Identify Drug Targets Research Articles

Deregulated Metabolic Pathways in Ovarian Cancer: Cause and Consequence.

Ovarian cancers are tumors that originate from the different cells of the ovary and account for almost 4% of all the cancers in women globally. More than 30 types of tumors have been identified based on the cellular origins. Epithelial ovarian cancer (EOC) is the most common and lethal type of ovarian cancer which can be further divided into high-grade serous, low-grade serous, endometrioid, clear cell, and mucinous carcinoma. Ovarian carcinogenesis has been long attributed to endometriosis which is a chronic inflammation of the reproductive tract leading to progressive accumulation of mutations. Due to the advent of multi-omics datasets, the consequences of somatic mutations and their role in altered tumor metabolism has been well elucidated. Several oncogenes and tumor suppressor genes have been implicated in the progression of ovarian cancer. In this review, we highlight the genetic alterations undergone by the key oncogenes and tumor suppressor genes responsible for the development of ovarian cancer. We also summarize the role of these oncogenes and tumor suppressor genes and their association with a deregulated network of fatty acid, glycolysis, tricarboxylic acid and amino acid metabolism in ovarian cancers. Identification of genomic and metabolic circuits will be useful in clinical stratification of patients with complex etiologies and in identifying drug targets for personalized therapies against cancer.

Predicting activatory and inhibitory drug-target interactions based on structural compound representations and genetically perturbed transcriptomes.

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.

Abstract 5885: A pathway-centric approach to characterising tumour heterogeneity and cell diversity across multiple cancer types

Abstract Biological tissues are composed of heterogeneous populations intricately organized in 3D architectures, yet cell type composition and spatial organization remain largely unknown for most tissue types. Single-cell sequencing analysis provides a systematic and quantitative approach to identify cell types and determine their composition in tissues. However, the spatial organization of cells and cell-cell interactions that are critical for tissue function are lost when cells are dissociated from tissue. This is especially important when working with the complex microenvironment of a tumour where such information is needed to fully understand patient prognosis. Complex diseases such as cancer can be better understood from the perspective of dysregulated pathways, rather than as a disease resulting from alterations of individual genes. Recently, spatially resolved, single-cell imaging platforms have provided a necessary solution to bridge the spatial information gap evident in previous transcriptomic methodologies. Vizgen’s MERSCOPE platform, built on multiplexed error robust in situ hybridisation (MERFISH) technology, enables the direct profiling of the spatial organisation of intact tissue with subcellular resolution. Here, we present a Pan-Cancer approach to characterise various cancers in human clinical samples with MERSCOPE. Using a 500-gene panel that includes the canonical signalling pathways of cancer, cancer type-specific genes, key immune genes, proto-oncogenes, and tumour-suppressor genes we demonstrate MERSCOPE’s ability to spatially profile gene expression across multiple tumour samples, including breast, colon, prostate, ovarian, lung, and skin cancer. To confirm the specificity and sensitivity of our pathway-focused gene panel, we assessed how tumour cell clusters in each dataset expressed different cancer type-specific markers. As expected, we found specific cancer types enriched in corresponding marker genes. To further evaluate how individual cell types in each tumour are dysregulated by cancer, we compared non-tumour cell clusters to similarly annotated clusters derived from single-cell RNA-sequencing data of normal tissue. Notably, the non-tumour cells in these datasets exhibit higher expression of stress and inflammation pathways, while immune cell clusters demonstrate higher immune activity and higher immune exhaustion compared to those in normal tissue. These results demonstrate the power of the MERSCOPE platform to generate individualised, accurate cell atlases from patient-derived tumours and to enable further insights into the relationship between genomic profiles, dysregulated pathways, and disease phenotype. Such network-centric approaches are critical for identifying genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. Citation Format: Leiam Colbert, Benjamin Patterson, Cheng Yi Chen, Nicolas Fernandez, Jiang He. A pathway-centric approach to characterising tumour heterogeneity and cell diversity across multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5885.

