Am J Orthod Dentofacial Orthop. 2025 Feb;167(2):242-249. doi: 10.1016/j.ajodo.2024.09.006.

ABSTRACT

INTRODUCTION: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.

METHODS: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model. To assess the model's performance, 15 cone-beam computed tomography scans from the test group were inputted into the model. The ground truth was obtained from manual segmentation data. Metrics including the Dice similarity coefficient, Hausdorff 95%, precision, recall, and segmentation times were calculated. In addition, surface deviations and volumetric differences between the automated and manual segmentation results were analyzed.

RESULTS: The automated model showed a high level of similarity to the manual segmentation results, with a mean Dice similarity coefficient of 0.926 ± 0.014. The Hausdorff distance was 1.358 ± 0.466 mm, whereas the mean recall and precision values were 0.941 ± 0.028 and 0.941 ± 0.022, respectively. There were no statistically significant differences in the arithmetic mean of the surface deviation for the entire mandible and 11 different anatomic regions. In terms of volumetric comparisons, the difference between the 2 groups was 1.62 mm³, which was not statistically significant.

CONCLUSIONS: The automated model was found to be suitable for clinical use, demonstrating a high degree of agreement with the reference manual method. Clinicians can use open-source software to develop custom automated segmentation models tailored to their specific needs.

PMID:39863342 | DOI:10.1016/j.ajodo.2024.09.006

J Immunother Cancer. 2025 Jan 25;13(1):e010083. doi: 10.1136/jitc-2024-010083.

ABSTRACT

BACKGROUND: B7 homolog 3 (B7-H3), an overexpressed antigen across multiple solid cancers, represents a promising target for CAR T cell therapy. This study investigated the expression of B7-H3 across various solid tumors and developed novel monoclonal antibodies (mAbs) targeting B7-H3 for CAR T cell therapy.

METHODS: Expression of B7-H3 across various solid tumors was evaluated using RNA-seq data from TCGA, TARGET, and GTEx datasets and by flow cytometry staining. B7-H3-specific mAbs were developed by immunizing mice with human B7-H3, screening with ELISA, and analyzing kinetics with surface plasmon resonance. These mAbs were used to create second-generation CAR constructs, which were evaluated in vitro and in vivo for their antitumor function.

RESULTS: We identified four mAb clones from immunized mice, with three demonstrating high specificity and affinity. The second-generation B7-H3 CAR T cells derived from these mAbs exhibited robust cytotoxicity against B7-H3-positive targets and successfully infiltrated and eliminated tumor spheroids in vitro. In a xenograft mouse model of glioblastoma, these CAR T cells, particularly those derived from clone A2H4, eradicated the primary tumor, and effectively controlled rechallenge tumor, resulting in prolonged survival of the xenograft mice. In vivo T cell trafficking revealed high accumulation and persistence of A2H4-derived CAR T cells at the tumor site.

CONCLUSIONS: Our results provide novel B7-H3-targeted CAR T cells with high efficacy, paving the way for clinical translation of solid tumor treatment.

PMID:39863300 | DOI:10.1136/jitc-2024-010083

Biochim Biophys Acta Mol Basis Dis. 2025 Jan 23:167688. doi: 10.1016/j.bbadis.2025.167688. Online ahead of print.

ABSTRACT

Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes. Herein, we present NetSDR, a Network-based Subtype-specific Drug Repurposing framework for prioritizing repurposed drugs specific to certain cancer subtypes, guided by subtype-specific proteomic signatures and network perturbations. First, by integrating cancer subtype information into a network-based method, we developed a pipeline to recognize subtype-specific functional modules. Next, we conducted drug response analysis for each module to identify the "therapeutic module" and then used deep learning to construct weighted drug response network for the particular subtype. Finally, we employed a perturbation response scanning-based drug repurposing method, which incorporates dynamic information, to facilitate the prioritization of candidate drugs. Applying the framework to gastric cancer, we attested the significance of the extracellular matrix module in treatment strategies and discovered a promising potential drug target, LAMB2, as well as a series of possible repurposed drugs. This study demonstrates a systems biology framework for precise drug repurposing in cancer and other complex diseases.

