Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm

In silico discovery of novel compounds for FAK activation using virtual screening, AI-based prediction, and molecular dynamics

Focal Adhesion Kinase (FAK) is a non-receptor tyrosine kinase that plays a crucial role in cell proliferation, migration, and signal transduction. FAK is overexpressed in metastatic and advanced-stage cancers, where it is considered a key kinase in cancer growth and metastasis. However, recent research has revealed that FAK activity decreases in various diseases. we aimed to identify compounds that could enhance FAK activity using structure-based virtual screening and artificial intelligence models from a vast chemical database. We began with an extensive chemical database containing over 10 million compounds and used our newly developed pipeline to screen candidate molecules. To select compounds structurally similar to ZINC40099027 (ZN27), a known FAK activator, we calculated Tanimoto Similarity scores and chose compounds with a score of at least 0.8. Clustering was performed using K-means based on the molecular properties. Subsequently, we utilized docking simulation, deep learning and SAScorer to evaluate and predict the protein-ligand docking affinity and physicochemical properties of the candidate compounds. The deep learning models were selected as state-of-the-art models: GLAM predicts the blood-brain barrier permeability of FAK, and elEmBERT predicts the potential toxicity of compound. The combined results were used to create an evaluation matrix. We selected 10 promising candidate compounds from the initial dataset of 10 million. To evaluate the stability of these top 10 candidate compounds in interaction with the FAK protein, we conducted Molecular Dynamics (MD) simulations. We performed a molecular dynamics simulation for a total of 50 ns and identified the top three promising candidate compounds.

Exploring disulfiram mechanisms in renal fibrosis: insights from biological data and computational approaches

Background

Disulfiram (DSF) is an anti-alcoholic drug that has been reported to inhibit the epithelial-to-mesenchymal transition and crosslinking during fibrosis, pyroptosis, and inflammatory NF-κB and Nrf-2 signaling pathways. However, there is insufficient evidence to support the mechanisms of DSF in preventing renal fibrosis (RF). Therefore, the current study aimed to elucidate the DSF-modulated targets and pathways in renal fibrosis.

Methods

The common proteins between DSF and RF were screened for protein-protein interaction, pathway enrichment, cluster, and gene ontology analysis. Molecular docking was executed for core genes using AutoDock Vina through the POAP pipeline. Molecular dynamics (MD) simulation (100 ns) was performed to infer protein-ligand stability, and conformational changes were analyzed by free energy landscape (FEL).

Results

A total of 78 targets were found to be common between DSF and RF, of which NFKB, PIK3CA/R1, MTOR, PTGS2, and MMP9 were the core genes. PI3K-Akt signaling followed by JAK-STAT, TNF, Ras, ErbB, p53, phospholipase D, mTOR, IL-17, NF-κB, AMPK, VEGF, and MAPK signaling pathways were modulated by DSF in RF. DSF showed a direct binding affinity with active site residues of core genes, and except for DSF with NF-κB, all other complexes, including the standard, were found to be stable during 100 ns MD simulation with minimal protein-ligand root mean squared deviation and residual fluctuations and higher compactness with broad conformational changes.

Conclusion

DSF protects against renal fibrosis, and this study paves the way for experimental investigation to repurpose DSF for treating RF.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

Systems biology approaches to identify driver genes and drug combinations for treating COVID-19

Corona virus 19 (Covid-19) has caused many problems in public health, economic, and even cultural and social fields since the beginning of the epidemic. However, in order to provide therapeutic solutions, many researches have been conducted and various omics data have been published. But there is still no early diagnosis method and comprehensive treatment solution. In this manuscript, by collecting important genes related to COVID-19 and using centrality and controllability analysis in PPI networks and signaling pathways related to the disease; hub and driver genes have been identified in the formation and progression of the disease. Next, by analyzing the expression data, the obtained genes have been evaluated. The results show that in addition to the significant difference in the expression of most of these genes, their expression correlation pattern is also different in the two groups of COVID-19 and control. Finally, based on the drug-gene interaction, drugs affecting the identified genes are presented in the form of a bipartite graph, which can be used as the potential drug combinations.