Abstract 3149: Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses

Abstract Cancer metabolism has been extensively explored to identify biomarkers and novel drug targets for effective cancer treatment. In particular, it is expected that metabolism-derived data somehow reflects unique features associated with a cancer patient’s prognosis. Here, we introduce a computational framework that predicts metabolic drug targets by considering metabolites associated with poor prognosis. For inputs, the computational framework requires genome-scale metabolic models (GEMs) and survival data that represent each of cancer patients with diverse prognoses. GEM is a computational model that allows predicting fluxes of entire metabolic reactions in a patient-specific manner. By using this computational framework, a risk score, which reflects the prognosis of cancer patients, was first established on the basis of metabolites significantly associated with survival. Finally, metabolic reaction targets were predicted that could reduce the risk score (i.e., improving the prognosis) upon their knockdown in silico. In this study, this framework was applied to bladder cancer patients as a proof-of-concept demonstration, and predicted 13 metabolic reaction targets. These metabolic reaction targets were expected to be also effective to bladder cancer patients with poor prognosis. The computational framework developed in this study is distinct from previous drug targeting methods because it also considers unique metabolic features associated with cancer patients with poor prognosis in order to identify drug targets that are effective to both low-risk and high-risk groups of cancer patients. Citation Format: Sang Mi Lee, GaRyoung Lee, Hyun Uk Kim. Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3149.

Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus

ObjectivesWe aimed at investigating the whole-blood transcriptome, expression quantitative trait loci (eQTLs), and levels of selected serological markers in patients with SLE versus healthy controls (HC) to gain insight into pathogenesis and identify drug targets. MethodsWe analyzed differentially expressed genes (DEGs) and dysregulated gene modules in a cohort of 350 SLE patients and 497 HC from the European PRECISESADS project (NTC02890121), split into a discovery (60%) and a replication (40%) set. Replicated DEGs qualified for eQTL, pathway enrichment, regulatory network, and druggability analysis. For validation purposes, a separate gene module analysis was performed in an independent cohort (GSE88887). ResultsAnalysis of 521 replicated DEGs identified multiple enriched interferon signaling pathways through Reactome. Gene module analysis yielded 18 replicated gene modules in SLE patients, including 11 gene modules that were validated in GSE88887. Three distinct gene module clusters were defined i.e., “interferon/plasma cells”, “inflammation”, and “lymphocyte signaling”. Predominant downregulation of the lymphocyte signaling cluster denoted renal activity. By contrast, upregulation of interferon-related genes indicated hematological activity and vasculitis. Druggability analysis revealed several potential drugs interfering with dysregulated genes within the “interferon” and “PLK1 signaling events” modules. STAT1 was identified as the chief regulator in the most enriched signaling molecule network. Drugs annotated to 15 DEGs associated with cis-eQTLs included bortezomib for its ability to modulate CTSL activity. Belimumab was annotated to TNFSF13B (BAFF) and daratumumab was annotated to CD38 among the remaining replicated DEGs. ConclusionsModulation of interferon, STAT1, PLK1, B and plasma cell signatures showed promise as viable approaches to treat SLE, pointing to their importance in SLE pathogenesis.

Acoustic streaming enabled moderate swimming exercise reduces neurodegeneration in C. elegans.

Regular physical exercise has been shown to delay and alleviate neurodegenerative diseases. Yet, optimum physical exercise conditions that provide neuronal protection and exercise-related factors remain poorly understood. Here, we create an Acoustic Gym on a chip through the surface acoustic wave (SAW) microfluidic technology to precisely control the duration and intensity of swimming exercise of model organisms. We find that precisely dosed swimming exercise enabled by acoustic streaming decreases neuronal loss in two different neurodegenerative disease models of Caenorhabditis elegans, a Parkinson's disease model and a tauopathy model. These findings highlight the importance of optimum exercise conditions for effective neuronal protection, a key characteristic of healthy aging in the elderly population. This SAW device also paves avenues for screening for compounds that can enhance or replace the beneficial effects of exercise and for identifying drug targets for treating neurodegenerative diseases.

SSELM-neg: spherical search-based extreme learning machine for drug–target interaction prediction

BackgroundThe experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug–target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed.MethodsIn this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine.ResultsThe results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively.ConclusionThe screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Cell-Free Synthesis Goes Electric: Dual Optical and Electronic Biosensor via Direct Channel Integration into a Supported Membrane Electrode.