PMID:39862994 | DOI:10.1016/j.bbadis.2025.167688

Cell. 2025 Jan 22:S0092-8674(25)00032-7. doi: 10.1016/j.cell.2025.01.003. Online ahead of print.

ABSTRACT

Knowledge of protein-metabolite interactions can enhance mechanistic understanding and chemical probing of biochemical processes, but the discovery of endogenous ligands remains challenging. Here, we combined rapid affinity purification with precision mass spectrometry and high-resolution molecular docking to precisely map the physical associations of 296 chemically diverse small-molecule metabolite ligands with 69 distinct essential enzymes and 45 transcription factors in the gram-negative bacterium Escherichia coli. We then conducted systematic metabolic pathway integration, pan-microbial evolutionary projections, and independent in-depth biophysical characterization experiments to define the functional significance of ligand interfaces. This effort revealed principles governing functional crosstalk on a network level, divergent patterns of binding pocket conservation, and scaffolds for designing selective chemical probes. This structurally resolved ligand interactome mapping pipeline can be scaled to illuminate the native small-molecule networks of complete cells and potentially entire multi-cellular communities.

PMID:39862855 | DOI:10.1016/j.cell.2025.01.003

EBioMedicine. 2025 Jan 24;112:105558. doi: 10.1016/j.ebiom.2025.105558. Online ahead of print.

ABSTRACT

BACKGROUND: Aging increases disease susceptibility and reduces vaccine responsiveness, highlighting the need to better understand the aging immune system and its clinical associations. Studying the human immune system, however, remains challenging due to its complexity and significant inter-individual variability.

METHODS: We conducted an immune profiling study of 550 elderly participants (≥60 years) and 100 young controls (20-40 years) from the RESIST Senior Individuals (SI) cohort. Extensive demographic, clinical, and laboratory data were collected. Multi-color spectral flow cytometry and 48-plex plasma cytokine assays were used for deep immune phenotyping. Data were analyzed using unsupervised clustering and multi-dataset integration approaches.

FINDINGS: We studied 97 innate and adaptive immune cell populations, revealing intricate age- and sex-related changes in the elderly immune system. Our large sample size allowed detection of even subtle changes in cytokines and immune cell clusters. Integrative analysis combining clinical, laboratory, and immunological data revealed systems-level aging signatures, including shifts in specific immune cell subpopulations and cytokine concentrations (e.g., HGF and CCL27). Additionally, we identified unique immune signatures associated with smoking, obesity, and diseases such as osteoporosis, heart failure, and gout.

INTERPRETATION: This study provides one of the most comprehensive immune profiles of elderly individuals, uncovering high-resolution immune changes associated with aging. Our findings highlight clinically relevant immune signatures that enhance our understanding of aging-related diseases and could guide future research into new treatments, offering translational insights into human health and aging.

FUNDING: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155-project number 390874280.

PMID:39862806 | DOI:10.1016/j.ebiom.2025.105558

Placenta. 2025 Jan 20;161:31-38. doi: 10.1016/j.placenta.2025.01.009. Online ahead of print.

ABSTRACT

INTRODUCTION: Fusion of mononucleated cytotrophoblasts into syncytium leads to trophoblast senescence. Yet, premature senescence is associated with preeclampsia, fetal growth restriction (FGR), and related obstetrical syndromes. A set of 28 transcripts that comprise senescence-associated secretory phenotype (SASP) was recently described in placentas from women with preeclampsia. We posited that this transcript set is uniquely regulated in late-term placentas or in placentas derived from participants with major obstetrical syndromes.

METHODS: Using our large placental RNAseq bank, we analyzed data from healthy participants (n = 33) with histologically normal placentas, representing delivery at 37-41 weeks. To represent diseases, we included RNAseq data from participants (n = 220) with severe preeclampsia, FGR, FGR with a hypertensive disorder (FGR + HDP), or spontaneous preterm delivery, and healthy controls (n = 129). We also assessed the expression of several SASPs in primary human trophoblasts that were exposed in vitro to hypoxia, reduced differentiation, or ferroptotic or apoptotic signals.

