Current drug discovery technologies最新文献

Medicinal plants are a rich source of therapeutic agents. Tuberculosis (TB) is a highly infectious disease causing significant morbidity and mortality, primarily due to its causative agent, Mycobacterium tuberculosis. The incidence of TB is rising globally, exacerbated by the emergence of drug-resistant strains. Resistance has developed against first-line and second-line drugs, compli-cating TB control programmes and diminishing their effectiveness. The development of Multi-Drug-Resistant (MDR) and extensively-Drug-Resistant (XDR) strains of Mycobacterium tubercu-losis highlights the urgent need for novel anti-TB drugs with unique mechanisms of action. Medic-inal plants present promising alternative sources for TB treatment, especially for MDR and XDR strains. These plants produce various secondary metabolites, such as alkaloids, coumarins, flavo-noids, polyphenols, terpenoids, and quinones, which exhibit antimicrobial properties. These com-pounds, while not directly involved in the plant's growth and development, serve as defence mech-anisms and hold potential for TB control. According to the literature, phytochemical constituents with anti-tubercular activity have been identified in various plants. These phytochemicals show promise in treating MDR and XDR TB. This review provides an overview of the current synthetic drugs used for TB treatment and highlights the work done on anti-tubercular plants and their phyto-chemicals.

Vitiligo, also known as leukoderma, is a chronic autoimmune skin disorder characterized by the progressive loss of melanocytes, leading to depigmented patches on the skin. While it is not life-threatening, the visible nature of the condition can significantly impact a patient's psychological and emotional well-being. This review aimed to provide a comprehensive overview of vitiligo, in-cluding its clinical presentation, pathogenesis, diagnostic methods, and current therapeutic options. Although various synthetic treatments are available, ranging from corticosteroids to phototherapy, their long-term effectiveness is often limited, and adverse effects are more common. As a result, there is a growing interest in natural and plant-based therapies that may offer safer and more sustainable alternatives. This review has highlighted the use of herbal bioactives and traditional medicines for vitiligo management, drawing upon the data sourced from PubMed, Google Scholar, Springer, and ClinicalTrials.gov databases. Key search terms for this review included vitiligo, herbal therapies, traditional medicine, animal models, Shwitra, and Baras. The review has also explored findings from animal models and clinical trials, contributing to our understanding of disease mechanisms and ther-apeutic efficacy. By integrating traditional knowledge with modern research, there is emerging po-tential for plant-derived compounds to serve as complementary or alternative options in vitiligo treat-ment. In conclusion, advancing our understanding of vitiligo's underlying mechanisms and embrac-ing safer, evidence-based herbal therapies may pave the way toward more effective and holistic pa-tient care.

Introduction: Today's world is grappling with numerous infectious diseases and pandemics caused by bacteria, viruses, fungi, or parasites, which are affecting people at an alarming rate. Molecular topology, a field that significantly influences drug design and discovery, involves the algebraic description of chemical compounds, enabling their distinctive and straightforward characterization.

Materials and methods: Among various applications, the topological indices can be generated from ℳ-polynomial. ℳ-polynomial is a generating function that has been proposed to unify the computation of diverse topological indices. It contains degree-based topological data of molecular graphs and facilitates the derivation of multiple degree-based topological indices in an efficient manner. The ℳ-polynomial can be used to derive different degree-based topological indices by using different transformations. Computational efficiency offers a common method for calculating several topological indices. QSAR/QSPR Models are employed to examine molecular properties and biological activity in drug design.

Results: The Sombor index, a molecular descriptor, was studied in the context of several antibacterial medications, including Amoxicillin, Ampicillin, Tetracycline, Doxycycline, Cefalexin, and Ciprofloxacin. These drugs are commonly used to treat conditions such as bladder infections, rickettsial infections, pneumonia, bronchitis, and other respiratory tract infections.

Discussion: In this study, the edge partition technique is employed to derive the ℳ-polynomial for selected antibacterial drug molecules. The graphical representation of the respective molecular structures is calculated and discussed based on the derived ℳ-polynomial.

Conclusion: To construct the ℳ -polynomial and derive the Sombor index for antibiotic drugs, then correlate them with the physicochemical properties of these drugs to analyze the regression models for the best fit.

Introduction: This study investigates the molecular docking of 306 phytochemicals from Iris, Daphne, and Chrysosplenium species against three key proteins of the H5N1 influenza virus: neuraminidase, polymerase, and hemagglutinin. Phytochemicals are recognized for their antiviral potential, but interactions between compounds from these genera and H5N1 proteins remain underexplored. Given the ongoing threat of H5N1, identifying novel inhibitors is essential. The main intent is to evaluate the binding affinities of selected phytochemicals through molecular docking and assess the drug-likeness of top candidates using pharmacokinetic and physicochemical filters.

Methods: Molecular docking was performed for 306 phytochemicals against the three H5N1 proteins. Fourteen promising compounds were further screened for physicochemical properties, compliance with Lipinski's Rule of Five, Veber's Rule, and PAINS alerts.

Results: All compounds exhibited no PAINS alerts, with several conforming to Lipinski's Rule of Five and Veber's Rule. Edgeworoside A emerged as the top-performing compound, showing strong binding affinity across all three targets and favorable interaction profiles. Triumbellin and daphnogi-rin A exhibited significant binding affinity for hemagglutinin and neuraminidase, as well as for polymerase, respectively. Compounds such as 3-isobutenylquercetin, irisoid E, junipegenin A, daphne-toxin, and excoecariatoxin exhibited high binding potential without violating drug-likeness criteria.

Conclusion: Several phytochemicals, particularly edgeworoside A, demonstrate promising multi-target potential against H5N1 influenza proteins. These findings highlight the therapeutic relevance of compounds from underexplored plant genera and support their further development through in vitro, in vivo, and preclinical studies.

Ethnopharmacology is the study of traditional medicinal knowledge and its application in modern drug discovery. It combines ethnobotanical insights with scientific research to identify bio-active compounds with therapeutic potential. Sustainable agriculture refers to farming practices that maintain ecological balance, support biodiversity, and ensure the long-term availability of resources. Integrating these fields can enhance drug discovery while preserving medicinal plants and promoting environmental sustainability. This review examines the collaboration between ethnopharmacology and sustainable agriculture in advancing drug discovery, conservation, and global food security. This review examines the role of ethnopharmacology in drug discovery, analyzing traditional medicinal practices, bioactivity-guided fractionation, and metabolomic profiling. It also investigates sustainable agriculture techniques, including organic farming, controlled cultivation, and conservation strategies for medicinal plants. Data were collected from peer-reviewed literature using sources such as Google Scholar, PubMed, Scopus, and journal databases like ScienceDirect. Ethnopharmacology has con-tributed to the discovery of key drugs with anticancer, anti-inflammatory, and antimicrobial proper-ties. Sustainable agriculture ensures a steady supply of medicinal plants while optimizing their bio-active compound production through improved cultivation techniques. The combination of these ap-proaches strengthens drug discovery efforts and supports ecological conservation. Integrating eth-nopharmacology with sustainable agriculture is a promising strategy for developing new drugs while protecting natural resources. Future research should focus on innovative cultivation techniques, com-munity-led conservation efforts, and advanced analytical methods to enhance the discovery of new drugs. The adoption of agroecological practices, technological advancements, and policy support will be crucial in ensuring sustainable and equitable benefits for healthcare and agriculture. Bridging tra-ditional knowledge with scientific research will foster new therapeutic discoveries while promoting environmental sustainability.

Introduction: Streptomyces species have complex genomes, including various biosynthetic gene clusters, frequently responsible for producing antibacterial and bioactive secondary metabolites under certain environmental conditions. To assess the impact of Magnesium and Iron on Streptomyces sp. VITGV100 secondary metabolite production and bioactivity, including molecular docking studies to predict their therapeutic potential.

Methods: Streptomyces sp. VITGV100 was grown in a nutrient broth supplemented with Magnesium and Iron elicitors. The secondary metabolites were analyzed for antioxidant activity via 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity, antimicrobial activity against Escherichia coli and Bacillus subtilis, and molecular docking studies of selected compounds.

Results: Magnesium and Iron supplementation elevated the production of metabolites with antioxidant activity (90% scavenging, IC50 value 0.025 mg/ml) at 6 mg/ml of Magnesium, and antimicrobial properties show the highest inhibition zone of 23 mm against Escherichia coli. Statistical analysis showed significant differences (p < 0.05) through two-way ANOVA. Docking study revealed substantial binding energy, supported by favorable Chemical Absorption, Distribution, Metabolism, Excretion, and Toxicity profiles.

Discussion: Magnesium and iron elicitation in Streptomyces sp. VITGV100 significantly enhances its antioxidant and antibacterial capabilities. Strong bioactivity and in-silico study confirmed. Although results lack in vivo efficacy and mechanistic insights, they are consistent with previous studies on trace element-induced metabolite synthesis. Clinical evaluations and mechanistic investigations of the discovered bioactive compounds should be prioritized.

Conclusion: Magnesium and Iron significantly improve the synthesis of bioactive compounds in Streptomyces sp. VITGV100, showing strong antioxidant and antimicrobial activities of these metabolites, combined with promising docking and ADMET profiles, shows promising therapeutic potential.

Introduction: Basil, an indigenous medicinal herb, is widely used in traditional medicine for its therapeutic and cosmetic benefits. Its properties include reducing oxidative stress, increasing melatonin production, protecting cells from free radical damage, and protecting the skin. Basil can be incorporated into daily skincare routines to address various skin conditions and promote overall health. This study examines the role of Basil in skin health, focusing on its antioxidant, antiinflammatory, antimicrobial, hydrating, and moisturizing properties. It examines its bioactive compounds and their impact on molecular pathways, such as NF-κB, MAPK, and COX-LOX, in reducing oxidative stress and inflammation. Additionally, it highlights Basil's potential as an anti-aging agent and its applications in skincare products.

Method: The data were gathered from various research studies and meta-analyses on basil cosmetics applications, sourced from multiple databases between 1999 and 2025, including PubMed, ScienceDirect, SpringerLink, Wiley Online Library, Elsevier, Nature, ResearchGate, and Google Scholar.

Result: Basil enhances skin health through its antioxidant, anti-inflammatory, antimicrobial, hydrating, and moisturizing effects. Its bioactive compounds, including flavonoids, phenolic acids, and terpenes, help neutralize ROS and RNS, preventing oxidative damage and inflammation. By modulating key inflammatory pathways, such as NF-κB, MAPK, and COX-LOX, Basil reduces proinflammatory cytokines and inhibits inflammatory mediators. These mechanisms contribute to its anti-aging and skin-protective properties, making it beneficial for various skincare applications.

Discussion: Basil is an indigenous herb with significant therapeutic potential. Traditionally, it has been used to manage various skin conditions, including acne and eczema. Studies have highlighted its anti-inflammatory and antioxidant properties, primarily due to its ability to inhibit the generation of free radicals. Additionally, Basil exhibits potent antimicrobial activity, attributed to the presence of essential oils that disrupt microbial cell membranes, inhibit biofilm formation, increase membrane permeability, and lead to intracellular leakage. Owing to these multifaceted properties, Basil holds promise for use in dermatological applications and cosmetic formulations.

Conclusion: Basil, a popular herb in various cultures, has antioxidant properties and is used in traditional medicine, skincare, and beauty treatments. Its essential oils, including linalool, estragole, and eugenol, have anti-aging, antifungal, and antibacterial properties. Basil's complex phytochemical composition makes it an excellent component for skincare, hair care, and dental care products. Its bioactive compounds neutralize reactive oxygen species and reactive nitrogen species, preventing oxidative damage

Artificial intelligence (AI) has developed into a powerful tool that employs human knowledge to swiftly resolve complex issues. Significant advancements in computer learning and artificial intelligence present a revolutionary opportunity for pharmaceutical formulation, drug dis-covery, and dosage form testing. AI algorithms would analyze a tremendous amount of biological reference data, such as proteomics and genomes, to assist researchers in identifying target diseases and predicting their possible interactions with proposed approaches to treatment. Just such focused and efficient drug development significantly augments the likelihood of acquiring drug approvals. AI may add, at the same time, develop costs and streamline research and development processes. Clas-sical machine learning techniques help not only in designing the experiment but also significantly in predicting the pharmacokinetics and toxicity of new drugs. This ability reduces the need for expen-sive, time-consuming animal testing by selection with optimization of lead compounds. Personalized medical strategies based on actual patient data assessments by algorithms such as those of AI may benefit patients through increased treatment adherence and outcomes. The review covers many ap-plications of AI for process optimization, testing, drug delivery dosage form design, and drug discov-ery. This study underlines the benefits brought by numerous types of techniques based on AI in phar-maceutical technology. However, there are exciting prospects to enhance patient care and medication development processes because of the pharmaceutical industry's continuous investment in and re-search into AI.

Introduction: Quantum computing represents a transformative advancement in computational science, with applications in drug discovery, molecular interaction simulation, drug-target binding optimization, and the analysis of complex biological data at unprecedented speeds and accuracy. Quantum computing emerges as a powerful tool to accelerate the development of new therapeutics, drug design, and the simulation of complex chemical interactions, including personalized medicine strategies. The objective of this study is to explore the potential of quantum computing in drug discovery and development, highlighting its ability to reduce time and costs while accelerating the identification of promising drug candidates.

Methods: Quantum computing algorithms, such as Grover's algorithm and the Variational Quantum Eigensolver (VQE), are utilized to simulate molecular interactions of drugs and optimize drug design. In case studies, such as IBM's use of VQE for molecular simulations, these technologies demonstrate their effectiveness.

Results: Quantum computing has shown promise in addressing several technological barriers, such as lengthy development timelines and high costs. Additionally, demonstrated success in molecular simulations and solving challenges during the drug development process. However, challenges related to error rates, qubit coherence, and regulatory compliance remain.

Discussion: This study examines the applications of quantum computing in drug discovery, highlighting key techniques such as quantum simulation, quantum machine learning, and optimization algorithms. Quantum computing is crucial for interdisciplinary collaboration among quantum physicists, computational chemists, biologists, and pharmaceutical professionals, as it is essential to overcoming these obstacles and realizing the full potential of quantum technologies in medicine.

Conclusion: Quantum computing holds great potential in drug discovery and development, offering accelerated, more accurate, and lower-cost research avenues, particularly in complex areas such as protein folding prediction and personalized medicine. This new paradigm has tremendous potential for guiding the future of pharmaceutical development and patient-focused medicine.