WC10 発表／参加報告
株式会社 インシリコデータ
湯田 浩太郎http://www.insilicodata.com

「ＱＳＡＲ」の言葉のルーツとなられた藤田稔夫先生が８月２２日に逝去されました

心よりご冥福をお祈りいたします
ＱＳＡＲ（Quantitative Structure Activity Relationships)

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□Scientific Program Overview
Themes:I. Ethics
II. Lessons Learned
III. Innovative Models for Safety and Efficacy
IV. Sustainability
V. Systems Biology and Big Data
VI. 3Rs in AcademiaVII. Translation
VIII. Refinement and Animal Welfare
IX. Global Cooperation

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Ｒｅａｄ Ａｃｒｏｓｓ 関連発表

招待講演：Thomas Hartung; Read-Across Based QSAR for REACH
口頭発表：
II-3-431 Grace Patlewicz: Using High-Throughput Literature Mining to Support Read-Across Predictions of Skin Sensitization
IV-1-165 Anne Bonhoff: Automated Read-Across for REACH
VII-3-776 Ivan Rusyn: Read-Across and Grouping of Complex Substances Using Bioactivity Data from Human Induced Pluripotent Stem Cell (iPSC)Derived Models
IX-3-491 Gladys Ouedraogo: Exploring the Value of New Approach Methodologies in Read-Across: The Parabens as a Collaborative EU-ToxRisk–Cosmetics Europe Case Study
Session II-5: Read Across
Co-Chairs: Grace Patlewicz, USEPA, RTP, NC, USA
Masamitsu Honma, National Institute of Health Sciences, Tokyo, Japan
II-5-432 Grace Patlewicz: Navigating Through the Minefield of Read-Across: From Research to Practical Tools
II-5-645 Sunil Kulkarni: Read-Across: Lessons Learned & Success Stories from Canada’s Chemicals Management Plan
II-5-561 Takashi Yamada: Our Recent Experiences for Development of Read-across Approach for Chemical Safety Assessment
II-5-671 Francis Kruszewski: Benefits of Using Read Across and in Silico Techniques to Fill Non-SIDS Data Gaps for High Production Volume ChemicalCategories
II-5-126 George Helman: Case Study of Read-across Predictions Using a Generalized Read-Across (GenRA) ApproachII-5-632 Q. Jay Zhao: Application of Read-Across in Quantitative Chemical Risk Assessment in a Regulatory Setting
ポスター発表：• Exploring the value of new approach methodologies in read-across: the parabens as a collaborative EU ToxRisk - Cosmetics Europe case study;
Ouedraogo G, Van der Burg B, Mahony C, Naciff J, Ellison C, Detroyer A, Bury A, Drewe W, Long T, Kamp7, Dinant Kroese H, Escher S, Cull T, WhiteA, Dent M, Blaauboer B, Keller D, Willighagen E, Cronin M, Currie R, Gräpel R, Van de Water B, Mombelli E.
• Lessons from Read-Across Case Studies for Repeated-Dose Toxicity; Mahony C, Schultz TW, Cronin MTD.

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Ｉｎ Ｓｉｌｉｃｏ 関連発表
招待講演：

Tuesday, August 22, 2017
Read-Across Based QSAR for REACH
12:15 – 13:00 Room 609
Speakers: Thomas Hartung, MD, Ph.D, The Johns Hopkins, University Bloomberg School of Public Health; Craig Rowlands, DABT Biosketch
This session will provide an update on the collaboration between UL and researchers from Johns Hopkins University on a read-across based QSAR toolfor REACH. We will discuss how this in silico tool will help to minimize animal consumption and will predict toxicity to fill data gaps for the REACH
registration process.

口頭発表：

II-2-445 Barbara Birk: The Effects of Active Transport in an In Silico-In Vitro Based Risk Assessment Approach for Potential Endocrine Disrupting
Substances
III-15-480 Miyoung Yoon: Incorporating Metabolism into a Tiered Testing Scheme Based on the Combination of In Vitro and In Silico Approaches
III-15-701 Barbara Wetmore: Advances in In Vitro and In Silico Tools for Toxicokinetic Dose Modeling and Predictive Toxicology
5-671 Francis Kruszewski: Benefits of Using Read Across and in Silico Techniques to Fill Non-SIDS Data Gaps for High Production Volume Chemical
Categories
II-6-291 Yuko Nukada: Construction of Mechanism-Based Hepatotoxicity Prediction System by Combining in Silico and In Vitro Technology
V-3-647 Glenn Myatt: The Development of in Silico Toxicology Protocols
IX-6-499 Paul Carmichael: Report on the Unilever and EPA Collaboration on Developing In Vitro and In Silico Methods for Toxicological Risk
Assessment

ポスター発表：・Paul Carmichael: Report on Unilever and EPA collaboration on developing in vitro and in silico methods for toxicological risk assessment• Bridging the gap from external to internal exposure: use of in silico, PBPK and in vitro methods to predict the biokinetics of topically applied cosmetic
ingredients; Klaric M, Cubberley R, Duplan H, Eilstein J, Ellison C, Grégoire S, Hewitt N, Jacques-Jamin C, Lange D, Roe A, Rothe H, Schepky A.

