Oral Presentation 5

日時： 2020 年 10 月 29 日 17:30-19:00
チャンネル： ２

口頭発表５『創薬応用（その他）』
Drug Discovery application
座長：関嶋 政和 （東京工業大学）
Masakazu Sekijima (Tokyo Institute of Technology)

O2-05"Deep generative model for designing antibody from antibody library data"
Taro Kakuzaki(Chugai Pharmaceuticals, Co., Ltd.)
O2-06"Search for preventive drugs against Cisplatin-induced nephrotoxicity using public
database and electronic medical records."
Eri Wakai(Department of Integrative Pharmacology, Mie University Graduate School ofMedicine)O2-07"Search for preventive drugs for drug-induced neuropathy using medical information
database"
Takahiro Niimura(Department of Clinical Pharmacology and Therapeutics, Tokushima University
Graduate School of Biomedical Sciences)O2-05

Deep generative model for designing antibody fromantibody library data
Taro Kakuzaki 1

Koichiro Saka 1

kakuzaki.taro99@chugai-pharm.co.jp

saka.koichiro55@chugai-pharm.co.jp

Shoichi Metsugi 1

Hiroyuki Tsunoda 1

metsugisui@chugai-pharm.co.jp

tsunodahry@chugai-pharm.co.jp

Reiji Teramoto 1
teramoto.reiji11@chugai-pharm.co.jp
1

Chugai Pharmaceuticals, Co., Ltd.200, Kajiwara, Kamakura, Kanagawa, 247-8530, Japan

Keywords: Antibody, Display library, Machine Learning, AI Drug Discovery
Antibodies are currently indispensable tools for therapeutic and biological research. Recently, NGS isfrequently used to screen a lot of clones to acquire antibody with strong affinity from an antibody
library panning using phage display technique. However, the frequently read antibody sequences do notnecessarily have the desirable property due to restriction of sequence diver sities of antibody libraries.
Therefore, it is difficult to find the antibody sequences with strong affinity only based on sequenced
data.
To address this problem, we employ long shot term memory network (LSTM) which is one of thepopular deep generative model to design sequences with the desirable property. We employ a trained
LSTM model to generative virtual sequences and then prioritize generated sequences according tolikelihood based on it. We applied our method to in-house antibody library data and confirmed that ourgenerated sequences have higher affinity than frequently read sequences. Moreover, we showed thatlikelihood of trained LSTM model correlates well with binding affinity. From our results, our LSTM
based sequence generation and prioritization procedure is quite useful for acquiring strong binder andexpanding library space from antibody library data.

[1] Mason, D. M. et al, Deep learning enables therapeutic antibody optimization in mammaliancells
bydecipheringhigh-dimensional
proteinsequence
space.
doi:https://doi.org/10.1101/617860, 2019.
[2] Yang, K. K., Wu, Z., Bedbrook, C. N. & Arnold, F. H. Learned protein embeddings for machinelearning. Bioinformatics 34, 4138, doi:10.1093/bioinformatics/bty455, 2018.
[3] Liu, G. et al. Antibody Complementarity Determining Region Design Using High-Capacity
Machine Learning. Bioinformatics, doi:10.1093/bioinformatics/btz895 ,2019.
[4] Gers, F. A., Schmidhuber, J. & Cummins, F. Learning to forget: continual prediction with
LSTM. Neural computation 12, 2451-2471, doi:10.1162/089976600300015015 ,2000.

O2-06

Search for preventive drugs against Cisplatin-induced
nephrotoxicity using public database and electronic
medical records.
Eri Wakai 1 ,Yuya Suzumura 1 ,318d026@m.mie-u.ac.jp
1Yuhei Nishimura 1

yuhei@doc.med.mie-u.ac.jp

Department of Integrative Pharmacology, Mie University Graduate School of Medicine, Mie,
2-174, Edobashi, Tsu, Mie, 514-8507, Japan

