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Current Medicinal Chemistry - Current Issue
Volume 32, Issue 10, 2025
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A Perspective of PI3K/AKT/mTOR Pathway Inhibitors to Overcome Drug-resistance in Breast Cancer Therapy
Authors: Sudip Kumar Mandal and Samir Kumar SamantaThe heterogeneous disease, breast cancer (BC), is a frequently detected cancer today, including hormone receptor-positive (HR+), human epidermal growth factor receptor-2-positive (HER2+), and triple-negative (ER-, PR-, HER2-) BC. Advanced endocrine therapies could improve about 85% HR+ BC patient survival. Still, 20% - 30% of cases of endocrine therapy resistance are observed. For all kinds of breast cancer, drug resistance is a common and dangerous phenomenon, comprised of two types: de novo resistance and acquired resistance (prolonged exposure). According to recent works of literature, the PI3K/AKT/mTOR pathway has become an emerging target for overcoming drug resistance in BC therapy due to its close association with tumour growth and resistance from current therapies. Activation of the PI3K/AKT/mTOR pathway was found to promote multidrug resistance by elevating drugs’ outflow. The first orally active PI3K inhibitor, Alpelisib (BYL-719) in fulvestrant combination, was approved for treating HR+/ HER2− metastatic BC. Therefore, utilizing PI3K/mTOR/AKT inhibitors in combination with currently available strategies could be an optimistic approach to overcoming drug resistance and resensitizing drug-resistant tumor cells of BC. Here, in this perspective, BC cancer therapies related to drug resistance, the involvement of PI3K/AKT/mTOR pathway in drug resistance and multi-drug resistance, and the role of PI3K/AKT/mTOR inhibitors in getting rid of drug resistance have been illuminated.
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Herbal Treatments for Obesity: A Review
Authors: Amin Gasmi, Sadaf Noor, Pavan Kumar Mujawdiya, Muhammad Akram, Aisha Manzoor and Geir BjørklundObesity is the most pervasive metabolic disorder, further linked with many other diseases, including diabetes, hypertension, cardiovascular disorders, and sleep apnea. To control the increasing weight of obese individuals, experts usually recommend exercise and lifestyle alterations, but medication and surgeries are also advised in severe cases. FDA-approved obesity-controlling drugs are effective but possess certain adverse effects, including dry mouth, drug abuse, dysregulation in monoamine neurotransmitters, insomnia, and many more. Medication processes are expensive; researchers have focused on safer and more effective alternative approaches than pharmaceutical drugs. Historically, a diverse array of herbal plants has been used due to their therapeutic effect, as in vitro and in vivo experimentations have proved the effectiveness of herbal plants without associated mortality. In this review, we present various herbs with their metabolically active secondary metabolites, including Berberis vulgaris L, Rhizoma Coptidis, Radix Lithospermi, Aloe vera, Clerodendrum multiflorum Burm f., Astragalus membranaceus (Fisch), Boerhaavia diffusa, Achyranthes aspera L., etc. All of these herbs are responsible for anti-obesity, anti-diabetic, and anti-inflammatory effects. Most previously published clinical trials and animal studies have confirmed the significant potential of these herbal plants and their active ingredients to reduce weight by decreasing the accumulated fats in the body have also been discussed in this review. Thus, it is concluded that scientists must consider and utilize these natural treasures for safe, effective, and cost-effective treatment. It will open new and novel ways for treatment regimes.
