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Purpose: Τhis study aimed to optimize the 89Zr-radiolabelling of bintrafusp alfa investigational drug product and controls, and perform the in vitro and in vivo characterization of 89Zr-Df-bintrafusp alfa and 89Zr-Df-control radioconjugates. Methods: Bintrafusp alfa (anti-PD-L1 human IgG1 antibody fused to TGF-β receptor II (TGF-βRII), avelumab (anti-PD-L1 human IgG1 control antibody), isotype control (mutated inactive anti-PD-L1 IgG1 control antibody), and trap control (mutated inactive anti-PD-L1 human IgG1 fused to active TGF-βRII) were chelated with p-isothiocyanatobenzyl-desferrioxamine (Df). After radiolabelling with zirconium-89 (89Zr), radioconjugates were assessed for radiochemical purity, immunoreactivity, antigen binding affinity, and serum stability in vitro. In vivo biodistribution and imaging studies were performed with PET/CT to identify and quantitate 89Zr-Df-bintrafusp alfa tumour uptake in a PD-L1/TGF-β-positive murine breast cancer model (EMT-6). Specificity of 89Zr-Df-bintrafusp alfa was assessed via a combined biodistribution and imaging experiment in the presence of competing cold bintrafusp alfa (1 mg/kg). Results: Nanomolar affinities for PD-L1 were achieved with 89Zr-Df-bintrafusp alfa and 89Zr-avelumab. Biodistribution and imaging studies in PD-L1- and TGF-β-positive EMT-6 tumour-bearing BALB/c mice demonstrated the biologic similarity of 89Zr-Df-bintrafusp alfa and 89Zr-avelumab indicating the in vivo distribution pattern of bintrafusp alfa is driven by its PD-L1 binding arm. Competition study with 1 mg of unlabelled bintrafusp alfa or avelumab co-administered with trace dose of 89Zr-labelled bintrafusp alfa demonstrated the impact of dose and specificity of PD-L1 targeting in vivo. Conclusion: Molecular imaging of 89Zr-Df-bintrafusp alfa biodistribution was achievable and allows non-invasive quantitation of tumour uptake of 89Zr-Df-bintrafusp alfa, suitable for use in bioimaging clinical trials in cancer patients. [ABSTRACT FROM AUTHOR]

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies., (© 2020 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)