Application of chemical biology in target identification and drug discovery

Inhibition of Tumor-Derived C-C Motif Chemokine Ligand 2 Expression Attenuates Tactile Allodynia in NCTC 2472 Fibrosarcoma-Inoculated Mice

Neuropathic pain associated with cancers is caused by tumor growth compressing and damaging nerves, which would also be enhanced by inflammatory factors through sensitizing nociceptor neurons. A troublesome hallmark symptom of neuropathic pain is hypersensitivity to innocuous stimuli, a condition known as "tactile allodynia", which is often refractory to NSAIDs and opioids. The involvement of chemokine CCL2 (monocyte chemoattractant protein-1) in cancer-evoked neuropathic pain is well established, but opinions remain divided as to whether CCL2 is involved in the production of tactile allodynia with tumor growth. In this study, we constructed Ccl2 knockout NCTC 2472 (Ccl2-KO NCTC) fibrosarcoma cells and conducted pain behavioral test using Ccl2-KO NCTC-implanted mice. Implantation of naïve NCTC cells around the sciatic nerves of mice produced tactile allodynia in the inoculated paw. Although the growth of Ccl2 KO NCTC-formed tumors was comparable to that of naïve NCTC-formed tumors, Ccl2-KO NCTC-bearing mice failed to show tactile pain hypersensitivity, suggesting the involvement of CCL2 in cancer-induced allodynia. Subcutaneous administration of controlled-release nanoparticles containing the CCL2 expression inhibitor NS-3-008 (1-benzyl-3-hexylguanidine) significantly attenuated tactile allodynia in naïve NCTC-bearing mice accompanied by a reduction of CCL2 content in tumor masses. Our present findings suggest that inhibition of CCL2 expression in cancer cells is a useful strategy to attenuate tactile allodynia induced by tumor growth. Development of a controlled-release system of CCL2 expression inhibitor may be a preventative option for the treatment of cancer-evoked neuropathic pain. SIGNIFICANCE STATEMENT: The blockade of chemokine/receptor signaling, particularly for C-C motif chemokine ligand 2 (CCL2) and its high-affinity receptor C-C chemokine receptor type 2 (CCR2), has been implicated to attenuate cancer-induced inflammatory and nociceptive pain. This study demonstrated that continuous inhibition of CCL2 production from cancer cells also prevents the development of tactile allodynia associated with tumor growth. Development of a controlled-release system of CCL2 expression inhibitor may be a preventative option for management of cancer-evoked tactile allodynia.

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.

Administration of isoliquiritigenin prevents nonalcoholic fatty liver disease through a novel IQGAP2-CREB-SIRT1 axis

Isoliquiritigenin (ISO) is a flavonoid extracted from the root of licorice, which serves various biological and pharmacological functions including antiinflammatory, antioxidation, liver protection, and heart protection. However, the mechanism of its action remains elusive and the direct target proteins of ISO have not been identified so far. Through cell-based screening, we identified ISO as a potent lipid-lowering compound. ISO treatment successfully ameliorated fatty acid-induced cellular lipid accumulation and improved nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) by increasing PPARα-dependent lipid oxidation and decreasing SREBPs-dependent lipid synthesis. Both these signaling required the activation of SIRT1. Knockdown of SIRT1 resulted in the reversal of ISO beneficiary effects suggesting that the lipid-lowering activity of ISO was regulated by SIRT1 expression. To identify the direct target of ISO, limited proteolysis combined with mass spectrometry (LiP-SMap) strategy was applied and IQGAP2 was identified as the direct target for ISO in regulating lipid homeostasis. In the presence of ISO, both mRNA and protein levels of SIRT1 were increased; however, this effect was abolished by blocking IQGAP2 expression using siRNA. To explore how IQGAP2 regulated the expression level of SIRT1, proteome profiler human phospho-kinase array kit was used to reveal possible phosphorylated kinases and signaling nodes that ISO affected. We found that through phosphorylation of CREB, ISO transduced signals from IQGAP2 to upregulate SIRT1 expression. Thus, we not only demonstrated the molecular basis of ISO in regulating lipid metabolism but also exhibited for the first time a novel IQGAP2-CREB-SIRT1 axis in treating NAFLD/NASH.

Identification of Psychoactive Metabolites from Cannabis sativa, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods

Cannabis sativa is a medicinal plant having a very complex matrix composed of mainly cannabinoids and terpenoids. The literature has numerous reports, which indicate that tetrahydrocannabinol (THC) is the only major psychoactive metabolite in C. sativa. It is important to explore other metabolites having the possibility of exhibiting the psychoactive character of various degrees and also to identify metabolites targeting other receptors such as opioid, γ amino butyric acid (GABA), glycine, serotonin, and nicotine present in C. sativa, the smoke of C. sativa, and other phytocannabinoid matrices. This article aims to achieve this goal by application of batteries of computational tools such as machine learning tools and multivariate methods on physiochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) descriptors of 468 metabolites from C. sativa, its smoke and, other phytocannabinoids. The structure-activity relationship (SAR) showed that 54 metabolites from C. sativa have high scaffold homology with THC. Its implications on the route of administration and factors affecting the SAR are discussed. C. sativa smoke has metabolites that have possibility of interacting with GABA, and glycine receptors.