Assembling transmembrane proteins on organic electronic materials is one promising approach to couple biological functions to electrical readouts. A biosensing device produced in such a way would enable both the monitoring and regulation of physiological processes and the development of new analytical tools to identify drug targets and new protein functionalities. While transmembrane proteins can be interfaced with bioelectronics through supported lipid bilayers (SLBs), incorporating functional and oriented transmembrane proteins into these structures remains challenging. Here, we demonstrate that cell-free expression systems allow for the one-step integration of an ion channel into SLBs assembled on an organic conducting polymer, poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS). Using the large conductance mechanosensitive channel (MscL) as a model ion channel, we demonstrate that MscL adopts the correct orientation, remains mobile in the SLB, and is active on the polyelectrolyte surface using optical and electrical readouts. This work serves as an important illustration of a rapidly assembled bioelectronic platform with a diverse array of downstream applications, including electrochemical sensing, physiological regulation, and screening of transmembrane protein modulators.

Abstract A020: Gene expression programs of cancer spread are targets for anti-metastasis treatments

Abstract The lack of specific anti-metastasis drugs is due to an incomplete understanding of the molecular mechanisms underlying this process, and hence a lack of potential drug targets. In the past, we have defined the genetic basis of cancer metastasis as aberrant expression or splicing by a unique set of developmentally non-essential stress response genes that physiologically mediate the homing of immune system cells. Thus, the genetic underpinning of metastasis is distinguishable from the genetic underpinnings for uncontrolled growth or immortalization (in contrast to oncogenes or tumor suppressor genes, metastasis genes are not mutated; when mutations do arise, they are typically located in the regulatory elements). More recently, we have expanded our research to gene expression programs with the goal to identify drug targets for combination therapies. Utilizing the gene expression profiles from 653 GEO datasets, we investigated the signatures by diverse cancers in various metastatic sites for common features and for site-specific characteristics. We corroborated the meta-analysis in a murine model. Metastases are generally characterized by a core program of gene expression that induces the oxidative metabolism, activates vascularization/tissue remodeling, silences extracellular matrix interactions, and alters ion homeostasis. This program distinguishes metastases from their originating primary tumors as well as from their target host tissues. For site specificity, the release from the primary tumor is associated with a suppression of functions that are important for the identity of the organ of origin, such as a down-regulation of steroid hormone responsiveness in the disseminated foci derived from prostate cancer. Metastases adjust to their target microenvironment by upregulating—even overexpressing—genes and genetic programs that are characteristic of that organ. Further, alterations in RNA and protein processing as well as immune deviation are common. Although the drug treatment of malignant tumors has made impressive progress in recent years, the successes have been achieved mostly against the primary tumors. Once a cancer has disseminated, the prognosis worsens dramatically. There is a paucity of suitable anti-metastasis drugs. Conceivably, chemotherapy of disseminated cancer might be more efficacious if selected to match the genetic makeup of the metastases rather than the organ of origin by the primary tumor. In this regard, new potential has opened up by our finding that the gene expression of metastases is characterized by a four-pronged core program, which is common to all metastases and distinguishes them from the primary tumor as well as from the target site. Drug combinations that suppress the genetic core program of metastasis may fill an important void in cancer chemotherapy. Our ongoing research has identified lead compounds that may lay the foundation for anti-metastasis combination chemotherapies. Citation Format: Gulimerouzi F. Fnu, Franz Hartung, Georg F. Weber. Gene expression programs of cancer spread are targets for anti-metastasis treatments [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A020.

From Natural Behavior to Drug Screening: Invertebrates as Models to Study Mechanisms Associated with Alcohol Use Disorders.

