Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

A number of genome-wide association studies (GWASs) and meta-analyses of genetic variants have been performed in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. We reinterpreted previous studies using false-positive report probability (FPRP) and Bayesian false discovery probability (BFDP). This study searched publications in PubMed and Excerpta Medica Database (EMBASE) up to February 2018. Identification of noteworthy associations were analyzed using FPRP and BFDP, and data (i.e., odds ratio (OR), 95% confidence interval (CI), p-value) related to significant associations were separately extracted. Using filtered gene variants, gene ontology (GO) enrichment analysis and protein–protein interaction (PPI) networks were performed. Overall, 241 articles were identified, and 7 were selected for analysis. Single nucleotide polymorphisms (SNPs) discovered by GWASs were shown to be noteworthy, whereas only 27% of significant results from meta-analyses of observational studies were noteworthy. Eighty-five percent of SNPs with borderline p-values (5.0 × 10⁻⁸ < p < 0.05) in GWASs were found to be noteworthy. No overlapping SNPs were found between PR3-ANCA and MPO-ANCA vasculitis. GO analysis revealed immune-related GO terms, including “antigen processing and presentation of peptide or polysaccharide antigen via major histocompatibility complex (MHC) class II”, “interferon-gamma-mediated (IFN-γ) signaling pathway”. By using FPRP and BFDP, network analysis of noteworthy genetic variants discovered genetic risk factors associated with the IFN-γ pathway as novel mechanisms potentially implicated in the complex pathogenesis of ANCA-associated vasculitis.

Epilepsy is a central neurological disorder affecting individuals of all ages and causing unpredictable seizures. In spite of the improved efficacy of new antiepileptic drugs and novel therapy, there are still approximately 20%~30% of patients, who have either intractable or uncontrolled seizures. The epilepsy drug–target network (EDT) is constructed and successfully demonstrates the characteristics and efficacy of popularly used AEDs through the identification of causative genes for 60 epilepsy patients. We discovered that the causative genes of most intractable patients were not the targets of existing AEDs, as well as being very far from the etiological mechanisms of existing AEDs in the functional networks. We show that the existence of new drugs that target the causative genes of intractable epilepsy patients, which will be potential candidates for refractory epilepsy patients. Our systematic approach demonstrates a new possibility for drug repositioning through the combination of the drug-target and functional networks.

This study aimed to verify noteworthy findings between genetic risk factors and autism spectrum disorder (ASD) by employing the false positive report probability (FPRP) and the Bayesian false-discovery probability (BFDP). PubMed and the Genome-Wide Association Studies (GWAS) catalog were searched from inception to 1 August, 2019. We included meta-analyses on genetic factors of ASD of any study design. Overall, twenty-seven meta-analyses articles from literature searches, and four manually added articles from the GWAS catalog were re-analyzed. This showed that five of 31 comparisons for meta-analyses of observational studies, 40 out of 203 comparisons for the GWAS meta-analyses, and 18 out of 20 comparisons for the GWAS catalog, respectively, had noteworthy estimations under both Bayesian approaches. In this study, we found noteworthy genetic comparisons highly related to an increased risk of ASD. Multiple genetic comparisons were shown to be associated with ASD risk; however, genuine associations should be carefully verified and understood.

Drug–drug interactions (DDI) occur because of the unexpected pharmacological effects of drug pairs. Although drug efficacy can be improved by taking two or more drugs in the short term, this may cause inevitable side effects. Currently, multiple drugs are prescribed based on the experience or knowledge of the clinician, and there is no standard database that can be referred to as safe co-prescriptions. Thus, accurately identifying DDI is critical for patient safety and treatment modalities. Many computational methods have been developed to predict DDIs based on chemical structures or biological features, such as target genes or functional mechanisms. However, some features are only available for certain drugs, and their pathological mechanisms cannot be fully employed to predict DDIs by considering the direct overlap of target genes. In this study, we propose a novel deep learning model to predict DDIs by utilizing chemical structure similarity and protein–protein interaction (PPI) information among drug-binding proteins, such as carriers, transporters, enzymes, and targets (CTET) proteins. We applied the random walk with restart (RWR) algorithm to propagate drug CTET proteins across a PPI network derived from the STRING database, which will lead to the successful incorporation of the hidden biological mechanisms between CTET proteins and disease-associated genes. We confirmed that the RWR propagation of CTET proteins helps predict DDIs by utilizing indirectly co-regulated biological mechanisms. Our method identified the known DDIs between clinically proven epilepsy drugs. Our results demonstrated the effectiveness of PRID in predicting DDIs in known drug combinations as well as unknown drug pairs. PRID could be helpful in identifying novel DDIs and associated pharmacological mechanisms to cause the DDIs.

Background: Drug–drug interactions (DDIs) involve pharmacokinetic or pharmacodynamic changes that occur when multiple drugs are co-administered, potentially leading to reduced efficacy or adverse effects. As polypharmacy becomes more prevalent, especially among patients with chronic diseases, scalable and accurate DDI prediction has become increasingly important. Although numerous computational approaches have been proposed to predict DDIs using various modalities such as chemical structure and biological networks, the intrinsic heterogeneity of these data complicates unified modeling; Methods: We address this challenge with a multimodal deep learning framework that integrates three complementary, heterogeneous modalities: (i) chemical structure, (ii) BioBERT-derived semantic embeddings (a domain-specific large language model, LLM), and (iii) pharmacological mechanisms through the CTET proteins. To incorporate indirect biological pathways within the PPI network, we apply a random walk with restart (RWR) algorithm. Results: Across features combinations, fusing structural feature with BioBERT embedding achieved the highest classification accuracy (0.9655), highlighting the value of readily available data and the capacity of domain-specific language models to encode pharmacological semantics from unstructured texts. Conclusions: BioBERT embeddings were particularly informative, capturing subtle pharmacological relationships between drugs and improving prediction of potential DDIs. Beyond predictive performance, the framework is readily applicable to real-world clinical workflows, providing rapid DDI references to support the polypharmacy decision-making.