RESULTS: Among the 28 SASP transcripts analyzed, eight had a significant change between deliveries at <37 weeks vs ≥ 41 weeks, including upregulation of FSTL3, IL1RL1, INHBA, and VEGFA and downregulation of STC1, RARRES2, MRC2, and SELP. The expression of SASP mRNAs was enriched in the placentas from the assessed syndromes, with most expression changes in placentas from FGR/HDP. Our in vitro analysis associated hypoxia or apoptosis with altered expression of FSTL3, VEGFA, and DKK1.

DISCUSSION: A set of placental SASPs defines late-term placentas, placental dysfunction-related clinical syndromes, and in vitro-defined trophoblast injury. Trophoblastic SASP signatures may assist in characterizing placental senescence in health and disease.

PMID:39862734 | DOI:10.1016/j.placenta.2025.01.009

Genomics Proteomics Bioinformatics. 2025 Jan 25:qzaf002. doi: 10.1093/gpbjnl/qzaf002. Online ahead of print.

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.

PMID:39862439 | DOI:10.1093/gpbjnl/qzaf002

STAR Protoc. 2025 Jan 24;6(1):103589. doi: 10.1016/j.xpro.2024.103589. Online ahead of print.

ABSTRACT

The intestine features a two-front nutrient supply environment, comprising an enteral side enriched with microbial and dietary metabolites and a serosal side supplied by systemic nutrients, collectively supporting intestinal and systemic homeostasis, but there is currently no optimal approach for extracting and assessing the local intestinal microenvironment. Here, we present a protocol for constructing a nutrient supply model in mice and extracting gut interstitial fluid (GIF) via centrifugation. This model and the extracted GIF are suitable for downstream analyses. For complete details on the use and execution of this protocol, please refer to Zhang et al.1.

PMID:39862429 | DOI:10.1016/j.xpro.2024.103589

Bioinformatics. 2025 Jan 25:btaf043. doi: 10.1093/bioinformatics/btaf043. Online ahead of print.

ABSTRACT

SUMMARY: Gene and genome duplications are major evolutionary forces that shape the diversity and complexity of life. However, different duplication modes have distinct impacts on gene function, expression, and regulation. Existing tools for identifying and classifying duplicated genes are either outdated or not user-friendly. Here, we present doubletrouble, an R/Bioconductor package that provides a comprehensive and robust framework for analyzing duplicated genes from genomic data. doubletrouble can detect and classify gene pairs as derived from six duplication modes (segmental, tandem, proximal, retrotransposon-derived, DNA transposon-derived, and dispersed duplications), calculate substitution rates, detect signatures of putative whole-genome duplication events, and visualize results as publication-ready figures. We applied doubletrouble to classify the duplicated gene repertoire in 822 eukaryotic genomes, and results were made available through a user-friendly web interface.

AVAILABILITY AND IMPLEMENTATION: doubletrouble is available on Bioconductor (https://bioconductor.org/packages/doubletrouble), and the source code is available in a GitHub repository (https://github.com/almeidasilvaf/doubletrouble). doubletroubledb is available online at https://almeidasilvaf.github.io/doubletroubledb/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online and at https://github.com/almeidasilvaf/doubletrouble_paper.

PMID:39862387 | DOI:10.1093/bioinformatics/btaf043

Pediatr Nephrol. 2025 Jan 25. doi: 10.1007/s00467-025-06656-x. Online ahead of print.

ABSTRACT

BACKGROUND: Limited research exists regarding the genetic profile, clinical characteristics, and outcomes of refractory rickets in children from India.

METHODS: Patients with refractory rickets aged ≤ 18 years were enrolled. Data regarding clinical features, etiology, genotype-phenotype correlation, and estimated glomerular filtration rate (eGFR) were recorded.

RESULTS: Seventy-two patients with refractory rickets (non-nutritional, with normal kidney function at presentation) from 65 families attending the pediatric nephrology clinic from 2005-2024 were included. Median (IQR) age at first presentation was 2 (1, 4) years. Clinical features included failure-to-thrive (49 [68.1%]), polyuria (37 [51.4%]), nephrocalcinosis (33 [45.8%]), fractures (10 [13.9%]), and hypokalemic paralysis (4 [5.6%]). Major etiologies included distal renal tubular acidosis (dRTA) [34(47.2%)], hereditary hypophosphatemic rickets (11 [15.3%]), cystinosis (9 [12.5%]), Lowe syndrome (3 [4.2%]), vitamin D-dependent rickets (4 [5.5%]), and Fanconi-Bickel syndrome (3 [4.2%]). Next-generation sequencing identified 61 variants among 71 children tested (85.9%), of which 56 variants (among 55 children) were pathogenic (P)/likely-pathogenic (LP) (77.5% diagnostic-yield). P/LP variants included SLC4A1 (n = 14), CTNS (n = 9), PHEX (n = 8), WDR72 (n = 5), OCRL (n = 2), SLC2A2 (n = 3), ATP6V0A4 (n = 4), VDR (n = 3), CLDN16 (n = 2), ATP6V1B1 (n = 1), SLC12A1 (n = 1), CLCN5 (n = 1), SLC34A3 (n = 1), ATP7B (n = 1), and KCNJ1 (n = 1). Fifteen novel P/LP variants and five novel variants-of-uncertain-significance (VUS) were identified. c.2573C > A in exon 19 among SLC4A1-dRTA (n = 14) was a recurrent mutation. Five patients with cystinosis, two patients with SLC4A1-dRTA, two with WDR72-dRTA, and two with Bartter syndrome showed progression to CKD stage 2 or greater during follow-up.

CONCLUSIONS: dRTA, X-linked hypophosphatemic rickets, and cystinosis were common causes of refractory rickets. The c.2573C > A variant in exon 19 was a recurrent mutation in SLC4A1-dRTA.

PMID:39862309 | DOI:10.1007/s00467-025-06656-x

BMJ Open. 2025 Jan 25;15(1):e086446. doi: 10.1136/bmjopen-2024-086446.

ABSTRACT

OBJECTIVES: To describe the prevalence of sub-optimal monitoring for selected higher-risk medicines in older community-dwelling adults and to evaluate patient characteristics and outcomes associated with sub-optimal monitoring.

STUDY DESIGN: Retrospective observational study (2011-2015) using historical general practice-based cohort data and linked dispensing data from a national pharmacy claims database.

SETTING: Irish primary care.

PARTICIPANTS: 625 community-dwelling adults aged ≥70 years and prescribed at least one higher-risk medicine during the 5-year study period.

PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the prevalence of sub-optimal laboratory monitoring using a composite measure of published medication monitoring indicators, with a focus on commonly prescribed higher-risk medicines such as diuretics and anticoagulants. Poisson regression was used to assess the patient characteristics associated with sub-optimal monitoring and explanatory variables included the number of medicines, age, sex, deprivation and anxiety/depression symptoms. Logistic regression was used to explore the association between baseline sub-optimal monitoring and the odds of adverse health outcomes (unplanned healthcare utilisation, adverse drug reactions and mortality).

RESULTS: Of 625 participants, the mean age was 77.7 years, 53% were female, the mean number of drugs was 7.3 (SD 3.3) and 499 (79.8%) had ≥1 unmonitored dispensing over 5 years. The number of drugs, deprivation and anxiety/depression symptoms were significantly associated with sub-optimal monitoring, with the strongest association seen for anxiety/depression symptoms (incidence rate ratio: 1.33, 95% CI 1.05 to 1.68). There was a small but significant association between baseline sub-optimal monitoring and emergency department visits at follow-up, but no evidence of an association with unplanned hospital admissions, mortality or adverse drug reactions.

CONCLUSION: The prevalence of sub-optimal medication monitoring was high, and number of drugs, deprivation and anxiety/depression symptoms were significantly associated with sub-optimal monitoring. However, the public health impact of these findings remains uncertain, as there was no clear evidence of an association between sub-optimal monitoring and adverse health outcomes. Further research is needed to evaluate the effect of improved monitoring strategies and the optimal timing for drug monitoring of higher risk medications.

PMID:39863414 | DOI:10.1136/bmjopen-2024-086446

Viruses. 2025 Jan 16;17(1):117. doi: 10.3390/v17010117.

ABSTRACT

Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome. Validation of docking protocols were achieved with reference inhibitors of the respective targets. Our in silico docking screen identified five drugs (dexamethasone, tadalafil, mefloquine, ergocalciferol, and flunarizine) with strong predicted binding affinity to LASV proteins involved in the formation of the vRNP. We used cell-based functional assays to evaluate the antiviral activity of the five selected drugs. We found that flunarizine, a calcium-entry blocker, inhibited the vRNP activity of LASV and LCMV and virus surface glycoprotein fusion activity required for mammarenavirus cell entry. Consistently with these findings, flunarizine significantly reduced peak titers of LCMV in a multi-step growth kinetics assay in human A549 cells. Flunarizine is being used in several countries worldwide to treat vertigo and migraine, supporting the interest in exploring its repurposing as a candidate drug to treat LASV infections.

PMID:39861906 | DOI:10.3390/v17010117

Viruses. 2025 Jan 13;17(1):92. doi: 10.3390/v17010092.

ABSTRACT

The ongoing monkeypox (mpox) disease outbreak has spread to multiple countries in Central Africa and evidence indicates it is driven by a more virulent clade I monkeypox virus (MPXV) strain than the clade II strain associated with the 2022 global mpox outbreak, which led the WHO to declare this mpox outbreak a public health emergency of international concern. The FDA-approved small molecule antiviral tecovirimat (TPOXX) is recommended to treat mpox cases with severe symptoms, but the limited efficacy of TPOXX and the emergence of TPOXX resistant MPXV variants has challenged this medical practice of care and highlighted the urgent need for alternative therapeutic strategies. In this study we have used vaccinia virus (VACV) as a surrogate of MPXV to assess the antiviral efficacy of combination therapy of TPOXX together with mycophenolate mofetil (MMF), an FDA-approved immunosuppressive agent that we have shown to inhibit VACV and MPXV, or the N-myristoyltransferase (NMT) inhibitor IMP-1088. Both MMF and IMP-1088 drugs exhibited strong dose-dependent antiviral activity against VACV and mpox, and potent synergistic effects in conjunction with TPOXX. Our findings support combination therapy of direct-acting (TPOXX) and host-targeted (MMF and IMP-1088) antivirals as a promising approach to treat mpox and prevent the emergence and spread of TPOXX-resistant MPXV variants.

PMID:39861882 | DOI:10.3390/v17010092

Pharmaceuticals (Basel). 2025 Jan 2;18(1):43. doi: 10.3390/ph18010043.

ABSTRACT

BACKGROUND/OBJECTIVES: Septic cardiomyopathy (SCM) is a severe cardiac complication of sepsis, characterized by cardiac dysfunction with limited effective treatments. This study aimed to identify repurposable drugs for SCM by integrated multi-omics and network analyses.

METHODS: We generated a mouse model of SCM induced by lipopolysaccharide (LPS) and then obtained comprehensive metabolic and genetic data from SCM mouse hearts using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and RNA sequencing (RNA-seq). Using network proximity analysis, we screened for FDA-approved drugs that interact with SCM-associated pathways. Additionally, we tested the cardioprotective effects of two drug candidates in the SCM mouse model and explored their mechanism-of-action in H9c2 cells.

