Multi-target Drugs Research Articles

Exploring Quinazoline as a Scaffold for Developing Novel Therapeutics in Alzheimer’s Disease

Quinazoline, a privileged scaffold in medicinal chemistry, offers promising potential in the synthesis of anti-Alzheimer’s disease (AD) drugs. This heterocyclic compound, characterized by its fused benzene and pyrimidine rings, enables the design of multifunctional agents targeting AD pathology. The drug-like aspects and pharmaceutical features of quinazoline derivatives have the potential to give rise to various therapeutic drugs. AD is a progressive neurodegenerative condition marked by memory decline, cognitive deterioration, and language disorders. Given its complexity and multifaceted nature, there is a pressing need to discover multi-target drugs to effectively address this debilitating disorder. A comprehensive literature review has demonstrated that quinazoline derivatives exhibit a wide range of therapeutic potential for AD. These compounds function as inhibitors of cholinesterases, β-amyloid aggregation, oxidative stress, and tau protein, among other protective effects. Here, we highlight the most significant and recent research on quinazoline-based anti-AD agents, aiming to support the development and discovery of novel treatments for AD.

Nanodrugs Targeting Key Factors of Ferroptosis Regulation for Enhanced Treatment of Osteoarthritis.

Osteoarthritis (OA) is a globally prevalent degenerative joint disease. Recent studies highlight the role of ferroptosis in OA progression. Targeting ferroptosis regulation presents a promising therapeutic strategy for OA; however, current research primarily focuses on single targets associated with ferroptosis. In this study, a reactive oxygen species (ROS)-responsive nanoparticle is developed by linking deferasirox (DEF) and pterostilbene (PTE) with thioketal and incorporating cerium ions (Ce), creating Ce@D&P nanoparticles (NPs), which offer multitarget regulation of ferroptosis. The characteristics of Ce@D&P NPs are evaluated and their therapeutic effects on IL-1β-stimulated chondrocytes are verified. Results show that Ce@D&P NPs reduce ROS levels, mitigate inflammation, chelate iron to inhibit ferroptosis, and balance extracellular matrix (ECM) metabolism in chondrocytes. Mechanistically, transcriptomics and metabolomics analyses suggest that Ce@D&P NPs exerted their effects by regulating oxidative stress and lipid metabolism in chondrocytes. To better treat destabilization of the medial meniscus (DMM)-induced OA in mice, Ce@D&P NPs via intra-articular injection are delivered. The results show that Ce@D&P NPs alleviate cartilage matrix damage and slow OA progression. Overall, the findings indicate that Ce@D&P NPs represent a promising multitarget drug delivery system, and Ce@D&P NPs may be an effective strategy for OA treatment.

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.

Computational Approaches for Multitarget Drug Design in Alzheimer's Disease: A Comprehensive Review.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative brain disorder, primarily affecting the elderly. Its socio-economic impact and mortality rate are alarming, necessitating innovative approaches to drug discovery. Unlike single-target diseases, Alzheimer's multifactorial nature makes single-target approaches less effective. To address this challenge, researchers are turning to drug design strategies targeting multiple disease pathways simultaneously. This approach has led to the promising identification of dual or multiple-target inhibitors, offering new perspectives for improving disease management. Computer-Aided Drug Design (CADD) such as virtual screening, docking, QSAR, molecular dynamics, ADMET prediction, etc., are valuable tools for designing and identifying new multi target directed ligands (MTDLs). These methods enable efficient screening of extensive compound libraries and accurate prediction of pharmacokinetic profiles, optimizing development costs and time. Challenges such as model accuracy, simulation complexity, and data integration persist. Addressing these issues requires advances in in silico modeling, high-performance computing, and experimental validation. In this regard, this review highlights recent advances using various computational methods to screen and identify new candidate compounds containing different heterocyclic motifs that could serve as potential bases for designing ligands targeting multiple targets for Alzheimer's disease.

GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.

Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.

Unraveling the Synergistic Neuroprotective Mechanism of Natural Drug Candidates Targeting TRPV1 and TRPM8 on an Ischemic Stroke.

The development of multitargeted drugs is urgent for ischemic stroke. TRPV1 and TRPM8 are important targets of ischemic stroke. Previous drug candidate screening has identified that muscone, l-borneol, and ferulic acid may target TRPV1 and TRPM8 for ischemic stroke. However, the mechanisms of these drug candidates on targets were ill-informed. Therefore, firstly, a tongue-tissue biosensor was constructed. It explored the activation or inhibition mechanisms of drug candidates targeting TRPV1 and TRPM8 in a near-physiological environment. It was found that muscone could specifically inhibit TRPM8 and selectively activate TRPV1, while l-borneol exhibited the opposite effect. It suggested a synergistic network between these two drug candidates. Furthermore, more selective protein biosensors were developed to delve deeper into the synergistic mechanisms. A strong synergistic effect of muscone and l-borneol was proved. Molecular docking revealed that the synergistic effect was caused by different action sites, respectively. Subsequently, the synergistic effect of muscone and l-borneol was further confirmed by hypoxic nerve injury models of Caenorhabditis elegans (C. elegans) and antithrombus and anti-ischemic models of zebrafish. Ultimately, through nontargeted metabolomics, it was found that muscone and l-borneol mainly regulated Ca2+ concentration and energy metabolism by pathways such as purine and amino acid metabolisms. In conclusion, this research identified critical targets and synergistic drug candidates for multitarget neuroprotection of ischemic stroke. In addition, it has systemically demonstrated the feasibility of the integration of tissue/protein biosensors and metabolomics for the research and development of multitarget drugs. Compared to other screening and validation methods for drugs and targets, the biosensors we developed not only achieved higher sensitivity and specificity in complex physiological environments, ensuring a wider detection range, but also greatly saved biological samples. Simultaneously, they could be extended to other complex systems, such as biomarker screening in clinical samples and exosomes isolated from stem cells.

New Promising Steroidal Aromatase Inhibitors with Multi-Target Action on Estrogen and Androgen Receptors for Breast Cancer Treatment.

Background/Objectives: Endocrine therapies that comprise anti-estrogens and aromatase inhibitors (AIs) are the standard treatment for estrogen receptor-positive (ER+) (Luminal A) breast cancer-the most prevalent subtype. However, the emergence of resistance restricts their success by causing tumor relapse and re-growth, which demands a switch towards other therapeutic approaches in order to minimize or overcome resistance. Indeed, this clinical limitation highlights the search for new molecules to improve cancer treatment. Recently, strategies that address multiple targets have been emerging, and multi-target drugs have the potential to become the future anti-cancer molecules. Our group has been searching for new multi-target compounds, and as part of this, our study aims to understand the anti-cancer and multi-target potential of three new steroidal aromatase inhibitors (AIs): 7α-methylandrost-4-en-17-one (6), 7α-methylandrost-4-ene-3,17-dione (10a) and androsta-4,9(11)-diene-3,17-dione (13). Methods: Their in vitro actions and molecular mechanisms were elucidated in a sensitive ER+ aromatase-overexpressing breast cancer cell line, MCF-7aro cells, as well as in an AI-resistant ER+ breast cancer cell line, LTEDaro cells. Results: All the new AIs (10 µM) prevented the proliferation of MCF-7aro cells by arresting cell cycle progression. Interestingly, all AIs (10 µM) act as androgen receptor (AR) agonists and modulate ER levels, synthesis and signaling to induce the apoptosis of ER+ breast cancer cells. Additionally, these new AIs (10 µM) also re-sensitize resistant cells by promoting apoptosis, offering a therapeutic benefit. Conclusions: Overall, new steroidal polypharmacological compounds have been discovered that, by acting as AIs, ER modulators and AR agonists, impair ER+ breast cancer cell growth. Overall, this study is a breakthrough on drug discovery as it presents new molecules with appealing anti-cancer properties and multi-target action for the treatment of ER+ breast cancer.

