Mark Penney1, Alberto Ippolito1, Elisa Fevola1, Scott Rata1, Liam Brown1, Pablo Morentin-Gutierrez1, Owen Jones1
1Early Oncology DMPK, AstraZeneca
Introduction/Objectives: T-Cell Engagers (TCEs) represent a rapidly expanding class of therapeutic proteins, primarily in oncology indications with nine now approved for a range of indications with over a hundred more in development. First generation TCEs were small in size, with molecular weights around 50 kDa, thus were subject to rapid renal clearance and required dosing continuous infusion. Future generations aimed at accessing more convenient routes of administration by adding an Fc arm to the molecule, adopting an IgG format or by the addition of a further binding arm to serum albumin. Such revised molecular formats, however, do not necessarily display the same low clearance of long terminal half-lives typical of IgG antibody therapeutics, Fc-fusion proteins or albumin-binding nanobodies ([1-3]). The half-life of an IgG biologic in humans can normally be estimated well from that in the cynomolgus monkey using allometric scaling ([1,4]), with a similar relationship seen for the hFcRn (Tg32) mouse [5]. However, this correlation but also the rank ordering is not conserved for TCEs, with many occurrences of shorter half-lives in humans versus the monkey. In this poster, we present a model for TCE PK that allows for the prediction of the human half-life that is superior to allometric scaling. Methods: The PKPD model consists of three components. First, we have an “intrinsic” clearance, or that not impacted by tumor mediated drug disposition (TMDD), which is still scalable allometrically, and may only be measured at a high dose where target saturation is assured, or more readily from pre-clinical species where the binding is not cross-reactive. The second component is the impact of TMDD via the binding to the activating receptor on T cells and its internalisation. The third is the complementary binding to the TAA(s) and internalisation, which is determined for each of the receptors that the TCE binds to and built into the PK model to derive a fuller projection for the PK of the TCE in patients. Results: The TMDD model for TCEs is applied to simulate the half-life observed in both non-human primates (NHP) and human. For NHP, the model simulates the half-life of 21 different constructs. We report that our predictions mostly lie within a two-fold error from the observed half-life, with only four lie outside of this prediction interval. Additionally, on average the simulations slightly under-predict the half-life with the bias at -16%. These 4 TCEs outside this range are ERY974 (GPC3), tarlatamab (DLL3), AMG199 (MUC17) and AMG160 (PSMA). For simulated clinical PK, the observed versus predicted half-lives for each TCE and dosing scenario (first dose and steady-state) are estimated with our method for our method. Out of 17 different constructs, only two TCEs are not predicted within two-fold margin: these are ERY974 (GPC3) and mosunetuzumab (CD20). Conclusion: Here we propose a model for TCE PK in which the elimination half-life is explained as the combination of the intrinsic clearance of the molecular format and its target-mediated clearances. While TMDD is commonplace for biological molecules and often cited as the likely cause for quicker clearance, we have quantified these phenomena and developed an accurate method for the estimation of the half-life of the TCE in humans as well as the cynomolgus monkey with no requirement for in vivo data. We hope that the method may be applied by others to TCEs in development, with improvements and refinements, and particularly any apparent confounders, published to further the collective knowledge on these exciting and novel therapeutic modalities.
[1] Rong Deng, Suhasini Iyer, Frank-Peter Theil, Deborah L. Mortensen, Paul J. Fielder & Saileta Prabhu (2011) Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data, mAbs, 3:1, 61-66, DOI: 10.4161/mabs.3.1.13799 [2] Population Pharmacokinetics and Exposure-Response Relationshipof Intravenous and Subcutaneous Abataceptin Patients With Rheumatoid Arthritis; Xiaohui Li, Amit Roy and Bindu Murthy; The Journal of Clinical Pharmacology2019, 59(2) 245–257; DOI: 10.1002/jcph.1308 [3] Caplacizumab: First Global Approval; Sean Duggan; Drugs (2018) 78:1639–1642; https://doi.org/10.1007/s40265-018-0989-0 [4] Isidro Hötzel, Frank-Peter Theil, Lisa J. Bernstein, Saileta Prabhu, Rong Deng, Leah Quintana, Jeff Lutman, Renuka Sibia, Pamela Chan, Daniela Bumbaca, Paul Fielder, Paul J. Carter & Robert F. Kelley (2012) A strategy for risk mitigation of antibodies with fast clearance, mAbs, 4:6, 753-760, DOI: 10.4161/mabs.22189 [5] Lindsay B. Avery, Mengmeng Wang, Mania S. Kavosi, Alison Joyce, Jeffrey C. Kurz, Yao-Yun Fan, Martin E. Dowty, Minlei Zhang, Yiqun Zhang, Aili Cheng, Fei Hua, Hannah M. Jones, Hendrik Neubert, Robert J. Polzer & Denise M. O’Hara (2016) Utility of a human FcRn transgenic mouse model in drug discovery for early assessment and prediction of human pharmacokinetics of monoclonal antibodies, mAbs, 8:6, 1064-1078, DOI: 10.1080/19420862.2016.1193660
Reference: PAGE 33 (2025) Abstr 11620 [www.page-meeting.org/?abstract=11620]
Poster: Drug/Disease Modelling - Oncology