Rapid in vivo evaluation system for cholestasis-related genes in mice with humanized bile acid profiles.

BACKGROUND: Pediatric cholestatic liver diseases (Ped-CLD) comprise many ultrarare disorders with a genetic basis. Pharmacologic therapy for severe cases of Ped-CLD has not been established. Species differences in bile acid (BA) metabolism between humans and rodents contribute to the lack of phenocopy of patients with Ped-CLD in rodents and hinder the development of therapeutic strategies. We aimed to establish an efficient in vivo system to understand BA-related pathogenesis, such as Ped-CLD. METHODS: We generated mice that express spCas9 specifically in the liver (L-Cas9Tg/Tg [liver-specific Cas9Tg/Tg] mice) and designed recombinant adeno-associated virus serotype 8 encoding small-guide RNA (AAV8 sgRNA) targeting Abcc2, Abcb11, and Cyp2c70. In humans, ABCC2 and ABCB11 deficiencies cause constitutional hyperbilirubinemia and most severe Ped-CLD, respectively. Cyp2c70 encodes an enzyme responsible for the rodent-specific BA profile. Six-week-old L-Cas9Tg/Tg mice were injected with this AAV8 sgRNA and subjected to biochemical and histological analysis. RESULTS: Fourteen days after the injection with AAV8 sgRNA targeting Abcc2, L-Cas9Tg/Tg mice exhibited jaundice and phenocopied patients with ABCC2 deficiency. L-Cas9Tg/Tg mice injected with AAV8 sgRNA targeting Abcb11 showed hepatomegaly and cholestasis without histological evidence of liver injury. Compared to Abcb11 alone, simultaneous injection of AAV8 sgRNA for Abcb11 and Cyp2c70 humanized the BA profile and caused higher transaminase levels and parenchymal necrosis, resembling phenotypes with ABCB11 deficiency. CONCLUSIONS: This study provides proof of concept for efficient in vivo assessment of cholestasis-related genes in humanized bile acid profiles. Our platform offers a more time- and cost-effective alternative to conventional genetically engineered mice, increasing our understanding of BA-related pathogenesis such as Ped-CLD and expanding the potential for translational research.

A general fluorescence off/on strategy for fluorogenic probes: Steric repulsion-induced twisted intramolecular charge transfer (sr-TICT).

Various control strategies are available for building fluorogenic probes to visualize biological events in terms of a fluorescence change. Here, we performed the time-dependent density functional theory (TD-DFT) computational analysis of the twisted intramolecular charge transfer (TICT) process in rhodamine dyes. On the basis of the results, we designed and synthesized a series of rhodamine dyes and established a fluorescence quenching strategy that we call steric repulsion-induced TICT (sr-TICT), in which the fluorescence quenching process is greatly accelerated by simple intramolecular twisting. As proof of concept of this design strategy, we used it to develop a fluorogenic probe, 2-Me PeER (pentyloxyethylrhodamine), for the N-dealkylation activity of CYP3A4. We applied 2-Me PeER for CYP3A4 activity-based fluorescence-activated cell sorting (FACS), providing access to homogeneous, highly functional human-induced pluripotent stem cell (hiPSC)-derived hepatocytes and intestinal epithelial cells. Our results suggest that sr-TICT represents a general fluorescence control method for fluorogenic probes.

Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations.

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.

Effect of N-o-nitrobenzylation on conformation and membrane permeability of linear peptides.

In this study, we explored the potential of the photoremovable o-nitrobenzyl (oNB) group as a tool to manipulate the membrane permeability and regulate the conformation of linear peptides by means of experimental and computational studies. We found that the introduction of one or more oNB groups markedly increased the permeability and altered the conformation, as compared to the corresponding unmodified peptides. We thoroughly investigated the impact of peptide length, number of oNB group, oNB insertion position, and introduction of N- and C-terminal protecting groups on the passive membrane permeability by means of parallel artificial membrane permeability assay (PAMPA). Photoreaction of peptides containing one or two oNB groups proceeded cleanly in moderate to high yields, releasing the unprotected parent linear peptide. The oNB-modified peptides showed a cis/trans conformational equilibrium, while after photolysis, the unprotected linear peptides showed only the trans-amide conformation. Furthermore, a comprehensive comparison of oNB-modified peptides and N-methylated peptides was conducted, encompassing conformational analysis and physicochemical properties. N-Substituted peptides favored a folded-like structure, which may contribute to the improvement in permeability.

Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset.

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.

Anti-Inflammatory Effects of a Novel Nuclear Factor-κB Inhibitory Derivative Derived from Pyrazolo[3,4-d]Pyrimidine in Three Inflammation Models.

Nuclear factor-κB (NF-κB) plays a central role in inflammatory responses, and its physiologic functions are essential for cell survival and proliferation. Currently, drugs targeting NF-κB inhibition have not yet been applied in clinical practice. We investigated the physiologic effect of a novel NF-κB inhibitory compound, 1H-pyrazolo[3,4-d]pyrimidin-4-amine derivative (INH #1), on three inflammatory animal models. The pharmacokinetics were measured by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. Acute hepatitis was induced by administrating lipopolysaccharide (LPS) and D-(+)-galactosamine hydrochloride followed by the analysis of survival time and inflammatory mediators. Collagen-induced arthritis (CIA) was induced by immunization with type II collagen (CII), and serum-transfer arthritis (STA) was caused by injecting K/BxN mice serum. Clinical and histologic scores were evaluated in both arthritis models. Immune cell subset analysis, CII-induced interferon-gamma (IFN-γ) production and proliferation, and measurement of anti-CII IgG antibodies were performed in the CIA model. In the acute hepatitis model, INH #1 suppressed tumor necrosis factor-α (TNF-α) production and prevented early death in a dose-dependent manner. INH #1 significantly attenuated arthritis scores and joint inflammation in both arthritis models. Additionally, in the CIA model, dendritic cells (DCs) in the regional lymph nodes were decreased in the treated mice and antigen-induced IFN-γ production and cell proliferation in splenocytes were inhibited, whereas the titers of anti-CII IgG antibodies were comparable regardless of the treatment. Here we revealed that INH #1 exerted anti-inflammatory effects in vivo via inhibition of inflammatory mediators and suppression of cellular immune responses. This compound could be a novel candidate for inhibition of NF-κB in certain inflammatory diseases. SIGNIFICANCE STATEMENT: A novel nuclear factor-κB (NF-κB) inhibitory compound, 1H-pyrazolo[3,4-d]pyrimidin-4-amine derivative (INH #1), which retains physiologically essential NF-κB bioactivity, suppressed inflammation in three different mouse models: the acute hepatitis model, the collagen-induced arthritis model, and the K/BxN serum-transfer arthritis model. These results suggest that this compound could be a novel and potent anti-inflammatory agent.

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo.

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.

Thioester-Based Coupled Fluorogenic Assays in Microdevice for the Detection of Single-Molecule Enzyme Activities of Esterases with Specified Substrate Recognition.

Single-molecule enzyme activity assay is a platform that enables the analysis of enzyme activities at single proteoform level. The limitation of the targetable enzymes is the major drawback of the assay, but the general assay platform is reported to study single-molecule enzyme activities of esterases based on the coupled assay using thioesters as substrate analogues. The coupled assay is realized by developing highly water-soluble thiol-reacting probes based on phosphonate-substituted boron dipyrromethene (BODIPY). The system enables the detection of cholinesterase activities in blood samples at single-molecule level, and it is shown that the dissecting alterations of single-molecule esterase activities can serve as an informative platform for activity-based diagnosis.

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases.

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.

Intestinal Atp8b1 dysfunction causes hepatic choline deficiency and steatohepatitis.

Choline is an essential nutrient, and its deficiency causes steatohepatitis. Dietary phosphatidylcholine (PC) is digested into lysoPC (LPC), glycerophosphocholine, and choline in the intestinal lumen and is the primary source of systemic choline. However, the major PC metabolites absorbed in the intestinal tract remain unidentified. ATP8B1 is a P4-ATPase phospholipid flippase expressed in the apical membrane of the epithelium. Here, we use intestinal epithelial cell (IEC)-specific Atp8b1-knockout (Atp8b1IEC-KO) mice. These mice progress to steatohepatitis by 4 weeks. Metabolomic analysis and cell-based assays show that loss of Atp8b1 in IEC causes LPC malabsorption and thereby hepatic choline deficiency. Feeding choline-supplemented diets to lactating mice achieves complete recovery from steatohepatitis in Atp8b1IEC-KO mice. Analysis of samples from pediatric patients with ATP8B1 deficiency suggests its translational potential. This study indicates that Atp8b1 regulates hepatic choline levels through intestinal LPC absorption, encouraging the evaluation of choline supplementation therapy for steatohepatitis caused by ATP8B1 dysfunction.

Evaluation of the risk of diarrhea induced by epidermal growth factor receptor tyrosine kinase inhibitors with cultured intestinal stem cells originated from crypts in humans and monkeys.

