Identification Of Drug Targets Research Articles

Dynamics of the splicing factor SRSF6 protein show metastable conformations with potentially druggable sites revealed by Markov state model

The splicing factor SRSF6 has emerged as a promising target for cancer treatment. In order to gain a comprehensive understanding of the structure and dynamics of SRSF6 and identify the potential binding sites of inhibitors, a combined approach of microseconds-time scale molecular dynamics simulations and Markov state models, followed by molecular docking, was employed here. We predicted that SRSF6 protein exhibits a variety of characteristic metastable conformations that are stabilized by specific hydrogen bonds and/or salt bridges formed between the RRM domains and the loop linker. The transitions between the metastable states are in the time scales ranging from nanoseconds to microseconds. For the most populated states, we identified their potential binding pockets. Then we performed molecular docking of the FDA-approved drugs library, and 15 drugs including reproterol were selected as potential hits inhibiting SRSF6. This work provides valuable insights into the structure and dynamics of SRSF6, which will aid in the structure-based drug design targeting SRSF6 to inhibit tumorigenesis.

Multimillion Atom Simulations of Di-8-ANEPPS Chromophores Embedded in a Model Plasma Membrane: Toward the Investigation of Realistic Dyed Cell Membranes.

A multistep computational approach has been employed to study a multimillion all-atom dyed plasma membrane, with no less than 42 different lipid species spanning the major head groups and a variety of fatty acids, as well as cholesterol, with the objective of investigating its structure and dynamics, as well as its impact on the embedded di-8-ANEPPS dyes. The latter are commonly used as bioimaging probes and serve as local microscopes. So, they provide information on membrane morphology via their second harmonic nonlinear optical (NLO) responses, which have the advantage of being specific to interface regions and sensitive to the chromophore environment. In previous studies, this chromophore has only been studied in simpler membrane models, far from the complexity of real lipid bilayers, while, owing to the ever-increasing computational resources, multimillion lipid bilayers have been studied, giving access to the effects of its heterogeneity. First, using molecular dynamics (MD) simulations, it is found that the combination of lipids produces a more ordered and denser membrane compared to its homogeneous model counterparts, while the local environment of the embedded dyes becomes enriched in phosphatidylcholine. Subsequently, the second harmonic first hyperpolarizability of the probes was calculated at the TDDFT level on selected frames of MD, highlighting the influence of the lipid environment. Due to the complexity of the system, machine learning (ML) tools have been employed to establish relationships between the membrane structural parameters, the orientation of the probes, and their NLO responses. These ML approaches have revealed influential features, including the presence of diacylglycerol lipids close to the dye. On the whole, this work provides a first step toward understanding the cooperation, synergy, and interactions that occur in such complex guest-host environments, which have emerged as new targets for drug design and membrane lipid therapy.

Ganoderma lucidum ethanol extracts ameliorate hepatic fibrosis and promote the communication between metabolites and gut microbiota g_Ruminococcus through the NF-κB and TGF-β1/Smads pathways

Ethnopharmacological relevanceGanoderma lucidum, a traditional edible medicinal mushroom, has been widely reported to improve liver diseases as a dietary intervention for people. Ganoderma lucidum extracts, primarily total triterpenoids (GLTTs), are one of the bioactive ingredients that have excellent beneficial effects on hepatic fibrosis. Therefore, its prevention and reversal are particularly critical due to the increasing number of patients with chronic liver diseases worldwide. Aim of the studyThe study aimed to evaluate whether GLTTs had a hepatoprotective effect against hepatic fibrosis through metabolic perturbations and gut microbiota changes and its underlying mechanisms. Materials and methodsThe compound compositions of GLTTs were quantified, and carbon tetrachloride (CCl4)-induced hepatic fibrosis rats were used to investigate the cause of the improvement in various physiological states with GLTTs treatment, and to determine whether its consequent effect was associated with endogenous metabolites and gut microbiota using UPLC-Q-TOF-MSE metabolomics and 16S rRNA gene sequencing technology. ResultsGLTTs alleviated physical status, reduced liver pathological indicators, proinflammatory cytokines, and deposition of hepatic collagen fibers via regulating the NF-κB and TGF-β1/Smads pathways. The untargeted metabolomics analysis identified 16 potential metabolites that may be the most relevant metabolites for gut microbiota dysbiosis and the therapeutic effects of GLTTs in hepatic fibrosis. Besides, although GLTTs did not significantly affect the α-diversity indexes, significant changes were observed in the composition of microflora structure. In addition, Spearman analysis revealed strong correlations between endogenous metabolites and gut microbiota g_Ruminococcus with hepatic fibrosis. ConclusionGLTTs could provide a potential target for the practical design and application of novel functional food ingredients or drugs in the therapy of hepatic fibrosis.

