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Pipeline Biotech PK/PD Mathematical Modeling
Pipeline Biotech offers a full set of mathematical pharmacokinetic and pharmacodynamic (PK/PD) modeling services as integrated support for clients’ in vivo studies.
Mathematical modeling and simulation services include optimal design of experimental in vivo studies, processing of experimental time series data, prediction of PK/PD for hypothetical dosing scenarios, and simulation and model-based analysis in general.
Benefits of PK/PD modeling
PK/PD (also known as PKPD or PK-PD) modeling combines classical pharmacological disciplines of pharmacokinetics and pharmacodynamics to integrate a PK or PD model component into a set of mathematical expressions that can map the time course of effect intensity following drug dose administration.
PK/PD modeling and simulation provide a valuable tool to support critical decisions in drug development. In fact, simulations made with sound models are indispensable when trying to quantify, and understand, the correlation between exposure (governed by dose size, dosing schedule) and some measured effect (e.g. reduction in tumour size).
Pipeline Biotech has developed its mathematical modeling services in collaboration with Danish-based CANDOR Simulation, who are world-class experts in building valid mathematical models of real-world problems.
Specific benefits of Pipeline Biotech model-based simulation and analysis include:
- Efficient and robust models that work well on both rich and sparse data
- Enhanced design of future studies that require minimum number of measurements and deliver maximal information through optimized dose size and sampling schedule
Modeling and simulation services
Depending on application and client’s needs, Pipeline Biotech can apply three different types of modeling and simulation services:
Optimal design of experimental in vivo studies
Population simulation-based analysis, preferably based on data from a pilot study, enables optimal choices to be made for sampling times, number of animals (power calculations by bootstrap technique), and dosage regimens for in vivo PD (drug effect) studies
Processing of experimental time series data
The service is based on standard PK non-compartmental analyses (finding T½, Tmax, Cmax, Vcc, cL, kel‘, AUC, etc.) or compartmental PK and PK/PD analysis, using semi-physiological or mechanism-based modeling. The latter can be used to quantify uncertainty and population variance based on confidence limits, etc. It can also be used to extract other statistics, such as probability of target attainment, by Monte Carlo simulation.
Experimental time series data processing can also check for nonlinear or time-dependent pharmacokinetics
Prediction of PK/PD for arbitrary (hypothetical) dosing scenarios
Hypothetical dosing scenarios can be modeled for future animal studies (using a fitted compartment simulation model) or as part of a translational simulation study to predict PK/PD in humans, based on available combined data