What is the clinical evidence from this study?

Study design: Other. Population: Venous thromboembolism (n=373). Intervention: Quantitative Systems Pharmacology (QSP) model predictions vs. Observed clinical data. Primary outcome: Prediction accuracy of INR and aPTT measurements.

July 14, 2020Open Access

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

Key Result

A quantitative systems pharmacology model accurately predicted INR and aPTT measurements for patients with different CYP2C9 and VKORC1 genetic polymorphisms receiving long-term warfarin or rivaroxaban therapy.

Structured PICO

Does a quantitative systems pharmacology model incorporating genetic polymorphisms accurately predict INR and aPTT in patients receiving steady-state warfarin or rivaroxaban?

Population

373 Caucasian adults receiving steady-state anticoagulation therapy for prophylactic VTE therapy (155 on warfarin/phenprocoumon, 218 on rivaroxaban).

Intervention

Quantitative systems pharmacology (QSP) model incorporating CYP2C9 and VKORC1 genetic polymorphisms to predict steady-state INR (for warfarin) and aPTT (for rivaroxaban).

Comparator

Observed routine clinical therapeutic drug monitoring data (INR and aPTT measurements).

Outcome

Accuracy of model-predicted INR and aPTT compared to observed clinical measurements.surrogate

A quantitative systems pharmacology model incorporating genetic polymorphisms can accurately predict INR and aPTT for patients on warfarin and rivaroxaban, offering a potential tool for precision dosing.

Limitations

The model does not account for demographic factors known to influence variability such as body weight and age.
The model does not account for clinical factors such as comorbidities and co-medications.
A general variability term of 20% was added instead of estimating true residual unexplained variability.
aPTT is generally not a reliable measure of rivaroxaban efficacy.

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 describing 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 amount of available vitamin K (VKORC1 GA: -22% from normal vitamin K amount; AA: -44% from normal vitamin K amount). 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 model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring and may be used as a tool during clinical practice to predict efficacy and safety of anticoagulants and help optimize anti-thrombotic therapy when used as a model-based precision dosing tool.

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