Emerging Trends in Metal‐based Anticancer Agents: Drug Design to Clinical Trials and their Mechanism of Action

AbstractMetal‐based therapeutic strategies have been effectively used to treat a number of diseases since the ancient period. However, the issue that emerged at this time was the indistinct barrier between therapeutic and toxic doses. After the discovery of cisplatin, a breakthrough was made for these challenges, and vital signs of progress were shown in the medicinal potential of metal complexes. Consequently, nowadays many metal‐based drugs, especially in cancer treatments, are under investigation. Nevertheless, due to a greater focus on clinical relevance, a few of these drugs are currently approved, and many more are in clinical trials waiting for approval. Therefore, the design, development, and clinical applications of several metal‐based anti‐cancer agents and their efficacy are highlighted in this review. Moreover, this review is intended to present a comprehensive overview of metal‐based anticancer agents, their targets of drug design and development and their mechanisms of action in cancer treatments.

Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment

BackgroundArtificial intelligence (AI) is capable of integrating a large amount of related information to predict therapeutic relationships such as disease treatment with known drugs, gene expression, and drug-target binding. AI has gained increasing attention as a promising tool for next-generation drug development.MethodsAn AI method was used for drug repurposing and target identification for cancer. Among 8 survived candidates after background checking, N-(1-propyl-1H-1,3-benzodiazol-2-yl)-3-(pyrrolidine-1-sulfonyl) benzamide (Z29077885) was newly selected as an new anti-cancer drug, and the anti-cancer efficacy of Z29077885 was confirmed using cell viability, western blot, cell cycle, apoptosis assay in MDA-MB 231 and A549 in vitro. Then, anti-tumor efficacy of Z29077885 was validated in an in vivo A549 xenograft in BALB/c nude mice.ResultsFirst, we discovered an antiviral agent, Z29077885, as a new anticancer drug candidate using the AI deep learning method. Next, we demonstrated that Z29077885 inhibits Serine/threonine kinase 33 (STK33) enzymatic function in vitro and showed the anticancer efficacy in various cancer cells. Then, we found enhanced apoptosis via S-phase cell cycle arrest as the mechanism underlying the anticancer efficacy of Z29077885 in both lung and breast cancer cells. Finally, we confirmed the anti-tumor efficacy of Z29077885 in an in vivo A549 xenograft.ConclusionsIn this study, we used an AI-driven screening strategy to find a novel anticancer medication targeting STK33 that triggers cancer cell apoptosis and cell cycle arrest at the s phase. It will pave a way to efficiently discover new anticancer drugs.

Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood

AbstractBackgroundGenome‐wide association studies (GWASs) have discovered numerous risk genes for Alzheimer’s disease (AD), but how these genes confer AD risk is challenging to decipher.MethodTo efficiently transform genetic associations into drug targets for AD, we employed an integrative analytical pipeline using proteomes in brain and blood by systematically applying proteome‐wide association study (PWAS), Mendelian randomization (MR) and Bayesian colocalization.ResultCollectively, we identified the brain protein abundance of 7 genes (ACE, ICA1L, TOM1L2, SNX32, EPHX2, CTSH, and RTFDC1) are causal in AD (P<0.05/proteins identified for PWAS and MR; PPH4>80% for Bayesian colocalization). The proteins encoded by these genes were mainly expressed on the surface of glutamatergic neurons and astrocytes. Of them, ACE with its protein abundance was also identified in significant association with AD on the blood‐based studies and showed significance at the transcriptomic level. SNX32 was also found to be associated with AD at the blood transcriptomic level.ConclusionOur current study results on genetic, proteomic and transcriptomic approaches has identified compelling genes, which may provide important leads to design future functional studies and potential drug targets for AD.

