Prediction Of Drug-drug Interactions Research Articles

Relation-aware graph structure embedding with co-contrastive learning for drug–drug interaction prediction

Relation-aware graph structure embedding (RaGSE) is promising for predicting multi-relational drug–drug interactions (DDIs). Most existing methods based on RaGSE commence by constructing a multi-relational DDI graph. They then learn the RaGSEs for drugs by aggregating their neighbor’s features under different relations. Nevertheless, new drugs lack neighbors in the DDI graph. This limitation hinders the ability of these methods to effectively learn RaGSEs for new drugs, resulting in suboptimal performance when evaluating DDIs that involve new drugs. To alleviate this issue, we propose a novel DDI prediction method based on relation-aware graph structure embedding with co-contrastive learning, RaGSECo. The proposed RaGSECo constructs two heterogeneous graphs: a multi-relational DDI graph and a multi-attribute drug–drug similarity (DDS) graph. The two graphs are used respectively for learning and propagating the RaGSEs of drugs, aiming to ensure all drugs, including new ones, can possess effective RaGSEs. Additionally, we present a novel co-contrastive learning module to learn feature representations for drug pairs (DPs). This module learns DP representations from two distinct views (interaction and similarity views) and encourages these views to supervise each other collaboratively to obtain more discriminative DP representations. We evaluate the effectiveness of our RaGSECo on three different tasks using two real datasets. Experimental outcomes unequivocally indicate that our RaGSECo surpasses several state-of-the-art methods in performance.

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach.

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI remains a challenge. Generalizable DDI predictions are closer to reality than source domain predictions. For existing methods, it is difficult to achieve out-of-distribution (OOD) predictions. In this article, focusing on substructure interaction, we propose DSIL-DDI, a pluggable substructure interaction module that can learn domain-invariant representations of DDIs from source domain. We evaluate DSIL-DDI on three scenarios: the transductive setting (all drugs in test set appear in training set), the inductive setting (test set contains new drugs that were not present in training set), and OOD generalization setting (training set and test set belong to two different datasets). The results demonstrate that DSIL-DDI improve the generalization and interpretability of DDI prediction modeling and provides valuable insights for OOD DDI predictions. DSIL-DDI can help doctors ensuring the safety of drug administration and reducing the harm caused by drug abuse.

PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug-Drug Interaction Prediction.

Adverse drug-drug interactions (DDIs) pose potential risks in polypharmacy due to unknown physicochemical incompatibilities between co-administered drugs. Recent studies have utilized multi-layer graph neural network architectures to model hierarchical molecular substructures of drugs, achieving excellent DDI prediction performance. While extant substructural frameworks effectively encode interactions from atom-level features, they overlook valuable chemical bond representations within molecular graphs. More critically, given the multifaceted nature of DDI prediction tasks involving both known and novel drug combinations, previous methods lack tailored strategies to address these distinct scenarios. The resulting lack of adaptability impedes further improvements to model performance. To tackle these challenges, we propose PEB-DDI, a DDI prediction learning framework with enhanced substructure extraction. First, the information of chemical bonds is integrated and synchronously updated with the atomic nodes. Then, different dual-view strategies are selected based on whether novel drugs are present in the prediction task. Particularly, we constructed Molecular fingerprint-Molecular graph view for transduc- tive task, and Bipartite graph-Molecular graph view for inductive task. Rigorous evaluations on benchmark datasets underscore PEB-DDI's superior performance. Notably, on DrugBank, it achieves an outstanding accuracy rate of 98.18% when predicting previously unknown interactions among approved drugs. Even when faced with novel drugs, PEB-DDI consistently exhibits outstanding generalization capabilities with an accuracy rate of 88.06%, attributing to the proper migrating of molecular basic structure learning.

Toward Unified AI Drug Discovery with Multimodal Knowledge.

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

Insights into the in-vitro Susceptibility and Drug-Drug Interaction Profiles Against Drug-Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates in Amhara, Ethiopia.

In Ethiopia, tuberculosis (TB) is a major public health problem. The aim of the study was to determine the in vitro susceptibility level of drugs and drug interaction profiles against drug-resistant and susceptible M. tuberculosis clinical isolates. A laboratory-based cross-sectional study was conducted between January 2023 and August 2023. GenoType MTBDRplus v.2.0 was facilitated in genetic mutation detection. Minimum inhibitory concentration (MIC) was determined using resazurin microtitre assay (REMA), while fractional inhibitory concentration index (FICI) using resazurin drug combination microtitre assay (REDCA) for in vitro quantitative susceptibility and drug interaction prediction. Among 32 clinical isolates, a total of 14 (43.8%) RIF, 20 (62.5%) INH, 2 (6.3%) EMB-related resistant and 14 (43.8%) MDR isolates were identified. Five of RIF-resistant isolates (55.6%) carrying rpoB common mutations at codon S450L were associated with high levels of RIF-resistance with MICs of ≥ 2μg/mL, whereas 100% of isolates harboring rpoB substitutions at codons D435V and H445Y were linked with moderate or low-level RIF-resistance in the MIC ranges from 0.5 to 1μg/mL. A proportion of 81.8% of isolates harboring katG S315T mutations were associated with high-level INH resistance (MIC ≥ 1μg/mL), while the 18.2% of isolates with S315T katG mutations and 100% of isolates with inhA C-15T mutations were linked to the low-level of INH resistance with MIC variability from 0.25 to 0.5μg/mL. Our results indicated that most FICIs of the dual drugs INH+RIF and INH+LEV combination for 9 (28.1%) and 4 (12.5%) INH-resistant isolates, respectively, were ≤0.5, whereas triple drugs INH+RIF+EMB, INH+RIF+LEV and INH+EMB+LEV combination for 6 (18.8%), 11 (34.4%) and 8 (25%) INH-resistant isolates were from 0.62 to 0.75, all showed synergistic effect. The study highlights that isolates with rpoB S450L and katG S315T substitutions were associated with high level of RIF and INH resistance. It is concluded that REDCA can quantitatively determine anti-mycobacterial synergy and that LEV being of potential use against INH-resistant isolates including MDR-TB when combined with RIF+INH and INH+EMB.

A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy.

Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.

TransFOL: A Logical Query Model for Complex Relational Reasoning in Drug-Drug Interaction.

Predicting drug-drug interaction (DDI) plays a crucial role in drug recommendation and discovery. However, wet lab methods are prohibitively expensive and time-consuming due to drug interactions. In recent years, deep learning methods have gained widespread use in drug reasoning. Although these methods have demonstrated effectiveness, they can only predict the interaction between a drug pair and do not contain any other information. However, DDI is greatly affected by various other biomedical factors (such as the dose of the drug). As a result, it is challenging to apply them to more complex and meaningful reasoning tasks. Therefore, this study regards DDI as a link prediction problem on knowledge graphs and proposes a DDI prediction model based on Cross-Transformer and Graph Convolutional Networks (GCN) in first-order logical query form, TransFOL. In the model, a biomedical query graph is first built to learn the embedding representation. Subsequently, an enhancement module is designed to aggregate the semantics of entities and relations. Cross-Transformer is used for encoding to obtain semantic information between nodes, and GCN is used to gather neighbour information further and predict inference results. To evaluate the performance of TransFOL on common DDI tasks, we conduct experiments on two benchmark datasets. The experimental results indicate that our model outperforms state-of-the-art methods on traditional DDI tasks. Additionally, we introduce different biomedical information in the other two experiments to make the settings more realistic. Experimental results verify the strong drug reasoning ability and generalization of TransFOL in complex settings. Data and code are available at https://github.com/Cheng0829/TransFOL.

An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network

Drug–drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI – a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.

SRR-DDI: A drug–drug interaction prediction model with substructure refined representation learning based on self-attention mechanism

Drug–drug interaction (DDI) is an important safety issue during clinical treatment, where the mechanism of action of drugs may interfere with each other thereby causing adverse effects on the body leading to therapeutic failure. Since the deep learning method provide a powerful tool in DDI prediction, in this paper, we propose a DDI prediction model based on substructure Refined Representation Learning based on Self-Attention Mechanism, SRR-DDI, to improve the robustness of the substructure features determining the properties of the drug, thus improving the performance of DDI prediction. To improve the generalization of the model, the drug similarity feature is designed and introduced to help the model extract potential associations between drugs. The comparative experiments are set up on real-word data with two scenarios of warm start and cold start for performance evaluation, and the experimental results show that SRR-DDI outperforms the state-of-the-art methods. Finally, the visual interpretation experiment demonstrates that SRR-DDI can gradually refine the substructure features, and highlight the important substructures of drugs in DDI. In summary, SRR-DDI provides a powerful tool for predicting DDI and in-depth understanding of DDI.

A Novel Deep Learning Model for Drug-Drug Interactions

Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy, emphasizing the need for accurate prediction and understanding of these interactions. This paper presents an innovative approach for DDI prediction, employing two distinct message-passing neural network (MPNN) models, each meticulously tailored to focus on one drug within a pair. By adeptly capturing the unique attributes and interactions of each drug, this novel method strives to enhance the accuracy of DDI prediction. Furthermore, we integrate the outcomes of individual MPNN models with information derived from both drugs and their molecular features. In addition to this, we harnessed a curated list of 82 existing FDA-approved drugs, which were evaluated against five vital SARS-CoV-2 proteins. Based on the Combined Score, we systematically scrutinized a selection of the top-10 drugs exhibiting the highest binding affinity to these proteins, employing a sophisticated deep learning architecture for predicting Drug-Drug Interactions. The results remarkably demonstrated the effectiveness of our approach, boasting an impressive accuracy of 0.92, an area under the curve (AUC) of 0.99, and an F1-score of 0.85. Moreover, the identified list of high-affinity drugs against SARS-CoV-2 proteins may potentially serve as a pivotal starting point for the development of new and invaluable pharmaceutical interventions.

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.

Drug-drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. Here we propose EmerGNN, a graph neural network that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The edges of the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

GADNN: A graph attention-based method for drug-drug association prediction considering the contribution rate of different types of drug-related features

The simultaneous use of multiple drugs, known as drug combinations, is increasingly common in the treatment of complex diseases such as cancer. However, drug-drug interactions (DDIs) caused by the simultaneous use of multiple drugs can lead to unexpected side effects and even death, resulting in high medical costs. Therefore, understanding potential DDI is an essential step in reducing the risk of adverse drug reactions before administering clinical drug compounds. Recently, many machine learning-based methods for DDI prediction have been introduced, but in these methods, no attention is paid to determining the contribution rate of each type of drug feature. Here, a Graph Attention-based Deep Neural Network method called GADNN is proposed to predict DDIs based on considering the influence or contribution rate of different drug-related features. The introduced method consists of two stages. In the first stage, four basic drug datasets, including target, enzyme, infrastructure, and pathway, are used as types of drug features. Using a graph neural network-based method, the embedding vector is generated for each drug based on each dataset separately. In the second stage, the contribution coefficient of each dataset is calculated dynamically through a new graph attention mechanism. Then, the weighted combination of the separate vectors is used to predict the probability of drug-drug interactions using a dense neural network. The experimental results confirmed that the presented innovation has been able to improve the accuracy compared to state-of-the-art methods.

Assessment of CYP3A-mediated drug interaction via cytokine (IL-6) elevation for mosunetuzumab using physiologically-based pharmacokinetic modeling.

