Pharmacokinetic modeling of therapies for systemic lupus erythematosus

Abstract

With the increasing use of different types of therapies in treating autoimmune diseases such as systemic lupus erythematosus (SLE), there is a need to utilize pharmacokinetic (PK) strategies to optimize the clinical outcome of these treatments. Various PK analysis approaches, including population PK modeling and physiologically based PK modeling, have been used to evaluate drug PK characteristics and population variability or to predict drug PK profiles in a mechanistic manner. This review outlines the PK modeling of major SLE therapies including immunosuppressants (methotrexate, azathioprine, mycophenolate and cyclophosphamide, among others) and immunomodulators (intravenous immunoglobulin). It summarizes the population PK modeling, physiologically based PK modeling and model-based individualized dosing strategies to improve the therapeutic outcomes in SLE patients.

Keywords:

• azathioprine
• cyclophosphamide
• methotrexate
• mycophenolate
• pharmacokinetic modeling
• systemic lupus erythematosus

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Key issues

• There are now an amazing array of therapies used for the treatment of systemic lupus erythematosus, including immunosuppressants (methotrexate, azathioprine, mycophenolate and cyclophosphamide, among others) and immunomodulators (intravenous immunoglobulin).

• Many of these drugs exhibit large between-variability in pharmacokinetics (PK), resulting in a wide range of exposure (and response) at standard dose.

• Population modeling approaches allow to quantitatively describe PK behavior and variability in patients and identify the factors that influence changes in the relationship between the administered dose and the achieved concentrations.

• These population models can be implemented in Bayesian estimators to facilitate real-time dose adjustment to defined target concentrations using concentration and biomarker measurements as feedback.

• An attractive approach uses population models as prior information, from this we can estimate each patient’s PK parameter values to construct a patient-specific Bayesian posterior model based on that patient’s relevant demographic characteristics, dosage history, and drug and biomarker concentrations.

• These individualized and tailored dosing approaches guided by PK algorithms may be safer, more effective and even cost-effective.