Humans consume ethanol-containing beverages, which may cause an uncontrollable or difficult-to-control intake of ethanol-containing liquids and may result in alcohol use disorders. How the transition at the molecular level from "normal" ethanol-associated behaviors to addictive behaviors occurs is still unknown. One problem is that the components contributing to normal ethanol intake and their underlying molecular adaptations, especially in neurons that regulate behavior, are not clear. The fruit fly Drosophila melanogaster and the earthworm Caenorhabditis elegans show behavioral similarities to humans such as signs of intoxication, tolerance, and withdrawal. Underlying the phenotypic similarities, invertebrates and vertebrates share mechanistic similarities. For example in Drosophila melanogaster, the dopaminergic neurotransmitter system regulates the positive reinforcing properties of ethanol and in Caenorhabditis elegans, serotonergic neurons regulate feeding behavior. Since these mechanisms are fundamental molecular mechanisms and are highly conserved, invertebrates are good models for uncovering the basic principles of neuronal adaptation underlying the behavioral response to ethanol. This review will focus on the following aspects that might shed light on the mechanisms underlying normal ethanol-associated behaviors. First, the current status of what is required at the behavioral and cellular level to respond to naturally occurring levels of ethanol is summarized. Low levels of ethanol delay the development and activate compensatory mechanisms that in turn might be beneficial for some aspects of the animal's physiology. Repeated exposure to ethanol however might change brain structures involved in mediating learning and memory processes. The smell of ethanol is already a key component in the environment that is able to elicit behavioral changes and molecular programs. Minimal networks have been identified that regulate normal ethanol consumption. Other environmental factors that influence ethanol-induced behaviors include the diet, dietary supplements, and the microbiome. Second, the molecular mechanisms underlying neuronal adaptation to the cellular stressor ethanol are discussed. Components of the heat shock and oxidative stress pathways regulate adaptive responses to low levels of ethanol and in turn change behavior. The adaptive potential of the brain cells is challenged when the organism encounters additional cellular stressors caused by aging, endosymbionts or environmental toxins or excessive ethanol intake. Finally, to underline the conserved nature of these mechanisms between invertebrates and higher organisms, recent approaches to identify drug targets for ethanol-induced behaviors are provided. Already approved drugs regulate ethanol-induced behaviors and they do so in part by interfering with cellular stress pathways. In addition, invertebrates have been used to identify new compounds targeting molecules involved in the regulation in ethanol withdrawal-like symptoms. This review primarily highlights the advances of the last 5years concerning Drosophila melanogaster, but also provides intriguing examples of Caenorhabditis elegans and Apis mellifera in support.

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.

Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.

Abstract P003: Computational identification of ovarian cancer candidate genes with mutated protein structures caused by missense variants

Abstract Ovarian cancer is one of the leading killers in the US for women. The American Cancer Society (ACR) ranks ovarian cancer as the fifth leading cause of cancer deaths among women. This cancer is hereditary, with higher risks if the patient has more relatives who have had ovarian cancer before. The Cancer Genome Atlas (TCGA) is a joint-effort project by NCI and NHGRI that generates genomic data for the cancer patients and matches normal data samples with these patients. Missense mutations are single nucleotide polymorphisms (SNPs) in which the amino acids are changed. A missense variant could disrupt protein structures; thus the function of the disrupted protein is altered. In this study, we investigated genes with high expression levels in the TCGA RNA-seq dataset by characterizing protein structure change patterns of candidate genes due to missense mutations in association with ovarian cancer. We also analyzed the association of these genes with ovarian cancer prognosis. We aim to select genes that have pathogenic consequences. We selected 44 genes with the highest expression values based on TCGA ovarian cancer RNA-seq data. We then retrieved protein sequences from the Ensembl BioMart database and missense mutation annotation information from cBioPortal for 44 input genes. Specifically, the missense mutation dataset, which is projected by the Pan-Cancer Analysis of Whole Genomes, was retrieved from cBioPortal. We then used the Phyre2 database to predict the 3-D structures of the wild and mutated type genes to identify structural changes. However, some genes were not suitable for Phyre2 analysis. For example, RPL41 was not long enough for analysis with Phyre2 as it only had 25 amino acids whereas Phyre2 required 30. TMSL3 (TMSB4XP8) is a pseudogene not found in both NCBI and AlphaFold. We then searched DrugBank for 42 genes with the predicted protein structure changes by Phyre2 to identify drug targets. We also performed Kaplan-Meier survival analysis to further evaluate the clinical consequences of the candidate genes. Our preliminary results show 26 genes that have drug targets and several of them have been identified to either decrease or increase the survival rate when the expression is high in ovarian cancer patients. This indicates that the highly expressed genes with protein structure changes due to missense mutations could be potential biomarkers for treating ovarian cancer. The functional annotation study could further validate ovarian cancer association for our prioritized gene list. In the future, we also plan to compare prediction results between genders and race groups. Our results could guide medical researchers in prioritizing drug targets and developing better treatment strategies for ovarian cancer. Our approach in prioritizing candidate genes is applicable to other cancers as well, and we plan to develop bioinformatic pipelines for automatically predicting candidate genes with significant effect on patient survival for any cancer type. Citation Format: Ian W. Hou, Yongsheng Bai. Computational identification of ovarian cancer candidate genes with mutated protein structures caused by missense variants. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P003.

