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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]

The dispersion model (DM) is a stochastic model describing the distribution of blood-borne substances within organ vascular beds. It is based on assumptions of concurrent convective and random-walk (pseudodiffusive) movements in the direction of flow, and is characterized by the mean transit time $$\left( {\overline {\text{t}} } \right)$$ and the dispersion number (inverse Peclet number), DN. The model is used with either closed (reflective) boundary conditions at the inflow and the outflow point (Danckwerts conditions) or a closed condition at the inflow and an open (transparent) condition at the outflow (mixed conditions). The appropriateness of DM was assessed with outflow data from single-pass perfused rat liver multiple indicator dilution (MID) experiments, with varying lengths of the inflow and outflow catheters. The studies were performed by injection of bolus doses of 51Cr-labeled red blood cells (vascular indicator),125I-labeled albumin and [14C] sucrose (interstitual indicators), and [3H]2O (whole tissue indicator) into the portal vein at a perfusion rate of 12 ml/min. The outflow profiles based on the DM were convolved with the transport function of the catheters, then fitted to the data. A fairly good fit was obtained for most of the MID curve, with the exception of the late-in-time data (prolonged tail) beyond $$3 \times \overline {\text{t}}$$ . The fitted DNs were found to differ among the indicators, and not with the length of the inflow and outflow catheters. But the differences disappeared when a delay parameter, t0=4.1 ± 0.7 sec $$\left( {\overline x \pm SD} \right)$$ , was included as an additional fitted parameter for all of the indicators except water. Using the short catheters, the average DNfor the model with delay was 0.31 ± 0.13 for closed and 0.22 ± 0.07 for mixed boundary conditions, for all reference indicators. Mean transit times and the variances of the fitted distributions were always smaller than the experimental ones (on average, by 6.8 ± 3.7% and 58 ± 19%, respectively). In conclusion, the DM is a reasonable descriptor of dispersion for the early-in-time data and not the late-in-time data. The existence of a common DN for all non-eliminated reference indicators suggests that intrahepatic dispersion depends only on the geometry of the vasculature rather than the diffusional processes. The role of the nonsinusoidal (“large”) vessels can be partly represented by a simple delay. [ABSTRACT FROM AUTHOR]