Curation and analysis of multitargeting agents for polypharmacological modeling

Mechanisms for the biological activity of Gastrodia elata Blume and its constituents: A comprehensive review on sedative-hypnotic, and antidepressant properties

BACKGROUND

Insomnia and depressive disorder are two common symptoms with a reciprocal causal relationship in clinical practice, which are usually manifested in comorbid form. Several medications have been widely used in the treatment of insomnia and depression, but most of these drugs show non-negligible side effects. Currently, many treatments are indicated for insomnia and depressive symptom, including Chinese herbal medicine such as Gastrodia elata Blume (G. elata), which has excellent sedative-hypnotic and antidepressant effects in clinical and animal studies.

PURPOSE

To summarize the mechanisms of insomnia and depression and the structure-activity mechanism for G. elata to alleviate these symptoms, particularly by hypothalamic-pituitary-adrenal (HPA) axis and intestinal flora, aiming to discover new approaches for the treatment of insomnia and depression.

METHODS

The following electronic databases were searched from the beginning to November 2023: PubMed, Web of Science, Google Scholar, Wanfang Database, and CNKI. The following keywords of G. elata were used truncated with other relevant topic terms, such as depression, insomnia, antidepressant, sedative-hypnotic, neuroprotection, application, safety, and toxicity.

RESULTS

Natural compounds derived from G. elata could alleviate insomnia and depressive disorder, which is involved in monoamine neurotransmitters, inflammatory response, oxidative stress, and gut microbes, etc. Several clinical trials showed that G. elata-derived natural compounds that treat depression and insomnia have significant and safe therapeutic effects, but further well-designed clinical and toxicological studies are needed.

CONCLUSION

G. elata exerts a critical role in treating depression and insomnia due to its multi-targeting properties and fewer side effects. However, more clinical and toxicological studies should be performed to further explore the sedative-hypnotic and antidepressant mechanisms of G. elata and provide more evidence and recommendations for its clinical application. Our review provides an overview of G. elata treating insomnia with depression for future research direction.

Development and evaluation of a java-based deep neural network method for drug response predictions

Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r ² as high as 0.81.

LigAdvisor: a versatile and user-friendly web-platform for drug design

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

Andrographis paniculata (Burm. f.) Wall. ex Nees: An Updated Review of Phytochemistry, Antimicrobial Pharmacology, and Clinical Safety and Efficacy

Infectious disease (ID) is one of the top-most serious threats to human health globally, further aggravated by antimicrobial resistance and lack of novel immunization options. Andrographis paniculata (Burm. f.) Wall. ex Nees and its metabolites have been long used to treat IDs. Andrographolide, derived from A. paniculata, can inhibit invasive microbes virulence factors and regulate the host immunity. Controlled clinical trials revealed that A. paniculata treatment is safe and efficacious for acute respiratory tract infections like common cold and sinusitis. Hence, A. paniculata, mainly andrographolide, could be considered as an excellent candidate for antimicrobial drug development. Considering the importance, medicinal values, and significant role as antimicrobial agents, this study critically evaluated the antimicrobial therapeutic potency of A. paniculata and its metabolites, focusing on the mechanism of action in inhibiting invasive microbes and biofilm formation. A critical evaluation of the secondary metabolites with the aim of identifying pure compounds that possess antimicrobial functions has further added significant values to this study. Notwithstanding that A. paniculata is a promising source of antimicrobial agents and safe treatment for IDs, further empirical research is warranted.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Multi-Targeting Bioactive Compounds Extracted from Essential Oils as Kinase Inhibitors

