2024 - Rome - Italy

PAGE 2024: Drug/Disease Modelling - Oncology
Oleg Demin Jr

Optimizing first-in-human dose for HPN536, a T-Cell Engager Targeting Mesothelin: comparison of MABEL PK-driven approach and mechanistic translational PK/RO/PA modeling

Oleg Demin Jr (1), Galina Kolesova (1), Dmitry Shchelokov (1)

(1) InSysBio CY, Cyprus


Mesothelin (MSLN) is a glycophosphatidylinositol-linked tumor antigen found to be significantly upregulated in various malignancies, including ovarian, pancreatic, and lung cancer. HPN536 is a 53-kDa, trispecific, T-cell-activating protein-based construct, which binds to MSLN-expressing tumor cells, CD3 on T cells, and serum albumin. While the minimal anticipated biological effect level (MABEL) approach is recommended for determining the safe clinical starting dose for T-cell engagers (TCE), it is noteworthy that this method can sometimes yield a low minimal recommended starting dose (MRSD) resulting in treatment of patients with sub-therapeutic doses and multiple dose escalations [1]. The objectives of our study were:

  • To predict the MRSD for HPN536 utilizing a comprehensive approach that integrates mechanistic translational pharmacokinetic (PK), receptor occupancy (RO), and pharmacological activity (PA) modeling.
  • To compare this predicted MRSD with that calculated using the MABEL PK-driven approach.

Methods: In vitro model was developed to describe published data on T-cell dependent cytotoxicity, cytokine secretion (IFNg, TNFa), and T-cell activation (% of CD25+ cells) in the presence of various HPN536 concentrations [2]. This model was used to estimate the EC50 values of PA expressed as numbers of timers of HPN536 bound with CD3 and MSLN in immunological synapse between T-cell and cancer cell.   PK/RO/PA model was developed to fit PK in cynomolgus monkey and translate it to humans using standard allometric scaling exponents without fitting [3]. The model for cancer patients includes the distribution of TCE into the tumor (ovarian cancer was considered) and PA (cytotoxicity, T-cells activation and cytokine secretion) based on EC50 values identified in the in vitro model. The binding of HPN536 with a soluble form of MSLN (sMSLN) in the tumor is described in the model despite its concentration is not known. sMSLN level in the tumor was estimated using murine data and its concentration in the blood. A conventional MABEL PK-driven approach was also used to estimate MRSD. MRSD was predicted for patients without dexamethasone premedication.

Results: PK data in cancer patients were successfully predicted via translation from cynomolgus monkey data. EC50 of cytotoxicity, T-cells activation and cytokine secretion were in the range of 500-900 trimers.  20% - 50% PA range was used to estimate the first-in-human dose. MRSD calculated by MABEL PK-driven approach (1 – 23 ng/kg) and starting dose used in phase 1 clinical trial of HPN536 (6 ng/kg) were lower than the lowest dose (54 ng/kg) resulted in CRS grade 3 without premedication with dexamethasone. MRSD prediction by PK/RO/PA model was 35 – 228 ng/kg. In the scenario where it is assumed that there is no soluble sMSLN present in the tumor, the predicted MRSD was lower, ranging from 18 to 114 ng/kg.

Conclusions: The conventional allometric scaling approach effectively facilitates the translation of PK from cynomolgus monkeys to humans for therapeutic antibody targeting albumin to prolong PK. PK/RO/PA model provided a more accurate prediction of MRSD for HPN536 considering specific features of the candidate and target, as well as the concentration of drug in the site of action and complexity of the mechanism of action (formation of trimers in immunological synapse). The concentration of sMSLN within the tumor significantly influences the prediction of the MRSD. 

[1] Saber et al. An Regul Toxicol Pharmacol. 2017 Nov;90:144–152.
[2] Molloy et al. Clin Cancer Res. 2021 Mar 1;27(5):1452–1462.
[3] Haraya et al. Drug Metab Pharmacokinet. 2017 Aug;32(4):208–217.

Reference: PAGE 32 (2024) Abstr 10911 [www.page-meeting.org/?abstract=10911]
Poster: Drug/Disease Modelling - Oncology