Clinical symptoms, biochemistry, and liver histology during the native liver period of progressive familial intrahepatic cholestasis type 2.

BACKGROUND: Progressive familial intrahepatic cholestasis type 2 (PFIC2) is an ultra-rare disease caused by mutations in the ABCB11 gene. This study aimed to understand the course of PFIC2 during the native liver period. METHODS: From November 2014 to October 2015, a survey to identify PFIC2 patients was conducted in 207 hospitals registered with the Japanese Society of Pediatric Gastroenterology, Hepatology, and Nutrition. Investigators retrospectively collected clinical data at each facility in November 2018 using pre-specified forms. RESULTS: Based on the biallelic pathogenic variants in ABCB11 and/or no hepatic immunohistochemical detection of BSEP, 14 Japanese PFIC2 patients were enrolled at seven facilities. The median follow-up was 63.2 [47.7-123.3] months. The median age of disease onset was 2.5 [1-4] months. Twelve patients underwent living donor liver transplantation (LDLT), with a median age at LDLT of 9 [4-57] months. Two other patients received sodium 4-phenylbutyrate (NaPB) therapy and survived over 60 months with the native liver. No patients received biliary diversion. The cases that resulted in LDLT had gradually deteriorated growth retardation, biochemical tests, and liver histology since the initial visit. In the other two patients, jaundice, growth retardation, and most of the biochemical tests improved after NaPB therapy was started, but pruritus and liver fibrosis did not. CONCLUSIONS: Japanese PFIC2 patients had gradually worsening clinical findings since the initial visit, resulting in LDLT during infancy. NaPB therapy improved jaundice and growth retardation but was insufficient to treat pruritus and liver fibrosis.

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.

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.

Identification of activity-based biomarkers for early-stage pancreatic tumors in blood using single-molecule enzyme activity screening.

Single-molecule enzyme activity-based enzyme profiling (SEAP) is a methodology to globally analyze protein functions in living samples at the single-molecule level. It has been previously applied to detect functional alterations in phosphatases and glycosidases. Here, we expand the potential for activity-based biomarker discovery by developing a semi-automated synthesis platform for fluorogenic probes that can detect various peptidases and protease activities at the single-molecule level. The peptidase/protease probes were prepared on the basis of a 7-amino-4-methylcoumarin fluorophore. The introduction of a phosphonic acid to the core scaffold made the probe suitable for use in a microdevice-based assay, while phosphonic acid served as the handle for the affinity separation of the probe using Phos-tag. Using this semi-automated scheme, 48 fluorogenic probes for the single-molecule peptidase/protease activity analysis were prepared. Activity-based screening using blood samples revealed altered single-molecule activity profiles of CD13 and DPP4 in blood samples of patients with early-stage pancreatic tumors. The study shows the power of single-molecule enzyme activity screening to discover biomarkers on the basis of the functional alterations of proteins.

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.

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.

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.

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

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.

[Development of Decomposition Approach for Comprehensive Understanding of Drug Effects].

As the term polypharmacology suggests, there are multiple actions of small-molecule compounds. We proposed a decomposition and understanding concept that sheds light on the small effects in comparison to the large effects by decomposing these multiple effects. This concept was embodied by describing the effects of the compounds in a transcriptome profile, followed by factor analysis to extract latent variables as decomposed effects. Application of this approach to public datasets resulted in the inferences of compound effects consistent with existing knowledge such as gene ontologies and pathways. In one experimental validation, the potential inducibility of endoplasmic reticulum stress of several commercial drugs was detected by decomposition. Another study successfully discriminated the effects of a natural product and its derivatives despite their structural similarity. In the era of big data, it is important to infer conceptual elements composed of measurable elements as a higher layer than the given data of a specimen, which can expand our perception and understanding of the specimen. This review introduces an example of such a philosophy by applying it to the multiple effects of drugs to contribute to the understanding.

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning.

Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not strictly match the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not clear due to the lack of comparable studies. To understand the differences, we compared the model performance between the time and random splits using nine types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events. We provide the analysis code and datasets used in the present study at https://github.com/mizuno-group/AE_prediction.

Effect of Cyclosporin A and Impact of Dose Staggering on OATP1B1/1B3 Endogenous Substrates and Drug Probes for Assessing Clinical Drug Interactions.

This study was designed to assess the quantitative performance of endogenous biomarkers for organic anion transporting polypeptide (OATP) 1B1/1B3-mediated drug-drug interactions (DDIs). Ten healthy volunteers orally received OATP1B1/1B3 probe cocktail (0.2 mg pitavastatin, 1 mg rosuvastatin, and 2 mg valsartan) and an oral dose of cyclosporin A (CysA, 20 mg and 75 mg) separated by a 1-hour interval (20 mg (-1 hour), and 75 mg (-1 hour)). CysA 75 mg was also given with a 3-hour interval (75 mg (-3 hours)) to examine the persistence of OATP1B1/1B3 inhibition. The area under the plasma concentration-time curve ratios (AUCRs) were 1.63, 3.46, and 2.38 (pitavastatin), 1.39, 2.16, and 1.81 (rosuvastatin), and 1.42, 1.77, and 1.85 (valsartan), at 20 mg, 75 mg (-1 hour) and 75 mg (-3 hours) of CysA, respectively. CysA effect on OATP1B1/1B3 was unlikely to persist at the dose examined. Among 26 putative OATP1B1/1B3 biomarkers evaluated, AUCR and maximum concentration ratio (Cmax R) of CP-I showed the highest Pearson's correlation coefficient with CysA AUC (0.94 and 0.93, respectively). Correlation between AUCR of pitavastatin, and Cmax R or AUCR of CP-I were consistent between this study and our previous study using rifampicin as an OATP1B1/1B3 inhibitor. Nonlinear regression analysis of AUCR-1 of pitavastatin and CP-I against CysA Cmax yielded Ki,OATP1B1/1B3,app (109 ± 35 and 176 ± 42 nM, respectively), similar to the Ki ,OATP1B1/1B3 estimated by our physiologically-based pharmacokinetic model analysis described previously (107 nM). The endogenous OATP1B1/1B3 biomarkers, particularly Cmax R and AUCR of CP-I, corroborates OATP1B1/1B3 inhibition and yields valuable information that improve accurate DDI predictions in drug development, and enhance our understanding of interindividual variability in the magnitude of DDIs.

Influence of food on pharmacokinetics and pharmacodynamics of 4-phenylbutyrate in patients with urea cycle disorders.

Urea cycle disorders (UCDs), inborn errors of hepatocyte metabolism, cause hyperammonemia and lead to neurocognitive deficits, coma, and even death. Sodium 4-phenylbutyrate (NaPB), a standard adjunctive therapy for UCDs, generates an alternative pathway of nitrogen deposition through glutamine consumption. Administration during or immediately after a meal is the approved usage of NaPB. However, we previously found that preprandial oral administration enhanced its potency in healthy adults and pediatric patients with intrahepatic cholestasis. The present study evaluated the effect of food on the pharmacokinetics and pharmacodynamics of NaPB in five patients with UCDs. Following an overnight fast, NaPB was administered orally at 75 mg/kg/dose (high dose, HD) or 25 mg/kg/dose (low dose, LD) either 15 min before or immediately after breakfast. Each patient was treated with these four treatment regimens with NaPB. With either dose, pre-breakfast administration rather than post-breakfast administration significantly increased plasma PB levels and decreased plasma glutamine availability. Pre-breakfast LD administration resulted in a greater attenuation in plasma glutamine availability than post-breakfast HD administration. Plasma levels of branched-chain amino acids decreased to the same extent in all tested regimens. No severe adverse events occurred during this study. In conclusion, preprandial oral administration of NaPB maximized systemic exposure of PB and thereby its efficacy on glutamine consumption in patients with UCDs.

Decomposition Profile Data Analysis for Deep Understanding of Multiple Effects of Natural Products.

It is difficult to understand the entire effect of a natural product because such products generally have multiple effects. We propose a strategy to understand these effects effectively by decomposing them with a profile data analysis method we developed. A transcriptome profile data set was obtained from a public database and analyzed. Considering their high similarity in structure and transcriptome profile, we focused on rescinnamine and syrosingopine. Decomposed effects predicted clear differences between the compounds. Two of the decomposed effects, SREBF1 activation and HDAC inhibition, were investigated experimentally because the relationship between these effects and the compounds had not yet been reported. Analyses in vitro validated these effects, and their strength was consistent with predicted scores. Moreover, the number of outliers in decomposed effects per compound was higher in natural products than in drugs in the data set, which is consistent with the nature of the effects of natural products.

A randomized trial to examine the impact of food on pharmacokinetics of 4-phenylbutyrate and change in amino acid availability after a single oral administration of sodium 4-phenylbutyrarte in healthy volunteers.

