Systems pharmacology modeling of rituximab and doxorubicin in non-Hodgkin's lymphoma.

摘要

Non-Hodgkin's lymphoma (NHL), a heterogeneous group of lymphocyte malignancies, is the most prevalent hematological cancer in adults, accounting for 4% of cancer-related deaths in the U.S. The overall 5-year survival rate of NHL is 69%. Patients with low-grade lymphomas are considered incurable with current therapy, and although aggressive lymphomas may respond to aggressive combination chemotherapy, more than half of patients eventually relapse. Rituximab, the first generation anti-CD20 antibody, has demonstrated substantial improvement in patient survival, especially when combined with standard chemotherapy, and has become the cornerstone of treatment for B-cell NHL (B-NHL) as well as other B-cell malignancies. A range of potential mechanisms has been proposed for rituximab actions, including complement-dependent cytotoxicity (CDC), antibody-dependent cell mediated cytotoxicity (ADCC), and direct signaling of cell death. In addition, chemo-sensitization has been reported in rituximab treated lymphoma cells, which may occur through modulation of cell apoptotic signaling mechanisms, and this effect might contribute to the clinical benefit of administering rituximab in combination with cytotoxic agents. However, the pharmacodynamic (PD) relationships of rituximab-based drug combinations are poorly understood, and dosing schedules are largely empirically based. Therefore, the major objective of this dissertation is to develop a quantitative systems pharmacology (QSP) platform of rituximab effects in B-NHL that enhance understanding of the biological mechanisms underlying rituximab action and interaction, predict rituximab-based chemotherapy in xenograft systems, and assess exposure-response relationships and the nature of rituximab-doxorubicin combinations on lymphoma cell survival and proliferation.;Characterization of rituximab pharmacokinetics (PK) and PD is challenging. Not only patient characteristics, but also disease-related factors (e.g., disease stage and target burden) can contribute to large variability in antibody disposition and therapeutic outcomes. A significant impact of CD20 antigen expression on rituximab clearance reflects the properties of target-mediated drug disposition. A CD20 systems model, which integrates target binding and dynamics of intracellular apoptotic signaling with tumor responses, successfully predicted the combinatorial chemotherapeutic effects of rituximab with fenretinide or rhApo2L in NHL xenografts (reviewed in Chapter 3). This model was further extended to the combination of rituximab-doxorubicin in a Ramos lymphoma xenograft system in Chapter 4. The predictive performance was assessed by comparing model simulations to experimental observations from a murine animal model of Ramos cell xenografts. The integrated systems pharmacodynamic model described tumor growth dynamics well in all treatment groups. In addition, the model simulated expression levels of the cellular interaction biomarker, Bcl-xL, were in good agreement with western blot analysis of Bcl-xL in ex vivo tumor samples.;To better understand the underlying biological mechanisms of rituximab-doxorubicin interactions, in vitro studies on lymphoma cell survival (Chapter 5) and proliferation (Chapter 6) were conducted, coupled with mechanism-based pharmacodynamic systems analyses. Rituximab induced homotypic adhesion, along with apoptotic and non-apoptotic modes of cell death. However, rituximab was found not to alter cell cycle distribution. In addition to accelerating cell apoptotic processes, doxorubicin also inhibited cell proliferation, which appears to be associated with cell cycle arrest in the G2/M phase. The final mechanism-based PD model captured all dynamic profiles for control, single agent, and combination drug treatments, and suggests that rituximab-induced chemo-sensitization in B-NHL cells follows an apoptotic pathway and might be synergistic when rituximab is combined with relatively low concentrations of cytotoxic agents.;Rituximab has revolutionized NHL treatment in the past two decades; however, suboptimal response and refractory/resistant disease continue to emerge in the clinic, and development of effective anti-lymphoma treatment remains an active area of research. Network-based approaches serve as efficient tools for analyzing complex biological systems and the dynamic interplay among the molecular components that give rise to emergent pathological and pharmacological system properties. A Boolean network of main intracellular mechanisms governing B-NHL proliferation and apoptosis was constructed, which included 102 nodes, 186 edges, and 12 drug interventions (Chapter 7). A graphic-based algorithm identified key structural features, such as network hubs, feedback loops, and species dependency. A logical steady-state analysis confirmed that the CD79B gain-of-function mutation contributes to uncontrolled lymphoma cell proliferation. Dynamical behavior of the B-NHL network following rituximab-based chemotherapy was calibrated with experimental data. This network-based systems pharmacology approach can be used to query key pharmacological targets in B-NHL and might provide a rationale for designing new intervention strategies.