Anti-Vibrio parahaemolyticus compounds from Streptomyces parvus based on Pan-genome and subtractive proteomics.

Vibrio parahaemolyticus is a foodborne pathogen commonly found in seafood, and drug resistance poses significant challenges to its control. This study aimed to identify novel drug targets for antibacterial drug discovery. To identify drug targets, we performed a pan-genome analysis on 58 strains of V. parahaemolyticus genomes to obtain core genes. Subsequently, subtractive proteomics and physiochemical checks were conducted on the core proteins to identify potential therapeutic targets. Molecular docking was then employed to screen for anti-V. parahaemolyticus compounds using a in-house compound library of Streptomyces parvus, chosen based on binding energy. The anti-V. parahaemolyticus efficacy of the identified compounds was further validated through a series of experimental tests. Pangenome analysis of 58 V. parahaemolyticus genomes revealed that there were 1,392 core genes. After Subtractive proteomics and physiochemical checks, Flagellar motor switch protein FliN was selected as a therapeutic target against V. parahaemolyticus. FliN was modeled and docked with Streptomyces parvus source compounds, and Actinomycin D was identified as a potential anti-V. parahaemolyticus agent with a strong binding energy. Experimental verification confirmed its effectiveness in killing V. parahaemolyticus and significantly inhibiting biofilm formation and motility. This study is the first to use pan-genome and subtractive proteomics to identify new antimicrobial targets for V. parahaemolyticus and to identify the anti-V. parahaemolyticus effect of Actinomycin D. These findings suggest potential avenues for the development of new antibacterial drugs to control V. parahaemolyticus infections.

UCSF researchers launch biotech Rezo

Scientists at the Quantitative Biosciences Institute at the University of California, San Francisco (UCSF) have launched Rezo Therapeutics with $78 million in series A funding. Drawing on close connections to its academic founders, the start-up wants to pioneer a computational approach that applies multiple technologies—proteomics, genetics, structural biology, chemistry, and bioinformatics—in parallel to probe disease mechanisms and identify drug targets. Rezo is advancing several therapeutic programs with an initial focus on solid tumor oncology.

An Epigenome‐wide association study of psychosis in Alzheimer's disease dorsolateral prefrontal cortex

AbstractBackgroundPsychosis is a debilitating syndrome occurring in 40‐60% of people with Alzheimer’s disease (AD) and corresponds with a more severe disease course. Evidence suggests that psychosis in AD (AD+P) is associated with a distinct profile of neurobiological changes, but little is known about the molecular processes driving etiology. In this study we performed an epigenome‐wide association study (EWAS) to investigate DNA methylation associated with AD+P in the dorsolateral prefrontal cortex of 192 post‐mortem brain samples.MethodBrain samples with corresponding in life neuropsychiatric assessments were obtained from the University of Pittsburgh Alzheimer’s disease Research Center (PITT‐ADRC). AD pathology was assessed and classified using CERAD neuritic plaque density score, Braak neurofibrillary tangle stages and NIA‐RI criteria. The presence or absence of delusions and hallucinations was scored on the CERAD Behavioral Rating Scale. DNA was extracted, bisulfite treated and then profiled on the Illumina Methylation EPIC Array.ResultAfter data processing and quality control we used linear regression models to compare 152 AD+P samples to 40 AD samples without psychosis (AD‐P). A regional analysis of spatially correlated p‐values highlighted four differentially methylated regions (DMRs) associated with psychosis, which are being replicated in other data sets and validated for cell specificity using fluorescence activated nuclei sorting (FANS).ConclusionThe development of effective therapies for AD+P is an urgent priority. To address this we have collated a well powered study cohort to interrogate the epigenetic basis of AD+P, finding significant methylomic variation. This variation provides an insight into potential mechanisms and biomarkers which will help to identify drug targets and enable better treatment with existing medications.

