Understand and quantify disease mechanisms and how your compounds impact them
Generate biological/pharmacological hypotheses in-silico to aid in the design of in-vitro or in-vivo non-clinical and clinical experiments, and guide biomedical experiments so that they yield more meaningful data
- Do we understand the disease mechanism?
- Do we understand how our compound acts on the disease?
- Why do in-vitro results not match observed in-vivo outcomes?
- How can the mechanism of action be translated into the clinic?
- What are predictive biomarkers for the clinical outcome of interest?
- What are beneficial drug-combinations for the given disease mechanics and available targets?
- Which experiments (in-vitro and/or in-vivo) should we conduct to better inform the disease mechanism and/or drug action?
- How can we leverage our clinical data to inform parameter values in QSP models?
- What are the main biological mechanisms that drive the observations and allow translation into the clinic?
- What are the most influential system parameters? Can we identify them based on available clinical data? What would need to be measured in clinical studies to be able to do so?
Quantitative Systems Pharmacology (QSP) can be seen as a sub-discipline of traditional Pharma Modeling and Simulation (Pharmacometrics). The main difference is that models tend to be more mathematically complex, which makes them unsuitable to handle and understand by the traditional approaches.
IntiQuan has strong experience and background in the area of complex biological and biomedical modeling and simulation approaches and can support you gaining the information you need about how your compound is working in the context of the targeted disease.
We have methodology and tools in place, which allow a seamless linking of even the most complex systems pharmacology models with your clinical data.