Development of a mechanistic model for rheumatoid arthritis is complex owing to the many molecular factors that affect disease progression. The model was derived based on biomarkers that are well-known as major mediators of inflammation and bone erosion. It is well established that there is an endocrine link between pro-inflammatory cytokine production and glucocorticoid receptor and corticosterone regulation. This partly explains disease remission with increased expression of GR mRNA and CST following CIA onset and permits a mechanistic framework to examine the effects of other corticosteroids.
The structure of the disease progression model integrates endogenous glucocorticoid effects with cytokine mRNA expression in an effort to model the effects of DEX in an animal model of arthritis (Part II). DEX is a synthetic glucocorticoid that perturbs many inflammatory pathways and all measured biomarkers acting through the same mechanisms as CST. The turnover of these markers within the disease progression and their effects on disease endpoints can be obtained from the PD data following DEX administration. The disease progression data alone cannot resolve all parameters. Rationalization of these remaining equations and parameters require information from DEX PD and is presented here to a limited extent and in more detail in part II.
The model describing disease progression in paw cytokine mRNA starts as a simple transduction model with a rate constant to account for the delay, baseline steady-state initial condition for the healthy response, and the kdis rate constant to increase the cytokine mRNA to a new disease steady-state at time of induction. This is a new application of transduction compartments to describe four aspects of disease progression: time of induction, delay in disease onset, rate of progression, and new disease-steady state response. Equations for individual cytokine mRNAs were complicated by inhibition of mRNA production by CST-GR complex in the nucleus, possible disease remission, and cross-talk between IL-6 and TNF-α mRNA. Since DEX and CST are assumed to act similarly, many of these modeling complexities were resolved from the DEX PD data (Part II).
Initially the decline in IL-6 paw mRNA was explained by CST binding to GR and DRN inhibiting IL-6 mRNA production as DRN rose above the IC50 for IL-6 (4.5 nM) mRNA inhibition during disease progression. However, early simulations indicated that CST alone could not account for the entire remission of IL-6. If DRN rose rapidly as in the case of CS administration CST would be suppressed rapidly and for a prolonged time (see Part II), such that when DRN returned to normal and CST remained suppressed, the IL-6 mRNA production would overshoot the measured response.
Abrupt DEX PD changes in cytokine mRNA indicated that rate constants relevant to the loss of mRNA were much faster than the transduction rate constants describing disease progression. This observation forced the production and loss constants for cytokine mRNA to be modeled separately from the transduction rate constants. Literature indicates rapid production and loss of cytokines within hours whereas disease effects may be drawn out over days. Using transduction equations to describe disease progression provides a conceptual link between slower rate-limiting disease steps and measured responses with faster turnover.
The localized nature of the disease in paw/joint tissue made measurements of TNF-α, IL-1β, or IL-6 protein in paw tissue impractical. The authors tried measuring these cytokine proteins in homogenized paw tissue diluted with phosphate buffered saline and plasma. Background/matrix effects were a problem and the TNF-α, IL-1β, and IL-6 ELISA assay kits (Promega Corp. Madison, WI) were not valid for tissue analysis. The cytokine ELISA kits for protein measurements were not sensitive enough for plasma cytokine concentrations in arthritic rats. Given these experimental limitations the model uses the available methods to relate physiogically relevant events. Owing to the rapid turnover of both cytokine protein and mRNA in tissue and that these proteins are constitutively expressed in response to an antigenic stimulus, time courses of cytokine mRNA may be reflective of synthesized cytokine. Thus correlations between cytokine mRNA and effects on disease progression may be useful if the turnover rates of disease endpoints are slower than those of cytokine protein.
The DEX PD data helped resolve Hill coefficients for DRN effect from different doses. For TNF-α and IL-6 mRNA these values were fixed to 2.0 as this agreed with both the initial model estimations and the physiological homo-dimerization of CST-GR complex in the nucleus.
Paw edema was monitored in every animal which developed the disease. There were 4943 paw edema data points used in the disease progression, healthy animal profiles, and pharmacodynamic analysis compared to 439 points for mRNA of each cytokine. Due to variation in paw edema progression among animals, describing paw swelling during simultaneous fitting of all data initially biased the model fitting as it placed most emphasis on the how the paw edema was modeled and little on the cytokine mRNA driving the edema. Without capturing the mRNA responses first, the development of the structural model describing cytokine mRNA and paw edema was difficult and unclear. The numerous feedback loops by DRN on most inflammatory factors and these factors on GR and CST meant that if one variable was altered, model descriptions for all other biomarkers were altered also. Thus the model describing cytokine mRNA, GR mRNA, and plasma CST was developed first and fixed for evaluating the structural model for paw edema dependent on the cytokine mRNA expression. This permitted shorter model fitting times and better model revisions for the complex feedback model between cytokine and CST-GR regulation.
The final model for paw edema was relatively simple and driven by contributions from all three cytokines. Paw edema increased early as affected by TNF-α and IL-6, and the decline in IL-6 mRNA after day 21 helped explain the similar decline to a steady-state in paw edema. These individual contributions of cytokines on edema were further resolved by administration of DEX which inhibited each cytokine mRNA profile at different rates and extents while correlating these mRNA amounts to paw edema.
There is increasing interest for treating rheumatoid arthritis with biologics aimed at inhibiting the effects of TNF-α and IL-1β. Model simulations continually inhibiting the effects of each cytokine by 50 or 100% were performed to predict extent of edema reduction. Because TNF-α, IL-1β, IL-6 and other pro-inflammatory cytokines have overlapping functions in producing edema, completely suppressing the inflammatory response by targeting only one or two of these pathways results in incomplete reduction of edema. The myriad pathways contributing to inflammation emphasizes the role of corticosteroids in treatment of RA for their inherent ability to suppress many different inflammatory processes simultaneously. Understanding the extent to which dexamethasone, prednisolone, or other CS reduce each inflammatory signaling pathway in light of how other therapies affect these common disease factors may contribute to determining optimal treatment regimens for agents given alone or in combination.
Bone mineral density was modeled in each region by the sum of the cancellous bone plus the cortical bone. As these amounts were not known, the fraction of cancellous bone was modeled in each region. Physiological understanding that bone turnover is more rapid in cancellous bone is in agreement with model estimates for initial and maximum BMD in both bone types. However, the turnover rate constant for cancellous bone was slightly lower than that for cortical bone. This is caused in part by the parameter ftc that reduces the effect of the ‘OC’ on cortical bone turnover.
The model does not consider proximity to inflamed joints as a factor and as such the lumbar region is treated as if equal to the knee joint in terms of inflammatory effects. While disease effects may not be readily apparent in the lumbar region of the rat, differences are observed in human RA in the spine. The time course of response may need to be carried out longer to observe these inflammatory effects on BMD in the rat lumbar region.
High CV% for bone parameters were rather surprising as these parameters appeared to stabilize quickly and shifted very little during model convergence. It is likely that the high CV% came from inter-animal variation in onset of disease progression and rate of bone loss. Precision measurements for Total BMD have been reported (Binkley et al., 2003
; Soon et al., 2006
). In all cases the CV% was less than 10%. It is possible this CV% also comes from not having measured cancellous or cortical bone directly. However, the value of the parameter estimates appeared reasonable.
Trends in both disease progression and pharmacodynamic responses to dexamethasone (Part II) were necessary to discern model behavior of pro-inflammatory cytokine mRNA and their effect on driving disease progression in paw edema as well as endogenous CST and GR mRNA that in turn regulate the production of TNF-α, IL-1β, and IL-6 mRNA. Other components such as immune cell presence in tissue, amounts of transcription factors in the nucleus, and additional inflammatory regulators (e.g. prostaglandins, nitric oxide, TH-2 cytokines) are valuable components to understanding inflammation. Despite the absence of these other factors in the model, edema and BMD appear to be well described by the major cytokines that drive the production of inflammation. This is the first detailed mechanistic model for CIA progression in the rat and model qualification may come with future studies. Defining and developing translational assumptions between disease progression in the rat and clinical disease observations may not be possible for all biomarkers. However, it is likely that for BMD the extent of disease or drug effect will correlate well, even if the rates of turnover are different because these are chronic conditions with chronic therapies and disease progression and drug responses tend toward steady-state values over time. Owing to the endogenous link between CST and cytokine mRNA, treatments with DEX and other corticosteroids in the clinic along with pre-determined knowledge of drug receptor binding may aid in relevant clinical predictions. Future experiments aim to validate the model by predicting the response to methylprednisolone dosing to arthritic rats. Experiments with other therapeutics may prove informative if the drug effects are limited individual pathways in the model. Anti-cytokine antibodies are a good example of therapeutic agents that may block specific signaling components in order to identifiy the relative contributions of certain cytokines on disease endpoints.