Abstract 678: Drug-induced TMDD: a novel class of PK models relevant to Immune-stimulating therapies
Authors: Fei Hua, David Flowers, Daniel C. Kirouac, John M. Burke, Joshua F. Apgar
Journal: Cancer Research (2019)

Abstract Purpose: Many biologics exhibit target-mediated drug disposition (TMDD). That is, given the high affinity and low doses typical for antibody-based therapeutics, drug clearance is often mediated through target binding and endocytosis, in addition to 1st-order elimination processes. Accounting for the process of TMDD in pharmacokinetic (PK) models is thus necessary to describe and predict drug exposure. TMDD is typically characterized by dose-dependent clearance, wherein drug elimination is faster at lower doses. In standard TMDD-models, target concentration and turnover are assumed to be constant. While this is generally a good approximation, the assumption does not always hold. Immune-stimulating drugs are the largest class of agents currently being developed in oncology, including checkpoint inhibitors (PD-1/PD-L1, CTLA4), cytokines (IL-2, IL-15), and immune agonists (GITR, OX40, ICOS). For immune-stimulating agents, drug treatment may induce lymphocyte proliferation, expanding the pool of target along with the target-expressing cells. TMDD in such cases is thus induced by drug treatment, and thereby target burden is dynamic; both dose- and time- dependent. PK dose response profiles in such cases could exhibit very different characteristics from standard (static) TMDD. Methods: The goal of this work is to invest the role of target dynamics on drug pharmacokinetics (PK) and receptor occupancy (RO). To do so, we have developed a mathematical modelling construct termed ‘Drug-induced TMDD’, wherein drug-target binding induces increase of the target synthesis rate, subsequently target concentration, leading to enhanced TMDD. We incorporate this construct into a 2-compartment model typical of monoclonal antibodies. Simulations were then used to characterize how dose, drug-target affinity, and the amplitude and the delay in target up-regulation affect the PK and RO profiles, and compared static vs. drug-induced TMDD behavior. Results: For Drug-induced TMDD, target expression increases with increased target engagement. This creates a pseudo-negative-feedback circuit, wherein the rate of TMDD-mediated clearance is dependent upon drug concentration. As a result, drug clearance does not appear dose-proportional at low doses, as is typical of static-TMDD. The AUC of drug concentration for drug-induced TMDD can be higher than static TMDD at low doses, but lower at higher doses. Similar results are predicted for the AUC of RO as well with the nominal parameter set. Increasing target affinity (decreasing KD) however results in faster clearance, typical of both cases of TMDD. Conclusions: Dynamic-TMDD could lead to different dose-dependent PK and RO behaviors from a static TMDD. Based on these characteristic profiles presented, one can determine whether a static vs. drug-induced TMDD model may be required for PK modelling, particularly important consideration for immune-stimulating biologics. Citation Format: Fei Hua, David Flowers, Daniel C. Kirouac, John M. Burke, Joshua F. Apgar. Drug-induced TMDD: a novel class of PK models relevant to Immune-stimulating therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 678.