Akkermansia muciniphila Mediated the Preventive Effect of Disulfiram on Acute Liver Injury via PI3K/Akt Pathway.

Acetaminophen induced acute liver injury (ALI) has a high incidence and is a serious medical problem, but there is a lack of effective treatment. The enterohepatic axis is one of the targets of recent attention due to its important role in liver diseases. Disulfiram (DSF) is a multitarget drug that has been proven to play a role in a variety of liver diseases and can affect intestinal flora, but whether it can alleviate ALI is not clear. We utilised bacterial 16S rRNA gene profiling, antimicrobial treatments, and faecal microbiota transplantation tests to explore whether DSF therapy for ALI is dependent on gut microbiota. Our findings indicate that DSF primarily restores intestinal microbiome balance by modulating the abundance of Akkermansia muciniphila (A. muciniphila), leading to significant alleviation of ALI symptoms in a gut microbiota dependent manner. We also found that A. muciniphila can promote the activation of PI3K/Akt pathway, correct the Bcl-2/Bax ratio, and further inhibit hepatocyte apoptosis. In conclusion, DSF ameliorates ALI by modulating the intestinal microbiome and activating the PI3K/AKT pathway through A. muciniphila.

Novel Insight into the Modulatory Effect of Traditional Chinese Medicine on Cerebral Ischemia-Reperfusion Injury by Targeting Gut Microbiota: A Review.

Cerebral ischemia-reperfusion injury (CIRI) is clinically characterized by high rates of morbidity, disability, mortality, and recurrence as well as high economic burden. The clinical manifestations of CIRI are often accompanied by gastrointestinal symptoms such as intestinal bacterial dysbiosis and gastrointestinal bleeding. Gut microbiota plays an important role in the pathogenesis of CIRI, and its potential biological effects have received extensive attention. The gut microbiota not only affects intestinal barrier function but also regulates gastrointestinal immunity and host homeostasis. Traditional Chinese medicine (TCM), a multi-component and multi-targeted drug, has shown remarkable effects and few adverse reactions in the prevention and treatment of CIRI. Notably, the effect of TCM on CIRI by regulating gut microbiota and maintaining gastrointestinal homeostasis has gradually become a hot topic. This review summarizes the functional role of the gut microbiota in the development and progression of CIRI and the therapeutic effects of TCM on CIRI by improving gut microbiota dysbiosis, affecting gut microbiota metabolism, and maintaining host immunity. The active ingredients of TCM used for the treatment of CIRI in relevant studies were saponins, triterpenoids, phenolics, and alkaloids. In addition, the clinical effects of TCM used to treat CIRI were briefly discussed. This review established the clinical significance and development prospects of TCM-based CIRI treatments and provided the necessary theoretical support for the further development of TCM resources for the treatment of CIRI.

Anti-inflammatory and antinociceptive effects of LQFM275 - A new multi-target drug.

Compound (4-(3,5-di-tert-butyl-4-hydroxybenzylamine)benzenesulfonamide) (LQFM275) was designed and synthesized from darbufelone and sulfanilamide as a new multi-target for the treatment of inflammatory diseases. LQFM275 showed a great range of safe cytotoxicity profile (100-400μM) evaluated by MTT assay, preventing damage induced by lipopolysaccharide (LPS) in EA.hy926 cell line. In mice, the acute oral treatment with LQFM275 (57, 114, and 228mg/kg) reduced the number of writhing by 26, 37, and 49%, respectively. LQFM275 (114mg/kg) also presented an antinociceptive effect, reducing by 57% the nociceptive response in the second phase of the formalin test and by 47% the Carrageenan(Carra)-induced hyperalgesia. That effect was dependent on its anti-inflammatory activity. LQFM275 (114mg/kg) also reduced 42% and 31% of the Carra and LPS-induced edema, respectively. The pleurisy test attenuated the leukocyte migration induced by Carra and LPS by reducing the number of polymorphonuclear cells (by 39 and 36%, respectively). The production of reactive oxygen species in the pleural exudate was reduced, which is shown by a decrease in myeloperoxidase (MPO) activity (Carra=35% and LPS=40%) and in levels of pro-inflammatory cytokines TNF-α and IL-1β (Carra=48% and LPS=47 e 36%). On the other hand, it increased the levels of anti-inflammatory cytokines, IL-4, and IL-10 (Carra=50% and LPS=21 and 53%). Moreover, LQFM275 demonstrated to be a dual COX-2 and 5-LOX inhibitor (IC50=81 and 167μM, respectively). Therefore, the promising anti-inflammatory and antinociceptive effects of LQFM275 provide an opportunity for a new multi-target drug development.

