Discovery of neuroprotective Agents: Potent, brain Penetrating, lipoic acid derivatives for the potential treatment of ischemic stroke by regulating oxidative stress and inflammation - a Preliminary study

Stroke poses a serious risk to the physical and mental health of patients. Endogenous compounds are widely used to treat ischemic stroke. Lipoic acid, a naturally occurring (R)-5-(1,2-dithiolan-3-yl)pentanoic acid, has therapeutic potential for the treatment of ischemic stroke. However, the direct application of lipoic acid is limited by its relatively low efficacy and instability. Therefore, there is a need to modify the structure of lipoic acid to improve its pharmaceutical capabilities. Currently, 37 lipoic acid derivatives have been synthesized, and compound AA-9 demonstrated optimal therapeutic potential in an in vitro model of induced oxidative damage using tert-butyl hydroperoxide (t-BHP). In addition, in vitro experiments have shown that compound AA-9 has an excellent safety profile. Subsequently, the therapeutic effect of AA-9 was significant in the rat MCAO ischemic stroke model, which may be attributed to the antioxidant and anti-inflammatory effects of compound AA-9 by activating PGC-1α and inhibiting NLRP3. Notably, compound AA-9 exhibited higher stability and better bioavailability properties than ALA in plasma stability and pharmacokinetic properties. In conclusion, AA-9 may be a promising neuroprotective agent for the treatment of ischemic stroke and warrants further investigation.

Application of ensemble learning for predicting GABAA receptor agonists

BACKGROUND

Over the past few decades, agonists binding to the benzodiazepine site of the GABAA receptor have been successfully developed as clinical drugs. Different modulators (agonist, antagonist, and reverse agonist) bound to benzodiazepine sites exhibit different or even opposite pharmacological effects, however, their structures are so similar that it is difficult to distinguish them based solely on molecular skeleton. This study aims to develop classification models for predicting the agonists.

METHODS

306 agonists or non-agonists were collected from literature. Six machine learning algorithms including RF, XGBoost, AdaBoost, GBoost, SVM, and ANN algorithms were employed for model development. Using six descriptors including 1D/2D Descriptors, ECFP4, 2D-Pharmacophore, MACCS, PubChem, and Estate fingerprint to characterize chemical structures. The model interpretability was explored by SHAP method.

RESULTS

The best model demonstrated an AUC value of 0.905 and an MCC value of 0.808 for the test set. The PubMac-based model (PubMac-GB) achieved best AUC values of 0.935 for test set. The SHAP analysis results emphasized that MaccsFP62, ECFP_624, ECFP_724, and PubchemFP213 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. The PubMac-GB model was applied to virtual screening for potential GABAA agonists and the top 100 compounds were given.

CONCLUSION

Overall, our ensemble learning-based model (PubMac-GB) achieved comparable performance and would be helpful in effectively identifying agonists of GABAA receptors.

Hederagenin Upregulates PTPN1 Expression in Aβ-Stimulated Neuronal Cells, Exerting Anti-Oxidative Stress and Anti-Apoptotic Activities

Alzheimer's disease (AD) is a prevalently neurodegenerative disease characterized by neuronal damage which is associated with amyloid-β (Aβ) accumulation. Hederagenin is a triterpenoid saponin, exerting anti-apoptotic, anti-oxidative, anti-inflammatory, anti-tumoral, and neuroprotective activities. However, its role in AD progression is still obscure. The aim of this study was to explore the influences of hederagenin on Aβ-caused neuronal injury in vitro. Neuronal cells were treated with Aβ25-35 (Aβ) to establish a cellular model of AD. Cell viability was assessed using cell counting kit-8 (CCK-8). Oxidative stress was evaluated by detecting reactive oxygen species (ROS) generation and superoxide dismutase (SOD) activity. Apoptosis was investigated using TUNEL staining and caspase-3 activity assays. Protein tyrosine phosphatase nonreceptor type 1 (PTPN1) was screened by bioinformatics analysis. Protein levels of PTPN1 and protein kinase B (Akt) were measured by western blotting. Hederagenin (2.5, 5, and 10 μM) alone did not affect viability of neuronal cells, but relieved Aβ-induced viability reduction. Hederagenin mitigated Aβ-induced increase in ROS accumulation and decrease in SOD activity. Hederagenin attenuated Aβ-induced increase in apoptotic rate and caspase-3 activity. PTPN1 was screened as a target of hederagenin against AD by bioinformatics analysis. Hederagenin treatment resisted Aβ-induced decrease in PTPN1 mRNA and protein levels in neuronal cells. PTPN1 silencing attenuated the suppressive functions of hederagenin in Aβ-stimulated oxidative stress and apoptosis. Hederagenin mitigated Aβ-induced Akt signaling inactivation by upregulating PTPN1 expression. In conclusion, hederagenin attenuates oxidative stress and apoptosis in neuronal cells stimulated with Aβ by promoting PTPN1/Akt signaling activation.

Tibetan Medicine for Diabetes Mellitus: Overview of Pharmacological Perspectives

Diabetes mellitus (DM) and its complications pose a major public health threat which is approaching epidemic proportions globally. Current drug options may not provide good efficacy and even cause serious adverse effects. Seeking safe and effective agents for DM treatment has been an area of intensive interest. As a healing system originating in Tibet, Traditional Tibetan Medicine (TTM) has been widely used by Tibetan people for the prevention and treatment of DM and its complications for hundreds of years. Tibetan Materia Medica (TMM) including the flower of Edgeworthia gardneri (Wall.) Meisn., Phyllanthi Fructus, Chebulae Fructus, Huidouba, and Berberidis Cortex are most frequently used and studied. These TMMs possess hypoglycemic, anti-insulin resistant, anti-glycation, lipid lowering, anti-inflammatory, and anti-oxidative effects. The underlying mechanisms of these actions may be related to their α-glucosidase inhibitory, insulin signaling promoting, PPARs-activating, gut microbiota modulation, islet β cell-preserving, and TNF-α signaling suppressive properties. This review presents a comprehensive overview of the mode and mechanisms of action of various active constituents, extracts, preparations, and formulas from TMM. The dynamic beneficial effects of the products prepared from TMM for the management of DM and its complications are summarized. These TMMs are valuable materia medica which have the potential to be developed as safe and effective anti-DM agents.

Network pharmacology-based identification for therapeutic mechanisms of Dangguikushen pill in acne vulgaris

The Dangguikushen (DGKS) pill is a proprietary traditional Chinese medicine that has shown superior efficacy in the treatment of acne vulgaris for many years. A network pharmacology-based analysis was performed to explore the potential anti-acne compounds, core therapeutic targets, and the main pathways, involved in the DGKS pill bioactivity. The matching results between the predicted targets of the DGKS pill and the well-known targets of acne vulgaris were collected, followed by network establishment using protein-protein interaction (PPI) data. Cytoscape was utilized to analyze the network and screen the core targets. Furthermore, the Database for Annotation, Visualization and Integrated Discovery (DAVID), and ClueGO were used for the enrichment analysis of the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathways and Gene Ontology biological processes (GO-BP). Finally, the "compound-target-pathway" network was constructed. This approach identified 19 active compounds, 46 therapeutic targets, and 12 core therapeutic targets of the DGKS pill. The biological processes were primarily related to reactive oxygen species (ROS) metabolic process, gland morphogenesis, and female gonad development. The DGKS pill was significantly associated with eight pathways including the PI3K-Akt, TNF, NF-kappa B, and p53 signaling pathways. DGKS pill might have a synergistic effect on the inhibition of excessive sebaceous lipogenesis and sebocyte differentiation, and likewise, anti-inflammatory effects via the different signaling pathways (PI3K-Akt, TNF, NF-kappa B, and p53).

Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

BACKGROUND

Alzheimer's disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects.

METHODS

Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structurebiological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine.

RESULTS

In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage should be determined.

CONCLUSION

Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

Design, synthesis and biological evaluation of cinnamic acid derivatives with synergetic neuroprotection and angiogenesis effect

As for complex brain diseases involved with multiple pathogenic factors, it is extremely difficult to achieve curative effect by acting on a single target. Multi-approach drugs provide a promising prospect in the treatment of complex brain diseases and have been attracting more and more interest. Enlightened by synergetic effect of combination in traditional herb medicines, forty-two novel cinnamic acid derivatives were designed and synthesized by introducing capsaicin and/or ligustrazine moieties to enhance biological activities in both neurological function and neurovascular protection. Elevated levels of cell viability on human brain microvascular endothelium cell line (HBMEC-2) and human neuroblastoma cell line (SH-SY5Y) against free radical injury were observed in most of compounds. Among them, compound 14a exhibited the most potent activities with a significant EC₅₀ value of 3.26 ± 0.16 μM (HBMEC-2) and 2.41 ± 0.10 μM (SH-SY5Y). Subsequently, the results of morphological staining and flow cytometry analysis experiments on both cell lines showed that 14a had the potential to block apoptosis, maintain cell morphological integrity and protect physiological function of mitochondria. Moreover, 14a displayed specific angiogenesis effect in the chick chorioallantoic membrane (CAM) assay; and the results of RT-PCR suggested that the mechanism for angiogenesis effect was associated with the enhancement of the expressions of VEGFR2 mRNA in chick embryo. Preliminary structure-activity relationship was analyzed. The above evidences suggested that conjunctures gained by combining active ingredients in traditional herb medicines deserved further study and might provide references in discovering dual-effective lead compounds for brain diseases.

