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.

Management of direct oral anticoagulant drug interactions in hospitalized patients

Moderate-strong CYP3A4 or Pgp inhibitors and inducers alter direct oral anticoagulant (DOAC) pharmacokinetics. Whether the presence of a DOAC drug-drug interaction (DDI) prompts in- hospital changes in management remains unknown. We identified all hospitalized patients at our institution who were admitted with a clinically relevant DOAC DDI from 01/2021 to 06/2021. Clinically relevant DOAC DDIs were defined as those listed in the prescribing information or FDA CYP3A4/Pgp inhibitors clinical indexes. We assessed the prevalence of DOAC DDIs and categorized their management as: drug stopped, drug held, or drug continued. For drugs that were continued we assessed whether the dose of the DOAC or interacting drug was increased, decreased or unchanged during the admission. We ascertained the number of DOAC DDIs that prompted an automated prescribing alert in our electronic health record (EHR). Finally, we conducted a logistic regression model to compare users of DOACs with DDI who had their regimen adjusted versus those without adjustments, focusing on outcomes of rehospitalization and death, adjusting for age and gender. Among 3,725 hospitalizations with a DOAC admission order, 197 (5%) had a clinically relevant DOAC DDI. The DOAC and the interacting drug were continued at discharge for 124 (63%) hospitalizations. The most frequent adjustments were stopping the interacting drug (73%) and stopping the DOAC (15%). Only 7 (4%) of DOAC DDIs prompted an EHR alert. The adjusted odds ratios for rehospitalizations and death, respectively, among patients whose regimens were adjusted compared to those whose were not, were 1.29 (95% CI, 0.67 to 2.48; P = 0.44) and 1.88 (95% CI, 0.91 to 3.89; P = 0.09). Clinically relevant DDIs with DOACs occur infrequently among hospitalized patients and usually are managed without stopping the DOAC. The clinical impact of such DDIs and subsequent adjustments on thrombotic and hemorrhagic outcomes requires further investigation.

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

Clinical relevance of potential self-medication drug interactions in antineoplastic and immune-modulating therapy among online pharmacy customers

Background

Modern oral antineoplastic and immune-modulating drugs offer an array of therapeutic advantages, and yet pose challenges in daily use for patients, physicians and pharmacists. In contrast to intravenous administration, these drugs are not subject to direct medical control. Recently, we have seen a huge rise in sales of non-prescription over-the-counter (OTC) medicines via the internet without any advice from a healthcare professional.

Objectives

The aim of this study was to investigate whether the risk of known potential drug-drug interactions between modern oral antineoplastic and immune-modulating drugs and OTC drugs differs between sales in traditional community pharmacies versus online pharmacies.

Design

Real-life sales data from community and online pharmacies were used as basis for the analysis.

Methods

We determined the most frequently purchased antineoplastic and immune-modulating drug-substances in 14 local community pharmacies within the Munich area, Germany and identified the OTC substance groups that could potentially cause interactions with oncological therapies. Using sales data from 11 local community pharmacies and three online pharmacies, we investigated whether OTC purchases differed between the two sales channels.

Results

We identified 10 relevant OTC substance classes and detected significant variations in patients' preferred sales channels between the drug classes. Certain OTC drugs, which seem to be bought more often over the internet, pose risks during antineoplastic and immune-modulating therapy.

Conclusion

Patients should therefore be proactively made aware of the corresponding risks in order not to jeopardize the activity of the antineoplastic and immune-modulating drugs and thus the success of their therapy.

Assessment of community pharmacists' knowledge about drug-drug interactions in Jeddah, Saudi Arabia

Background: Drug-drug interactions (DDIs) have the potential to result in severe adverse drug events and profoundly affect patient outcomes. The pivotal role community pharmacists assume in recognizing and effectively managing these interactions necessitates a comprehensive understanding and heightened awareness of their implications. Such knowledge and awareness among community pharmacists are fundamental for ensuring the delivery of safe and efficacious care to patients. Aim: This study aimed to assess the knowledge of community pharmacists in Jeddah, Saudi Arabia, regarding drug-drug interactions (DDIs). Method: A cross-sectional survey was administered to a cohort of 147 community pharmacists through the utilization of a self-administered questionnaire. The questionnaire encompassed a comprehensive range of 30 multiple-choice questions, encompassing various facets pertaining to drug-drug interactions (DDIs). Results: A total of 147 community pharmacists working in Jeddah City, Saudi Arabia, completed the survey. The majority of them were male (89.1%, n = 131), and had bachelor's degrees in pharmacy. Results showed that the lowest correct response of DDIs was between Theophylline/Omeprazole, while the highest was between amoxicillin and acetaminophen. Results revealed that among the 28 drug pairs, only six pairs were determined correctly by most participants. The study found that majority of the studied community pharmacist could not determine the correct answer on drug-drug interaction knowledge, as also seen with the measured below half mean DDIs knowledge of 38.22 ± 22.0 (min = 0, max = 89.29, median = 35.71). Conclusion: The study highlights the need for ongoing training and education programs for community pharmacists in Saudi Arabia to enhance their knowledge and understanding of DDIs, ultimately leading to improved patient care and safety.

Evaluation of Drug Interactions in Hospitalized Patients with Respiratory Disorders in Greece

Background: Patients with respiratory disorders often have additional diseases and are usually treated with more than one medication to manage their respiratory conditions as well as additional comorbidities. Thus, they are frequently exposed to polypharmacy (≥5 drugs), which raises the risk for drug-drug interactions (DDIs) and adverse drug reactions (ADRs). In this work, we present the results regarding the prevalence of DDIs in hospitalized patients with respiratory disorders in Greece. Methods: A 6-month descriptive single-center retrospective observational study enrolled 102 patients with acute or chronic respiratory disorders. Clinical characteristics and medication regimens were recorded upon admission, hospitalization, and discharge. The prevalence of DDIs and their clinical significance was recorded and analyzed. Results: Unspecified acute lower respiratory tract infection (25%), exacerbations of chronic obstructive pulmonary disease (12%) and pneumonia (8%) were the most frequent reasons for admission. Cardiovascular disorders (46%), co-existing respiratory disorders (32%), and diabetes (25%) were the most prevalent comorbidities. Polypharmacy was noted in 61% of patients upon admission, 98% during hospitalization, and 63% upon discharge. Associated DDIs were estimated to be 55% upon admission, 96% throughout hospitalization, and 63% on discharge. Pharmacodynamic (PD) DDIs were the most prevalent cases (81%) and referred mostly to potential risk for QT-prolongation (31.4% of PD-DDIs) or modulation of coagulation process as expressed through the international normalized ratio (INR) (29.0% of DDIs). Pharmacokinetic (PK) DDIs (19% of DDIs) were due to inhibition of Cytochrome P450 mediated metabolism that could lead to elevated systemic drug concentrations. Clinically significant DDIs characterized as "serious-use alternative" related to 7% of cases while 59% of DDIs referred to combinations that could be characterized as "use with caution-monitor". Clinically significant DDIs mostly referred to medication regimens upon admission and discharge and were associated with outpatient prescriptions. Conclusions: Hospitalized patients with respiratory disorders often experience multimorbidity and polypharmacy that raise the risk of DDIs. Clinicians should be conscious especially if any occurring arrhythmias, INR modulations, and prolonged or increased drug action is associated with DDIs.

A comparison of four drug-drug interaction databases for patients undergoing haematopoietic stem cell transplantation

WHAT IS KNOWN AND OBJECTIVE

Patients who have undergone haematopoietic stem cell transplantation are prone to drug-drug interactions due to polypharmacy. Drug-drug interaction databases are essential tools for identifying interactions in this patient group. However, drug-drug interaction checkers, which help manage interactions, may have disagreements about assessing the existence or severance of the interactions. The study aimed to determine differences among popular drug-drug interaction databases from several angles for patients who underwent haematopoietic stem cell transplantation.

METHODS

The 21-day treatment sheets of one hundred patients who underwent haematopoietic stem cell transplantation were examined in two subscription-based (Uptodate and Micromedex) and two open-access databases (Drugs.com and Epocrates) in terms of several categories two years in a row. Statistical analysis was utilized to understand the compatibility of databases in terms of severity scores, evidence levels, given references, and word counts in interaction reports. Fleiss' and Cohen's kappa statistics were used to analyse the databases' agreement levels.

RESULTS AND DISCUSSION

A total of 1393 and 1382 different drug-drug interactions were detected in subsequent versions of the databases, namely the 2021 and 2022 versions. The Fleiss kappa overall agreement among databases was slight. Uptodate and Micromedex showed fair agreement, and other database pairs showed slight agreement in severity ratings.

CONCLUSION

There was a poor agreement among databases for interactions seen in bone marrow transplantation patients. Therefore, it would be safer to use more than one database in daily practice. Further work needs to be done to understand the agreement level of databases for different types of interactions.

