Alpha-1 antitrypsin inhibits Clostridium botulinum C2 toxin, Corynebacterium diphtheriae diphtheria toxin and B. anthracis fusion toxin.

The bacterium Clostridium botulinum, well-known for producing botulinum neurotoxins, which cause the severe paralytic illness known as botulism, produces C2 toxin, a binary AB-toxin with ADP-ribosyltranferase activity. C2 toxin possesses two separate protein components, an enzymatically active A-component C2I and the binding and translocation B-component C2II. After proteolytic activation of C2II to C2IIa, the heptameric structure binds C2I and is taken up via receptor-mediated endocytosis into the target cells. Due to acidification of endosomes, the C2IIa/C2I complex undergoes conformational changes and consequently C2IIa forms a pore into the endosomal membrane and C2I can translocate into the cytoplasm, where it ADP-ribosylates G-actin, a key component of the cytoskeleton. This modification disrupts the actin cytoskeleton, resulting in the collapse of cytoskeleton and ultimately cell death. Here, we show that the serine-protease inhibitor α1-antitrypsin (α1AT) which we identified previously from a hemofiltrate library screen for PT from Bordetella pertussis is a multitoxin inhibitor. α1AT inhibits intoxication of cells with C2 toxin via inhibition of binding to cells and inhibition of enzyme activity of C2I. Moreover, diphtheria toxin and an anthrax fusion toxin are inhibited by α1AT. Since α1AT is commercially available as a drug for treatment of the α1AT deficiency, it could be repurposed for treatment of toxin-mediated diseases.

In silico comparative analysis of cestode and human NPC1 provides insights for ezetimibe repurposing to visceral cestodiases treatment.

Visceral cestodiases, like cysticercoses and echinococcoses, are caused by cystic larvae from parasites of the Cestoda class and are endemic or hyperendemic in many areas of the world. Current therapeutic approaches for these diseases are complex and present limitations and risks. Therefore, new safer and more effective treatments are urgently needed. The Niemann-Pick C1 (NPC1) protein is a cholesterol transporter that, based on genomic data, would be the solely responsible for cholesterol uptake in cestodes. Considering that human NPC1L1 is a known target of ezetimibe, used in the treatment of hypercholesterolemia, it has the potential for repurposing for the treatment of visceral cestodiases. Here, phylogenetic, selective pressure and structural in silico analyses were carried out to assess NPC1 evolutive and structural conservation, especially between cestode and human orthologs. Two NPC1 orthologs were identified in cestode species (NPC1A and NPC1B), which likely underwent functional divergence, leading to the loss of cholesterol transport capacity in NPC1A. Comparative interaction analyses performed by molecular docking of ezetimibe with human NPC1L1 and cestode NPC1B pointed out to similarities that consolidate the idea of cestode NPC1B as a target for the repurposing of ezetimibe as a drug for the treatment of visceral cestodiases.

Drug Repurposing Approach to Identify Candidate Drug Molecules for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a high mortality rate due to the limited therapeutic options. Systemic drug treatments improve the patient's life expectancy by only a few months. Furthermore, the development of novel small molecule chemotherapeutics is time-consuming and costly. Drug repurposing has been a successful strategy for identifying and utilizing new therapeutic options for diseases with limited treatment options. This study aims to identify candidate drug molecules for HCC treatment through repurposing existing compounds, leveraging the machine learning tool MDeePred. The Open Targets Platform, UniProt, ChEMBL, and Expasy databases were used to create a dataset for drug target interaction (DTI) predictions by MDeePred. Enrichment analyses of DTIs were conducted, leading to the selection of 6 out of 380 DTIs identified by MDeePred for further analyses. The physicochemical properties, lipophilicity, water solubility, drug-likeness, and medicinal chemistry properties of the candidate compounds and approved drugs for advanced stage HCC (lenvatinib, regorafenib, and sorafenib) were analyzed in detail. Drug candidates exhibited drug-like properties and demonstrated significant target docking properties. Our findings indicated the binding efficacy of the selected drug compounds to their designated targets associated with HCC. In conclusion, we identified small molecules that can be further exploited experimentally in HCC therapeutics. Our study also demonstrated the use of the MDeePred deep learning tool in in silico drug repurposing efforts for cancer therapeutics.

