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Analytics in Life Sciences: New Product Development

With a myriad of good investment opportunities for life science organizations, why invest in data analytics? And should you take the plunge, how do you properly leverage analytics to extract value and realize a competitive advantage? In this blog series, we invite you to reflect on your organization’s capacity to leverage data throughout the product lifecycle, including new product development, manufacturing & quality, supply chain management and continued market intelligence efforts. Let’s get started. Offering innovative products with the potential to positively impact societal health and welfare is the very promise of life science companies worldwide. Reducing the time to market of these innovations remains a challenge, but a true competitive advantage when addressed effectively.

Drug Development

Imagine a catalog of disease prevalence, seasonal trends, and environmental indicators to help predict new market needs. Useful. Now, also imagine a data platform which includes clinical, medical, genomic information profiles and historical data to help predict compound viability. That is, the ability to shift attrition appreciably earlier in the development process, improving transition through, and ultimately the outcome of expensive clinical trials. A single clinical trial can cost up to $100 million – and approach $1 billion when factoring in manufacturing and clinical testing. Leveraging a similar custom Precision Medicine Analytics Ecosystem and predictive analytics, Xalkori was successfully developed by Pfizer and approved by the FDA in 2011.

Clinical Trial Management

As development activity intensifies, so does the complexity of clinical trial management. A consortium of life science companies and research organizations formed the Project Data Sphere initiative to share de-identified, patient-level data from late-stage comparative studies with hopes of accelerating development and designing more efficient clinical trials. Given the variety of trial designs and data collection requirements across multiple sites, reviewing and efficiently analyzing mass amounts of real-time operational, clinical and safety data is critical to timely and proactive decision-making. For example, real-time operational and clinical data review can prompt action to ensure protocol adherence and salvage off-course trials; eliminate non-performing sites and execute contingency plans; or terminate studies altogether to minimize costly adverse results downstream. Pre-marketing safety analytics can include standard exposure-response correlations and dose optimization, but with equal ease, also determine risk-adjusted net present value computations for compound prioritization across the clinical trial portfolio. Further, through ongoing reporting and analytics, companies can make informed decisions to adjust the execution of the clinical supply plan in light of changing trial conditions. The appropriate quantity of drug substance can be made available at all times via alerts to supply planners when trial progress approaches predefined thresholds. The supply planners, can, in turn, then account for any variances from the baseline clinical forecast and ensure that materials are ordered with sufficient lead times, and bulk substance can be produced and packaged on time. The adjusted trial performance data (previously mentioned) can be further leveraged to predict and overcome future risks and emergency requests.

Intellectual Property Assessment

Portfolio assessments can also be completed in the intellectual property domain. Advanced data research and analytical tools can uncover new revenue sources in the competitive landscape. As companies identify new or significant niche players and promising advances or technology trajectories, an organization’s intellectual property can point to novel opportunities and investment strategies – whether a new indication, licensing potential, or another area for further research and development. That’s a start. In the next post, we will address analytics applications in manufacturing and quality for life science companies.

Tags: Analytics, Competitive Differentiation & Execution, Product Lifecycle Management