Search Results (133)

Drosophila melanogaster is one of the most known and used organisms worldwide, not just to study general biology problems but above all for modeling complex human diseases. During the decades, it has become a central tool to understand the genetics of human disease, how mutations alter the behavior and health of cells, tissues, and organs, and more recently to test new compounds with a potential therapeutic use. But how did this small insect become so crucial in genetics? And how is it currently used in the study of human conditions affecting millions of people? In this review, we retrace the historical origins of its adoption in genetics laboratories and list all the advantages it provides to scientific research, both for its daily usage and for the fine tuning of gene regulation through genetic engineering approaches. We also provide some examples of how it is used to study human diseases such as cancer, neurological and infectious diseases, and its importance in drug discovery and testing. Full article

Animal venoms are complex biochemical secretions rich in highly potent and selective bioactive molecules, including peptides, enzymes, and small organic compounds. Once associated primarily with toxicity, these venoms are now recognized as a promising source of therapeutic agents for a wide range of medical conditions. This review provides a comprehensive analysis of the pharmacological potential of venom-derived compounds, highlighting their mechanisms of action, such as ion channel modulation, receptor targeting, and enzyme inhibition. Successful venom-derived drugs like captopril and ziconotide exemplify the translational potential of this biological arsenal. We discuss therapeutic applications in cardiovascular diseases, chronic pain, cancer, thrombosis, and infectious diseases, as well as emerging peptide candidates in clinical development. Technological advancements in omics, structural biology, and synthetic peptide engineering have significantly enhanced the discovery and optimization of venom-based therapeutics. Despite challenges related to stability, immunogenicity, and ecological sustainability, the integration of AI-driven drug discovery and personalized medicine is expected to accelerate progress in this field. By synthesizing current findings and future directions, this review underscores the transformative potential of animal venoms in modern pharmacotherapy and drug development. We also discuss current therapeutic limitations and how venom-derived compounds may address unmet needs in specific disorders. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article

In recent decades, peptide-based therapeutics have undergone transformative advancements driven by breakthroughs in production, modification, and analytical technologies. Innovations in chemical and biological synthesis, coupled with novel design and delivery strategies, have systematically addressed historical limitations (e.g., poor stability and bioavailability). These advancements have facilitated the characterization and clinical translation of diverse natural and engineered peptides across therapeutic domains, including metabolic disorders, oncology, and infectious diseases. This review synthesizes critical developments in peptide drug discovery, production technologies, and clinical applications, while highlighting emerging challenges and opportunities. We further evaluate the therapeutic potential of peptides in addressing unmet medical needs and propose strategic directions to accelerate their integration into precision medicine paradigms. Full article

Background/Objectives: The increasing threat of antibiotic resistance and the declining efficiency of traditional drug discovery pipelines highlight the urgent need for novel drug targets and effective enzyme inhibitors against infectious diseases. Oligopeptidase B (OPB), a serine protease with trypsin-like specificity that processes low-molecular-weight peptides and oligopeptides, is present in bacteria and certain parasites but absent in mammals. This unique distribution makes OPB an attractive and selective target for antimicrobial drug development. Methods: Three-dimensional models of OPB from Serratia marcescens and Stenotrophomonas maltophilia, previously identified by our research group, were constructed via homology modeling using the best available OPB template from the RCSB Protein Data Bank. The S. marcescens OPB model was subjected to high-throughput virtual screening (HTVS) against the Natural Products Atlas (npatlas) database. Top-ranking compounds were further evaluated using Glide standard precision (SP) and extra precision (XP) docking protocols. Binding affinities were refined using molecular mechanics with generalized born and surface area (MM–GBSA) calculations. Molecular dynamics (MD) simulations assessed binding stability, while absorption distribution metabolism excretion and toxicity (ADMET) profiling evaluated drug-likeness and pharmacokinetic properties. Results: Ten natural product compounds demonstrated stronger binding affinities than antipain, a well-known oligopeptide-based protease inhibitor, as indicated by their more favorable MM–GBSA scores of −60.90 kcal/mol (S. marcescens) and −27.07 kcal/mol (S. maltophilia). Among these, dichrysobactin and validamycin E consistently exhibited favorable binding profiles across both OPB models. MD simulations confirmed the stability of their interactions with OPB active sites, maintaining favorable binding conformations throughout the simulation period. ADMET analysis suggested that while both compounds show promise, lead optimization is required to enhance their drug-like characteristics. Conclusions: This study identifies dichrysobactin and validamycin E as promising OPB inhibitors with potential antimicrobial activity. These findings support their further development as selective and potent agents against bacterial pathogens, including resistant strains, and underscore the need for experimental validation to confirm their efficacy and safety. Full article

Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to microfluidic biosensors in the field of laboratory medicine, focusing on their applications in the above three areas. In cancer liquid biopsy, microfluidic biosensors facilitate the isolation, enrichment, and detection of tumor markers such as CTCs, ctDNA, miRNA, exosomes, and so on, providing support for early diagnosis, precise treatment, and prognostic assessment. In terms of pathogenic bacteria detection, microfluidic biosensors can achieve the rapid, highly sensitive, and highly specific detection of a variety of pathogenic bacteria, helping disease prevention and control as well as public health safety. Pertaining to the realm of POCT, microfluidic biosensors bring the convenient detection of a variety of diseases, such as tumors, infectious diseases, and chronic diseases, to primary health care. Future microfluidic biosensor research will focus on enhancing detection throughput, lowering costs, innovating new recognition elements and signal transduction methods, integrating artificial intelligence, and broadening applications to include home health care, drug discovery, food safety, and so on. Full article

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature. Full article

The aim of this review is to summarize the current knowledge on the use of coumarin-derived compounds in the zebrafish (Danio rerio) model. Coumarins, a class of naturally occurring compounds with diverse biological activities, including compounds such as coumarin, angelicin, and warfarin, have attracted considerable attention in the study of potential therapeutic agents for cancer, central nervous system disorders, and infectious diseases. The capabilities of coumarins as active compounds have led to synthesizing various derivatives with their own properties. While such variety is certainly promising, it is also cumbersome due to the large amount of research needed to find the most optimal compounds. The zebrafish model offers unique advantages for such studies, including high genetic and physiological homology to mammals, optical transparency of the embryos, and rapid developmental processes, facilitating the assessment of compound toxicity and underlying mechanisms of action. This review provides an in-depth analysis of the chemical properties of coumarins, their mechanisms of biological activity, and the results of previous studies evaluating the toxicity and efficacy of these compounds in zebrafish assays. The zebrafish model allows for a holistic assessment of the therapeutic potential of coumarin derivatives, offering valuable insights for advancing drug discovery and development. Full article

Background/Objectives: Infectious diseases caused by Staphylococcus aureus (S. aureus) have become alarming health issues worldwide due to the ever-increasing emergence of multidrug resistance. In silico approaches can accelerate the identification and/or design of versatile antibacterial chemicals with the ability to target multiple S. aureus strains with varying degrees of drug resistance. Here, we develop a perturbation theory machine learning model based on a multilayer perceptron neural network (PTML-MLP) for the prediction and design of versatile virtual inhibitors against S. aureus strains. Methods: To develop the PTML-MLP model, chemical and biological data associated with antibacterial activity against S. aureus strains were retrieved from the ChEMBL database. We applied the Box–Jenkins approach to convert the topological indices into multi-label graph-theoretical indices; the latter were used as inputs for the creation of the PTML-MLP model. Results: The PTML-MLP model exhibited accuracy higher than 80% in both training and test sets. The physicochemical and structural interpretation of the PTML-MLP model was performed through the fragment-based topological design (FBTD) approach. Such interpretations permitted the analysis of different molecular fragments with favorable contributions to the multi-strain antibacterial activity and the design of four new drug-like molecules using different fragments as building blocks. The designed molecules were predicted/confirmed by our PTML model as multi-strain inhibitors of diverse S. aureus strains, thus representing promising chemotypes to be considered for future synthesis and biological testing of versatile anti-S. aureus agents. Conclusions: This work envisages promising applications of PTML modeling for early antibacterial drug discovery and related antimicrobial research areas. Full article

Plasmodium, Leishmania, and Trypanosoma parasites are responsible for infectious diseases threatening millions of people worldwide. Despite more recent efforts devoted to the search for new antiprotozoal agents, efficacy, safety, and resistance issues still hinder the development of suited therapeutic options. The lack of robustly validated targets and the complexity of parasite’s diseases have made phenotypic screening a preferential drug discovery strategy for the identification of new chemical entities. However, via this approach, no information on biological target(s) and mechanisms of action of compounds are provided. Among the target deconvolution strategies useful to fill this gap, photoaffinity labeling (PAL) has emerged as one of most suited to enable investigation in a complex cellular environment. More recently, PAL has been exploited to unravel the molecular basis of bioactive compounds’ function in live parasites, allowing elucidation of the mechanism of action of both approved drugs and new chemical entities. Besides highlighting new potential drug targets, PAL can provide valuable information on efficacy and liabilities of small molecules at the molecular level, which could be exploited to greatly facilitate the rational optimization of compounds in terms of potency and safety. In this review, we will report the most recent studies that have leveraged PAL to disclose the biological targets and mechanism of action of phenotypically active compounds targeting kinetoplastid diseases (i.e., human African trypanosomiasis, leishmaniasis, and Chagas disease) and malaria. Moreover, we will comment on potential perspectives that this innovative approach can provide in aiding the discovery and development of new antiprotozoal drugs. Full article

