Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells.

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.

Simulations of site-specific target-mediated pharmacokinetic models for guiding the development of bispecific antibodies.

Bispecific antibodies (BAbs) are novel constructs that are under development and show promise as new therapeutic modalities for cancer and autoimmune disorders. The aim of this study is to develop a semi-mechanistic modeling approach to elucidate the disposition of BAbs in plasma and possible sites of action in humans. Here we present two case studies that showcase the use of modeling to guide BAb development. In case one, a BAb is directed against a soluble and a membrane-bound ligand for treating systemic lupus erythematosus, and in case two, a BAb targets two soluble ligands as a potential treatment for ulcerative colitis and asthma. Model simulations revealed important differences between plasma and tissues, when evaluated for drug disposition and target suppression. Target concentrations at tissue sites and type (soluble vs membrane-bound), tissue-site binding, and binding affinity are all major determinants of BAb disposition and subsequently target suppression. For the presented case studies, higher doses and/or frequent dosing regimens are required to achieve 80 % target suppression in site specific tissue (the more relevant matrix) as compared to plasma. Site-specific target-mediated models may serve to guide the selection of first-in-human doses for new BAbs.