Urea cycle disorders (UCDs), inborn errors of hepatocyte metabolism, result in the systemic accumulation of ammonia to toxic levels. Sodium 4-phenylbutyrate (NaPB), a standard therapy for UCDs for over 20 years, generates an alternative pathway of nitrogen deposition through glutamine consumption. Administration during or immediately after a meal is the accepted use of NaPB. However, this regimen is not based on clinical evidence. Here, an open-label, single-dose, five-period crossover study was conducted in healthy adults to investigate the effect of food on the pharmacokinetics of NaPB and determine any subsequent change in amino acid availability. Twenty subjects were randomized to one of four treatment groups. Following an overnight fast, NaPB was administered orally at 4.3 g/m2 (high dose, HD) or 1.4 g/m2 (low dose, LD) either 30 min before or just after breakfast. At both doses, compared with post-breakfast administration, pre-breakfast administration significantly increased systemic exposure of PB and decreased plasma glutamine availability. Pre-breakfast LD administration attenuated plasma glutamine availability to the same extent as post-breakfast HD administration. Regardless of the regimen, plasma levels of branched-chain amino acids (BCAA) were decreased below baseline in a dose-dependent manner. In conclusion, preprandial oral administration of NaPB maximized systemic exposure of the drug and thereby its potency to consume plasma glutamine. This finding may improve poor medication compliance because of the issues with odor, taste, and pill burden of NaPB and reduce the risk of BCAA deficiency in NaPB therapy.

Current progress in identifying endogenous biomarker candidates for drug transporter phenotyping and their potential application to drug development.

Drug transporters play important roles in the elimination of various compounds from the blood. Genetic variation and drug-drug interactions underlie the pharmacokinetic differences for the substrates of drug transporters. Some endogenous substrates of drug transporters have emerged as biomarkers to assess differences in drug transporter activity-not only in animals, but also in humans. Metabolomic analysis is a promising approach for identifying such endogenous substrates through their metabolites. The appropriateness of metabolites is supported by studies in vitro and in vivo, both in animals and through pharmacogenomic or drug-drug interaction studies in humans. This review summarizes current progress in identifying such endogenous biomarkers and applying them to drug transporter phenotyping.

Functional Investigation of Solute Carrier Family 35, Member F2, in Three Cellular Models of the Primate Blood-Brain Barrier.

Understanding the mechanisms of drug transport across the blood-brain barrier (BBB) is an important issue for regulating the pharmacokinetics of drugs in the central nervous system. In this study, we focused on solute carrier family 35, member F2 (SLC35F2), whose mRNA is highly expressed in the BBB. SLC35F2 protein was enriched in isolated mouse and monkey brain capillaries relative to brain homogenates and was localized exclusively on the apical membrane of MDCKII cells and brain microvascular endothelial cells (BMECs) differentiated from human induced pluripotent stem cells (hiPS-BMECs). SLC35F2 activity was assessed using its substrate, YM155, and pharmacological experiments revealed SLC35F2 inhibitors, such as famotidine (half-maximal inhibitory concentration, 160 µM). Uptake of YM155 was decreased by famotidine or SLC35F2 knockdown in immortalized human BMECs (human cerebral microvascular endothelial cell/D3 cells). Furthermore, famotidine significantly inhibited the apical (A)-to-basal (B) transport of YM155 in primary cultured monkey BMECs and hiPS-BMECs. Crucially, SLC35F2 knockout diminished the A-to-B transport and intracellular accumulation of YM155 in hiPS-BMECs. By contrast, in studies using an in situ brain perfusion technique, neither deletion of Slc35f2 nor famotidine reduced brain uptake of YM155, even though YM155 is a substrate of mouse SLC35F2. YM155 uptake was decreased significantly by losartan and naringin, inhibitors for the organic anion transporting polypeptide (OATP) 1A4. These findings suggest SLC35F2 is a functional transporter in various cellular models of the primate BBB that delivers its substrates to the brain and that its relative importance in the BBB is modified by differences in the expression of OATPs between primates and rodents. SIGNIFICANCE STATEMENT: This study demonstrated that SLC35F2 is a functional drug influx transporter in three different cellular models of the primate blood-brain barrier (i.e., human cerebral microvascular endothelial cell/D3 cells, primary cultured monkey BMECs, and human induced pluripotent stem-BMECs) but has limited roles in mouse brain. SLC35F2 facilitates apical-to-basal transport across the tight cell monolayer. These findings will contribute to the development of improved strategies for targeting drugs to the central nervous system.