Infectious diseases have significantly shaped human history, leading to significant advancements in medical science. The discovery and development of antibiotics represented a critical breakthrough, but the rise of antibiotic-resistant pathogens now presents a serious global health threat. Due to the limitations of current synthetic antimicrobials, such as toxicity and environmental concerns, it is essential to explore alternative solutions. Algae, particularly microalgae and cyanobacteria, have emerged as promising sources of bioactive antimicrobial compounds. This review provides a comprehensive analysis of the antimicrobial properties of algal-derived compounds, including polysaccharides, fatty acids, and phenols, which have shown effectiveness against multi-drug-resistant bacteria. A co-occurrence bibliometric analysis using VOSviewer highlighted five key research clusters: antibiotic resistance, algal extracts, biosynthesis, water treatment, and novel pharmacological compounds. Furthermore, the primary mechanisms of action of these bioactive compounds, such as the inhibition of protein synthesis and cell membrane disruption, were identified, demonstrating their potential against both common and multi-resistant pathogens. Future research should prioritize optimizing algal biomass production, utilizing genetic and metabolic engineering, and creating innovative delivery systems to enhance the efficient production of bioactive compounds. Full article

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts. Full article

Background/Objectives. The rise of antibiotic-resistant pathogens represents a significant global challenge in infectious disease control, which is amplified by the decline in the discovery of novel antibiotics. Staphylococcus aureus continues to be a highly significant pathogen, causing infections in multiple organs and tissues in both healthcare institutions and community settings. The bacterium has become increasingly resistant to all available antibiotics. Consequently, there is an urgent need for novel small molecules that inhibit the growth or impair the survival of bacterial pathogens. Given their large structural and chemical diversity, as well as often unique mechanisms of action, natural products represent an excellent avenue for the discovery and development of novel antimicrobial treatments. Anaephene A and B are two such naturally occurring compounds with significant antimicrobial activity against Gram-positive bacteria. Here, we report the rapid syntheses and biological characterization of five novel anaephene derivatives, which display low cytotoxicity against mammalian cells but potent antibacterial activity against various S. aureus strains, including methicillin-resistant S. aureus (MRSA) and the multi-drug-resistant community-acquired strain USA300LAC. Methods. A Sonogashira cross-coupling reaction served as the key step for the synthesis of the alkyl pyridinol products. Results/Conclusions. Using the compound JC-01-074, which displays bactericidal activity already at low concentrations (MIC: 16 μg/mL), we provide evidence that alkyl pyridinols target actively growing and biofilm-forming cells and show that these compounds cause disruption and deformation of the staphylococcal membrane, indicating a membrane-associated mechanism of action. Full article

The prevalence of antimicrobial resistance reduces the effectiveness of antimicrobial drugs in the prevention and treatment of infectious diseases caused by pathogens such as bacteria, fungi, and viruses. Microbial secondary metabolites have been recognized as important sources for new drug discovery and development, yielding a wide range of structurally novel and functionally diverse antimicrobial drugs for the treatment of a variety of diseases that are considered good producers of novel antimicrobial drugs. Bacteria produce a wide variety of antimicrobial compounds, and thus, antibiotics derived from natural products still dominate over purely synthetic antibiotics among the antimicrobial drugs developed and introduced over the last four decades. Among them, Pseudomonas aeruginosa secondary metabolites constitute a richly diverse source of antimicrobial substances with good antimicrobial activity. Therefore, they are regarded as an outstanding resource for finding novel bioactive compounds. The exploration of antimicrobial compounds among Pseudomonas aeruginosa metabolites plays an important role in drug development and biomedical research. Reports on the secondary metabolites of Pseudomonas aeruginosa, many of which are of pharmacological importance, hold great promise for the development of effective antimicrobial drugs against microbial infections by drug-resistant pathogens. In this review, we attempt to summarize published articles from the last twenty-five years (2000–2024) on antimicrobial secondary metabolites from Pseudomonas aeruginosa. Full article

Alzheimer’s Disease is among the major chronic neurodegenerative diseases that affects more than 50 million people worldwide. This disease irreversibly destroys memory, cognition, and the overall daily activities which occur mainly among the elderly. Few drugs are approved for Alzheimer’s Disease management despite its high prevalence. To date, the available drugs in the market cannot reverse the damage of neurons caused by the disease leading to the exacerbation of symptoms and possibly death. Medicinal plants are considered a rich source of chemical constituents and have been contributing to modern drug discovery in many therapeutic areas including cancer, infectious, cardiovascular, neurodegenerative and Central Nervous System (CNS) diseases. Moreover, essential oils that are extracted from plant organs have been reported for a wide array of biological activities, and their roles as antioxidants, antiaging, cytotoxic, anti-inflammatory, antimicrobial, and enzyme inhibitory activities. This article highlights the promising potential of plants’ essential oils in the discovery of novel therapeutic options for Alzheimer’s Disease and halting its progression. In this article, 428 compounds were reported from the essential oils isolated from 21 plants. A comparative study is carried out by employing a variety of machine learning techniques, validation, and evaluation metrics, to predict essential oils’ efficacy against Alzheimer’s Disease progression. Extensive experiments on essential oil data suggest that a prediction accuracy of up to 82% can be achieved given the proper data preprocessing, feature selection, and model configuration steps. This study underscores the potential of integrating machine learning with natural product research to prioritize and expedite the identification of bioactive essential oils that could lead to effective therapeutic interventions for Alzheimer’s Disease. Further exploration and optimization of machine learning techniques could provide a robust platform for drug discovery and development, facilitating faster and more efficient screening of potential treatments. Full article