Application of systems pharmacology modeling approach to optimize Interferon therapy of hepatitis C
Sergey Smirnov (1), Ekaterina Goryacheva (1), Neil Benson (2), Piet Van der Graaf (2), Victoria Flores (3), Oleg Demin (1)
(1) Institute for Systems Biology SPb, Moscow, Russia; (2) Pfizer, department of Pharmacokinetics Dynamics and Metabolism, Sandwich, UK; (3) Pfizer, Antivirals Research Unit, Sandwich, UK
Motivation: The interferon-based therapy is a common method of hepatitis C treatment. However, interferon-based therapy is lengthy (48 weeks), expensive and results in significant toxic side effects. Moreover, interferon-based therapy results in a positive outcome only in about 50% of patients. In accordance with individual susceptibility to interferon-based therapy, all patients can be subdivided into two main groups: (i) responders and (ii) non-responders. If 48 week course of interferon therapy results in complete recovery of a patient from hepatitis C this patient can be referred as responder. On the contrary, patients who failed to be cure of the disease during the 48 week course can be considered as non-responders. The main challenge of interferon therapy of hepatitis C is to predict before, or at the beginning of the interferon course, whether the patient is responder or non-responder.
One of the possible tools which can allow us to address the challenge is a systems pharmacology model of HCV dynamics and interferon-based therapy. This model could give an opportunity (i) to predict final therapy outcome for individual patients on the basis of his/her individual data profile, obtained at the initial stage of treatment; (ii) to design individual therapy regimen for each patient (or group of patients) in compliance with individual data of the patient such as interferon PK/PD characteristics.
The monitoring of the main features (such as virus load, serum IFN concentration, biomarkers production etc.) during first weeks of therapy can provide us with individual sets of kinetic parameters for each patient. These sets of parameters could be used for prediction of therapy outcome for each patient and/or for development of individual therapy protocol (dosage and regimen), that is more suitable for each patient.
Objectives:
- to reconstruct molecular mechanisms of IFN action and HCV reproduction
- to develop system pharmacology model of hepatitis C dynamics for simulation of long-term interferon-based therapy
- to identify the set of kinetic parameters, that necessary for personalized prediction of therapy outcome (responder vs non-responder) with above mentioned system pharmacology model.
- to optimize dosing regime of classical and modified INFs for different groups of patients (e.g. responders/non-responders)
Methods: To address the problem a systems pharmacology modeling approach has been applied. This approach enables us to collect and integrate all known in vitro, in vivo and clinical data, to analyze possible regulatory mechanism involved in the response to the drug administration at intracellular, cellular and organism levels and to test various hypotheses to explain the phenomena observed.
Results: A mathematical model, combining (i) virus dynamics (Neumann model), (ii) interferon PK taking into account various dosage regimes, (iii) response to long-term therapy, has been developed.
Parameters of the model have been identified on the basis of the published clinical data on dynamics of IFN and HCV RNA in serum during the first week of treatment measured for each patient individually.
The outcome of full-scale (48 weeks) therapy for the individual patient has been predicted. It has been shown, that the model predicts outcome of full-scale (48 weeks) interferon-based therapy for 88% of the patients involved in the clinical trials. Thus, the model can be used as a tool for choice of personalized interferon therapy of hepatitis C patients.
The model enables us to predict following ways to increase efficacy of interferon therapy for potential non-responders: (i) to modify preparation of peg-interferon in such a way to reduce absorption from subcutaneous site of injection; (ii) to identify optimal (personalized) interferon administration regimen (reducing of single dose with corresponding increasing of injection frequency)