Network target for screening synergistic drug combinations with application to traditional Chinese medicine

<p>Abstract</p> <p>Background</p> <p>Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.</p> <p>Results</p> <p>In this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.</p> <p>Conclusions</p> <p>NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.</p

Network Pharmacology and Traditional Chinese Medicine

Gynaecology & obstetric

A call for using natural compounds in the development of new antimalarial treatments – an introduction

Natural compounds, mostly from plants, have been the mainstay of traditional medicine for thousands of years. They have also been the source of lead compounds for modern medicine, but the extent of mining of natural compounds for such leads decreased during the second half of the 20th century. The advantage of natural compounds for the development of drugs derives from their innate affinity for biological receptors. Natural compounds have provided the best anti-malarials known to date. Recent surveys have identified many extracts of various organisms (mostly plants) as having antiplasmodial activity. Huge libraries of fractionated natural compounds have been screened with impressive hit rates. Importantly, many cases are known where the crude biological extract is more efficient pharmacologically than the most active purified compound from this extract. This could be due to synergism with other compounds present in the extract, that as such have no pharmacological activity. Indeed, such compounds are best screened by cell-based assay where all potential targets in the cell are probed and possible synergies identified. Traditional medicine uses crude extracts. These have often been shown to provide many concoctions that deal better with the overall disease condition than with the causative agent itself. Traditional medicines are used by ~80 % of Africans as a first response to ailment. Many of the traditional medicines have demonstrable anti-plasmodial activities. It is suggested that rigorous evaluation of traditional medicines involving controlled clinical trials in parallel with agronomical development for more reproducible levels of active compounds could improve the availability of drugs at an acceptable cost and a source of income in malaria endemic countries

Conceptions of Potency, Purity, and Synergy-by-Design

Sowa Rigpa institutions and practitioners have growing interest in examining and legitimizing Sowa Rigpa formulas vis-à-vis pharmacological research methods, seeking scientific validation of what they view as ‘potency’ and ‘purity’ for their formulas. Likewise, the pharmacology researchers have demonstrated renewed interest in herbal medical traditions in mining for new drugs to address resistance, toxicity, and optimize what they view as ‘potency’ and ‘purity.’ However, differing conceptualizations emerge when the pharmacological drug discovery process is examined to determine what is being analyzed, how it is doing so, and what assumptions underlie such methods. Whether a formula is ‘active,’ ‘toxic’ or ‘effective’ hinges on assumptions, processes, and methods that typically have low fidelity to how Sowa Rigpa formulations function from the Tibetan tradition’s perspective and are actually administered to patients. This paper argues that standard mainstream biochemical pharmacology screening methods may not be suitable for analyzing Sowa Rigpa formulas, as they are traditionally compounded and understood to function in concert with multiple physiological pathways, rather than one specific target. We examine the pharmaceutical research processes to identify points of adherence and divergence with conceptions of ‘potency’ and ‘purity' in Tibetan medical theory, and believe pharmacological research institutions will be receptive to traditional Sowa Rigpa menjor (sman sbyor), or ‘medicine compounding,’ theory due to benefits it could provide biomedical drug discovery via complementary understandings of compound synergy and distinctly different concepts of toxicity and purity. Accordingly, we suggest that efficacy, activity and safety of Tibetan medicinal formulas will be more accurately assessed by retaining fidelity to its own conceptions of potency and purity

Development of novel herbal compound combinations targeting neuroinflammation : network pharmacology, molecular docking and experimental verification

This study aimed to develop a novel herbal compound combination to attenuate neuroinflammation with a synergistic approach in vitro. Two novel herbal compound combinations targeting neuroinflammation with a synergistic approach have been identified through a series of studies using network pharmacology, molecular docking, and in vitro experimental verification. The major research findings are shown as follows: 1) AN-SG and BA-SG synergistically inhibited pro-inflammatory mediators, NO, IL-6 and TNF-α in LPS-induced mono-cultured microglial N11 cells; 2) The observed synergy of AN-SG and BA-SG was associated with downregulation of phosphor-MAPKp38/MAPKp38, iNOS and NF-κB p65 nuclear translocation. 3) AN-SG and BA-SG exhibited enhanced anti-neuroinflammatory activities than their individual component in the neuroinflammation tri-culture model and subsequently restored the endothelial tight junction, protected the neuronal survival and reduced p-tau expression. Chapter 3 constructed the compound-gene targets-signaling pathway networks, which highlight the hub gene targets and KEGG pathways for eight selected phytochemicals against neuroinflammation. The top five gene targets included MAPK14, MAPK8, NOS3, EGFR and SRC, and the top KEGG pathway was the MAPK signaling pathway. The molecular docking partly verified the network pharmacology results that demonstrated good bind affinities of all phytochemicals with MAPK14 and NOS3. In Chapter 4, two herbal-compound combinations, AN-SG and BA-SG, were found to exhibit synergistic effects (CI<1) on inhibiting NO, IL-6 and TNF-α productions in LPS-induced N11 cells. Western blot analysis suggested that the observed synergy was associated with downregulation of iNOS and phosphor-MAPKp38/MAPKp38 protein expressions, which were in line with the findings of network pharmacology and molecular docking studies. In Chapter 5, an LPS-induced neuroinflammation tri-culture model was established to further investigate the anti-neuroinflammatory effects of AN-SG and BA-SG. The LPS-induced neuroinflammation tri-culture model provides an efficient and practical cellular model for a wide range of investigations on neuroinflammation mechanisms and for screening potential compounds or drugs candidates to treat neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and schizophrenia (Wolfe, 2012). Our results showed that two combinations significantly reduced the NO, IL-6, and TNF-α in the N11 cell in the tri-culture model. They also protected the endothelial tight junction as evidenced by the honeycomb structure of ZO-1 tight junction protein, reduced permeability and restored TEER values in the MVEC cells. Furthermore, the combinations were found to restore the impaired cell viability and reduced p-tau protein expression in N2A cells in the tri-culture model

A Computational Drug-Target Network for Yuanhu Zhitong Prescription

Yuanhu Zhitong prescription (YZP) is a typical and relatively simple traditional Chinese medicine (TCM), widely used in the clinical treatment of headache, gastralgia, and dysmenorrhea. However, the underlying molecular mechanism of action of YZP is not clear. In this study, based on the previous chemical and metabolite analysis, a complex approach including the prediction of the structure of metabolite, high-throughput in silico screening, and network reconstruction and analysis was developed to obtain a computational drug-target network for YZP. This was followed by a functional and pathway analysis by ClueGO to determine some of the pharmacologic activities. Further, two new pharmacologic actions, antidepressant and antianxiety, of YZP were validated by animal experiments using zebrafish and mice models. The forced swimming test and the tail suspension test demonstrated that YZP at the doses of 4 mg/kg and 8 mg/kg had better antidepressive activity when compared with the control group. The anxiolytic activity experiment showed that YZP at the doses of 100 mg/L, 150 mg/L, and 200 mg/L had significant decrease in diving compared to controls. These results not only shed light on the better understanding of the molecular mechanisms of YZP for curing diseases, but also provide some evidence for exploring the classic TCM formulas for new clinical application

Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples