Trial design and analyses
Every trial has its challenges, and we stand ready to complement teams’ capabilities to overcome them. We understand clinical trial demands and can support identifying vital questions and conduct the right analysis, while effectively communicating the results necessary to facilitate the decision-making process and progress the drug development program.
Efficiency in decision-making
The swift pace and the many variables that must be considered before every decision in designing clinical trials or analyzing collected data can present a diverse range of contexts of use. Based on extensive individual and collective experience, Pharmetheus has expert knowledge of how these challenges could implicate your trial and can help you manage each one.
- Reliable translation of pre-clinical results to humans is a key challenge in designing First-In-Human trials. Pharmetheus offers a range of fit-for-purpose modeling solutions ranging from simpler empirical models to advanced systems pharmacology solutions.
- The choice of study design and analysis methods for dose finding trials can make a very substantial difference in cost, probability of success and the amount of information gained. We are experienced in using clinical trial simulations to help drug development teams making well-informed decisions that best serve their program.
- Late phase trials (Phase III and IV) face different types of challenges in terms of design but also efficient analysis that fit regulatory requirements and support key claims. Pharmetheus has vast experience in this field and a proven track record of rapidly producing submission-ready analysis reports that meet the requirements of all key regulatory agencies.
Our process
Our platforms
We draw from a variety of platforms’ ways of working and solutions to assist your team in planning and conducting efficient analyses of collected data. Here are the Pharmetheus Platforms that can be leveraged.
To analyze both drug concentration (pharmacokinetic) and effects (pharmacodynamic) data using a population modeling approach. The adopted quantitative approach enables efficient integration of longitudinal information across subjects and across study protocols.
When the objective is to leverage accumulated knowledge to inform mechanistic analyses and or achieve predictions beyond observations.
A quantitative framework to support and inform development program strategies and decisions.