Dynamic Proteomics Reveals High Plasticity of Cellular Proteome: Growth-Related and Drug-Induced Changes in Cancer Cells are Comparable

In chemical proteomics, the changes occurring in cellular proteomes upon drug treatment are used to identify the drug targets and the mechanism of action. However, proteomes of cultured cells undergo also natural alteration associated with changes in the media, attaining a degree of confluence as well as due to cell division and cell metabolism. These changes are implicitly assumed to be smaller in magnitude than the drug-induced changes that ultimately lead to cell demise. In this study, it is shown that growth-related proteome changes in the untreated control group are comparable in magnitude to drug-induced changes over the course of 48 h treatment. In two well-characterized cancer cell lines, growth-related effects assessed with deep proteomics analysis (10 481 proteins quantified with at least two peptides) show common trends, the steady downregulation of cell division processes, and the upregulation of metabolism-related pathways. The magnitude of these variations, which are present even before reaching 100% confluence reveals unexpectedly high plasticity of the cellular proteome. This finding reinforces the need, generally accepted in theory but not always followed in practice, to use a time-matched control when measuring drug-induced proteome changes.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Family-wide Analysis of the Inhibition of Arf Guanine Nucleotide Exchange Factors with Small Molecules: Evidence of Unique Inhibitory Profiles

Arf GTPases and their guanine nucleotide exchange factors (ArfGEFs) are major regulators of membrane traffic and organelle structure in cells. They are associated with a variety of diseases and are thus attractive therapeutic targets for inhibition by small molecules. Several inhibitors of unrelated chemical structures have been discovered, which have shown their potential in dissecting molecular pathways and blocking disease-related functions. However, their specificity across the ArfGEF family has remained elusive. Importantly, inhibitory responses in the context of membranes, which are critical determinants of Arf and ArfGEF cellular functions, have not been investigated. Here, we compare the efficiency and specificity of four structurally distinct ArfGEF inhibitors, Brefeldin A, SecinH3, M-COPA, and NAV-2729, toward six ArfGEFs (human ARNO, EFA6, BIG1, and BRAG2 and Legionella and Rickettsia RalF). Inhibition was assessed by fluorescence kinetics using pure proteins, and its modulation by membranes was determined with lipidated GTPases in the presence of liposomes. Our analysis shows that despite the intra-ArfGEF family resemblance, each inhibitor has a specific inhibitory profile. Notably, M-COPA is a potent pan-ArfGEF inhibitor, and NAV-2729 inhibits all GEFs, the strongest effects being against BRAG2 and Arf1. Furthermore, the presence of the membrane-binding domain in Legionella RalF reveals a strong inhibitory effect of BFA that is not measured on its GEF domain alone. This study demonstrates the value of family-wide assays with incorporation of membranes, and it should enable accurate dissection of Arf pathways by these inhibitors to best guide their use and development as therapeutic agents.

Chemical genetics-based development of small molecules targeting hepatitis C virus

Hepatitis C virus (HCV) infection is a major worldwide problem that has emerged as one of the most significant diseases affecting humans. There are currently no vaccines or efficient therapies without side effects, despite today's advanced medical technology. Currently, the common therapy for most patients (i.e. genotype 1) is combination of HCV-specific direct-acting antivirals (DAAs). Up to 2011, the standard of care (SOC) was a combination of peg-IFNα with ribavirin (RBV). After approval of NS3/4A protease inhibitor, SOC was peg-IFNα and RBV with either the first-generation DAAs boceprevir or telaprevir. In the past several years, various novel small molecules have been discovered and some of them (i.e., HCV polymerase, protease, helicase and entry inhibitors) have undergone clinical trials. Between 2013 and 2016, the second-generation DAA drugs simeprevir, asunaprevir, daclatasvir, dasabuvir, sofosbuvir, and elbasvir were approved, as well as the combinational drugs Harvoni^®, Zepatier^®, Technivie^®, and Epclusa^®. A number of reviews have been recently published describing the structure-activity relationship (SAR) in the development of HCV inhibitors and outlining current therapeutic approaches to hepatitis C infection. Target identification involves studying a drug's mechanism of action (MOA), and a variety of target identification methods have been developed in the past few years. Chemical biology has emerged as a powerful tool for studying biological processes using small molecules. The use of chemical genetic methods is a valuable strategy for studying the molecular mechanisms of the viral lifecycle and screening for anti-viral agents. Two general screening approaches have been employed: forward and reverse chemical genetics. This review reveals information on the small molecules in HCV drug discovery by using chemical genetics for targeting the HCV protein and describes successful examples of targets identified with these methods.