COVID-19 Therapeutic Potential of Natural Products

Despite the fact that coronavirus disease 2019 (COVID-19) treatment and management are now considerably regulated, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still one of the leading causes of death in 2022. The availability of COVID-19 vaccines, FDA-approved antivirals, and monoclonal antibodies in low-income countries still poses an issue to be addressed. Natural products, particularly traditional Chinese medicines (TCMs) and medicinal plant extracts (or their active component), have challenged the dominance of drug repurposing and synthetic compound libraries in COVID-19 therapeutics. Their abundant resources and excellent antiviral performance make natural products a relatively cheap and readily available alternative for COVID-19 therapeutics. Here, we deliberately review the anti-SARS-CoV-2 mechanisms of the natural products, their potency (pharmacological profiles), and application strategies for COVID-19 intervention. In light of their advantages, this review is intended to acknowledge the potential of natural products as COVID-19 therapeutic candidates.

Deciphering the Underlying Mechanisms of Formula Le-Cao-Shi Against Liver Injuries by Integrating Network Pharmacology, Metabonomics, and Experimental Validation

Le-Cao-Shi (LCS) has long been used as a folk traditional Chinese medicine formula against liver injuries, whereas its pharmacological mechanisms remain elusive. Our study aims to investigate the underlying mechanism of LCS in treating liver injuries via integrated network pharmacology, metabonomics, and experimental validation. By network pharmacology, 57 compounds were screened as candidate compounds based on ADME parameters from the LCS compound bank (213 compounds collected from the literature of three single herbs). According to online compound–target databases, the aforementioned candidate compounds were predicted to target 87 potential targets related to liver injuries. More than 15 pathways connected with these potential targets were considered vital pathways in collectively modulating liver injuries, which were found to be relevant to cancer, xenobiotic metabolism by cytochrome P450 enzymes, bile secretion, inflammation, and antioxidation. Metabonomics analysis by using the supernatant of the rat liver homogenate with UPLC-Q-TOF/MS demonstrated that 18 potential biomarkers could be regulated by LCS, which was closely related to linoleic acid metabolism, glutathione metabolism, cysteine and methionine metabolism, and glycerophospholipid metabolism pathways. Linoleic acid metabolism and glutathione metabolism pathways were two key common pathways in both network pharmacology and metabonomics analysis. In ELISA experiments with the CCl₄-induced rat liver injury model, LCS was found to significantly reduce the levels of inflammatory parameters, decrease liver malondialdehyde (MDA) levels, and enhance the activities of hepatic antioxidant enzymes, which validated that LCS could inhibit liver injuries through anti-inflammatory property and by suppressing lipid peroxidation and improving the antioxidant defense system. Our work could provide new insights into the underlying pharmacological mechanisms of LCS against liver injuries, which is beneficial for its further investigation and modernization.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer's disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

Screening drug target combinations in disease-related molecular networks

Background

For treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed.

Results

We developed a strategy to screen the synergistic combinations of two drug targets in disease networks based on the classification of single drug targets. The method tried to identify the sensitivity of single intervention and then the combination of multiple interventions that can restore the disease network to a desired normal state. In our strategy of screening drug target combinations, we first classified all drug targets into sensitive and insensitive single drug targets. Then, we identified the synergistic and antagonistic of drug target combinations, including the combinations of sensitive drug targets, the combinations of insensitive drug target and the combination of sensitive and insensitive targets. Finally, we applied our strategy to Arachidonic Acid (AA) metabolic network and found 18 pairs of synergistic drug target combinations, five of which have been proven to be viable by biological or medical experiments.

Conclusions

Different from traditional methods for judging drug synergy and antagonism, we propose the framework of how to enhance the efficiency by perturbing two sensitive targets in a combinatorial way, how to decrease the drug dose and therefore its side effect and cost by perturbing combinatorially a main sensitive target and an auxiliary insensitive target, and how to perturb two insensitive targets to realize the transition from a disease state to a healthy one which cannot be realized by perturbing each insensitive target alone. Although the idea is mainly applied to an AA metabolic network, the strategy holds for more general molecular networks such as combinatorial regulation in gene regulatory networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2730-8) contains supplementary material, which is available to authorized users.

Screening and analysis of key active constituents in Guanxinshutong capsule using mass spectrum and integrative network pharmacology

Guanxinshutong capsule (GXSTC) is an effective and safe traditional Chinese medicine used in the treatment of cardiovascular diseases (CVDs) for many years. However, the targets of this herbal formula and the underlying molecular mechanisms of action involved in the treatment of CVDs are still unclear. In the present study, we used a systems pharmacology approach to identify the active ingredients of GXSTC and their corresponding targets in the calcium signaling pathway with respect to the treatment of CVDs. This method integrated chromatographic techniques, prediction of absorption, distribution, metabolism, and excretion, analysis using Kyoto Encyclopedia of Genes and Genomes, network construction, and pharmacological experiments. 12 active compounds and 33 targets were found to have a role in the treatment of CVDs, and four main active ingredients, including protocatechuic acid, cryptotanshinone, eugenol, and borneol were selected to verify the effect of (GXSTC) on calcium signaling system in cardiomyocyte injury induced by hypoxia and reoxygenation. The results from the present study revealed the active components and targets of GXSTC in the treatment of CVDs, providing a new perspective to enhance the understanding of the role of the calcium signaling pathway in the therapeutic effect of GXSTC.

An integrated strategy by using target tissue metabolomics biomarkers as pharmacodynamic surrogate indices to screen antipyretic components of Qingkaikling injection

Traditional Chinese medicine (TCM) treatment can be valuable therapeutic strategies. However, the active components and action mechanisms that account for its therapeutic effects remain elusive. Based on the hypothesis that the components of a formula which exert effect would be measurable in target tissue, a target tissue metabolomics-based strategy was proposed for screening of antipyretic components in Qingkaikling injection (QKLI). First, we detected the components of QKLI which could reach its target tissue (hypothalamus) by determining the hypothalamus microdialysate and discovered that only baicalin and geniposide could be detected. Then, by conducting hypothalamus metabolomics studies, 14 metabolites were screened as the potential biomarkers that related to the antipyretic mechanisms of QKLI and were used as its pharmacodynamic surrogate indices. Subsequently, the dynamic concentration of baicalin and geniposide in hypothalamus microdialysates and biomarkers in hypothalamus were measured and correlated with each other. The results indicated that only baicalin shown a good correlation with these biomarkers. Finally, a network pharmacology approach was established to validate the antipyretic activity of baicalin and the results elucidated its antipyretic mechanisms as well. The integrated strategy proposed here provided a powerful means for identifying active components and mechanisms contributing to pharmacological effects of TCM.

Innovating Chinese Herbal Medicine: From Traditional Health Practice to Scientific Drug Discovery

As one of the major contemporary alternative medicines, traditional Chinese medicine (TCM) continues its influence in Chinese communities and has begun to attract the academic attention in the world of western medicine. This paper aims to examine Chinese herbal medicine (CHM), the essential branch of TCM, from both narrative and scientific perspectives. CHM is a traditional health practice originated from Chinese philosophy and religion, holding the belief of holism and balance in the body. With the development of orthodox medicine and science during the last centuries, CHM also seized the opportunity to change from traditional health practice to scientific drug discovery illustrated in the famous story of the herb-derived drug artemisinin. However, hindered by its culture and founding principles, CHM faces the questions of the research paradigm posed by the convention of science. To address these questions, we discussed two essential questions concerning the relationship of CHM and science, and then upheld the paradigm of methodological reductionism in scientific research. Finally, the contemporary narrative of CHM in the 21st century was discussed in the hope to preserve this medical tradition in tandem with scientific research.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components

Chinese medicine is known to treat complex diseases with multiple components and multiple targets. However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula (LQF). The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target (PT) network. An influenza-related protein-protein interaction (PPI) network was constructed and integrated with the PT network. Then the compound-effective target (CET) network and compound-ineffective target network (CIT) were extracted, respectively. A novel approach was developed to identify effective components by comparing CET and CIT networks. As a result, 15 main effective components were identified along with 61 corresponding targets. 7 of these main effective components were further experimentally validated to have antivirus efficacy in vitro. The main effective component-target (MECT) network was further constructed with main effective components and their key targets. Gene Ontology (GO) analysis of the MECT network predicted key functions such as NO production being modulated by the LQF. Interestingly, five effective components were experimentally tested and exhibited inhibitory effects on NO production in the LPS induced RAW 264.7 cell. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions.

