AI, Data and Partnerships: The Building Blocks for More Sustainable Clinical Trials
Sas Maheswaran discusses how artificial intelligence, data and strategic partnerships can make clinical trials more sustainable – both financially and environmentally.
5 minutes with… Andrew Cooper, CEO, CluePoints
Andrew Cooper, CEO, CluePoints, explores how AI is transforming clinical trials, the power of a purpose-driven culture and what it takes to lead with innovation
Benchmarking Risks Across Therapeutic Areas
The monitoring of key risk indicators (KRIs) is an established form of risk control in the conduct of clinical trials. It is widely acknowledged that
Where is RBQM Heading in The Future With AI?
Richard Young, Chief Strategy Officer at CluePoints, shares insights on AI and risk-based quality management (RBQM) in clinical trials. With experience at both CluePoints and
Think Tank 2025 Pharma Forecast: What Lies Ahead for the Industry in 2025?
The pharmaceutical industry is poised for a transformative evolution by 2025, driven by the possibilities of emerging technologies, regulatory shifts, personalized medicine, and lessons from
Central Monitoring Use in Early-Phase and Small Enrollment Trials
Central monitoring is highly effective in detecting emerging quality issues in clinical trials, but debate continues over its use in early-phase and small enrollment studies
Risk Planning: A Review of Industry Trends
An essential component of risk-based quality management (RBQM), as detailed in both the ICH E8 (R1) and ICH E6 (R2) GCP guidelines, is to perform
Achieving Optimal Adoption of Risk-Based Quality Management
The need to boost education, shift culture, and embrace new technologies.
The Negative Impact of Failing to Implement Risk-Based Quality Management
As an industry, we regularly discuss the benefits of adopting risk-based quality management (RBQM). The good news is that adoption is on the increase with
There’s No Such Thing as Too Much Data
François Torche dispels the myth that, at least in RBQM, there is such a thing as too much data, and discusses how advanced data analytics