Overview of the Advancement in the Drug Discovery and Contribution in the Drug Development Process

The drug discovery and development of a new chemical entity from original idea to the regulatory approval for launch into the market is a very long and complex journey. The process is complex and can take between 10-15 years, with a significant financial implication of cost of about $1 billion and more. The bioactive molecules as lead compounds may be derived from several sources such as plant, animal sources or as synthetic compounds. The odyssey of drug discovery/development may take several years elucidate valid evidence-based information from data mining, before a target selection for the progression of the drug discovery platform. The successful selection of identified drug target is an important objective for many Pharmaceutical industries before laying down a platform for research and development on hit target identification of compounds with promising biological and pharmacological activities with the potential to produce a good drug. The objective of this review paper is to develop a comprehensive review on the odyssey of drug discovery and development from the research and development standpoint with special bias within the frame of the preclinical level, hit and lead compound identification, optimization and validation, using different high throughput assay development platform. The consideration of a potential active pharmaceutical product necessitates a good test battery for hit identification, lead optimization a good process for candidate f selection of a candidate compound for drug development and putting into consideration the regulatory compliance guideline for new drug approval before the attain the market and subsequent post marketing surveillance process.

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology.

Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.

IKZF1 Deletions Induce Resistance to AraC in BCP-ALL Reversible By CDK8/19 Inhibition

Background Deletions and mutations affecting lymphoid transcription factor IKZF1 occur in about 10-15% of pediatric patients and up to 50% of adults with B cell precursor acute lymphoblastic leukemia (BCP-ALL) and predict a poor outcome. We have shown previously that loss of IKZF1 function compromises the therapeutic efficacy of glucocorticoids (GCs). Here, we investigated whether loss of IKZF1 function also affects therapy response to other chemotherapeutic agents used in the treatment of ALL. Aim To develop tailored treatment protocols for IKZF1 deficient leukemia Results We used CRISPR-Cas9 to target the IKZF1 locus in SEM pro-B ALL cells and evaluated the response to standard chemotherapy used in the treatment of ALL. We observed that IKZF1-/- SEM cells show a markedly reduced response to the pyrimidine analogue cytarabine (AraC) relative to IKZF1 wild type cells, with an 8-fold increase in IC50. The same drug resistance profile was observed upon shRNA-mediated knockdown of IKZF1 as well as in B-cells isolated from 8 to 14 weeks-old Ikzf1+/- mice. Using ex vivo cultures of patient derived xenografts (PDX), we observed that IKZF1 deficient leukemic cells were significantly more resistant to AraC relative to cells wild type for IKZF1 (3-fold increase in area under the curve (AUC) (Figure A). In addition, we targeted the IKZF1 protein for proteasomal degradation using the thalomide Iberdomide in PDX cells wildtype for IKZF1, showing that this drug-induced IKZF1 loss leads to AraC resistance in 7 out of 8 PDX samples. To provide in-patient evidence for AraC resistance, we compared the reduction in tumor load between patients with leukemias wildtype for IKZF1 with those carrying IKZF1 deficiencies after treatment with an AraC containing treatment block (cumulative dose of 1200 mg/m2 AraC, 6-mercaptopurine 60 mg/m2/day p.o., for 28 days and 2 doses of 1000 mg/m2 cyclophosphamide). In the Dutch ALL10/11 protocol, this treatment block is flanked by minimal residual disease (MRD) measurements. We selected all patients with a high tumor load (MRD of 10-2) before the start of the AraC containing block and calculated the reduction in leukemia burden by comparing MRD levels before and after treatment with the AraC containing interval. This showed that the response to AraC treatment was attenuated in patients with IKZF1 deleted ALL (n=25) compared to patients WT for IKZF1 (n=35) (**p=0.0065). Because IKZF1 deficient cells did not show resistance to other compounds used in this treatment block, we attribute the reduced treatment response to increased AraC resistance. To further confirm the involvement of IKZF1 loss in AraC resistance, we performed a single mouse trial in which we compared AraC treatment response in mice transplanted with IKZF1 wildtype leukemias (n=5) with IKZF1 deficient leukemias (n=7), confirming that the latter respond more poorly to treatment. To study whether resistance in response to IKZF1 loss can be reversed and obtain more insights into the biology behind the observed AraC resistance, we performed CRISPR/Cas9 screens to identify drug targets that can restore response to AraC therapy. Briefly, IKZF1 deficient cells were transduced with a library of gRNAs targeting subset of genes (kinases and genes involved in cellular metabolism). Transduced cells were treated with a sublethal dose of AraC and the relative abundance of each gRNA before and after treatment was calculated. In addition to known modifiers of the AraC therapy response such as deoxycytidine kinase, we also identified novel targets that may be targeted to sensitize cells to cytarabine treatment. Guide RNAs targeting the mediator kinases CDK8/19 were selectively and strongly depleted during treatment, suggesting that these proteins modify response to cytarabine treatment. Indeed, small-molecule inhibitors targeting CDK8/19 kinase activity synergized with cytarabine induced killing of leukemic cells (Figure B). Conclusion Together, our results demonstrate that loss of IKZF1 function confers resistance to AraC in BCP-ALL. Moreover, this resistance can be overcome by use of small molecule inhibitors such as those targeting CDK8/19. We expect that these studies will aid in the rational design of combination therapies that will further improve treatment response in these high-risk leukemias. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Differential Affinity Chromatography Coupled to Mass Spectrometry: A Suitable Tool to Identify Common Binding Proteins of a Broad-Range Antimicrobial Peptide Derived from Leucinostatin.

