Search Results (374)

Background/Objective: Pharmacological interventions often exert systemic effects beyond their primary targets, underscoring the need for a comprehensive evaluation of their metabolic impact. Etodolac is a nonsteroidal anti-inflammatory drug (NSAID) that alleviates pain, fever, and inflammation by inhibiting cyclooxygenase-2 (COX-2), thereby reducing prostaglandin synthesis. While its pharmacological effects are well known, the broader metabolic impact and potential mechanisms underlying improved clinical outcomes remain underexplored. Untargeted metabolomics, which profiles the metabolome without prior selection, is an emerging tool in clinical pharmacology for elucidating drug-induced metabolic changes. In this study, untargeted metabolomics was applied to investigate metabolic changes following a single oral dose of etodolac in healthy male volunteers. By analyzing serial blood samples over time, we identified endogenous metabolites whose concentrations were positively or inversely associated with the drug’s plasma levels. This approach provides a window into both therapeutic pathways and potential off-target effects, offering a promising strategy for early-stage drug evaluation and multi-target discovery using minimal human exposure. Methods: Thirty healthy participants received a 400 mg dose of Etodolac. Plasma samples were collected at five time points: pre-dose, before Cmax, at Cmax, after Cmax, and 36 h post-dose (n = 150). Samples underwent LC/MS-based untargeted metabolomics profiling and pharmacokinetic analysis. A total of 997 metabolites were significantly dysregulated between the pre-dose and Cmax time points, with 875 upregulated and 122 downregulated. Among these, 80 human endogenous metabolites were identified as being influenced by Etodolac. Results: A total of 17 metabolites exhibited time-dependent changes closely aligned with Etodolac’s pharmacokinetic profile, while 27 displayed inverse trends. Conclusions: Etodolac influences various metabolic pathways, including arachidonic acid metabolism, sphingolipid metabolism, and the biosynthesis of unsaturated fatty acids. These selective metabolic alterations complement its COX-2 inhibition and may contribute to its anti-inflammatory effects. This study provides new insights into Etodolac’s metabolic impact under healthy conditions and may inform future therapeutic strategies targeting inflammation. Full article

Drug repurposing or drug repositioning is the process of identifying new therapeutic uses for approved or investigational drugs beyond the original treatment indication. The discovery of new drugs for cancer therapy needs this cost-effective and time-saving alternative strategy to traditional drug development for a rapid clinical translation in Phase II/III studies, especially for unmet medical needs and rare diseases. Neuroendocrine tumors (NETs) are a heterogeneous group of rare neoplasms arising from cells of the neuroendocrine system that, though often indolent, can be aggressive and metastatic. In this context, drug repurposing has emerged as a promising strategy to improve treatment options due to the limited number of effective treatments and the heterogeneity of the disease. Indeed, a large number of non-oncology drugs have the potential to address more than one target that could be therapeutic for cancer patients. Although many repurposed drugs are used off-label, efficacy for the new use must be demonstrated in clinical trials. Within regulatory frameworks, both the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have procedures to reduce the need for extensive new studies and to expedite the review of drugs for serious conditions when preliminary evidence indicates substantial clinical improvement over available therapy. In spite of several advantages, including reduced development time, lower costs, known safety profiles, and faster regulatory approval, difficulty in obtaining new patents for old drugs with limited protection for intellectual property may reduce commercial returns and disincentivize investments. This review aims to provide comprehensive information on some marketed drugs currently under investigation to be repurposed or used in clinical practice for NETs and to discuss the major clinical challenges. Although drug repurposing is a useful strategy for early access to medicines, the monitoring of the clinical benefit of oncologic drugs during the post-marketing authorization is crucial to support the safety and effectiveness of treatments. Full article

In hormone-dependent cancers, front-line treatment options include surgery and therapies that target hormone dependance. These therapies are effective initially but fail in tumors that recur, develop resistance or present at an advanced stage. Consequently, new therapeutic avenues are urgently needed. Increasing evidence implicates epigenetic modulators in tumor initiation, progression and therapeutic response, making them attractive biomarkers for patient stratification and targets for intervention. Over the past two decades, the discovery and development of small-molecule inhibitors directed against key epigenetic regulators have accelerated. This review provides a comprehensive overview of the major epigenetic targets, the inhibitors developed against them and the clinical trials currently underway in endocrine-related cancers. While epigenetic agents have shown limited benefits as monotherapies, their use in combination regimens is emerging as a strategy to overcome resistance and enhance the efficacy of existing treatments. We summarize the current landscape of combination trials, highlight early signs of clinical activity and discuss the opportunities and challenges inherent in integrating epigenetic drugs into the management of advanced endocrine-related cancers. Full article

Early medical traditions include those of ancient Babylonia, China, Egypt, and India. The roots of modern Western medicine, however, go back to ancient Greece. During the Renaissance, physicians increasingly relied on observation and experimentation to understand the human body and develop new techniques for diagnosis and treatment. The discovery of antibiotics, antiseptics, and other drugs in the 19th century accelerated the development of modern medicine, the latter being fueled further by advances in technology, research, a better understanding of the human body, and, most recently, the introduction of evidence-based medicine (EBM). The EBM model de-emphasized intuition, unsystematic clinical experience, and pathophysiologic rationale as sufficient grounds for clinical decision-making and stressed the examination of evidence from clinical research. A later EBM model additionally incorporated clinical expertise and the latest model of EBM patients’ preferences and actions. In this review article, we argue that in the era of precision medicine, major EBM principles must be based on (a) the systematic identification, analysis, and utility of big data using artificial intelligence; (b) the magnifying effect of medical interventions by means of the physician–patient interaction, the latter being guided by the physician’s expertise, intuition, and philosophical beliefs; and (c) the patient preferences, since, in healthcare under precision medicine, the patient will be a central stakeholder contributing data and actively participating in shared decision-making. Full article

The discovery of effective therapeutics against Alzheimer’s disease (AD) and other neurological disorders remains a significant challenge. Artificial intelligence (AI) tools are of considerable interest in modern drug discovery processes and, by exploiting machine learning (ML) algorithms and deep learning (DL) tools, as well as data analytics, can expedite the identification of new drug targets and potential lead molecules. The current study was aimed at assessing the role of AI-based tools in the discovery of new drug targets against AD and other related neurodegenerative diseases and their efficacy in the discovery of new drugs against these diseases. AD represents a multifactorial neurological disease with limited therapeutics available for management and limited efficacy. The discovery of more effective medications is limited by the complicated pathophysiology of the disease, involving amyloid beta (Aβ), neurofibrillary tangles (NFTs), oxidative stress, and inflammation-induced damage in the brain. The integration of AI tools into the traditional drug discovery process against AD can help to find more effective, safe, highly potent compounds, identify new targets of the disease, and help in the optimization of lead molecules. A detailed literature review was performed to gather evidence regarding the most recent AI tools for drug discovery against AD, Parkinson’s disease (PD), multiple sclerosis (MLS), and epilepsy, focusing on biological markers, early diagnoses, and drug discovery using various databases like PubMed, Web of Science, Google Scholar, Scopus, and ScienceDirect to collect relevant literature. We evaluated the role of AI in analyzing multifaceted biological data and the properties of potential drug candidates and in streamlining the design of clinical trials. By exploring the intersection of AI and neuroscience, this review focused on providing insights into the future of AD treatment and the potential of AI to revolutionize the field of drug discovery. Our findings conclude that AI-based tools are not only cost-effective, but the success rate is extremely high compared to traditional drug discovery methods in identifying new therapeutic targets and in the screening of the majority of molecules for clinical trial purposes. Full article

Plasmodium falciparum is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse reactions, are not efficacious enough due to factors such as drug resistance. In silico approaches can speed up the discovery and design of new molecules with wide-spectrum antimalarial activity. Here, we report a unified computational methodology combining a perturbation theory machine learning model based on multilayer perceptron networks (PTML-MLP) and the fragment-based topological design (FBTD) approach for the prediction and design of novel molecules virtually exhibiting versatile antiplasmodial activity against diverse P. falciparum strains. Our PTML-MLP achieved an accuracy higher than 85%. We applied the FBTD approach to physicochemically and structurally interpret the PTML-MLP, subsequently extracting several suitable molecular fragments and designing new drug-like molecules. These designed molecules were predicted as multi-strain antiplasmodial inhibitors, thus representing promising chemical entities for future synthesis and biological experimentation. The present work confirms the potential of combining PTML modeling and FBTD for early antimalarial drug discovery while opening new horizons for extended computational applications for antimicrobial research and beyond. Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article

Background and Objective: Dysregulated tyrosine kinase signaling is a central driver of tumorigenesis, metastasis, and therapeutic resistance. While tyrosine kinase inhibitors (TKIs) have revolutionized targeted cancer treatment, identifying compounds with optimal bioactivity remains a critical bottleneck. This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. Methods: A curated dataset of 28,314 small molecules from the ChEMBL database targeting 11 tyrosine kinases was analyzed. Using Morgan fingerprints and physicochemical descriptors (e.g., molecular weight, LogP, hydrogen bonding), ten supervised models, including dANN, SVM, CatBoost, and CNN, were trained and optimized through a randomized hyperparameter search. Model performance was evaluated using F1-score, ROC–AUC, precision–recall curves, and log loss. Results: SVM achieved the highest F1-score (87.9%) and accuracy (85.1%), while dANNs yielded the lowest log loss (0.25096), indicating superior probabilistic reliability. CatBoost excelled in ROC–AUC and precision–recall metrics. The integration of Morgan fingerprints significantly improved bioactivity prediction across all models by enhancing structural feature recognition. Conclusions: This work highlights the transformative role of machine learning—particularly dANNs and SVM—in rational drug discovery. By enabling accurate bioactivity prediction, our model pipeline can effectively reduce experimental burden, optimize compound selection, and support personalized cancer treatment design. The proposed framework advances kinase inhibitor screening pipelines and provides a scalable foundation for translational applications in precision oncology. By enabling early identification of bioactive compounds with favorable pharmacological profiles, the results of this study may support more efficient candidate selection for clinical drug development, particularly in regards to cancer therapy and kinase-associated disorders. Full article

The global burden of tuberculosis (TB), exacerbated by the rise of drug-resistant Mycobacterium tuberculosis (M. tuberculosis), underscores the need for alternative intervention strategies. One promising approach is to block the infection at its earliest stage—bacterial adhesion to host cells—thereby preventing colonization and transmission without exerting selective pressure. Adhesins, surface-exposed molecules mediating this critical interaction, have therefore emerged as attractive targets for early prevention. This review outlines the infection process driven by bacterial adhesion and describes the architecture of the M. tuberculosis outer envelope, emphasizing components that contribute to host interaction. We comprehensively summarize both non-protein and protein adhesins, detailing their host receptors, biological roles, and experimental evidence. Recent progress in the computational prediction of adhesins, particularly neural network-based tools like SPAAN, is also discussed, highlighting its potential to accelerate adhesin discovery. Additionally, we present a detailed, generalized workflow for predicting M. tuberculosis adhesins, which synthesizes current approaches and provides a comprehensive framework for future studies. Targeting bacterial adhesion presents a therapeutic strategy that interferes with the early stages of infection while minimizing the risk of developing drug resistance. Consequently, anti-adhesion strategies may serve as valuable complements to conventional therapies and support the development of next-generation TB vaccines and treatments. Full article

On 11 March 2020, the World Health Organisation (WHO) declared a global pandemic caused by the SARS-CoV-2 coronavirus that earlier in February 2020 the WHO had named COVID-19 (coronavirus disease 2019). There were neither drugs nor vaccines that were known to be effective against the virus, stimulating an urgent worldwide search for treatments. An evaluation of existing drugs by ‘repurposing’ was initiated followed by a transition to de novo drug discovery. Repurposing of an already approved drug may accelerate the introduction of that drug into clinical use by circumventing early, including preclinical studies otherwise essential for a de novo drug and reducing development costs. Early in the pandemic three drugs were identified as repurposing candidates for the treatment of COVID-19: (i) hydroxychloroquine, an anti-malarial also used to treat rheumatoid arthritis and lupus; (ii) ivermectin, an antiparasitic approved for both human and veterinary use; (iii) remdesivir, an anti-viral originally developed to treat hepatitis C. The scientific evidence, both for and against the efficacy of these three drugs as treatments for COVID-19, vied with public demand and politicization as unqualified opinions clashed with evidence-based medicine. To quote Hippocrates, “There are in fact two things, science and opinion; the former begets knowledge, the latter ignorance”. Full article

Effective postoperative pain management remains a major clinical challenge in spinal surgery, with poorly controlled pain affecting up to 50% of patients and contributing to delayed mobilization, prolonged hospitalization, and risk of chronic postsurgical pain. This review synthesizes current and emerging strategies in postoperative spinal pain management, tracing the evolution from opioid-centric paradigms to individualized, multimodal approaches. Multimodal analgesia (MMA) has become the cornerstone of contemporary care, combining pharmacologic agents, such as non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and gabapentinoids, with regional anesthesia techniques, including erector spinae plane blocks and liposomal bupivacaine. Adjunctive nonpharmacologic modalities like early mobilization, cognitive behavioral therapy, and mindfulness-based interventions further optimize recovery and address the biopsychosocial dimensions of pain. For patients with refractory pain, neuromodulation techniques such as spinal cord and peripheral nerve stimulation offer promising results. Advances in artificial intelligence (AI), biomarker discovery, and nanotechnology are poised to enhance personalized pain protocols through predictive modeling and targeted drug delivery. Enhanced recovery after surgery protocols, which integrate many of these strategies, have been shown to reduce opioid use, hospital length of stay, and complication rates. Nevertheless, variability in implementation and the need for individualized protocols remain key challenges. Future directions include AI-guided analytics, regenerative therapies, and expanded research on long-term functional outcomes. This review provides an evidence-based framework for pain control following spinal surgery, emphasizing integration of multimodal and innovative approaches tailored to diverse patient populations. Full article

Cellular senescence is a fundamental mechanism in aging, marked by irreversible growth arrest and diverse functional changes, including, but not limited to, the development of a senescence-associated secretory phenotype (SASP). While transient senescence contributes to beneficial processes such as tissue repair and tumor suppression, the persistent accumulation of senescent cells is implicated in tissue dysfunction, chronic inflammation, and age-related diseases. Notably, the SASP can exert both pro-inflammatory and immunosuppressive effects, depending on cell type, tissue context, and temporal dynamics, particularly in early stages where it may be profibrotic and immunomodulatory. Recent advances in senotherapeutics have led to two principal strategies for targeting senescent cells: senolytics, which selectively induce their apoptosis, and senomorphics, which modulate deleterious aspects of the senescence phenotype, including the SASP, without removing the cells. This review critically examines the molecular mechanisms, therapeutic agents, and clinical potential of both approaches in the context of anti-aging interventions. We discuss major classes of senolytics, such as tyrosine kinase inhibitors, BCL-2 family inhibitors, and natural polyphenols, alongside senomorphics including mTOR and JAK inhibitors, rapalogs, and epigenetic modulators. Additionally, we explore the biological heterogeneity of senescent cells, challenges in developing specific biomarkers, and the dualistic role of senescence in physiological versus pathological states. The review also highlights emerging tools, such as targeted delivery systems, multi-omics integration, and AI-assisted drug discovery, which are advancing precision geroscience and shaping future anti-aging strategies. Full article

Type 2 diabetes mellitus (T2DM) is a complex and prevalent metabolic disorder, and dipeptidyl peptidase 4 (DPP4) inhibitors have proven effective, yet the identification of novel inhibitors remains challenging due to the vastness of chemical space. In this study, we developed DPPPRED-IV, a web-based ensembled system integrating both binary classification and continuous regression Quantitative Structure Activity Relationships (QSAR) models to predict human DPP4 inhibitory activity. A curated dataset of 4 676 ChEMBL compounds was subjected to genetic algorithm descriptor selection and multiple machine learning algorithms; classification models were combined via a soft voting ensemble, while regression models estimated IC₅₀ values. All models underwent external 10-fold cross-validation and applicability domain analysis. The final models were integrated into a user-friendly web server, allowing predictions from SMILES inputs. Experimental testing of 29 MolPort compounds at 1.5 µM confirmed that 14 predicted actives exhibited significant inhibition, supporting the tool’s performance in early-stage screening. DPPPRED IV is freely available within the ChemoPredictionSuite and offers a resource to accelerate decision making, reduce costs and minimize animal use in T2DM drug discovery. Full article

Emerging clinical data support the paradoxical notion that glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) agonism and antagonism can provide additive weight loss when combined with a glucagon-like peptide 1 (GLP-1) receptor (GLP-1R) agonist. In this review, we examine data that motivated the initiation of these seemingly contradictory drug discovery programs. We focus on the physiologic role of GIP in humans, human genetics evidence, rodent genetic models, and preclinical rodent and non-human primate pharmacology studies. Furthermore, we highlight where early preclinical findings translated into relevant clinical efficacy in the development of tirzepatide and maridebart cafraglutide (MariTide). Full article

Background/Objectives: Colony stimulating factor 1 receptor kinase (CSF1R) is a well-validated molecular target in drug discovery for various reasons. Based on the structure of an early lead molecule identified in our lab and the marketed drug Pexidartinib (PLX3397), we merged fragments of Pexidartinib with our pyrrolo[2,3-d]pyrimidine nucleus, and the idea was supported by initial molecular docking studies. Thus, several new compounds were synthesized with Pexidartinib fragments on C4, C5, and C6 on the pyrrolopyrimidine scaffold using molecular hybridization. Methods: Nine final products were synthesized using a combination of Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions in three to four steps and in good yields. The analogues were subsequently profiled as CSF1R inhibitors in enzymatic and cellular assays, and ADME properties were evaluated for some derivatives. Results: N-Methyl-N-(3-methylbenzyl)-6-(6-((pyridin-3-ylmethyl)amino)pyridin-3-yl)-7H-pyrrolo[2,3-d]pyrimidin-4-amine (12b) emerged as the most potent CSF1R inhibitor, showing low-nanomolar enzymatic activity, cellular efficacy, and favorable ADME properties, highlighting its promise as a lead compound for further development. Conclusions: These findings suggest that combining structural elements from previously reported CSF1R inhibitors such as Pexidartinib could guide the development of improved drug candidates targeting this kinase. Full article