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INSILICODATA LIMITED 2017ＷＥＴとＤＲＹの融合による新たな化合物安全性評価への可能性

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プライバシー保護のため写真を省略いたします。

Moo-Yeal Lee, Ph.D.
I really want to find out a way to predict human toxicity(e.g., Cmax or true/false of toxicity in humans) from in vitro
data.
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省略
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省略
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省略
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省略
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３次元細胞を用いたスクリーニングデータによる毒性評価
条件１：新しいＷＥＴ実験手法が日進月歩で開発・研究されている。
二次元培養細胞（従来の高速スクリーニング）から、より生体に近いと考えられる３次元培養細胞利用による in vitro データ利用で、in vivoデータに近い結果が期待
条件２：実験手法の開発のみならず、新たに得られるデータを用いて、従来よりも付加価値の高い結果に導きたいが、そのデータ解析をできる環境がない。
（３次元細胞を用いたスクリーニングデータ毒性評価）
ＷＥＴとＤＲＹの連携により、より高い目標を実現するチャンスがある
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ＫＹ法関連のポスター発表

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Development of new data analysis methods : The KY-methods
Kohtaro Yuta “The In Silico Data , Ltd.

K-yuta@insilicodata.com

High reliability
No classification
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Positive Zone

Negative Zone

Gray Zone○○○ ○○ ○ ○○○ ○ ○ ×○○ ×
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○ ○××× ×××××× ××××××× × ××× ×× ××“One model KY-method”
ClassGray zone
Class21>Big data era has come and data analysis methods
are required to handle large amounts of data.
>Now data analytical methods must handle large
amounts of data and have high accuracy arerequired.
>The KY-methods are developed state of the art
data analysis methods for which can implement
the above requirements.
>By adopting the multistage iterative analysis
method, the KY-method can achieve highanalytical precision even when handling with alarge number of samples.
>The KY-method easy to incorporate new data
analysis methods which will be developed in theA series of the KY methods
future.

Discriminant
Analysis

Fitting

Two model KY

KY Fittingwith DA
KY Fittingwith no DA
Model free
KY Fitting

Single model KY
Model free KY

Correctzone

Mis
classified

Correct
zone“Model free KY-method”
ClassGray zone
Class2
1

Correctzone

Mis
classified“ KY Fitting with DA ”

◆Introduction“ Two model KY-method ”
High reliability

Correctzone

calcdSta
ge1

Take outsamples
from innermeasurzone

measureded inner samplesSelect out
Extreme high correlation
coefficients“ KY Fitting with no DA ”
group
3group1◆Conclusion>Discriminant analysis (3 methods) and fitting(3 methods) were developed as KY-method.
>Discriminant analysis by KY-method (2 classclassification) achieve 100% classification rate
constantly.
> High correlation coefficient was obtained by KY
fitting method.
> In the prediction rate, the model free KY-method
may achieve the highest value in discriminant
analysis, and the model free KY fitting is expectedto achieve high value in fitting.

calcdgrou
p2

Pick upsamplesfrom each zone

“ Model free KY Fitting ”
Sample S(2): Residue D(2) =Σmin Dextrapolationoutlier

TargetsampleSimilar
sampleoutlier

□ＫＹ法の基本的特徴（データ解析能力）
１．二クラス分類では常に１００％分類実現
２．重回帰手法では極めて高い相関係数や決定係数実現□ＫＹ法の基本的特徴（解析手順）
１．良サンプルおよびグレーサンプル選択
２． 多段階手続き□ＫＹ法の基本的特徴（手法的特徴）
１．基本となる解析手法は通常の手法を適用：メタ解析手法
２．新たに開発される手法の取り込みが容易
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Two model KY
Original KY
二クラス分類手法

ＫＹ（K-step Yard sampling）法
オリジナル＊二本の判別関数を用いる

Single model KY( Super KY )二クラス分類手法□手法の特徴１．１本の判別関数でＫＹ法
実施
２．２モデルＫＹ法より手順が簡単且つより高い予測性

Model free KY( Miracle KY )二クラス分類手法□手法の特徴１．判別関数を用いるが、分類目的ではない
２．良好なサンプルを外部より順に取り出してゆく
３．簡単且つより高い予測性
４．自動化に適したアルゴリズム

KY Fittingwith DA

良サンプル群

アウトライアー
重回帰手法

CONFIDENTIAL MATERIAL / RESTRICTED ACCESSKY Fittingwith no DA
重回帰手法□手法の特徴
１．アウトライアーを残差の＋／－で細かく調整可能

Model freeKY Fitting
重回帰手法□手法の特徴１．残差が最少のサンプルから順番に取り出す
２．残ったサンプル群で重回帰
３．簡単且つより高い予測性
４．自動化に適したアルゴリズム

結論：
１．ＫＹ法としての基本手続き、アルゴリズム等を確立
２．二クラス分類に加えて重回帰への対応を実現
３．二クラスおよび重回帰のそれぞれで３種類のＫＹ法を展開
４．今後は他の手法との連携等を実施し、高度な解析実現を目指す

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学会会場およびｶﾞｰﾗﾃﾞｨﾅｰ

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シアトル観光

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Thank you for your attention
株式会社 インシリコデータ
湯田 浩太郎http://www.insilicodata.com