Pharmacodynamics and potential synergistic effects of Mai-Luo-Ning injection on cardiovascular protection, based on molecular docking

As a computer-assisted approach, molecular docking has been universally applied in drug research and development and plays an important role in the investigation and evaluation of herbal medicines. Herein, the method was used to estimate the pharmacodynamics of Mai-Luo-Ning injection, a traditional Chinese compound herbal prescription. Through investigating the interactions between several important proteins in cardiovascular system and characteristic components of the formula, its effect on cardiovascular protection was evaluated. Results showed the differences in the interactions between each component and the selected target proteins and revealed the possible mechanisms for synergistic effects of various characteristic components on cardiovascular protection. The study provided scientific evidence supporting the mechanistic study of the interactions among multi-components and targets, offering a general approach to investigating the pharmacodynamics of complicated materials in compound herbal prescriptions.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Characterization of rational biomarkers accompanying fever in yeast-induced pyrexia rats using urine metabolic footprint analysis

Fever is a prominent feature of diseases and is an ongoing process that is always accompanied by metabolic changes in the body system. Despite the success of temperature regulation theory, the underlying biological process remains unclear. To truly understand the nature of the febrile response, it is crucial to confirm the biomarkers during the entire biological process. In the current study, a 73-h metabolic footprint analysis of the urine from yeast-induced pyrexia rats was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Potential biomarkers were selected using orthogonal partial least squares-discriminate analysis (OPLS-DA), the rational biomarkers were verified by Pearson correlation analysis, and the predictive power was evaluated using receiver operator characteristic (ROC) curves. A metabolic network constructed using traditional Chinese medicine (TCM) grammar systems was used to validate the rationality of the verified biomarkers. Finally, five biomarkers, including indoleacrylic acid, 3-methyluridine, tryptophan, nicotinuric acid and PI (37:3), were confirmed as rational biomarkers because their correlation coefficients were all greater than 0.87 and because all of the correlation coefficients between any pair of these biomarkers were higher than 0.75. The areas under the ROC curves were all greater than 0.84, and their combined predictive power was considered reliable because the greatest area under the ROC curve was 0.968. A metabolic network also demonstrated the rationality of these five biomarkers. Therefore, these five metabolites can be adopted as rational biomarkers to reflect the process of the febrile response in inflammation-induced pyrexia.

A systems-pharmacology analysis of herbal medicines used in health improvement treatment: predicting potential new drugs and targets

For thousands of years, tonic herbs have been successfully used all around the world to improve health, energy, and vitality. However, their underlying mechanisms of action in molecular/systems levels are still a mystery. In this work, two sets of tonic herbs, so called Qi-enriching herbs (QEH) and Blood-tonifying herbs (BTH) in TCM, were selected to elucidate why they can restore proper balance and harmony inside body, organ and energy system. Firstly, a pattern recognition model based on artificial neural network and discriminant analysis for assessing the molecular difference between QEH and BTH was developed. It is indicated that QEH compounds have high lipophilicity while BTH compounds possess high chemical reactivity. Secondly, a systematic investigation integrating ADME (absorption, distribution, metabolism, and excretion) prediction, target fishing and network analysis was performed and validated on these herbs to obtain the compound-target associations for reconstructing the biologically-meaningful networks. The results suggest QEH enhance physical strength, immune system and normal well-being, acting as adjuvant therapy for chronic disorders while BTH stimulate hematopoiesis function in body. As an emerging approach, the systems pharmacology model might facilitate to understand the mechanisms of action of the tonic herbs, which brings about new development for complementary and alternative medicine.