Certara informs drug development decisions with CODEx  

The Clinical Outcomes Database Explorer streamlines the analysis of therapeutic area-specific literature data to assess a drug candidate’s likelihood of regulatory and commercial success 

Certara recently launched Clinical Outcomes Database Explorer (CODEx), a web-based platform designed to assist drug development teams.

It enables researchers to easily visualise, explore, analyse and communicate content from Certara’s Clinical Trial Outcomes Databases and assess a given compound’s probability of success against competitor drugs.

Leon Bax, Certara Director of Consulting Services, said: “CODEx is a user-friendly and highly interactive interface used to make sense of the large and valuable amount of competitor clinical data available from the literature.

"Through CODEx, scientists can efficiently explore data trends regardless of knowledge of the therapeutic area or coding.”

Certara provides decision support technology and consulting services for optimising drug development and improving health outcomes.

Certara state that publicly available clinical trial data represents an under utilised source of information.

If properly extracted and analysed, the information will provide valuable input for facilitating go/no go decisions, perform comparative effectiveness and portfolio evaluations in support of in/out licensing and optimise overall drug development decisions, including dose and dosing regimens.

CODEx facilitates the effective use of information from the Certara Clinical Outcomes Databases, which are a collection of well-organised clinical drug efficacy and safety data from the published biomedical literature for different diseases.

The 40 Certara databases are divided into four major categories: CNS and pain; inflammation; oncology; and cardiovascular, metabolic and other therapeutic diseases.

Nancy Zhang, Certara Vice President of Database Products, said: “CODEx presents data in a clear, consolidated format to give users insights into data availability and data patterns, which can subsequently be leveraged in model-based meta-analyses for assessment of comparative effectiveness, endpoint-to-endpoint correlations, and probability of clinical trial success.

"Its selection of interactive tools allows scientists to choose the best plots, graphs and tables to communicate and brainstorm best analysis strategies.”

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