A new risk and issue management system to improve productivity, quality, and compliance in clinical trials.

Objective: We present a new system to track, manage, and report on all risks and issues encountered during a clinical trial.

Materials And Methods: Our solution utilizes JIRA, a popular issue and project tracking tool for software development, augmented by third-party and custom-built plugins to provide the additional functionality missing from the core product.

Results: The new system integrates all issue types under a single tracking tool and offers a range of capabilities, including configurable issue management workflows, seamless integration with other clinical systems, extensive history, reporting, and trending, and an intuitive web interface.

A dimensional warehouse for integrating operational data from clinical trials.

Timely, consistent and integrated access to clinical trial data remains one of the pharmaceutical industry's most pressing needs. As part of a comprehensive clinical data repository, we have developed a data warehouse that can integrate operational data from any source, conform it to a canonical data model and make it accessible to study teams in a timely, secure and contextualized manner to support operational oversight, proactive risk management and other analytic and reporting needs. Our solution consists of a dimensional relational data warehouse, a set of extraction, transformation and loading processes to coordinate data ingestion and mapping, a generalizable metrics engine to enable the computation of operational metrics and key performance, quality and risk indicators and a set of graphical user interfaces to facilitate configuration, management and administration.

A late-binding, distributed, NoSQL warehouse for integrating patient data from clinical trials.

Clinical trial data are typically collected through multiple systems developed by different vendors using different technologies and data standards. That data need to be integrated, standardized and transformed for a variety of monitoring and reporting purposes. The need to process large volumes of often inconsistent data in the presence of ever-changing requirements poses a significant technical challenge.

A cross-source, system-agnostic solution for clinical data review.

Assembly of complete and error-free clinical trial data sets for statistical analysis and regulatory submission requires extensive effort and communication among investigational sites, central laboratories, pharmaceutical sponsors, contract research organizations and other entities. Traditionally, this data is captured, cleaned and reconciled through multiple disjointed systems and processes, which is resource intensive and error prone. Here, we introduce a new system for clinical data review that helps data managers identify missing, erroneous and inconsistent data and manage queries in a unified, system-agnostic and efficient way.

Risk-based Monitoring of Clinical Trials: An Integrative Approach.

Purpose: Clinical trial monitoring is an essential component of drug development aimed at safeguarding subject safety, data quality, and protocol compliance by focusing sponsor oversight on the most important aspects of study conduct. In recent years, regulatory agencies, industry consortia, and nonprofit collaborations between industry and regulators, such as TransCelerate and International Committee for Harmonization, have been advocating a new, risk-based approach to monitoring clinical trials that places increased emphasis on critical data and processes and encourages greater use of centralized monitoring. However, how best to implement risk-based monitoring (RBM) remains unclear and subject to wide variations in tools and methodologies.

Universal screening methods and applications of ThermoFluor.

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins.

Enzyme classification by ligand binding.

The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes.

Exploring the nonlinear geometry of protein homology.

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity.