Discovery Of Therapeutics Research Articles

FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking

Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, are major drivers of various pediatric cancers. These proteins are intrinsically disordered and lack druggable pockets, making them highly challenging therapeutic targets for both small molecule-based and structure-based approaches. Protein language models (pLMs) have recently emerged as powerful tools for capturing physicochemical and functional protein features but have yet to be trained on fusion oncoprotein sequences. We introduce FusOn-pLM, a fine-tuned pLM trained on a newly curated, comprehensive set of fusion oncoprotein sequences, FusOn-DB. Employing a unique cosine-scheduled masked language modeling strategy, FusOn-pLM dynamically adjusts masking rates (15%–40%) to optimize feature extraction and representation quality, surpassing baseline embeddings in fusion-specific tasks, including localization, puncta formation, and disorder prediction. FusOn-pLM uniquely predicts drug-resistant mutations, providing insights for therapeutic design that anticipates resistance mechanisms. In total, FusOn-pLM provides biologically relevant representations for advancing therapeutic discovery in fusion-driven cancers.

Neuroimaging as a Tool for Advancing Pediatric Psychopharmacology.

Neuroimaging, specifically magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET), plays an important role in improving the therapeutic landscape of pediatric neuropsychopharmacology by detecting target engagement, pathway modulation, and disease-related changes in the brain. This review provides a comprehensive update on the application of neuroimaging to detect neural effects of psychotropic medication in pediatrics. Additionally, we discuss opportunities and challenges for expanding the use of neuroimaging to advance pediatric neuropsychopharmacology. PubMed and Embase were searched for studies published between 2012 and 2024 reporting neural effects of attention deficit hyperactivity disorder (ADHD) medications (e.g., methylphenidate, amphetamine, atomoxetine, guanfacine), selective serotonin reuptake inhibitors (e.g., fluoxetine, escitalopram, sertraline), serotonin/norepinephrine reuptake inhibitors (e.g., duloxetine, venlafaxine), second-generation antipsychotics (e.g., aripiprazole, olanzapine, risperidone, quetiapine, ziprasidone), and others (e.g., lithium, carbamazepine, lamotrigine, ketamine, naltrexone) used to treat pediatric psychiatric conditions. Of the studies identified (N = 57 in 3314 pediatric participants), most (86%, total participants n = 3045) used MRI to detect functional pathway modulation or anatomical changes. Fewer studies (14%, total participants n = 269) used MRS to understand neurochemical modulation. No studies used PET. Studies that included healthy controls detected normalization of disease-altered pathways following treatment. Studies that focused on affected youth detected neuromodulation following single-dose and ongoing treatment. Neuroimaging is positioned to serve as a biomarker capable of demonstrating acute brain modulation, predicting clinical response, and monitoring disease, yet biomarker validation requires further work. Neuroimaging is also well suited to fill the notable knowledge gap of long-term neuromodulatory effects of psychotropic medications in the context of ongoing brain development in children and adolescents. Future studies can leverage advancements in neuroimaging technology, acquisition, and analysis to fill these gaps and accelerate the discovery of novel therapeutics, leading to more effective prescribing and ensuring faster recovery.

Validation of a biomarker-based mortality score for cardiogenic shock patients: Comparison with a clinical risk score.

Cardiogenic shock (CS) is the deadliest manifestation of acute heart failure, with persistently high mortality rates and a lack of recent therapeutic breakthroughs. Accurate risk prediction is crucial in clinical decision-making and the design of future clinical trials. We aimed to validate the CLIP score, a biomarker-based risk score comprising cystatin C, lactate, interleukin-6 and NT-proBNP, for predicting mortality in acute coronary syndrome (ACS) related CS, and to compare its predictive value with the previously published CardShock risk score. The study is a post hoc analysis of the CardShock Study, a prospective, observational European multicentre study on CS. The CLIP score was calculated 12h after hospital admission, and its ability to predict 90-day mortality was assessed using are under the curve (AUC) of the receiver-operating characteristics (ROC) curve analysis. The discriminative ability of the CLIP score was compared with the CardShock risk score by comparing the AUC's. The cohort was dichotomized into low and high risk groups by the optimal cut-off value derived from the ROC analysis of the CLIP score. Kaplan-Meier curves were constructed to evaluate risk stratification when combining the CLIP and CardShock risk scores. The cohort (n=121) comprised 77% (n=93) men and the median age was 67years (IQR 61-76). A total of 21% (n=25) of the patients had non-ACS related CS. The CLIP score demonstrated appropriate predictive accuracy for 90-day mortality (AUC 0.84, 95% CI 0.77-0.91), comparable with the CardShock risk score (AUC 0.77 [95% CI 0.69-0.85]; P=0.064 for comparison). A CLIP score cut-off of 0.28 stratified patients into high risk (65% mortality) and low risk (16% mortality) groups. In addition, incorporating the CLIP score enhanced risk stratification in all CardShock risk score categories. The CLIP score, calculated within 12h of hospital admission, accurately predicted 90-day mortality in CS and complemented the CardShock risk score. The biomarker-based score has potential utility in dynamic mortality risk assessment and could inform clinical management and trial design.

Novel Reporter Constructs to Accelerate Antiviral and Therapeutic Discovery for Enterovirus-A71.

Enterovirus A71 (EV-A71) is an important human pathogen and 'prototype pathogen' for studies of other Enteroviruses of pandemic potential. Understanding the biology of EV-A71 would inform generalizable strategies for antiviral drug, vaccine, and monoclonal antibody development. Such studies are accelerated by robust reagents to evaluate efficacy. Here, we describe and evaluate a suite of synthetic reporter constructs to accelerate EV-A71 research and therapeutic discovery. These constructs include replicons and infectious clones carrying luminescent and fluorescent reporter proteins. Among the reporters we tested were shorter luminescent and de novo-designed synthetic fluorescent proteins, which enhance genetic stability, reduce reporter gene loss and improve the utility of these reporters. This toolbox provides free access to robust and flexible assays for EV-A71 infection and replication through public repositories, promoting and accelerating open scientific discovery for this understudied emerging pathogen.

The Gastric Cancer Registry: A multi-omic cellular and molecular resource for cancer biomarker and therapeutic discovery.

491 Background: The Gastric Cancer Registry (GCR) is a comprehensive clinical and genomic data resource focused on gastric cancer (GC) patients and individuals with a family history of GC or a germline CDH1 mutation. As part of the GCR’s ongoing enrollment, patients contribute cancer history information, archival tumor samples and other biospecimens. The cancer samples undergo extensive molecular and cellular analysis that includes genomic sequencing and spatial analysis. We have generated a robust dataset, publicly accessible online through the GCR Genome Explorer (GCR-GE). This genomic, molecular and cellular resource provides a rich data set that accelerates multidisciplinary research aimed at improving detection and treatment strategies for GC. GC disproportionately affects Latin American populations - we have included new cohorts from Latin America. The GCR enables researchers, clinicians and patients to address specific questions about the molecular and cellular basis of GC. Methods: GC samples underwent whole genome, whole exome, and bulk RNA sequencing were conducted. The genomic features identified include copy number variation, gene mutations, gene expression, microbiome composition, estimation of tumor-infiltrating immune cells, tumor neoantigens, MSI/MSS status, and HLA subtypes. In addition to genomic data, clinical data was compiled from survey responses and pathology reports and integrated into the GCR-GE. We have incorporated single cell and spatial analysis that includes diverse cell types, single cell gene expression and cellular architecture in native tumor tissues. This data was released to the GCR Genome Explorer website. Results: A total of 817 subjects have enrolled in the GCR including 598 diagnosed with GC and 219 at high-risk for GC through family history of GC and/or a CDH1 mutation. Of the 598 with GC, some met multiple criteria including at least 40 with a family history of GC, 10 with a CDH1 mutation, and 18 with both a family history of GC and a CDH1 mutation. The GCR-GE includes data from 257 gastric tumors in addition to external datasets from TCGA. In the latest data release, clinical and genomic data from 78 gastric tumors from patients in Latin America were integrated into the GCR-GE. Conclusions: The GCR is a comprehensive database of crucial clinical and genomic data necessary for driving forward GC research. Through global expansion, the GCR has generated a more representative dataset by increasing racial diversity and including underrepresented populations that are at higher risk for GC.

Abstract TP140: The Blood Proteome Profiling Identifies Biomarkers Associated With Ischemic Stroke Prognosis

Blackground: Due to the narrow therapeutic time window and strict indications, there are still a large number of patients unable to receive thrombolysis and endovascular therapy, resulting in a poor long-term functional prognosis. Moreover, it is unclear what effective therapeutic targets as well as molecular systemic changes in stroke in the chronic phase. Therefore, to provide useful insights into its pathogenesis, identification of therapeutic targets and biomarker discovery, we analyze the proteomic profile of blood in ischemic stroke patients in order to gain insights into the disease. Method: We use proteomics technologies to examine serum at baseline and use the modified Rankin Scale (mRS) to evaluate neurological function at baseline and 3-month follow-up after ischemic stroke. Based on data-independent acquisition mass spectrometry and parallel reaction monitoring (PRM) technology, we implement a staged discovery-confirmatory pipeline in serum samples from better prognosis patients(n=48) and poor prognosis patients(n=50) according to mRS to comprehensively identify and validate more accurate biomarker performance characteristics of candidate proteins. Results: In this proteomic study, we characterize 4,740 circulating proteins and 324 differentially expressed proteins in 98 patients. We identify circulating proteomic signatures for ischemic stroke prognosis, and correlate these with clinical data to develop a proteomic profile of 14 markers. Based on machine learning algorithms such as LASSO regression and exhaustive feature selection (EFS), we finally identify some biomarkers (IQGAP2, LYZ, PDGFD, EHD3, CETP, FYB1) are validated as contributors to the ischemic stroke prognosis model through verification of an additional 58 serum samples. (AUC range:0.65-0.99). Conclusion: We identify potential biomarkers and therapeutic targets and offer an opportunity for more precise prognosis and treatment in ischemic stroke.

Wnt/β-catenin signalling underpins juvenile Fasciola hepatica growth and development.

Infection by the liver fluke, Fasciola hepatica, places a substantial burden on the global agri-food industry and poses a significant threat to human health in endemic regions. Widespread resistance to a limited arsenal of chemotherapeutics, including the frontline flukicide triclabendazole (TCBZ), renders F. hepatica control unsustainable and accentuates the need for novel therapeutic target discovery. A key facet of F. hepatica biology is a population of specialised stem cells which drive growth and development - their dysregulation is hypothesised to represent an appealing avenue for control. The exploitation of this system as a therapeutic target is impeded by a lack of understanding of the molecular mechanisms underpinning F. hepatica growth and development. Wnt signalling pathways govern a myriad of stem cell processes during embryogenesis and drive tumorigenesis in adult tissues in animals. Here, we identify five putative Wnt ligands and five Frizzled receptors in liver fluke transcriptomic datasets and find that Wnt/β-catenin signalling is most active in juveniles, the most pathogenic life stage. FISH-mediated transcript localisation revealed partitioning of the five Wnt ligands, with each displaying a distinct expression pattern, consistent with each Wnt regulating the development of different cell/tissue types. The silencing of each individual Wnt or Frizzled gene yielded significant reductions in juvenile worm growth and, in select cases, blunted the proliferation of neoblast-like cells. Notably, silencing FhCTNNB1, the key effector of the Wnt/β-catenin signal cascade led to aberrant development of the neuromuscular system which ultimately proved lethal - the first report of a lethal RNAi-induced phenotype in F. hepatica. The absence of any discernible phenotypes following the silencing of the inhibitory Wnt/β-catenin destruction complex components is consistent with low destruction complex activity in rapidly developing juvenile worms, corroborates transcriptomic expression profiles and underscores the importance of Wnt signalling as a key molecular driver of growth and development in early-stage juvenile fluke. The putative pharmacological inhibition of Wnt/β-catenin signalling using commercially available inhibitors phenocopied RNAi results and provides impetus for drug repurposing. Taken together, these data functionally and chemically validate the targeting of Wnt signalling as a novel strategy to undermine the pathogenicity of juvenile F. hepatica.

MUC1 and MUC4 expression are inversely correlated and trigger immunological response and transport pathways in adult-type diffuse gliomas.

Adult-type diffuse gliomas arise from glial or progenitor cells. These tumors are currently classified as astrocytoma isocitrate dehydrogenase (IDH)-mutant or IDH-mutant oligodendroglioma with co-deletion of chromosomal arms 1p and 19q, both of which could be either slow-growing tumors, or glioblastoma (GBM), which is a more aggressive tumor. Despite advances in diagnosis and treatment, the median survival time after GBM diagnosis remains low at approximately 15 months, with a 5-year overall survival (OS) rate of 6.8%. Therefore, new biomarker and therapeutic target discoveries are required to improve prognosis. Mucin 1 (MUC1) and MUC4 are membrane-bound mucins and potential biomarkers of several tumors. However, the role of these mucins in adult gliomas has not been well explored. In this retrospective study, in silico analysis of data from patients with adult-type diffuse glioma revealed differential methylation and expression patterns of MUC1 and MUC4 between GBM and non-GBM groups. In the GBM group, decreased methylation and elevated expression of MUC1 were observed (r=-0.25, p<0.0001), whereas increased methylation and decreased expression of MUC4 were observed (r=-0.13, p=0.1344). Conversely, in the non-GBM group, MUC1 exhibited higher methylation and lower expression (r=-0.27, p<0.0001), whereas MUC4 showed lower methylation and higher expression (r=-0.32, p<0.0001). The expression of these genes influenced OS in adult patients with glioma (p=0.0344), with high MUC1 and low MUC4 expression associated with worse OS. MUC1 and MUC4 expression correlated with that of MUC20 in both GBM (r=0.54) and non-GBM (r=0.53) groups (p<0.0001). Functional enrichment analysis identified the biological roles of MUC1-co-expressed genes as involvement in innate immunity, antigen processing, and proinflammatory responses in both the non-GBM and GBM groups, and integrin-based signaling pathways in the GBM group. MUC4-co-expressed genes are involved in ion transport in GBM patients. Using molecular docking, we observed that MUC1 domains physically interact with immune response-related proteins, such as receptors for advanced glycation end products (RAGE), major histocompatibility complex II (MHC-II), and extracellular matrix receptor integrin alpha 2 (ITGA2). To our knowledge, this is the first retrospective study and in silico analysis demonstrating the relevance and correlation of MUC1 and MUC4 in adult gliomas. These findings elucidate the molecular mechanisms underlying adult-type diffuse glioma progression and highlight MUC1 and MUC4 as potential prognostic markers and therapeutic targets for glioma management.

Association of Sleep Spindle Rate With Memory Consolidation in Children With Rolandic Epilepsy.

Rolandic epilepsy (RE), the most common childhood focal epilepsy syndrome, is characterized by a transient period of sleep-activated epileptiform activity in the centrotemporal regions and variable cognitive deficits. Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in RE and related sleep-activated epileptic encephalopathies. To further evaluate the association between this electrographic biomarker and cognitive dysfunction in this common disease, we investigate whether children with RE have deficient sleep-dependent memory consolidation and whether impaired memory consolidation is associated with reduced sleep spindles in the centrotemporal regions. In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using an automated sleep spindle detector validated for use in children with and without epilepsy. Twenty-three children with RE (9 with active disease, 5F, age 6.9-12.8 years; 14 with resolved disease, 8F, age 8.8-17.8 years) and 19 age-matched and sex-matched controls (8F, age 6.9-18.7 years) were enrolled. Children with active epilepsy had decreased memory consolidation compared with control children (p = 0.001, mean percentage reduction 25.7%, 95% CI 10.3%-41.2%) and compared with children with resolved epilepsy (p = 0.007, mean percentage reduction 21.9%, 95% CI 6.2%-37.6%). Children with active epilepsy had decreased sleep spindle rates in the centrotemporal region compared with controls (p = 0.008, mean decrease 2.5 spindles per minute, 95% CI 0.7-4.4 spindles per minute). Spindle rate, but not spike rate or spike-wave index, correlated with sleep-dependent memory consolidation (p = 0.004, mean MST improvement of 3.9%, 95% CI 1.3%-6.4%, for each unit increase in spindles per minute). Children with RE have impaired sleep-dependent memory consolidation during the active period of disease that correlates with a deficit in the sleep spindle rate. This finding identifies a noninvasive biomarker to aid diagnosis and a potential etiologic mechanism to guide therapeutic discovery of cognitive dysfunction in RE and related sleep-activated epilepsy syndromes.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

RETRACTION: The Predictive Efficacy of Programmed Cell Death in Immunotherapy of Melanoma: A Comprehensive Analysis of Gene Expression Data for Programmed Cell Death Biomarker and Therapeutic Target Discovery.

C. Yue, W. Lian, M. Duan, D. Xia, X. Cao, and J. Peng, "The Predictive Efficacy of Programmed Cell Death in Immunotherapy of Melanoma: A Comprehensive Analysis of Gene Expression Data for Programmed Cell Death Biomarker and Therapeutic Target Discovery," Environmental Toxicology 39, no. 3 (2024): 1858-1873, https://doi.org/10.1002/tox.24051. The above article, published online on 22 December 2023, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the authors; the journal Editor-in-Chief, Paul B. Tchounwou; and Wiley Periodicals LLC. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. Therefore, the article must be retracted.

P0172 In silico drug repurposing approach for the discovery of therapeutics for intestinal fibrosis in crohn’s disease

Abstract Background Intestinal fibrosis in Crohn’s disease (CD) can lead to stricture formation and other disease-related complications.1,2 Given the lack of anti-fibrotic drugs, current treatment strategies are limited to anti-inflammatory therapies, endoscopic balloon dilation and surgery.1 Our aim was to identify compounds and druggable targets that can reverse the gene expression signature in mucosal fibroblasts associated with intestinal fibrosis. Methods A signature associated with fibrotic CD was derived from three publicly available transcriptomic datasets of fibroblasts isolated from fibrostenotic strictures of CD patients. We conducted a meta-regression analysis across the transcriptomic datasets to identify significant differentially expressed genes (DEGs) associated with fibrostenotic CD. Three drug repurposing platforms (iLINCS, L1000 CDS2 and CLUE io), connected to the NIH LINCS database,3,4 were used to identify compounds that could reverse the fibrotic signature, ranked by their anti-fibrotic potential using the CoDReS (Composite Drug Reranking Scoring) tool. Transcription factors and miRNAs targeting the fibrostenotic signature were identified via the TRRUST and miRWalk databases. A gene interaction network was constructed, incorporating transcription factors (TRRUST TF), miRNAs and DEGs. Drugs targeting key network components were identified using the DGiDb database. Results Via the analysis of combined transcriptomic datasets from fibrostenotic- versus non-stenosis-associated mucosal fibroblasts, fibroblast activation protein (FAP), IL7 receptor and transcription factor AP-2 gamma, emerged as the most significantly upregulated genes in fibrostenotic CD (Figure 1). Out of 6,783 compounds, PI3K/Akt/mTOR inhibitors, Src kinase family inhibitors and histone deacetylase inhibitors were predicted as being most effective in reversing this pathologic gene signature. The gene interaction network revealed 15 hub genes as central components with the most interconnections (Table 1). Among these top 15 key gene regulators, IL6, PPARγ and angiotensin receptor type 1 (AGTR1) were the highest ranked druggable targets, based on the drug-gene interaction analysis. Conclusion Our in silico drug repurposing approach identified several potential anti-fibrotic compounds and druggable targets for the treatment of CD-associated intestinal fibrosis. These findings open novel avenues for the development of anti-fibrotic drugs and provide a foundation for future research on cell and pathway-specific mechanisms driving fibrosis in CD.

The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy.

The application of quantitative systems pharmacology (QSP) has enabled substantial progress and impact in many areas of therapeutic discovery and development. This new technology is increasingly accepted by industry, academia, and solution providers, and is enjoying greater interest from regulators. In this chapter, we summarize key aspects regarding how effective collaboration among institutions and disciplines can support the growth of QSP and expand its application domain. We exemplify these considerations through a selection of successful cross-institutional or cross-functional collaborations, which resulted in reuse, repurposing, or extension of QSP modeling results or infrastructure, with important and novel results.

Innovative Nitrogen-Based Heterocycles: Pioneering Advances in Anticancer Therapeutics

Structural diversity, pharmacological relevance, and the ability to include N-containing heterocyclic derivatives through medicinal chemistry make these heterocyclic compounds known as essential scaffolds for the design and development of anticancer agents. Uncontrolled cell proliferation with metastasis remains a leading cause of global mortality from cancer. Enzyme binding sites interact with compounds with N-heterocycle to inhibit critical processes in cancer cells. The relative ease of synthesis, selective penetration, and low toxicity of these compounds make them desirable for use as a source of new therapeutic discoveries (over 90% of new therapeutic discoveries with approximately 65% of FDA-approved anticancer drugs (2010-2015). Focusing on selectivity, bioavailability, and low toxicity, this article compares the latest advancements in anticancer drug discovery, emphasizing the significance of SAR analysis in enhancing potency, efficacy, and development of N-heterocycle-based anticancer agents to overcome drug resistance.

Acceptance of Evidence Transfer Within German Early Benefit Assessment of New Drugs for Pediatric and Adolescents Target Populations.

In Germany, all new drugs undergo an early benefit assessment (EBA) by the decision-making body (G-BA). Due to limited access to clinical data in pediatric healthcare since 2017, evidence transfer has allowed for data from adult studies to be used in the EBA of pediatric drugs. This study examines the acceptance of evidence transfer, aiming to understand its correlation with granted added benefit. By searching the G-BA database, relevant EBAs were identified. In addition to descriptive statistics, agreement statistics regarding binary and ordinal extent of added benefit and binary logistic regression with and without intercept were performed to investigate acceptance of evidence transfer, juxtaposing it with manufacturers' claims, and to evaluate the impact of identified factors on evidence transfer. In 14 of 36 identified EBAs, the evidence transfer was accepted by the G-BA. They referred to four therapeutic areas, received a non-quantifiable added benefit and were subject to a pediatric investigation program. Non-quantifiable added benefit implies an added value in itself which can range from minor to major added benefit and is considering the genuine uncertainty mainly induced in theese EBAs due to evidence transfer, which is not allowing a quantification of an added benefit. The binary agreement between manufacturers' claims and G-BA's appraisals was less than by chance [kappa - 0.054 (- 0.158 to 0.050)] whereas the ordinal agreement became fair [kappa 0.333 (0.261-0.406)]. Congruence of the mechanism of action, alignment of disease pattern, transferability of efficacy and safety, and same comparator were fundamental for evidence transfer. Additionally, supportive evidence, therapeutic breakthroughs, and small-scale approval enhanced the acceptance of evidence transfer. The regression models yielded similar results showing different model fit and explained variance. Evidence transfer hinges upon fulfilling various minimum criteria and additional supportive evidence. Availability of study data from adult or older patients and the pediatric group under evaluation is crucial.

Tudor-Containing Methyl-Lysine and Methyl-Arginine Reader Proteins: Disease Implications and Chemical Tool Development.

Tudor domains are histone readers that can recognize various methylation marks on lysine and arginine. This recognition event plays a key role in the recruitment of other epigenetic effectors and the control of gene accessibility. The Tudor-containing protein family contains 42 members, many of which are involved in the development and progression of various diseases, especially cancer. The development of chemical tools for this family will not only lead to a deeper understanding of the biological functions of Tudor domains but also lay the foundation for therapeutic discoveries. In this review, we discuss the role of several Tudor domain-containing proteins in a range of relevant diseases and progress toward the development of chemical tools such as peptides, peptidomimetics, or small-molecules that bind Tudor domains. Overall, we highlight how Tudor domains are promising targets for therapeutic development and would benefit from the development of novel chemical tools.

Advancing COVID-19 Treatment: The Role of Non-covalent Inhibitors Unveiled by Integrated Machine Learning and Network Pharmacology.

The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR). Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features. Molecular docking and molecular dynamics simulations, conducted over a 100 nanosecond period, enhanced the ML-based screening by providing insights into the binding affinities and interaction dynamics with BCL2 and EGFR. Network pharmacology analysis identified these proteins as hub targets within the COVID-19 protein-protein interaction network, highlighting their roles in apoptosis regulation and cellular signaling. The identified inhibitors exhibited strong binding affinities, suggesting potential efficacy in disrupting viral life cycles and impeding disease progression. Comparative analysis with existing literature affirmed the relevance of BCL2 and EGFR in COVID-19 therapy and underscored the novelty of integrating network pharmacology with ML. This multidisciplinary approach establishes a framework for emerging pathogen treatments and advocates for subsequent in vitro and in vivo validation, emphasizing a multi-targeted drug design strategy against viral adaptability. This study's findings are crucial for the ongoing development of therapeutic agents against COVID-19, leveraging computational and network-based strategies.

Alternative Splicing: Emerging Roles in Anti-Aging Strategies.

Alternative splicing plays a fundamental role in gene expression and protein complexity. Aberrant splicing impairs cell homeostasis and is closely associated with aging and cellular senescence. Significant changes to alternative splicing, including dysregulated splicing events and the abnormal expression of splicing factors, have been detected during the aging process or in age-related disorders. Here, we highlight the possibility of suppressing aging and cellular senescence by controlling alternative splicing. In this review, we will summarize the latest research progress on alternative splicing in aging and cellular senescence, discuss the roles and regulatory mechanisms of alternative splicing during aging, and then excavate existing and potential approaches to anti-aging by controlling alternative splicing. Novel therapeutic breakthroughs concerning aging and senescence entail a further understanding of regulating alternative splicing mechanically and accurately.

Targeting InhA in drug-resistant Mycobacterium tuberculosis: potent antimycobacterial activity of diaryl ether dehydrozingerone derivatives.

Tuberculosis (TB) remains a major global threat, with 10 million new cases and 1.5 million deaths each year. In multidrug-resistant tuberculosis (MDR-TB), resistance is most commonly observed against isoniazid (INH) and rifampicin (RIF), the two frontline drugs. Isoniazid resistance is predominantly linked to mutations in the InhA gene, which encodes an enzyme involved in mycolic acid synthesis, a vital component of the mycobacterial cell wall. Mutations in InhA reduce drug binding, rendering INH ineffective. These morbidity and mortality figures, along with the fact that the rise and global spread of drug-resistant TB, underscores the need for the discovery of novel therapeutics. In this direction, we have previously synthesized, characterized, and screened a library of diaryl ether dehydrozingerone derivatives against mycobacteria and identified two best hits, 7 and 14, based on bacteriostatic activities. The present study aimed to thoroughly investigate the antituberculosis potential of these compounds, particularly regarding drug-resistant TB. Our findings revealed that both compounds exhibited tuberculocidal activity against the standard laboratory strain Mycobacterium tuberculosis (M. tb) H37Rv, with minimal bactericidal concentrations (MBC) of 4μg/ml for compound 7 and 8μg/ml for compound 14. Next, concentration vs time-kill kinetics of both these compounds showed concentration-dependent bactericidal activities against M. tb and complete pathogen eradication from culture at just 16× MIC. Both compounds were found to be suitable for combination regimens as their interactions with isoniazid and rifampicin against M. tb were observed to be synergistic. Additionally, 7 and 14 exhibited minimal hemolysis against human RBCs and less cytotoxicity was observed against three human cell lines up to 1000μM. Molecular docking revealed that these compounds bind more effectively to M. tb InhA, including its mutant forms where isoniazid binding is impaired, outperforming both isoniazid and triclosan in binding affinity. Importantly 7 and 14 showed potent activity against drug-susceptible clinical isolates and two isoniazid-resistant M. tb clinical isolates equivalent to that against M. tb H37Rv. The most interesting observation was that both compounds were found to be effective against three multi-drug resistant (MDR) strains of M. tb, thereby depicting their potential against drug-resistant TB. An ex vivo assay on RAW 264 cells infected with M. tb demonstrated a significant reduction in bacterial load at 8× MIC, revealing the fact that these compounds are highly effective against intracellular M. tb H37Rv. To the best of our knowledge, this is the first study that reports promising antimycobacterial potential of 7 and 14 against drug-susceptible, isoniazid-resistant, and MDR tuberculosis which warrants further exploration considering the need for new anti-TB medicine.

Geroscience in heart failure: the search for therapeutic targets in the shared pathobiology of human aging and heart failure

Age is a major risk factor for heart failure, but one that has been historically viewed as non-modifiable. Emerging evidence suggests that the biology of aging is malleable, and can potentially be intervened upon to treat age-associated chronic diseases, such as heart failure. While aging biology represents a new frontier for therapeutic target discovery in heart failure, the challenges of translating Geroscience research to the clinic are multifold. In this review, we propose a strategy that prioritizes initial target discovery in human biology. We review the rationale for starting with human omics, which has generated important insights into the shared (patho)biology of human aging and heart failure. We then discuss how this knowledge can be leveraged to identify the mechanisms of aging biology most relevant to heart failure. Lastly, we provide examples of how this human-first Geroscience approach, when paired with rigorous functional assessments in preclinical models, is leading to early-stage clinical development of gerotherapeutic approaches for heart failure.