Clinical endpoint sensitivity in rheumatoid arthritis: modeling and simulation

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Development of PBPK model for intra-articular injection in human: methotrexate solution and rheumatoid arthritis case study

A physiologically based model describing the dissolution, diffusion, and transfer of drug from the intra-articular (IA) space to the plasma, was developed for GastroPlus® v9.8. The model is subdivided into compartments representing the synovial fluid, synovium, and cartilage. The synovium is broken up into two sublayers. The intimal layer acts as a diffusion barrier between the synovial fluid and the subintimal layer. The subintimal layer of the synovium has fenestrated capillaries that allow the free drug to be transported into systemic circulation. The articular cartilage is broken up into 10 diffusion sublayers as it is much thicker than the synovium. The cartilage acts as a depot tissue for the drug to diffuse into from synovial fluid. At later times, the drug will diffuse from the cartilage back into synovial fluid once a portion of the dose enters systemic circulation. In this study, a listing of all relevant details and equations for the model is presented. Methotrexate was chosen as a case study to show the application and utility of the model, based on the availability of intravenous (IV), oral (PO) and IA administration data in patients presenting rheumatoid arthritis (RA) symptoms. Systemic disposition of methotrexate in RA patients was described by compartmental pharmacokinetic (PK) model with PK parameters extracted using the PKPlus™ module in GastroPlus®. The systemic PK parameters were validated by simulating PO administration of methotrexate before being used for simulation of IA administration. For methotrexate, the concentrations of drug in the synovial fluid and plasma were well described after adjustments of physiological parameters to account for RA disease state, and with certain assumptions about binding and diffusion. The results indicate that the model can correctly describe PK profiles resulting from administration in the IA space, however, additional cases studies will be required to evaluate ability of the model to scale between species and/or doses.

Randomised, double-blind, phase III study comparing the infliximab biosimilar, PF-06438179/GP1111, with reference infliximab: efficacy, safety and immunogenicity from week 30 to week 54

Objective

To investigate the efficacy, safety and immunogenicity of PF-06438179/GP1111 (PF-SZ-IFX) compared with European reference infliximab (Remicade^®; ref-IFX) in patients with moderate-to-severe, active rheumatoid arthritis after continued long-term use of PF-SZ-IFX, and in patients who were switched from ref-IFX to PF-SZ-IFX.

Methods

REFLECTIONS B537-02 was a double-blind, active-controlled, multinational study in which patients (N=650) were initially randomised to PF-SZ-IFX or ref-IFX for 30 weeks (treatment period [TP] 1). During weeks 30–54 (TP2), the PF-SZ-IFX group (n=280) continued treatment with PF-SZ-IFX (PF-SZ-IFX/PF-SZ-IFX) and patients in the ref-IFX group (n=286) were rerandomised (1:1) to continue ref-IFX (ref-IFX/ref-IFX) (n=143) or switch to PF-SZ-IFX (ref-IFX/PF-SZ-IFX) (n=143) for a further 24 weeks. Efficacy, safety, immunogenicity and pharmacokinetics were evaluated.

Results

During TP2, patients in all three treatment groups continued to maintain comparable treatment response. At week 54, the American College of Rheumatology (ACR20) response rates were 71.1% (PF-SZ-IFX/PF-SZ-IFX), 64.3% (ref-IFX/ref-IFX) and 70.6% (ref-IFX/PF-SZ-IFX). Observations for other endpoints, including ACR50/70, Disease Activity Score in 28 Joints Based on High-Sensitivity C Reactive Protein(DAS28-CRP) remission, and mean change in DAS28-CRP and Health Assessment Questionnaire-Disability Index, were also comparable. Treatment-emergent adverse events were reported in 36.8% (PF-SZ-IFX/PF-SZ-IFX), 33.6% (ref-IFX/ref-IFX) and 37.8% (ref-IFX/PF-SZ-IFX) of patients; there were no clinically meaningful differences in the safety profiles between groups. The percentage of patients who were antidrug antibody-positive was generally stable through the treatment period and comparable overall between the PF-SZ-IFX/PF-SZ-IFX (52.1%; neutralising: 80.8%), ref-IFX/ref-IFX (60.1%; neutralising: 84.9%) and ref-IFX/PF-SZ-IFX (58.0%; neutralising 78.3%) groups.

Conclusions

The similar efficacy, safety and immunogenicity of PF-SZ-IFX compared with ref-IFX were maintained for up to 54 weeks and were not affected by blinded treatment switch from ref-IFX to PF-SZ-IFX at week 30.

Trial registration number

NCT02222493.

Exposure-Response Modeling and Power Analysis of Components of ACR Response Criteria in Rheumatoid Arthritis (Part 2: Continuous Model)

Population pharmacokinetic/pharmacodynamic (PK/PD) models were developed to quantitate the exposure-response relationships using continuous longitudinal data on American College of Rheumatology (ACR) subcomponents, that is, tender-joint count (TJC), swollen-joint count (SJC), C-reactive protein, patient's assessment of pain, patient's global assessment of disease activity, physician's global assessment of disease activity, and patient's assessment of physical function for 5 biologics approved for use in rheumatoid arthritis. The models were then used to simulate the time courses of clinical outcomes following different treatment regimens. The relative sensitivity of the 7 subcomponents was assessed using Monte Carlo simulation-based power analysis. The developed population PK/PD models adequately described the relationship between serum concentrations and changes in ACR subcomponents. The trial simulation and subsequent power analysis showed that SJC and TJC appeared to be more sensitive than the other 5 ACR subcomponents to detect treatment effect over placebo/methotrexate. These 7 ACR subcomponents had similar power in detecting the treatment difference between different doses. In addition, the continuous measures of ACR subcomponents did not appear to be more sensitive than binary measures.

Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis

Improving the quality of exposure-response modeling is important in clinical drug development. The general joint modeling of multiple endpoints is made possible in part by recent progress on the latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate, when modeling a continuous and a categorical clinical endpoint, the level of improvement achievable by joint modeling in the latent variable IDR modeling framework through the sharing of model parameters for the individual endpoints, guided by the appropriate representation of drug and placebo mechanism. This was illustrated with data from two phase III clinical trials of intravenously administered mAb X for the treatment of rheumatoid arthritis, with the 28-joint disease activity score (DAS28) and 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria were used as efficacy endpoints. The joint modeling framework led to a parsimonious final model with reasonable performance, evaluated by visual predictive check. The results showed that, compared with the more common approach of separately modeling the endpoints, it is possible for the joint model to be more parsimonious and yet better describe the individual endpoints. In particular, the joint model may better describe one endpoint through subject-specific random effects that would not have been estimable from data of this endpoint alone.