CEP-1347 Boosts Chk2-Mediated p53 Activation by Ionizing Radiation to Inhibit the Growth of Malignant Brain Tumor Cells.

Radiation therapy continues to be the cornerstone treatment for malignant brain tumors, the majority of which express wild-type p53. Therefore, the identification of drugs that promote the ionizing radiation (IR)-induced activation of p53 is expected to increase the efficacy of radiation therapy for these tumors. The growth inhibitory effects of CEP-1347, a known inhibitor of MDM4 expression, on malignant brain tumor cell lines expressing wild-type p53 were examined, alone or in combination with IR, by dye exclusion and/or colony formation assays. The effects of CEP-1347 on the p53 pathway, alone or in combination with IR, were examined by RT-PCR and Western blot analyses. The combination of CEP-1347 and IR activated p53 in malignant brain tumor cells and inhibited their growth more effectively than either alone. Mechanistically, CEP-1347 and IR each reduced MDM4 expression, while their combination did not result in further decreases. CEP-1347 promoted IR-induced Chk2 phosphorylation and increased p53 expression in concert with IR in a Chk2-dependent manner. The present results show, for the first time, that CEP-1347 is capable of promoting Chk2-mediated p53 activation by IR in addition to inhibiting the expression of MDM4 and, thus, CEP-1347 has potential as a radiosensitizer for malignant brain tumors expressing wild-type p53.

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug-target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.

Free Radical Production Induced by Nitroimidazole Compounds Lead to Cell Death in Leishmania infantum Amastigotes.

Leishmania infantum is the vector-borne trypanosomatid parasite causing visceral leishmaniasis in the Mediterranean basin. This neglected tropical disease is treated with a limited number of obsolete drugs that are not exempt from adverse effects and whose overuse has promoted the emergence of resistant pathogens. In the search for novel antitrypanosomatid molecules that help overcome these drawbacks, drug repurposing has emerged as a good strategy. Nitroaromatic compounds have been found in drug discovery campaigns as promising antileishmanial molecules. Fexinidazole (recently introduced for the treatment of stages 1 and 2 of African trypanosomiasis), and pretomanid, which share the nitroimidazole nitroaromatic structure, have provided antileishmanial activity in different studies. In this work, we have tested the in vitro efficacy of these two nitroimidazoles to validate our 384-well high-throughput screening (HTS) platform consisting of L. infantum parasites emitting the near-infrared fluorescent protein (iRFP) as a biomarker of cell viability. These molecules showed good efficacy in both axenic and intramacrophage amastigotes and were poorly cytotoxic in RAW 264.7 and HepG2 cultures. Fexinidazole and pretomanid induced the production of ROS in axenic amastigotes but were not able to inhibit trypanothione reductase (TryR), thus suggesting that these compounds may target thiol metabolism through a different mechanism of action.

Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors.

Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery.

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Tanomastat exerts multi-targeted inhibitory effects on viral capsid dissociation and RNA replication in human enteroviruses.

BACKGROUND: Global cyclical outbreaks of human enterovirus infections has positioned human enterovirus A71 (EV-A71) as a neurotropic virus of clinical importance. However, there remains a scarcity of internationally approved antivirals and vaccines. METHODS: In pursuit of repurposing drugs for combating human enteroviruses, we employed a comprehensive pharmacophore- and molecular docking-based virtual screen targeting EV-A71 capsid protein VP1-4, 3C protease, and 3D polymerase proteins. Among 15 shortlisted ligand candidates, we dissected the inhibitory mechanism of Tanomastat in cell-based studies and evaluated its in vivo efficacy in an EV-A71-infected murine model. FINDINGS: We demonstrated that Tanomastat exerts dose-dependent inhibition on EV-A71 replication, with comparable efficacy profiles in enterovirus species A, B, C, and D in vitro. Time-course studies suggested that Tanomastat predominantly disrupts early process(es) of the EV-A71 replication cycle. Mechanistically, live virus particle tracking and docking predictions revealed that Tanomastat specifically impedes viral capsid dissociation, potentially via VP1 hydrophobic pocket binding. Bypassing its inhibition on entry stages, we utilized EV-A71 replication-competent, 3Dpol replication-defective, and bicistronic IRES reporter replicons to show that Tanomastat also inhibits viral RNA replication, but not viral IRES translation. We further showed that orally administered Tanomastat achieved 85% protective therapeutic effect and alleviated clinical symptoms in EV-A71-infected neonatal mice. INTERPRETATION: Our study establishes Tanomastat as a broad-spectrum anti-enterovirus candidate with promising pre-clinical efficacy, warranting further testing for potential therapeutic application. FUNDING: MOE Tier 2 grants (MOE-T2EP30221-0005, R571-000-068-592, R571-000-076-515, R571-000-074-733) and A∗STARBiomedical Research Council (BMRC).

Shared etiology of Mendelian and complex disease supports drug discovery.

BACKGROUND: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. METHODS: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. RESULTS: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. CONCLUSIONS: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

The future of pharmacology and therapeutics of the arachidonic acid cascade in the next decade: Innovative advancements in drug repurposing.

Many drugs can act on multiple targets or disease pathways, regardless of their original purpose. Drug repurposing involves reevaluating existing compounds for new medical uses. This can include repositioning approved drugs, redeveloping unapproved drugs, or repurposing any chemical, nutraceutical, or biotherapeutic product for new applications. Traditional drug development is slow, expensive, and has high failure rates. Drug repurposing can speed up the process, costing less and saving time. This approach can save 6-7 years of early-stage research time. Drug repurposing benefits from existing compounds with optimized structures and approved for clinical use with associated structure-activity relationship publications, supporting the development of new effective compounds. Drug repurposes can now utilize advanced in silico screening enabled by artificial intelligence (AI) and sophisticated tissue and organ-level in vitro models. These models more accurately replicate human physiology and improve the selection of existing drugs for further pre-clinical testing and, eventually, clinical trials for new indications. This mini-review discusses some examples of drug repurposing and novel strategies for further development of compounds for targets of the arachidonic acid cascade. In particular, we will delve into the prospect of repurposing antiplatelet agents for cancer prevention and addressing the emerging noncanonical functionalities of 5-lipoxygenase, potentially for leukemia therapy.

Repurposing antihypertensive drugs for pain disorders: a drug-target mendelian randomization study.

Objective: Addressing the rising prevalence of pain disorders and limitations of current analgesics, our study explores repurposing antihypertensive drugs for pain management, inspired by the link between hypertension and pain. We leverage a drug-target Mendelian Randomization (MR) approach to explore their dual benefits and establish causal connections. Methods: A comprehensive compilation of antihypertensive drug classes was undertaken through British National Formulary, with their target genes identified using the DrugBank database. Relevant single nucleotide polymorphisms (SNPs) associated with these targets were selected from published genomic studies on systolic blood pressure (SBP) as genetic instruments. These SNPs were validated through MR against acute coronary artery disease (CAD) to ensure genes not linked to CAD were excluded from acting as proxies for antihypertensive drugs. An MR analysis of 29 pain-related outcomes was conducted using the FinnGen R10 database employing the selected and validated genetic instruments. We utilized the Inverse Variance Weighted (IVW) method for primary analysis, applying Bonferroni correction to control type I error. IVW's multiplicative random effects (MRE) addressed heterogeneity, and MR-PRESSO managed pleiotropy, ensuring accurate causal inference. Results: Our analysis differentiates strong and suggestive evidence in linking antihypertensive drugs to pain disorder risks. Strong evidence was found for adrenergic neuron blockers increasing migraine without aura risk, loop diuretics reducing panniculitis, and vasodilator antihypertensives lowering limb pain risk. Suggestive evidence suggests alpha-adrenoceptor blockers might increase migraine risk, while beta-adrenoceptor blockers could lower radiculopathy risk. Adrenergic neuron blockers also show a potential protective effect against coxarthrosis (hip osteoarthritis) and increased femgenpain risk (pain and other conditions related to female genital organs and menstrual cycle). Additionally, suggestive links were found between vasodilator antihypertensives and reduced radiculopathy risk, and both alpha-adrenoceptor blockers and renin inhibitors possibly decreasing dorsalgianas risk (unspecified dorsalgia). These findings highlight the intricate effects of antihypertensive drugs on pain disorders, underlining the need for further research. Conclusion: The findings indicate that antihypertensive medications may exert varied effects on pain management, suggesting a repurposing potential for treating specific pain disorders. The results advocate for further research to confirm these associations and to explore underlying mechanisms, to optimize pain management practices.

Anticancer effect of the antipsychotic agent penfluridol on epithelial ovarian cancer.

OBJECTIVE: Chemoresistant-epithelial ovarian cancer (EOC) has a poor prognosis, prompting the search for new therapeutic drugs. The diphenylbutylpiperidine (DPBP) class of antipsychotic drugs used in schizophrenia has shown anticancer effects. This study aimed to investigate the preclinical efficacy of penfluridol, fluspirilene, and pimozide (DPBP) using in vitro and in vivo models of EOC. METHODS: Human EOC cell lines A2780, HeyA8, SKOV3ip1, A2780-CP20, HeyA8-MDR, and SKOV3-TR were treated with penfluridol, fluspirilene, and pimozide, and cell proliferation, apoptosis, and migration were assessed. The preclinical efficacy of DPBP was also investigated using in vivo mouse models, including cell lines and patient-derived xenografts (PDX) of EOC. RESULTS: DPBP drugs significantly decreased cell proliferation in chemosensitive (A2780, HeyA8, and SKOV3ip1) and chemoresistant (A2780-CP20, HeyA8-MDR, and SKOV3-TR) cell lines. Among these drugs, penfluridol exerted a relatively stronger cytotoxic effect on all cell lines. Penfluridol significantly increased apoptosis and inhibited migration of EOC cells. In the cell line xenograft mouse model with HeyA8, the penfluridol group showed significantly decreased tumor weight compared with the control group. In the paclitaxel-resistant model with HeyA8-MDR, the penfluridol group had significantly decreased tumor weight compared with the paclitaxel or control groups. Penfluridol exerted anticancer effects on the PDX model. CONCLUSION: Penfluridol exerted significant anticancer effects on EOC cells and xenograft models, including PDX. Thus, penfluridol therapy, as a drug repurposing strategy, might be a potential therapeutic for EOCs.

Treatment of congenital disorders of glycosylation: An overview.

While the identification and diagnosis of congenital disorders of glycosylation (CDG) have rapidly progressed, the available treatment options are still quite limited. Mostly, we are only able to manage the disease symptoms rather than to address the underlying cause. However, recent years have brought about remarkable advances in treatment approaches for some CDG. Innovative therapies, targeting both the root cause and resulting manifestations, have transitioned from the research stage to practical application. The present paper aims to provide a detailed overview of these exciting developments and the rising concepts that are used to treat these ultra-rare diseases.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

Drug repurposing against antibiotic resistant bacterial pathogens.

The growing prevalence of MDR and XDR bacterial pathogens is posing a critical threat to global health. Traditional antibiotic development paths have encountered significant challenges and are drying up thus necessitating innovative approaches. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, offers a promising alternative to combat resistant pathogens. By leveraging pre-existing safety and efficacy data, drug repurposing accelerates the development of new antimicrobial therapy regimes. This review explores the potential of repurposing existing FDA approved drugs against the ESKAPE and other clinically relevant bacterial pathogens and delves into the identification of suitable drug candidates, their mechanisms of action, and the potential for combination therapies. It also describes clinical trials and patent protection of repurposed drugs, offering perspectives on this evolving realm of therapeutic interventions against drug resistance.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction.

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.