Multi-targeted nanogel drug delivery system alleviates neuroinflammation and promotes spinal cord injury repair

Multi-targeted nanogel drug delivery system alleviates neuroinflammation and promotes spinal cord injury repair

Anticancer benzimidazole derivatives as inhibitors of epigenetic targets: a review article.

Cancer is one of the leading causes of morbidity and mortality worldwide. One of the primary causes of cancer development and progression is epigenetic dysregulation, which is a heritable modification that alters gene expression without changing the DNA sequence. Therefore, targeting these epigenetic changes has emerged as a promising therapeutic strategy. Benzimidazole derivatives have gained attention for their potent epigenetic modulatory effects as they interact with various epigenetic targets, including DNA methyltransferases, histone deacetylases and histone methyltransferases. This review provides a comprehensive overview of benzimidazole derivatives that inhibit different acetylation and methylation reader, writer and eraser epigenetic targets. Herein, we emphasize the therapeutic potential of these compounds in developing targeted, less toxic cancer therapies. Presently, some promising benzimidazole derivatives have entered clinical trials and shown great advancements in the fields of hematological and solid malignancy therapies. Accordingly, we highlight the recent advancements in benzimidazole research as epigenetic agents that could pave the way for designing new multi-target drugs to overcome resistance and improve clinical outcomes for cancer patients. This review can help researchers in designing new anticancer benzimidazole derivatives with better properties.

BMY 7378, a selective α1D-adrenoceptor antagonist, is a new angiotensin converting enzyme inhibitor: In silico, in vitro and in vivo approach

BMY 7378 is a multitarget drug primarily known for its selective antagonism of α1D-adrenoceptors (α1D-AR), exhibiting both hypotensive effects and the ability to prevent or reverse angiotensin II-induced vascular hypertrophy. Notably, BMY 7378 contains a phenylpiperazine moiety, a structural feature associated with angiotensin-converting enzyme (ACE) inhibition. This study aimed to investigate ACE inhibition as a potential pharmacological mechanism of BMY 7378. Using an in silico approach we predicted BMY 7378 interactions with the ACE active site, followed by in vitro activity assays. Additionally, ACE protein expression in the heart was analyzed following four weeks of BMY 7378 treatment in 7–8-month-old spontaneously hypertensive rats (SHR). All assays were benchmarked against captopril, a standard ACE inhibitor. In silico results showed that BMY 7378 binds to the ACE active site, though with reduced interaction with Zn701 (73.7 % compared to captopril), likely due to the pKa of its amino group. The inhibitory concentration 50 (IC50) for BMY 7378 was 136 μM, lower than other reported phenylpiperazine derivatives. Furthermore, BMY 7378 significantly increased ACE expression in the hearts of SHR, with an increase of 8.5-fold compared to captopril. In conclusion, BMY 7378 exhibits dual activity as an α1D-AR antagonist and an ACE inhibitor, making it a promising pharmacological tool for investigating and potentially treating hypertension and its associated cardiovascular complications.

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles.

Polypharmacological Drug Design Guided by Integrating Phenotypic and Restricted Fragment Docking Strategies.