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A Comprehensive Review of Therapeutic Compounds from Plants for Neurodegenerative Diseases
Neurodegenerative diseases (NDDs) comprise a large number of disorders that affects the structure and functions of the nervous system. The major cause of various neurodegenerative diseases includes protein aggregation, oxidative stress and inflammation. Over the last decade, there has been a gradual inclination in neurological research in order to find drugs that can prevent, slow down, or treat these diseases. The most common NDDs are Alzheimer's, Parkinson's, and Huntington's illnesses, which claims the lives of 6.8 million people worldwide each year and it is expected to rise by 7.1%. The focus on alternative medicine, particularly plant-based products, has grown significantly in recent years. Plants are considered as a good source of biologically active molecules and hence phytochemical screening of plants will pave way for the discovering new drugs. Neurodegeneration has been linked to oxidative stress, either as a direct cause or as a side effect of other variables. Therefore, it has been proposed that the use of antioxidants to combat cellular oxidative stress within the nervous system may be a viable therapeutic strategy for neurological illnesses. In order to prevent and treat NDDs, this review article covers the therapeutic compounds/metabolites from plants with the neuroprotective role. However, these exhibit other beneficial molecular functions in addition to antioxidative activity, making them a potential application in the management or prevention of neurodegenerative disorders. Further, it gives the insights to the future researchers about considering the peptide based therapeutics through various mechanisms for delaying or curing neurodegenerative diseases.
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Adjuvant Anti-tumor Therapy with Polyphenolic Compounds: A Review
The search for effective methods of treatment and prevention of oncological diseases, despite the successes achieved in recent decades, remains one of the most urgent issues in modern medicine. It is known that chemotherapy and radiation therapy are based on the induction of cell death by increasing the intracellular concentration of reactive oxygen species (ROS). To increase the effectiveness of chemo- and radiotherapy, inducing and increasing oxidative stress in tumor cells has been proposed. A new class of promising adjuvants in combination with anticancer therapy, which has already been shown to be effective in preclinical and clinical studies, includes natural and synthetic polyphenols. Polyphenolic compounds not only exhibit antitumor activity but also significantly reduce the resistance of tumor cells to chemo- and radiotherapy. However, almost all chemotherapeutic drugs and regimens of radiation treatment have a damaging toxic effect on normal tissues, which significantly affects the quality of life of patients, and treatment options for managing these side effects are limited. In this regard, some of the most promising agents for the management of toxic side effects are natural polyphenols. This study discusses the possible molecular mechanisms and prospects for the clinical use of natural and synthetic polyphenolic compounds in chemo- and radiotherapy. In addition, the protective role/effect of polyphenols on the effects of chemo- and radiotherapy in tumor patients is discussed.
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Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives
Authors: Mai Hanh Nguyen, Ngoc Dung Tran and Nguyen Quoc Khanh LeGastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.
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A Golden Shield: The Protective Role of Curcumin against Liver Fibrosis
More LessSeveral chronic liver injuries can result in liver fibrosis, a wound-healing response defined by an excessive buildup of diffuse extracellular matrix (ECM). Liver fibrosis may progress to liver cirrhosis, liver failure, or hepatocellular carcinoma. Many cellular routes are implicated in the fibrosis process; however, hepatic stellate cells appear to be the main cell type involved. Curcumin, a polyphenolic substance extracted from the Curcuma longa plant, has a diversity of pharmacologic impacts, including anti-inflammatory, antioxidant, antiproliferative and antiangiogenic actions. The anti-fibrotic property of curcumin is less clear, but curcumin's ability to influence inflammatory cytokines, inflammatory pathways, the expression of pro-apoptotic (up-regulated) and anti-apoptotic (down-regulated) proteins, and its ability to lower oxidative stress likely underlie its anti-fibrotic properties. In this review, we investigate and analyze the impact of curcumin on several disorders that lead to liver fibrosis, and discuss the therapeutic applications of curcumin for these disorders.
