Systems Modeling of Bortezomib Exposure -- Response Relationships in Multiple Myeloma
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Multiple myeloma (MM) is an incurable bone marrow plasma cell malignancy that accounts for 20% of hematological malignancy related deaths in the United States. One molecular hallmark of MM is constitutively elevated proteasome activity, which results in abnormal degradation of key regulatory proteins and subsequent perturbation of multiple signaling pathways involved in apoptosis and cell cycle regulation. Proteasomes are proteolytic complexes responsible for the targeted degradation of proteins that are crucial in the regulation of cell survival. Bortezomib (Velcade ® ), a first-in-class reversible proteasome inhibitor, shows potent antineoplastic activity by inhibiting elevated proteasome activity in myeloma cells and is approved as a first-line agent for treating MM patients. The introduction of bortezomib has revolutionized MM pharmacotherapy; however, significant inter-individual variability in response, acquired bortezomib resistance, and dose-limiting toxicities continue to pose challenges for effective anti-MM therapy. Optimization of bortezomib-based combinatorial regimens requires improved understanding of the factors controlling bortezomib exposure-response relationships under such challenging conditions. Therefore, this dissertation focuses on mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) systems analysis of preclinical and clinical data in order to achieve following aims: 1) identify the critical factors that regulate the time-course of bortezomib exposure and subsequent pharmacological responses in MM; and 2) propose optimal bortezomib-based single-agent and combination regimens to improve clinical outcomes in MM pharmacotherapy. Bortezomib exhibits dose- and time-dependent PK, and proteasome-mediated disposition of bortezomib is proposed as the primary source of its nonlinear and nonstationary behavior. A target-mediated physiologically-based PK (PBPK) model was developed to describe plasma and tissue PK profiles of bortezomib simultaneously in mice. The final PBPK model was scaled to predict drug exposure in humans and successfully recapitulated plasma bortezomib PK profiles resulting from multiple-dosing in patients with MM. The model analysis supports the hypothesis that bortezomib binding to proteasome is the primary source of its dose- and time-dependent kinetics. In addition, model simulations suggest that renal function exerts a minimal influence on bortezomib exposure in humans. This simulation result is consistent with clinical observations from patients with renal dysfunction, confirming that bortezomib dose adjustment may not be needed for MM patients with renal function impairment. Bortezomib also exhibits a relatively narrow therapeutic index, and numerous clinical trials have been conducted to identify optimal bortezomib chemotherapeutic regimens that maximize efficacy but minimize toxicity. A quantitative, predictive systems pharmacological framework was developed that integrates drug-target interactions and downstream cellular apoptosis signal transduction with biomarkers of clinical efficacy (e.g., M-protein) and adverse events (e.g., platelet counts). Bortezomib exposure, myeloma progression, and platelet dynamic profiles were well characterized in MM patients, and model simulations qualitatively demonstrate that increased systemic proteasome concentrations could ultimately attenuate bortezomib antineoplastic activity in MM patients. In addition, model simulations support that a bortezomib regimen of 1.9 mg/m 2 once weekly might represent an optimal therapeutic strategy, producing comparable anti-MM efficacy but a significantly reduced risk of thrombocytopenia in MM patients. Rituximab, a CD20 monoclonal antibody, is under Phase II clinical development for treating MM patients. Rituximab can suppress anti-apoptotic signals and has been shown to sensitize cancer cells to other cytotoxic drugs. Our cell culture studies provide evidence for the first time that pretreatment of human myeloma cells that express CD20 with rituximab can potentiate the cytotoxicity of bortezomib and lead to sequence-dependent synergistic effects at clinical achievable drug concentrations. The exposure sequence of rituximab pretreatment, followed by bortezomib, produced the most pronounced effects as compared to concurrent combination or the alternate sequence of bortezomib treatment prior to rituximab exposure in human myeloma cells. Osteolytic bone disease is one of the most debilitating manifestations of MM, and currently bortezomib is being evaluated for its positive effects on MM with skeletal complications. Dexamethasone is a synthetic corticosteroid that is often given in combination with bortezomib for its potent anti-myeloma activity; however, osteolysis is a major adverse event of chronic steroid-based therapies. A systems pharmacology model was successfully developed to characterize the time-course of clinical biomarkers following bortezomib and dexamethasone combination therapy in MM patients. The interaction model was based on codifying multiple regulatory mechanisms of drug action and provided a platform for probing optimal bortezomib and dexamethasone combination dosing regimens to minimize skeletal side effects during MM therapy. In summary, proteasome inhibitor bortezomib has yielded important clinical benefit for patients with multiple myeloma in the past decade. Optimization of bortezomib-based therapy, though, requires a further understanding of the multiple challenges associated with use of the drug. This dissertation attempts to use pharmacological systems analysis to preclinical and clinical PK/PD data for hypothesis testing and to identify the crucial factors that determine bortezomib exposure and response in MM patients. The models and approaches developed in this dissertation could provide initial guidance for the optimization of bortezomib based combinational regimens to improve clinical outcome of multiple myeloma pharmacotherapy.