A method for analyzing the co-mechanism of hepatotoxicity and nephrotoxicity of non-steroidal anti-inflammatory drugs

Abstract

The application provides a method for analyzing the synergistic mechanism of hepatotoxicity and nephrotoxicity of non-steroidal anti-inflammatory drugs. The method comprises the following steps: preliminary toxicity prediction of NSAIDs and collection of toxicity target points, collection of liver and kidney disease target points, then cross and screening of the target points to obtain core target points and common core target points of NSAIDs induced liver and kidney diseases, and then constructing a protein interaction network of the common core target points; enrichment analysis of the common core target points to obtain the common action pathway of NSAIDs induced liver and kidney diseases; finally, further screening of the common core target points to obtain the key target points of NSAIDs induced liver and kidney diseases, and verification by using molecular docking technology. Compared with the traditional method, the advantages of the method are: first, the method does not depend on large-scale patient clinical data and a large number of animal or cell experiments, avoiding the ethical controversy in animal experiments and human experiments; second, the method can identify the potential cross-pathway and synergistic toxicity mechanism when a compound triggers multiple diseases, which is helpful for more comprehensive evaluation of the toxicity risk of NSAIDs.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics technology, specifically to a method for analyzing the combined hepatotoxicity and nephrotoxicity mechanism of nonsteroidal anti-inflammatory drugs. Background Technology

[0002] Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most commonly used over-the-counter medications globally, primarily used to treat pain, inflammation, and fever. The global NSAID market was reportedly valued at US$15.58 billion in 2019 and is projected to reach US$24.35 billion by 2027, representing a CAGR of 5.8% during the forecast period. Despite their wide range of therapeutic uses and market demand, NSAIDs are also controversial due to various toxic effects, including gastrointestinal toxicity, hepatotoxicity, nephrotoxicity, and cardiovascular toxicity.

[0003] Network toxicology, based on network pharmacology and network biology, employs network-based methods to construct networks relating chemical substances, biological targets, and adverse outcomes, thereby revealing toxic pathways and their potential association with disease. Molecular docking techniques are used to predict the spatial orientation and binding modes of small molecules (such as drug ligands) within the active sites of macromolecules (such as proteins, enzymes, and receptors), providing valuable insights into the potential therapeutic effects or side effects of drugs. In toxicological research, molecular docking techniques can predict and elucidate how toxins interact with biomolecules, revealing the toxic mechanisms of toxins and their potential harm to organisms.

[0004] Current methods for studying the toxicity risks of NSAIDs have the following problems:

[0005] (1) Traditional NSAID toxicology studies involve random-effects general inverse variance method, corrected proportional hazards model, cell experiments, animal experiments, and other methods. These methods require a large amount of experimental data, consume a lot of human, material and financial resources, and may also involve ethical issues. Therefore, there is an urgent need for innovative methods to comprehensively and effectively assess the toxicity of NSAIDs and their potential disease risks.

[0006] (2) There is existing literature on the use of network toxicology to study the toxicity of compounds, but most studies only consider the toxic effects of compounds on a single disease or a class of diseases, ignoring the common pathways and targets when they induce two or more diseases. Therefore, there is an urgent need for a method that can study the common mechanisms by which a compound induces multiple diseases. Summary of the Invention

[0007] To address the shortcomings of existing methods, this invention provides a more comprehensive, economical, and efficient method for assessing the hepatotoxicity and nephrotoxicity risk of NSAIDs, thereby delving into the dual risk characteristics of NSAIDs in causing both hepatotoxicity and nephrotoxicity, and elucidating the common molecular mechanisms of action of these two types of toxicity. This method first requires preliminary toxicity prediction of NSAIDs, collection of toxicity targets, and collection of targets for hepatotoxic and nephrotoxic diseases. Then, the targets for both diseases are cross-referenced with the toxicity targets of NSAIDs to obtain potential targets induced by NSAIDs. Next, the obtained potential targets are imported into the STRING12.0 database for protein-protein interaction analysis, and the results are imported into Cytoscape 3.9.0 for network visualization analysis, calculating topological properties and generating a protein-protein interaction (PPI) network graph. Then, based on specific topological criteria, the core targets for each type of NSAID-induced hepatotoxic and nephrotoxic disease are screened. Finally, the core targets for both types of NSAID-induced hepatotoxic and nephrotoxic diseases are cross-referenced to obtain the common core targets for NSAID-induced hepatotoxic and nephrotoxic diseases. The R package "clusterProfiler" was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on common core targets, revealing the common pathways by which NSAIDs induce liver and kidney diseases. Subsequently, the MCODE and CytoHubba plugins in Cytoscape were used to further screen for key targets inducing these two diseases. Finally, molecular docking validation of NSAIDs and key targets was performed using Autodock Vina 1.2.7 software. The methodological steps are illustrated in the figure below. Figure 1 As shown, the specific steps are as follows:

[0008] 1. NSAID structure acquisition: Enter the name of a specific NSAID into the small organic molecule activity database (Pubchem) to obtain its simplified molecular input line entry system (SMILES) sequence.

[0009] 2. Toxicity prediction and toxicity target collection of NSAIDs: The SMILES sequences obtained in step 1 were uploaded to the ADMETlab 3.0 and TOXicity of chemicals (ProTox-3.0) toxicity prediction databases, as well as the SwissTargetPrediction, Targetnet and CTD websites, to obtain the toxicity prediction results and toxicity targets of NSAIDs.

[0010] 3. Disease target collection: Enter keywords for two diseases into the GeneCards, Disgenet, and CTD databases respectively to obtain targets for liver and kidney diseases.

[0011] 4. Identification of potential targets: The NSAID toxicity targets collected in step 2 are cross-referenced with the targets for the two diseases collected in step 3 to obtain the intersection targets. The intersection portion is considered as potential targets for NSAID-induced liver and kidney diseases.

[0012] 5. Construction of PPI Network and Screening of Common Core Targets: The potential targets obtained in step 4 were imported into the STRING 12.0 database for protein-protein interaction analysis. The results were then imported into Cytoscape 3.9.0 for network visualization analysis, calculating topological properties and generating a protein-protein PPI network diagram. Based on the criteria of betweenness, closeness, eigenvector, local average connectivity-based method, and network weight values ​​> median and degree weight values ​​> twice the median, core targets of NSAIDs inducing two diseases were screened. Cross-referencing the core targets of NSAIDs inducing these two diseases yielded the common core targets of NSAIDs inducing liver and kidney diseases.

[0013] 6. GO and KEGG enrichment analysis: The common core targets screened in step 5 were enriched using the R package "clusterProfiler". The common pathways of NSAID-induced liver and kidney diseases were obtained through the enrichment analysis results.

[0014] 7. Screening of Key Targets: Using the MCODE and CytoHubba plugins in Cytoscape, key targets for NSAID-induced diseases were further screened from the common core targets obtained in step 5. The sub-network modules with the highest scores in the MCODE analysis were selected for further study. The CytoHubba plugin was then used to reveal the top 7 targets, which were considered the most critical targets for NSAID-induced liver and kidney diseases.

[0015] 8. Molecular docking verification: Autodock Vina 1.2.7 software was used to perform molecular docking between NSAIDs and the key targets screened in step 7 to verify the accuracy and scientific validity of the results. If the docking affinity was less than -5.0 kcal / mol, the key targets were considered to have a stable binding ability with NSAIDs; if the docking affinity was less than -7.0 kcal / mol, the key targets were considered to have a strong binding ability with NSAIDs.

[0016] This invention provides a method for analyzing the combined hepatotoxicity and nephrotoxicity mechanism of nonsteroidal anti-inflammatory drugs (NSAIDs), which has the following advantages:

[0017] 1. This invention utilizes multiple databases to obtain the hepatotoxicity and nephrotoxicity of NSAIDs and their common core targets that induce liver and kidney diseases. The biological functions of these targets are analyzed, and they are further screened using a series of rigorous criteria to identify key targets. Finally, molecular docking is performed between NSAIDs and the screened key targets to obtain the common molecular mechanism of action of both toxicities. This method does not rely on large-scale patient clinical data or extensive animal or cell experiments, thus significantly reducing the investment of human, material, and financial resources. It avoids the ethical controversies associated with animal and human experiments and aligns with modern trends in drug safety assessment and ethical review.

[0018] 2. Compared with traditional network toxicology methods, this study breaks through the limitations of "single disease, single target" and can identify potential cross-pathways and synergistic toxicity mechanisms when compounds cause multiple diseases. This not only helps to more comprehensively assess the toxicity risk of NSAIDs, but also provides a scientific basis for the prevention and control of related adverse clinical reactions and the development of novel detoxification strategies. Attached Figure Description

[0019] Figure 1 This is a diagram illustrating the steps of the method of the present invention.

[0020] Figure 2 Radar chart of SLD toxicity prediction results

[0021] Figure 3 Venn diagram of the common core target of CLD and AKI

[0022] Figure 4 Bar chart for GO enrichment analysis of common core targets

[0023] Figure 5 Bubble chart for KEGG enrichment analysis of common core target

[0024] Figure 6 PPI network diagram for common core target

[0025] Figure 7 PPI network diagram of key (top 7) targets

[0026] Figure 8 Molecular docking affinity heatmap of SLD with key target sites Detailed Implementation

[0027] To provide a clearer understanding of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the scope of the invention.

