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Article ; Online: Progress in the Quantitative Assessment of Transporter-Mediated Drug-Drug Interactions Using Endogenous Substrates in Clinical Studies.

Mochizuki, Tatsuki / Kusuhara, Hiroyuki

Drug metabolism and disposition: the biological fate of chemicals

2023 Volume 51, Issue 9, Page(s) 1105–1113

Abstract: Variations in drug transporter activities, caused by genetic polymorphism and drug-drug interactions (DDIs), alter the systemic exposure of substrate drugs, leading to differences in drug responses. Recently, some endogenous substrates of drug ... ...

Abstract	Variations in drug transporter activities, caused by genetic polymorphism and drug-drug interactions (DDIs), alter the systemic exposure of substrate drugs, leading to differences in drug responses. Recently, some endogenous substrates of drug transporters, particularly the solute carrier family transporters such as OATP1B1, OATP1B3, OAT1, OAT3, OCT1, OCT2, MATE1, and MATE2-K, have been identified to investigate variations in drug transporters in humans. Clinical data obtained support their performance as surrogate probes in terms of specificity and reproducibility. Pharmacokinetic parameters of the endogenous biomarkers depend on the genotypes of drug transporters and the systemic exposure to perpetrator drugs. Furthermore, the development of physiologically based pharmacokinetic models for the endogenous biomarkers has enabled a top-down approach to obtain insights into the effect of perpetrators on drug transporters and to more precisely simulate the DDI with victim drugs, including probe drugs. The endogenous biomarkers can address the uncertainty in the DDI prediction in the preclinical and early phases of clinical development and have the potential to fulfill regulatory requirements. Therefore, the endogenous biomarkers should be able to predict disease effects on the variations in drug transporter activities observed in patients. This mini-review focuses on recent progress in the identification and use of the endogenous drug transporter substrate biomarkers and their application in drug development. SIGNIFICANCE STATEMENT: Advances in analytical methods have enabled the identification of endogenous substrates of drug transporters. Changes in the pharmacokinetic parameters (C
MeSH term(s)	Humans ; Organic Cation Transport Proteins ; Reproducibility of Results ; Drug Interactions ; Biomarkers
Chemical Substances	Organic Cation Transport Proteins ; Biomarkers
Language	English
Publishing date	2023-05-11
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	186795-7
ISSN	1521-009X ; 0090-9556
ISSN (online)	1521-009X
ISSN	0090-9556
DOI	10.1124/dmd.123.001285
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations.

Yoshikai, Yasuhiro / Mizuno, Tadahaya / Nemoto, Shumpei / Kusuhara, Hiroyuki

Nature communications

2024 Volume 15, Issue 1, Page(s) 1197

Abstract: Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular ... ...

Abstract	Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address this black box, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer. We show that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures. Consistently, the accuracy of molecular property predictions using descriptors generated from models at different learning steps was similar from the beginning to the end of training. Furthermore, we found that the Transformer requires particularly long training to learn chirality and sometimes stagnates with low performance due to misunderstanding of enantiomers. These findings are expected to deepen the understanding of NLP models in chemistry.
Language	English
Publishing date	2024-02-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-45102-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Anti-Inflammatory Effects of a Novel Nuclear Factor-

Baba, Hiroyuki / Hosoya, Tadashi / Ishida, Ryosuke / Tai, Kenpei / Hatsuzawa, Saki / Kondo, Yuma / Kusuhara, Hiroyuki / Kagechika, Hiroyuki / Yasuda, Shinsuke

The Journal of pharmacology and experimental therapeutics

2024 Volume 388, Issue 3, Page(s) 788–797

Abstract: Nuclear factor- ...

Abstract	Nuclear factor-
MeSH term(s)	Mice ; Animals ; NF-kappa B/metabolism ; Arthritis, Experimental/pathology ; Chromatography, Liquid ; Tandem Mass Spectrometry ; Inflammation/drug therapy ; Anti-Inflammatory Agents/pharmacology ; Anti-Inflammatory Agents/therapeutic use ; Hepatitis/drug therapy ; Pyrimidines/adverse effects ; Inflammation Mediators/metabolism ; Amines/therapeutic use ; Immunoglobulin G
Chemical Substances	NF-kappa B ; Anti-Inflammatory Agents ; Pyrimidines ; Inflammation Mediators ; Amines ; Immunoglobulin G
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	3106-9
ISSN	1521-0103 ; 0022-3565
ISSN (online)	1521-0103
ISSN	0022-3565
DOI	10.1124/jpet.123.001904
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Article ; Online: Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset.

Azuma, Iori / Mizuno, Tadahaya / Morita, Katsuhisa / Suzuki, Yutaka / Kusuhara, Hiroyuki

NAR genomics and bioinformatics

2024 Volume 6, Issue 1, Page(s) lqad111

Abstract: Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it ...

Abstract	Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.
Language	English
Publishing date	2024-01-05
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqad111
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Article: Investigation of chemical structure recognition by encoder-decoder models in learning progress.

Nemoto, Shumpei / Mizuno, Tadahaya / Kusuhara, Hiroyuki

Journal of cheminformatics

2023 Volume 15, Issue 1, Page(s) 45

Abstract: Descriptor generation methods using latent representations of encoder-decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized ...

Abstract	Descriptor generation methods using latent representations of encoder-decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input-output substructure similarity using substructure-based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time-consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals.
Language	English
Publishing date	2023-04-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2486539-4
ISSN	1758-2946
ISSN	1758-2946
DOI	10.1186/s13321-023-00713-z
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Article ; Online: Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo.

Maedera, Shotaro / Mizuno, Tadahaya / Kusuhara, Hiroyuki

Computers in biology and medicine

2023 Volume 168, Page(s) 107748

Abstract: Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, ... ...

Abstract	Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.
MeSH term(s)	Neural Networks, Computer ; Machine Learning
Language	English
Publishing date	2023-11-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107748
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Article ; Online: [Understanding of Multiple Effects of Low Molecular Weight Compounds with Factor Analysis].

Mizuno, Tadahaya / Nemoto, Shumpei / Morita, Katsuhisa / Kusuhara, Hiroyuki

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan

2023 Volume 143, Issue 2, Page(s) 127–132

Abstract: The effects of drugs and other low-molecular-weight compounds are complex and may be unintended by the developer. These compounds and drugs should be avoided if these unintended effects are harmful; however, unintended effects are not always as harmful ... ...

Abstract	The effects of drugs and other low-molecular-weight compounds are complex and may be unintended by the developer. These compounds and drugs should be avoided if these unintended effects are harmful; however, unintended effects are not always as harmful as suggested by drug repositioning. Therefore, a comprehensive understanding of complex drug actions is essential. Omics data can be regarded as the nonarbitrary transformation of biological information about a sample into comprehensive numerical information comprising multivariate data with a large number of variables. However, the changes are often based on a small number of elements in different dimensions (i.e., latent variables). The omics data of compound-treated samples comprehensively capture the complex effects of compounds, including their unrecognized aspects. Therefore, finding latent variables in these data is expected to contribute to the understanding of multiple effects. In particular, it can be interpreted as decomposing multiple effects into a smaller number of easily understandable effects. Although latent variable models of omics data have been used to understand the mechanisms of diseases, no approach has considered the multiple effects of compounds and their decomposition. Therefore, we propose to decompose and understand the multiple effects of low-molecular-weight compounds without arbitrariness and have been developing analytical methods and verifying their usefulness. In particular, we focused on classical factor analysis among latent variable models and have been examining the biological validity of the estimates obtained under linear assumptions.
MeSH term(s)	Molecular Weight ; Factor Analysis, Statistical ; Drug Repositioning
Language	Japanese
Publishing date	2023-02-01
Publishing country	Japan
Document type	English Abstract ; Journal Article
ZDB-ID	200514-1
ISSN	1347-5231 ; 0031-6903 ; 0372-7750 ; 0919-2085 ; 0919-2131
ISSN (online)	1347-5231
ISSN	0031-6903 ; 0372-7750 ; 0919-2085 ; 0919-2131
DOI	10.1248/yakushi.22-00156-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: NRBdMF: A Recommendation Algorithm for Predicting Drug Effects Considering Directionality.

Azuma, Iori / Mizuno, Tadahaya / Kusuhara, Hiroyuki

Journal of chemical information and modeling

2023 Volume 63, Issue 2, Page(s) 474–483

Abstract: Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature ...

Abstract	Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive (+1) label and known treatment effects were assigned a negative (-1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.
MeSH term(s)	Humans ; Algorithms ; Drug-Related Side Effects and Adverse Reactions
Language	English
Publishing date	2023-01-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.2c01210
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Article ; Online: Investigation of chemical structure recognition by encoder–decoder models in learning progress

Shumpei Nemoto / Tadahaya Mizuno / Hiroyuki Kusuhara

Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-

2023 Volume 9

Abstract: Abstract Descriptor generation methods using latent representations of encoder–decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is ... ...

Abstract	Abstract Descriptor generation methods using latent representations of encoder–decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input–output substructure similarity using substructure-based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time-consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals. Graphical Abstract
Keywords	Encoder–decoder model ; Descriptor ; Information technology ; T58.5-58.64 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases.

Morita, Katsuhisa / Mizuno, Tadahaya / Azuma, Iori / Suzuki, Yutaka / Kusuhara, Hiroyuki

Toxicological sciences : an official journal of the Society of Toxicology

2023

Abstract: Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are ... ...

Abstract	Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.
Language	English
Publishing date	2023-11-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	1420885-4
ISSN	1096-0929 ; 1096-6080
ISSN (online)	1096-0929
ISSN	1096-6080
DOI	10.1093/toxsci/kfad117
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