Oral Presentation 8

日時： 2020 年 10 月 30 日 13:00-14:30
チャンネル： ２

口頭発表 8『ADME・毒性/その他』
ADMET/Others
座長： 水間 俊 （帝京平成大学）
Takashi Mizuma (Teikyo Heisei University)
小長谷 明彦 （恵泉女学園大学）
Akihiko Konagaya (Keisen University)
O2-08"The prediction of food effect on drug oral absorption by machine learning"
Yusuke Hoshino(Chiba University)
O2-09"Prediction of the inhibitory activity of rat drug-metabolizing enzyme by in silico
method"Mizuki Nakamori(Graduate School of Pharmaceutical Sciences, Nagoya City University)
O2-10"Introduction to the Issues of Dataset Construction in Pharmacokinetic Models"
Daitaro Misawa(SyntheticGestalt)
O2-11"Analysis of monkey pose estimation using deep learning."
Tetsuo Kitamura(Nonclinical Research Center, LSI Medience Corporation)

O2-08The prediction of food effect on drug oral absorption bymachine learning
Yusuke Hoshino 1

Hideki Yoshioka 1

Akihiro Hisaka 1
1

Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences,
Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba 260-8675, Japan

Keywords: Pharmacokinetics, oral absorption, food effects, machine learning, random forestThe prediction of food effect on drug absorption has been previously reported by several methods
including BCS, GUT framework and PBPK model, which were investigated based on observed
availability changes of approximately 100 drugs. The purpose of present study was to develop a
reliable model for prediction of food effect on drug absorption from the chemical structure basedon observations of 470 drugs by using machine learning. Changes in oral availability by food werecollected from previous reports 1-4 and the product labels. The food effects were classified intonegative, no, and positive for drugs whose availability ratios (fed/fasted), mainly AUC ratios, areless than 0.8(14% of the drugs), 0.8 to 1.25 (57% of the drugs), and greater than 1.25 (29% of thedrugs), respectively. Approximately 360 features including solubilities in various conditions werecalculated from chemical structures with ADMET Predictor (Simulations Plus, Inc.). Features ofmonotonous or highly correlated were excluded, and then Boruta method was used to selectinformative features. The data was divided into a training set and a test set, and a training set was
further divided into a real training set and a validation set for hyperparameter optimization (nestedcross validation). The decision tree algorithm of random forest was used for training of food effectfrom the selected features. F1-score was used for evaluation of the model performance. F1-scoresof the final model in the test set (average of five trainings of different division ways) for negative,
no, and positive food effects were 0.304, 0.651 and 0.669, respectively. The F1-score of negative
food effect was the smallest due to the lowest frequency of this class (14%). Using informationcollected in this study, the prediction performance was evaluated also with the BCS method inwhich drugs were classified by dose, predicted permeability and solubility. F1-scores of negative(class 3), no (class 1), and positive (class 2 and 4) effects were 0.350, 0.379, and 0.580,respectively. The ratio of erroneous negative prediction for positive drugs and that of erroneous
positive prediction for negative drugs were 0.058 and 0.166, respectively, in the machine learningmodel, whereas those in the BCS method were 0.146 and 0.328, respectively. Therefore, themachine learning model predicted changes in availability of drugs more accurately for no orpositive food effects and with noticeably less risk of opposite predictions, compared with the BCS
method.
[1] Singh, B.N., A quantitative approach to probe the dependence and correlation of food‐effectwith aqueous solubility, dose/solubility ratio, and partition coefficient (Log P) for orally active
drugs administered as immediate‐release formulations, Drug Dev Res, 65: 55-75, 2005.
[2] Gu, C.H. et al., Predicting effect of food on extent of drug absorption based on
physicochemical properties, Pharm Res, 24: 1118-1130, 2007.
[3] O'Shea, J.P. et al., Food for thought: formulating away the food effect - a PEARRL review, J
Pharm Pharmacol, 71:510-535, 2019.
[4] Deng, J. et al., A Review of Food-Drug Interactions on Oral Drug Absorption, Drugs,
77:1833-1855, 2017.

O2-09Prediction of the inhibitory activity of rat
drug-metabolizing enzyme by in silico method
Mizuki Nakamori 1
c172528@ed.nagoya-cu.ac.jp

Kaori Ambe 1ambek@phar.nagoya-cu.ac.jp

12

Riku Tohno 1
c162036@ed.nagoya-cu.ac.jp

Masahiro Tohkin 1
tohkin@phar.nagoya-cu.ac.jp

Takamitsu Sasaki 2

Kouichi Yoshinari 2

t-sasaki@u-shizuoka-ken.ac.jp

yoshinari@u-shizuoka-ken.ac.jp

Graduate School of Pharmaceutical Sciences, Nagoya City University, 3 -1
Tanabe-dori, Mizuho-ku, Nagoya-shi, Aichi, 467-8603, Japan
School of Pharmaceutical Sciences, University of Shizuoka, 52 -1, Yada, Suruga-ku,Shizuoka-shi, Shizuoka, 422-8526, Japan

Keywords: Applicability domain, Drug-metabolizing enzymes, Machine learning method, Synthetic
Minority Over-sampling TEchniqueSome chemical substances exhibit their toxicity, especially hepatotoxicity, through their reactive
metabolites formed by drug-metabolizing enzymes. In this study, we sought to develop in silico
models for predicting the inhibitory activity of chemical substances against rat drug-metabolizing
enzymes based on chemical structure information to gain an insight into the chemical-induced
toxicity.
We selected 218 commercially available substances from the rat repeated-dose toxicity test
database of Hazard Evaluation Support System Integrated Platform (HESS) [1] and used them as
test substances for inhibition assays against rat drug-metabolizing enzymes. We used the data of 6rat cytochrome P450s (CYP1A1, CYP1A2, CYP2B1, CYP2C6, CYP2D1, CYP3A2) and rat
UDP-glucuronosyltransferases (UGT) as targets. Substance that showed inhibition by 15% or more
were considered as “reactive” and the others as “non-reactive”. The performance of generated
prediction models was evaluated in terms of sensitivity and area under the cur ve (ROC-AUC).
Using the results of the in vitro inhibition assays, we constructed classification models for eachtarget discriminating “reactive” and “non-reactive” using a random forest (RF) algorism withmolecular descriptors calculated by Mordred [2]. We randomly divided the 218 substances into thetraining set and test set at a 3 to 1 ratio, maintaining the reactive/non-reactive ratio. To improve theprediction of imbalanced data, we applied the Synthetic Minority Over-sampling TEchnique(SMOTE). We also set the applicability domains (ADs), which are the chemical spaces that canprovide highly reliable prediction results in the test s et. The established models showed thefollowing prediction performance: Sensitivity; 0.79 or more, and ROC-AUC; 0.83 or more.
In this study, we have demonstrated that the incorporation of the SMOTE and ADs is useful todevelop high-performance and reliable classification models that predict the inhibitory activity ofchemical substances against drug-metabolizing enzymes.
This study was supported in part by the Artificial Intelligence -based Substance Hazard Integrated
Prediction System project of the Ministry of Economy, Trade and Industry of Japan.
[1] http://www.nite.go.jp/en/chem/qsar/hess-e.html[2] http://mordred-descriptor.github.io/documentation/master/index.html

O2-10Introduction to the Issues of Dataset Constructionin Pharmacokinetic Models
Daitaro Misawa1

Yoshihiro Hori1

Kotaro Kamiya1d.misawa@syntheticgestalt.com

y.hori@syntheticgestalt.com

k.kamiya@syntheticgestalt.com

Reiko Watanabe2

Hitoshi Kawashima2

Masataka Kuroda2, 3

Kenji Mizuguchi2

reiko-watanabe@nibiohn.go.jp

hkawashi@nibiohn.go.jp

m-kuroda@nibiohn.go.jp

kenji@nibiohn.go.jp

123SyntheticGestalt, 1-6 Naitomachi Shinjuku-ku, Tokyo 160-0014, Japan
National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi Ibaraki City, Osaka567-0085, Japan
Mitsubishi Tanabe Pharma Corporation, 1000 Kamoshida-cho Aoba-ku Yokohama-shi, Kanagawa 2270033, Japan

