Modern Drug Research Research Articles

MolCFL: A personalized and privacy-preserving drug discovery framework based on generative clustered federated learning

In today’s era of rapid development of large models, the traditional drug development process is undergoing a profound transformation. The vast demand for data and consumption of computational resources are making independent drug discovery increasingly difficult. By integrating federated learning technology into the drug discovery field, we have found a solution that both protects privacy and shares computational power. However, the differences in data held by various pharmaceutical institutions and the diversity in drug design objectives have exacerbated the issue of data heterogeneity, making traditional federated learning consensus models unable to meet the personalized needs of all parties. In this study, we introduce and evaluate an innovative drug discovery framework, MolCFL, which utilizes a multi-layer perceptron (MLP) as the generator and a graph convolutional network (GCN) as the discriminator in a generative adversarial network (GAN). By learning the graph structure of molecules, it generates new molecules in a highly personalized manner and then optimizes the learning process by clustering federated learning, grouping compound data with high similarity. MolCFL not only enhances the model’s ability to protect privacy but also significantly improves the efficiency and personalization of molecular design. MolCFL exhibits superior performance when handling non-independently and identically distributed data compared to traditional models. Experimental results show that the framework demonstrates outstanding performance on two benchmark datasets, with the generated new molecules achieving over 90% in Uniqueness and close to 100% in Novelty. MolCFL not only improves the quality and efficiency of drug molecule design but also, through its highly customized clustered federated learning environment, promotes collaboration and specialization in the drug discovery process while ensuring data privacy. These features make MolCFL a powerful tool suitable for addressing the various challenges faced in the modern drug research and development field.

Role of the Caco-2 cell culture model developed in Dr. Ronald T. Borchardt’s lab in modern drug research and development: a perspective from a practitioner

Role of the Caco-2 cell culture model developed in Dr. Ronald T. Borchardt’s lab in modern drug research and development: a perspective from a practitioner

Usnea longissima Ach. ekstraktları ve onun sekonder metaboliti usnik asidin farklı hücreler üzerindeki sitotoksisitesi

The biological activities of lichens, known as organisms based on a symbiotic relationship, are attracting more and more attention in traditional medicine and modern drug research. Lichens can possess various pharmacological effects such as antimicrobial, antioxidant, antitumor, anti-inflammatory, and many others due to the bioactive compounds they contain. In the present study, Usnea longissima Ach. and its secondary metabolite, usnic acid on human gastric adenocarcinoma cells (AGS), human colorectal adenocarcinoma cells (Caco-2), and mouse fibroblasts (NIH/3T3) were investigated. In this context, methanol and water extracts from U. longissima were obtained by Soxhlet extractor. The characterization of usnic acid was carried out by fourier transform infrared spectroscopy (FTIR). The cytotoxic activities of the extracts and the metabolite on cells were determined by 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) analysis. Considering the median inhibitory concentration (IC50) values, the application with the greater effect on AGS and NIH/3T3 cells was the methanol extract (373.17 µg/ml and 318.81 µg/ml, respectively). Considering the Caco-2 cells, it was determined that the water extract had the lowest IC50 value (230.05 µg/ml). The high cytotoxic activity of usnic acid on cancer cells (AGS; IC50: 395.03 µg/ml and Caco-2; IC50: 462.35 µg/ml) compared to normal cell (NIH/3T3; IC50: 472.41 µg/ml) was noted. As a result, it has been revealed that methanol and water extracts of U. longissima, especially usnic acid, are products that can be used within the scope of complementary therapy.

