To explore the pharmacological mechanism of action using digital twin

With the advent of medical technology and science, the number of animals used in research has increased. For decades, the use of animals in research and product testing has been a point of conflict. Experts and pharmaceutical manufacturers are harming animals worldwide during laboratory research. Animals have also played a significant role in the advancement of science; animal testing has enabled the discovery of various novel drugs. The misery, suffering, and deaths of animals are not worth the potential human benefits. As a result, animals must not be exploited in research to assess the drug mechanism of action (MOA). Apart from the ethical concern, animal testing has a few more downsides, including the requirement for skilled labor, lengthy processes, and cost. Because it is critical to investigate adverse effects and toxicities in the development of potentially viable drugs. Assessment of each target will consume the range of resources as well as disturb living nature. As the digital twin works in an autonomous virtual world without influencing the physical structure and biological system. Our proposed framework suggests that the digital twin is a great reliable model of the physical system that will be beneficial in assessing the possible MOA prior to time without harming animals. The study describes the creation of a digital twin to combine the information and knowledge obtained by studying the different drug targets and diseases. Mechanism of Action using Digital twin (MOA-DT) will enable the experts to use an innovative approach without physical testing to save animals, time, and resources. DT reflects and simulates the actual drug and its relationships with its target, however presenting a more accurate depiction of the drug, which leads to maximize efficacy and decrease the toxicity of a drug. In conclusion, it has been shown that drug discovery and development can be safe, effective, and economical in no time through the combination of the digital and physical models of a pharmaceutical as compared to experimental animals.

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

Exploring the Mechanism of Quercetin for the Treatment of Atrial Fibrillation by Geo Gene Chip Combined With Network Pharmacology and Molecular Docking

PurposeAtrial fibrillation (AF) is a common atrial arrhythmia. Quercetin (Que) has some advantages in the treatment of cardiovascular disease arrhythmias, but its specific drug mechanism of action needs further investigation. To explore the mechanism of action of Que in AF, core target speculation and analysis were performed using network pharmacology and molecular docking methods.MethodsQue chemical structures were obtained from Pubchem. TCMSP, Swiss Target Prediction, Drugbank , STITCH, Binding DB, Pharmmapper, CTD, GeneCards, DISGENET and TTD were used to obtain drug component targets and AF-related genes, and extract AF from normal tissues by GEO database differentially expressed genes. Then, the intersecting genes were obtained by online Wayne mapping tool. The intersection genes were introduced into the top five targets selected for molecular docking via protein-protein interaction (PPI) network to verify the binding activity between Que and the target proteins. GO and KEGG enrichment analysis of the intersected genes using program R was performed to further screen for key genes and key pathways.ResultsThere were 65 effective targets for Que and AF. Through further screening, the top 5 targets were IL6, VEGFA, JUN, MMP9 and EGFR. Que treatment of AF may involve signaling pathways such as lipid and atherosclerosis pathway, AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and IL-17 signaling pathway. Molecular docking suggests that Que has strong binding to key targets.ConclusionThis study systematically elucidates the key targets of Que treatment for AF and the specific mechanisms through network pharmacology as well as molecular docking, providing a new direction for further basic experimental exploration and clinical treatment.

High Throughput Drug Discovery in S. Cerevisiae: the Characterisation of FC-592 and FC-888

<p>The discovery and characterisation of novel small molecule drug candidates is a medical priority. Recent advances in synthetic organic chemistry allow the de novo production of diversity oriented synthetic compound libraries and synthetic modification of natural products to provide candidate compounds for screening as potential therapeutics, bioactive agents or genetic probes. Small drugs function through interaction with complex genetic networks and pathways. However, it is difficult to characterise these interactions on a genome wide level to achieve understanding of drug mechanism. Here, discovery based approaches are utilised to achieve system wide parsing of biological mechanism, in an attempt to characterise the action of novel synthetic compounds and natural product derivatives. Chemical genomic analysis allows for such understanding by examining growth profiles of a genomic deletion library of Saccharomyces cerevisiae mutants in the presence of sub-inhibitory concentrations of drug. The gene targets of small molecule compounds can be identified by noting deletion strains which display increased sensitivity, indicating chemical interaction with the associated gene network. In addition, the development and characterisation of resistant mutants can be used to identify putative drug targets. In this strategy, characterisation of the mechanism of resistance gives insight into drug mode-of-action. This study develops a high throughput yeast inhibition assay to identify bioactive compounds from a synthetic organic compound library, and attempts to characterise mechanism of action by establishing a profile of each compound’s interaction with these gene networks; and mapping a resistance mutation to provide evidence of inhibitory mechanism. Two candidate compounds are identified, FC-592 and FC-888. FC-592 displayed cytostatic inhibition. Further, yeast tag microarray homozygous profiling (HOP), chemical structure analysis, and cell-cycle analysis via flow cytometry for this compound provided evidence for a mechanism of poor specificity that targets glycoprotein biosynthesis and the secretory (Sec) pathway, as well as the cell-division cycle (CDC) pathway. Attempts to characterise a mutant resistant to this compound via synthetic genetic array mapping were unsuccessful when the resistance mutation proved to mediate a slow growth phenotype, abrogating the Synthetic Genetic Array Mapping approach utilised. Pending further analysis, it is suggested that this compound could have a role as a genetic probe in future exploration of the Sec and CDC pathways. Chemical structure analysis and a non-specific HOP screen chemigenomic profile suggested that FC-888 is an alkylating agent with a broad affinity for cellular nucleophiles. The compound demonstrates cytotoxic activity, and its efflux is not mediated by the pleiotropic drug resistance (PDR) network. It is suggested that the compound could find utility as a probe dissecting processes related to cellular defence against non-DNA specific alkylation.</p>

High Throughput Drug Discovery in S. Cerevisiae: the Characterisation of FC-592 and FC-888

<p>The discovery and characterisation of novel small molecule drug candidates is a medical priority. Recent advances in synthetic organic chemistry allow the de novo production of diversity oriented synthetic compound libraries and synthetic modification of natural products to provide candidate compounds for screening as potential therapeutics, bioactive agents or genetic probes. Small drugs function through interaction with complex genetic networks and pathways. However, it is difficult to characterise these interactions on a genome wide level to achieve understanding of drug mechanism. Here, discovery based approaches are utilised to achieve system wide parsing of biological mechanism, in an attempt to characterise the action of novel synthetic compounds and natural product derivatives. Chemical genomic analysis allows for such understanding by examining growth profiles of a genomic deletion library of Saccharomyces cerevisiae mutants in the presence of sub-inhibitory concentrations of drug. The gene targets of small molecule compounds can be identified by noting deletion strains which display increased sensitivity, indicating chemical interaction with the associated gene network. In addition, the development and characterisation of resistant mutants can be used to identify putative drug targets. In this strategy, characterisation of the mechanism of resistance gives insight into drug mode-of-action. This study develops a high throughput yeast inhibition assay to identify bioactive compounds from a synthetic organic compound library, and attempts to characterise mechanism of action by establishing a profile of each compound’s interaction with these gene networks; and mapping a resistance mutation to provide evidence of inhibitory mechanism. Two candidate compounds are identified, FC-592 and FC-888. FC-592 displayed cytostatic inhibition. Further, yeast tag microarray homozygous profiling (HOP), chemical structure analysis, and cell-cycle analysis via flow cytometry for this compound provided evidence for a mechanism of poor specificity that targets glycoprotein biosynthesis and the secretory (Sec) pathway, as well as the cell-division cycle (CDC) pathway. Attempts to characterise a mutant resistant to this compound via synthetic genetic array mapping were unsuccessful when the resistance mutation proved to mediate a slow growth phenotype, abrogating the Synthetic Genetic Array Mapping approach utilised. Pending further analysis, it is suggested that this compound could have a role as a genetic probe in future exploration of the Sec and CDC pathways. Chemical structure analysis and a non-specific HOP screen chemigenomic profile suggested that FC-888 is an alkylating agent with a broad affinity for cellular nucleophiles. The compound demonstrates cytotoxic activity, and its efflux is not mediated by the pleiotropic drug resistance (PDR) network. It is suggested that the compound could find utility as a probe dissecting processes related to cellular defence against non-DNA specific alkylation.</p>

Using BERT to identify drug-target interactions from whole PubMed

Background: Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of articles providing this data (~0.1 million) likely constitutes only a fraction of all articles on PubMed that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we propose Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format. Results: Our novel method identified ~2.1 million articles (along with drug and protein information) that are not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~99% accuracy for identifying articles containing quantitative drug-target profiles. The accuracy for the prediction of assay format is ~90%, which leaves room for improvement in future studies. Conclusion: The BERT model in this study is robust and the proposed pipeline can be used to identify previously overlooked articles containing quantitative DTIs. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing.

A Primer on Harnessing Non-enzymatic Posttranslational Modifications for Drug Design

Of the manifold concepts in drug discovery and design, covalent drugs have re-emerged as one of the most promising over the past 20-or so years. All such drugs harness the ability of a covalent bond to drive an interaction between a target biomolecule, typically a protein, and a small molecule. Formation of a covalent bond necessarily prolongs target engagement, opening avenues to targeting shallower binding sites, protein complexes, and other difficult to drug manifolds, amongst other virtues. This opinion piece discusses frameworks around which to develop covalent drugs. Our argument, based on results from our research program on natural electrophile signaling, is that targeting specific residues innately involved in native signaling programs are ideally poised to be targeted by covalent drugs. We outline ways to identify electrophile-sensing residues, and discuss how studying ramifications of innate signaling by endogenous molecules can provide a means to predict drug mechanism and function and assess on- versus off-target behaviors.

Dr. Sim: Similarity Learning for Transcriptional Phenotypic Drug discovery

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as Connectivity Map (CMap) and Library of Integrated Network-Based Cellular Signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed, and among them, a fundamental issue is to define the proper similarity between the transcriptional profiles to elucidate the drug mechanism of actions and identify new drug indications. Traditionally, this similarity has been defined in an unsupervised way, and due to the high dimensionality and the existence of high noise in those high-throughput data, it lacks robustness with limited performance. In our study, we present Dr. Sim, which is a general learning-based framework that automatically infers similarity measurement rather than being manually designed and can be used to characterize transcriptional phenotypic profiles for drug discovery with generalized good performance. We evaluated Dr. Sim on comprehensively publicly available in vitro and in vivo datasets in drug annotation and repositioning using high-throughput transcriptional perturbation data and indicated that Dr. Sim significantly outperforms the existing methods and is proved to be a conceptual improvement by learning transcriptional similarity to facilitate the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and usage of Dr. Sim is available at https://github.com/bm2-lab/DrSim/.

Roadmap for the use of base editors to decipher drug mechanism of action

CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context focusing on G-protein coupled receptors. A pooled mutagenesis screening framework was set up based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. The β2 Adrenergic Receptor (B2AR) was targeted in this way employing a 6xCRE-mCherry reporter system to monitor its response to isoproterenol. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology.