Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue.

Nanoparticle (NP) technology holds significant promise to mediate targeted drug delivery to specific organs in the body. Understanding the 3D biodistribution of NPs in heterogeneous environments such as the tumor tissue can provide crucial information on efficacy, safety and potential clinical outcomes. Here we present a novel end-to-end workflow, VIOLA, which makes use of tissue clearing methodology in conjunction with high resolution imaging and advanced 3D image processing to quantify the spatiotemporal 3D biodistribution of fluorescently labeled ACCURIN® NPs. Specifically, we investigate the spatiotemporal biodistribution of NPs in three different murine tumor models (CT26, EMT6, and KPC-GEM) of increasing complexity and translational relevance. We have developed new endpoints to characterize NP biodistribution at multiple length scales. Our observations reveal that the macroscale NP biodistribution is spatially heterogeneous and exhibits a gradient with relatively high accumulation at the tumor periphery that progressively decreases towards the tumor core in all the tumor models. Microscale analysis revealed that NP extravasation from blood vessels increases in a time dependent manner and plateaus at 72 h post injection. Volumetric analysis and pharmacokinetic modeling of NP biodistribution in the vicinity of the blood vessels revealed that the local NP density exhibits a distance dependent spatiotemporal biodistribution which provide insights into the dynamics of NP extravasation in the tumor tissue. Our data represents a comprehensive analysis of NP biodistribution at multiple length scales in different tumor models providing unique insights into their spatiotemporal dynamics. Specifically, our results show that NPs exhibit a dynamic equilibrium with macroscale heterogeneity combined with microscale homogeneity.

Development of Highly Optimized Antibody-Drug Conjugates against CD33 and CD123 for Acute Myeloid Leukemia.

PURPOSE: Mortality due to acute myeloid leukemia (AML) remains high, and the management of relapsed or refractory AML continues to be therapeutically challenging. The reapproval of Mylotarg, an anti-CD33-calicheamicin antibody-drug conjugate (ADC), has provided a proof of concept for an ADC-based therapeutic for AML. Several other ADCs have since entered clinical development of AML, but have met with limited success. We sought to develop a next-generation ADC for AML with a wide therapeutic index (TI) that overcomes the shortcomings of previous generations of ADCs. EXPERIMENTAL DESIGN: We compared the TI of our novel CD33-targeted ADC platform with other currently available CD33-targeted ADCs in preclinical models of AML. Next, using this next-generation ADC platform, we performed a head-to-head comparison of two attractive AML antigens, CD33 and CD123. RESULTS: Our novel ADC platform offered improved safety and TI when compared with certain currently available ADC platforms in preclinical models of AML. Differentiation between the CD33- and CD123-targeted ADCs was observed in safety studies conducted in cynomolgus monkeys. The CD33-targeted ADC produced severe hematologic toxicity, whereas minimal hematologic toxicity was observed with the CD123-targeted ADC at the same doses and exposures. The improved toxicity profile of an ADC targeting CD123 over CD33 was consistent with the more restricted expression of CD123 in normal tissues. CONCLUSIONS: We optimized all components of ADC design (i.e., leukemia antigen, antibody, and linker-payload) to develop an ADC that has the potential to translate into an effective new therapy against AML.