Drug-drug Interaction Network Research Articles

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Identification of Repurposable Drugs for Colorectal Cancer Using Drug-Network-Based Classification Models

Colorectal cancer (CRC) is the second most lethal cancer with more than one million new cases diagnosed worldwide every year. To defuse the increasing CRC threat, more effective and less harmful treatments for CRC patients are urgently needed. Computational drug repurposing, which is an in silico based approach to uncover new indications of approved drugs, is a promising strategy to accelerate the time to market of drugs. However, there are not many computational drug repurposing methods for CRC. In this work, we proposed drug-network-based classification models to identify repurposable drugs for CRC. Initially, four drug networks, the chemical structure network (CSN), the target protein network (TPN), the drug pathway network (PWN), and the drug-drug interaction network (DIN), were formulated. Based on the drug features properly extracted from the networks, we created four multi-layer perceptron (MLP) models. By comparing the performance of the models, the DIN model outperformed the others with the highest accuracy and an F1 score of 96.9%. After predicting the repurposability of over 1,200 non-CRC approved drugs using the DIN model, 306 drugs discovered as potentially repurposable drugs for CRC. In summary, the drug-network-based classification models can efficiently identify repurposable drug candidates for CRC, which would be applicable for efficient therapeutic treatment of CRC.

Decoding the conformational binding of drug mixtures on ovalbumin: An integrated multimodal network

This research addresses the crucial necessity for a deeper understanding of the binding interactions between surfactants and proteins, with a specific focus on ovalbumin. Considering ovalbumin's role in diverse biochemical processes, it remains a subject of significant interest for drug discovery and design. To fill existing knowledge gaps, we investigated the binding interaction between dicloxacillin and cetyltrimethylammonium bromide (CTAB) on ovalbumin, employing a comprehensive approach that combines computational modeling with experimental validations. Using the ezPocket tool, the computational phase predicted ten relevant binding sites on ovalbumin's surface. The isobologram combination index (CI) heatmap strongly suggested a complex interplay of antagonistic and synergistic effects. Besides, a conformational drug-drug interaction network was proposed to explore the stability of the surfactant mixture within specific binding sites of ovalbumin, revealing a dynamic landscape of suggested antagonist effects. Experimental validations through UV–vis, Fluorescence, and circular dichroism (CD) spectroscopy further corroborated the computational findings, confirming the formation of stable complexes. Finally, this study not only advances our comprehension of ovalbumin's interactions with surfactants but also offers a multidimensional perspective and an advanced methodological framework for efficient therapeutic strategies, opening new avenues for future applications in drug development and applied biochemistry.

Revealing the dynamic landscape of drug-drug interactions through network analysis.

Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape. Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time. Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and clinical applications. Recently, researchers have proposed several deep learning methods to predict DDIs. However, these methods mainly exploit the chemical or biological features of drugs, which is insufficient and limits the performances of DDI prediction. Here, we propose a new deep multimodal feature fusion framework for DDI prediction, DMFDDI, which fuses drug molecular graph, DDI network and the biochemical similarity features of drugs to predict DDIs. To fully extract drug molecular structure, we introduce an attention-gated graph neural network for capturing the global features of the molecular graph and the local features of each atom. A sparse graph convolution network is introduced to learn the topological structure information of the DDI network. In the multimodal feature fusion module, an attention mechanism is used to efficiently fuse different features. To validate the performance of DMFDDI, we compare it with 10 state-of-the-art methods. The comparison results demonstrate that DMFDDI achieves better performance in DDI prediction. Our method DMFDDI is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDEBLab/DMFDDI.git.

An Improved Graph Isomorphism Network for Accurate Prediction of Drug–Drug Interactions

Drug–drug interaction (DDI) prediction is one of the essential tasks in drug development to ensure public health and patient safety. Drug combinations with potentially severe DDIs have been verified to threaten the safety of patients critically, and it is therefore of great significance to develop effective computational algorithms for identifying potential DDIs in clinical trials. By modeling DDIs with a graph structure, recent attempts have been made to solve the prediction problem of DDIs by using advanced graph representation learning techniques. Still, their representational capacity is limited by isomorphic structures that are frequently observed in DDI networks. To address this problem, we propose a novel algorithm called DDIGIN to predict DDIs by incorporating a graph isomorphism network (GIN) such that more discriminative representations of drugs can thus be learned for improved performance. Given a DDI network, DDIGIN first initializes the representations of drugs with Node2Vec according to the topological structure and then optimizes these representations by propagating and aggregating the first-order neighboring information in an injective way. By doing so, more powerful representations can thus be learned for drugs with isomorphic structures. Last, DDIGIN estimates the interaction probability for pairwise drugs by multiplying their representations in an end-to-end manner. Experimental results demonstrate that DDIGIN outperforms several state-of-the-art algorithms on the ogbl-ddi (Acc = 0.8518, AUC = 0.8594, and AUPR = 0.9402) and DDInter datasets (Acc = 0.9763, AUC = 0.9772, and AUPR = 0.9868). In addition, our case study indicates that incorporating GIN enhances the expressive power of drug representations for improved performance of DDI prediction.

Prediction of Drug-Drug Interactions by Means of a Graph Look Network Novel Algorithm

Abstract: With the growing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unexpected adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a severe public health issue for polypharmacy safety. Traditional investigational methods for detecting DDIs are expensive and time- consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly exposed drugs without an existing DDI network, remains a challenge. In this study, we proposea predicting method of DDIs named HAG-DDI based on graph look networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on superior representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: https://github.com/xtnenu/DDIFramework.

Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug-Drug Interactions.

Previous studies have shown that antibiotics can be divided into groups, and drug-drug interactions (DDI) depend on their groups. However, these studies focused on a specific bacteria strain (i.e., Escherichia coli BW25113). Existing datasets often contain noise. Noisy labeled data may have a bad effect on the clustering results. To address this problem, we developed a multi-source information fusion method for integrating DDI information from multiple bacterial strains. Specifically, we calculated drug similarities based on the DDI network of each bacterial strain and then fused these drug similarity matrices to obtain a new fused similarity matrix. The fused similarity matrix was combined with the T-distributed stochastic neighbor embedding algorithm, and hierarchical clustering algorithm can effectively identify antibiotic subgroups. These antibiotic subgroups are strongly correlated with known antibiotic classifications, and group-group interactions are almost monochromatic. In summary, our method provides a promising framework for understanding the mechanism of action of antibiotics and exploring multi-species group-group interactions.

Integrating multi-source drug information to cluster drug-drug interaction network

Characterizing drug-drug interactions is important to improve efficacy and/or slow down the evolution of antimicrobial resistance. Experimental methods are both time-consuming and laborious for characterizing drug-drug interactions. In recent years, many computational methods have been proposed to explore drug-drug interactions. However, these methods failed to effectively integrate multi-source drug information. In this study, we propose a similarity matrix fusion (SMF) method to integrate four drug information (i.e., structural similarity, pharmaceutical similarity, phenotypic similarity and therapeutic similarity). SMF combined with t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering algorithm can effectively identify drug groups and group-group interactions are almost monochromatic (purely synergetic or purely antagonistic). To evaluate clustering quality (i.e., monochromaticity), two measures (edge purity and edge normalized mutual information) are proposed, and SMF showed the best performance. In addition, clustered drug-drug interaction network can also be used to predict new drug-drug interactions (accuracy = 0.741). Overall, SMF provides a comprehensive view to understand drug groups and group-group interactions.

Attention-based cross domain graph neural network for prediction of drug-drug interactions.

Drug-drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI network, ignoring the potential information contained in drug-related biological entities such as targets and genes. Besides, existing DDI network-based models could not make effective predictions for drugs without any known DDI records. To address the above limitations, we propose an attention-based cross domain graph neural network (ACDGNN) for DDI prediction, which considers the drug-related different entities and propagate information through cross domain operation. Different from the existing methods, ACDGNN not only considers rich information contained in drug-related biomedical entities in biological heterogeneous network, but also adopts cross-domain transformation to eliminate heterogeneity between different types of entities. ACDGNN can be used in the prediction of DDIs in both transductive and inductive setting. By conducting experiments on real-world dataset, we compare the performance of ACDGNN with several state-of-the-art methods. The experimental results show that ACDGNN can effectively predict DDIs and outperform the comparison models.