[0028] This example is to verify the feasibility and effectiveness of the method of the present invention, specifically as follows: a method based on network toxicology and molecular docking analysis of sulindc (SLD)-induced cholestatic liver disease (CLD) and acute kidney injury (AKI).

[0029] 1. Toxicity prediction and toxicity target collection of SLD

[0030] The toxicity of SLD was predicted and analyzed using the ADMETlab 3.0 and ProTox-3.0 databases, and a basic overview of SLD toxicity was obtained. Figure 2 ADMETlab 3.0 showed that the probability of SLD causing human hepatotoxicity was 0.863, drug-induced liver injury was 0.999, and drug-induced nephrotoxicity was 0.934. ProTox-3.0 showed that the predicted results for SLD hepatotoxicity and nephrotoxicity were both active, with probabilities of 0.80 and 0.61, respectively. This indicates that SLD toxicity is closely related to CLD and AKI. Then, a comprehensive screening was performed using the SwissTargetPrediction website, Targetnet website, and CTD database, obtaining 103, 63, and 626 SLD toxicity targets, respectively. These targets were integrated and duplicates removed, ultimately resulting in 754 SLD toxicity targets.

[0031] 2. Collection of disease targets for CLD and AKI

[0032] By entering the keywords "Cholestatic liver disease" and "Acute kidney injury" into the GeneCards, Disgenet, and CTD databases, a total of 2222 CLD targets and 4812 AKI targets were obtained.

[0033] 3. Identification of potential targets and construction of PPI networks

[0034] Cross-analysis was performed on 754 SLD toxicity targets with 2222 CLD targets and 4812 AKI targets, resulting in 266 potential targets particularly associated with SLD-induced CLD and 437 potential targets particularly associated with SLD-induced AKI. A PPI network of these potential targets was then constructed using STRING 12.0, and the results were imported into Cytoscape software for analysis.

[0035] 4. Screening of core targets and common core targets

[0036] Based on the criteria in step 5, 82 core targets for SLD-induced CLD and 106 core targets for SLD-induced AKI were selected. Cross-analysis of these two types of core targets yielded a set of 32 common core targets that are particularly relevant to SLD-induced CLD and AKI. Figure 3 ).

[0037] 5. GO and KEGG enrichment analysis

[0038] Enrichment analysis was performed on these 32 common core targets. GO enrichment analysis showed that, in terms of biological processes (BP), these targets are mainly related to the regulation of inflammatory response, smooth muscle cell proliferation, and tissue remodeling; for cellular components (CC), the targets are located in the Bcl-2 family protein complex, the endoplasmic reticulum lumen, and the peptidase inhibitor complex; in terms of molecular function (MF), the targets are enriched in nuclear receptor activity, phosphatase binding, and cytokine activity. Figure 4 KEGG enrichment analysis showed that these common targets are involved in key pathways, including lipid and atherosclerosis, the AGE-RAGE signaling pathway, the JAK-STAT signaling pathway, and the TNF signaling pathway. Figure 5The above results suggest that SLD mainly induces CLD and AKI by regulating apoptosis and inflammatory signaling pathways.

[0039] 6. Screening of key targets

[0040] To further screen key targets, we constructed a protein-protein interaction network of these 32 common core targets. Figure 6 The MCODE analysis yielded one subnetwork module with a score of 17.8, which was selected for further study. CytoHubba analysis revealed the top 7 key targets, including interferon-gamma (IFNG), albumin (ALB), vascular cell adhesion molecule 1 (VCAM1), signal transducer and transcription activator 3 (STAT3), transforming growth factor β1 (TGFB1), interleukin-6 (IL6), and prostaglandin intraperoxidase 2 (PTGS2). Figure 7 These results suggest that patients with either CLD or AKI may exhibit significant alterations in the expression levels of the aforementioned genes compared to healthy individuals, and that these alterations may predict the development of the other disease.

[0041] 7. Molecular docking verification

[0042] To further validate the seven key targets identified by SLD that induce CLD and AKI, molecular docking was performed between them and SLD using AutoDock Vina software. To compare the hepatotoxicity and nephrotoxicity of different NSAIDs, in addition to sulindac, the binding affinity of non-selective cyclooxygenase (COX) inhibitors (ibuprofen and diclofenac) and selective cyclooxygenase-2 (COX-2) inhibitors (celecoxib, etoricoxib, and nimesulide) to the key targets was compared, and heatmaps were used to visualize the docking affinity results. Figure 8 The results showed that the docking affinity of all key targets to SLD was close to or less than -5.0 kcal / mol, indicating that they could stably bind to SLD. Among them, four targets had docking affinities to SLD less than -7.0 kcal / mol: ALB (-9.236 kcal / mol), PTGS2 (-8.141 kcal / mol), IFNG (-7.831 kcal / mol), and STAT3 (-7.202 kcal / mol). This indicates that SLD can spontaneously bind to these targets, thereby inducing CLD and AKI.

