Certara offers predictive science and informatics solutions that enable cross-disciplinary and translational approaches to drug development.
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Phoenix Trial Designer will include the functionality of Pharsight Trial Simulator within the Phoenix platform. Phoenix Trial Designer will provide code-based simulation (Pharsight Modeling Language) with an intuitive User Interface. If you are interested in evaluating pre-release versions when available, please email us at Phoenix.Trial.Designer@certara.com.
The Pharsight Trial Simulator provides efficient and powerful process for drug development teams to test proposed clinical trials in a series of "what if" scenarios, and helps minimize risks and guide decision making by formalizing assumptions and quantifying uncertainties about the drug being investigated and upcoming trials.
Pharsight Trial Simulator balances ease-of-use with patented technology (U.S. Application No. 7,043,415) for defining and testing interactive drug models, exploring and communicating study design attributes, and performing statistical and sensitivity analysis through graphical and statistical summaries. Pharsight Trial Simulator helps you to anticipate risks and preview the range of expected results before R&D dollars are committed to further development of a drug, and human subjects are exposed to experimental therapies.
Development team members of diverse disciplines use the Pharsight Trial Simulator to conduct a series of simulations comparing different trial, patient and drug activity scenarios. By varying simulation input parameters, your team can test the impact on trial results of changes in known information, assumptions about the drug and subjects, and variations in the trial design. Simulation addresses questions such as:
Test trial designs against expected drug and subject characteristics to predict the probability of success.
Trial Simulator models predict subject responses from subject, drug and disease characteristics.
Repeated, Monte-Carlo simulation of each Trial Simulator scenario generates a distribution of the most likely outcomes, so you know not only the most likely subject and trial outcomes, but also the confidence bounds on those outcomes.
Trial Simulator’s simulation scenarios and replicate-level variability allow your modeling team to simulate outcomes based on different trial design and model settings, reducing the likelihood of failed trails.
New data are easily incorporated by editing the drug model, or swapping in alternate model segments.
As knowledge accumulates in your Trial Simulator models, the uncertainty in outcomes decreases.