Drug-drug Interaction Model Research Articles

A Novel Deep Learning Model for Drug-drug Interactions.

Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions. In this paper, we propose a novel approach for DDI prediction using two separate message-passing neural network (MPNN) models, each focused on one drug in a pair. By capturing the unique characteristics of each drug and their interactions, the proposed method aims to improve the accuracy of DDI prediction. The outputs of the individual MPNN models combine to integrate the information from both drugs and their molecular features. Evaluating the proposed method on a comprehensive dataset, we demonstrate its superior performance with an accuracy of 0.90, an area under the curve (AUC) of 0.99, and an F1-score of 0.80. These results highlight the effectiveness of the proposed approach in accurately identifying potential drugdrug interactions. The use of two separate MPNN models offers a flexible framework for capturing drug characteristics and interactions, contributing to our understanding of DDIs. The findings of this study have significant implications for patient safety and personalized medicine, with the potential to optimize treatment outcomes by preventing adverse events. Further research and validation on larger datasets and real-world scenarios are necessary to explore the generalizability and practicality of this approach.

Physiologically based pharmacokinetic models for predicting lamotrigine exposure and dose optimization in pediatric patients receiving combination therapy with carbamazepine or valproic acid.

Lamotrigine (LTG) is an antiepileptic drug that has been used in pediatric epilepsy as a combination therapy or monotherapy after stabilization in recent years. However, there are significant drug-drug interactions (DDI) between LTG and combined drugs such as carbamazepine (CBZ) and valproic acid (VPA). It is particularly important to consider the risk of DDI in combination therapy for intractable epilepsy in pediatric patients. Therefore, it is necessary to adjust the dosage of LTG accordingly. The aim of this study was to establish and validate a pediatric physiologically based pharmacokinetic (PBPK) model for predicting LTG exposure. The model is designed to explore the potential for quantifying pharmacokinetic (PK) DDI of LTG when administered concurrently with CBZ or VPA in pediatric patients. Adult and pediatric PBPK models for LTG and VPA were developed using PK-Sim® software in combination with physiological information and drug-specific parameters, and a DDI model was developed in combination with the published CBZ model. The models were validated against available PK data. Predictive and observational results in adults, children, and the DDI model were in good agreement. The recommended doses of LTG for preschool children (2-6 years) and school-aged children (6-12 years) in the absence of drug interactions were 1.47 and 1.2 times higher than those for adults, respectively; 3.1 and 2.6 times higher than those for adults in combination with CBZ; and 0.67 and 0.57 times lower than those for adults in combination with VPA. In addition, plasma exposures in adolescents (12-18 years) were similar to those in adults at the same doses. We have successfully developed PBPK models and DDI models for LTG in adults and children, which provide a reference for rational drug use in the pediatric population.

Modeling the complexity of drug-drug interactions: A physiologically-based pharmacokinetic study of Lenvatinib with Schisantherin A/Schisandrin A

BackgroundLenvatinib's efficacy as a frontline targeted therapy for radioactive iodine-refractory thyroid carcinoma and advanced hepatocellular carcinoma owes to its inhibition of multiple tyrosine kinases. However, as a CYP3A4 substrate, lenvatinib bears susceptibility to pharmacokinetic modulation by co-administered agents. Schisantherin A (STA) and schisandrin A (SIA) — bioactive lignans abundant in the traditional Chinese medicinal Wuzhi Capsule — act as CYP3A4 inhibitors, engendering the potential for drug-drug interactions (DDIs) with lenvatinib. MethodsTo explore potential DDIs between lenvatinib and STA/SIA, we developed a physiologically-based pharmacokinetic (PBPK) model for lenvatinib and used it to construct a DDI model for lenvatinib and STA/SIA. The model was validated with clinical trial data and used to predict changes in lenvatinib exposure with combined treatment. ResultsFollowing single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (Cmax) of lenvatinib increased 1.00- to 1.03-fold and 1.00- to 1.01-fold, respectively, in the presence of STA/SIA. Simulations of multiple-dose regimens revealed slightly greater interactions, with lenvatinib AUC0-t and Cmax increasing up to 1.09-fold and 1.02-fold, respectively. ConclusionOur study developed the first PBPK and DDI models for lenvatinib as a victim drug. STA and SIA slightly increased lenvatinib exposure in simulations, providing clinically valuable information on the safety of concurrent use. Given the minimal pharmacokinetic changes, STA/SIA are unlikely to interact with lenvatinib through pharmacokinetic alterations synergistically but rather may enhance efficacy through inherent anti-cancer efficacy of STA/ SIA.

A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

DeepMedFeature: An Accurate Feature Extraction and Drug-Drug Interaction Model for Clinical Text in Medical Informatics

Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may prove to be fatal in the worst case. Finding drug interactions early in diagnosis can be pivotal in side-effect prevention. The growth of big data provides a rich source of information for clinical studies to investigate DDIs. We propose a hierarchical classification model which is double-pass in nature. The first pass predicts the occurrence of an interaction and then the second pass further predicts the type of interaction such as effect, advice, mechanism, and int. We applied different deep learning algorithms with Convolutional Bi-LSTM (ConvBLSTM) proving to be the best. The results show that pre-trained vector embeddings prove to be the most appropriate features. The F1-score of the ConvBLSTM algorithm turned out to be 96.39% and 98.37% in Russian and English language respectively which is greater than the state-of-the-art systems. According to the results, it can be concluded that adding a convolution layer before the bi-directional pass improves model performance in the automatic classification and extraction of drug interactions, using pre-trained vector embeddings such as Fasttext and Bio-Bert.

Physiologically based pharmacokinetic modeling for confirming the role of CYP3A1/2 and P-glycoprotein in detoxification mechanism between glycyrrhizic acid and aconitine in rats.

Fuzi, an effective common herb, is often combined with Gancao to treat disease in clinical practice with enhancing its efficacy and alleviating its toxicity. The major toxic and bioactive compounds in Fuzi and Gancao are aconitine (AC) and glycyrrhizic acid (GL), respectively. This study aims to elucidate detoxification mechanism between AC and GL from pharmacokinetic perspective using physiologically based pharmacokinetic (PBPK) model. In vitro experiments exhibited that AC was mainly metabolized by CYP3A1/2 in rat liver microsomes and transported by P-glycoprotein (P-gp) in Caco-2 cells. Kinetics assays showed that the Km and Vmax of AC towards CYP3A1/2 were 2.38 μM and 57.3pmol/min/mg, respectively, whereas that of AC towards P-gp was 11.26 μM and 147.1pmol/min/mg, respectively. GL markedly induced the mRNA expressions of CYP3A1/2 and MDR1a/b in rat primary hepatocytes. In vivo studies suggested that the intragastric and intravenous administration of GL significantly reduced systemic exposure of AC by 27% and 33%, respectively. Drug-drug interaction (DDI) model of PBPK predicted that co-administration of GL would decrease the exposure of AC by 39% and 45% in intragastric and intravenous dosing group, respectively. The consistency between predicted data and observed data confirmed that the upregulation of CYP3A1/2 and P-gp was the crucial detoxification mechanism between AC and GL. Thus, this study provides a demonstration for elucidating the compatibility mechanisms of herbal formula using PBPK modeling and gives support for the clinical co-medication of Fuzi and Gancao.

Mapping combinatorial expression perturbations to growth in Escherichia coli

The connection between growth and gene expression has often been considered in a single gene. Repurposing a drug-drug interaction model, the multidimensional effects of several simultaneous gene expression perturbations on growth have been examined in the model bacteria Escherichia coli.

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.

Prediction of herb-drug interactions involving consumption of furanocoumarin-mixtures and cytochrome P450 1A2-mediated caffeine metabolism inhibition in humans

Herb-drug interactions (HDI) has become important due to the increasing popularity of natural health product consumption worldwide. HDI is difficult to predict as botanical drugs usually contain complex phytochemical-mixtures, which interact with drug metabolism. Currently, there is no specific pharmacological tool to predict HDI since almost all in vitro-in vivo-extrapolation (IVIVE) Drug-Drug Interaction (DDI) models deal with one inhibitor-drug and one victim-drug. The objectives were to modify-two IVIVE models for the prediction of in vivo interaction between caffeine and furanocoumarin-containing herbs, and to confirm model predictions by comparing the DDI predictive results with actual human data. The models were modified to predict in vivo herb-caffeine interaction using the same set of inhibition constants but different integrated dose/concentration of furanocoumarin mixtures in the liver. Different hepatic inlet inhibitor concentration ([I]H) surrogates were used for each furanocoumarin. In the first (hybrid) model, the [I]H was predicted using the concentration-addition model for chemical-mixtures. In the second model, the [I]H was calculated by adding individual furanocoumarins together. Once [I]H values were determined, the models predicted an area-under-curve-ratio (AUCR) value of each interaction. The results indicate that both models were able to predict the experimental AUCR of herbal products reasonably well. The DDI model approaches described in this study may be applicable to health supplements and functional foods also.

Evaluation of drug-drug interaction between rilpivirine and rifapentine using PBPK modelling.

Tuberculosis remains the leading cause of death among people living with HIV. Rifapentine is increasingly used to treat active disease or prevent reactivation, in both cases given either as weekly or daily therapy. However, rifapentine is an inducer of CYP3A4, potentially interacting with antiretrovirals like rilpivirine. This in silico study investigates the drug-drug interaction (DDI) magnitude between daily oral rilpivirine 25mg with either daily 600mg or weekly 900mg rifapentine. A physiologically based pharmacokinetic (PBPK) model was built in Simbiology (Matlab R2018a) to simulate the drug-drug interaction. The simulated PK parameters from the PBPK model were verified against reported clinical data for rilpivirine and rifapentine separately, daily rifapentine with midazolam, and weekly rifapentine with doravirine. The simulations of concomitant administration of rifapentine with rilpivirine at steady-state lead to a maximum decrease on AUC0-24 and Ctrough by 83% and 92% on day 5 for the daily rifapentine regimen and 68% and 92% for the weekly regimen on day 3. In the weekly regimen, prior to the following dose, AUC0-24 and Ctrough were still reduced by 47% and 53%. In both simulations, the induction effect ceased 2weeks after the interruption of rifapentine's treatment. A daily double dose of rilpivirine after initiating rifapentine 900mg weekly was simulated but failed to compensate the drug-drug interaction. The drug-drug interaction model suggested a significant decrease on rilpivirine exposure which is unlikely to be corrected by dose increment, thus coadministration should be avoided.

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.

An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES.

The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

Predicting the Drug-Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation.

The prediction of drug-drug interactions (DDIs) plays critical roles for the estimation of DDI risk caused by inhibition of CYP3A4. The aim of this paper is to develop a physiologically based pharmacokinetic (PBPK)-DDI model for prediction of the DDI co-administrated with ketoconazole in humans and evaluate the predictive performance of the model. The pharmacokinetic and biopharmaceutical properties of 35 approved drugs, as victims, were collected for the development of a PBPK model, which were linked to the PBPK model of ketoconazole for the DDI prediction. The PBPK model of victims and ketoconazole were validated by matching actual in vivo pharmacokinetic data. The predicted results of DDI were compared with actual data to evaluate the predictive performance. The percentage of predicted ratio of AUC (AUCR), Cmax (CmaxR), and Tmax (TmaxR) was 75%, 69%, and 91%, respectively, which were within the twofold threshold (range, 0.5-2.0×) of the observed values. Only 3% of the predicted AUCRs are obviously underestimated. After integration of the reported fraction of metabolism (fm) into the PBPK-DDI model for limited four cases, the model-predicted AUCRs were improved from the twofold range of the observed AUCRs to the 90% confidence interval. The developed method could reasonably predict drug-drug interaction with a low risk of underestimation. The present accuracy of the prediction was improved compared with that of static mechanistic models. The evaluation of predictive performance increases the confidence using the model to evaluate the risk of DDIs co-administrated with ketoconazole before the in vivo DDI study.

Does the circulating ketoconazole metabolite N-deacetyl ketoconazole contribute to the drug-drug interaction potential of the parent compound?

