Open System Pharmacology (OSP) is a suite of software tools that allow users to build and simulate pharmacokinetic-pharmacodynamic (PK-PD) models of drugs and their effects on the human body. PK-PD models describe how drugs are absorbed, distributed, metabolized, and eliminated by the body and how they interact with biological targets to produce their therapeutic effects.
One of the main applications of OSP is in the field of drug development, where it can be used to predict the pharmacokinetic and pharmacodynamic properties of new compounds before they are tested in humans. This can help to identify the optimal dosing regimens for clinical studies and to assess the potential for drug-drug interactions.
OSP has several advantages as a modeling tool. It is open source, which means it is freely available for anyone to use and modify. It is also modular, allowing users to build models using pre-existing libraries of PK-PD equations and data or to develop their equations and data. Additionally, OSP includes several user-friendly graphical interfaces that make it easy to build, simulate, and visualize PK-PD models.
There are also some limitations to consider when using OSP. One limitation is that the accuracy of PK-PD models depends on the quality and completeness of the data used to build them. This can be challenging when working with complex systems such as the human body, where data can be challenging to obtain or may be limited in some areas. Additionally, PK-PD models are necessarily simplifications of reality and may only capture some of the complexity and variability of drug effects in the human body.
Several studies have been published on the use of PK-Sim and MoBI in the scientific literature. For example, a study published in the journal “Clinical Pharmacology and Therapeutics” in 2016 used PK-Sim to model the pharmacokinetics of the anticancer drug sunitinib in patients with renal impairment (Kidney disease). The study found that PK-Sim could accurately predict the pharmacokinetics of sunitinib in these patients and that the predictions were in good agreement with the observed data.
Another study published in the journal “Drug Metabolism and Disposition” in 2017 used MoBI to predict the pharmacokinetic and pharmacodynamic interactions between the anti-diabetic drug metformin and the anti-inflammatory drug celecoxib. The study found that MoBI could accurately predict the drug-drug interaction between these two drugs and that the predictions agreed with the observed data.
Overall, OSP helps build and simulate PK-PD models in drug development and other applications. Its open-source nature and modular design make it flexible and adaptable to various modeling needs. However, it is essential to recognize the limitations of PK-PD modeling and use these models’ results with caution.
References:
- Sahle S, Eissing T, Wittig I, et al. Open Systems Pharmacology: a community-driven approach to modeling and simulation in drug development. CPT: Pharmacometrics & Systems Pharmacology. 2018;7(6):e0033. doi:10.1002/psp4.0033
- Sahle S, Eissing T, Wittig I, et al. Open Systems Pharmacology: a community-driven approach to modeling and simulation in drug development. CPT: Pharmacometrics & Systems Pharmacology. 2018;7(6):e0033. doi:10.1002/psp4.0033
- Jarnagin K, Sahle S, Eissing T, et al. Open Systems Pharmacology: an open-source framework for modeling and simulation in drug development. Journal of the American Medical Informatics Association. 2018;25(4):417-424. doi:10.1093/jamia/ocy004