Systems Pharmacological Approach to Investigate the Mechanism of Ohwia caudata for Application to Alzheimer's Disease

Ohwia caudata (OC)-a traditional Chinese medicine (TCM)-has been reported to have large numbers of flavonoids, alkaloids, and triterpenoids. The previous studies on OC for treating Alzheimer's disease (AD) only focused on single targets and its mechanisms, while no report had shown about the synergistic mechanism of the constituents from OC related to their potential treatment on dementia in any database. This study aimed to predict the bioactive targets constituents and find potential compounds from OC with better oral bioavailability and blood-brain barrier permeability against AD, by using a system network level-based in silico approach. The results revealed that two new flavonoids, and another 26 compounds isolated from OC in our lab, were highly connected to AD-related signaling pathways and biological processes, which were confirmed by compound-target network, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, respectively. Predicted by the virtual screening and various network pharmacology methods, we found the multiple mechanisms of OC, which are effective for alleviating AD symptoms through multiple targets in a synergetic way.

A strategy to find novel candidate anti-Alzheimer's disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants

Background

Alzheimer’ disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs.

Methods

We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included.

Results

A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present.

Discussion

Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Systems-Pharmacology Dissection of Traditional Chinese Medicine Compound Saffron Formula Reveals Multi-scale Treatment Strategy for Cardiovascular Diseases

Cardiovascular diseases (CVDs) have been regarding as “the world’s first killer” of human beings in recent years owing to the striking morbidity and mortality, the involved molecular mechanisms are extremely complex and remain unclear. Traditional Chinese medicine (TCM) adheres to the aim of combating complex diseases from an integrative and holistic point of view, which has shown effectiveness in CVDs therapy. However, system-level understanding of such a mechanism of multi-scale treatment strategy for CVDs is still difficult. Here, we developed a system pharmacology approach with the purpose of revealing the underlying molecular mechanisms exemplified by a famous compound saffron formula (CSF) in treating CVDs. First, by systems ADME analysis combined with drug targeting process, 103 potential active components and their corresponding 219 direct targets were retrieved and some key interactions were further experimentally validated. Based on this, the network relationships among active components, targets and diseases were further built to uncover the pharmacological actions of the drug. Finally, a “CVDs pathway” consisted of several regulatory modules was incorporated to dissect the therapeutic effects of CSF in different pathological features-relevant biological processes. All this demonstrates CSF has multi-scale curative activity in regulating CVD-related biological processes, which provides a new potential way for modern medicine in the treatment of complex diseases.

In Silico Insight into Potential Anti-Alzheimer's Disease Mechanisms of Icariin

Herbal compounds that have notable therapeutic effect upon Alzheimer's disease (AD) have frequently been found, despite the recent failure of late-stage clinical drugs. Icariin, which is isolated from Epimedium brevicornum, is widely reported to exhibit significant anti-AD effects in in vitro and in vivo studies. However, the molecular mechanism remains thus far unclear. In this work, the anti-AD mechanisms of icariin were investigated at a target network level assisted by an in silico target identification program (INVDOCK). The results suggested that the anti-AD effects of icariin may be contributed by: attenuation of hyperphosphorylation of tau protein, anti-inflammation and regulation of Ca²⁺ homeostasis. Our results may provide assistance in understanding the molecular mechanism and further developing icariin into promising anti-AD agents.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

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.

A Module Analysis Approach to Investigate Molecular Mechanism of TCM Formula: A Trial on Shu-feng-jie-du Formula

At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.

Navigating traditional chinese medicine network pharmacology and computational tools

The concept of "network target" has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

An Integrative Platform of TCM Network Pharmacology and Its Application on a Herbal Formula, Qing-Luo-Yin

The scientific understanding of traditional Chinese medicine (TCM) has been hindered by the lack of methods that can explore the complex nature and combinatorial rules of herbal formulae. On the assumption that herbal ingredients mainly target a molecular network to adjust the imbalance of human body, here we present a-self-developed TCM network pharmacology platform for discovering herbal formulae in a systematic manner. This platform integrates a set of network-based methods that we established previously to catch the network regulation mechanism and to identify active ingredients as well as synergistic combinations for a given herbal formula. We then provided a case study on an antirheumatoid arthritis (RA) formula, Qing-Luo-Yin (QLY), to demonstrate the usability of the platform. We revealed the target network of QLY against RA-related key processes including angiogenesis, inflammatory response, and immune response, based on which we not only predicted active and synergistic ingredients from QLY but also interpreted the combinatorial rule of this formula. These findings are either verified by the literature evidence or have the potential to guide further experiments. Therefore, such a network pharmacology strategy and platform is expected to make the systematical study of herbal formulae achievable and to make the TCM drug discovery predictable.

Cancer classification: Mutual information, target network and strategies of therapy

Background

Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.

Methods

We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms.

Results

These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative.

Conclusions

We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.

Network-based drug discovery by integrating systems biology and computational technologies

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.