing and controlling the drug, device and system supply chain is significant for many virtual organizations. Finally, some may argue that the true potential for the life sciences will hinge on the ability of big data to identify new and effective therapies for disease.

The common denominator of these market drivers is information and data. The following piece will explore the technology, regulations, security strategies, industry solutions and challenges behind this elevated emphasis on information and data management.

Several technologies will have a profound impact on how drug companies bring products to market:

E-clinical Mobile Platforms
The e-clinical solution software market, valued at $3 billion in 2014, is expected to grow at a CAGR of 13.8 percent from 2014 to 2020 to reach an estimated value of $6.52 billion in 20202. Technology platforms have evolved with the market, focusing on functional re-usability, standardization and quality, resulting in decreased cost and implementation timelines. These solutions allow clinicians to rapidly tailor clinical data gathering and protocol requirements as commercial off-the-shelf (COTS) applications that are configurable. These systems will allow many pharma companies, on a global basis, to more easily comply with GCP requirements and best-in-class data management requirements from the Clinical Data Interchange Standards Consortium (CDISC) and Clinical Data Acquisition Standards Harmonization (CDASH).

E-clinical platforms are showing significant growth with the Software as a Service (SaaS) business models. All pharma organizations are managing risks in the extended enterprise — suppliers, contractors and vendors. The SaaS model supports third-party risk management by providing an application to external users outside of the company’s firewall.

Challenges — The two dominant mobile computing platforms are Google’s Android and Apple’s IOS operating systems (OS). The market share for these two platforms cannot be more different between the rest of the world and the U.S., which causes near-term and long-term management challenges. A recent study3 by Atredis Partners summarized the U.S. market share for IOS and Android, as 43 percent and 53 percent, respectively. The rest of the world is far more one-sided with Android commanding 83 percent and IOS only 14 percent. The challenge for mobile platform software is the issue of fragmentation. Fragmentation describes a software solution provider’s ability to keep up with OS changes. Android launches incomplete OSs and then iteratively optimizes them while Apple launches complete operating systems to the market. The disparity between U.S. and the rest of the world (ROW) is the ability to keep pace with patches. In the United States, the assumption is you will change your phone every two years, while the ROW may be closer to four to five years. This makes the challenge of managing security and updates incredibly complex for any organization banking on a mobile solution as a core vehicle for product development.

Cloud-Based Electronic Data Capture (Cloud EDC)
Augmenting the positive opportunities created by mobile data collection is the expansion of Cloud EDC. Clinical trials data management is the biggest opportunity for Cloud EDC and is the fastest growing application within pharma. The advantages of traditional EDC are well understood. By moving away from human data recording, the consistency of data acquisition and accuracy of data acquired has increased exponentially. Anyone who attempted clinical trials 10 years ago in the emerging markets experienced the frustration and regulatory consequences of having to repeat clinical studies because of poorly acquired data that could not be verified. These systems provided rapid search and query functions, the ability to make field edits, and the ability to establish rules and get early notification of anomalous data. The downside of these systems is that they were fairly inflexible, very expensive and typically required an external software team with the specific system expertise, making training and startup potentially a rate limiting step.

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