Abstract C059: Probabilistic graph-based model uncovers druggable vulnerabilities in major solid cancers

Abstract We present A3D3a’s Molecular Vulnerability Picker (MVP), a novel probabilistic approach to determine vulnerabilities in networks of cancer interactions for the identification of new therapeutic targets. Successes in targeted therapies driven by molecular profiling have helped 47% of cancer patients achieve long-lasting remission. However, over half of patients still lack safe, effective, and long-lasting treatment options. Successes have often clustered around exploitation of major driver mechanisms that can be clearly discerned from molecular data. Heterogeneity, rarity, and complexity of these remaining cancers mean that the signal of key molecular vulnerabilities, i.e., protein targets that can be exploited for therapy, can be drowned out by noise. We developed A3D3a’s MVP to increase the signal-to-noise ratio in cancer networks of interaction and alteration information to determine vulnerabilities toward the identification of new potential drug targets. To implement A3D3a’s MVP, we constructed protein networks of major solid cancers from TCGA data that we used as a framework for information flow. We developed a novel weight-biased Markov Chain model to highlight cooperativity of weak signals arising from related regions of the protein network, emphasizing previously hidden signal within these cancer networks. To validate A3D3a’s MVP, we examined the top genes it returned in the Dependency Map and the Genomics of Drug Sensitivity in Cancer. We further validated our findings by comparing the number of genetic dependencies and drug targets recovered by the model to that recovered by the state of the art. Finally, we applied A3D3a’s MVP to identify therapeutic opportunities across cancer indications and extended our analysis to highlight well-ranked genes whose proteins contain druggable pockets and thus, could serve as new drug targets. For the 19 cancer types included in this analysis, A3D3a’s MVP returned significantly more genetic dependencies (p < 0.01) and drug targets (p < 0.001) than genes ranked by the state of the art. We demonstrate that A3D3a’s MVP is able to increase the signal of weakly altered genes and is also able to identify genuine dependencies that themselves are not mutated or altered in any way. Using A3D3a’s MVP, we identified 56 drug repurposing opportunities and 49 potential druggable targets for solid cancers and also highlight novel potential druggable targets for future exploitation and new therapeutics. In summary, A3D3a’s MVP is a novel mathematical modeling approach that increases the signal-to-noise ratio in cancer molecular data and helps uncover previously hidden molecular vulnerabilities towards new potential drug targets to address unmet patient needs. Citation Format: Stephanie T Schmidt, Ying Zhu, Li Zhao, Chunjie Jiang, Patrizio Di Micco, Costas Mitsopoulos, Andrew Futreal, Bissan Al-Lazikani. Probabilistic graph-based model uncovers druggable vulnerabilities in major solid cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C059.

In silico Identification of Putative Drug Targets in Mycobacterium ulcerans Virulence Proteins

Background: Buruli ulcer (BU), caused by Mycobacterium ulcerans is a neglected tropical disease characterized by necrotic skin lesions. Antibiotic therapy and excision of the lesions are the treatments for this chronic disease. During the management of the disease, the emergence of drug resistance in these bacilli is a major challenge. Therefore, there is a need to identify new drug targets against this important pathogen. Objective: The study aimed to investigate novel drug targets exploring virulence factors of M. ulcerans by in silico analysis. Methods: Virulence proteins encoded by the chromosome of Mycobacterium ulcerans strain Agy99 were retrieved and analyzed for their cellular localization, human non-homology and essentiality. Further, proteins were analyzed for their physio-chemical characterization, drug resistance analysis, protein interaction analysis, metabolic pathway prediction, and druggability prediction by various databases and online software to find their suitability as drug targets. The structure of the predicted drug targets was also modeled and validated. Among three predicted drug targets, MUL_4536 was subjected to molecular docking with some known inhibitor compounds also. Receptor-ligand complex with the highest binding energy was selected for molecular dynamic (MD) simulation to determine the structural stability of the complex. Results: Three virulence proteins MUL_4536, MUL_3640, and MUL_2329 encoding enzymes iso-citrate lyase, lysine-N-oxygenase, pup-protein ligase, respectively were predicted as a drug target against M. ulcerans. Isocitrate lyase has been identified as a potential drug target in many other mycobacterial and non-mycobacterial diseases. Lysine-N-oxygenase is the enzyme of mycobactin biosynthesis pathway and pup-protein ligase is associated with the pup-proteasome system. Proteins of these pathways have been studied as attractive drug targets in previous research works, which further support our predictions. Conclusion: Our computational analysis predicted new drug targets, which could be used to design drugs against M. ulcerans. However, these predicted proteins require further experimental validation for their potential use as drug targets.

