2SHQLQJUHPDUNV‫؟‬6KLJHWVXJX+DWDNH\DPD 'HDQRI)DFXOW\RI0HGLFLQH  㻌ٛ6HVVLRQٜ    &KDLU‫؟‬7HWVX\D7DNHWVXJX‫ق‬,&5H''‫ك‬㻌‫؞‬6DWRVKL0DHGD‫ق‬,&5H''‫ك‬㻌
n$UWLILFLDOIRUFH LQGXFHG UHDFWLRQ PHWKRG $ FRPSXWDWLRQDO DSSURDFK IRU H[SORULQJFKHPLFDOUHDFWLRQVEDVHGRQTXDQWXPFKHPLFDOFDOFXODWLRQV|  㻌‫؞‬7RPRKLUR2QRGHUD‫)ق‬DFXOW\RI0HGLFLQH‫ ك‬㻌n&OLQLFDO GHYHORSPHQW DQG FRPPHUFLDOL]DWLRQ RI XOWUDSXULILHG DOJLQDWH 83$/  JHOWUDQVSODQWDWLRQ  IRUDUWLFXODUFDUWLODJHLQMXU\|㻌‫؞‬+DMLPH,WR‫ق‬,&5H''‫ك‬㻌
n&KHPLVWU\IRUVHQVLQJDQGXWLOL]LQJPHFKDQLFDOIRUFH|㻌
ٛ6HVVLRQٜ    &KDLU‫؟‬0DVXPL7VXGD‫ق‬,&5H'')DFXOW\RI0HGLFLQH‫ك‬㻌‫؞‬-LDQ3LQJ*RQJ‫ق‬,&5H''‫ك‬㻌
n&KDOOHQJHVDQGRSSRUWXQLWLHVRIK\GURJHOUHVHDUFKLQ,&5H''|㻌‫؞‬6KLQ\D7DQDND‫ق‬,&5H'')DFXOW\RI0HGLFLQH‫ك‬㻌
n$SSOLFDWLRQRIPXOWLIXQFWLRQDOK\GURJHOIRUELRPHGLFDOUHVHDUFK|㻌‫؞‬7DPLNL.RPDWVX]DNL ,&5H''‫ك‬㻌
n$FFHOHUDWLRQ RI PHDVXUHPHQWV SUHVHUYLQJ GLDJQRVLV DFFXUDF\ E\ UHLQIRUFHPHQWOHDUQLQJ5DPDQLPDJLQJDQGPHGLFDODSSOLFDWLRQV|㻌,QWHUPLVVLRQ 
㻌

㻌







㻌
ٛ6HVVLRQٜ    &KDLU‫<؟‬DVXKLGH,QRNXPD ,&5H''‫ك‬㻌‫<؞‬DVXFKLND+DVHJDZD‫ق‬,&5H''‫ ك‬㻌
n&DQFHULPDJLQJV\VWHPXVLQJOXPLQHVFHQWODQWKDQLGHFRPSOH[|㻌‫؞‬.HQML+LUDWD‫)ق‬DFXOW\RI0HGLFLQH‫ك‬㻌
n$UWLILFLDOLQWHOOLJHQFHLQSRVLWURQHPLVVLRQWRPRJUDSK\|㻌‫<؞‬DVXKLUR+LGD‫ق‬+RNNDLGR8QLYHUVLW\+RVSLWDO‫    ك‬㻌n([SDQGLQJWKHDSSOLFDWLRQRIQHDULQIUDUHGSKRWRLPPXQRWKHUDS\RIFDQFHU|㻌
ٛ6HVVLRQٜ    &KDLU0DVDPLFKL,PDMR ,&5H'' 㻌‫؞‬.RML7DQLJXFKL‫)ق‬DFXOW\RI0HGLFLQH‫ ك‬㻌n,QIODPPDWLRQLQGXFHGUHJHQHUDWLRQDQGFDQFHU|㻌‫؞‬,FKLJDNX7DNLJDZD‫ق‬,&5H''‫ك‬㻌
n0DFKLQHOHDUQLQJIRUPROHFXOHV |㻌‫؞‬7DNDVXNH)XNXKDUD‫)ق‬DFXOW\RI0HGLFLQH‫ك‬㻌n(VWDEOLVKPHQWRIDVLPSOHKLJKVSHHGUHYHUVHJHQHWLFVV\VWHPIRU6$56&R9|㻌&ORVLQJUHPDUNV‫؟‬6DWRVKL0DHGD 'LUHFWRURI,&5H'' 㻌


















$EVWUDFWV















Artificial force induced reaction method: A computational approach for
exploring chemical reactions based on quantum chemical calculations
Satoshi Maeda
Professor
Affiliation:

