Annual Meeting of the Japanese Society of Toxicology

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The pig has been introduced more than 20 years ago in drug development following attempts of finding a species which shares better homology with human than the dog based on bio-physiological parameters. However, miniaturization, standardized breeding and health status control were required before the pig could find a broader than niche application in pharmaceutical industry. During the years of experience with minipigs in pharmaceutical research and the science evolving rapidly, the selection of a non-rodent animal species for preclinical safety testing became primarily driven by pharmacological (target expression homologous function), pharmacokinetic and bio-physiological considerations. This offered a broad field of application for the minipig, besides the well-established use in dermal projects in all areas of drug development but also in novel approaches including genetically modified animals. In this symposium we look at recent approaches and requirements in the optimal selection of a non-rodent model in pharmaceutical development and critically ask how good of a choice the minipig offers for the scientist, how did the testing environment evolve and what are the key requirements for a broader use of the minipig compared to the other well established non-rodent species like dog or monkey. In the end, recent advances like the Microminipig and genetically-engineered pigs using CRISPR/Cas appear to offer future potential in the field of translational research and drug development.

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We characterized microminipig as an experimental model for evaluating cardiac safety of drugs in comparison with a dog. Microminipig was anesthetized with ketamine (16 mg/kg, i.m.) and xylazine (1.6 mg/kg, i.m.) followed by 1 % halothane inhalation. Pilsicainide (1 mg/kg), verapamil (0.1 mg/kg), E-4031 (0.01 and 0.1 mg/kg), moxifloxacin (0.03, 0.3 and 3 mg/kg), terfenadine (0.03, 0.3 and 3 mg/kg), dipyridamole (0.056 and 0.56 mg/kg), azithromycin (0.3, 3 and 30 mg/kg), oseltamivir (0.3, 3 and 30 mg/kg) and fluvoxamine (0.1, 1 and 10 mg/kg) were intravenously administered over 10 min (n=4 for each treatment). Pilsicainide, E-4031, moxifloxacin and terfenadine prolonged QTc, but its reverse was true for verapamil, azithromycin, oseltamivir and fluvoxamine, whereas no significant change was detected by dipyridamole. Changes in QTc were in good accordance with those in our previous canine studies except for those by verapamil, azithromycin, oseltamivir and fluvoxamine. Moreover, the high dose of fluvoxamine increased the heart rate and mean blood pressure together with skin flush and myoclonus, which was not observed in dogs. The discrepancy in the responses between the Microminipig and dog can be partly explained by the smaller volume of distribution of a drug in Microminipig; thus, the high dose of fluvoxamine may have induced serotonin syndrome. These observations may help apply this new animal for safety pharmacology study.

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‘Percellome’ database is a unique source of toxicity response data for more than 100 chemicals obtained on various organs of mice (Kanno group, NIHS). Originally developed “per cell” readout in mRNA copy method with a high precision in measuring gene expression permits to include each molecule to the network analysis, treating cell as an Open System.

We have developed two software tools: AGCT (A Geometric Clustering Tool) and SHOE (Sequence HOmology in higher Eukaryotes) promoter analyzer suitable for ‘Percellome’ data. AGCT analyses large-scale (40,000ⁿ probes) time-series data with spectral clustering manifold that represents and visualize complex data in low dimension.

Subsequently low dimension manifold is clustered with novel techniques such as Bregman K-means, Affinity Propagation (AP), Non-Negative Matrix Factorization (NNMF), Expecation Maximization and others to reach the finite number of clusters with similar expression profile. Those clusters are hereafter analyzed on SHOE, which aims to predict transcription factors network basing on phylogenetic footprinting of three genomes: human, mouse and rat.

Since AGCT and SHOE are connected to Garuda platform (Systems Biology Institute), their outputs can be automatically passed to other tools on Garuda platform for further analysis or visualization.

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