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Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations.

Infantile onset hereditary spastic paralysis (IAHSP) is a rare neurological disease diagnosed in less than 50 children worldwide. It is transmitted with a recessive pattern and originates from mutations of the ALS2 gene, encoding for the protein alsin and involved in differentiation and maintenance of the upper motoneuron. The exact pathogenic mechanisms of IAHSP and other neurodevelopmental diseases are still largely unknown. However, previous studies revealed that, in the cytosolic compartment, alsin is present as an active tetramer, first assembled from dimer pairs. The C-terminal VPS9 domain is a key interaction site for alsin dimerization. Here, we present an innovative drug discovery strategy, which identified a drug candidate to potentially treat a patient harboring two ALS2 mutations: one truncation at lysine 1457 (not considered) and the substitution of arginine 1611 with a tryptophan (R1611W) in the C-terminus VPS9. With a protein modeling approach, we obtained a R1611W mutant model and characterized the impact of the mutation on the stability and flexibility of VPS9. Furthermore, we showed how arginine 1611 is essential for alsin’s homo-dimerization and how, when mutated to tryptophan, it leads to an abnormal dimerization pattern, disrupting the formation of active tetramers. Finally, we performed a virtual screening, individuating an already therapy-approved compound (MK4) able to mask the mutant residue and re-establishing the alsin tetramers in HeLa cells. MK4 has now been approved for compassionate use.

Background Drug repurposing offers a promising strategy for drug discovery. Drug repurposing involves identifying new therapeutic indications for existing, marketed drugs, thereby reducing the risks, costs, and time typically required for drug development. Various methods exist for drug repurposing, including high-throughput screening of drug compound libraries, computation in silico approaches, literature-based methods, etc. Currently, numerous methods utilize literature for data mining in drug repositioning; however, relatively few approaches leverage literature citation networks for this purpose. Results We identified 19,553 potential drug pairs for repurposing by analyzing biomedical literature data through the Jaccard coefficient. Our results demonstrated that the literature-based Jaccard coefficient was the most effective similarity metric for identifying drug repurposing opportunities. To refine our selection process, we applied a threshold defined by the upper th quantile value of the Jaccard coefficient, enabling us to prioritize promising de novo drug repurposing candidates. Among the identified drug pairs, we found several with strong potential for repurposing, including combinations such as adapalene and bexarotene, guanabenz and tizanidine, alvimopan and methylnaltrexone, etc. Conclusion We created a validation set consisting of both true positives and true negatives for drug pairs using the repoDB dataset, a widely recognized resource for drug repurposing. To evaluate the performance of various similarity metrics for drug pairs, we compared their effectiveness based on AUC, F 1 score, and AUCPR using the validation set.

Schistosomiasis is a neglected tropical disease affecting more than 200 million people worldwide. Chemotherapy relies on one single drug, praziquantel, which is safe but ineffective at killing larval stages of this parasite. Furthermore, concerns have been expressed about the rise in resistance against this drug. In the absence of an antischistosomal vaccine, it is, therefore, necessary to develop new drugs against the different species of schistosomes. Protein kinases are important molecules involved in key cellular processes such as signaling, growth, and differentiation. The kinome of schistosomes has been studied and the suitability of schistosomal protein kinases as targets demonstrated by RNA interference studies. Although protein kinase inhibitors are mostly used in cancer therapy, e.g., for the treatment of chronic myeloid leukemia or melanoma, they are now being increasingly explored for the treatment of non-oncological conditions, including schistosomiasis. Here, we discuss the various approaches including screening of natural and synthetic compounds, de novo drug development, and drug repurposing in the context of the search for protein kinase inhibitors against schistosomiasis. We discuss the status quo of the development of kinase inhibitors against schistosomal serine/threonine kinases such as polo-like kinases (PLKs) and mitogen-activated protein kinases (MAP kinases), as well as protein tyrosine kinases (PTKs).

Chronic widespread pain (CWP) remains challenging due to its heterogeneous causes and complex mechanisms. A total of 2920 plasma proteins are analyzed from 29,254 UK Biobank participants. A total of 256 proteins are identified as cross‐sectionally correlated with CWP. A simple (top 10 proteins) and comprehensive (all significant proteins) proteomic‐based score (ProtS) is created for CWP diagnosis, both outperforming and improving the existing clinical score (area under the curve, AUC: 0.801, 0.723, and 0.791 alone, and 0.856 and 0.880 in combination). In addition, the protein score predicted 13‐years risk of pain‐related traits over the body, including pain onset, progression, and intensity; Moreover, it has stronger associations with nociplastic pain and fibromyalgia compared to nociceptive and neuropathic pain, implying a unique protein signature of different pain mechanisms. Finally, among 434 candidate proteins prioritized in the observational analysis, 18 are corroborated with causal relevance by Mendelian randomization, and importantly, four (CA14, DPEP1, LGALS3, and TNF) showed potential as novel drug targets repurposed for treating CWP. Plasma proteomics is leveraged to decode the biological underpinnings of chronic widespread pain. A nested machine learning framework integrates proteomic signatures, prospective outcomes, and Mendelian randomization to uncover 18 causal proteins. Several targets are druggable, including CA14 and TNF, highlighting new avenues for precision pain therapeutics and drug repurposing in nociplastic pain.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [ 33 ]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [ 204 ]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [ 7 , 11 , 14 , 33 ]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [ 1 , 7 , 155 ]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [ 114 ]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [ 7 ]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [ 151 , 159 ]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation. Highlights Genetic, epigenetic, hormonal, immunological, metabolic, and environmental factors affect sex-biased drug responses. Drug repurposing approaches provide a significant advantage over novel drug development by reducing lengthy and costly clinical trials. Advances in compute processing power and optimized algorithms for computational systems have increased the efficiency and feasibility of computational drug repurposing. Multiple challenges still need to be addressed for sex-aware drug repurposing, including the insufficient understanding of the cause of variation of drug responses due to sex differences, better performing sex-aware repurposing methods, and the lack of large and balanced datasets to develop improved methods. Future low-dimensional representations of molecular association and network approaches could significantly impact the field of sex-aware drug repurposing.

A significant proportion of meningiomas are clinically aggressive, but there is currently no effective chemotherapy for meningiomas. An increasing number of studies have been conducted to develop targeted therapies, yet none have focused on the p53 pathway as a potential target. In this study, we aimed to determine the in vitro and in vivo effects of CEP-1347, a small-molecule inhibitor of MDM4 with known safety in humans. The effects of CEP-1347 and MDM4 knockdown on the p53 pathway in human meningioma cell lines with and without p53 mutation were examined by RT-PCR and Western blot analyses. The growth inhibitory effects of CEP-1347 were examined in vitro and in a mouse xenograft model of meningioma. In vitro, CEP-1347 at clinically relevant concentrations inhibited MDM4 expression, activated the p53 pathway in malignant meningioma cells with wild-type p53, and exhibited preferential growth inhibitory effects on cells expressing wild-type p53, which was mostly mimicked by MDM4 knockdown. CEP-1347 effectively inhibited the growth of malignant meningioma xenografts at a dose that was far lower than the maximum dose that could be safely given to humans. Our findings suggest targeting the p53 pathway with CEP-1347 represents a novel and viable approach to treating aggressive meningiomas.

Objective Prostate cancer remains a prevalent global health challenge, with limited treatment options for advanced stages. There is a critical need to identify effective therapies through systematic integration of genomic and biological data. Methods We analyzed 10,911 single nucleotide polymorphisms (SNPs) in 554 genes from genome- and phenome-wide association studies to identify biological risk genes for prostate cancer. Bioinformatic analysis was used to map these genes to key pathways and potential drug targets. Drug repurposing opportunities were assessed through Connectivity Map (CMap) transcriptomic signature analysis in the PC3 prostate cancer cell line, with additional molecular docking studies to evaluate drug-target interactions. Results We identified 77 prostate cancer-associated genes. Drug repurposing analysis revealed 59 drugs targeting 13 genes, including 11 approved for prostate cancer and 22 in clinical or preclinical development. Notably, 26 candidate drugs had not been previously linked to prostate cancer. CMap analysis prioritized five candidates: estradiol-benzoate and estradiol-cypionate (targeting ESR2), which showed the highest CMap scores, danazol and oxymetholone (targeting AR), and selumetinib (targeting MAP2K1/MEK), each demonstrating potential to modulate key pathways in prostate cancer. Molecular docking analysis further supported these findings, revealing that estradiol-benzoate and estradiol-cypionate have strong predicted binding affinities for ESR2, while selumetinib robustly interacts with MAP2K1. Conversely, danazol and oxymetholone displayed weaker predicted binding, suggesting a more limited capacity for direct protein engagement. Conclusions Integrating genomics, bioinformatics, and molecular docking provides an effective strategy for identifying and prioritizing drug repurposing candidates in prostate cancer. Estradiol-benzoate, estradiol-cypionate, and selumetinib emerge as promising candidates, meriting further preclinical and clinical evaluation for advanced prostate cancer therapy.

In the field of drug repurposing, the use of statins for treating dyslipidemia is considered promising in ovarian cancer treatment based on epidemiological studies and basic research findings. Biomarkers should be established to identify patients who will respond to statin treatment to achieve clinical application. In the present study, we demonstrated that statins have a multifaceted mode of action in ovarian cancer and involve pathways other than protein prenylation. To identify biomarkers that predict the response to statins, we subjected ovarian cancer cells to microarray analysis and calculated Pearson’s correlation coefficients between gene expression and cell survival after statin treatment. The results showed that VDAC1 and LDLRAP1 were positively and negatively correlated with the response to statins, respectively. Histoculture drug response assays revealed that statins were effective in clinical samples. We also confirmed the synergistic effects of statins with paclitaxel and panobinostat and determined that statins are hematologically safe to administer to statin-treated mice. Future clinical trials based on the expression of the biomarkers identified in this study for repurposing statins for ovarian cancer treatment are warranted.

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies.