preclinical-positron-emission-tomography-with-body-conforming-animal

Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as ...

Pfizer, Inc.

Age-related Macular Degeneration (AMD) is the leading cause of visual impairment and blindness in the elderly in developed countries. Neovascular/exudative (wet) AMD is the aggressive form of AMD and can involve choroidal neovascularization and vascular leakage. Anti-vascular endothelial growth factor (anti-VEGF) medications have significantly improved treatment of wet-AMD. However, only approximately 40% of patients obtain full benefit from anti-VEGF therapy and the medications are given by intravitreal injection. Axitinib, a small molecule multi-receptor tyrosine kinase inhibitor used for the treatment of advanced renal cell carcinoma, is taken orally and inhibits VEGF activity by blocking VEGF receptors. Axitinib also has the advantage of blocking platelet derived growth factor (PDGF) receptors which play a role in neovascularization. Using in vitro human retinal microvascular endothelial cells (HRMVECs), human brain vascular pericytes (HBVRs), 3D co-culture vessel sprout assay, and in vivo laser induced rat choroidal neovascularization (CNV) models, the effect of axitinib on neovascularization was evaluated. Axitinib inhibited neovascularization better than anti-VEGF and/or anti-hPDGF-B mAb in the in vitro models demonstrating that combined inhibition of both VEGF and PDGF pathways may be synergistic in treating wet-AMD. Additionally, axitinib showed good efficacy at a low dose (0.875 mg/day) in laser-induced CNV model in rats. In conclusion our data shows that axitinib, an inhibitor of VEGF and PDGF-B pathways may be useful in ameliorating wet-AMD therapy.

Axitinib inhibits retinal and choroidal neovascularization in in vitro and in vivo models.

Understanding a drug's whole-body biodistribution and tumor targeting can provide important information regarding efficacy, safety, and dosing parameters. Current methods to evaluate biodistribution include in vivo imaging technologies like positron electron tomography and single-photon emission computed tomography or ex vivo quantitation of drug concentrations in tissues using autoradiography and standard biochemical assays. These methods use radioactive compounds or are cumbersome and do not give whole-body information. Here, for the first time, we show the utility of fluorescence molecular tomography (FMT) imaging to determine the biodistribution and targeting of an antibody-drug conjugate (ADC). An anti-5T4-antibody (5T4-Ab) and 5T4-ADC were conjugated with a near-infrared (NIR) fluorophore VivoTag 680XL (VT680). Both conjugated compounds were stable as determined by SEC-HPLC and plasma stability studies. Flow cytometry and fluorescence microscopy studies showed that VT680-conjugated 5T4-ADC specifically bound 5T4-expressing cells in vitro and also exhibited a similar cytotoxicity profile as the unconjugated 5T4-ADC. In vivo biodistribution and tumor targeting in an H1975 subcutaneous xenograft model demonstrated no significant differences between accumulation of VT680-conjugated 5T4-Ab or 5T4-ADC in either normal tissues or tumor. In addition, quantitation of heart signal from FMT imaging showed good correlation with the plasma pharmacokinetic profile suggesting that it (heart FMT imaging) may be a surrogate for plasma drug clearance. These results demonstrate that conjugation of VT680 to 5T4-Ab or 5T4-ADC does not change the behavior of native biologic, and FMT imaging can be a useful tool to understand biodistribution and tumor-targeting kinetics of antibodies, ADCs, and other biologics. Mol Cancer Ther; 15(10); 2530-40. ©2016 AACR.

Biodistribution and Targeting of Anti-5T4 Antibody-Drug Conjugate Using Fluorescence Molecular Tomography.

P-cadherin-LP-DART is a bispecific antibody targeting P-cadherin expressed on the tumor cells and CD3 on the T-cells. Previously we demonstrated the development and efficacy of P-cadherin-LP-DART in  and  models. Here, we evaluated the three pillars: exposure, targeting specificity and pharmacodynamic modulation for P-cadherin-LP-DART using fluorescence molecular tomography (FMT). Bispecific antibodies and T-cells were conjugated with a near-infrared fluorophores: VivoTag680XL (VT680) and CellVueNIR815 (CV815), respectively.  binding and cytotoxic T-lymphocyte assay demonstrated that P-cadherin-LP-DART significantly retained its properties after VT680 conjugation.  FMT imaging was performed to determine the bispecific biodistribution and T-cell trafficking in HCT-116 xenograft model. Peak tumor exposure (2.71%ID) was observed at 96 hr post-injection with measurable quantity even at 240 hr (1.46%ID) (Pillar 1). P-cadherin-LP-DART accumulation in tumor was 20-25 fold higher compared to Control-LP-DART demonstrating the targeting specificity (Pillar 2). Imaging after engraftment of CV815 labeled T-cells showed P-cadherin-LP-DART mediated T-cell trafficking in tumors (Pillar 3). This study harnessed the multichannel capability of FMT and demonstrated the targeting of drug and trafficking of T cells to tumors, simultaneously. Our results show the impact of molecular imaging in demonstrating three pillars of pharmacology, longitudinally and non-invasively.

Molecular imaging reveals biodistribution of P-cadherin LP-DART bispecific and trafficking of adoptively transferred T cells in mouse xenograft model.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

A sensitive and specific imaging biomarker to monitor immune activation and quantify pharmacodynamic responses would be useful for development of immunomodulating anti-cancer agents. PF-07062119 is a T cell engaging bispecific antibody that binds to CD3 and guanylyl cyclase C, a protein that is over-expressed by colorectal cancers. Here, we used Zr-Df-IAB22M2C (Zr-Df-Crefmirlimab), a human CD8-specific minibody to monitor CD8+ T cell infiltration into tumors by positron emission tomography. We investigated the ability of Zr-Df-IAB22M2C to track anti-tumor activity induced by PF-07062119 in a human CRC adoptive transfer mouse model (with injected activated/expanded human T cells), as well as the correlation of tumor radiotracer uptake with CD8+ immunohistochemical staining.

Preclinical Evaluation of Zr-Df-IAB22M2C PET as an Imaging Biomarker for the Development of the GUCY2C-CD3 Bispecific PF-07062119 as a T Cell Engaging Therapy.

αvβ8 integrin, a key activator of transforming growth factor β (TGF-β), inhibits anti-tumor immunity. We show that a potent blocking monoclonal antibody against αvβ8 (ADWA-11) causes growth suppression or complete regression in syngeneic models of squamous cell carcinoma, mammary cancer, colon cancer, and prostate cancer, especially when combined with other immunomodulators or radiotherapy. αvβ8 is expressed at the highest levels in CD4+CD25+ T cells in tumors, and specific deletion of β8 from T cells is as effective as ADWA-11 in suppressing tumor growth. ADWA-11 increases expression of a suite of genes in tumor-infiltrating CD8+ T cells normally inhibited by TGF-β and involved in tumor cell killing, including granzyme B and interferon-γ. The in vitro cytotoxic effect of tumor CD8 T cells is inhibited by CD4+CD25+ cells, and this suppressive effect is blocked by ADWA-11. These findings solidify αvβ8 integrin as a promising target for cancer immunotherapy.

Integrin αvβ8 on T cells suppresses anti-tumor immunity in multiple models and is a promising target for tumor immunotherapy.

IL13Rα2 is a cell surface tumor antigen that is overexpressed in multiple tumor types. Here, we studied biodistribution and targeting potential of an anti-IL13Rα2 antibody (Ab) and anti-tumor activity of anti-IL13Rα2-antibody-drug conjugate (ADC). The anti-IL13Rα2 Ab was labeled with fluorophore AF680 or radioisotope Zr for  tracking using fluorescence molecular tomography (FMT) or positron emission tomography (PET) imaging, respectively. Both imaging modalities showed that the tumor was the major uptake site for anti-IL13Rα2-Ab, with peak uptake of 5-8% ID and 10% ID/g as quantified from FMT and PET, respectively. Pharmacological  competition with excess of unlabeled anti-IL13Rα2-Ab significantly reduced the tumor uptake, indicative of antigen-specific tumor accumulation. Further, FMT imaging demonstrated similar biodistribution and pharmacokinetic profiles of an auristatin-conjugated anti-IL13Rα2-ADC as compared to the parental Ab. Finally, the anti-IL13Rα2-ADC exhibited a dose-dependent anti-tumor effect on A375 xenografts, with 90% complete responders at a dose of 3 mg/kg. Taken together, both FMT and PET showed a favorable biodistribution profile for anti-IL13Rα2-Ab/ADC, along with antigen-specific tumor targeting and excellent therapeutic efficacy in the A375 xenograft model. This work shows the great potential of this anti-IL13Rα2-ADC as a targeted anti-cancer agent.