Ketoconazole is a strong inhibitor of cytochrome P450 3A4 (CYP3A4) and of P-glycoprotein (P-gp) and is often used as an index inhibitor especially for CYP3A4-mediated drug metabolism. A preliminary physiologically based pharmacokinetic (PBPK) model for drug-drug interactions indicated possible involvement of a metabolite to the perpetrator potential of ketoconazole. Still unknown for humans, in rodents, N-deacetyl ketoconazole (DAK) has been identified as the major ketoconazole metabolite. We therefore investigated in vitro, whether DAK also inhibits the human CYPs and drug transporters targeted by ketoconazole and quantified DAK in human plasma from healthy volunteers after receiving a single oral dose of 400 mg ketoconazole. Our data demonstrated that DAK also inhibits CYP3A4 (2.4-fold less potent than ketoconazole), CYP2D6 (13-fold more potent than ketoconazole), CYP2C19 (equally potent), P-gp (3.4-fold less potent than ketoconazole), breast cancer resistance protein (more potent than ketoconazole) and organic anion transporting polypeptide 1B1 and 1B3 (7.8-fold and 2.6-fold less potent than ketoconazole). After a single oral dose of 400 mg ketoconazole, maximum concentrations of DAK in human plasma were only 3.1 ‰ of the parent compound. However, assuming that DAK also highly accumulates in the human liver as demonstrated for rodents, inhibition of the proteins investigated could also be conceivable in vivo. In conclusion, DAK inhibits several CYPs and drug transporters, which might contribute to the perpetrator potential of ketoconazole.

Para-aminosalicylic acid significantly reduced tenofovir exposure in human subjects: Mismatched findings from in vitro to in vivo translational research.

Tenofovir and para-aminosalicylic acid (PAS) may be coprescribed to treat patients with concomitant infections of human immunodeficiency virus and Mycobacterium tuberculosis bacteria. Both drugs are known to have remarkable renal uptake transporter-mediated clearance. Owing to the lack of clinical studies on drug-drug interaction between the 2 drugs, we conducted a translational clinical study to investigate the effect of PAS on tenofovir pharmacokinetics (PK). Initially, we studied in vitro renal uptake transporter-mediated drug-drug interactions using stably transfected cells with human organic anion transporters (OAT1 and OAT3). Later, we estimated clinical drug interactions using static and physiologically based PK modelling. Finally, we investigated the effects of PAS-calcium formulation (PAS-Ca) on tenofovir disoproxil fumarate PK in healthy male Korean subjects. PAS inhibited OAT1- and OAT3-mediated tenofovir uptake in vitro. The physiologically based PK drug-drug interaction model suggested a 1.26-fold increase in tenofovir peak plasma concentration when coadministered with PAS. By contrast, an open-label, randomized, crossover clinical trial evaluating the effects of PAS-Ca on tenofovir PK showed significantly altered geometric mean ratio (90% confidence intervals) of maximum plasma concentration (Cmax ) and area under the curve (AUC0-inf ) by 0.33 (0.28-0.38) and 0.29 (0.26-0.33), respectively. Our study findings suggest that the PAS-Ca formulation significantly reduced systemic exposure to tenofovir through an unexplained mechanism, which was contrary to the initial prediction. Caution should be exercised while predicting in vivo PK profiles from in vitro data, particularly when there are potential confounders such as pharmaceutical interactions.

Population Pharmacokinetics of Diethylcarbamazine in Patients with Lymphatic Filariasis and Healthy Individuals.

Diethylcarbamazine (DEC) is a drug of choice to treat lymphatic filariasis (LF) either used alone or in combination as mass drug administration (MDA) preventive strategies. The objective of this study was to develop a population pharmacokinetics (PK) model for DEC in subjects infected with lymphatic filariasis (LF) compared to healthy individuals, and to evaluate the effect of covariates on the volume of distribution (V/F) and oral clearance (CL/F) of DEC. This was an open-label cohort study of treatment-naive Wuchereria bancrofti-infected (n = 32) and uninfected (n = 24) adults residing in the Agboville District of Côte d'Ivoire. The population pharmacokinetics model for DEC was built using Phoenix NLME 8.0 software. The covariates included in the model-building process were age, gender, body weight, infection status, creatinine clearance (CLCR), and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. A total of 56 adults were enrolled in the study, and a total of 728 samples were obtained over 168 h. A one-compartment linear pharmacokinetics model with first-order absorption with an absorption lag time (Tlag) best described the data. After determining the pharmacokinetics (PK) parameters of DEC, body weight and gender were found to be the significant covariates for DEC V/F. The final population pharmacokinetics model adequately described the pharmacokinetics of DEC in the studied population. Model-based simulation indicated that the body weight significantly impacted the exposure in both the male and female populations. This analysis may further support the drug-drug interaction model development of DEC with different coadministered drugs or agents in disease control programs. (This study is registered at clinicaltrials.gov under identifier NCT02845713.).