ICReDD and Department of Chemistry,
Faculty of Science, Hokkaido University

e-mail:Specialty:

smaeda@eis.hokudai.ac.jp
Theoretical Chemistry

ABSTRACTThe motion of atoms during a chemical reaction, called reaction path hereafter, can in principle beelucidated by repeatedly performing quantum chemical calculations at all energetically feasible atomic
configurations. However, the number of configurations that can possibly be involved in a reaction pathcan be huge. Previous studies have thus relied on assumptions (human inputs) concerning the atomic
configurations along the targetted reaction path.
The human inputs may bias the results. To avoid that, we have developed an automated reaction
path search method called artificial force induced reaction (AFIR) [1]. AFIR explores possible
reaction paths automatically by inducing geometrical transformations in a molecule systematicallyusing a virtual force. Combining it with a chemical kinetics method called rate constant matrix
contraction (RCMC), on-the-fly kinetics simulation can be performed (FIG. 1).

FIG 1. A schematic illustrating on-the-fly kinetics simulation by AFIR and RCMC. Based on the input, i.e.,
reactants, reaction temperature, reaction time, and computational level, AFIR generates many reaction paths.
RCMC identifies the most feasible reaction path from the network of reaction paths.

Recently, we proposed a concept, quantum chemistry-aided retrosynthetic analysis (QCaRA) [2].
QCaRA predicts reaction paths affording a given product by an inverse reaction path search from theproduct toward various reactant candidates using AFIR. In a proof-of-concept study, we set
difluoroglycine as the synthetic target. Then, a new synthetic route of producing a difluoroglycine
derivative was proposed as the reverse process of one of the obtained reaction paths.
REF.
1. Maeda, S.; Harabuchi, Y. WIREs Comput. Mol. Sci. 2021, 11, e1538.
2. Mita, T.; Harabuchi, Y.; Maeda, S. Chem. Sci 2020, 11, 7569.

Clinical development and commercialization of ultrapurified alginate(UPAL) gel transplantation for articular cartilage injury
Tomohiro Onodera
Lecturer
Affiliation:

Department of Orthopaedic Surgery,
Hokkaido University Hospitale-mail:Specialty:

tomozou@med.hokudai.ac.jp
Orthopaedics

ABSTRACT
Articular cartilage has limited healing capacity. Once injured, it cannot be repaired, typically leading
to progressive joint damage and cannot be restored predictably by conventional treatments. We havedeveloped an injectable ultra-purified alginate gel (UPAL gel) as a scaffolding material for cartilage
tissue engineering (1, 2). The UPAL gel transplantation, with a simple and easy handling procedure,
could provide a 1-step, minimally invasive, cost-effective cartilage tissue reparative medicine without
harvesting donor cells. Since UPAL gel is safe and has a biological effect beneficial on chondrogenesis,
it was applied for clinical application of cartilage regeneration. Subsequently, we have conducted apreclinical study using a large animal, the beagle dog, to demonstrate the cartilage regeneration effect(3, 4). Since the implantation of UPAL) gl enhances cartilage repair in small and large animal models,
this material is expected to improve the efficacy of the current treatment strategies for cartilage lesions
in human. The first-in-human trial was performed on 12 patients (12 knees) with symptomatic, posttraumatic, full-thickness cartilage lesions. This trial provided evidence for the safety and efficacy ofan acellular UPAL gel transplantation to facilitate cartilage repair.
Our current aim is to the proof of the efficacy and safety of UPAL gel transplantation into articularcartilage defects in human knee joints affected by osteochondral lesionsas the final step tocommercialization. At this symposium, I will introduce the process of development andcommercialization of this new scaffold, UPAL gel, for more than 10 years.
REF.
1. Igarashi T, Iwasaki N, Kasahara Y, Minami A.: A cellular implantation system using an injectable ultra-purified
alginate gel for repair of osteochondral defects in a rabbit model. J Biomed Mater Res A. 2010 Sep 1;94(3):84455. doi: 10.1002/jbm.a.32762.
2. Sukegawa A, Iwasaki N, Kasahara Y, Onodera T, Igarashi T, Minami A. Repair of rabbit osteochondral defects
by an acellular technique with an ultrapurified alginate gel containing stromal cell-derived factor-1. Tissue Eng
Part A. 2012 May;18(9-10):934-45. doi: 10.1089/ten.TEA.2011.0380.
3. Baba R, Onodera T, Matsuoka M, Hontani K, Joutoku Z, Matsubara S, Homan K, Iwasaki N.: Bone Marrow
Stimulation Technique Augmented by an Ultrapurified Alginate Gel Enhances Cartilage Repair in a Canine Model.
Am J Sports Med. 2018 Jul;46(8):1970-1979. doi: 10.1177/0363546518770436. Epub 2018 May 15.
4. Onodera T, Baba R, Kasahara Y, Tsuda T, Iwasaki N.: Therapeutic effects and adaptive limits of an acellular
technique by ultrapurified alginate (UPAL) gel implantation in canine osteochondral defect models. Regen Ther.
2020 Feb 20;14:154-159. doi: 10.1016/j.reth.2020.01.001. eCollection 2020 Jun., PMID: 32110684