Keywords: Pharmacokinetics, Dataset
Developing a new drug normally takes over ten years and costs hundreds of billions of yen. In addition,
Significant loss of time and money occurs when development is discontinued due to pharmacokinetic problems.
The prediction of pharmacokinetic parameters using machine learning is expected to allow the developmentof new drugs at a lower cost over a shorter period of time. However, the available data are limited, and it israre that the framework of machine learning can be applied immediately. In this presentation, we will discuss
the problems faced when combining the Well-Stirred Model and machine learning, using the estimation ofhepatic clearance (CLh), one of a pharmacokinetic index, as an example. Machine learning is used for theregression prediction of pharmacokinetic parameters (intrinsic clearance (CLint), fraction unbound in plasma(fu,p,), blood/plasma ratio (Rb)). In particular, we point out two problems that were revealed through theconstruction of evaluation data for CLh and the model construction of Rb. The first is "lack of data." The
number of compounds with a set of ((CLint, fu,p, Rb), CLh) necessary for the evaluation of CLh prediction was
only 5 out of 5,201 (0.096%). The second is "quality of data." With regard to the distribution of Rb,approximately 20% of all data are unevenly distributed at Rb=1. It can be inferred that 20% of the values for
Rb have been set to 1 for some reason. The identification flag is necessary to eliminate the mixture of data, but
no metadata for quality assurance was included to flag which values are set and which are actual measurements.
Finally, we would like to summarize the insights gained from this activity and propose the data curation policy
for machine-learning on pharmacokinetic parameters and its predictions.

[1] Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP. Physiological parameter values forphysiologically based pharmacokinetic models. Toxicol Ind Health. 1997;13(4):407-484.
doi:10.1177/074823379701300401[2] DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimatesof R&D costs. J Health Econ. 2016;47:20-33. doi:10.1016/j.jhealeco.2016.01.012[3] Levy V. A new future for R&D? Measuring the return from pharmaceutical innovation 2017, Deloitte
Centre for Health Solutions 2017(https://www2.deloitte.com/ch/en/pages/ life-sciences-andhealthcare/articles/new-future-for-r-and- d.html)

O2-11Analysis of monkey pose estimation using deep learning.
Tetsuo Kitamura 1
kitamura.tetsuo@mh.medience.co.jp

Munehiro Nakagawa 1
nakagawa.munehiro@md.medience.co.jp
12Modong Tan 2
modong.tan@phchd.com

Y ousuke Ochiai 1

ochiai.yousuke@mn.medience.co.jp

Yusuke Suzuki 2
yusuke.suzuki@phchd.com

Takumi Sakimura 2
takumi.sakimura@phchd.com

Nonclinical Research Center, LSI Medience Corporation, 14-1, Sunayama, Kamisu-shi,Ibaraki, 314-0255, Japan
Advanced Technology Development Center, PHC Corporation, Shonan Health Innovation Park,
2-26-1, Muraoka-Higashi, Fujisawa-shi, Kanagawa, 251-8555, Japan

Keywords: Nonclinical study, behavior, deep learning
Recent technological innovations have had a great impact on the development of new drugs, oneof which is deep learning. This technology allows us to utilize a much larger amount of data for
drug development than humans alone could. This study was conducted with the expectation ofdiscovering a new method of utilizing deep learning in video monitoring of animals, which would
allow monitoring for a longer period than previously performed by humans alone. The ultimate goalof this study is to develop a deep learning model that can classify toxic features such as changes inactivity level, abnormal posture, ataxia, and vomiting/nausea from videos recorded for a long periodof time in normal cages with a shield. As a preliminary step, we herein report on detection of monkey
skeleton.
A skeleton detection model was used. In order to avoid bias in the learning data, still images cut
out from RGB + depth videos were clustered by image pattern, and still images were selected evenly
from each cluster. The coordinates of the head, neck and extremities were input to the selected stillimages and used as learning data. The skeletal detection was learned by CNN from the still images,
and inferences were made based on videos of the same individuals over different time zones.
In the same individuals, the accuracy of the detected parts in the selected still images was 94%.
As a result, identification of the positions of the monkey's head, neck and extremities using askeleton detection model was considered effective. We will further investigate whether toxic
features can be detected from these positions in monkeys showing toxic features.