Kinetics and dynamics of corticosteroids in a rat model of arthritis
Earp, Justin C.
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Pharmacodynamic drug-drug interaction modeling. Mechanistic understanding of how multiple disease factors or multiple drugs interact to influence the production or loss of disease endpoints and drug effect is essential for modeling of disease progression and drug dynamics. Characterization of drug-drug interactions in the context of indirect response models provides useful mechanistic equations for testing the underlying mechanisms by which two or more molecular factors or drugs interact. Chapter 1 characterizes drug-drug interactions relevant to pharmacodynamic indirect response models when two agents jointly inhibit or stimulate either the production or loss processes of response or both. Simulations of time-dependent response indicate enhanced, suppressed, biphasic, or no-interaction responses. Isobolograms are used to identify the apparent synergy, antagonism, or additivity relevant to the various interaction cases without the need of additional empirical interaction parameters. The model for non-competitive effects is derived and introduced in Chapter 2. Monte Carlo simulations were performed, and sensitivity analysis of the steady-state parameter (E SS ) indicates how sample intensity and the value of ESS affect the accuracy and precision of model parameter estimates. Chapter 3 applies semi-mechanistic and mechanistic PD drug-drug interaction models to compare interactions between three anti-cancer agents on the inhibition of cancer cell growth, each in combination with arsenic tri-oxide. Interaction parameters are used to compare the magnitude of the synergy or antagonism observed in protein expression for combinations of heat shock protein-90 inhibitors. Isobolograms are shown to compare the degree of synergy found in protein expression with the degree of synergy observed in the cell-survival data. This application of the non-competitive model demonstrates the utility of isobolograms for comparing drug combinations and the degree of synergy found between different types of measured drug response. Pharmacokinetics and pharmacodynamics of dexamethasone in arthritis. Corticosteroid therapy remains a mainstay in RA treatment when other drug therapies fail. It is hypothesized that mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling of CS effects and disease progression in a rat model of RA will identify optimal doses of dexamethasone for effects on edema and bone mineral density and major inflammatory pathways relevant for optimizing CS therapy in the human disease. Chapter 5 introduces two animal models of RA: collagen induced arthritis (CIA) and adjuvant induced arthritis (AIA). Disease was induced and paw edema was monitored as the arthritis advanced. A population disease progression model is applied to compare inter-animal variation of paw edema between CIA and AIA in Lewis and Dark-Agouti (DA) rats. No differences are detected in variability between groups. Lewis rats with CIA are chosen for the clinical relevance and ease of obtaining these rats. The RA disease-state kinetics of DEX are examined in Chapter 6. A pharmacokinetic model is applied to plasma concentrations of DEX in CIA and healthy rats. A mechanistic disease progression model for CIA in Lewis rats is presented in Chapter 7. Primary cytokine mediators of arthritis, plasma concentrations of corticosterone (CST), and bone mineral density (BMD), paw edema and body weight are measured. Time-dependent models are developed for paw edema and BMD as a function of the pro-inflammatory cytokine mRNA expression. Simulations of BMD and paw edema with inhibition of different cytokine pathways indicate the advantages of therapeutically targeting specific cytokines in the disease state and the importance of how PD drug interactions may yield favorable responses in the clinic. In Chapter 8 the effect of DEX is incorporated into each relevant disease pathway of the mechanistic disease progression model in order to identify the extent to which DEX suppresses each cytokine mRNA, paw edema, and BMD. This integration of pharmacologic effect with disease progression identifies which pro-inflammatory factors and disease endpoints are most influenced by DEX. Various models have been constructed, applied, and presented to understand: (1) mechanisms by which two factors may interact, (2) inter-animal variation in disease states, (3) differences in pharmacokinetic parameter estimates, (4) individual contributions of proinflammatory mediators on disease endpoints, and (5) the mechanisms by which disease processes and DEX mediate the end therapeutic result. The final model equations and parameters are sets of mechanistic tools capable of characterizing PD drug interactions, disease progression, and dexamethasone drug effect. These models aim for simplicity but are sufficient to characterize the major mechanisms governing the end therapeutic result, ultimately expanding our understanding of DEX therapy in chronic arthritis and inflammation. (Abstract shortened by UMI.)