Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

An NLP-based technique to extract meaningful features from drug SMILES

NLP is a well-established field in ML for developing language models that capture the sequence of words in a sentence. Similarly, drug molecule structures can also be represented as sequences using the SMILES notation. However, unlike natural language texts, special characters in drug SMILES have specific meanings and cannot be ignored. We introduce a novel NLP-based method that extracts interpretable sequences and essential features from drug SMILES notation using N-grams. Our method compares these features to Morgan fingerprint bit-vectors using UMAP-based embedding, and we validate its effectiveness through two personalized drug screening (PSD) case studies. Our NLP-based features are sparse and, when combined with gene expressions and disease phenotype features, produce better ML models for PSD. This approach provides a new way to analyze drug molecule structures represented as SMILES notation, which can help accelerate drug discovery efforts. We have also made our method accessible through a Python library.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Acacetin exerts antitumor effects on gastric cancer by targeting EGFR

Background: Gastric cancer (GC) is a common malignant tumor with a poor prognosis. Combination treatments may prolong the survival of patients with GC. Acacetin, which is a flavonoid, exerts potent inhibitory effects on several types of cancer cells; however, the mechanisms of action remain poorly understood. Methods: Network pharmacology and RNA sequencing were used to predict the targets of acacetin, which were then verified by drug affinity responsive target stability (DARTS), cellular thermal shift assay (CETSA) and molecular docking. The biological functions of acacetin in MKN45 and MGC803 cells were investigated using TUNEL assays, crystal staining and colony formation assays. The pathways affected by acacetin were verified through reverse experiments. The in vivo antitumor efficacy of acacetin was assessed in a subcutaneous xenotransplanted tumor model. Results: In this study, we identified EGFR from more than a dozen predicted targets as a protein that directly binds to acacetin. Moreover, acacetin affected the level of phosphorylated EGFR. In vitro, acacetin promoted the apoptosis of GC cells. Importantly, EGFR agonists reversed the inhibitory effects of acacetin on the STAT3 and ERK pathways. In vivo, acacetin decreased the protein levels of pEGFR in tumors, resulting in increased GC xenograft tumor regression without obvious toxicity. Conclusion: Our findings highlight EGFR as one of the direct targets of acacetin in GC cells. Acacetin inhibited the phosphatase activity of EGFR in vitro and in vivo, which played a role in the antitumor effects of acacetin. These studies provide new evidence for the use of acacetin as a potential reagent for the treatment of GC.

Hepatocellular Carcinoma Chemoprevention with Generic Agents

Liver cancer, mainly hepatocellular carcinoma (HCC), remains a major cause of cancer-related death worldwide. With the global epidemic of obesity, the major HCC etiologies have been dynamically shifting from viral to metabolic liver diseases. This change has made HCC prevention difficult with increasingly elusive at-risk populations as rational target for preventive interventions. Besides ongoing efforts to reduce obesity and metabolic disorders, chemoprevention in patients who already have metabolic liver diseases may have a significant impact on the poor HCC prognosis. Hepatitis B- and hepatitis C-related HCC incidences have been substantially reduced by the new antivirals, but HCC risk can persist over a decade even after successful viral treatment, highlighting the need for HCC-preventive measures also in these patients. Experimental and retrospective studies have suggested potential utility of generic agents such as lipophilic statins and aspirin for HCC chemoprevention given their well-characterized safety profile, although anticipated efficacy may be modest. In this review, we overview recent clinical and translational studies of generic agents in the context of HCC chemoprevention under the contemporary HCC etiologies. We also discuss newly emerging approaches to overcome the challenges in clinical testing of the agents to facilitate their clinical translation.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

On the road to explainable AI in drug-drug interactions prediction: a systematic review

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A systematic review on applications of explainable AI in drug-drug interaction prediction.

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Review is conducted on a comprehensive set of 94 papers from five prestigious databases.

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Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.

Revealing the Mechanism of Friedelin in the Treatment of Ulcerative Colitis Based on Network Pharmacology and Experimental Verification

Objectives

Ulcerative colitis (UC) is a chronic inflammatory disease affecting the colon, and its incidence is rising worldwide. This study was designed to uncover the healing effect of friedelin, a bioactive compound against UC through bioinformatics of network pharmacology and experimental verification of UC model mice.

Materials and Methods

Targets of friedelin and potential mechanism of friedelin on UC were predicted through target searching, PPI network establishing, and enrichment analyzing. We explored effects of friedelin on dextran sulfate sodium (DSS)-induced colitis. Severity of UC was investigated by body weight, disease activity index (DAI), and length of the colon. Inflammation severity was examined by determination of proinflammatory and anti-inflammatory cytokines. The numbers of autophagosome around the epithelial cells were observed by autophagy inhibition via a transmission electron microscope. The expressions of autophagy-related ATG5 protein and AMPK-mTOR signaling pathway were determined by immunofluorescence staining.

Results

In this study, 17 potential targets of friedelin and 1111 UC-related targets were identified. 10 therapeutic targets of friedelin against UC were acquired from overlapped targets of UC and friedelin. PPI network construction filtered 14 core targets through target amplification and confidence enhancement. The results of molecular docking showed that the docking scores of the top 5 active targets were higher than the threshold values. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out, showing friedelin alleviates UC through anti-inflammatory pathways and molecular function of autophagy. Subsequently, animal-based experiments revealed the intraperitoneal injection of friedelin ameliorated DSS-induced body weight loss, DAI decrease, colon length shortening and colonic pathological damage with lower myeloperoxidase and proinflammatory cytokines (IL-1β and IL-6) and higher IL-10 levels, and more autophagosomes in transmission electron microscope results. The AMPK-mTOR signaling pathway plays important role in the friedelin's effect in autophagy as KEGG pathway result and experiment verification. Furthermore, the 3 ma validated the role of autophagy as an improvement in the friedelin's pharmacologic effect to UC model mice.

Conclusions

Friedelin ameliorated DSS-induced colitis in mice through of inflammatory inhibition and regulation of autophagy.

On the robustness of generalization of drug-drug interaction models

BACKGROUND

Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug-drug interactions (DDIs). The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful. Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages.

RESULTS

We rigorously tested various structure-based models that predict drug interactions using different splitting strategies to simulate different real-world scenarios. In addition to the effects of different training and testing setups on the robustness and generalizability of the models, we then explore the contribution of traditional approaches such as multitask learning and data augmentation.

CONCLUSION

Structure-based models tend to generalize poorly to unseen drugs despite their ability to identify new DDIs among drugs seen during training accurately. Indeed, they efficiently propagate information between known drugs and could be valuable for discovering new DDIs in a database. However, these models will most probably fail when exposed to unknown drugs. While multitask learning does not help in our case to solve the problem, the use of data augmentation does at least mitigate it. Therefore, researchers must be cautious of the bias of the random evaluation scheme, especially if their goal is to discover new DDIs.