Clinical Trial
Outcome Databases

Leverage Databases to Answer Key Drug Development and Commercial Viability Questions

For sponsors, the hurdle of commercial success is no longer just about regulatory approval, but the competitive positioning of novel compounds in the market. Understanding a new compound’s predicted product profile and how it compares with those of competitors is key to that success. Publicly available clinical trial data represent an underutilized source of information and if properly extracted and analyzed, provide a great value proposal.

It is within the context of this critical drug development need that we created a series of therapeutic area-based Clinical Trial Outcomes Databases. Based on years of experience exploring and analyzing publicly available data to perform model-based meta-analyses for our clients, we created a set of databases that comprise key attributes (ie, always up-to date, analysis ready, flexible and high quality) that make them relevant and effective in supporting drug development decisions.

Our databases are designed and managed by experienced modelers who are therapeutic area experts. Certara clinical trial outcome databases provide the highest quality data on the market and are supported by multiple clients. Our modeling and simulation experts perform vigorous quality control (QC) and testing, and keep the databases up-to-date.

Example questions addressed by leveraging the Certara databases

  • Competitive landscape: What are the key competitor compounds in development? How many competitor trials are available for certain outcomes? What are characteristics of these trial in terms of key covariates such as populations, baseline values, demographics
  • Disease and trial characteristics: What do placebo effects (and their variability) look like? How excessive is the heterogeneity and selectivity within outcomes? How the trials were conducted and what was the risk of study bias?
  • Drug characteristics: Is there any evidence of dose response? What are typical time courses and how do they vary across drugs?
  • Drug: What are the characteristics of the dose-response curves for existing drugs that are in the same class as a new compound? What are typical ranges? How does onset of effect differ between drug classes? How do baseline characteristics or background treatments impact drug response?
  • Trial design: What is the impact of trial design features (e.g. time, endpoints) on treatment effects? How are specific subsets of the population represented? What is the impact of region? How do biomarker and clinical endpoint results compare? Can we predict trial results?
  • Go / no go, portfolio, marketing: Can we differentiate our drug as best-in-class? How can we best position a drug between existing and developing competitors
Learn More