Systems Biology Software Suite with PK-Sim and MoBi: Features
Unmatched Flexibility, Accuracy, and Data Sharing:
Building block concept: In PK-Sim, „Individual/Population“, „Compound”, “Administration Protocol”, "Formulation", and „Event“ are set up as separate and re-usable units. To create a simulation, building blocks are simply combined. In MoBi, these building blocks are defined in a way more usable for modelers: "Spatial Structure", "Molecules", "Reactions", "Observers", "Events", "Start Values" and "Passive Transport"
Each building block can be saved to a database and be available for different projects and simulations. This makes swapping and comparing different simulated scenarios easier. The building block database can be made readily accessible to other users within the network. When a building block is modified, the user has the option to apply these changes to any simulation that uses that building block.
Simulations and building blocks can easily be compared to mark out differences between stages in a model building process or to get an overview where the significant changes between two simulations lie.
Population simulations are conveniently performed in PK-Sim, any model structurally modified in MoBi can be re-imported to PK-Sim for this purpose. For example, a physiological tumor can be incorporated into an organ of a whole-body PBPK model for e.g. an Antibody-Drug Conjugate (ADC) using MoBi and then a population simulation can be performed using the intuitive population simulation function of PK-Sim.
Strong flexibility in defining administration protocols: application patterns can be customized with the ability to combine different administration routes into one protocol. This allows the user to simulate any clinical scenario.
Transparency and Convenience: History and Working Journal functions
In both PK-Sim and MoBi, all user actions performed within a project are sequentially listed and stored. All previous project states during one session can be reinstated at any time. Rollback to a previous state is not an undo function but rather a new action that is sequentially stored in the history. It also provides a documentation of the model building process.
With this feature, traceability of all user actions is ensured. With the goal of being fully transparent in regards to parameters and functions used within PK-Sim, all are made visible. This makes the Computational Systems Biology Software suite a “white box” in its entirety.
Moreover, the working journal feature newly introduced in Version 6.0 enables users to document, track, and revisit their projects in an unprecedentedly convenient fashion.
Advanced Parameter Identification (PI) Toolbox
A fully integrated PI Toolbox provides a straightforward means to adjust key parameters of your PBPK models automatically within user-defined ranges. It is possible to optimize multiple simulations, for example with different dose levels, and multiple observed data sets, simultaneously. The PI Toolbox is also capable to deal with values below limit of quantification. A clear visualization of the optimization process and of the optimization results gives you full control and direct feedback whether the identification process was successful. More…
Advanced Drug-Drug-Interaction Modeling
Inhibition and/or induction of a metabolizing enzyme or transporter by a drug can be defined directly in PK‐Sim in a simple and user friendly manner (for reference, see this exemplified workflow).
For more convenient simulation of parent‐metabolite interactions, PK‐Sim® offers two alternatives to define drug metabolites. Firstly, metabolites can be a "sink" which means that they are not actively or passively transported. In this case, they possess no physico‐chemical or ADME properties and are not used as compounds in a simulation.
Secondly, compounds in a simulation can be assigned to be a metabolite of another compound. The metabolite then possesses physico‐chemical and ADME properties and is transported. In addition, the metabolite can be used in further reactions and thus a metabolic network can be built.
A list of pre-configured interacting compound models is provided with the SB Suite. Like all PK-Sim and MoBi models, these compound models are fully transparent and all parameters, calculation methods and equations are easily accessible and can be managed as necessary and desired.
Detailed compartmental gastrointestinal (GI) transit model
The GI model of PK-Sim is represented with a compartmental model in which the alimentary canal from the stomach to the rectum is divided into 12 compartments, each representing a distinct GI segment . Furthermore, a mucosal compartment is added to each luminal segment of the intestine. More…
With this GI model, colonic absorption is modeled as well as a more accurate description of gut wall metabolism and transport. Within the model, gastric emptying and GI transit of solid formulations (e.g.tablet) and solutions can be treated separately.
Inclusion of Gene expression data: PK-Sim Express®
Protein expression levels collected from publicly available databases such as UNIGENE, ArrayExpress, etc. have been added to an expression database that is integrated with PK-Sim. Expression levels for one or more proteins (e.g. enzyme, transporter) playing a role in pathways of interest can be added to individuals or populations. More...
- Possibility to define “Events” such as meals and discrete gallbladder emptying
- Possibility to model continuous and discrete bile flow in rats
- Modeling of specific binding
- More flexible display of time windows for simulations
- Choice of different GUI-Skins matching mood and/or season
March 28, 2017
Published in npj Systems Biology and Applications: Translational learning from clinical studies predicts drug pharmacokinetics across patient populations
December 19, 2016
New publications regarding PBPK modeling: Elderly, Isoniazid, DDI and Diabetes. In addition: A white paper regarding good practices in drug discovery and development
October 18, 2016
Published in CPT Pharmacometrics & Systems Pharmacology: Applied Concepts in PBPK modeling: How to build a PBPK/PD model