An Updated Review on Medicinal Plants with Hepato-protective Activity

Many precious medicines from traditional medicinal plants can be produced using a phototherapeutic approach to modern drug research. Finding pure phytochemicals that can be used as medicines requires time and money. The treatment of liver problems involves the use of numerous herbs and polyherbal mixtures. However, the treatments are ineffective in the majority of severe instances. Even though many of these plants and mixtures were subjected to experimental evaluations, the research was frequently insufficient and uncompleted. It was found that the therapeutic effects were evaluated in animals with subclinical liver damage brought on by a few substances. Antioxidants included in common foods can offer this kind of defence against liver damage brought on by hazardous substances’ oxidative processes. Therefore, hepato-protective natural products such as Phyllanthus amarus, Silybum marianum, Solanum nigrum, Tephrosia purpurea, Eclipta alba, Cichorium intybus, Phyllanthus Niruri, Centella asiatica, Capparis spinosa, Terminalia arjuna, Cassia occidentalis, Fumaria parviflora, Embelia ribes, Boerhaavia diffusa, Tamarix gallica, Achillea millefolium, Terminalia chebula, Ocimum sanctum, Glycyrrhiza glabra, Foeniculum vulgare, Garcinia mangostana, Acacia Catechu, Pergularia daemia, Annona squamosa, Flacourtia indica. The goal of the current review is to gather information on promising phytochemicals from medicinal plants that have been evaluated in hepatotoxicity models utilising cutting-edge scientific methods.

Curcumin – Extraction, Synthesis and Application in Modern Drug Research: A Mini Review

Curcumin – Extraction, Synthesis and Application in Modern Drug Research: A Mini Review

Comparison of Herbal Medicines Used for Women's Menstruation Diseases in Different Areas of the World.

Aims: This review aims to compare the use of herbal medicine used to treat women’s menstruation and the prevalence of menstrual diseases in different regions, which reveal the use of herbal medicine globally and provide scientific guidance for improving women’s health. Materials and Methods: The information available on herbal medicines for women between the years 2000 and 2021 was systematically collected via the library and electronic search systems such as Google Scholar, PubMed, ScienceDirect, and Web of Science as well as secondary resources including books and conference proceedings. Results: Totally, 571 ethnic medicines commonly used for women’s menstruation health in Asia, Europe, Oceania, Africa, and America were accounted. Zingiber officinale Roscoe (Ginger), Ruta graveolens L. (Common rue), Angelica sinensis (Oliv.) Diels (Angelica sinensis), Foeniculum vulgare Mill (Fennel), Catharanthus roseus (L.) G. Don (Catharanthus roseus) and other medicines which have obvious advantages and long-term usage are utilized in the treatment of menstrual diseases. Family Asteraceae, Lamiaceae, Apiaceae, Fabaceae, and Zingiberaceae are the most common medicinal plant families used for such treatments. In many instances, the application of fresh parts of plants was observed because of the healers’ belief regarding the higher efficiency of the medicine made from fresh plants. Edible plants are used in a wide range of countries. Conclusion: Women’s menstruation health is directly related to their health condition. Traditional medicines of most ethnic groups have contributed to women’s health care and treatment of gynecological diseases. Practitioners in this field have gained elaborate experience in treatments and medication, and assembled a large number of effective drugs and prescriptions. These experiences have also been inherited and developed by modern clinical application and scientific research. However, the basic research on these drugs is not sufficient, the knowledge of drug use has not been fully popularized, the advantages of drugs have not been fully utilized, and the guiding potential to modern drug research continues to be insufficient. As such, it is necessary to further promote and make a significant contribution to women’s health.

Thoughts on path of R&D and registration of innovative traditional Chinese medicine with synchronous transformation of "series prescriptions"

Since the implementation of drug registration in China, the classification of Chinese medicine has greatly met the needs of public health and effectively guided the transformation, inheritance, and innovation of research achievements on traditional Chinese medicine(TCM). In the past 30 years, the development of new Chinese medicine has followed the registration transformation model of " one prescription for single drug". This model refers to the R&D and registration system of modern drugs, and approximates to the " law-abiding" medication method in TCM clinic, while it rarely reflects the sequential therapy of syndrome differentiation and comprehensive treatment with multiple measures. In 2017, Opinions on Deepening the Reform of Review and Approval System and Encouraging the Innovation of Drugs and Medical Devices released by the General Office of the CPC Central Committee and the General Office of the State Council pointed out that it is necessary to " establish and improve the registration and technical evaluation system in line with the characteristics of Chinese medicine, and handle the relationship between the traditional advantages of Chinese medicine and the requirements of modern drug research". Therefore, based on the development law and characteristics of TCM, clinical thinking should be highlighted in the current technical requirements and registration system of research and development of Chinese medicine. Based on the current situation of registration supervision of Chinese medicine and the modern drug research in China, the present study analyzed limitations and deficiency of " one prescription for single drug" in the research and development of Chinese medicine. Additionally, a new type of " series prescriptions" was proposed, which was consistent with clinical thinking and clinical reality. This study is expected to contribute to the independent innovation and high-quality development of the TCM industry.

