Clinical Trial Analytics Prescription for Bio-pharmaceutical R&D...
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Clinical Trial Analytics Prescription for Bio-pharmaceutical R&D Industry

Ankit Lodha, Associate Director, Clinical Analytics & Innovation, Takeda

Clinical Trial Analytics Prescription for Bio-pharmaceutical R&D Industry Ankit Lodha, Associate Director, Clinical Analytics & Innovation, Takeda

Bio-pharmaceutical R&D suffers from declining success rates and a stagnant pipeline. Real world data and advancements in analytics that go with it could be a key element of the cure. After transforming customer-facing functions such as sales and marketing, big data technology is extending its reach to other parts of the enterprise.

Analytics, with its rich set of advanced tools for business insights, provides an ideal means to bring rigor to the decision-making processes and management of clinical research projects. There are a lot of different areas of technology for pharma companies to invest into, and each one has its own share of benefits and challenges.

Raise Clinical-Trial Efficiency

A combination of new, smarter devices and fluid data exchange has enabled improvements in clinical-trial design and outcomes as well as greater efficiency. Clinical trials are on a path to become increasingly adaptable to react to drug-safety signals seen only in small but identifiable subpopulations of patients.

Dynamic sample-size estimation (or re-estimation) and other protocol changes could enable rapid responses to emerging insights from the clinical data. Efficiency gains are achieved by enabling smaller trials for equivalent power or shortening the time necessary to expand a trial.

Adapting to differences in site patient-recruitment rates has allowed bio-pharmaceutical companies to address lagging sites, bring new sites online if necessary, and increase recruiting from more successful sites.

Next-generation remote monitoring of sites, enabled by fluid, real-time data access, is improving management and responses to issues that arise in trials.

Sharpen Focus on Real-World Evidence

Real-world outcomes are becoming more important to biopharmaceutical companies as payors increasingly impose value-based pricing. Companies are now responding to this cost-benefit pressure by pursuing drugs for which they can show differentiation through real-world outcomes, such as therapies targeted at specific patient populations. In addition, the FDA and other government organizations have created incentives for research on health economics and outcomes. To expand their data beyond clinical trials, some leading pharmaceutical companies are creating proprietary data networks to gather, analyze, share, and respond to real-world outcomes and claims data. Partnerships with payors, providers, and other institutions are critical to these efforts.

The big-data opportunity is especially compelling in complex business environments experiencing an explosion in the types and volumes of available data. In the healthcare and pharmaceutical industries, data growth is generated from several sources, including the R&D process itself, retailers, patients, and caregivers. Effectively utilizing these data will help pharmaceutical companies better identify new potential drug candidates and develop them into effective, approved and reimbursed medicines more quickly.

Today, we see a greater focus on the insights derived from analytics, real-world data, as well as recognition of the impact these can have on improving decision making and patient care both internally and by regulators, physicians, and payors. Advancements of technology in real-world evidence has significantly improved healthcare decisions across the health system and ultimately improve patient care. Expanding its use, however, will require multi-stakeholder action on several priorities, as well as company-specific campaigns. The broad healthcare community is best equipped to make progress on the following goals:
1. Increasing understanding and communication of RWE value drivers while focusing on high-impact use cases.
2. Creating an operating model that drives integration and adoption of RWE and manages risk.
3. Shaping an integrated, adaptive partner ecosystem.

At the execution level, analytics has enabled biopharmaceutical companies to focus their efforts where they matter the most—accurate forecasts for planning and riskbased leveraging of resources.

Weekly Brief

Top 10 eClinical Trial Management Solution Companies - 2021
Top 10 eClinical Trail Management Service Companies - 2021

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