Drug target identification through systems biology

The metabolic control theory: Its development and its application to mitochondrial oxidative phosphorylation

Metabolic Control Theory (MCT) and Metabolic Control Analysis (MCA) are the two sides, theoretical and experimental, of the measurement of the sensitivity of metabolic networks in the vicinity of a steady state. We will describe the birth and the development of this theory from the first analyses of linear pathways up to a global mathematical theory applicable to any metabolic network. We will describe how the theory, given the global nature of mitochondrial oxidative phosphorylation, solved the problem of what controls mitochondrial ATP synthesis and then how it led to a better understanding of the differential tissue expression of human mitochondrial pathologies and of the heteroplasmy of mitochondrial DNA, leading to the concept of the threshold effect.

RNAseq Analysis of Novel 1,3,4-Oxadiazole Chalcogen Analogues Reveals Anti-Tubulin Properties on Cancer Cell Lines

1,3,4-Oxadiazole derivatives are among the most studied anticancer drugs. Previous studies have analyzed the action of different 1,3,4-oxadiazole derivatives and their effects on cancer cells. This study investigated the characterization of two new compounds named 6 and 14 on HeLa and PC-3 cancer cell lines. Based on the previously obtained IC₅₀, cell cycle effects were monitored by flow cytometry. RNA sequencing (RNAseq) was performed to identify differentially expressed genes, followed by functional annotation using gene ontology (GO), KEGG signaling pathway enrichment, and protein-protein interaction (PPI) network analyses. The tubulin polymerization assay was used to analyze the interaction of both compounds with tubulin. The results showed that 6 and 14 strongly inhibited the proliferation of cancer cells by arresting them in the G2/M phase of the cell cycle. Transcriptome analysis showed that exposure of HeLa and PC-3 cells to the compounds caused a marked reprograming of gene expression. Functional enrichment analysis indicated that differentially expressed genes were significantly enriched throughout the cell cycle and cancer-related biological processes. Furthermore, PPI network, hub gene, and CMap analyses revealed that compounds 14 and 6 shared target genes with established microtubule inhibitors, indicating points of similarity between the two molecules and microtubule inhibitors in terms of the mechanism of action. They were also able to influence the polymerization process of tubulin, suggesting the potential of these new compounds to be used as efficient chemotherapeutic agents.

Target Identification-Based Analysis of Mechanism of Betulinic Acid-Induced Cells Apoptosis of Cervical Cancer SiHa

Cervical cancer is the fourth most common female malignancy with high morbidity and mortality, which urgently needs novel anti-cancer drugs. Accumulating investigations have focused on the antitumor activity of betulinic acid (BA), which is a natural compound with low toxicity and high efficiency. Although the effect of BA on SiHa cells is obvious, the specific mechanism is seldom studied. Target identification is an important part of research on the internal mechanism of action. In this current study, an integrated method based on literature collection, target prediction, enrichment analysis, network analysis, and western blotting experiments was performed to identify the potential key targets of BA-induced apoptosis. Then, combined with the identified potential key targets, the specific mechanism of BA-induced cervical cancer SiHa cells apoptosis was elucidated. Our present study demonstrated that BA significantly reduces the viability of cervical cancer SiHa cells in a dose- and time-dependent manner. In addition, 8 potential key targets (AKT1, CASP8, LMNA, TNF, BCL2, CASP3, PARP1, and XIAP) were obtained through our integrated target identification method. Meanwhile, western blotting showed that within a certain concentration range, the expression of cleaved-caspase 3, cleaved-PARP, and cytochrome c increased with the BA concentration, while XIAP was almost unchanged. Therefore, the effect of BA on cervical cancer is noticeable. BA-induced SiHa cells apoptosis is a multi-molecule coordinated process. In this process, BA is not only a participant in either the extrinsic or intrinsic pathways, but also a regulator of apoptosis effector molecules of the CASP3/PARP1 axis.

From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

Facing Diseases Caused by Trypanosomatid Parasites: Rational Design of Pd and Pt Complexes With Bioactive Ligands

Human African Trypanosomiasis (HAT), Chagas disease or American Trypanosomiasis (CD), and leishmaniases are protozoan infections produced by trypanosomatid parasites belonging to the kinetoplastid order and they constitute an urgent global health problem. In fact, there is an urgent need of more efficient and less toxic chemotherapy for these diseases. Medicinal inorganic chemistry currently offers an attractive option for the rational design of new drugs and, in particular, antiparasitic ones. In this sense, one of the main strategies for the design of metal-based antiparasitic compounds has been the coordination of an organic ligand with known or potential biological activity, to a metal centre or an organometallic core. Classical metal coordination complexes or organometallic compounds could be designed as multifunctional agents joining, in a single molecule, different chemical species that could affect different parasitic targets. This review is focused on the rational design of palladium(II) and platinum(II) compounds with bioactive ligands as prospective drugs against trypanosomatid parasites that has been conducted by our group during the last 20 years.

How Physiologic Targets Can Be Distinguished from Drug-Binding Proteins

In clinical trials, some drugs owe their effectiveness to off-target activity. This and other observations raise a possibility that many studies identifying targets of drugs are incomplete. If off-target proteins are pharmacologically important, it will be worthwhile to identify them early in the development process to gain a better understanding of the molecular basis of drug action. Herein, we outline a multidisciplinary strategy for systematic identification of physiologic targets of drugs in cells. A drug-binding protein whose genetic disruption yields very similar molecular effects as treatment of cells with the drug may be defined as a physiologic target of the drug. For a drug developed with a rational approach, it is desirable to verify experimentally that a protein used for hit optimization in vitro remains the sole polypeptide recognized by the drug in a cell. SIGNIFICANCE STATEMENT: A body of evidence indicates that inactivation of many drug-binding proteins may not cause the pharmacological effects triggered by the drugs. A multidisciplinary cell-based approach can be of great value in identifying the physiologic targets of drugs, including those developed with target-based strategies.

Target identification of a novel unsymmetrical 1,3,4-oxadiazole derivative with antiproliferative properties

1,3,4-Oxadiazole derivatives are widely used in research on antineoplastic drugs. Recently, we discovered a novel unsymmetrical 1,3,4-oxadiazole compound with antiproliferative properties called 2j. To further investigate its possible targets and molecular mechanisms, RNA-seq was performed and the differentially expressed genes (DEGs) were obtained after treatment. Data were analyzed using functional (Gene Ontology term) and pathway (Kyoto Encyclopedia of Genes and Genomes) enrichment of the DEGs. The hub genes were determined by the analysis of protein-protein interaction networks. The connectivity map (CMap) information provided insight into the model action of antitumor small molecule drugs. Hub genes have been identified through function gene networks using STRING analysis. The small molecular targets obtained by CMap comparison showed that 2j is a tubulin inhibitor and it acts mainly affecting tumor cells through the cell cycle, FoxO signaling pathway, apoptotic, and p53 signaling pathways. The possible targets of 2j could be TUBA1A and TUBA4A. Molecular docking results indicated that 2j interacts at the colchicine-binding site on tubulin.

Discovery of potential asthma targets based on the clinical efficacy of Traditional Chinese Medicine formulas

ETHNOPHARMACOLOGICAL RELEVANCE

Standard therapy for asthma, a highly heterogeneous disease, is primarily based on bronchodilators and immunosuppressive drugs, which confer short-term symptomatic relief but not a cure. It is difficult to discover novel bronchodilators, although potential new targets are emerging. Traditional Chinese Medicine (TCM) formulas have been used to treat asthma for more than 2000 years, forming the basis for representative asthma treatments.

AIM OF THE STUDY

Based on the efficacy of TCM formulas, anti-asthmatic herbal compounds bind proteins are potential targets for asthma therapy. This analysis will provide new drug targets and discovery strategies for asthma therapy.

MATERIALS AND METHODS

A list of candidate herbs for asthma was selected from the classical formulas (CFs) of TCM for the treatment of wheezing or dyspnea recorded in Treatise on Cold Damage and Miscellaneous Diseases (TCDMD) and from modern herbal formulas identified in the SAPHRON TCM Database using the keywords "wheezing" or "dyspnea". Compounds in the selected herbs and compounds that directly bind target proteins were acquired by searching the Herbal Ingredients' Targets Database (HITD), TCM Data Bank (TCMDB) and TCM Integrated Database (TCMID). Therapeutic targets of conventional medicine (CM) for asthma were collected by searching Therapeutic Target Database (TTD), DrugBank and PubMed as supplements. Finally, the enriched gene ontology (GO) terms of the targets were obtained using the Database for Annotation Visualization and Integrated Discovery (DAVID) and protein-protein interactions (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The effects of two selected TCM compounds, kaempferol and ginkgolide A, on cellular resistance in human airway smooth muscle cells (ASMCs) and pulmonary resistance in a mouse model were investigated.

RESULTS

The list of 32 candidate herbs for asthma was selected from 10 CFs for the treatment of wheezing or dyspnea recorded in TCDMD and 1037 modern herbal formulas obtained from the SAPHRON TCM Database. A total of 130 compounds from the 32 selected herbs and 68 herbal compounds directly bind target proteins were acquired from HITD and TCMDB. Eighty-eight therapeutic targets of CM for asthma were collected by searching TTD and PubMed as supplements. DAVID and STRING analyses showed targets of TCM formulas are primarily related to cytochrome P450 (CYP) family, transient receptor potential (TRP) channels, matrix metalloproteinases (MMPs) and ribosomal protein. Both TCM formulas and CM act on the same types of targets or signaling pathways, such as G protein-coupled receptors (GPCRs), steroid hormone receptors (SHRs), and JAK-STAT signaling pathway. The proteins directly targeted by herbal compounds, TRPM8, TRPA1, TRPV3, CYP1B1, CYP2B6, CYP1A2, CYP3A4, CYP1A1, PPARA, PPARD, NR1I2, MMP1, MMP2, ESR1, ESR2, RPLP0, RPLP1 and RPLP2, are potential targets for asthma therapy. In vitro results showed kaempferol (1 × 10^-2 mM) and ginkgolide A (1 × 10^-5 mM) significantly increased the cell index (P < 0.05 vs. histamine, n = 3) and therefore relaxed human ASMCs. In vivo results showed kaempferol (145 μg/kg) and ginkgolide A (205 μg/kg) significantly reduced pulmonary resistance (P < 0.05 vs. methacholine, n = 6).

CONCLUSION

Potential target discovery for asthma treatment based on the clinical effectiveness of TCM is a feasible strategy.

Computational medicine: quantitative modeling of complex diseases

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Metabolic Control Analysis for Drug Target Prioritization in Trypanosomatids

To validate therapeutic targets in metabolic pathways of trypanosomatids, the criterion of enzyme essentiality determined by gene knockout or knockdown is usually being applied. Since, it is often found that most of the enzymes/proteins analyzed are essential, additional criteria have to be implemented for drug target prioritization. Metabolic control analysis (MCA), often in conjunction with kinetic pathway modeling, offers such possibility for prioritization. MCA is a theoretical and experimental approach to analyze how metabolic pathways are controlled. It involves strategies to perform quantitative analyses to determine the degree in which an enzyme controls a pathway flux, a value called flux control coefficient ([Formula: see text]). By determining the [Formula: see text] of individual steps in a metabolic pathway, the distribution of control of the pathway is established, that is, the identification of the main flux-controlling steps. Therefore, MCA can help in ranking pathway enzymes as drug targets from a metabolic perspective. In this chapter, three approaches to determine [Formula: see text] are reviewed: (1) In vitro pathway reconstitution, (2) manipulation of enzyme activities within parasites, and (3) in silico kinetic modeling of the metabolic pathway. To perform these methods, accurate experimental data of enzyme activities, metabolite concentrations and pathway fluxes are necessary. The methodology is illustrated with the example of trypanothione metabolism of Trypanosoma cruzi and protocols to determine such experimental data for this metabolic process are also described. However, the MCA strategy can be applied to any metabolic pathway in the parasite and general directions to perform it are provided in this chapter.

Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer's disease

Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

A systems level analysis of epileptogenesis-associated proteome alterations

Despite intense research efforts, the knowledge about the mechanisms of epileptogenesis and epilepsy is still considered incomplete and limited. However, an in-depth understanding of molecular pathophysiological processes is crucial for the rational selection of innovative biomarkers and target candidates. Here, we subjected proteomic data from different phases of a chronic rat epileptogenesis model to a comprehensive systems level analysis. Weighted Gene Co-expression Network analysis identified several modules of interconnected protein groups reflecting distinct molecular aspects of epileptogenesis in the hippocampus and the parahippocampal cortex. Characterization of these modules did not only further validate the data but also revealed regulation of molecular processes not described previously in the context of epilepsy development. The data sets also provide valuable information about temporal patterns, which should be taken into account for development of preventive strategies in particular when it comes to multi-targeting network pharmacology approaches. In addition, principal component analysis suggests candidate biomarkers, which might inform the design of novel molecular imaging approaches aiming to predict epileptogenesis during different phases or confirm epilepsy manifestation. Further studies are necessary to distinguish between molecular alterations, which correlate with epileptogenesis versus those reflecting a mere consequence of the status epilepticus.

A computational study on role of 6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol in the regulation of blood glucose level

6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol (SID 242078875) was isolated from the fruits of Syzygium densiflorum Wall. ex Wight & Arn (Myrtaceae), which has been traditionally used in the treatment of diabetes by the tribes of The Nilgiris, Tamil Nadu, India. In this study, reverse pharmacophore mapping approach and text-based database search identified the dipeptidyl peptidase-IV, protein-tyrosine phosphatase 1B, phosphoenolpyruvate carboxykinase, glycogen synthase kinase-3β and glucokinase as potential targets of SID 242078875 in diabetes management. Further, molecular docking was performed to predict the binding pose of SID 242078875 in the active site region of the target protein. In addition, dynamic behaviour and stability of protein-ligand complexes were observed for a period of 50 ns through molecular dynamics simulation.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges

A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.