BioModels

Blood coagulation model investigating effects of Xa-inhibitors (Rivaroxaban and Apixaban). Model is an extension of Pohl1994 and reduced from Wajima2009. Encoding the model from the supplementary files results in 43 species, 82 reactions and 111 parameters. Including the drug (Xa-inhibitor) and drug-Xa complexes results in 46 species, 84 reactions and 115 parameters (+1 dummy variable to change inhibitory kinetic parameters depending on which drug is simulated). The publication lists there being 45 species, 84 reactions and 116 parameters. Publication figure 2 has 45 species present however the complex VIIa:Xa (reaction 51 involving Xa and VIIa) is not shown. This figure also has numerous small errors such as listing IXa:ATIII complex twice (instead of one being XIa:ATIII), not showing XIa:ATIII, typo ('Va' -> Va), typo (IXa + VIIIa -> IXA:'VIIa'). Rate laws for Xa-drug interactions were assumed to be mass action.

• A Systems Pharmacology Model for Predicting Effects of Factor Xa Inhibitors in Healthy Subjects: Assessment of Pharmacokinetics and Binding Kinetics.
• Zhou X, Huntjens D, Gilissen R
• CPT: Pharmacometrics & Systems Pharmacology , 11/ 2015 , Volume 4 , Issue 11 , pages: 650-659 , PubMed ID: 26783501
Affiliation: Department of Pharmacokinetics, Metabolism and Dynamics Discovery Sciences, Janssen R&D, a division of Janssen Pharmaceutica Beerse Belgium.
Abstract: Factor Xa (FXa) emerged as a promising target for effective anticoagulation and several FXa inhibitors are now available for the prevention of venous thromboembolism. However, in previously reported pharmacokinetic/pharmacodynamic (PK/PD) models, the complex coagulation processes and detailed information of drug action are usually unclear, which makes it difficult to predict clinical outcome at the drug discovery stage. In this study, a large-scale systems pharmacology model was developed based on several published models and clinical data. It takes into account all pathways of the coagulation network, and captures drug-specific features: plasma pharmacokinetics and drug-target binding kinetics (BKs). We aimed to predict the anticoagulation effects of FXa inhibitors in healthy subjects, and to use this model to compare the effects of compounds with different binding properties. Our model predicts the clotting time and anti-FXa effects and could thus serve as a predictive tool for the anticoagulant potential of a new compound.