Mechanism Of Drug-drug Interactions Research Articles

Innovative Approaches to Optimize Clinical Transporter Drug-Drug Interaction Studies.

Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.

Kinetic modelling of olanzapine interactions with ciprofloxacin and norfloxacin in adult male Wistar rats: unraveling the mechanism of drug-drug interaction

Kinetic modelling of olanzapine interactions with ciprofloxacin and norfloxacin in adult male Wistar rats: unraveling the mechanism of drug-drug interaction

Investigation of the drug–drug interaction and incompatibility mechanism between Aconitum carmichaelii Debx and Pinellia ternata (Thunb.) Breit

Ethnopharmacological relevanceThe combination of Aconitum carmichaelii Debx (Chuanwu, CW) and Pinellia ternata (Thunb.) Breit (Banxia, BX) forms an herbal pair within the eighteen incompatible medicaments (EIM), indicating that BX and CW are incompatible. However, the scientific understanding of this incompatibility mechanism, especially the corresponding drug–drug interaction (DDI), remains complex and unclear. Aim of the studyThis study aims to explain the DDI and potential incompatibility mechanism between CW and BX based on pharmacokinetics and cocktail approach. Materials and methodsUltraperformance liquid chromatography–tandem mass spectrometry methods were established for pharmacokinetics and cocktail studies. To explore the DDI between BX and CW, in the pharmacokinetics study, 10 compounds were determined in rat plasma after administering CW and BX–CW herbal pair extracts. In the cocktail assay, the pharmacokinetic parameters of five probe substrates were utilized to assess the influence of BX on cytochrome P450 (CYP) isoenzyme (dapsone for CYP3A4, phenacetin for CYP1A2, dextromethorphan for CYP2D6, tolbutamide for CYP2C9, and omeprazole for CYP2C19). Finally, the DDI and incompatibility mechanism of CW and BX were integrated to explain the rationality of EIM theory. ResultsBX not only enhances the absorption of aconitine and benzoylaconine but also accelerates the metabolism of mesaconitine, benzoylmesaconine, songorine, and fuziline. Moreover, BX affects the activity of CYP enzymes, which regulate the metabolism of toxic compounds. ConclusionsBX altered the activity of CYP enzymes, consequently affecting the metabolism of toxic compounds from CW. This incompatibility mechanism may be related to the increased absorption of these toxic compounds in vivo.

MKG-FENN: A Multimodal Knowledge Graph Fused End-to-End Neural Network for Accurate Drug–Drug Interaction Prediction

Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI events. However, DDI events are associated with complex relationships and mechanisms among drugs, targets, enzymes, transporters, molecular structures, etc. Existing approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. Different from them, this paper proposes a Multimodal Knowledge Graph Fused End-to-end Neural Network (MKGFENN) that consists of two main parts: multimodal knowledge graph (MKG) and fused end-to-end neural network (FENN). First, MKG is constructed by comprehensively exploiting DDI events-associated relationships and mechanisms from four knowledge graphs of drugs-chemical entities, drug-substructures, drugs-drugs, and molecular structures. Correspondingly, a four channels graph neural network is designed to extract high-order and semantic features from MKG. Second, FENN designs a multi-layer perceptron to fuse the extracted features by end-to-end learning. With such designs, the feature extractions and fusions of DDI events are guaranteed to be comprehensive and optimal for prediction. Through extensive experiments on real drug datasets, we demonstrate that MKG-FENN exhibits high accuracy and significantly outperforms state-of-the-art models in predicting DDI events. The source code and supplementary file of this article are available on: https://github.com/wudi1989/MKG-FENN.

Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer

Identifying novel drug-drug interactions (DDIs) is a crucial task in pharmacology, as the interference between pharmacological substances can pose serious medical risks. In recent years, several network-based techniques have emerged for predicting DDIs. However, they primarily focus on local structures within DDI-related networks, often overlooking the significance of indirect connections between pairwise drug nodes from a global perspective. Additionally, effectively handling heterogeneous information present in both biomedical knowledge graphs and drug molecular graphs remains a challenge for improved performance of DDI prediction. To address these limitations, we propose a Transformer-based relatIon-aware Graph rEpresentation leaRning framework (TIGER) for DDI prediction. TIGER leverages the Transformer architecture to effectively exploit the structure of heterogeneous graph, which allows it direct learning of long dependencies and high-order structures. Furthermore, TIGER incorporates a relation-aware self-attention mechanism, capturing a diverse range of semantic relations that exist between pairs of nodes in heterogeneous graph. In addition to these advancements, TIGER enhances predictive accuracy by modeling DDI prediction task using a dual-channel network, where drug molecular graph and biomedical knowledge graph are fed into two respective channels. By incorporating embeddings obtained at graph and node levels, TIGER can benefit from structural properties of drugs as well as rich contextual information provided by biomedical knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of TIGER in DDI prediction. Furthermore, case studies highlight its ability to provide a deeper understanding of underlying mechanisms of DDIs.

The molecular basis of dapsone activation of CYP2C9-catalyzed nonsteroidal anti-inflammatory drug oxidation

Positive heterotropic cooperativity, or "activation," results in an instantaneous increase in enzyme activity in the absence of an increase in protein expression. Thus, cytochrome P450 (CYP) enzyme activation presents as a potential drug-drug interaction mechanism. It has been demonstrated previously that dapsone activates the CYP2C9-catalyzed oxidation of a number of nonsteroidal anti-inflammatory drugs invitro. Here, we conducted molecular dynamics simulations (MDS) together with enzyme kinetic investigations and site-directed mutagenesis to elucidate the molecular basis of the activation of CYP2C9-catalyzed S-flurbiprofen 4'-hydroxylation and S-naproxen O-demethylation by dapsone. Supplementation of incubations of recombinant CYP2C9 with dapsone increased the catalytic efficiency of flurbiprofen and naproxen oxidation by 2.3- and 16.5-fold, respectively. MDS demonstrated that activation arises predominantly from aromatic interactions between the substrate, dapsone, and the phenyl rings of Phe114 and Phe476 within a common binding domain of the CYP2C9 active site, rather than involvement of a distinct effector site. Mutagenesis of Phe114 and Phe476 abrogated flurbiprofen and naproxen oxidation, and MDS and kinetic studies with the CYP2C9 mutants further identified a pivotal role of Phe476 in dapsone activation. MDS additionally showed that aromatic stacking interactions between two molecules of naproxen are necessary for binding in a catalytically favorable orientation. In contrast to flurbiprofen and naproxen, dapsone did not activate the 4'-hydroxylation of diclofenac, suggesting that the CYP2C9 active site favors cooperative binding of nonsteroidal anti-inflammatory drugs with a planar or near-planar geometry. More generally, the work confirms the utility of MDS for investigating ligand binding in CYP enzymes.

Effect of P-glycoprotein and Cotreatment with Sofosbuvir on the Intestinal Permeation of Tenofovir Disoproxil Fumarate and Tenofovir Alafenamide Fumarate.

We aimed to compare the effects of P-glycoprotein (ABCB1) on the intestinal uptake of tenofovir disoproxil fumarate (TDF), tenofovir alafenamide fumarate (TAF), and metabolites, tenofovir isoproxil monoester (TEM) and tenofovir (TFV), and to study the molecular mechanism of drug-drug interaction (DDI) between sofosbuvir (SOF) and TDF/TAF. Bidirectional transport experiments in Caco-2 cells and accumulation studies in precision-cut intestinal slices prepared from the ileal segment of rodent (rPCIS) and human (hPCIS) intestines were performed. TDF and TAF were extensively metabolised but TAF exhibited greater stability. ABCB1 significantly reduced the intestinal transepithelial transfer and uptake of the TFV(TDF) and TFV(TAF)-equivalents. However, TDF and TAF were absorbed more efficiently than TFV and TEM. SOF did not inhibit intestinal efflux of TDF and TAF or affect intestinal accumulation of TFV(TDF) and TFV(TAF)-equivalents but did significantly increase the proportion of absorbed TDF. TDF and TAF likely produce comparable concentrations of TFV-equivalents in the portal vein and the extent of permeation is reduced by the activity of ABCB1. DDI on ABCB1 can thus potentially affect TDF and TAF absorption. SOF does not inhibit ABCB1-mediated transport of TDF and TAF but does stabilise TDF, albeit without affecting the quantity of TFV(TDF)-equivalents crossing the intestinal barrier. Our data thus suggest that reported increases in the TFV plasma concentrations in patients treated with SOF and TDF result either from a DDI between SOF and TDF that does not involve ABCB1 or from a DDI involving another drug used in combination therapy.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity.

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy and toxicity, as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biological membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiological functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions (DDIs), and variability caused by genetic polymorphisms. The data collected contributes towards safer and more effective pharmacological treatments. Significance Statement The experimental structure of ABC and SLC transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of DDIs, and differences caused by genetic polymorphisms.

