Assessment of near-infrared fluorophores to study the biodistribution and tumor targeting of an IL13 receptor α2 antibody by fluorescence molecular tomography.

Non-invasive imaging using radiolabels is a common technique used to study the biodistribution of biologics. Due to the limited shelf-life of radiolabels and the requirements of specialized labs, non-invasive optical imaging is an attractive alternative for preclinical studies. Previously, we demonstrated the utility of fluorescence molecular tomography (FMT) an optical imaging modality in evaluating the biodistribution of antibody-drug conjugates. As FMT is a relatively new technology, few fluorophores have been validated for in vivo imaging. The goal of this study was to characterize and determine the utility of near-infrared (NIR) fluorophores for biodistribution studies using interleukin-13 receptor subunit alpha-2 antibody (IL13Rα2-Ab). Eight fluorophores (ex/em: 630/800 nm) with an N-hydroxysuccinimide (NHS) linker were evaluated for Ab conjugation. The resulting antibody-fluorophore (Ab-F) conjugates were evaluated in vitro for degree of conjugation, stability and target-binding, followed by in vivo/ex vivo FMT imaging to determine biodistribution in a xenograft model. The Ab-F conjugates (except Ab-DyLight800) showed good in vitro stability and antigen binding. All Ab-F conjugates (except for Ab-BOD630) resulted in a quantifiable signal in vivo and had similar biodistribution profiles, with peak tumor accumulation between 6 and 24 h post-injection. In vivo/ex vivo FMT imaging showed 17-34% ID/g Ab uptake by the tumor at 96 h. Overall, this is the first study to characterize the biodistribution of an Ab using eight NIR fluorophores. Our results show that 3-dimensional optical imaging is a valuable technology to understand biodistribution and targeting, but a careful selection of the fluorophore for each Ab is warranted.

Feasibility of Singlet Analysis for Ligand Binding Assays: a Retrospective Examination of Data Generated Using the Gyrolab Platform.

There are many sources of analytical variability in ligand binding assays (LBA). One strategy to reduce variability has been duplicate analyses. With recent advances in LBA technologies, it is conceivable that singlet analysis is possible. We retrospectively evaluated singlet analysis using Gyrolab data. Relative precision of duplicates compared to singlets was evaluated using 60 datasets from toxicokinetic (TK) or pharmacokinetic (PK) studies which contained over 23,000 replicate pairs composed of standards, quality control (QC), and animal samples measured with 23 different bioanalytical assays. The comparison was first done with standard curve and QCs followed by PK parameters (i.e., Cmax and AUC). Statistical analyses were performed on combined duplicate versus singlets using a concordance correlation coefficient (CCC), a measurement used to assess agreement. Variance component analyses were conducted on PK estimates to assess the relative analytical and biological variability. Overall, 97.5% of replicate pairs had a %CV of <11% and 50% of the results had a %CV of ≤1.38%. There was no observable bias in concentration comparing the first replicate with the second (CCC of 0.99746 and accuracy value of 1). The comparison of AUC and Cmax showed no observable difference between singlet and duplicate (CCC for AUC and Cmax >0.99999). Analysis of variance indicated an AUC inter-subject variability 35.3-fold greater than replicate variability and 8.5-fold greater for Cmax. Running replicates from the same sample will not significantly reduce variation or change PK parameters. These analyses indicated the majority of variance was inter-subject and supported the use of a singlet strategy.

A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate.

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.