Keywords: Cisplatin, Nephrotoxicity, Palonosetron, Public database, Real world data
Cisplatin (CDDP) is used widely for the treatment of several types of cancer. CDDP-induced
nephrotoxicity is serious adverse events, previous treatment such as hydration therapy isinsufficient to prevent nephrotoxicity [1]. In the present study, we investigated novel preventive
drugs against CDDP-induced nephrotoxicity using public databases and electronic medical records.
Using public databases related to gene expression profiling (Gene Expression Omnibus [2] andConnectivity map [3]), we identified palonosetron, a 5HT3-receptor antagonist, as the candidacy ofeffective preventive drugs for CDDP-induced nephrotoxicity. Using the Food and Drug
Administration Adverse Event Reporting System (FAERS) database , we revealed that the reporting
odds ratio of palonosetron for CDDP-induced nephrotoxicity was 0.56 (95% CI: 0.199 – 0.929)
whereas no significant signals were not found with other 5HT3-receptor antagonists. Moreover, weretrospectively investigated the effects of palonosetron and other 5HT3-receptor antagonist
palonosetron in 135 patients who received CDDP and fluorouracil therapy at Mie University
Hospital . The rate of nephrotoxicity in the palonosetron group (17%, n = 77) was significantly
lower than that in the ramosetron group (33%, n = 58). Severe nephrotoxicity greater than Grade 2(by the Common Terminology Criteria for Adverse Events version 5.0) was more observed in theramosetron groups than the palonosetron groups. These findings suggest that palonosetron canreduce CDDP-induced nephrotoxicity. This study was approved by the Ethics Committee of Mie
University Graduate School of Medicine and Faculty of Medicine .

[1] de Jongh FE., et al., Weekly high-dose cisplatin is feasible treatment option: analysis on
prognostic factors for toxicity in 400 patients. Br J Cancer, 88:1199-1206, 2003.
[2] Barrett T., et al., NCBI GEO: archive for functional genomics data sets —update. Nucleic
Acids Res. 41:991-995, 2013.
[3] Montero-melendez T., et al., Connections in pharmacology: innovation serving translational
medicine. Drug Discov Today, 19:820-823, 2014.

O2-07

Search for preventive drugs for drug-induced neuropathy
using medical information database
Takahiro Niimura 1

Yoshito Zamami 1, 2

c201756030@tokushima-u.ac.jp

zamami@tokushima-u.ac.jp

Takehiro Kawashiri 3
tkawa@med.kyushu-u.ac.jp

Masayuki Chuma 4
chuma.masayuki@tokushima-u.ac.jp

1

2

3

4

5

Yutaro Naitoh 1
c401831018@tokushima-u.ac.jp

Mitsuhiro Goda 2

Kenta Yagi 4

mgoda@tokushima-u.ac.jp

yagi.kenta@tokushima-u.ac.jp

Yuki Izawa-Ishizawa 5

Keisuke Ishizawa 1, 2

ishizawa.yuki@tokushima-u.ac.jp ishizawa@tokushima-u.ac.jp

Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate
School of Biomedical Sciences, 2-50-1 Kuramoto-cho, Tokushima 770-8503, Japan.
Department of Pharmacy, Tokushima University Hospital, 2 -50-1 Kuramoto, Tokushima,770-8503, Japan.
Department of Clinical Pharmacy and Pharmaceutical Care, Graduate School of PharmaceuticalSciences, Kyushu University, 3–1–1 Maidashi, Higashi-ku, Fukuoka 812–8582, Japan.
Clinical Research Center for Developmental Therapeutics , Tokushima University Hospital,
2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan.
AWA Support Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503,
Japan.

Keywords: drug repositioning, medical information database, adverse event
Drug repositioning enables identification of novel uses of approved drugs . It is cost effective andhas a shorter development period than conventional drug discovery. In recent years, v arious
databases including a medical information database have been utilized for drug development . In
this study, we aimed to identify prophylactic drugs for oxaliplatin -induced peripheral neuropathy(OIPN) to facilitate drug repositioning, utilizing medical information databases.
First, we analyzed the Library of Integrated Network -based Cellular Signatures (LINCS) of theNational Institutes of Health (NIH) and extracted 23 therapeutic drug candidate s counteracting
OIPN-related gene expression changes. We searched for approved drugs that reduce OIPN using
the FDA Adverse Event Reporting System (FAERS). Analysis using a medical information
database revealed that simvastatin, used to treat d yslipidemia, significantly reduced reports ofOIPN. Its neuroprotective effect was evaluated by the von Frey test using OIPN model rats.
Simvastatin significantly reduced oxaliplatin-induced hyperalgesia. In model rat nerve tissue, themRNA expression of the antioxidant enzyme Gstm1 increased with statin administration.
We, therefore, conclude that drug repositioning, utilizing a clinical database, would allow drug
discovery for various diseases.