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Abstract Background Fibrodysplasia Ossificans Progressiva (FOP) is a genetic, progressive and devastating disease characterized by severe heterotopic ossification (HO), loss of mobility and early death. There are no FDA approved medications. The STOPFOP team identified AZD0530 (saracatinib) as a potent inhibitor of the ALK2/ACVR1-kinase which is the causative gene for this rare bone disease. AZD0530 was proven to prevent HO formation in FOP mouse models. The STOPFOP trial investigates the repositioning of AZD0530, originally developed for ovarian cancer treatment, to treat patients with FOP. Methods The STOPFOP trial is a phase 2a study. It is designed as a European, multicentre, 6-month double blind randomized controlled trial of AZD0530 versus placebo, followed by a 12-month trial comparing open-label extended AZD0530 treatment with natural history data as a control. Enrollment will include 20 FOP patients, aged 18–65 years, with the classic FOP mutation (ALK2 R206H). The primary endpoint is objective change in heterotopic bone volume measured by low-dose whole-body computer tomography (CT) in the RCT phase. Secondary endpoints include 18F NaF PET activity and patient reported outcome measures. Discussion Clinical trials in rare diseases with limited study populations pose unique challenges. An ideal solution for limiting risks in early clinical studies is drug repositioning – using existing clinical molecules for new disease indications. Using existing assets may also allow a more fluid transition into clinical practice. With positive study outcome, AZD0530 may provide a therapy for FOP that can be rapidly progressed due to the availability of existing safety data from 28 registered clinical trials with AZD0530 involving over 600 patients. Trial registration EudraCT, 2019–003324-20. Registered 16 October 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2019-003324-20/NL . Clinicaltrials.gov , NCT04307953 . Registered 13 March 2020.

Abstract Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which also will introduces noise into the data and affect the performance of the model. In this paper, first, a novel drug repositioning method, namely DVGEDR, is proposed, which generates two subgraphs of the target drug–disease pair to fuse biological information and integrate drug–disease associations from two distinct perspectives: drug–disease heterogeneous network and similarity networks. Next, a Multiple Attention Graph encoder (MAGencoder) module is designed to learn subgraph features and explore relationships between entities, which also improve the interpretability of the model. Finally, a graph enhancement mechanism is devised to improve the perception of critical information of model, enabling the model to flexibly process different graph structures. Performance comparisons with baseline models on three public datasets validate the state-of-the-art performance of DVGEDR in the field of drug repositioning. In case study, DVGEDR identifies 10 new candidate drugs for breast cancer and COVID-19, demonstrating not only superior performance in experimental settings but also potential therapeutic advantages in clinical environments. Furthermore, we select two sets of instances and further analyzed the attention distribution of the different nodes in the subgraph to explain the decision process of the model.

The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) is a growing concern for the high incidence rate of hepatocellular carcinoma (HCC) globally. The progression of NAFLD to HCC is heterogeneous and non-linear, involving intermediate stages of non-alcoholic steatohepatitis (NASH), liver fibrosis, and cirrhosis. There is a high unmet clinical need for appropriate diagnostic, prognostic, and therapeutic options to tackle this emerging epidemic. Unfortunately, at present, there is no validated marker to identify the risk of developing HCC in patients suffering from NAFLD or NASH. Additionally, the current treatment protocols for HCC don’t differentiate between viral infection or NAFLD-specific etiology of the HCC and have a limited success rate. The mammalian target of rapamycin complex 1 (mTORc1) is an important protein involved in many vital cellular processes like lipid metabolism, glucose homeostasis, and inflammation. These cellular processes are highly implicated in NAFLD and its progression to severe liver manifestations. Additionally, hyperactivation of mTORc1 is known to promote cell proliferation, which can contribute to the genesis and progression of tumors. Many mTORc1 inhibitors are being evaluated for different types of cancers under various phases of clinical trials. This paper deliberates on the strong pathological implication of the mTORc1 signaling pathway in NAFLD and its progression to NASH and HCC and advocates for a systematic investigation of known mTORc1 inhibitors in suitable pre-clinical models of HCC having NAFLD/NASH-specific etiology.

Following the coronavirus disease-2019 outbreak caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), there is an ongoing need to seek drugs that target COVID-19. First off, novel drugs have a long development cycle, high investment cost, and are high risk. Second, novel drugs must be evaluated for activity, efficacy, safety, and metabolic performance, contributing to the development cycle, investment cost, and risk. We searched the Cochrane COVID-19 Study Register (including PubMed, Embase, CENTRAL, ClinicalTrials.gov, WHO ICTRP, and medRxiv), Web of Science (Science Citation Index, Emerging Citation Index), and WHO COVID-19 Coronaviral Disease Global Literature to identify completed and ongoing studies as of February 20, 2024. We evaluated the pharmacological effects, in vivo and in vitro data of the 16 candidates in the paper. The difficulty of studying these candidates in clinical trials involving COVID-19 patients, dosage of repurposed drugs, etc. is discussed in detail. Ultimately, Metformin is more suitable for prophylactic administration or mildly ill patients; the combination of Oseltamivir, Tamoxifen, and Dexamethasone is suitable for moderately and severely ill patients; and more clinical trials are needed for Azvudine, Ribavirin, Colchicine, and Cepharanthine to demonstrate efficacy.

Xiaona Lang,1 Jinlei Liu,2 Guangzhong Zhang,3 Xin Feng,1 Wenchao Dan3 1Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China; 2Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China; 3Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email dan9521@foxmail.comBackground: In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.Methodology: The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.Results: The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.Conclusion: The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.Keywords: drug repositioning, drug discovery, bibliometric, citation analysis, trend analysis

In a previous paper, we demonstrated the synergistic action of the anti-ischemic lubeluzole (Lube S) on the cytotoxic activity of doxorubicin (Dox) and paclitaxel in human ovarian cancer A2780 and lung cancer A549 cells. In the present paper, we extended in vitro the study to the multi-drug-resistant A2780/DX3 cell line to verify the hypothesis that the Dox and Lube S drug association may potentiate the antitumor activity of this anticancer compound also in the context of drug resistance. We also evaluated some possible mechanisms underlying this activity. We analyzed the antiproliferative activity in different cancer cell lines. Furthermore, apoptosis, Dox accumulation, MDR1 downregulation, ROS, and NO production in A2780/DX3 cells were also evaluated. Our results confirm that Lube S improves Dox antiproliferative and apoptotic activities through different mechanisms of action, all of which may contribute to the final antitumor effect. Moderate stereoselectivity was found, with Lube S significantly more effective than its enantiomer (Lube R) and the corresponding racemate (Lube S/R). Docking simulation studies on the ABCB1 Cryo-EM structure supported the hypothesis that Lube S forms a stable MDR1-Dox-Lube S complex, which hampers the protein transmembrane domain flipping and blocks the efflux of Dox from resistant A2780/DX3 cells. In conclusion, our in vitro studies reinforce our previous hypothesis for repositioning the anti-ischemic Lube S as a potentiating agent in anticancer chemotherapy.

Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs. According to the simplified molecular input line entry specification (SMILES) code of drugs, firstly, the atomic features were extracted to construct the point sets, and edge sets were constructed according to atomic bonds. Then the undirected graph with atomic features was generated by RDKit tool and the graph attention layer was used to extract the drug feature information. Five ion channel target proteins were screened from the whole SARS-CoV-2 genome sequences of NCBI database, and the protein features were extracted by convolution neural network (CNN). Using attention mechanism and graph convolutional network (GCN), the extracted drug features and target features information were connected. After two full connection layers operation, the drug-target affinity was output, and model was obtained. Kiba dataset was used to train the model and determine the model parameters. Compared with DeepDTA, WideDTA, graph attention network (GAT), GCN and graph isomorphism network (GIN) models, it was proved that the mean square error (MSE) of the proposed model was decreased by 0.055, 0.04, 0.001, 0.046, 0.013 and the consistency index (CI) was increased by 0.028, 0.016, 0.003, 0.03 and 0.01, respectively. It can predict the drug-target affinity more accurately. According to the prediction results of drug-target affinity of SARS-CoV-2 ion channel targets, seven kinds of small molecule drugs acting on five ion channel targets were obtained, namely SCH-47112, Dehydroaltenusin, alternariol 5-o-sulfate, LPA1 antagonist 1, alternariol, butin, and AT-9283.These drugs provide a reference for drug repositioning and precise treatment of COVID-19.

Abstract Background The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to predict drugs for the rare disease. Method In this paper, we focus on father–son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What’s more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network. Result Comparing with traditional methods, GCAN makes full use of father–son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning. Conclusion The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Abstract In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80–85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

d-Glucosamine is a protective dietary supplement or medicine for osteoarthritis of the knee, a musculoskeletal disease that leads to a significant deterioration in daily activities and quality of life. As glucosamine can restore damaged cartilage worn down by joint disease, there was hope it could also improve symptoms. Data from several clinical studies on the efficacy of glucosamine on knee joint function conducted since the 1980s have been used in certain meta-analyses and epidemiological studies since 2010, yet the effect of glucosamine on the knee joints remains controversial. Concurrently, many drugs have been investigated for their anti-aging properties. Among these drugs, glucosamine has recently been discovered to be a potential substance with convincing evidence for increasing human lifespan. More interestingly, Zhi-Hao et al. have recently reported that the use of glucosamine was associated with a reduction in total mortality regardless of its effect on the knee (Annals of the Rheumatic Diseases 79(2020)829–836). Additionally, glucosamine prolongs the lifespan of the nematode Caenorhabditis elegans, possibly due to its calorie restriction-mimicking effect by improving energy metabolism and inducing autophagy. Thus, the recent large-scale epidemiological report on glucosamine intake and mortality, as well as our animal studies (Journal of Applied Glycoscience 65(2018)37–43), has become relevant. However, the potential significance of metabolism of glucosamine in anti-aging should be more clearly investigated in the future. This paper presents the novel concept of repositioning glucosamine from a dietary supplement or an OTC drug for osteoarthritis improvements to an anti-aging drug for healthy lifespan extension.

Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the homogeneous multi-view information with multiple drug similarity features and then extracts the intra-domain embedding of drugs through the combination of HGCN and channel attention mechanism. Secondly, inter-domain information of known drug-disease associations is extracted by graph convolutional networks combining node and edge embedding (NEEGCN), and a heterogeneous network composed of drugs, proteins and diseases is built as an important auxiliary to enhance the inter-domain message passing of drugs and diseases. Besides, the intra-domain embedding of diseases is also extracted through HGCN. Ultimately, intra-domain and inter-domain embeddings of drugs and diseases are integrated as the final embedding for calculating the drug-disease correlation matrix. Through 10-fold cross-validation on some benchmark datasets, we find that the AUPR of EMPHCN reaches 0.593 (T1) and 0.526 (T2), respectively, and the AUC achieves 0.887 (T1) and 0.961 (T2) respectively, which shows that EMPHCN has an advantage over other state-of-the-art prediction methods. Concerning the new disease association prediction, the AUC of EMPHCN through the five-fold cross-validation reaches 0.806 (T1) and 0.845 (T2), which are 4.3% (T1) and 4.0% (T2) higher than the second best existing methods, respectively. In the case study, EMPHCN also achieves satisfactory results in real drug repositioning for breast carcinoma and Parkinson’s disease.

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

Inflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required. This article has an associated First Person interview with the joint first authors of the paper.

Abstract Background Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. Results We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock. Conclusions Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

Drug repositioning, which refers to the identification of new clinical indications for existing drugs, has become an important strategy for drug discovery. The most recent studies in pharmacogenomics have demonstrated that drugs can target microRNAs (miRNAs) and regulate their expression levels. Given the intriguing fact that the inappropriate expression of miRNAs is related to many kinds of human diseases, developing small-molecule drugs to target specific miRNAs and modulate their activities would be a promising approach to disease treatment, which offers an innovative insight for drug repositioning. In this paper, we proposed a miRNA-based computational method HNBI to infer novel drug-disease associations for drug repositioning. Similarity measurements and experimentally supported association information were first integrated to construct a three-layer drug-miRNA-disease heterogeneous network. Our method then updated the strength of weight between unlinked drug-miRNA, miRNA-disease, and drug-disease pairs iteratively till stabilized. Based on information flow on the heterogeneous network, the final weight of drug-disease associations was received by summarizing the values of paths connecting the two types of nodes. We prioritized the potential drug-disease associations according to the new weight. When applied to the collected data set for cross-validation experiments, our method showed superior performance in drug-disease association predictions compared with two state-of-the-art methods. Furthermore, our method HNBI incorporated information of target miRNAs to understand the mechanisms of action of drugs and the molecular mechanisms of diseases. A case study on the drug Terazosin indicated that some predicted indications with high ranks were supported by the recent literature, which further illustrated the practical usefulness of our method. Finally, comprehensive predictions of associations between drugs and diseases were released for future drug repositioning studies.

As a multifactorial and multiorgan syndrome, cancer cachexia is associated with decreased tolerance to antitumor treatments and increased morbidity and mortality rates. The current approaches for the treatment of this syndrome are not always effective and well established. Drug repurposing or repositioning consists of the investigation of pharmacological components that are already available or in clinical trials for certain diseases and explores if they can be used for new indications. Its advantages comparing to de novo drugs development are the reduced amount of time spent and costs. In this paper, we selected drugs already available or in clinical trials for non-cachexia indications and that are related to the pathways and molecular components involved in the different phenotypes of cancer cachexia syndrome. Thus, we introduce known drugs as possible candidates for drug repurposing in the treatment of cancer-induced cachexia.

Abstract Background Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs. Methods We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. Results In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence. Conclusions This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Penfluridol has robust antipsychotic efficacy and is a first-generation diphenylbutylpiperidine. Its effects last for several days after a single oral dose and it can be administered once a week to provide better compliance and symptom control. Recently; strong antitumour effects for penfluridol were discovered in various cancer cell lines; such as breast; pancreatic; glioblastoma; and lung cancer cells via several distinct mechanisms. Therefore; penfluridol has drawn much attention as a potentially novel anti-tumour agent. In addition; the anti-cancer effects of penfluridol have been demonstrated in vivo: results showed slight changes in the volume and weight of organs at doses tested in animals. This paper outlines the potential for penfluridol to be developed as a next-generation anticancer drug.