Essential oils (EOs) are popular in aromatherapy, a branch of alternative medicine that claims their curative effects. Moreover, several studies reported EOs as potential anti-cancer agents by inducing apoptosis in different cancer cell models. In this study, we have considered EOs as a potential resource of new kinase inhibitors with a polypharmacological profile. On the other hand, computational methods offer the possibility to predict the theoretical activity profile of ligands, discovering dangerous off-targets and/or synergistic effects due to the potential multi-target action. With this aim, we performed a Structure-Based Virtual Screening (SBVS) against X-ray models of several protein kinases selected from the Protein Data Bank (PDB) by using a chemoinformatics database of EOs. By evaluating theoretical binding affinity, 13 molecules were detected among EOs as new potential kinase inhibitors with a multi-target profile. The two compounds with higher percentages in the EOs were studied more in depth by means Induced Fit Docking (IFD) protocol, in order to better predict their binding modes taking into account also structural changes in the receptor. Finally, given its good binding affinity towards five different kinases, cinnamyl cinnamate was biologically tested on different cell lines with the aim to verify the antiproliferative activity. Thus, this work represents a starting point for the optimization of the most promising EOs structure as kinase inhibitors with multi-target features.

Interaction between nanoparticles and charged phospholipid membranes

Charged lipids in cell membranes and subcellular organelles are arranged in the form of a bilayer with the hydrocarbon tails sequestered away from the water and the polar head groups exposed to the aqueous environment. Most of them bear net negative charges leading to the negatively charged cell membranes. Charged lipid-lipid and lipid-protein interactions are generally dynamic and heavily depend on their local molecular concentrations. To examine the electrostatic properties of charged lipid layers in contact with an electrolyte solution, we incorporate the single chain mean field theory with Poisson-Boltzmann theory to explore the equilibrium structure of charged phospholipid membranes. Using the three bead coarse-grained model we reproduced the essential equilibrium properties of the charged phospholipid bilayer. We also investigate the influence of the mobile ions on the thickness of the layer, the area per lipid (APL), and the electrostatic potential of the membrane. Then we investigate the attraction-repulsion property of two charged nanoparticles which are stuck on the charged lipid molecules surrounded with mobile ions. After that we simulated the interaction between the Pleckstrin homology domain (PH domain) of Akt and the cytoplasmic membrane. Taking into account the electrostatic interaction, we observe the structure changes of the membrane at different concentrations of mobile ions in its equilibrium state. Also we discuss the influence of mobile ions on the size of the pore opened in the membrane by the charged protein. Such an observation may shed light on the activation of oncogenic Akt (or protein kinase B) around the membrane at the molecular level.

MTLD, a Database of Multiple Target Ligands, the Updated Version

Polypharmacology plays an important role in drug discovery and polypharmacology drug strategies provide a novel path in drug design. However, to develop a polypharmacology drug with the desired profile remains a challenge. Previously, we developed a free web-accessible database called Multiple Target Ligand Database (MTLD, www.mtdcadd.com). Herein, the MTLD database has been updated, containing 2444 Multiple Target Ligands (MTLs) that bind with 21,424 binding sites from 18,231 crystal structures. Of the MTLs, 304 entries are approved drugs, and 1911 entries are drug-like compounds. Also, we added new functions such as multiple conditional search and linkage visualization. Through querying the updated database, extremely useful information for the development of polypharmacology drugs may be provided.

Computational polypharmacology: a new paradigm for drug discovery

INTRODUCTION

Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

Comprehensive Modeling and Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting Kinase Inhibitor

TRAF2- and NCK-interacting kinase (TNIK) represents one of the crucial targets for Wnt-activated colorectal cancer. In this study, we curated two datasets and conducted a comprehensive modeling study to explore novel TNIK inhibitors with desirable biopharmaceutical properties. With Dataset I, we derived Comparative Molecular Similarity Indices Analysis (CoMSIA) and variable-selection k-nearest neighbor models, from which 3D-molecular fields and 2D-descriptors critical for the TNIK inhibitor activity were revealed. Based on Dataset II, predictive CoMSIA-SIMCA (Soft Independent Modelling by Class Analogy) models were obtained and employed to screen 1,448 FDA-approved small molecule drugs. Upon experimental evaluations, we discovered that mebendazole, an approved anthelmintic drug, could selectively inhibit TNIK kinase activity with a dissociation constant K_d = ~1 μM. The subsequent CoMSIA and kNN analyses indicated that mebendazole bears the favorable molecular features that are needed to bind and inhibit TNIK.

Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs

PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.

Polypharmacology in Drug Development: A Minireview of Current Technologies

Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.