Mosunetuzumab is a CD3/CD20 bispecific antibody. As an on-target effect, transient elevation of interleukin-6 (IL-6) occurs in early treatment cycles. A physiologically-based pharmacokinetic (PBPK) model was developed to assess potential drug interaction caused by IL-6 enzyme suppression on cytochrome P450 3A (CYP3A) during mosunetuzumab treatment. The model's performance in predicting IL-6 CYP3A suppression and subsequent drug-drug interactions (DDIs) was verified using existing clinical data of DDIs caused by chronic and transient IL-6 elevation. Sensitivity analyses were performed for a complete DDI risk assessment. The IL-6 concentration- and time-dependent CYP3A suppression during mosunetuzumab treatment was simulated using PBPK model with incorporation of invitro IL-6 inhibition data. At clinically approved doses/regimens, the DDI at maximum CYP3A suppression was predicted to be a midazolam maximum drug concentration in plasma (Cmax ) and area under the plasma drug concentration-time curve (AUC) ratio of 1.17 and 1.37, respectively. At the 95th percentile of IL-6 concentration level or when gut CYP3A suppression was considered, the predicted DDI risk for mosunetuzumab remained low (<2-fold). The PBPK-based DDI predictions informed the mosunetuzumab product label to monitor, in early cycles, the concentrations and toxicities for sensitive CYP3A substrates with narrow therapeutic windows.

Quantitative prediction of CYP3A-mediated drug-drug interactions by correctly estimating fraction metabolized using human liver chimeric mice.

Fraction metabolized (fm ) and fraction transported (ft ) are important for understanding drug-drug interactions (DDIs) in drug discovery and development. However, current in vitro systems cannot accurately estimate in vivo fm due to inability to reflect the ft by efflux transporters (ft,efflux ). This study demonstrates how CYP3A-mediated DDI for CYP3A/P-gp substrates can be predicted using Hu-PXB mice as human liver chimeric mice. For estimating human in vitro fm by CYP3A enzyme (fm,CYP3A,in vitro ), six drugs, including CYP3A/P-gp substrates (alprazolam, cyclosporine, docetaxel, midazolam, prednisolone, and theophylline) and human hepatocytes were incubated with or without ketoconazole as a CYP3A inhibitor. We calculated fm,CYP3A,in vitro based on hepatic intrinsic clearance. To estimate human in vivo fm,CYP3A (fm,CYP3A,in vivo ), we collected information on clinical DDI caused by ketoconazole for these six drugs. We calculated fm,CYP3A,in vivo using the change of total clearance (CLtotal ). For evaluating the human DDI predictability, the six drugs were administered intravenously to Hu-PXB and SCID mice with or without ketoconazole. We calculated the change of CLtotal caused by ketoconazole. We compared the CLtotal change in humans with that in Hu-PXB and SCID mice. The fm,CYP3A,in vitro was overestimated compared to the fm,CYP3A,in vivo . Hu-PXB mice showed much better correlation in the change of CLtotal with humans (R2 = 0.95) compared to SCID mice (R2 = 0.0058). CYP3A-mediated DDI can be predicted by correctly estimating human fm,CYP3A,in vivo using Hu-PXB mice. These mice could be useful predicting hepatic fm and ft,efflux .

GGI-DDI: Identification for key molecular substructures by granule learning to interpret predicted drug–drug interactions

Deep learning-based approaches have achieved promising performance in predicting drug–drug interactions (DDIs). Nevertheless, a significant drawback of these approaches is their limited interpretability, hindering their practical applicability for clinicians. Based on current research findings, drug interactions frequently arise from specific substructures or functional groups present in drugs. To enhance the interpretability of deep learning models, we propose a novel end-to-end learning approach that employs granular computing to identify pivotal substructures instead of using conventional atom-based or predefined molecular fingerprint methods to predict DDIs. We refer to this model as ”GGI-DDI” (Granule–Granule Interaction for Drug–Drug Interaction). In this method, drugs are granulated into a set of coarser granules that represent the key substructures or functional groups of drugs. Subsequently, the detection of DDIs occurs through the examination of interactions among these granules, aligning more closely with human cognitive patterns. We conducted thorough experiments on the TWOSIDES dataset, and the results show that GGI-DDI achieved impeccable accuracy in predicting DDIs. We compared GGI-DDI to state-of-the-art baseline models including DDIMDL, GoGNN, DNN, STNN-DDI and GMPNN-CS, GGI-DDI almost consistently outperforms the baselines across all metrics in terms of Accuracy (Acc), Area under the receiver operating characteristic (Auc), Area under precision recall curve (Aupr) and Precision (Pre) in both transductive and inductive scenarios. Finally, we provide case studies to illustrate how GGI-DDI can effectively reveal important substructure pairs across drugs about a specific DDI type, offering insights into the underlying mechanism of these interactions. We find that this interpretability can serve as valuable guidance in the advancement of novel drug development and poly-drug therapy strategies.

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction – DrugSchizoNet

Drug discovery relies on the precise prognosis of drug–target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome the above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction of Schizophrenia. Our model leverages drug-related data from the DrugBank and repoDB databases, employing three key preprocessing techniques. First, data cleaning eliminates duplicate or incomplete entries to ensure data integrity. Next, normalization is performed to enhance security and reduce costs associated with data acquisition. Finally, feature extraction is applied to improve the quality of input data. The three layers of the DrugSchizoNet model are the input, hidden and output layers. In the hidden layer, we employ dropout regularization to mitigate overfitting and improve generalization. The fully connected (FC) layer extracts relevant features, while the LSTM layer captures the sequential nature of DIs. In the output layer, our model provides confidence scores for potential DIs. To optimize the prediction accuracy, we utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that DrugSchizoNet achieves a superior accuracy of 98.70%. The existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, and CNN, across various evaluation metrics such as accuracy, recall, specificity, precision, F1 score, AUPR, and AUROC are compared with the proposed model. By effectively addressing the challenges of imbalanced data, noise, poor generalization, high cost and time-consuming processes, DrugSchizoNet offers a promising approach for accurate DTI prediction in Schizophrenia. Its superior performance demonstrates the potential of DL in advancing drug discovery and development processes.

A simplified similarity-based approach for drug-drug interaction prediction.

Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning-based approaches for DDI prediction have been developed; however, in many cases, their ability to achieve high accuracy relies on data only available towards the end of the molecule lifecycle. Here, we propose a simple yet effective similarity-based method for preclinical DDI prediction where only the chemical structure is available. We test the model on new, unseen drugs. To focus on the preclinical problem setting, we conducted a retrospective analysis and tested the models on drugs that were added to a later version of the DrugBank database. We extend an existing method, adjacency matrix factorization with propagation (AMFP), to support unseen molecules by applying a new lookup mechanism to the drugs' chemical structure, lookup adjacency matrix factorization with propagation (LAMFP). We show that using an ensemble of different similarity measures improves the results. We also demonstrate that Chemprop, a message-passing neural network, can be used for DDI prediction. In computational experiments, LAMFP results in high accuracy, with an area under the receiver operating characteristic curve of 0.82 for interactions involving a new drug and an existing drug and for interactions involving only existing drugs. Moreover, LAMFP outperforms state-of-the-art, complex graph neural network DDI prediction methods.

Predicting drug-drug interactions based on multi-view and multichannel attention deep learning.

Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.

Development of a Novel In Vitro Model to Study Lymphatic Uptake of Drugs via Artificial Chylomicrons.

The lymphatic system plays a crucial role in the absorption of lipophilic drugs, making it an important route for drug delivery. In this study, an in vitro model using Intralipid® was developed to investigate the lymphatic uptake of drugs. The model was validated using cannabidiol, halofantrine, quercetin, and rifampicin. Remarkably, the uptake of these drugs closely mirrored what would transpire in vivo. Furthermore, adding peanut oil to the model system significantly increased the lymphatic uptake of rifampicin, consistent with meals containing fat stimulating lymphatic drug uptake. Conversely, the inclusion of pluronic L-81 was observed to inhibit the lymphatic uptake of rifampicin in the model. This in vitro model emerges as a valuable tool for investigating and predicting drug uptake via the lymphatic system. It marks the first phase in developing a physiologically based predictive tool that can be refined further to enhance the precision of drug interaction predictions with chylomicrons and their subsequent transport via the lymphatic system. Moreover, it can be employed to explore innovative drug formulations and excipients that either enhance or hinder lymphatic drug uptake. The insights gained from this study have significant implications for advancing drug delivery through the lymphatic system.