Industry Perspectives in Modern Drug Research and Development Strategies

Industry Perspectives in Modern Drug Research and Development Strategies

Application prospects of single-cell transcriptome sequencing in traditional Chinese medicine research

Single-cell transcriptome sequencing(scRNA-seq) can be used to analyze the expression characteristics of the transcriptome at the level of individual cell, and discover the heterogeneity of gene expression in individual cell that is "diluted" or averaged in study of group organization. The scRNA-seq, with the characteristics of standardization, high-throughput, and high integration, can greatly simplify the experimental operation and significantly reduce the consumption of reagents. At the same time, a variety of cells are screened and the gene expression patterns are analyzed at the single-cell level to provide a more efficient detection technique and more rich and accurate information for drug research. In the field of traditional Chinese medicine(TCM), the scRNA-seq is still a new technology, but the individual and precision concepts embodied by scRNA-seq and the theory of TCM syndrome differentiation and treatment have reached the same effect between the micro and macro aspects. This study tried to broaden the thinking for the modernization of TCM by introducing the development of scRNA-seq technology and its application in modern drug research and discussing the application prospects of scRNA-seq in TCM research.

In Silico Identification of Flavonoids from Corriandrum sativum Seeds against Coronavirus Covid-19 Main Protease

Molecular docking analysis is routinely used in modern drug research to understand and predict the relationship between a drug molecule and a target protein from a microbe. The entry and replication of pathogens in host cells can be prevented by drugs identified in this way. The coronavirus disease associated with SARS-CoV-2, COVID-19, has become today's most infectious and lethal pandemic disease in the world. Burgeoning in the absence of any particular vaccine or therapeutic agent against SARS-CoV-2.The situation urges the need for appropriate medications to treat patients infected with the virus. Consequently, the study focus on evaluate the therapeutic potential of flavonoids present in Corriandrum sativum seeds that could serve as suitable remedies for COVID19.We analyzed the binding affinity of four flavonoids were screened against Mpro protein of SARS-CoV-2 by PyRx Virtual Screening tool and also results are validated with Lig-Plot Plus. Lopinavir shows binding affinity of -8.3 Kcal/mol and exhibit stable, strong interaction with active site of COVID19 main protease. Besides flavonoids, Rutin found to have the highest binding affinity compared to Lopinavir with the Mpro protease, followed by Chlorogenic acid, Quercetin and Caffeic acid. The present study concludes that Rutin present in the integrant of seeds shows the highest potentiality for acting as in inhibitor of main protease enzyme. Further, characterization of the amino acid residues comprising the viral binding site and the nature of the hydrogen bonding involved in the ligand receptor interaction shows significant findings with Rutin binding to the MPro protein at amino acid. The amino acid acid present in active sites of Mpro protease responsible for virus pathogenicity. The findings of the present study need in vivo experiments to prove the utility of Rutin compounds and further use in making Corriandrum sativum seeds as anti-SARS-CoV-2 product in near future.
 Keywords: Corriandrum sativum seeds,Novel Coronavirus, SARS-CoV2, COVID-19, Protease, Molecular Docking.