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network.

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug–drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug–gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Modeling of Drug-Drug Interactions between Omeprazole and Erythromycin in the Cytochrome P450-Dependent System In vitro

In the present study an electrochemical system based on recombinant cytochrome P450 3A4 (CYP3A4) has been developed for analysis of potential drug-drug interactions between drugs prescribed for Helicobacter pylori eradication therapy. The drug-drug interaction between omeprazole (a proton pump inhibitor; PPI) and macrolide antibiotic erythromycin induced by cytochrome P450 3A4 was demonstrated. In the presence of omeprazole, the rate of erythromycin N-demethylation by CYP3A4, measured by the reaction product (formaldehyde) formation, decreased, while erythromycin had no effect on omeprazole metabolism evaluated using mass-spectrometry analysis of omeprazole sulfone as a metabolite formed during CYP3A4-dependent metabolism. These drug-drug interactions may be explained by a higher affinity of CYP3A4 for omeprazole (the spectral dissociation constant Kd = 18 ± 2 µM) than that for erythromycin (Kd = 52 µM). Using the developed model system, it is possible to analyze drug-drug interactions induced by cytochrome P450 3A4. The results obtained by means of in vitro experiments well correspond to the results of in silico modeling performed using the PASS program and PoSMNA descriptors, which also showed the possibility of drug-drug interactions between omeprazole and erythromycin at the level of biotransformation carried out by cytochrome P450 3A4.

Modeling of drug-drug interactions between omeprazole and erythromycin with cytochrome P450 3A4 in vitro assay

In the present study the electrochemical system based on recombinant cytochrome P450 3A4 (CYP3A4) was used for the investigation of potential drug-drug interaction between medicinal preparations employed for Helicobacter pylori eradication therapy. Drug interactions were demonstrated in association of omeprazole as a proton pump inhibitor (PPI) and macrolide antibiotic erythromycin during cytochrome P450 3A4-mediated metabolism. It was shown that in the presence of omeprazole the rate of N-demethylase activity of CYP3A4 to erythromycin measured by means of product (formaldehyde) formation decreased. Mass-spectrometry analysis of omeprazole sulfone as a CYP3A4-mediated metabolite demonstrated the absence of erythromycin influence on CYP3A4-dependent omeprazole metabolism. This phenomenon may be explained by lower spectral dissociation constant of CYP3A4-omeprazole complex (Kd = 18±2 μM) than that of CYP3A4-erythromycin complex (Kd = 52 μM). Using the electrochemical model of electrochemically-driven drug metabolism it is possible to register CYP3A4-mediated catalytic conversion of certain drugs. In vitro experiments of potential CYP3A4-mediated drug-drug interactions are in accordance with in silico modeling with program PASS and PoSMNA descriptors in the case of omeprazole/erythromycin combinations.

Clinical Studies on Drug-Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation.

Many drug–drug interactions (DDIs) are based on alterations of the plasma concentrations of a victim drug due to another drug causing inhibition and/or induction of the metabolism or transporter‐mediated disposition of the victim drug. In the worst case, such interactions cause more than tenfold increases or decreases in victim drug exposure, with potentially life‐threatening consequences. There has been tremendous progress in the predictability and modeling of DDIs. Accordingly, the combination of modeling approaches and clinical studies is the current mainstay in evaluation of the pharmacokinetic DDI risks of drugs. In this paper, we focus on the methodology of clinical studies on DDIs involving drug metabolism or transport. We specifically present considerations related to general DDI study designs, recommended enzyme and transporter index substrates and inhibitors, pharmacogenetic perspectives, index drug cocktails, endogenous substrates, limited sampling strategies, physiologically‐based pharmacokinetic modeling, complex DDIs, methodological pitfalls, and interpretation of DDI information.