Neurology. 2024 Feb 27;102(4):e209128. doi: 10.1212/WNL.0000000000209128. Epub 2024 Jan 23.

ABSTRACT

The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.

PMID:38261980 | DOI:10.1212/WNL.0000000000209128

Acta Pharm Sin B. 2024 Jan;14(1):223-240. doi: 10.1016/j.apsb.2023.09.015. Epub 2023 Sep 25.

ABSTRACT

Lenvatinib, a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer, facing limitations due to drug resistance. Here, we applied a multidimensional, high-throughput screening platform comprising patient-derived resistant liver tumor cells (PDCs), organoids (PDOs), and xenografts (PDXs) to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings. Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment, expediting drug repurposing screens. Pharmacological screening identified romidepsin, YM155, apitolisib, NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models. Notably, romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway. A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models. Collectively, our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer, providing a feasible multidimensional platform for personalized medicine.

PMID:38261805 | PMC:PMC10793100 | DOI:10.1016/j.apsb.2023.09.015

Fundam Clin Pharmacol. 2024 Jan 23. doi: 10.1111/fcp.12981. Online ahead of print.

ABSTRACT

BACKGROUND: Due to the limited success in the treatment of lung adenocarcinomas, new treatment protocols are urgently needed to increase the curability rate and the survival of lung cancer patients.

OBJECTIVES: Although statins, like atorvastatin (Ator), and metformin (Met) are widely accepted as hypolipidemic and hypoglycemic drugs, respectively, there are many predictions about their enhancing antitumor effect when they are combined with traditional chemotherapeutics.

METHODS: The individual and combined antiproliferative potential of Ator and Met was tested by MTT-assay in non-small cell lung cancer (NSCLC) A549 cell line, compared to the corresponding effect of Gemcitabine (Gem) with implication on the mechanisms of action.

RESULTS: Initially, both drugs demonstrated concentration-dependent cytotoxicity in A549 cells. Also, their combination index (CI) indicated their synergistic effect at equi-IC50 concentration (CI = 0.00984). Moreover, Ator and/or Met-treated cells revealed disrupted patterns of SOD, CAT, GSH, MDA, and TAC, developed apoptosis, and larger fractions of the cell population were arrested in G0/G1 phase, particularly in cells dually-treated both Ator and Met. These observations were accompanied by downregulation in the expression of iNOS, HO-1, and the angiogenic marker VEGF, meanwhile, an altered expression of MAPK and AMPK was observed.

CONCLUSION: Conclusively, these data suggest that repurposing of Ator and Met demonstrates their individual and combined antiproliferative effect in non-small cell lung cancer and they may adopt a similar mechanism of action.

PMID:38258539 | DOI:10.1111/fcp.12981

Viruses. 2023 Dec 27;16(1):45. doi: 10.3390/v16010045.

ABSTRACT

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

PMID:38257744 | DOI:10.3390/v16010045

Viruses. 2023 Dec 22;16(1):24. doi: 10.3390/v16010024.

ABSTRACT

Zika virus (ZIKV) is a pathogenic member of the flavivirus family, with several unique characteristics. Unlike any other arbovirus, ZIKV can be transmitted sexually and maternally, and thus produce congenital syndromes (CZS) due to its neurotropism. This challenges the search for safe active molecules that can protect pregnant women and their fetuses. In this context, and in the absence of any existing treatment, it seemed worthwhile to test whether the known cytoprotective properties of adiponectin and its pharmacological analog, AdipoRon, could influence the outcome of ZIKV infection. We showed that both AdipoRon and adiponectin could significantly reduce the in vitro infection of A549 epithelial cells, a well-known cell model for flavivirus infection studies. This effect was particularly observed when a pre-treatment was carried out. Conversely, ZIKV revealed an ability to downregulate adiponectin receptor expression and thereby limit adiponectin signaling.

PMID:38257725 | DOI:10.3390/v16010024

Pharmaceuticals (Basel). 2024 Jan 8;17(1):82. doi: 10.3390/ph17010082.

ABSTRACT

Isoniazid is a first-line drug in antitubercular therapy. Isoniazid is one of the most commonly used drugs that can cause liver injury or acute liver failure, leading to death or emergency liver transplantation. Therapeutic approaches for the prevention of isoniazid-induced liver injury are yet to be established. In this study, we identified the gene expression signature for isoniazid-induced liver injury using a public transcriptome dataset, focusing on the differences in susceptibility to isoniazid in various mouse strains. We predicted that lansoprazole is a potentially protective drug against isoniazid-induced liver injury using connectivity mapping and an adverse event reporting system. We confirmed the protective effects of lansoprazole against isoniazid-induced liver injury using zebrafish and patients' electronic health records. These results suggest that lansoprazole can ameliorate isoniazid-induced liver injury. The integrative approach used in this study may be applied to identify novel functions of clinical drugs, leading to drug repositioning.

PMID:38256915 | DOI:10.3390/ph17010082

Pharmaceuticals (Basel). 2023 Dec 29;17(1):63. doi: 10.3390/ph17010063.

ABSTRACT

Paclitaxel is an anticancer agent efficacious in various tumors. There is large interindividual variability in drug plasma concentrations resulting in a wide variability in observed toxicity in patients. Studies have shown the time the concentration of paclitaxel exceeds 0.05 µM is a predictive parameter of toxicity, making dose individualization potentially useful in reducing the adverse effects. To determine paclitaxel drug concentration, a venous blood sample collected 24 h following the end of infusion is required, often inconvenient for patients. Alternatively, using a microsampling device for self-sampling would facilitate paclitaxel monitoring regardless of the patient's location. We investigated the feasibility of collecting venous and capillary samples (using a Mitra® device) from cancer patients to determine the paclitaxel concentrations. The relationship between the venous plasma and whole blood and venous and capillary blood (on Mitra®) paclitaxel concentrations, defined by a Passing-Bablok regression, were 0.8433 and 0.8569, respectively. Demonstrating a clinically acceptable relationship between plasma and whole blood paclitaxel concentration would reduce the need to establish new target concentrations in whole blood. However, in this study, comparison of venous and capillary blood using Mitra® for sampling displayed wide confidence intervals suggesting the results from the plasma and whole blood on this device may not be interchangeable.

PMID:38256896 | DOI:10.3390/ph17010063

Pharmaceuticals (Basel). 2023 Dec 29;17(1):60. doi: 10.3390/ph17010060.

ABSTRACT

Protein tyrosine kinase 6 (PTK6), also known as breast tumor kinase (BRK), serves as a non-receptor intracellular tyrosine kinase within the Src kinases family. Structurally resembling other Src kinases, PTK6 possesses an Src homology 3 (SH3) domain, an Src homology 2 (SH2) domain, and a tyrosine kinase domain (SH1). While considerable efforts have been dedicated to designing PTK6 inhibitors targeting the SH1 domain, which is responsible for kinase activity in various pathways, it has been observed that solely inhibiting the SH1 domain does not effectively suppress PTK6 activity. Subsequent investigations have revealed the involvement of SH2 and SH3 domains in intramolecular and substrate binding interactions, which are crucial for PTK6 function. Consequently, the identification of PTK6 inhibitors targeting not only the SH1 domain but also the SH2 and SH3 domains becomes imperative. Through an in silico structural-based virtual screening approach, incorporating drug repurposing and a consensus docking approach, we have successfully identified four potential ligands capable of concurrently inhibiting the tyrosine kinase domain and SH2/SH3 domains of PT6K simultaneously. This finding suggests potential pathways for therapeutic interventions in PTK6 inhibition.

PMID:38256893 | DOI:10.3390/ph17010060

J Clin Med. 2024 Jan 11;13(2):403. doi: 10.3390/jcm13020403.

ABSTRACT

Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on "prescription drug use-related software" illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug-device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer's disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx "active ingredients" also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug-device combination products are illustrated by Humira® (adalimumab) facing a "patent-cliff" competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.

PMID:38256537 | DOI:10.3390/jcm13020403

Int J Mol Sci. 2024 Jan 16;25(2):1092. doi: 10.3390/ijms25021092.

ABSTRACT

Cancer is one of the primary global causes of death, thus addressing cancer therapy remains a significant challenge, especially in cases where cancers exhibit resistance to treatment [...].

PMID:38256165 | DOI:10.3390/ijms25021092

Int J Mol Sci. 2024 Jan 7;25(2):765. doi: 10.3390/ijms25020765.

ABSTRACT

Drug repurposing is a strategy for discovering new applications of existing drugs for use in various diseases. Despite the use of structured networks in drug research, it is still unclear how drugs interact with one another or with genes. Prostate adenocarcinoma is the second leading cause of cancer mortality in the United States, with an estimated incidence of 288,300 new cases and 34,700 deaths in 2023. In our study, we used integrative information from genes, pathways, and drugs for machine learning methods such as clustering, feature selection, and enrichment pathway analysis. We investigated how drugs affect drugs and how drugs affect genes in human pancreatic cancer cell lines that were derived from bone metastases of grade IV prostate cancer. Finally, we identified significant drug interactions within or between clusters, such as estradiol-rosiglitazone, estradiol-diclofenac, troglitazone-rosiglitazone, celecoxib-rofecoxib, celecoxib-diclofenac, and sodium phenylbutyrate-valproic acid.

PMID:38255837 | DOI:10.3390/ijms25020765

Biomedicines. 2024 Jan 15;12(1):183. doi: 10.3390/biomedicines12010183.

ABSTRACT

The management of metastatic cancer is complicated by chemotherapy resistance. This manuscript provides a comprehensive academic review of strategies to overcome chemotherapy resistance in metastatic cancer. The manuscript presents background information on chemotherapy resistance in metastatic cancer cells, highlighting its clinical significance and the current challenges associated with using chemotherapy to treat metastatic cancer. The manuscript delves into the molecular mechanisms underlying chemotherapy resistance in subsequent sections. It discusses the genetic alterations, mutations, and epigenetic modifications that contribute to the development of resistance. Additionally, the role of altered drug metabolism and efflux mechanisms, as well as the activation of survival pathways and evasion of cell death, are explored in detail. The strategies to overcome chemotherapy resistance are thoroughly examined, covering various approaches that have shown promise. These include combination therapy approaches, targeted therapies, immunotherapeutic strategies, and the repurposing of existing drugs. Each strategy is discussed in terms of its rationale and potential effectiveness. Strategies for early detection and monitoring of chemotherapy drug resistance, rational drug design vis-a-vis personalized medicine approaches, the role of predictive biomarkers in guiding treatment decisions, and the importance of lifestyle modifications and supportive therapies in improving treatment outcomes are discussed. Lastly, the manuscript outlines the clinical implications of the discussed strategies. It provides insights into ongoing clinical trials and emerging therapies that address chemotherapy resistance in metastatic cancer cells. The manuscript also explores the challenges and opportunities in translating laboratory findings into clinical practice and identifies potential future directions and novel therapeutic avenues. This comprehensive review provides a detailed analysis of strategies to overcome chemotherapy resistance in metastatic cancer. It emphasizes the importance of understanding the molecular mechanisms underlying resistance and presents a range of approaches for addressing this critical issue in treating metastatic cancer.

PMID:38255288 | DOI:10.3390/biomedicines12010183

EBioMedicine. 2023 Dec;98:104859. doi: 10.1016/j.ebiom.2023.104859. Epub 2023 Oct 28.

ABSTRACT

BACKGROUND: Drug repurposing provides a cost-effective approach to address the need for breast cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR) and then validate the candidate drugs using population-based analyses.

METHODS: We identified genetic instruments for 1406 actionable targets of approved non-oncological drugs based on gene expression, DNA methylation, and protein expression quantitative trait loci (eQTL, mQTL, and pQTL, respectively). Genome-wide association study (GWAS) summary statistics were obtained from the Breast Cancer Association Consortium (122,977 cases, 105,974 controls). We further conducted a nested case-control study using data retrieved from Swedish registers to validate the candidate drugs that were identified from MR analyses.

FINDINGS: We identified six significant MR associations with gene expression levels (TUBB, MDM2, CSK, ULK3, MC1R and KCNN4) and two significant associations with gene methylation levels across 21 CpG islands (RPS23 and MAPT). Results from the nested case-control study showed that the use of raloxifene (targeting MAPT) was associated with 35% reduced breast cancer risk (odds ratio, OR, 0.65; 95% confidence interval, CI, 0.51-0.83). However, usage of estradiol, tolterodine, and nitrofurantoin (also targeting MAPT) was associated with increased breast cancer risk, with adjusted ORs and 95% CI of 1.10 (1.07-1.13), 1.16 (1.09-1.24), and 1.09 (1.05-1.13), respectively. The effect of raloxifene and nitrofurantoin lost significance in further validation analyses using active-comparator and new-user design.

INTERPRETATION: This large-scale MR analysis, combined with population-based validation, identified eight druggable target genes for breast cancer and suggested that raloxifene is an effective chemoprevention against breast cancer.

FUNDING: Swedish Research Council, Cancerfonden, Crafoordska Stiftelsen, Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, 111 Project and MAS cancer.

PMID:38251461 | DOI:10.1016/j.ebiom.2023.104859

J Biomol Struct Dyn. 2024 Jan 21:1-11. doi: 10.1080/07391102.2024.2302343. Online ahead of print.

ABSTRACT

Non-muscle invasive bladder cancer (NMIBC) refers to a subtype of bladder carcinoma where cancer is localized in the inner lining of bladder. NMIBC consider as one of most costly malignancy and requires significant surgical and therapeutic measure. However, recurrence and progression of tumor is common in treated patients. Here we presented an integrated OMICs approach for the identification and inhibition of NMIBC specific genes. We utilized a case study where three group of patients were compared: 1) Relapsed tumors 2) recurrent tumors and 3) tumor in progression. Common transcriptome signature between patients facing recurrence and progression allowed us to identify three NMIBC specific genes FLT-1, WHSC-1 and CD34. We further utilized novel approach of Co-expressed gene-set enrichment analysis (COGENA) on the differentially expressed genes of this case study. Three drugs (paroxetine, adiphenine and H-89) with role of receptors inhibition were identified and predicted as repurposed drugs for the inhibition NMIBC specific genes. We further tested this hypothesis by performing molecular docking and simulation analysis between cancer specific proteins and drugs. FLT-1 have shown significant stable interaction with both drugs paroxetine and adiphenine whereas WHSC-1 have shown compact interaction with adiphenine and H-89. In the light of these evidence, we suggest that adiphenine could be repositioned as alternate targeted medicine for the treatment of NMIBC. In the future, this study will help for strengthening the strategies development at the molecular level for the control of carcinomas at early as well as detection of active and binding site, receptor-ligand interaction and also make drug repurposing for the early treatment of the carcinomas.Communicated by Ramaswamy H. Sarma.

PMID:38247255 | DOI:10.1080/07391102.2024.2302343

Transl Res. 2024 Jan 19:S1931-5244(24)00018-5. doi: 10.1016/j.trsl.2024.01.008. Online ahead of print.

ABSTRACT

Traumatic brain injury (TBI) has a significant impact on cognitive function, affecting millions of people worldwide. Myelin loss is a prominent pathological feature of TBI, while well-functioning myelin is crucial for memory and cognition. Utilizing drug repurposing to identify effective drug candidates for TBI treatment has gained attention. Notably, recent research has highlighted the potential of clemastine, an FDA-approved allergy medication, as a promising pro-myelinating drug. Therefore, in this study, we aim to investigate whether clemastine can enhance myelination and alleviate cognitive impairment following mild TBI using a clinically relevant rat model of TBI. Mild diffuse TBI was induced using the Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA). Animals were treated with either clemastine or an equivalent volume of the vehicle from day 1 to day 14 post-injury. Following treatment, memory-related behavioral tests were conducted, and myelin pathology in the cortex and hippocampus was assessed through immunofluorescence staining and ProteinSimple® capillary-based immunoassay. Our results showed that TBI leads to significant myelin loss, axonal damage, glial activation, and a decrease in mature oligodendrocytes in both the cortex and hippocampus. The TBI animals also exhibited notable deficits in memory-related tests. In contrast, animals treated with clemastine showed an increase in mature oligodendrocytes, enhanced myelination, and improved performance in the behavioral tests. These preliminary findings support the therapeutic value of clemastine in alleviating TBI-induced cognitive impairment, with substantial clinical translational potential. Our findings also underscore the potential of remyelinating therapies for TBI.