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An Overview of the Pharmacological Properties of Calebin-A
The natural polyphenol, calebin-A, was recently discovered and identified as a novel phytopharmaceutical with anti-inflammatory, anti-tumor, and antiproliferative properties. Calebin-A occurs naturally in trace quantities in Curcuma longa/C cassia, commonly known as turmeric, from the Zingiberaceae family. Calebin-A is a curcumin analog or 'chemical cousin' of curcumin with a similar chemical structure. Although few research studies have been conducted on the pharmacological and therapeutic properties of calebin-A, it is a very promising molecule with a variety of pharmacological properties. Some studies have suggested that calebin-A is helpful in treating various cancers due to its inhibitory effect on cell growth and anti-inflammatory properties. Other studies have suggested that calebin-A may improve neurocognitive status associated with neurodegeneration caused by Alzheimer’s disease (AD) by inhibiting the aggregation of β-amyloid. Finally, several studies have proposed that calebin-A may potentially be therapeutically beneficial in treating patients with obesity. This novel compound downregulates nuclear factor (NF)-κB-mediated processes involved with cancer, such as tumor cell invasion, proliferation, metastasis, and, most profoundly, inflammation. Moreover, calebin-A influences the activities of mitogen-activated protein kinases (MAPKs) in cancer cells. The present review identifies and discusses the pharmacological and phytochemical properties of calebin-A, as well as its therapeutic benefits and limitations, for future scientists and clinicians interested in exploring calebin-A’s medicinal qualities.
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Neopterin as a Potential Biomarker for the Early Diagnosis of Heart Failure: A Systematic Review and Meta-analysis
BackgroundNeopterin (NEO) is an inflammatory biomarker with proposed diagnostic value in cardiovascular diseases. Some correlations have been discovered between NEO levels and the incidence, severity, and adverse outcomes of heart failure (HF). However, there are discrepancies in the results reported in the literature.
MethodsWe conducted a systematic review and meta-analysis of studies comparing urinary and blood NEO concentrations between individuals with HF, cardiac insufficiency, or dilated cardiomyopathy (DCM) with control groups or those monitoring the role of NEO concentrations as a predictive marker of adverse outcomes in HF patients.
ResultsA total of 24 studies that met the inclusion criteria were reviewed. The studies demonstrated the alteration of NEO in blood or urine samples in subjects with HF, cardiac insufficiency, or DCM compared with control groups. Also, reviewing the studies suggested a link between reduced ejection fraction, higher NYHA classes, and a higher risk of adverse cardiac outcomes with increased NEO levels. The meta-analysis of three studies revealed a significant increase in serum NEO levels in HF cases compared to that in healthy controls with an effect size of 3.72 (95% CI 0.16 to 7.28; p = 0.04).
ConclusionMeta-analysis demonstrated a significant difference between serum NEO levels of HF cases and healthy subjects. This evidence implies the potential of serum NEO as a valuable diagnostic biomarker in HF patients. Also, the review of the studies revealed the prognostic potential of NEO. Further research is required to assess the usefulness of NEO as a diagnostic/prognostic biomarker for HF.
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EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides
Authors: Shengli Zhang, Yuanyuan Jing and Yunyun LiangBackgroundThe novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-CoV) and SARS-CoV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study.
MethodsGiven this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset.
ResultsExperimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides.
ConclusionThe results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.com/JYY625/EACVP.git.
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iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network
Authors: Yunyun Liang, Xinyan Ma, Jin Li and Shengli ZhangBackgroundOver the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP.
MethodsIn this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively.
ResultsThe accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models.
ConclusionThe iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.
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Volumes & issues
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Volume 32 (2025)
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Volume 31 (2024)
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Volume 30 (2023)
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Volume 29 (2022)
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Volume 28 (2021)
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Volume 27 (2020)
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Volume 26 (2019)
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Volume 25 (2018)
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Volume 24 (2017)
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Volume 23 (2016)
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Volume 22 (2015)
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Volume 21 (2014)
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Volume 20 (2013)
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Volume 19 (2012)
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Volume 18 (2011)
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Volume 17 (2010)
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Volume 16 (2009)
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Volume 15 (2008)
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Volume 14 (2007)
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Volume 13 (2006)
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Volume 12 (2005)
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Volume 11 (2004)
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Volume 10 (2003)
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Volume 9 (2002)
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Volume 8 (2001)
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Volume 7 (2000)
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