Leucinostatins are antimicrobial peptides with a broad range of activities against infectious agents as well as mammalian cells. The leucinostatin-derivative peptide ZHAWOC_6027 (peptide 6027) was tested in vitro and in vivo for activity against the intracellular apicomplexan parasite Toxoplasma gondii. While highly efficacious in vitro (EC50 = 2 nM), subcutaneous application of peptide 6027 (3 mg/kg/day for 5 days) in mice experimentally infected with T. gondii oocysts exacerbated the infection, caused mild clinical signs and elevated cerebral parasite load. Peptide 6027 also impaired the proliferation and viability of mouse splenocytes, most notably LPS-stimulated B cells, in vitro. To identify common potential targets in Toxoplasma and murine splenocytes, we performed differential affinity chromatography (DAC) with cell-free extracts from T. gondii tachyzoites and mouse spleens using peptide 6027 or an ineffective analogue (peptide 21,358) coupled to N-hydroxy-succinimide sepharose, followed by mass spectrometry. Proteins specifically binding to peptide 6027 were identified in eluates from the peptide 6027 column but not in peptide 21,358 nor the mock column eluates. In T. gondii eluates, 269 proteins binding specifically to peptide 6027 were identified, while in eluates from mouse spleen extracts 645 proteins specifically binding to this peptide were detected. Both datasets contained proteins involved in mitochondrial energy metabolism and in protein processing and secretion. These results suggest that peptide 6027 interacts with common targets in eukaryotes involved in essential pathways. Since this methodology can be applied to various compounds as well as target cell lines or organs, DAC combined with mass spectrometry and proteomic analysis should be considered a smart and 3R-relevant way to identify drug targets in pathogens and hosts, thereby eliminating compounds with potential side effects before performing tedious and costly safety and efficacy assessments in animals or humans.

Plant-derived natural products for drug discovery: current approaches and prospects.

Nature has abundant source of drugs that need to be identified/purified for use as essential biologics, either individually or in combination in the modern medical field. These drugs are divided into small bio-molecules, plant-made biologics, and a recently introduced third category known as phytopharmaceutical drugs. The development of phytopharmaceutical medicines is based on the ethnopharmacological approach, which relies on the traditional medicine system. The concept of ‘one-disease one-target drug’ is becoming less popular, and the use of plant extracts, fractions, and molecules is the new paradigm that holds promising scope to formulate appropriate drugs. This led to discovering a new concept known as polypharmacology, where natural products from varying sources can engage with multiple human physiology targets. This article summarizes different approaches for phytopharmaceutical drug development and discusses the progress in systems biology and computational tools for identifying drug targets. We review the existing drug delivery methods to facilitate the efficient delivery of drugs to the targets. In addition, we describe different analytical techniques for the authentication and fingerprinting of plant materials. Finally, we highlight the role of biopharming in developing plant-based biologics.