Chemistry for sensing and utilizing mechanical force
Hajime Ito
Professor, Vice director of WPI-ICReDD
Affiliation:

WPI-ICReDD and Department of Applied Chemistry
Faculty of Engineering, Hokkaido University

e-mail:Specialty:

hajito@eng.hokudai.ac.jp
Organic Chemistry, Catalysis Development

ABSTRACTHeat and light have long been recognized as effective ways to promote chemical reactions. On theother hand, mechanical stimulation as a method to promote chemical reactions has not received
much attention for a long time. Considering the phenomena of life, even though the sensing of
mechanical stimuli and their conversion into chemical reactions and the conversion of chemical
reactions into forces have been done extensively, little has been done to understand this chemical
reaction mechanism and to construct artificial systems. The discovery of the luminescentmechanochromism phenomenon in gold isocyanide complexes has led us to study crystals whose
structure and luminescence are changed by mechanical stimuli1. We have also succeeded indeveloping new chemical synthesis methods utilizing mechanical stimuli, and have developed
completely new technologies for enviromental benign chemical synthesis, such as solid-state SuzukiMiyaura coupling reactions and mechanoredox reactions2,3. In this talk, I will give an overview ofthese studies.

REF.
1. Ito, H.; Saito, T.; Oshima, N.; Kitamura, N.; Ishizaka, S.; Hinatsu, Y.; Wakeshima, M.; Kato, M.; Tsuge, K.;
Sawamura, M. J. Am. Chem. Soc. 130, 10044 – 10045, 2008.
2. Kubota, K.; Seo, T.; Koide, K.; Hasegawa, Y.; Ito, H. Nature Commun. 10, 111, 2019.
3. Kubota, K.*; Pang, Y.; Miura, A.; Ito, H.* Science, 366, 1500 – 1504, 2019

Challenges and Opportunities of Hydrogel Research in ICReDD
Jian Ping Gong
Professor
Affiliation:

ICReDD and Laboratory of Soft & Wet Matter
Faculty of Advanced Life Science, Hokkaido University

e-mail:Specialty:

gong@sci.hokudai.ac.jp
Polymer Science, Soft Matter

ABSTRACTA polymer gel, in general, is defined as a three-dimensional polymer network containing a large
amount of solvent. Hydrogel refers to gel with water solvent. Hydrogels and biological tissues havesome common features such as softness, permeability to small molecules, and are both belong to soft
matter. Biological tissues possess elaborated structures and exhibit outstanding functions. On theother hand, hydrogels are usually amorphous in structure and have poor functions. Especially,
conventional hydrogels have very weak mechanical strength, which limits the use of the material for
applications. For a long time, the insoluble gels have been considered as side reaction products inchemical reaction. We have developed tough double network hydrogels and established a generalprinciple for designing high strength and toughness hydrogels and soft materials. In recent years,
various gels with robust functionalities along with high mechanical strength have been developed byintroducing structures into the gels, which widely extends their applications in diverse fields,
including soft actuators, biological sensors, and biomaterials. In this symposium, after a brief
introduction of hydrogels, I will discuss challenges of hydrogels and the opportunities of thecollaborative research among experimental science, computational science and information science in
ICReDD.

REF.
1. Kunpeng Cui, Jian Ping Gong, "Aggregated Structures and Their Functionalities in Hydrogels", Aggregate, 2(2),e33 (2021).
2. Hailong Fan, Jian Ping Gong, "Fabrication of Bioinspired Hydrogels: Challenges and Opportunities",
Macromolecules, 53(8), 2769-2782 (2020).

Application of multi-functional hydrogel for biomedical research
Shinya Tanaka
Professor
Affiliation:

ICReDD and Department of Cancer Pathology,
Faculty of Medicne, Hokkaido University

e-mail:Specialty:

tanaka@med.hokudai.ac.jp
Medicine, Pathology

ABSTRACTAs one of the flagship projects in ICReDD, we are involving in the biomedical research using multifunctional polymer hydrogel collaborated with the information science. Polymer hydrogels mimic
microenvironment and is expected to be utilized to investigate various biological reactions forapplications in advanced medical care including cancer diagnosis/treatment and regenerative
medicine. For cancer research, we utilized double network (DN) hydrogel composed of poly-(2acrylamido-2-methylpropanesulfonic acid) (PAMPS) and poly-(N,N’-dimethyl acrylamide)(PDMAAm), which possesses physical feature as soft and tough1. On DN gel, cancer cells rapidly
formed spherical structures within 24 hours that expressed stemness markers including Sox2, Oct3/4,
and Nanog. In this process DN gels can rapidly modulate cellular gene expression and facilitate thereprogramming of differentiated cancer cells towards cancer stem cells (CSCs), in a process termed
HARP (hydrogel-activated reprogramming) phenomenon2. In ICReDD, molecular mechanism of
HARP phenomenon is further analyzed3 and collaborative with information science, we hope that
DN gel will contribute to discovery of therapeutic reagents. In addition, applicability of the hydrogelis widening and currently, cartilagous3 and neuronal tissue engineering have been achieved by using
charged hydrogel. Furthermore, hydrogel may be beneficial for studies of organoids, iPS, andembryonic stem cells. In this symposium, I will introduce the merit of the collaboration research in
ICReDD.

REF.
1. Gong, JP, Science, 344, 161-162, 2014.
2. Suzuka J, Tsuda M, Wang L, Kohsaka S, Kishida K, Semba S, Sugino H, Aburatani S, Frauenlob M, Kurokawa
T, Kojima S, Ueno T, Ohmiya Y, Mano H, Yasuda K, Gong J P, and Tanaka S. Rapid reprogramming of tumour
cells into cancer stem cells on double-network hydrogels. Nat Biomed Eng, DOI: 10.1038/s41551-021-00692-2,
2021.
3. Frauenlob M, King D R, Guo H, Ishihara S, Tsuda M, Kurokawa T, Haga H, Tanaka S, and Gong J P.
Modulation and characterization of the double network hydrogel surface-bulk transition. Macromolecules, 52,
6704-6713, 2019.
4. Semba S, Kitamura N, Tsuda M, Goto K, Kurono S, Ohmiya Y, Kurokawa T, Gong J P, Yasuda K, and Tanaka S.
Synthetic PAMPS gel induces chondrogenic differentiation of ATDC5 cells via a novel protein reservoir function. J
Biomed Mater Res A, 109, 354-364, 2021.

Acceleration of Measurements Preserving Diagnosis Accuracy byReinforcement Learning: Raman Imaging and Medical Applications
Tamiki Komatsuzaki
Professor
Affiliation:e-mail:Specialty:

ICReDD and Research Center of Mathematics for Social
Creativity, Research Institute for Electronic Science (RIES),
Hokkaido University
tamiki@es.hokudai.ac.jp
Mathematical and Information Science, Chemical andBiological Physics

ABSTRACTIn this talk, we present our recent study combined multi-arm Bandits algorithm in reinforcement
learning, utilized in the Monte Carlo tree search in alpha-GO, with spontaneous Raman
measurements for the acceleration of the experiments by designing and generating optimal
experimental conditions “on the fly” during the measurements while keeping the accuracy of
diagnosis, andLQVLWX Raman imaging analysis in which single cell Raman spectra reveal the
distinctive chemical features between differentiated cancer cells cultivated under the existence ofheterogeneous PAMPS gels background and those on quartz.
In brief, multi-arm Bandits algorithm [1] enables us to analytically evaluate the upper and lower
confidence bounds of the quantity to be quantified such as cancer index before the full distribution
will be acquired, and then the algorithm feedbacks the desired experimental conditions to accelerate
the diagnosis during the measurement to the Raman microscope.:e present our simulation using
Raman images in the diagnosis of follicular thyroid carcinoma, and show that this protocol canaccelerate more than 5,000-20,000 times in speedy and accurate diagnoses faster than the standard
point-scanned Raman measurement that requires the full detailed scanning over all pixels (If timeallows, we will also show our developed on-the-fly Raman microscope using programmable spatial
light modulator combined with our Bandits algorithm).
In ICReDD we have been collaborating with Prof. Gong and Prof. Tanaka on the acceleration of drug
screening designed to eradicate cancer stem cells (CSCs) selectively by using multi-arm Bandits
algorithm and LQVLWX single cell Raman imaging under the existence of PAMPS hydrogels for CSCs[2]. We briefly present our preliminary result on the LQVLWX single cell Raman imaging analysis basedon 2D Bayesian optimization for PRXVHP\REODVW cells (C2C12 SS18 SSX) cultivated on PAMPS gel
and quartz dish.
REF.
1. K. Tabata, A. Nakamura, J. Honda, T. Komatsuzaki, A bad arm existence checking problem: How to utilize
asymmetric problem structure? Machine Learning, 109, 327-372 (2020)
2. Suzuka J, Tsuda M, Wang L, Kohsaka S, Kishida K, Semba S, Sugino H, Aburatani S, Frauenlob M, Kurokawa
T, Kojima S, Ueno T, Ohmiya Y, Mano H, Yasuda K, Gong J P, and Tanaka S. Rapid reprogramming of tumour
cells into cancer stem cells on double-network hydrogels. Nat Biomed Eng, DOI: 10.1038/s41551-021-00692-2,
2021Cancer-imaging system using luminescent lanthanide complex
Yasuchika Hasegawa
Professor
Affiliation:

ICReDD and Division of Applied Chemistry
Faculty of Engineering, Hokkaido University

e-mail:Specialty:

hasegaway@eng.hokudai.ac.jp
Photophysical Chemistry

ABSTRACT
Photophysical and informatic analysis for cancer growth process and mechanism (reaction) opens up
new medical science and engineering. In this study, we hope the following three points: 1)
Visualization of cancer cells using water-soluble luminescent lanthanide(Ln(III)) complexes supports
future medical check for understanding the growth mechanism of cancer. 2) Photophysical informationand data mining of Luminescent Ln(III) micelles on cancer is useful for finding new aspects of cancer
growth. 3) Using the obtained lanthanides, we hope to be able to detect cancer cells on patient’s tissue
specimen and finally in cancer patients LQYLYR, that is, to develop novel “cancer GPS system”.
The prediction of cancer growth is one of expansion-study for chemical reaction discovery. In order tounderstand the cancer-cell growth reaction, we attempted to prepare the Cancer growth positioning
system (Cancer-GPS) using luminescent lanthanide complex. Using water-solved Eu-ntfa complex,
luminescent images of cancer cells (Hela: colon cancer, Lung cancer, Brain cancer etc.) aresuccessfully observed. We also real-time monitored the endocytosis process in living cancer cells using
luminescent images with Tanaka-group, Faculty of Medicine, Hokkaido Univ. The use of machinelearning methods for images for pattern detection, prediction, and quantification is under investigation.

REF.
1. Kitagawa Y, Wada S, Islam MD J, Saita K, Gon M, Fushimi K., Tanaka K, Maeda S, Hasegawa Y, Chirallanthanide lumino-glass for a circularly polarized light security device. Communications Chemistry(Nature.com), 3, 119 (2020).
2. Kitagawa Y, Suzue F, Nakanishi T, Fushimi K, Seki T, Ito H, Hasegawa Y, Stacked nanocarbon photosensitizer
for efficient blue light excited Eu(III) emission, Communications Chemistry (Nature.com) 3, 3 (2020).
3. Wada S, Kitagawa Y, Nakanishi T, Gon M, Tanaka K, Fushimi K, Chujo Y, Hasegawa Y, Electronic chirality
inversion of lanthanide complex. Scientific Reports (Nature.com), 8: 16395(2018).
4. Hasegawa Y, Kitagawa Y, Nakanishi T, Effective photosensirized, electrosensitized, and mechanosensitizedluminescence of lanthanide complexes. NPG Asia Mater. (Nature.com), 10: 52-70 (2018).

Artificial intelligence in positron emission tomography
Kenji Hirata
Associate Professor
Affiliation:

Department of Diagnostic Imaging,
Faculty of Medicine, Hokkaido University

e-mail:Specialty:khirata@med.hokudai.ac.jp
Radiology, Nuclear medicine, PET, AI, Radiomics

ABSTRACT
Positron emission tomography (PET) is an in vivo functional imaging technique that can visualize
biological phenomena in organs and tumors. PET using F-18 labeled fluorodeoxyglucose (FDG) is
routinely used in clinical practice of oncology. Traditionally, PET images have been interpreted bytrained nuclear medicine physicians or analyzed quantitatively using mathematical models such ascompartmental models. More recently, artificial intelligence (AI) has been applied to different typesof medical imaging, including PET. In general, AI can perform four tasks on medical images: 1)
image classification, 2) object detection, 3) image segmentation, and 4) image generation. Image
classification is the task of classifying an image into several categories, such as benign lesions andmalignant tumors. Object detection is the task of detecting a certain lesion (e.g., lung cancer) in thewhole image. These tasks are expected to help doctors diagnose and avoid oversights. Image
segmentation is the task of defining the boundaries of an organ or tumor in order to measure its
volume or plan radiation therapy. Finally, image generation is the task of transforming an image froma related image. For example, a noisy or low-resolution image can be converted to a normal quality
image. It can also convert a PET image into a different type of image, such as CT or MRI. Image
generation techniques will reduce radiation exposure and scan time. AI techniques are creating a newresearch area in medical image processing. Important things to remember when using AI are that 1)
developing AI models requires a significant amount of training data; 2) AI does not "think", butrather uses statistical inference; 3) AI cannot replace physicians, but only assist them. In mypresentation, I will introduce some examples of the use of AI for PET.

REF.
1. Uchiyama Y, Hirata K, Watanabe S, et al. Development and validation of a prediction model based on the organbased metabolic tumor volume on FDG-PET in patients with differentiated thyroid carcinoma. Ann Nucl Med.
2021 Aug 11.
2. Hirata K, Manabe O, Magota K, et al. A Preliminary Study to Use SUVmax of FDG PET-CT as an Identifier ofLesion for Artificial Intelligence. Front Med (Lausanne). 2021 Apr 28;8:647562.
3. Yamaguchi S, Hirata K, Okamoto M, et al. Determination of brain tumor recurrence using 11 C-methionine
positron emission tomography after radiotherapy. Cancer Sci. 2021 Jun 1.
4. Hirata K, Tamaki N. Quantitative FDG PET Assessment for Oncology Therapy. Cancers (Basel). 2021 Feb
19;13(4):869.

Expanding the application of near-infrared photoimmunotherapy ofcancerYasuhiro Hida
Associate professor
Affiliation:

Department of Cardiovascular and Thoracic Suegery,
Faculty of Medicine, Hokkaido University

e-mail:Specialty:

yhida@med.hokudai.ac.jp
Thoracic surgery

ABSTRACT
Near-infrared photoimmunotherapy (NIR-PIT) is a new cancer treatment that utilizes antibodyIRDye700DX (IR700) conjugates. IR700 changes its conformation immediately after NIR light
exposure and damages the cell membrane and eventually causes rapture of the targeted cells1.
Mechanisms of IR700 action were found and revealed by Dr. Hisataka Kobayashi, a seniorinvestigator of NCI, and Professor Mikako Ogawa, in Graduate School of Pharmaceutical Sciences,
Hokkaido University2. IR700 can be conjugated with any antibodies targeting varioustransmembrane cell surface molecules. Antibody-IR700 conjugates activated by NIR has much morepotent specific cytotoxicity compared to antibody alone. Unlike radiation therapy, NIR does notaffect surrounding cells because of its low energy. Moreover, it has been shown in preclinical animalmodels that target regulatory T cells (Treg) in tumor environment by using anti-CD25 or anti-CTLA4
antibody-IR700 conjugates to eliminate Treg and enhance cytotoxic T cell attack to tumor cells3,4.
This strategy could be applied to any type of tumor regardless of histology or original organs. In
September 2020, the clinical use of NIR-PIT was approved in Japan for patients with inoperable
head and neck cancer-targeting epidermal growth factor receptor (EGFR). It is anticipated that NIRPIT is applied to many other malignant tumors. To expand the application, there would be several
issues to overcome. The selection of cell surface antigen for targeting is critical. High prevalence andhomogeneous expression of a target molecule in the tumor cells are crucial. If there is marked
heterogeneity of target molecule expression, a cocktail of antibodies conjugated with IR700 might beneeded. Although immunogenic cell death following NIR-PIT might elicit adopted tumor-specificimmune response against target negative tumor cells, it will be investigated in the future. Delivery of
NIR to the target tumor is a complex task in human patients, especially in the case of the deep
internal organs and tumors adjacent to great vessels or nerves. Although NIR is relatively safe,
probes must deliver it proximate to tumors since it penetrates only about a centimeter of humantissue. Various medical, surgical and interventional radiology techniques developed for conventional
therapy will accelerate safe delivery of NIR to the complex and deep anatomical area, such asintestines, liver, lungs, and central nerves. Ultimately, proofs of superiority or non-inferiority plus
benefits to preexisting treatments are demanded to make NIR-PIT available for many patients.
Clinical trials with an anti-EGFR antibody conjugate, an anti-CD25 antibody conjugate are ongoingfor patients with head and neck, gastric esophageal, cutaneous squamous cancers and malignant
melanoma5.
REF.
1. Mitsunaga M, Ogawa M, Kosaka N, Rosenblum LT, Choyke PL, Kobayashi H. Cancer cell-selective in vivo nearinfrared photoimmunotherapy targeting specific membrane molecules. Nat. Med. 2011;17:1685–91.
2. Sato K, Ando K, Okuyama S, Moriguchi S, Ogura T, Totoki S, et al. Photoinduced ligand release from a silicon
phthalocyanine dye conjugated with monoclonal antibodies: a mechanism of cancer cell cytotoxicity after nearinfrared photoimmunotherapy. ACS Cent Sci 2018;4:1559–69.
3. Okada R, Kato T, Furusawa A, Inagaki F, Wakiyama H, Choyke PL, et al. Local deple- tion of immune
checkpoint ligand CTLA4 expressing cells in tumor beds enhances antitumor host immunity. Advanced
Therapeutics 2021;4:2000269.
4. Sato K, Sato N, Xu B, Nakamura Y, Nagaya T, Choyke PL, et al. Spatially selective depletion of tumorassociated regulatory T cells with near-infrared photoimmu- notherapy. Sci Transl Med 2016;8:352ra110.
https://rakuten-med.com/jp/illuminox/pipeline/Inflammation-induced regeneration and cancer
Koji Taniguchi
Professor
Affiliation:

Department of Pathology,
Faculty of Medicine, Hokkaido University

e-mail:Specialty:

kotaniguchi@med.hokudai.ac.jp
Pathology, Immunology, Molecular Oncology, Gastrointestinal surgery

ABSTRACTInflammation is divided into two types, acute inflammation and chronic inflammation. Acuteinflammation plays an important role in intestinal regeneration, while chronic inflammation plays anessential role in colorectal tumorigenesis and metastasis. In our previous study, we discovered thatduring intestinal epithelial regeneration, IL-6 family cytokines activate YAP via Src family kinases(SFKs) in addition to the JAK-STAT3 pathway and promotes the regeneration. We also found thatloss of adenomatous polyposis coli (APC), an important tumor suppressor in colorectal cancer,
induces the activation of the SFK-YAP and JAK-STAT3 pathways in addition to ȕ-catenin.
Moreover, the simultaneous activation of these signaling pathways is found in 60-70% of humancolorectal cancer samples. We revealed that YAP activation, induced by loss of APC, induces
upregulation of gp130/IL6ST, which results in the activation of the SFK-YAP and JAK-STAT3
pathways and establishment of an feedforward loop. The concomitant inhibition of these signaling
pathways efficiently suppressed colorectal tumor growth in vitro and in vivo. In this symposium, I
will introduce the recent advances of inflammation-related studies.

REF.
1. Hibino S, Kawazoe T, Kasahara H, Itoh S, Ishimoto T, Sakata-Yanagimoto M, Taniguchi K. Inflammationinduced tumorigenesis and metastasis. Int J Mol Sci. 22(11):5421. 2021
2. Kawazoe T, Saeki H, Oki E, Oda Y, Maehara Y, Mori, M, Taniguchi K. Autocrine leukemia inhibitory factor
promotes esophageal squamous cell carcinoma progression via Src family kinase-dependent Yes-associated protein
activation. Mol Cancer Res 18(12):1876-1888. 2020
3. Taniguchi K, Karin M. NF-țB, inflammation, immunity and cancer: coming of age. Nat Rev Immunol.
18(5):309-324. 2018
4. Taniguchi K, Moroishi T, de Jong PR, Krawczyk M, Grebbin BM, Luo H, Xu RH, Golob-Schwarzl N,
Schweiger C, Wang K, Di Caro G, Feng Y, Fearon ER, Raz E, Kenner L, Farin HF, Guan KL, Haybaeck J, Datz C,
Zhang K, Karin M. YAP-IL-6ST autoregulatory loop activated on APC loss controls colonic tumorigenesis. Proc
Natl Acad Sci U S A. 14;114(7):1643-1648. 2017
5. Taniguchi K, Wu LW, Grivennikov SI, de Jong PR, Lian I, Yu FX, Wang K, Ho SB, Boland BS, Chang JT,
Sandborn WJ, Hardiman G, Raz E, Maehara Y, Yoshimura A, Zucman-Rossi J, Guan KL, Karin M. A gp130-SrcYAP module links inflammation to epithelial regeneration.Nature. 5;519(7541):57-62. 2015



Machine Learning for Molecules
Ichigaku Takigawa
Principal Investigator
Affiliation:

ICReDD, Hokkaido University
RIKEN Center for Advanced Intelligence Projecte-mail:Specialty:

takigawa@icredd.hokudai.ac.jp
Computer Science, Machine Learning

ABSTRACTThe goal of ICReDD is to innovate the way chemical reactions —chemical transformations of one
set of molecules to another— are developed, and thus we hope that sharing our interests can be also
beneficial to have precise understanding of biomedical phenomena in the light of molecular biology.
Now that we are often overwhelmed by diverse data and facts from experiments and simulations,data-intensive technologies such as machine learning come into play in both chemistry and life
sciences1-2.
The main technical difficulties come from the fact that the molecules are combinatorial. Molecules
are combinations of atoms, and chemical reactions are recombination of these atoms in molecules.
Even for a very limited number of atomic elements (99% of the human-body mass is made up of 6
elements), the number of all possible combinations is astronomical due to the combinatorial
explosion, and hence too huge for brute-force exploration. My long-standing interests lie in this
aspect of machine learning involving combinatorial objects such as graphs. Graphs are mathematicalabstractions by nodes and edges (or dots and lines), and also everyday tools for computer scientists
to represent combinatorial objects. In this talk, I will briefly give an overview on recent excitement,
advances, and implications on machine learning over graphs3-9 for molecules and molecular
networks.
REF.
1. Mine S, Takao M, Yamaguchi T, Toyao T*, Maeno Z, Siddiki S M A H, Takakusagi S, Shimizu K*, Takigawa I*,
Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative
Machine-Learning Method to Identify Novel Catalysts. ChemCatChem. 2021; 13(16): 3636-3655.
2. Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I*, Shimizu K*, Machine learning for catalysis
informatics: Recent applications and prospects.ACS Catalysis. 2020; 10: 2260-2297.
3. Harada S, Akita H, Tsubaki M, Baba Y, Takigawa I, Yamanishi Y, Kashima H, Dual graph convolutional neuralnetwork for predicting chemical networks. BMC Bioinformatics. 2020; 21(Suppl 3):94.
4. Kikuchi S, Takigawa I, Oyama S, Kurihara M, Learning relevant molecular representations via self-attentive
graph neural networks. Workshop on Deep Graph Learning: Methodologies and Applications (DGLMA'19), IEEE
BigData'19 Workshop, Los Angeles, USA, December 9, 2019.
5. Shirakawa R, Yokoyama Y, Okazaki F, Takigawa I. Jointly learning relevant subgraph patterns and nonlinear
models of their indicators. The 14th International Conference on Mining and Learning with Graphs (MLG 2018)(KDD'18 Workshop), London, U.K., August 20, 2018
6. Takigawa I, Mamitsuka H, Generalized sparse learning of linear models over the complete subgraph feature set.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017; 39(3): 617-624.
7. Takigawa I, Mamitsuka H, Graph mining: procedure, application to drug discovery and recent advances. Drug
Discovery Today. 2013;18(1-2):50-57.
8. Takigawa I, Tsuda K, Mamitsuka H, Mining significant substructure pairs for interpreting polypharmacology in
drug-target network. PLoS One. 2011;6(2): e16999.
9. Takigawa I, Mamitsuka H, Efficiently mining delta-tolerance closed frequent subgraphs. Machine Learning.
2011;82(2): 95-121.

Establishment of a simple high-speed reverse genetics system for SARSCoV-2
Takasuke Fukuhara
Professor
Affiliation:

Department of Microbiology and Immunology,
Graduate School of Medicine, Hokkaido University

e-mail:Specialty:

fukut@pop.med.hokudai.ac.jp
Virology

ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of a global
pandemic of coronavirus disease 2019. We know various mutations have been accumulated on the
genome of SARS-CoV-2. To understand the effects of those mutations in the viral life cycle of
SARS-CoV-2, it is essential to generate recombinant viruses with each mutation. However, classicalreverse genetics systems of SARS-CoV-2 are very complicated and time-consuming due to the large
genome size of coronaviruses. To overcome the problem, we attempted to establish a quick reverse
genetics system to generate recombinant SARS-CoV-2 using circular polymerase extension reaction(CPER).
We generated 9 cDNA fragments (‫׽‬5,000 base pairs) covering the full-length of SARS-CoV-2, and alinker fragment encoding the sequences of cytomegalovirus promoter, hepatitis delta virus ribozyme,
and bovine growth hormone polyA signal. All of these fragments were designed to possess
complementary ends with 25 to 452 overlapping nucleotides with adjacent fragments. The amplified
10 fragments were assembled by CPER and transfected into the tetracycline-inducible ACE2 andTMPRSS-expressing HEK293 cells.
Cytopathic effects were observed in cells at 7 days post-transfection of the CPER products. We
confirmed that the recovery of recombinant SARS-CoV-2 at high titer with high accuracy by titrationand genome sequencing. The recombinant SARS-CoV-2 exhibited similar propagation property withthe parental virus in the Vero cells expressing TMPRSS2. We also applied the CPER method for theconstruction of SARS-CoV-2 carrying reporter genes or mutants. The infectious clones of therecombinant viruses could be completed in the following steps: introduction of reporter genes ormutations into the desirable cDNA fragments by PCR and assembly of cDNA fragments by CPER.
In this study, we establish a PCR-based and bacterium-free method to generate recombinant SARSCoV-2. We hope that our reverse genetics system can contribute to a further understanding of themolecular characteristics of SARS-CoV-2.
REF.
1. Torii S, Ono C, Suzuki R, Morioka Y, Anzai I, Fauzyah Y, Maeda Y, Kamitani W, Fukuhara T (Corresponding
author) and Matsuura Y. A novel reverse genetics system for SARS-CoV-2 by using circular polymerase extension
reaction. Cell Rep. 35(3): 109014, 2021
2. Motozono C, Toyoda M, Zahradnik J, Saito A, Nasser H, Tan TS, Ngare I, Kimura I, Uriu K, Kosugi Y, Yue Y,
Shimizu R, Ito J, Torii S, Yonekawa A, Shimono N, Nagasaki Y, Minami R, Toya T, Sekiya N, Fukuhara T,
Matsuura Y, Schreiber G; Genotype Japan (G2P-Japan) Consortium, Ikeda T, Nakagawa S, Ueno T and Sato K.
SARS-CoV-2 spike L452R variant evades cellular immunity and increases infectivity. Cell Host Microbe. 29(7):1124-36, 202