A Review of Selected Phyto-derivative Compounds Evaluated by In silico studie as Potential Effective Options to Combat Life Threatening COVID-19

SARS-COV-2 identified as COVID-19, has become the world's most contagious and dangerous pandemic disease today. It was firstly reported in Wuhan, China, in December 2019, then due to its strong infectious nature, it had spread to almost 214 countries. Precautionary steps remain the only mandatory technique before a successful form of treatment or vaccine is created to avoid person-to-person transmissions. In the absence of any unique or therapeutic vaccine against this virus, current attempts are being made to find a cure for this pandemic. Using derivatives from previously known antiviral drugs are a beneficial strategy until a specific treatment methodology for COVID-19 is available. Since ancient times, herbal medicines have been used as natural remedies for treating different infectious diseases. A good way to treat COVID-19 will be to look for new compounds from natural sources known for their high safety and applicability since the development of innovative drugs takes a long time and cost. Molecular docking analysis is routinely used in modern drug research to understand and predict the interaction between the molecule of the drug and the microbe's target protein. Drugs designed in this way can prevent access of pathogens into host cells and replication. The present study gives an insight about some plant phytoderivatives that were examined via in silico studies to have the potentiality in treating coronavirus disease through various potential mechanisms such as hindering genome replication, inhibition of spike proteins or preventing inflammatory storm that causes lung injury.

The Immobilization of Oxindole Derivatives with Use of Cube Rhombellane Homeomorphs

A key aspect of modern drug research is the development of delivery methods that ensure the possibility of implementing targeted therapy for a specific biological target. The use of nanocarriers enables to achieve this objective, also allowing to reduce the toxicity of used substances and often extending their bioavailability. Through the application of docking methods, the possibility of using cube rhombellanes as potential carriers for two oxindole derivatives was analyzed. In the studies, compounds identified as inhibitors of the CDK2 enzyme and a set of nanostructures proposed by the Topo Cluj Group were used. The popular fullerene molecule C60 was used as the reference system. The estimated binding affinities and structures of obtained complexes show that use of functionalized cube rhombellanes containing hydrogen bond donors and acceptors in their external molecular shell significantly increases ligand affinity toward considered nanocariers, compared to classic fullerenes. The presented values also allow to state that an important factor determining the mutual affinity of the tested ligands and nanostructures is the symmetry of the analyzed nanocarriers and its influence on the distribution of binding groups (aromatic systems, donors and acceptors of hydrogen bonds) on the surface of nanoparticles.

The Development of Pharmacophore Modeling: Generation and Recent Applications in Drug Discovery.

The pharmacophore concept in modern drug research is of great importance and promotes the development of drug industry as indicated by the number of publications available. In this article, we reviewed and highlighted some successful examples of pharmacophore modeling, which was applied either in virtual screening for efficient hit discovery or in the optimization of the lead compounds. Meanwhile, the analysis of some important aspects of pharmacophore modeling such as a database, the software was listed as well. Based on the analysis of these examples, we intended to provide an overview of pharmacophore-based virtual screening, which we hope to help readers gain insight into this powerful technique. Owing to its' convenience and efficiency, pharmacophore presents an essential method for drug discovery.

Capillary electrophoresis in the enantioseparation of modern antidepressants: An overview.

Chirality is a key subject in modern drug research as well as in the pharmaceutical industry and drug development. Almost all second-generation modern antidepressants are chiral substances; however in therapy some are used as racemic mixtures while others are used as pure enantiomers. The development of enantioseparation methods of chiral antidepressants and their metabolites is one of the keys in understanding their enantioselective drug action. For this purpose, efficient and reliable analytical methods are needed, and capillary electrophoresis has proved to be an interesting and advantageous alternative to the more frequently used chromatographic techniques. In this review electrodriven methods available for the chiral discrimination of selective serotonin reuptake inhibitors (fluoxetine, citalopram, sertraline) and selective serotonin and norepinephrine reuptake inhibitors (venlafaxine, duloxetine) are presented and discussed.

Regulatory considerations on endpoints in ovarian cancer drug development.

Ovarian cancer remains a disease entity that is responsible for considerable morbidity and mortality among women worldwide. Modern drug research pipelines and accelerated drug development timelines applied to other disease entities have begun to make an impact on treatment options for patients with advanced ovarian cancer, as exemplified by the recent accelerated approval of 2 agents for this disease as the forerunners of a growing number of registrational trials. Regulatory flexibility for this serious and life-threatening condition spurs the consideration of intermediate endpoints for regulatory trial design, including potential applications in the development of newer therapeutic classes such as targeted therapies and immunotherapies for patients with advanced ovarian cancer. Cancer 2017;123:2604-8. © 2017 American Cancer Society.