Targeting and pharmacology of an anti-IL13Rα2 antibody and antibody-drug conjugate in a melanoma xenograft model.

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.

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

KRAS is one of the most commonly mutated oncogenes in lung, colorectal, and pancreatic cancers. Recent clinical trials directly targeting KRAS G12C presented encouraging results for a large population of non-small cell lung cancer (NSCLC), but resistance to treatment is a concern. Continued exploration of new inhibitors and preclinical models is needed to address resistance mechanisms and improve duration of patient responses. To further enable the development of KRAS G12C inhibitors, we present a preclinical framework involving translational, non-invasive imaging modalities (CT and PET) and histopathology in a conventional xenograft model and a novel KRAS G12C knock-in mouse model of NSCLC. We utilized an in-house developed KRAS G12C inhibitor (Compound A) as a tool to demonstrate the value of this framework in studying in vivo pharmacokinetic/pharmacodynamic (PK/PD) relationship and anti-tumor efficacy. We characterized the Kras G12C-driven genetically engineered mouse model (GEMM) and identify tumor growth and signaling differences compared to its Kras G12D-driven counterpart. We also find that Compound A has comparable efficacy to sotorasib in the Kras G12C-driven lung tumors arising in the GEMM, but like observations in the clinic, some tumors inevitably progress on treatment. These findings establish a foundation for evaluating future KRAS G12C inhibitors that is not limited to xenograft studies and can be applied in a translationally relevant mouse model that mirrors human disease progression and resistance.

Efficacy and Imaging-Enabled Pharmacodynamic Profiling of KRAS G12C Inhibitors in Xenograft and Genetically Engineered Mouse Models of Cancer.

Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r=0.98, DSC=0.78) performed within inter-reader variability (r=0.79, DSC=0.69) and close to intra-reader variability (r=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.

Article	Year
Axitinib inhibits retinal and choroidal neovascularization in in vitro and in vivo models. Experimental eye research\| PubMed ID: 26927930	2016
Biodistribution and Targeting of Anti-5T4 Antibody-Drug Conjugate Using Fluorescence Molecular Tomography. Molecular cancer therapeutics\| PubMed ID: 27466353	2016
Molecular imaging reveals biodistribution of P-cadherin LP-DART bispecific and trafficking of adoptively transferred T cells in mouse xenograft model. Oncotarget\| PubMed ID: 32341754	2020
Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography. PloS one\| PubMed ID: 34138905	2021
Preclinical Evaluation of Zr-Df-IAB22M2C PET as an Imaging Biomarker for the Development of the GUCY2C-CD3 Bispecific PF-07062119 as a T Cell Engaging Therapy. Molecular imaging and biology\| PubMed ID: 34143379	2021
Integrin αvβ8 on T cells suppresses anti-tumor immunity in multiple models and is a promising target for tumor immunotherapy. Cell reports\| PubMed ID: 34233193	2021
Targeting and pharmacology of an anti-IL13Rα2 antibody and antibody-drug conjugate in a melanoma xenograft model. mAbs\| PubMed ID: 34347577	2021
Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue. Journal of controlled release : official journal of the Controlled Release Society\| PubMed ID: 36596340	2023
Efficacy and Imaging-Enabled Pharmacodynamic Profiling of KRAS G12C Inhibitors in Xenograft and Genetically Engineered Mouse Models of Cancer. Molecular cancer therapeutics\| PubMed ID: 37186518	2023
Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography. Translational oncology\| PubMed ID: 38128467	2024

Anand Giddabasappa

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