All‐in‐Human Target Discovery in Alzheimer’s Disease and Resilient Brains

AbstractBackgroundAlzheimer’s disease (AD) is characterized by neuropathological hallmarks including amyloid plaques and tau tangles and neurodegeneration in the frontal cortex. While there is a strong association of tau pathology with neurodegeneration and cognitive decline, around 30% of amyloid plaque‐carrying seniors remain cognitively healthy. We hypothesize that protective cellular mechanisms upstream of tau phosphorylation and neurodegeneration may underlie this resilience.MethodWe applied high resolution spatial transcriptomics and single nuclei RNA sequencing on postmortem frontal lobes from 10 demented and 10 non‐demented individuals with comparable degrees of amyloid pathology. We integrated the generated transcriptomics data with proteomics, genomics and drug action databases and interpret cellular transcriptome responses to pathology in the context of human specific protein‐protein interaction networks.ResultUsing spatial and single nuclei transcriptomics, we identify specific transcriptomic signatures and regulatory networks in cellular microenvironments around amyloid plaques and pTau pathology niches. Our results suggest that specific neuronal and glial subpopulations carry enriched vulnerability towards amyloid plaque and pTau depositions. We further find that certain cell death signaling pathways related to neurodegeneration are overrepresented in the context of pTau pathology. Finally, our multi‐omics database integration approach has led to the identification of potential drug targets for therapeutics addressing cellular mechanisms of neuroprotection.ConclusionSpatial transcriptomics from human brain tissue is a powerful approach to reveal cellular mechanisms of pathogenic lesions and enables us to identify neuroprotective mechanisms that may prevent the development of tauopathy and ultimately neuronal loss. The combination of single cell and spatial transcriptomics together with protein‐protein interaction networks provides a promising approach for the identification of novel therapeutic targets for AD.

A Comprehensive Review of Small Interfering RNAs (siRNAs): Mechanism, Therapeutic Targets, and Delivery Strategies for Cancer Therapy.

Small interfering RNA (siRNA) delivery by nanocarriers has been identified as a promising strategy in the study and treatment of cancer. Short nucleotide sequences are synthesized exogenously to create siRNA, which triggers RNA interference (RNAi) in cells and silences target gene expression in a sequence-specific way. As a nucleic acid-based medicine that has gained popularity recently, siRNA exhibits novel potential for the treatment of cancer. However, there are still many obstacles to overcome before clinical siRNA delivery devices can be developed. In this review, we discuss prospective targets for siRNA drug design, explain siRNA drug properties and benefits, and give an overview of the current clinical siRNA therapeutics for the treatment of cancer. Additionally, we introduce the siRNA chemical modifications and delivery systems that are clinically sophisticated and classify bioresponsive materials for siRNA release in a methodical manner. This review will serve as a reference for researchers in developing more precise and efficient targeted delivery systems, promoting ongoing advances in clinical applications.

Pyroptosis inhibiting nanobodies block Gasdermin D pore formation

Human Gasdermin D (GSDMD) is a key mediator of pyroptosis, a pro-inflammatory form of cell death occurring downstream of inflammasome activation as part of the innate immune defence. Upon cleavage by inflammatory caspases in the cytosol, the N-terminal domain of GSDMD forms pores in the plasma membrane resulting in cytokine release and eventually cell death. Targeting GSDMD is an attractive way to dampen inflammation. In this study, six GSDMD targeting nanobodies are characterized in terms of their binding affinity, stability, and effect on GSDMD pore formation. Three of the nanobodies inhibit GSDMD pore formation in a liposome leakage assay, although caspase cleavage was not perturbed. We determine the crystal structure of human GSDMD in complex with two nanobodies at 1.9 Å resolution, providing detailed insights into the GSDMD–nanobody interactions and epitope binding. The pore formation is sterically blocked by one of the nanobodies that binds to the oligomerization interface of the N-terminal domain in the multi-subunit pore assembly. Our biochemical and structural findings provide tools for studying inflammasome biology and build a framework for the design of GSDMD targeting drugs.

Mangostenone Bioactive Compound from Garcinia mangostana L. as Antiviral Agent via Dual Inhibitors Against E6 HPV 16/18 Oncoprotein through Computational Simulation

HPV is a DNA virus from Papillomaviridae about 170 types have been identified and most of these viruses can triger cervial cancer disease. Types of HPV that can trigger cervical cancer consist of HPV-16 and HPV-18 with around 70% of cases, HPV-6 and HPV-11 only trigger genital warts. Types of HPV-16 and HPV-18 are high risk in triggering cervical cancer. High risk HPV types have the ability to interfere with the performance of tumor suppressors in cells through oncoprotein activity. E6 is a crucial oncoprotein because it allows degradation of tumor suppressors in host cells, E6 can be a major target in antiviral drug design. Inhibition of the E6 domain by antiviral candidate compounds is an important part of preventing the formation of the E6-p53 complex and preventing cancer development. Garcinia mangostana L. (Mangosteen) is a traditional medicine for treating bacterial, viral, fungal infections, as an antioxidant, and for degenerative diseases. This study aims to explore the potential of mangostenone compounds from Garcinia mangostana L. as HPV antivirals through inhibition of the E6 oncoprotein on HPV-16 and HPV-18 through in silico study. In silico analysis methods such as drug likeness, antiviral probability, docking simulation, chemical interaction analysis, and molecular visualization were used in this study to reveal HPV antiviral candidates from Mangostenone derivatives. Mangostenone derivative compounds from Garcinia mangostana L. can be antiviral candidates for HPV through a dual inhibitory mechanism by Mangostenone A. These compounds have strong activity through more negative binding affinity values and weak bonds such as hydrogen and hydrophobic bonds compared to other mangostenone derivative compounds.

Lipoprotein lipids and apolipoproteins in primary immune thrombocytopenia: Results from a clinical characteristics and causal relationship verification, potential drug target identification by Mendelian randomization analyses.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disease. Cellular and systemic lipid metabolism plays a significant role in the regulation of immune cell activities. However, the role of lipoprotein lipids and apolipoproteins in ITP remains elusive. The automatic biochemistry analyser was used to measure the levels of serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoA-I), apoB, apoE and lipoprotein a [LP(a)]. Genetic variants strongly associated with circulating lipoprotein lipids and apolipoproteins (LDL-C, apoB, TG, HDL-C and apoA-I) were extracted to perform Mendelian randomization (MR) analyses. Finally, drug-target MR and passive ITP mice model was used to investigate the potential druggable targets of ITP. Levels of HDL-C, apoA-I, decreased and LP(a) increased in ITP patients compared with healthy controls. Low HDL-C was causally associated with ITP susceptibility. Through drug-target MR and animal modelling, ABCA1 was identified as a potential target to design drugs for ITP. Our study found that lipid metabolism is related to ITP. The causative association between HDL-C and the risk of ITP was also established. The study provided new evidence of the aetiology of ITP. ABCA1 might be a potential drug target for ITP.

A Functional Genomic Screen to Identify Novel Genes Involved in Multiple Myeloma Tumour Development

Multiple myeloma (MM) is an incurable malignancy characterised by uncontrolled proliferation of plasma cells (PCs) in the bone marrow (BM). MM is a genetically heterogeneous disease with each patient's PCs harbouring unique genetic mutations, however the development of MM tumours is not only dependent on the underlying genetics but also on the selective pressures applied by the BM microenvironment. Hence, we hypothesise that identifying the dependencies which promote MM cell outgrowth in the BM microenvironment will allow for the identification of new drug targets. This project aims to use an in vivo functional screen, combining CRISPR-Cas9 gene editing, using a single guide RNA (sgRNA) library targeting thousands of genes, with a murine model of MM, to identify novel genes involved in MM tumour development. The Bassik human apoptosis and cancer CRISPR knockout library (Addgene #101926) was used to transduce MM cells with 31,324 unique sgRNAs targeting 3,015 genes and 1,500 control regions. The human MM cell line OPM2 was transduced with the Cas9 transgene (FuCas9GFP), followed by the sgRNA expression vector (pMCB320) generating OPM2-Cas9-sgRNA cells. These cells were then injected (5x10 5 cells/mouse, n=8 mice + 1 tumour-naive control) into the tibiae of immunodeficient NOD.Cg-Prkdc scid Il2rg tm1Wjl/SzJ (NSG) mice. Four weeks post-injection, the primary tumour within the injected tibia was isolated and sgRNA frequencies were assessed via next generation sequencing and compared with that of the initial library and the injected cells using the MAGeCK algorithm. To identify genes that play roles in MM tumour formation in patients, we assessed the expression of our identified genes in CD138-selected BM PCs from newly diagnosed MM patients (n=155) when compared with normal PCs from healthy donors (n=5) in microarray dataset E-MTAB-363 (ArrayExpress). Furthermore, to identify potential pathway involvement of our identified genes we performed gene set enrichment analysis (GSEA) using the University of California San Diego and Broad Institute developed GSEA software, comparing to Hallmark Gene Sets. Comparison of the in vitro cultured cells with the initial library identified 34 genes with undetectable (p<0.05) sgRNAs post in vitro culture, suggesting these genes play key roles in the survival of OPM2 cells. This list contained a number of genes known to control MM cell survival, such as IRF4 and CCND2. Additionally, sgRNAs targeting a further 115 genes were significantly depleted (p<0.05) in vitro and were completely undetectable in vivo, suggesting that these are implicated in cell proliferation and tumour formation in vivo. This gene list contained some already known MM drivers such as MYC, KRAS and DIS3. Furthermore, we identified 28 genes where CRISPR knockout had no significant effect in vitro but were critical dependencies for tumour development, with sgRNAs being undetectable (p<0.05) in vivo. This list contained genes known to be important for MM and BM stromal cell interaction such as ALCAM and SPP1. Notably, 25% of the genes identified as critical dependencies for OPM2 cells in vitro and/or in vivo are significantly upregulated >2-fold (p<0.05; limma) in MM patient PCs when compared with normal PCs (E-MTAB-363), suggesting these may play a role in tumour development in patients. Gene set enrichment analysis revealed that the genes that were essential in vitro were predominantly involved in cell survival related pathways such as cell cycle regulation (G2M checkpoint) and DNA replication/repair; however, the uniquely in vivo genes are predominantly involved in cell metabolism pathways such as MTORC1 signalling, oxidative phosphorylation and glycolysis. Overall, this study has successfully used a functional genetic screen to identify novel gene dependencies required for in vitro MM cell growth and in vivo MM tumour development. A secondary genetic screen will be performed in vivo to validate and refine this list of candidate genes. These validated genes will be further investigated using patient derived data sets to determine if they represent common vulnerabilities in patients and in multiple MM cell lines to investigate their specific mechanistic and pathway involvement. Identification and understanding the driving dependencies which promote MM cell outgrowth in the BM microenvironment will allow for the identification and development of new drug targets to improve patient outcomes.

Inflammatory Response to Intravenously Administered Lipopolysaccharide (LPS) in the Bone Marrow of Healthy Volunteers As a Model for Early Phase Drug Development

Introduction Inflammatory responses in the peripheral and central hematological compartments are important for identification of new biomarkers and drug targets in early phase drug development. There is an increasing interest to investigate the modulation of the inflammatory response upon experimental drug exposure using cellular and molecular analyses, specifically in the central compartment. The lipopolysaccharide (LPS) challenge model allows to study TLR4-mediated local and systemic inflammation and therefore the effects of anti-inflammatory compounds. In addition, there is an interest in the effects of TLR4 agonists on early immune cell populations. The aim of the current study is to investigate the effects of intravenous (i.v.) LPS on the peripheral blood and central bone marrow compartment and to perform a proof-of-methodology study for bone marrow analysis in early phase drug research in healthy volunteers. Methods This was an open-label study in 13 healthy young male subjects, aged 18 - 35 years (inclusive). The study was conducted at the Centre for Human Drug Research, Leiden, The Netherlands. The study protocol was approved by the Medical Ethics Committee. All subjects provided written informed consent prior to any study activity. Subjects were divided into control group (n=3), 1 ng/kg i.v. LPS group (n=5) and 2 ng/kg i.v. LPS group (n=5). Blood and bone marrow samples were collected at baseline and at 4h post-dose timepoints. Primary objective was to evaluate the safety and tolerability of bone marrow sampling in healthy volunteers, by continuous safety monitoring, visual analogue scale (VAS) for pain and subject experience questionnaires. To investigate the pharmacodynamic (PD) effects of i.v. LPS challenge on the bone marrow and whole-blood immune compartments, flow cytometry, cytokine analysis and RNA sequencing was performed. Results All 13 subjects completed the study. Adverse events were mild and reversible, and included puncture site pain related to bone marrow puncture, and flu-like symptoms related to LPS administration. Twelve subjects (92%) indicated they would undergo the procedure again in the future. There was an observable difference in VAS pain score between the baseline bone marrow puncture and LPS treatment groups, but no difference between 1 ng/kg and 2 ng/kg groups. Phenotypic changes of inflammatory signatures in peripheral blood and bone marrow cell populations upon LPS administration were detected by multi-parameter flow cytometry. An LPS-induced inflammatory cytokine profile was present. RNA sequencing revealed differential gene expression in inflammatory gene signatures between peripheral blood mononuclear cells and bone marrow cells. Conclusion We demonstrated that bone marrow analysis is feasible and well-tolerable in early phase studies with healthy volunteers and can be used to assess inflammatory responses in the central hematological compartment upon systemic LPS challenge. Access to bone marrow can provide important novel insights into drug effects on the hematopoiesis and differentiation of immune cells and provides added value to early drug development.

Ceftriaxone exerts antitumor effects in MYCN-driven retinoblastoma and neuroblastoma by targeting DDX3X for translation repression.

MYCN proto-oncogene, bHLH transcription factor (MYCN) amplification is associated with aggressive retinoblastoma (RB) and neuroblastoma (NB) cancer recurrence that is resistant to chemotherapies. Therefore, there is an urgent need to identify new therapeutic tools. This study aimed to evaluate the potential repurposing of ceftriaxone for the treatment of MYCN-amplified RB and NB, based on the clinical observations that the drug was serendipitously found to decrease the volume of the MYCN-driven RB subtype. Using patient-derived tumor organoids and tumor cell lines, we demonstrated that ceftriaxone is a potent and selective growth inhibitor targeting MYCN-driven RB and NB cells. Profiling of drug-induced transcriptomic changes, cell-cycle progression, and apoptotic death indicated cell-cycle arrest and death of drug-treated MYCN-amplified tumor cells. Drug target identification, using an affinity-based proteomic and molecular docking approach, and functional studies of the target proteins revealed that ceftriaxone targeted DEAD-box helicase 3 X-linked (DDX3X), thereby inhibiting translation in MYCN-amplified tumors but not in MYCN-nonamplified cells. The data suggest the feasibility of repurposing ceftriaxone as an anticancer drug and provide insights into the mechanism of drug action, highlighting DDX3X as a potential target for treating MYCN-driven tumors.

Searching for significant reactions and subprocesses in models of biological systems based on Petri nets

The primary aim of this research was to propose algorithms enabling the identification of significant reactions and subprocesses within models of biological systems constructed using classical Petri nets. These solutions allow to performance of two analysis methods: an importance analysis for identifying individual reactions critical to the functioning of the model and an occurrence analysis for finding essential subprocesses. To demonstrate the utility of these methods, analyses of an example model have been performed. In this case, it was a model related to the DNA damage response mechanism. It is worth noting that the proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods represent an extension of classical Petri net-based analyses. Their utility lies in their potential to enhance our comprehension of the biological phenomena under investigation. Furthermore, they can lead to the development of more effective medical therapies, as they can aid in the identification of potential molecular targets for drugs.

Microcolin H, a novel autophagy inducer, exerts potent antitumour activity by targeting PITPα/β

The identification of effective drug targets and the development of bioactive molecules are areas of high need in cancer therapy. The phosphatidylinositol transfer protein alpha/beta isoform (PITPα/β) has been reported to play an essential role in integrating phosphoinositide trafficking and lipid metabolism in diverse cellular processes but remains unexplored as a potential target for cancer treatment. Herein, data analysis of clinical cancer samples revealed that PITPα/β expression is closely correlated with the poor prognosis. Target identification by chemical proteomic methods revealed that microcolin H, a naturally occurring marine lipopeptide, directly binds PITPα/β and displays antiproliferative activity on different types of tumour cell lines. Furthermore, we identified that microcolin H treatment increased the conversion of LC3I to LC3II, accompanied by a reduction of the level of p62 in cancer cells, leading to autophagic cell death. Moreover, microcolin H showed preeminent antitumour efficacy in nude mouse subcutaneous tumour models with low toxicity. Our discoveries revealed that by targeting PITPα/β, microcolin H induced autophagic cell death in tumours with efficient anti-proliferating activity, which sheds light on PITPα/β as a promising therapeutic target for cancer treatment.

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel

Background The rapid adoption of next-generation sequencing in clinical oncology has enabled detection of molecular biomarkers which are shared between multiple tumour types. Intra-tumour heterogeneity is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the tumour-related copy number variants (CNVs), as key regulators of cancer origination, development, and progression, across various types of cancers are poorly understood. Methods We performed pan-cancer CNV analysis of cancer-related genes in 15 types of cancers including 1438 cancerous patients by next-generation sequencing using a commercially available pan-cancer panel (Onco PanScan™). Downstream bioinformatics analysis was performed in order to detect CNVs, cluster analysis of the found CNVs, and comparison of the frequency of gained CNVs between different types of cancers. LASSO analysis was used for identification of the most important CNVs. Results We also identified 523 CNVs among which 16 CNVs were common while 22 CNVs were caner-specific CNVs. Meanwhile, FAM58A was most commonly found in all studied cancers in this study and significant differences were found in FAM58A between female and male patients (p = .001). Common CNVs, such as FOXA1, NFKBIA, HEY1, MECOM, CHD7, AGO2, were mutated in 6.79%, 8.45%, 7.51%, 6.43%, 7.59%, 8.16% of tumours, while most of these mutations have proven roles in positive regulation of transcription from RNA polymerase II promoter. 11 features including sex, DIS3, EPHB1, ERBB2, FLT1, HCK, KEAP1, MYD88, PARP3, TBX3, and TOP2A were found as the key features for classification of cancers using CNVs. Conclusion The 16 common CNVs between cancers can be used to identify the target of pan-cancer drug design and targeted therapies. Additionally, 22 caner-specific CNVs can be used as unique diagnostic markers for each cancer type.

Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer.

The precise mechanism of resistance to anti-cancer drugs such as platinum drugs is not fully revealed. To reveal the mechanism of drug resistance, the molecular networks of anti-cancer drugs such as cisplatin, carboplatin, oxaliplatin, and arsenic trioxide were analyzed in several types of cancers. Since diffuse-type stomach adenocarcinoma, which has epithelial-mesenchymal transition (EMT)-like characteristics, is more malignant than intestinal-type stomach adenocarcinoma, the gene expression and molecular networks in diffuse- and intestinal-type stomach adenocarcinomas were analyzed. Analysis of carboplatin revealed the causal network in diffuse large B-cell lymphoma. The upstream regulators of the molecular networks of cisplatin-treated lung adenocarcinoma included the anti-cancer drug trichostatin A (TSA), a histone deacetylase inhibitor. The upstream regulator analysis of cisplatin revealed an increase in FAS, BTG2, SESN1, and CDKN1A, and the involvement of the tumor microenvironment pathway. The molecular networks were predicted to interact with several microRNAs, which may contribute to the identification of new drug targets for drug-resistant cancer. Analysis of oxaliplatin, a platinum drug, revealed that the SPINK1 pancreatic cancer pathway is inactivated in ischemic cardiomyopathy. The study showed the importance of the molecular networks of anti-cancer drugs and tumor microenvironment in the treatment of cancer resistant to anti-cancer drugs.