DGANDDI: Double Generative Adversarial Networks for Drug-Drug Interaction Prediction.

Co-administration of multiple drugs may cause adverse drug interactions and side effects that damage the body. Therefore, accurate prediction of drug-drug interaction (DDI) events is of great importance. Recently, many computational methods have been proposed for predicting DDI associated events. However, most existing methods merely considered drug associated attribute information or topological information in DDI network, ignoring the complementary knowledge between them. Therefore, to effectively explore the complementarity of drug attribute and topological information of DDI network, we propose a deep learning model based adversarial learning strategy, which is named as DGANDDI. In DGANDDI, we design a two-GAN architecture to deeply capture the complementary knowledge between drug attribute and topological information of DDI network, thus more comprehensive drug representations can be learned. We conduct extensive experiments on real world dataset. The experimental results show that DGANDDI can effectively predict DDI occurrence and outperforms the comparison of the state-of-the-art models. We also perform ablation studies that demonstrate that DGANDDI is effective and that it is robust in DDI prediction tasks, even in the case of a scarcity of labeled DDIs.

A novel drug-drug interactions prediction method based on a graph attention network

<abstract><p>With the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety. Traditional experimental methods for detecting DDIs are expensive and time-consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly discovered drugs without an existing DDI network, remains a challenge. In this study, we propose a predicting method of DDIs named HAG-DDI based on graph attention networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on advanced representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/xtnenu/DDIFramework">https://github.com/xtnenu/DDIFramework</ext-link>.</p></abstract>

Network-based Drug Repurposing: A Critical Review.

New drug development for a disease is a tedious, time-consuming, complex, and expensive process. Even if it is done, the chances for success of newly developed drugs are still very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data about medications, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to simplify data interpretation. Some of the data sets now available in standard, and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods allows a more efficient and easy way to repurpose an exact drug for the desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drugdisease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with this, we briefly discussed the drug's limitations, prediction methodologies, and data sets utilised in various biological networks for drug repurposing.

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

BackgroundDrug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability.ResultsIn this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance.ConclusionsOur proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

In Silico Screening of Synthetic and Natural Compounds to Inhibit the Binding Capacity of Heavy Metal Compounds against EGFR Protein of Lung Cancer.

Inorganic pollutant, specifically heavy metals' contamination, is a significant matter of concern and is one of the key contributors in various health disorders including cancer. However, the interaction of heavy metals (HMs) with lung cancer has rarely been explored yet. Therefore, the present study was intended with the aim to identify the interactions of HMs with the target protein “epidermal growth factor receptor (EGFR)” of lung cancer and explore potential drug candidates, which could inhibit the active site of EGFR against HM exposure. The molecular operating environment (MOE) tool was used to study the interactions of HMs with EGFR protein. The drug-drug interaction (DDI) network approach was used to identify the potential drug candidates, which were further confirmed and compared with the commercial medicines/control group. Various compounds of twenty-three HMs were docked with EGFR protein. Out of which tinidazole, thallium bromodimethyl, and silver acetate (Sn, Ti, and Ag compounds) showed strong interactions with EGFR based on lowest-scoring values (-20.42, -7.86, and -7.74 kcal/mol, respectively). Among 1280 collected drug candidates, three synthetic compounds viz., ZINC00602803, ZINC00602685, and ZINC06718468 and three natural compounds (berberine chloride, transresveratrol, and ellagic acid) depicted strong binding capacity with EGFR. Specifically, the scoring value of ZINC00602803 (-30.99 kcal/mol) was even lowest than standard lung cancer drugs (afatinib, erlotinib, and gefitinib). Our findings revealed that both natural and synthetic compounds having strong associations with EGFR protein could be potential candidates to inhibit the interaction between HMs and lung cancer protein and can also be used as an alternative for the prevention and treatment of lung cancer. However, in vitro and in vivo studies should be conducted to validate the aforementioned natural and synthetic compounds.

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.

The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.

Predicting drug-drug interactions by graph convolutional network with multi-kernel.

Drug repositioning is proposed to find novel usages for existing drugs. Among many types of drug repositioning approaches, predicting drug-drug interactions (DDIs) helps explore the pharmacological functions of drugs and achieves potential drugs for novel treatments. A number of models have been applied to predict DDIs. The DDI network, which is constructed from the known DDIs, is a common part in many of the existing methods. However, the functions of DDIs are different, and thus integrating them in a single DDI graph may overlook some useful information. We propose a graph convolutional network with multi-kernel (GCNMK) to predict potential DDIs. GCNMK adopts two DDI graph kernels for the graph convolutional layers, namely, increased DDI graph consisting of 'increase'-related DDIs and decreased DDI graph consisting of 'decrease'-related DDIs. The learned drug features are fed into a block with three fully connected layers for the DDI prediction. We compare various types of drug features, whereas the target feature of drugs outperforms all other types of features and their concatenated features. In comparison with three different DDI prediction methods, our proposed GCNMK achieves the best performance in terms of area under receiver operating characteristic curve and area under precision-recall curve. In case studies, we identify the top 20 potential DDIs from all unknown DDIs, and the top 10 potential DDIs from the unknown DDIs among breast, colorectal and lung neoplasms-related drugs. Most of them have evidence to support the existence of their interactions. fangxiang.wu@usask.ca.

RANEDDI: Relation-aware network embedding for drug-drug interaction prediction

Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI.

Role of Persistent Organic Pollutants in Breast Cancer Progression and Identification of Estrogen Receptor Alpha Inhibitors Using In-Silico Mining and Drug-Drug Interaction Network Approaches

Simple SummaryThe role of persistent organic pollutants (POPs) in breast cancer progression and their bioaccumulation in adipose tissue has been reported. We used a computational approach to study molecular interactions of POPs with breast cancer proteins and identified natural and synthetic compounds to inhibit these interactions. Moreover, for comparative analysis, standard drugs and screened compounds were also docked against estrogen receptor alpha (ERα) and identification of the finest inhibitor was performed using in-silico mining and drug-drug interaction (DDI) network approaches. Based on scoring values, short-chained chlorinated paraffins demonstrated strong interactions with ERα compared to organo-chlorines and PCBs. Synthetic and natural compounds demonstrating strong associations with the active site of the ERα protein could be potential candidates to treat breast cancer specifically caused by POPs and other organic toxins and can be used as an alternative to standard drugs.The strong association between POPs and breast cancer in humans has been suggested in various epidemiological studies. However, the interaction of POPs with the ERα protein of breast cancer, and identification of natural and synthetic compounds to inhibit this interaction, is mysterious yet. Consequently, the present study aimed to explore the interaction between POPs and ERα using the molecular operating environment (MOE) tool and to identify natural and synthetic compounds to inhibit this association through a cluster-based approach. To validate whether our approach could distinguish between active and inactive compounds, a virtual screen (VS) was performed using actives (627 compounds) as positive control and decoys (20,818 compounds) as a negative dataset obtained from DUD-E. Comparatively, short-chain chlorinated paraffins (SCCPs), hexabromocyclododecane (HBCD), and perfluorooctanesulfonyl fluoride (PFOSF) depicted strong interactions with the ERα protein based on the lowest-scoring values of −31.946, −18.916, −17.581 kcal/mol, respectively. Out of 7856 retrieved natural and synthetic compounds, sixty were selected on modularity bases and subsequently docked with ERα. Based on the lowest-scoring values, ZINC08441573, ZINC00664754, ZINC00702695, ZINC00627464, and ZINC08440501 (synthetic compounds), and capsaicin, flavopiridol tectorgenin, and ellagic acid (natural compounds) showed incredible interactions with the active sites of ERα, even more convening and resilient than standard breast cancer drugs Tamoxifen, Arimidex and Letrozole. Our findings confirm the role of POPs in breast cancer progression and suggest that natural and synthetic compounds with high binding affinity could be more efficient and appropriate candidates to treat breast cancer after validation through in vitro and in vivo studies.

A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine.

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.