RESULTS: Network analysis identified 129 drugs associated with SCM, which were refined to 14 drug candidates based on strong network predictions, proven anti-infective effects, suitability for ICU use, and minimal side effects. Among them, acetaminophen and pyridoxal phosphate significantly improved cardiac function in SCM moues, as demonstrated by the increased ejection fraction (EF) and fractional shortening (FS), and the reduced levels of cardiac injury biomarkers: B-type natriuretic peptide (BNP) and cardiac troponin I (cTn-I). In vitro assays revealed that acetaminophen inhibited prostaglandin synthesis, reducing inflammation, while pyridoxal phosphate restored amino acid balance, supporting cellular function. These findings suggest that both drugs possess protective effects against SCM.

CONCLUSIONS: This study provides a robust platform for drug repurposing in SCM, identifying acetaminophen and pyridoxal phosphate as promising candidates for clinical translation, with the potential to improve treatment outcomes in septic patients with cardiac complications.

PMID:39861106 | DOI:10.3390/ph18010043

Pharmaceuticals (Basel). 2024 Dec 27;18(1):21. doi: 10.3390/ph18010021.

ABSTRACT

Infection with the protozoan parasite Trypanosoma cruzi causes human Chagas disease. Benznidazole (BNZ) and nifurtimox are the current drugs for the treatment; however, they induce severe adverse side effects in patients; therefore, there is a need to improve the treatment effectiveness and efficiency of these drugs for its safer use. Background/Objective: Glyburide, glipizide, and gliquidone, hypoglycemic drugs for diabetes treatment, were previously predicted to bind to dihydrofolate reductase-thymidylate synthase from T. cruzi by in silico docking analysis; they also showed antiproliferative effects against T. cruzi epimastigotes, the stage of the insect vector. In the present study, the potential parasiticidal effect of these antidiabetic drugs was tested in monotherapy and bi-therapy with BNZ in human cells in vitro and in animals. Methods: Evaluation was performed in (a) a model of in vitro infection of T. cruzi trypomastigotes using human fibroblasts as host cells and (b) in mice infected with T. cruzi. Results: The antidiabetic drugs in monotherapy showed antiparasitic effects in preventing infection progression (trypomastigotes release), with an IC50 of 8.4-14.3 µM in comparison to that of BNZ (0.26 µM) in vitro. However, in bi-therapy, the presence of just 0.5 or 1 µM of the antidiabetics decreased the BNZ IC50 by 5-10 times to 0.03-0.05 µM. Remarkably, the antidiabetic drugs in monotherapy decreased the infection in mice by 40-60% in a similar extent to BNZ (80%). In addition, the combination of BNZ plus antidiabetics perturbed the antioxidant metabolites in epimastigotes. Conclusions: These results identified antidiabetics as potential drugs in combination therapy with BNZ to treat T. cruzi infection.

PMID:39861083 | DOI:10.3390/ph18010021

Pharmaceuticals (Basel). 2024 Dec 25;18(1):14. doi: 10.3390/ph18010014.

ABSTRACT

BACKGROUND: Chemoresistance is a major obstacle in high-grade serous carcinoma (HGSC) treatment. Although many patients initially respond to chemotherapy, the majority of them relapse due to Carboplatin and Paclitaxel resistance. Drug repurposing has surfaced as a potentially effective strategy that works synergically with standard chemotherapy to bypass chemoresistance. In a prior study, using 2D cultures and two HGSC chemoresistant cell lines, it was demonstrated that combining Carboplatin or Paclitaxel with Pitavastatin or Ivermectin resulted in the most notable synergy. Acknowledging that 2D culture systems are limited in reflecting the tumor architecture, 3D cultures were generated to provide insights on treatment efficacy tests in more complex models.

OBJECTIVES: We aimed to investigate whether combining Carboplatin or Paclitaxel with Pitavastatin or Ivermectin offers therapeutic benefits in a Cultrex-based 3D model.

METHODS: Here, the cytotoxicity of Carboplatin and Paclitaxel, both alone and in combination with Pitavastatin or Ivermectin, were analyzed on two chemoresistant tumor cell lines, OVCAR8 and OVCAR8 PTX R C, in 3D cultures. Cellular viability was assessed using CellTiter-Glo® Luminescent assays. Also, it explored synergistic interactions using zero interaction potency, Loewe, Bliss independence, and High-single agent reference models.

RESULTS: Our research indicates combining chemotherapeutic drugs with Pitavastatin or Ivermectin yields significantly more cytotoxic effects than chemotherapy alone. For all the combinations tested, at least one model indicated an additive effect; however, only the combination of Paclitaxel and Ivermectin consistently demonstrated an additive effect across all chemoresistant cell lines cultured in 3D models, as well as in all four synergy reference models used to assess drug interactions.

CONCLUSIONS: Combining Paclitaxel with Ivermectin has the highest cytotoxic and the strongest additive effect for both chemoresistant cell lines compared to Paclitaxel alone.

PMID:39861076 | DOI:10.3390/ph18010014

Pharmaceuticals (Basel). 2024 Dec 25;18(1):13. doi: 10.3390/ph18010013.

ABSTRACT

Background: Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. Methods: This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations. Using this pipeline, we analyzed 4680 investigational and approved drugs from DrugBank database. Results: The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. The candidates demonstrated strong binding energies, key molecular interactions with critical residues (e.g., Phe88, Glu161), and stable MD trajectories, particularly saredutant, a neurokinin-2 (NK2) antagonist. Conclusions: Beyond identifying potential AurB inhibitors, this study highlights an integrated methodology that can be applied to other challenging drug targets.

PMID:39861075 | DOI:10.3390/ph18010013

Pathogens. 2024 Dec 31;14(1):20. doi: 10.3390/pathogens14010020.

ABSTRACT

The development of antivirals for respiratory viruses has advanced markedly in response to the growing threat of pathogens such as Influenzavirus (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. This article reviews the advances and challenges in this field, highlighting therapeutic strategies that target critical stages of the viral replication cycle, including inhibitors of viral entry, replication, and assembly. In addition, innovative approaches such as inhibiting host cellular proteins to reduce viral resistance and repurposing existing drugs are explored, using advanced bioinformatics tools that optimize the identification of antiviral candidates. The analysis also covers emerging technologies such as nanomedicine and CRISPR gene editing, which promise to improve the stability and efficacy of treatments. While current antivirals offer valuable options, they face challenges such as viral evolution and the need for accessible treatments for vulnerable populations. This article underscores the importance of continued innovation in biotechnology to overcome these limitations and provide safe and effective treatments. Combining traditional and advanced approaches in developing antivirals is essential in order to address respiratory viral diseases that affect global health.

PMID:39860981 | DOI:10.3390/pathogens14010020

Molecules. 2025 Jan 12;30(2):274. doi: 10.3390/molecules30020274.

ABSTRACT

Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug-target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development.

PMID:39860144 | DOI:10.3390/molecules30020274

Molecules. 2025 Jan 10;30(2):260. doi: 10.3390/molecules30020260.

ABSTRACT

Drug development faces significant financial and time challenges, highlighting the need for more efficient strategies. This study evaluated the druggability of the entire human proteome using Fpocket. We identified 15,043 druggable pockets in 20,255 predicted protein structures, significantly expanding the estimated druggable proteome from 3000 to over 11,000 proteins. Notably, many druggable pockets were found in less studied proteins, suggesting untapped therapeutic opportunities. The results of a pairwise pocket similarity analysis identified 220,312 similar pocket pairs, with 3241 pairs across different protein families, indicating shared drug-binding potential. In addition, 62,077 significant matches were found between druggable pockets and 1872 known drug pockets, highlighting candidates for drug repositioning. We repositioned progesterone to ADGRD1 for pemphigus and breast cancer, as well as estradiol to ANO2 for shingles and medulloblastoma, which were validated via molecular docking. Off-target effects were analyzed to assess the safety of drugs such as axitinib, linking newly identified targets with known side effects. For axitinib, 127 new targets were identified, and 46 out of 48 documented side effects were linked to these targets. These findings demonstrate the utility of pocket similarity in drug repositioning, target expansion, and improved drug safety evaluation, offering new avenues for the discovery of new indications and side effects of existing drugs.

PMID:39860130 | DOI:10.3390/molecules30020260