The Chemical Space of Terpenes: Insights from Data Science and AI

Terpenes are a widespread class of natural products with significant chemical and biological diversity, and many of these molecules have already made their way into medicines. In this work, we employ a data science-based approach to identify, compile, and characterize the diversity of terpenes currently known in a systematic way, in a total of 59,833 molecules. We also employed several methods for the purpose of classifying terpene subclasses using their physicochemical descriptors. Light gradient boosting machine, k-nearest neighbours, random forests, Gaussian naïve Bayes and Multilayer perceptron were tested, with the best-performing algorithms yielding accuracy, F1 score, precision and other metrics all over 0.9, thus showing the capabilities of these approaches for the classification of terpene subclasses. These results can be important for the field of phytochemistry and pharmacognosy, as they allow the prediction of the subclass of novel terpene molecules, even when biosynthetic studies are not available.

Drug Repurposing in Non-Small Cell Lung Carcinoma: Old Solutions for New Problems

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths in 2022. The majority (80%) of lung cancer cases belong to the non-small cell lung carcinoma (NSCLC) subtype. Despite the increased screening efforts, the median five-year survival of metastatic NSCLC remains low at approximately 3%. Common treatment approaches for NSCLC include surgery, multimodal chemotherapy, and concurrent radio and chemotherapy. NSCLC exhibits high rates of resistance to treatment, driven by its heterogeneity and the plasticity of cancer stem cells (CSCs). Drug repurposing offers a faster and cheaper way to develop new antineoplastic purposes for existing drugs, to help overcome therapy resistance. The decrease in time and funds needed stems from the availability of the pharmacokinetic and pharmacodynamic profiles of the Food and Drug Administration (FDA)-approved drugs to be repurposed. This review provides a synopsis of the drug-repurposing approaches and mechanisms of action of potential candidate drugs used in treating NSCLC, including but not limited to antihypertensives, anti-hyperlipidemics, anti-inflammatory drugs, anti-diabetics, and anti-microbials.

Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing

Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical's mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

PRKAR2A-derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis-associated colorectal cancer

BACKGROUND

Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis-associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown.

METHODS

In this study, mouse colitis and CAC models were established, and the RNA-seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC.

RESULTS

The K-means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu_circ_0001109 closely correlated to the pro-inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu_circ_0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1- and PRKAR2A-derived circRNAs, which might be considered as homeotic circRNAs of mmu_circ_0001109 and mmu_circ_0001845, respectively, were identified. The clinical data revealed that RTEL1-derived circRNAs had no clinical significance in human IBD and CAC. However, three PRKAR2A-derived circRNAs, which had the high RNA similarities to mmu_circ_0001845, were remarkably up-regulated in CAC tissue samples and promoted the transition from colitis to CAC.

CONCLUSIONS

Our results suggested that these human PRKAR2A-derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC.

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network

Drug repositioning is the identification of interactions between drugs and target proteins in pharmaceutical sciences. Traditional large-scale validation through chemical experiments is time-consuming and expensive, while drug repositioning can drastically decrease the cost and duration taken by traditional drug development. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning methods have been used to systematically identify potential drug-target interactions. Some of them are based on a particular class of machine learning algorithms called kernel methods. In this paper, we propose a new machine learning prediction method combining multiple kernels into a tripartite heterogeneous drug-target-disease interaction spaces in order to integrate multiple sources of biological information simultaneously. This novel network algorithm extends the traditional drug-target interaction bipartite graph to the third disease layer. Meanwhile, Gaussian kernel functions on heterogeneous networks and the regularized least square method of the Kronecker product are used to predict new drug-target interactions. The values of AUPR (area under the precision-recall curve) and AUC (the area under the receiver operating characteristic curve) of the proposed algorithm are significantly improved. Especially, the AUC values are improved to 0.99, 0.99, 0.97, and 0.96 on four benchmark data sets. These experimental results substantiate that the network topology can be used for predicting drug-target interactions.

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph

Background

Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions.

Results

We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types.

Conclusions

We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.

Circular RNA hsa_circ_0007059 restrains proliferation and epithelial-mesenchymal transition in lung cancer cells via inhibiting microRNA-378

As newly discovered non-coding RNA (ncRNA), circular RNA (circRNA) has become a research hotspot in manifold cancers. But, the influences of hsa_circ_0007059 in lung cancer remain obscure. Expression of hsa_circ_0007059 in lung cancer tissues was firstly determined through RT-qPCR. After overexpressing hsa_circ_0007059, cell viability, apoptosis, p53/CyclinD1, Bax and Pro/Cleaved-Caspase-3 and EMT-correlative factors (E-cadherin, Vimetin, Twist1 and Zeb1) were tested in A549 and H1975 cells. MiR-378 expression in lung cancer tissues and cells was evaluated after miR-378 mimic transfection. Wnt/β-catenin and ERK1/2 pathways were finally evaluated in A549 and H1975 cells. Inhibition of hsa_circ_0007059 was discovered in lung cancer tissues. Overexpressed hsa_circ_0007059 evidently restrained cell proliferation, elevated p53 and repressed CyclinD1 expression, meanwhile triggered apoptosis and enhanced Bax and Cleaved-Caspase-3 expression. Increased hsa_circ_0007059 abated EMT via enhancement of E-cadherin and inhibition of Vimentin, Twist and Zeb1 in A549 and H1975 cells. MiR-378 was up-regulated in lung cancer tissues, declined by hsa_circ_0007059 overexpression in A549 and H1975 cells. Overexpressed hsa_circ_0007059 hindered Wnt/β-catenin and ERK1/2 pathways via suppressing miR-378 in A549 and H1975 cells. The investigations manifested that hsa_circ_0007059 abated cell proliferation and EMT process in lung cancer cells via inactivation of Wnt/β-catenin and ERK1/2 pathways via suppressing miR-378.

The Applications of Clustering Methods in Predicting Protein Functions

Background:

The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem.

Objective:

In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

Role of cytokines in combinatorial immunotherapeutics of non-small cell lung cancer through systems perspective

Lung cancer is the leading cause of deaths related to cancer and accounts for more than a million deaths per year. Various new strategies have been developed and adapted for treatment; still the survival for 5 years is just 16% in patients with non‐small cell lung cancer (NSCLC). Most of these strategies to combat NSCLC whether it is a drug molecule or immunotherapy/vaccine candidate require a big cost and time. Integration of computational modeling with systems biology has opened new avenues for understanding complex cancer biology. Resolving the complex interactions of various pathways and their crosstalk leading to oncogenic changes could identify new therapeutic targets with lesser cost and time. Herein, this review provides an overview of various aspects of NSCLC along with available strategies for its cure concluding with our insight into how systems approach could serve as a therapeutic intervention dissecting the immunologic parameters and cross talk between various pathways involved.

Phosphoinositide 3-kinase-delta could be a biomarker for eosinophilic nasal polyps

Nasal polyps (NP) cause diverse clinical symptoms of chronic rhinosinusitis (CRS). Chronic inflammation of sinonasal mucosa is known to be crucial in NP formation. We aimed to define the implications of phosphoinositide 3-kinase (PI3K)-δ in nasal inflammation associated with NP by analyzing NP tissue obtained from CRS patients. Results showed that expression of p110δ, a regulatory subunit of PI3K-δ, in NP tissue was increased compared to control tissue. Increased p110δ expression was closely correlated with more severe CRS features. Interestingly, p110δ expression was increased in eosinophilic NP, which are closely related to more complicated clinical courses of the disease. Furthermore, CRS patients possessing NP with higher p110δ expression displayed more eosinophils in NP tissue and blood, higher levels of IL-5 in NP tissue, and more severe features of the disease. Therefore, PI3K-δ may contribute to the formation of NP, especially eosinophilic NP associated with more severe clinical presentations and radiological features.

Review of Drug Repositioning Approaches and Resources

Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.

A computational method for the identification of candidate drugs for non-small cell lung cancer

Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.