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Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug–target–disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro . The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical–protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

Background Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is required to identify the target proteins associated with the phenotype. Methods In this study, we propose a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data in Connectivity Map and a machine learning classification technique, which we call the “transcriptomic approach.” Results Unlike existing methods such as the chemogenomic approach, the transcriptomic approach enabled the prediction of target proteins without dependence on prior knowledge of compound chemical structures. The prediction accuracy of the chemogenomic approach was highly depended on compounds structure similarities in data sets. In contrast, the prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds. Conclusions The transcriptomic approach reported here is expected to be a useful tool for structure-independent prediction of target proteins for drug candidate compounds.

Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases. Here, we present KampoDB ( http://wakanmoview.inm.u-toyama.ac.jp/kampo/ ), a novel platform for the analysis of natural medicines, which provides various useful scientific resources on Japanese traditional formulas Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these constituent compounds. Potential target proteins of these constituent compounds were predicted by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins, and has functional annotations for biological pathways and molecular functions. KampoDB is useful for mode-of-action analysis of natural medicines and prediction of new indications for a wide range of diseases.

Background Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. Methods The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). Results Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. Conclusions The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases. Plain Language Summary Adding drug treatments together can sometimes produce better results for patients. We introduced a new computer-based method called SyndrumNET, designed to identify effective drug combinations for treating diseases. The method uses data about how diseases and drugs interact at a molecular level to predict which drugs work well together. Tested on six different diseases, such as asthma and different types of cancer, SyndrumNET proved to be more accurate than previous approaches. For example, most of the drug combinations predicted by SyndrumNET to rank highly have shown better combination effects on leukemia cells. This method also helped understand why certain drug combinations work better by analyzing their effects on cellular pathways. The findings suggest that SyndrumNET could be a valuable tool in developing more effective treatment for various complex diseases. Iida et al. predict synergistic drug combinations using a computational method termed SyndrumNET. Validation of predictions in chronic myeloid leukemia using in vitro cell survival assays reveal synergistic anticancer effects in 14 of 17 top predicted drug pairings.

In an open pilot trial, six patients with various hereditary forms of spinocerebellar ataxia (SCA) were assigned to topiramate (50 mg/day) for 24 weeks. Four patients completed the protocol without adverse events. Of these four patients, topiramate was effective for three patients. Some patients with SCA could respond to treatment with topiramate. We proposed a computational drug repositioning approach, performed a screening of exsiting drugs, and selected topiramate as a drug candidate for spinocerebellar ataxia (SCA). This pilot study suggests that topiramate could be effective for some patients with hereditary SCA.

Background Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. Results We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers. Conclusions Our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design.

C-type natriuretic peptide (CNP) is known to promote chondrocyte proliferation and bone formation; however, CNP's extremely short half-life necessitates continuous intravascular administration to achieve bone-lengthening effects. Vosoritide, a CNP analog designed for resistance to neutral endopeptidase, allows for once daily administration. Nonetheless, it distributes systemically rather than localizing to target tissues, which may result in adverse effects such as hypotension. To enhance local drug delivery and therapeutic efficacy, we developed a novel synthetic protein by fusing a collagen-binding domain (CBD) to CNP, termed CBD-CNP. This fusion protein exhibited stability under heat conditions and retained the collagen-binding ability and bioactivity as CNP. CBD-CNP localized to articular cartilage in fetal murine tibiae and promoted bone elongation. Spatial transcriptomic analysis revealed that the upregulation of chondromodulin expression may contribute to its therapeutic effects. Treatment of CBD-CNP mixed with collagen powder to a fracture site of a mouse model increased bone mineral content and bone volume rather than CNP-22. Intra-articular injection of CBD-CNP to a mouse model of knee osteoarthritis suppressed subchondral bone thickening. By addressing the limitations of CNP's rapid degeneration, CBD-CNP leverages its collagen-binding capacity to achieve targeted, sustained delivery in collagen-rich tissues, offering a promising strategy for enhancing chondrogenesis and osteogenesis.

Sessile serrated adenomas (SSAs) are known to be precursors of sporadic colorectal cancers (CRCs) with microsatellite instability (MSI), and to be tightly associated with BRAF mutation and the CpG island methylator phenotype (CIMP). Consequently, colonoscopic identification of SSAs has important implications for preventing CRCs, but accurate endoscopic diagnosis is often difficult. Our aim was to clarify which endoscopic findings are specific to SSAs. The morphological, histological and molecular features of 261 specimens from 226 colorectal tumors were analyzed. Surface microstructures were analyzed using magnifying endoscopy. Mutation in BRAF and KRAS was examined by pyrosequencing. Methylation of p16, IGFBP7, MLH1 and MINT1, -2, -12 and -31 was analyzed using bisulfite pyrosequencing. Through retrospective analysis of a training set (n=145), we identified a novel surface microstructure, the Type II open-shape pit pattern (Type II-O), which was specific to SSAs with BRAF mutation and CIMP. Subsequent prospective analysis of an independent validation set (n=116) confirmed that the Type II-O pattern is highly predictive of SSAs (sensitivity, 65.5%; specificity, 97.3%). BRAF mutation and CIMP occurred with significant frequency in Type II-O-positive serrated lesions. Progression of SSAs to more advanced lesions was associated with further accumulation of aberrant DNA methylation and additional morphological changes, including the Type III, IV and V pit patterns. Our results suggest the Type II-O pit pattern is a useful hallmark of the premalignant stage of CRCs with MSI and CIMP, which could serve to improve the efficacy of colonoscopic surveillance.

Background Adequate blood supply for the reconstructed organ is important for safe esophagogastric anastomosis during esophagectomy. Recently, indocyanine green (ICG) has been used for visualization of the blood supply when anastomosis is performed in vascular surgery. To visualize the blood supply for reconstruction, we employed ICG fluorescence during esophagectomy. Methods From August 2008, 40 patients received cervical or thoracic esophagectomy. They consisted of 33 patients having esophagectomy for thoracic esophageal cancer, 3 being treated for cervical esophageal cancer, and 4 with double cancer of the thoracic and cervical regions. Before and after pulling up the reconstructed organ, 2.5 mg of ICG was injected as a bolus. Then ICG fluorescence was detected by a camera and recorded. Results ICG fluorescence was easily detected in all patients at 1 min after injection. The vascular network was well visualized in the gastric wall, colonic grafts, and free jejunal grafts. In five patients, we also performed anastomosis between the short gastric vein and the external cervical vein or superficial cervical vein. The intraoperative and postoperative course of all patients was uneventful apart from three anastomotic leakages. Conclusions ICG fluorescence can be employed to evaluate the blood supply to reconstructed organs and can be useful in selecting the patients who do not need additional vessel anastomosis. However, anastomotic leakage was not reduced, so the microcirculation detected by ICG fluorescence did not necessarily provide appropriate blood supply for a viable anastomosis.