9 out of 10 drugs in development fail to make it to market, costing pharmaceutical companies billions every year. Even the majority of drugs that get to Phase 3 do not get approved. These late stage failures are especially costly as companies have already invested hundreds of millions of dollars in discovery, development, research, and testing.
One reason for trial failures is sub-optimal trial design. There are many variables to fine tune to optimize study design and maximize probability of trial success.
Certara’s Trial Simulator has been trusted for over a decade by leading pharmaceutical companies to maximize chances of trial success. Leveraging existing knowledge for a drug under study with simulation, you can find answers to critical questions to increase your probability of meeting study endpoints.
With Certara’s Trial Simulator software, find answers to critical questions like:
- How likely is a trial to succeed?
- How can we reduce the cost of the next trial?
- What is the optimal dosing and treatment schedule for a particular indication?
- How will a change in inclusion/exclusion criteria affect outcomes?
- What is the impact of poor compliance or concomitant disease?
- Can we shorten Phase 1 and Phase 2 clinical trials?
Access existing scientific knowledge and test ideas and plan effective trials with Trial Simulator:
- Define study design attributes
- Create novel study designs
- Compare development strategies
- Use our library of pre-built models
- Conduct statistical and sensitivity analysis
- Leverage analysis routines such as ANOVA, ANCOVA, bioequivalence analysis, and Kaplan-Meier survival analysis
- Access descriptive statistics on input and output data including weighted descriptive statistics
- Create graphical summaries
- Generate plots in R and ggplot2
- Export data to Excel-ready files, Phoenix WinNonlin, Phoenix NLME, SAS, NONMEM, and R
- Comprehensive modeling of drug action: build population-based drug, disease models that describe drug actions over time in subjects
- Protocol design: supports a variety of trial designs, including parallel, n-by-n Latin square, and crossover designs
- Integrated study analysis plans: Create and analyze calculated field variables and handle missing or below quantifiable-limit data
- Comparison of “what if” scenarios: explore and analyze simulation results over a range of assumptions and design parameters
- Simulation results and analysis: Plot, export, and analyze the whole or subset of the dataset