Clinical Researcher

The potential of personalized medicine presents an opportunity for life sciences to leverage big data to target therapies to specific patients better. With artificial intelligence and machine learning technologies continuing to develop, research and development teams can finally bring this vision of personalized medicine to life, provided that the data they are using are clean, standardized, interpretable, and secure.

Is risk-based quality management working? Is it supporting the primary mission of improving quality in clinical trials? To answer these questions, we need to understand the limitations of traditional approaches to quality and explore the latest evidence which demonstrates how the components of centralized monitoring are helping to find the errors that matter.

As companies seek ways to bring their products to multiple markets, understanding those markets and their regulatory approval process is imperative. Choosing the most appropriate pathway can reduce the burden on both the sponsor and on the health authorities, and potentially expand access to more patients. One such pathway is the Access Consortium, which was formed in 2007 by four health authorities.

Studies of major depressive disorder require knowledge of its biology, the biomarkers that can be used as signals for efficacy, the optimal study design, and the availability of a study population that meets the protocol-specified inclusion and exclusion criteria.

Preventive antimicrobials are a promising solution to the antibiotic resistance problem, driven in part by recent advances in cell engineering technology and the important safety advantages of biologic drugs over their small-molecule cousins. However, not all of the barriers to wider adoption are scientific or biological—this novel therapeutic modality has important clinical, regulatory, and patient recruitment implications.