Pharmacodynamic systems analysis of HDAC and proteasome inhibition in multiple myeloma
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Multiple myeloma (MM) is B-cell neoplasm characterized by abnormal proliferation of plasma cells. Principal signaling pathways such as NFκB, JAK-STAT, PI3K, RAS, and MAPK are deregulated in MM resulting in the abnormal proliferation and survival of myeloma cells. Consequently, inhibition of one pathway by any targeted agent likely results in compensation by other pathways, ultimately leading to ineffective therapy. Myeloma patients thus exhibit a repetitive pattern of remission and relapse and ultimately stop responding to any kind of therapy. The combination of bortezomib (a proteasome inhibitor) and vorinostat (a histone deacetylase inhibitor) has been proposed to have an enhanced cell killing effect by aggravation of cellular stress and apoptosis and has shown promising preclinical results. However, clinical studies emphasize the need for optimization of dosing regimens for these drugs for better efficacy. This highlights a gap in preclinical-clinical translation and warrants systematic studies to identify factors regulating drug interactions along with mechanistic and quantitative models for guiding the design of dosing schedules. Systems pharmacology provides a promising complementary approach to reductionist, semi-mechanistic, and empirical methods, as it aids in evaluating interactions between drugs and biological systems from a mechanistic-global perspective. The purpose of this dissertation is to evaluate the utility of a systems pharmacology-based approach in studying the efficacy of bortezomib and vorinostat in MM and advance the methods for analyzing drug combinations in oncology. A Boolean network model, consisting of 79 proteins and 225 connections, was developed for bortezomib and vorinostat interactions in U266 myeloma cells, based on information regarding known apoptotic and cell survival pathways and mechanisms of drug action curated from the literature. A reduction algorithm was applied to identify critical proteins, and the network was qualified by comparing experimental data with dynamic model simulations. Interestingly, the systems network predicted an enhanced efficacy for the drug combination a priori of any experimentation (Chapter 2). Pharmacological studies were conducted in U266 cells for bortezomib and vorinostat exposures as single agents and in combinations that ranged 30 different concentrations across 5 combination regimens. Semi-mechanistic modeling, using a non-competitive interaction model and empirical interaction parameters, suggested a synergistic effect for the sequential combination (bortezomib treatment for 24 hours followed by adding vorinostat for the next 24 hours) and an additive effect for their simultaneous combination for 48 hours. This emphasizes the importance of considering a timed dosing schedule in comparison to the norm of using maximal doses over long durations of exposure (Chapter 3). A signaling model was constructed to bridge in vitro and in vivo vorinostat exposure-response relationships. Firstly, a LC/MS/MS method was developed and validated to measure vorinostat concentrations in cell culture medium to determine its temporal in vitro stability under culture conditions. Interestingly, vorinostat exhibited a biphasic degradation at both tested concentrations (2 and 5 μM), which were modeled with a bi-exponential decay function and subsequently used to drive pharmacodynamic effects (Chapter 4). A unified cellular model incorporating dynamics of p21, p53, BCL-xL, pNFκB, and cleaved PARP was successfully linked to describe in vitro cellular proliferation and in vivo tumor growth inhibition by vorinostat (Chapter 5). This analysis assisted in deriving a basic structural model for U266 pathway dynamics and their associated turnover parameters under vorinostat perturbation. Results from Chapters 2-5 were assimilated to design a quantitative study in which the time-course of all critical proteins (identified from the systems network) was determined simultaneously using the MAGPIX Luminex system (multiplex immunoassay) at efficacious bortezomib and vorinostat concentrations. A systems model was developed with integrated turnover and time-dependent transduction functions that bridged exposure-response relationships for borteozmib and vorinostat as single agents and in two combination regimens with a single set of parameters. The model captured the trends in protein profiles well and was able to quantitatively characterize the biological basis of the sequence-dependent enhanced efficacy of bortezomib and vorinostat without the need for empirical interaction terms. The model was further extended in vivo wherein it effectively captured tumor volume inhibition in xenograft mice for single and combination dosing schedules of bortezomib and vorinostat (Chapter 6). In summary, network based methodologies and traditional pharmacodynamics were successfully integrated and applied to conduct a mechanistic and quantitative evaluation of bortezomib and vorinostat combinations in MM. The final Boolean network can be used to study additional combinations with these agents and to identify potential drug targets in MM. In addition, the final dynamical systems model can be used to explore their in vivo preclinical dosing schedules. This approach provides a translational platform to evaluate and potentially optimize bortezomib/vorinostat combinations with biomarker driven interactions that can eventually be applied to make clinical dose projections. The overall approach may be adopted and generalized to study any drug combination and might aid in making drug development decisions from first principles.