Polypharmacological drugs are of great value for treating complex human diseases by the combinative modulation of several biological targets. However, multitarget drug design with more than two targets is challenging and generally discovered by serendipity. Herein, a restricted fragment docking (RFD) computational method combined with a phenotypic discovery approach was developed for rational polypharmacological drug design. Via genetic and drug combination studies in a microglial phenotype, we first identified novel synergistic effects by triple target modulation toward RIPK1, MAP4K4, and ALK. Drawing on the RFD method to explore virtual chemical spaces in three target pockets, we identified a lead compound, LP-10d, that precisely modulated RIPK1/MAP4K4/ALK for synergistic microglial protection with low nanomolar potency. LP-10d showed polypharmacology against multiple neuropathologies in the 3xTg Alzheimer's disease mouse model. Our study revealed a potential application of the RFD method, which is valuable to further polypharmacological drug discovery involved in clinical studies for treating complex human diseases.

Polyphenols-rich Portulaca oleracea L. (purslane) alleviates ulcerative colitis through restiring the intestinal barrier, gut microbiota and metabolites.

Ulcerative colitis (UC) is a recurrent intestinal disease caused by a complex of factors, and there are serious adverse effects and tolerance problems associated with the current long-term use of therapeutic drugs. The development of natural food sources and multi-targeted drugs for the treatment of UC is imminent. Portulaca oleracea L. (PO), as a vegetable, has been shown in studies to have an anti-UC effects. However, the relationship between the abundant active ingredients contained in Portulaca oleracea L. and the improvement of intestinal barrier, gut microbiota and metabolites is unclear. In the present study, Portulaca oleracea L. which was found to be rich in phenolic acid-based active ingredients, were effective in alleviating dextran sulfate sodium (DSS)-induced body weight loss, disease activity index (DAI) score and colon length in mice. It also decreased C-reactive protein (CRP) and myeloperoxidase (MPO) responses, reduced the permeation of fluorescein isothiocyanate (FITC)-dextran, lipopolysaccharide (LPS) and evans blue (EB), and improved histopathological scores. Meanwhile, in vitro and in vivo validation revealed the protective effects of purslane on the intestinal barrier indicators ZO-1, Occludin and Claudin-1, and inhibited the expression of inflammation-associated iNOS and NLRP3 proteins through the NF-κB signaling pathway. In addition, purslane increased the diversity of the intestinal flora, enhancing the proportion of the genera Butyricoccus, Dorea and Bifidobacterium and decreasing the percentage of Bacteroides, Turicibacter and Parabacteroides. Serum metabolomics analysis showed that the imbalance of 39 metabolites was significantly reversed after PO deployment. Enrichment analysis showed that Pentose phosphate pathway and Pyruvate metabolism pathway were the key pathways of PO against UC. Overall, purslane effectively improved the intestinal barrier disruption and intestinal inflammation by inhibiting the NF-κB signaling pathway, and adjusted the disorder of gut microbiota and metabolites to exert anti-UC effects.

Computational dissection through network pharmacology and structure-based analysis unravels mechanistic actions of bioactive compounds in a hepatoprotective herb, Picrorhiza kurroa for the treatment of NAFLD and NASH

Non-Alcoholic fatty liver disease has become a silent pandemic worldwide with no authorized medicine available. Picrorhiza kurroa is a traditional hepatoprotective herb wherein extracts provide therapeutic efficacy but not the individual compounds. Hence, the aim of the study is exploration of active molecules in P. kurroa extracts and identification of mechanistic actions to pinpoint potential leads towards drug development. We employed network pharmacology to identify the significance of combinatorial effect of compounds on multiple targets. The NAFLD/NASH associated genes encoding protein targets overlapped with the predicted protein targets of P. kurroa compounds. Then, overlapping targets were considered further to capture the interactive targets from Protein-Protein-Interaction network of NAFLD and NASH. The networks were generated to capture the role of proteins in different signaling pathways, diseases, and effective compounds as therapeutics. Furthermore, structural, and biophysical analysis was performed for significant complexes. We observed that the compounds like astragalin, Picroside-I, Vernicoside, Rutin, Quercetin, Kaempferol, Gallic acid, Ellagic acid in P. kurroa acted synergistically by enhancing the bioavailability of active compounds and affecting various morbidities of NAFLD through involvement in different signaling and disease pathways such as oxidative phosphorylation, FoxO signaling, inflammation, several cancerous and diabetic pathways. The network pharmacology revealed the interactive behavior of proteins involved in NAFLD treated by P. kurroa compounds. Furthermore, molecular docking and molecular dynamic simulation study showed potential candidates in therapeutics. Overall, the study suggested multi-target drug discovery for treating complex diseases by providing leads in herbal extracts as potential therapeutic botanicals.