Generation of Major Human Excretory and Circulating Drug Metabolites Using a Hepatocyte Relay Method

The prediction of human drug metabolites using in vitro experiments containing human-derived reagents is an important approach in modern drug research; however, this can be challenging for drugs that are slowly metabolized. In this report, we describe the use of a recently developed human hepatocyte relay method for the purpose of predicting human drug metabolite profiles. Five compounds for which in vivo human metabolism data were available were selected for the investigation of this method, and the results were compared with data gathered in hepatocyte suspensions as well as previous data from a micropatterned hepatocyte coculture method. The hepatocyte relay method demonstrated an improved performance (generation of 75% of human in vivo metabolites) for those drugs for which previous methods showed a relatively low rate of success (50% of human in vivo metabolites). Metabolites included those arising from both oxidative and conjugative reactions and metabolites that required sequential reactions. Two 4-hour relays were shown to adequately generate metabolites, and no further benefit was derived from more relays. Overall, it can be concluded that the hepatocyte relay assay method can be successfully used in the generation of relevant human metabolites, even for challenging drugs.

Predicting network of drug–enzyme interaction based on machine learning method

It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug–enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug–enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Pharmacophore Modeling for Antitargets

The pharmacophore modeling in modern drug research has been applied for both bioactivity profiling and early stage of risk assessment of potential side effects and toxicity due to interactions of drug candidates with antitargets namely P-glycoprotein, hERG, cytochrome P450 and pregnane X-receptor. In this article, an existing state concerning with pharmacophore modeling applied for promiscuous proteins in drug research were updated and reviewed. In an attempt to create new safe medicines faster, the partial overlap of substrate properties of hERG, P-glycoprotein, pregnane X-receptor and cytochrome P450 has to be considered and drug safety has to be dealt on a system level on the off-targets.

Using Grey System GM(1,1) Model to Predict the Drug-GPCRs Couples

It is an important topic to find and identify molecular drug targets for modern drug research. Naturally, to find new or potential drug-GPCRs couples is very useful for determining the new effective medicine target, because GPCRs are among the most frequent targets of therapeutic drugs and proven evolutionary pharmacology value. To realize this, this paper incorporates chemical functional groups of drugs and grey model GM(1,1) based on digital coding of amino acids to formulate the drug-GPCRs couples for statistical prediction. The overall success rate using the fuzzy K-Nearest Neighbor algorithm by the jackknife test is about 81%. This novel approach will further stimulate the development of scanning target via a computational approach.

An update on the strategies in multicomponent activity monitoring within the phytopharmaceutical field

BackgroundTo-date modern drug research has focused on the discovery and synthesis of single active substances. However, multicomponent preparations are gaining increasing importance in the phytopharmaceutical field by demonstrating beneficial properties with respect to efficacy and toxicity.DiscussionIn contrast to single drug combinations, a botanical multicomponent therapeutic possesses a complex repertoire of chemicals that belong to a variety of substance classes. This may explain the frequently observed pleiotropic bioactivity spectra of these compounds, which may also suggest that they possess novel therapeutic opportunities. Interestingly, considerable bioactivity properties are exhibited not only by remedies that contain high doses of phytochemicals with prominent pharmaceutical efficacy, but also preparations that lack a sole active principle component. Despite that each individual substance within these multicomponents has a low molar fraction, the therapeutic activity of these substances is established via a potentialization of their effects through combined and simultaneous attacks on multiple molecular targets. Although beneficial properties may emerge from such a broad range of perturbations on cellular machinery, validation and/or prediction of their activity profiles is accompanied with a variety of difficulties in generic risk-benefit assessments. Thus, it is recommended that a comprehensive strategy is implemented to cover the entirety of multicomponent-multitarget effects, so as to address the limitations of conventional approaches.SummaryAn integration of standard toxicological methods with selected pathway-focused bioassays and unbiased data acquisition strategies (such as gene expression analysis) would be advantageous in building an interaction network model to consider all of the effects, whether they were intended or adverse reactions.