in vitro permeability & hepatocyte modeling software

Choose a Module:

The MembranePlus™ Optimization Module

The Optimization Module for MembranePlus extends and enhances the program’s basic capabilities in several important ways. It uses a powerful direct search optimization algorithm to minimize an objective function (error function) that you specify within the MembranePlus framework.  In simple terms, the optimizer adjusts your selected set of model parameters in order to minimize the difference between predicted and observed values of the drug concentrations from your in vitro permeability or hepatocytes studies.

Using your measured concentration vs. time data, fit parameters to build more robust models (e.g., nonlinear kinetics) and “learn” from your chemical series. Or, apply the feature to help design your in vitro Caco-2, PAMPA, or MDCK permeability experiment – based on your compound’s properties, how long do you need to run the experiment? At what pH? Or what shaking rate?

Model Building

As a model building tool, the Optimization Module allows you to calibrate simulation models to best match your experimental data.

Building a general model from a set of membrane experiments is accomplished by first assembling a database for which experimental data are available.  With this data for a significant number of membrane experiments, you can then select a set of parameters to be optimized (fitted) to make the simulation match the data as closely as possible.

Experimental Design

As a design tool, the Optimization Module provides the ability to determine optimum values for various parameters that define a membrane system and experiments in order to match a desired concentration-time profile or a set of concentration-time profiles.

Model parameters can be fitted to data for a single record, or across multiple records simultaneously.

The program will run one simulation for each record each time it changes the value(s) of one or more model parameters. Typically, hundreds of iterations will be performed, each with N simulations, where N is the number of records whose observations are being used to compare predicted and observed values. Objective function weighting is user-defined, and includes the most common weighting schemes.

The Simulation Modes - What Can I Do?

  • Single simulation: based on your drug properties (whether measured or predicted through ADMET Predictor) and Caco-2, PAMPA, or MDCK experimental setup, easily run a simulation to predict the time course changes in concentrations (e.g., apical, basolateral, cellular or lysosomal). Also calculate the different permeabilities (transcellular and paracellular).
  • Parameter Sensitivity Analysis: during early drug development, researchers have a large number of compounds to evaluate and limited resources. With Parameter Sensitivity Analysis, quickly assess the impact of changes to certain properties (e.g., physicochemical or experimental) on critical endpoints. This can help guide your resource allocation plans and identify which Caco-2, PAMPA, or MDCK experiments should be done next.
  • Batch Simulations: quickly screen a library of compounds based on predicted permeability or run the same compound through a series of different Caco-2, PAMPA, or MDCK experiments. Prioritize compounds from high to low permeability, or based upon potential issues with lysosomal trapping.

Regardless of the ‘run’ mode, report-quality results can be easily generated and shared with others.