Claims

1. A method for analyzing the combined mechanism of hepatotoxicity and nephrotoxicity of nonsteroidal anti-inflammatory drugs, characterized in that, Includes the following steps: 1) Enter the name of a specific nonsteroidal anti-inflammatory drug (NSAID) into the Pubchem organic small molecule activity database to obtain its simplified molecular linear input canonical SMILES sequence; 2) Upload the SMILES sequences obtained in step 1) to the ADMETlab3.0 and ProTox-3.0 toxicity prediction databases, as well as the SwissTargetPrediction, Targetnet and CTD websites, to obtain the toxicity prediction results and toxicity targets of NSAIDs. 3) Enter keywords for two diseases into the GeneCards, Disgenet, and CTD databases respectively to obtain targets for liver and kidney diseases; 4) Cross the NSAID toxicity targets collected in step 2) with the targets of the two diseases collected in step 3) to obtain the intersection targets; the intersection part is regarded as the potential targets of NSAIDs to induce the two diseases respectively. 5) Import the potential targets obtained in step 4) into the STRING 12.0 database for protein interaction analysis. Import the generated results into Cytoscape 3.9.0 for network visualization analysis, calculate the topological properties, and generate a protein interaction PPI network diagram. Based on specific criteria, screen out the core targets that NSAIDs induce in two diseases respectively. Cross-reference the core targets that NSAIDs induce in these two diseases to obtain the common core targets that NSAIDs induce in liver and kidney diseases. 6) Use the R package "clusterProfiler" to perform enrichment analysis on the common core targets screened in step 5), and obtain the common pathways of NSAID-induced liver and kidney diseases through the enrichment analysis results; 7) Using the MCODE and CytoHubba plugins in Cytoscape, further screen the key targets that NSAIDs induce in these two diseases from the common core targets obtained in step 5); select the sub-network modules with the highest scores in the MCODE analysis for further study; and then use the CytoHubba plugin to reveal the top 7 targets, which are regarded as the most critical targets for NSAIDs to induce liver and kidney diseases. 8) Use Autodock Vina 1.2.7 software to perform molecular docking of NSAIDs and key targets screened in step 7) to verify the accuracy and scientific validity of the results.

2. The method of claim 1, wherein: Step 2) requires that: only if the toxicity prediction results show that this NSAID has clear hepatotoxicity and nephrotoxicity can the subsequent steps be carried out; if there is no clear hepatotoxicity and nephrotoxicity, it means that this NSAID does not meet the prerequisite for analyzing the combined mechanism of hepatotoxicity and nephrotoxicity.

3. The method of claim 1, wherein: The diseases mentioned in step 3) must include a liver disease and a kidney disease.

4. The method of claim 1, wherein: The specific criteria in step 5) are as follows: targets with betweenness, closeness, eigenvector, local average connectivity-based method and network weight value > median, and degree weight value > twice the median are selected as the core targets for NSAIDs to induce two diseases respectively.

5. The method of claim 1, wherein: The specific criteria for verifying the accuracy and scientific validity of the results in step 8) are as follows: if the docking affinity is less than -5.0 Kcal / mol, the key target is considered to be able to bind stably to NSAIDs; if the docking affinity is less than -7.0 Kcal / mol, the key target is considered to have a strong binding ability to NSAIDs.

6. The method according to any one of claims 1 to 5, characterized in that: The NSAIDs mentioned are one of the following: aspirin, dichloroaniline salicylic acid, ibuprofen, naproxen, flurbiprofen, ketoprofen, oxybutynin, indomethacin, sulindac, tometidine, etordoxime, diclofenac, mephenazine, antipyrine, aminopyrine, phenacetin, piroxicam, meloxicam, celecoxib, etordoxib, parecoxib, and vardecoxib.

7. The method of claim 3, wherein: The liver disease mentioned is one of the following: intrinsic drug-induced liver injury, idiosyncratic drug-induced liver injury, acute liver failure, hepatocellular injury, cholestatic liver disease, and mixed liver injury; the kidney disease mentioned is one of the following: nonsteroidal anti-inflammatory drug-induced nephropathy, acute kidney injury, acute interstitial nephritis, nephrotic syndrome / minimal change disease, chronic interstitial nephritis, renal papillary necrosis, and electrolyte imbalance.

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