Severe diarrhea is a common side effect of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). We aimed to evaluate the risk of EGFR-TKI-induced diarrhea using spheroids of human and monkey crypt-derived intestinal stem cells. Intestinal spheroids exhibited higher toxic susceptibility to EGFR-TKIs than Caco-2 cells. As concentration of EGFR-TKIs increased, cellular ATP first decreased relative to the control condition, followed by an increase in LDH release, in contrast with their simultaneous changes with traditional cytotoxic anticancer drugs. The toxic sensitivity of spheroids to various EGFR-TKIs corresponded to clinical diarrhea incidence. Afatinib, a second-generation EGFR-TKI, exhibited higher toxic sensitivity compared with the first-generation ones, corresponding to the clinical evidence that afatinib-induced diarrhea is almost inevitable and severe. By contrast, the third-generation osimertinib, which reduces the risk of diarrhea, showed mitigated cytotoxicity compared with afatinib. For irreversible EGFR-TKIs, the decreased ATP level persisted or its recovery was delayed even after drug removal compared with reversible ones. Furthermore, the highest drug accumulation in spheroids (TKIspheroids) and inhibition potency against EGFR (TKIspheroids/Ki) were observed for afatinib. This system would be useful for predicting the risk of EGFR-TKI-induced diarrhea; moreover, on-target cytotoxicity against intestinal stem cells might contribute to clinically observed diarrhea.

Model-based meta-analysis of ethnic differences and their variabilities in clearance of oral drugs classified by clearance mechanism.

In this study, the ethnic ratios (ERs) of oral clearance between Japanese and Western populations were subjected to model-based meta-analysis (MBMA) for 81 drugs evaluated in 673 clinical studies. The drugs were classified into eight groups according to the clearance mechanism, and the ER for each group was inferred together with interindividual variability (IIV), interstudy variability (ISV), and inter-drug variability within a group (IDV) using the Markov chain Monte Carlo (MCMC) method. The ER, IIV, ISV, and IDV were dependent on the clearance mechanism, and, except for particular groups such as drugs metabolized by polymorphic enzymes or their clearance mechanism is not confirmative, the ethnic difference was found to be generally small. The IIV was well-matched across ethnicities, and the ISV was approximately half of the IIV as the coefficient of variation. To adequately assess ethnic differences in oral clearance without false detections, phase I studies should be designed with full consideration of the mechanism of clearance. This study suggests that the methodology of classifying drugs based on the mechanism that causes ethnic differences and performing MBMA with statistical techniques such as MCMC analysis is helpful for a rational understanding of ethnic differences and for strategic drug development.

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

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 (Cmax, AUC, or CLR) of these endogenous biomarkers relative to baseline values can serve as a quantitative index to assess variations in drug transporter activities during clinical studies and thereby provide more precise DDI predictions.

A strategy for low-cost portable monitoring of plasma drug concentrations using a sustainable boron-doped-diamond chip.

On-site monitoring of plasma drug concentrations is required for effective therapies. Recently developed handy biosensors are not yet popular owing to insufficient evaluation of accuracy on clinical samples and the necessity of complicated costly fabrication processes. Here, we approached these bottlenecks via a strategy involving engineeringly unmodified boron-doped diamond (BDD), a sustainable electrochemical material. A sensing system based on a ∼1 cm2 BDD chip, when analysing rat plasma spiked with a molecular-targeting anticancer drug, pazopanib, detected clinically relevant concentrations. The response was stable in 60 sequential measurements on the same chip. In a clinical study, data obtained with a BDD chip were consistent with liquid chromatography-mass spectrometry results. Finally, the portable system with a palm-sized sensor containing the chip analysed ∼40 µL of whole blood from dosed rats within ∼10 min. This approach with the 'reusable' sensor may improve point-of-monitoring systems and personalised medicine while reducing medical costs.

Advanced translational PBPK model for transferrin receptor-mediated drug delivery to the brain.

Transferrin receptor (TfR)-mediated transcytosis is an attractive pathway for delivering large-molecule therapeutics to the central nervous system across the blood-brain barrier. Despite the clinical success of some drugs conjugated with TfR-binder, the desired drug profile for efficient TfR-mediated delivery to the targeted compartment within the brain, especially considering the species-related differences, has not been fully elucidated. To provide a prospective direction in the TfR-mediated drug delivery system, we developed an advanced physiologically based pharmacokinetic (PBPK) model. The model addresses TfR-mediated trans- and intracellular disposition of anti-TfR antibodies from brain capillary blood, endothelial cells, extracellular fluid (ECF), and eventually to brain parenchymal cells (BPCs), which correspond to pharmacological target sites of interest. The PBPK model is applicable in rats, monkeys, and human TfR knock-in (hTfR-KI) mice with satisfactory prediction accuracy through model calibration using the brain and plasma PK data of anti-TfR monoclonal antibodies, including their fused protein, with diverse binding affinity to TfR (TfR-Kd). The sensitivity analysis to determine drug properties required for the optimal brain delivery revealed 1) a bell-shaped relationship between TfR-Kd and brain exposure; 2) a minimum species difference between monkeys and hTfR-KI mice in the optimal TfR-Kd range, but not with rats; 3) a low TfR-Kd range to be preferably targeted for BPCs compared with ECF; and 4) an increase in brain exposure when using the pH-sensitive antibody. This may advance model-informed drug development, improve molecular design optimization, and provide precise human dose projection of drugs leveraging TfR-mediated shuttle technology into the brain.

Investigation of chemical structure recognition by encoder-decoder models in learning progress.

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.

The influence of serial 50 µL microsampling on rats administered azathioprine, the immunosuppressive drug.

According to the ICH S3A Q&A, microsampling is applicable to pharmaceutical drugs and toxicological analysis. Few studies have reported the effect of microsampling on the toxicity of immunotoxicological drugs. The aim of this multicenter study was to evaluate the toxicological effects of serial microsampling on rats treated with azathioprine as a model drug with immunotoxic effects. Fifty microliters of blood were collected from the jugular vein of Sprague-Dawley rats at six time points from day 1 to 2 and 7 time points from day 27 to 28. The study was performed at three organizations independently. The microsampling effect on clinical signs, body weights, food consumption, hematological parameters, biochemical parameters, urinary parameters, organ weights, and tissue pathology was evaluated. Azathioprine-induced changes were observed in certain hematological and biochemical parameters and thymus weight and pathology. Microsampling produced minimal or no effects on almost all parameters; however, at 2 organizations, azathioprine-induced changes were apparently masked for two leukocytic, one coagulation, and two biochemical parameters. In conclusion, azathioprine toxicity could be assessed appropriately as overall profiles even with blood microsampling. However, microsampling may influence azathioprine-induced changes in certain parameters, especially leukocytic parameters, and its usage should be carefully considered.

Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method.

Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner.

[Understanding of Multiple Effects of Low Molecular Weight Compounds with Factor Analysis].

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.

Impact of Direction of Transport on the Evaluation of Substrate Recognition of Mouse Multidrug and Toxin Extrusion Protein 1.

Multidrug and toxin extrusion protein (MATE/SLC47A) secretes metabolites and xenobiotics into the urine in the proximal tubules of the kidney. Uptake assays have been commonly used for evaluating MATE-mediated transport of new chemical entities in drug development. The purpose of this study was to examine the relationship between in vitro uptake activities by MATEs and the impact of MATE-mediated transport in in vivo renal secretion. In vitro uptake in mouse Mate1 (mMate1)-expressing human embryonic kidney 293 (HEK293) cells and several in vivo parameters from mMate1 knockout and wild-type mice were compared using nine cationic compounds (almotriptan, naratriptan, talinolol, sumatriptan, alogliptin, sitagliptin, rivaroxaban, saxagliptin, and vildagliptin). Compounds that showed statistically significant decrease in secretory clearances with respect to kidney concentrations (CLR,kidney) in mMate1 knockout mice were categorized as in vivo substrates in this study. A good correlation (R2 = 0.637) was observed between the in vitro uptake ratio and the in vivo ratio of CLR,kidney of mMate1 knockout mice and wild-type mice. This study supported the rationale of using an uptake assay to determine whether investigational compounds are the substrate of MATEs and to predict drug-drug interaction risk via renal secretion by MATE from the viewpoint of drug development in pharmaceutical companies. SIGNIFICANCE STATEMENT: We revealed that substrates judged by in vitro experiments using mouse multidrug and toxin extrusion (mMate)1-expressing cells were excreted in urine via mMate1 in vivo, and a good correlation (R2 = 0.637) was observed between in vitro uptake ratio and in vivo ratio of secretory clearance with respect to the kidney concentrations (CLR,kidney) of mMate1 knockout and wild-type mice. This study supported the rationale of using an uptake assay to predict potential human MATE1-mediated drug-drug interaction as a victim.