PMID:38246342 | DOI:10.1016/j.trsl.2024.01.008

BMC Chem. 2024 Jan 20;18(1):14. doi: 10.1186/s13065-023-01110-1.

ABSTRACT

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer's randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, - 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein-ligand complexes, yielding five promising novel UPPS inhibitors.

PMID:38245752 | DOI:10.1186/s13065-023-01110-1

Ageing Res Rev. 2024 Jan 18:102195. doi: 10.1016/j.arr.2024.102195. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) has infected over 700 million people, with up to 30% developing neurological manifestations, including dementias. However, there is a lack of understanding of common molecular brain markers causing Alzheimer's disease (AD). COVID-19 has etiological cofactors with AD, making patients with AD a vulnerable population at high risk of experiencing more severe symptoms and worse consequences. Both AD and COVID-19 have upregulated several shared kinases, leading to the repositioning of kinase inhibitors (KIs) for the treatment of both diseases. This review provides an overview of the interactions between the immune system and the nervous system in relation to receptor tyrosine kinases, including epidermal growth factor receptors, vascular growth factor receptors, and non-receptor tyrosine kinases such as Bruton tyrosine kinase, spleen tyrosine kinase, c-ABL, and JAK/STAT. We will discuss the promising results of kinase inhibitors in pre-clinical and clinical studies for both COVID-19 and Alzheimer's disease (AD), as well as the challenges in repositioning KIs for these diseases. Understanding the shared kinases between AD and COVID-19 could help in developing therapeutic approaches for both.

PMID:38244862 | DOI:10.1016/j.arr.2024.102195

Comput Biol Med. 2024 Jan 6;170:107936. doi: 10.1016/j.compbiomed.2024.107936. Online ahead of print.

ABSTRACT

Drug repurposing is a strategy aiming at uncovering novel medical indications of approved drugs. This process of discovery can be effectively represented as a link prediction task within a medical knowledge graph by predicting the missing relation between the disease entity and the drug entity. Typically, the links to be predicted pertain to rare types, thereby necessitating the task of few-shot link prediction. However, the sparsity of neighborhood information and weak triplet interactions result in less effective representations, which brings great challenges to the few-shot link prediction. Therefore, in this paper, we proposed a meta-learning framework based on a multi-level attention network (MLAN) to capture valuable information in the few-shot scenario for drug repurposing. First, the proposed method utilized a gating mechanism and a graph attention network to effectively filter noise information and highlight the valuable neighborhood information, respectively. Second, the proposed commonality relation learner, employing a set transformer, effectively captured triplet-level interactions while remaining insensitive to the size of the support set. Finally, a model-agnostic meta-learning training strategy was employed to optimize the model quickly on each meta task. We conducted validation of the proposed method on two datasets specifically designed for few-shot link prediction in medical field: COVID19-One and BIOKG-One. Experimental results showed that the proposed model had significant advantages over state-of-the-art few-shot link prediction methods. Results also highlighted the valuable insights of the proposed method, which successfully integrated the components within a unified meta-learning framework for drug repurposing.

PMID:38244473 | DOI:10.1016/j.compbiomed.2024.107936

J Infect Dis. 2024 Jan 19:jiae022. doi: 10.1093/infdis/jiae022. Online ahead of print.

NO ABSTRACT

PMID:38243873 | DOI:10.1093/infdis/jiae022

Database (Oxford). 2024 Jan 18;2024:baad095. doi: 10.1093/database/baad095.

ABSTRACT

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL: http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

PMID:38242684 | DOI:10.1093/database/baad095