Harnessing network pharmacology in drug discovery: an integrated approach.

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Design, synthesis and biological evaluation of novel pyrazolinone derivatives as multifunctional ligands for the treatment of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the depletion of cholinergic neurons and the accumulation of amyloid β (Aβ) plaques. The complexity and multifaceted nature of AD necessitate further exploration of multi-target drugs for its treatment. In this study, a series of novel pyrazolinone-based compounds were designed, synthesized, and evaluated as acetylcholinesterase (AChE) inhibitors and antioxidants. The lead compounds ET11 and ET21 showed strong inhibitory activity against human AChE, with IC50 values of 6.34 and 1.81nM, respectively. In vitro DPPH and ORACFL assays confirmed the compounds' strong antioxidant capabilities. ET11 exhibited excellent neuroprotective activity in the tBHP-induced SH-SY5Y cell damage model. Benefiting from the pyridopyrazolone moiety, ET11 showed significant Cu2+ chelating ability and effectively inhibited Cu2+-induced Aβ aggregation. In vivo behavioral studies and histopathology analysis preliminarily confirmed the compound's cognitive improvement and neuroprotective effects. Overall, these findings suggested that compound ET11 is expected to play a synergistic role in the treatment of AD, potentially slowing disease progression.

Highly Biocompatible Lamellar Liquid Crystals Based on Hempseed or Flaxseed Oil with Incorporated Betamethasone Dipropionate: A Bioinspired Multi-Target Dermal Drug Delivery System for Atopic Dermatitis Treatment.

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease that severely impairs patient's life quality and represents significant therapeutic challenge due to its pathophysiology arising from skin barrier dysfunction. Topical corticosteroids, the mainstay treatment for mild to moderate AD, are usually formulated into conventional dosage forms that are impeded by low drug permeation, resulting in high doses with consequent adverse effects, and also lack properties that would strengthen the skin barrier. Herein, we aimed to develop biomimetic lamellar lyotropic liquid crystals (LLCs), offering a novel alternative to conventional AD treatment. In screening studies, pseudoternary phase diagrams alongside polarized light microscopy (PLM) and viscosity measurements were utilized. Next, the selected LCCs underwent comprehensive characterization via PLM, small-angle X-ray scattering, differential scanning calorimetry, and rheological analysis. Lastly, their performance was evaluated and compared with the commercially available reference medicine in chemical stability study, in vitro permeation testing, in vitro safety assessment using cell proliferation assay, inverted light microscopy, and Raman mapping of keratinocytes, besides gap closure assay performed by live-cell imaging. Formulation (L/T)Ho30, containing the highest amount of lecithin/Tween 80 mixture (21%) and hempseed oil (28%), demonstrated lamellar microstructure with high skin hydration potential and favourable rheological features for skin administration. Moreover, in comparison with the reference medicine, it stood out by providing suitable chemical BD (betamethasone dipropionate) stability, improved 3-fold BD permeation, and excellent biocompatibility with over 85% cell proliferation at all tested concentrations, ensuring keratinocytes' integrity, as well as promoting skin healing with gap closure observed after 36hours. Unique multi-target drug delivery strategy depicted in newly developed bioinspired lamellar LCCs structurally resembling stratum corneum intercellular lipids, with incorporated BD drug, and composed of multifunctional components that synergistically strengthen skin barrier, was presented here and shows a promising approach for improved AD treatment.