A comprehensive electrochemical study on anti-tuberculosis drug rifampicin. Investigating reactions of rifampicin-quinone with other anti-tuberculosis drugs, isoniazid, pyrazinamide and ethambutol

Electrochemical behavior of rifampicin (RIF) as an anti-tuberculosis drug was studied in detail in water/ethanol mixture using different voltammetric techniques. The results indicate that rifampicin is oxidized in two steps, the first step is related to the reversible oxidation of hydroquinone moiety to benzoquinone and the second one is related to the irreversible oxidation of phenolic ring of RIF. In this work, the adsorption activity and diffusion coefficient of RIF was also determined and shows that the first oxidation step is diffusion control while second one is a diffusion/adsorption control process. Furthermore, our results show that the oxidation of RIF (especially in the first step) is complex and highly pH-dependent. The Pourbaix diagram was drawn for this drug and useful information was obtained regarding the oxidized and reduced forms of RIF and the acid/base properties of the species obtained from RIF and its oxidized form (RIF-Q). The first line of tuberculosis drug treatment currently includes the simultaneous use of rifampicin, isoniazid, pyrazinamide and ethambutol (Fixed Dose Combinations, FDCs). Understanding the mechanism of drug-drug interactions is not only useful in preventing drug toxicity or side effects, but also in devising safer treatments for diseases. In this study, based on the obtained electrochemical data, we suggest plausible mechanisms for the reaction of RIF-Q with the medicines prescribed in FDC. The obtained results show that the mentioned drugs through their free amino group can react with RIF-Q. Our findings also show that the rate of these reactions is low in acidic environments such as the stomach and high in neutral or alkaline environments such as the small intestine. In these reactions, ethambutol has the highest rate and pyrazinamide has the lowest rate for the reaction with RIF.

Incidence, Patterns, and Severity of Potential Drug Interactions Among Cancer Patients on Chemotherapy in a Tertiary Care Hospital

Objective To study the incidence of potential drug−drug interactions (DDIs) and evaluate their pattern and severity in cancer inpatients. Materials and Methods A detailed clinical data and prescriptions of 150 inpatients with different malignancies were subjected to DDI screening using Micromedex software. The frequency of potential DDIs and their types, patterns, and severity were investigated. Results A total of 360 potential DDIs were present in 111 (74%) of 150 inpatients, dominated by female (67.33%) and breast cancer (30%) patients. The incidence of severe interactions was 63.88%, moderate interactions 35.83%, and mild interactions 0.27%. The potential mechanisms of DDIs were 38.33% pharmacodynamic, 48.33% pharmacokinetic, and 13.33% unspecified. The drug interactions were found to be positively correlated ( p < 0.01) with the 6–10 number of prescribed medicines. Conclusion According to this study, the number of medicines prescribed to cancer inpatients increased the chance of DDIs. As a result, the drug surveillance program could save a sizable number of patients from the potentially hazardous clinical effects of DDIs.

Preincubation Time-Dependent, Long-Lasting Inhibition of Drug Transporters and Impact on the Prediction of Drug-Drug Interactions.

Transporter-mediated drug-drug interaction (DDI) is of clinical concern, and the quantitative prediction of DDIs is an indispensable part of drug development. Cell-based inhibition assays, in which a representative probe substrate and a potential inhibitor are coincubated, are routinely performed to assess the inhibitory potential of new molecular entities on drug transporters. However, the inhibitory effect of cyclosporine A (CsA) on organic anion transporting polypeptide (OATP) 1B1 is substantially potentiated with CsA preincubation, and this effect is both long-lasting and dependent on the preincubation time. This phenomenon has also been reported with transporters other than OATP1Bs, but it is considered more prevalent among OATP1Bs and organic cation transporters. Regulatory agencies have also noted this preincubation effect and have recommended that pharmaceutical companies consider inhibitor preincubation when performing in vitro OATP1B1 and OATP1B3 inhibition studies. Although the underlying mechanisms responsible for the preincubation effect are not fully understood, a trans-inhibition mechanism was recently demonstrated for OATP1B1 inhibition by CsA, in which CsA inhibited OATP1B1 not only extracellularly (cis-inhibition) but also intracellularly (trans-inhibition). Furthermore, the trans-inhibition potency of CsA was much greater than that of cis-inhibition, suggesting that trans-inhibition might be a key driver of clinical DDIs of CsA with OATP1B substrate drugs. Although confidence in transporter-mediated DDI prediction is generally considered to be low, the predictability might be further improved by incorporating the trans-inhibition mechanism into static and dynamic models for preincubation-dependent inhibitors of OATP1Bs and perhaps other transporters. SIGNIFICANCE STATEMENT: Preincubation time-dependent, long-lasting inhibition has been observed for OATP1B1 and other solute carrier transporters in vitro. Recently, a trans-inhibition mechanism for the preincubation effect of CsA on OATP1B1 inhibition was identified, with the trans-inhibition potency being greater than that of cis-inhibition. The concept of trans-inhibition may allow us to further understand the mechanism of transporter-mediated DDIs not only for OATP1B1 but also for other transporters and to improve the accuracy and confidence of DDI predictions.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Retracted: A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms.

[This retracts the article DOI: 10.1155/2022/9132477.].

MOPDDI: Predicting Drug-Drug Interaction Events Based on Multimodal Mutual Orthogonal Projection and Intermodal Consistency Loss.

Predicting accurately the mechanisms of drug-drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0.9500, and an area under the precision-recall (AUPR) curve is 0.9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs. The source code is available at https://github.com/xiaqixiaqi/MOPDDI.

Pharmacometric model of agalsidase-migalastat interaction in human: a novel mechanistic model of drug-drug interaction between a therapeutic protein and a small molecule.

Recently, a new mechanism of drug-drug interaction (DDI) was reported between agalsidase, a therapeutic protein, and migalastat, a small molecule, both of which are treatment options of Fabry disease. Migalastat is a pharmacological chaperone that stabilizes the native form of both endogenous and exogenous agalsidase. In Fabry patients co-administrated with agalsidase and migalastat, the increase in active agalsidase exposure is considered a pharmacokinetic effect of agalsidase infusion but a pharmacodynamic effect of migalastat administration, which makes this new DDI mechanism even more interesting. To quantitatively characterize the interaction between agalsidase and migalastat in human, a pharmacometric DDI model was developed using literature reported concentration-time data. The final model includes three components: a 1-compartment linear model component for migalastat; a 2-compartment linear model component for agalsidase; and a DDI component where the agalsidase-migalastat complex is formed via second order association constant kon, dissociated with first order dissociation constant koff, and distributed/eliminated with same rates as agalsidase alone, albeit the complex (i.e., bound agalsidase) has higher enzyme activity compared to free agalsidase. The final model adequately captured several key features of the unique interaction between agalsidase and migalastat, and successfully characterized the kinetics of migalastat as well as the kinetics and activities of agalsidase when both drugs were used alone or in combination following different doses. Most parameters were reasonably estimated with good precision. Because the model includes mechanistic basis of therapeutic protein and small molecule pharmacological chaperone interaction, it can potentially serve as a foundational work for DDIs with similar mechanism.

Involvement of esterases in the pulmonary metabolism of beclomethasone dipropionate and the potential influence of cannabis use

Beclomethasone dipropionate (BDP) is an inhaled glucocorticoid used for maintenance treatment of asthma in adults and children. BDP is a prodrug activated in lung when hydrolyzed to its major active metabolite beclomethasone-17-monopropionate (17-BMP), which can be further deactivated to beclomethasone (BOH). The specific hydrolases contributing to these processes have not been identified which warrants an investigation to enable a better assessment of the drug-drug interaction (DDI) liability and a better management of drug efficacy and systemic toxicity. In the present study, the pulmonary metabolism of BDP was investigated using both human lung S9 (HLuS9) and recombinant carboxylesterase 1 (CES1) S9. By employing the relative activity approach, we tested the hypothesis of CES1 being the major enzyme involved. Assessment of other hydrolases were conducted in an assay with selective esterase inhibitors. In addition, the DDI potentials between BDP and Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) were evaluated due to the increasing use of inhaled cannabis both recreationally and medically. The mechanism of DDI was conducted in an in vitro time-dependent inhibition assay, and further interpreted utilizing a proposed model. In HLuS9, BDP was efficiently metabolized almost completely to 17-BMP, which was then converted to BOH at a much lower rate. CES1 was found as a minor contributor accounting for only 1.4% of BDP metabolism in HLuS9, while arylacetamide deacetylase might be the main enzyme involved. Both THC and CBD inhibited the HLuS9 mediated BDP hydrolysis in a reversible manner, with reported IC50 values estimated as 8.98 and 36.8 μM, respectively. Our proposed model suggested a moderately decreased 17-BMP exposure in lung by concomitant THC from a cannabis cigarette, while the effects from orally administered CBD was expected to be of no clinical relevance.

A review: drug-drug interactions of epithelial growth factor receptor-tyrosine kinase inhibitors

Mutations in the epithelial growth factor receptor (EGFR) is a driving factor that causes non-small cell lung carcinoma (NSCLC). The epithelial growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) is a crucial discovery in the treatment of lung cancer, particularly the efficacy of EGFR-TKIs is superior to that of the standard chemotherapy for patients with EGFR mutation-positive advanced NSCLC. Patients with NSCLC use EGFR-TKIs and other medications simultaneously is commonly seen, especially among those with comorbidities, which increases the risk of drug-drug interactions (DDIs) of EGFR-TKIs. The most common mechanisms underlying the DDIs of EGFR-TKIs are modulations of cytochrome P450 (CYP) and drug transporters [including P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP)], as well as gastrointestinal acid-inhibitory drugs [proton pump inhibitors (PPIs) and H(2) receptor antagonists (H(2)RA)]. Inhibitors or inducers of CYP enzymes and drug transporters can inhibit or accelerate the metabolism of EGFR-TKIs, which increase or reduce the exposure of EGFR-TKIs, thereby affect the efficacy and safety of EGFR-TKIs. In addition, PPIs or H(2)RA can decrease the solubility, bioavailability and efficacy of EGFR-TKIs. This review summarizes the mechanisms of DDIs of gefitinib, erlotinib, icotinib, afatinib, dacomitinib and osimertinib; the management recommendations for DDIs of those EGFR-TKIs from the Chinese and global guideline, as well as from the recent pre-clinical and clinical studies, which provide the reference and evidence for managing the combination therapies of EGFR-TKIs and other medications in clinics.

Energy Decomposition Analysis of the Nature of Coordination Bonding at the Heme Iron Center in Cytochrome P450 Inhibition

Inhibition of cytochrome P450 enzymes brings about the problem of drug–drug interaction (DDI); however, much remains to be elucidated regarding the microscopic DDI mechanisms. This study theoretically investigates the coordination bonding formed in the reversible and quasi-irreversible inhibition of cytochrome P450 enzymes. The generalized Kohn–Sham energy decomposition analysis (GKS-EDA) and natural bond orbital (NBO) analysis provide valuable quantum mechanical insights. More information can be found in the Research Article by Shuyang Liu and Hajime Hirao.

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.

Prediction of Carboxylesterase 1-mediated In Vivo Drug Interaction between Methylphenidate and Cannabinoids using Static and Physiologically Based Pharmacokinetic Models.

The use of cannabis products has increased substantially. Cannabis products have been perceived and investigated as potential treatments for attention-deficit/hyperactivity disorder (ADHD). Accordingly, co-administration of cannabis products and methylphenidate (MPH), a first-line medication for ADHD, is possible. Oral MPH undergoes extensive presystemic metabolism by carboxylesterase 1 (CES1), a hepatic enzyme which can be inhibited by two prominent cannabinoids, Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). This prompts further investigation into the likelihood of clinical interactions between MPH and these two cannabinoids through CES1 inhibition. In the present study, inhibition parameters were obtained from a human liver S9 system and then incorporated into static and physiologically-based pharmacokinetic (PBPK) models for prediction of potential clinical significance. The inhibition of MPH hydrolysis by THC and CBD was reversible, with estimated unbound inhibition constants (Ki,u) of 0.031 and 0.091 µM, respectively. The static model predicted a mild increase in MPH exposure by concurrent THC (34%) and CBD (94%) from smoking a cannabis cigarette and ingestion of prescriptive CBD, respectively. PBPK models suggested no significant interactions between single doses of MPH and CBD (2.5 - 10 mg/kg) when administered simultaneously, while a mild interaction (area under drug concentration-time curve increased by up to 55% and maximum concentration by up to 45%) is likely if multiple doses of CBD (10 mg/kg twice daily) are administered. In conclusion, the pharmacokinetic disposition of MPH can be potentially influenced by THC and CBD under certain clinical scenarios. Whether the magnitude of predicted interactions translates into clinically relevant outcomes requires verification in an appropriately designed clinical study. SIGNIFICANCE STATEMENT: This work demonstrated a potential mechanism of drug-drug interactions between methylphenidate (MPH) and two major cannabinoids (Δ9-tetrahydrocannabinol [THC] and cannabidiol [CBD]) not previously reported. We predicted a mild interaction between MPH and THC when the cannabinoid exposure occurred via cannabis smoking. Mild interactions between MPH and CBD were predicted with multiple oral administrations of CBD.