Zebrafish animal models lend themselves to behavioral assays that can facilitate rapid screening of ototoxic, otoprotective, and otoregenerative drugs. Structurally similar to human inner ear hair cells, the mechanosensory hair cells on their lateral line allow the zebrafish to sense water flow and orient head-to-current in a behavior called rheotaxis. This rheotaxis behavior deteriorates in a dose-dependent manner with increased exposure to the ototoxin cisplatin, thereby establishing itself as an excellent biomarker for anatomic damage to lateral line hair cells. Building on work by our group and others, we have built a new, fully automated high-throughput behavioral assay system that uses automated image analysis techniques to quantify rheotaxis behavior. This novel system consists of a custom-designed swimming apparatus and imaging system consisting of network-controlled Raspberry Pi microcomputers capturing infrared video. Automated analysis techniques detect individual zebrafish, compute their orientation, and quantify the rheotaxis behavior of a zebrafish test population, producing a powerful, high-throughput behavioral assay. Using our fully automated biological assay to test a standardized ototoxic dose of cisplatin against varying doses of compounds that protect or regenerate hair cells may facilitate rapid translation of candidate drugs into preclinical mammalian models of hearing loss.

Zebrafish have emerged as a powerful biological system for drug development against hearing loss. Zebrafish hair cells, contained within neuromasts along the lateral line, can be damaged with exposure to ototoxins, and therefore, pre-exposure to potentially otoprotective compounds can be a means of identifying promising new drug candidates. Unfortunately, anatomical assays of hair cell damage are typically low-throughput and labor intensive, requiring trained experts to manually score hair cell damage in fluorescence or confocal images. To enhance throughput and consistency, our group has developed an automated damage-scoring algorithm based on machine-learning techniques that produce accurate damage scores, eliminate potential operator bias, provide more fidelity in determining damage scores that are between two levels, and deliver consistent results in a fraction of the time required for manual analysis. The system has been validated against trained experts using linear regression, hypothesis testing, and the Pearson's correlation coefficient. Furthermore, performance has been quantified by measuring mean absolute error for each image and the time taken to automatically compute damage scores. Coupling automated analysis of zebrafish hair cell damage to behavioral assays for ototoxicity produces a novel drug discovery platform for rapid translation of candidate drugs into preclinical mammalian models of hearing loss.

the vascular disrupting agent crolibulin binds to the colchicine binding site and produces antivascular and apoptotic effects. In a multisite phase 1 clinical study of crolibulin (NCT00423410), we measured treatment-induced changes in tumor perfusion and water diffusivity (ADC) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI), and computed correlates of crolibulin pharmacokinetics. 11 subjects with advanced solid tumors were imaged by MRI at baseline and 2-3 days post-crolibulin (13-24 mg/m 2). ADC maps were computed from DW-MRI. Pre-contrast T 1 maps were computed, co-registered with the DCE-MRI series, and maps of area-under-thegadolinium-concentration-curve-at-90 s (AUC 90s) and the Extended Tofts Model parameters k trans , v e , and v p were calculated. There was a strong correlation between higher plasma drug C max and a linear combination of (1) reduction in tumor fraction with AUC 90s > 15.8 mM s, and, (2) increase in tumor fraction with v e < 0.3. A higher plasma drug AUC was correlated with a linear combination of (1) increase in tumor fraction with ADC < 1.1 × 10 −3 mm 2 /s , and, (2) increase in tumor fraction with v e < 0.3. These findings are suggestive of cell swelling and decreased tumor perfusion 2-3 days post-treatment with crolibulin. The multivariable linear regression models reported here can inform crolibulin dosing in future clinical studies of crolibulin combined with cytotoxic or immune-oncology agents. Tumor vasculature differs fundamentally from normal blood vessels, presenting opportunities for selective targeting that have led to two main categories of therapeutics: antiangiogenic agents designed to prevent neovascularization, and Vascular Disrupting Agents (VDAs) that target endothelial cells and pericytes of established tumor vasculature and induce vascular collapse 1,2. Efforts in the former category have been more successful, with FDA approval being granted to bevacizumab, sunitinib, sorafenib, lenvatinib, and multiple other antiangiogenic agents. VDAs that have entered clinical testing as anti-cancer therapeutics include, combretastatin A4 phosphate 3 , ZD6126 4 , ombrabulin 5,6 , plinabulin 7 , and crolibulin 8,9. Clinical development of VDAs has been hampered by non-availability of effective biomarkers to identify an Optimal Biological Dose (OBD) rather than the Maximum Tolerated Dose (MTD) 10,11. The choice of companion diagnostic depends on the mode of drug action. For example, agents targeted to genetic alterations can be guided by assays of the specific molecular aberration or frequency of target presence in a given patient's tumor 12 , while nanoparticle drug penetration into solid tumors may be predicted by imaging biomarkers such as ferumoxytol-enhanced MRI 13 .

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