Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy

Abstract

Background Tight monitoring of efficacy and safety of anticoagulants such as warfarin is imperative to optimize the benefit-risk ratio of anticoagulants in patients. The standard tests used are measurements of prothrombin time (PT), usually expressed as international normalized ratio (INR), and activated partial thromboplastin time (aPTT). Objective To leverage a previously developed quantitative systems pharmacology (QSP) model of the human coagulation network to predict INR and aPTT for warfarin and rivaroxaban, respectively. Methods A modeling and simulation approach was used to predict INR and aPTT measurements of patients receiving steady-state anticoagulation therapy. A previously developed QSP model was leveraged for the present analysis. The effect of genetic polymorphisms known to influence dose response of warfarin ( CYP2C9, VKORC1 ) were implemented into the model by modifying warfarin clearance (CYP2C9 *1: 0.2 L/h; *2: 0.14 L/h, *3: 0.04 L/h) and the concentration of available vitamin K (VKORC1 GA: −22% from normal vitamin K concentration; AA: −44% from normal vitamin K concentration). Virtual patient populations were used to assess the ability of the model to accurately predict routine INR and aPTT measurements from patients under long-term anticoagulant therapy. Results The introduced model accurately described the observed INR of patients receiving long-term warfarin treatment. The model was able to demonstrate the influence of genetic polymorphisms of CYP2C9 and VKORC1 on the INR measurements. Additionally, the model was successfully used to predict aPTT measurements for patients receiving long-term rivaroxaban therapy. Conclusion The QSP model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring. This is an exemplar of how a QSP model can be adapted and used as a model-based precision dosing tool during clinical practice and drug development to predict efficacy and safety of anticoagulants to ultimately help optimize anti-thrombotic therapy.

Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring

“…FIRM can be expanded to include novel biological findings relevant to sirolimus, such as incorporating novel medications that target antigen-presenting cells (B-cells), T-cell subsets, T-cell signal transduction, costimulatory molecules, or cytokines into QSP models. Early steps are being taken to apply QSP models to precision dosing, specifically in the context of the well-characterized coagulation cascade (Hartmann et al, 2016). However, the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient's immune state in vivo makes it challenging to develop such QSP models of immune response (Laubenbacher et al, 2022).…”

Asia‐Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

“…QSP models can provide an understanding of the relative contributions of the drug candidate or its metabolites to efficacy and safety at a pathway level, including insight into how receptor variability, signaling heterogeneity, and genetic variability in xenobiotic metabolism and transport can impact outcomes. QSP modeling can provide predictive value in dose selection, the need for alternative dosing, and the probability of demonstrating an outcome under different intrinsic and extrinsic factor conditions that differentiate Asian and Western populations 57–60 . Additionally, advances in the ability to create mathematical models of tumor immunology and the cancer‐immune cycle have resulted in the development of QSP frameworks that can incorporate multidimensional sources of variation 61 .…”

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

“…The values of these parameters can be either simulated with a priori distributions of anthropometric parameters and a particular model or directly resampled from an existing database. To simulate the inter-patient variation, the pathophysiological parameters can be used as covariations of drug-specific parameters in the estimated models, such as PK/PD, PBPK, and QSP models [ 152 , 153 , 154 ]. With VPs’ estimation, virtual clinical trials can be conducted to predict the clinical response expressed by survival response or frequent response.…”