The key role of organic anion transporter 3 in the drug-drug interaction between tranilast and methotrexate

Tranilast, N-(3',4'-dimethoxycinnamoyl)-anthranilic acid, is an anti-allergic drug and is considered for use in the treatment of rheumatoid arthritis. Methotrexate, an antimetabolite and folate antagonist to treat some cancers, is also a first-line drug for RA. The aim of this study was to understand whether tranilast could inhibit renal uptake transporters (Oat1, Oat3, and Oct2) and whether MTX combined with TL would have drug-drug interactions. The results of kidney slices and HEK293T-OAT3 cell uptake experiments showed that TL (10 μM) could inhibit the uptake of penicillin G and MTX, which are substrates of OAT3. When TL (10 mg/kg) was combined with MTX (5 mg/kg), the area under the curve and peak concentration of MTX increased by 46.46% and 113.51%, respectively, while the pharmacokinetic process of tranilast (10 mg/kg) was not changed by methotrexate (5 mg/kg). TL could increase plasma exposure of MTX by inhibiting Oat3 in vitro and in vivo.

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients

Background

It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs.

Methods

Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs.

Results

MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding.

Conclusions

An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02594-z.

Establishment and Verification of UPLC-MS/MS Technique for Pharmacokinetic Drug-Drug Interactions of Selinexor with Posaconazole in Rats

Background

A method for the determination of selinexor by UPLC-MS/MS was established to study the effect of posaconazole on the pharmacokinetics of selinexor in rats.

Methods

The experiment rats were divided into group A (0.5% CMC-Na) and group B (posaconazole, 20 mg/kg), 6 rats in each group. 30 minutes after administration of 0.5% CMC-Na or posaconazole, all the rats were given selinexor (8 mg/kg), and plasma samples were collected. The plasma samples underwent acetonitrile protein precipitation, and were separated by UPLC on an Acquity UPLC BEH C18 column with gradient elution. Acetonitrile and 0.1% formic acid were used as the mobile phases. The analyte detection was used a Xevo TQ-S triple quadrupole tandem mass spectrometer and multiple reaction monitoring (MRM) for analyte monitoring. We use acetonitrile for protein precipitation.

Results

Selinexor had good linearity (1.0-1000 ng/mL, r² ₌0.996 2), and the accuracy and precision, recovery rate and matrix effects(ME) were also met the FDA approval guidelines. Compared with group A, the C_max, AUC_(0-t) and AUC_(0-∞) of selinexor in group B increased by 60.33%, 48.28% and 48.27%, and T_max increased by 53.92%, CLz/F reduced by 32.08%.

Conclusion

This bioanalysis method had been applied to the study of drug interactions in rats. It was found that posaconazole significantly increased the concentration of selinexor in rats. Therefore, when selinexor and posaconazole are combined, we should pay attention to the possible drug-drug interactions to reduce adverse reactions.

Effect of pharmacodynamical interaction between nutlin-3a and aspirin in the activation of p53

BACKGROUND AND OBJECTIVE

p53, an anti-tumour protein, is significantly inactivated in most tumours. A small molecule of nutlin-3a is used to activate its function by repressing (Mouse double minute 2 homolog) Mdm2 protein which inhibits its activity. In cancer patients, a high risk of drug-drug interactions (DDIs) is observed owing to their multi-dosing prescriptions, which may lead them to harmful effects. In the presented work, we have aimed to investigate the effect of pharmacodynamical interaction between two anti-cancer drugs, nutlin-3a and aspirin in the activation of p53 protein.

METHODS

We have adapted control system techniques and designed a Proportional-Integral-Derivative (PID) controller. This controller is used to activate p53 protein. A drug interaction parameter is used to incorporate the effect of both drugs. Extensive simulation is performed using two different doses of aspirin, i.e. a low and a high dose of aspirin.

RESULTS

The result shows no harmful effects of pharmacodynamical interaction when a low dose is administered along with nutlin-3a. When a high dose of aspirin is administered it acts as input disturbance and leads to undesirable over-expression of p53 protein. This can further harm other growth cells, thus inducing harmful effects. A comparative analysis is also tabulated with different dosing regimens which shows that a combination of nutlin-3a and a low dose of aspirin provides better results than a high dose of aspirin.

CONCLUSION

Overall, the work provides an insight to the activation of p53 protein in cancer patients under the presence of pharmacodynamical interaction and might contribute to the effective management of cancer patients.

A Review: Effects of Macrolides on CYP450 Enzymes

As a kind of haemoglobin, cytochrome P450 enzymes (CYP450) participate in the metabolism of many substances, including endogenous substances, exogenous substances and drugs. It is estimated that 60% of common prescription drugs require bioconversion through CYP450. The influence of macrolides on CYP450 contributes to the metabolism and drug-drug interactions (DDIs) of macrolides. At present, most studies on the effects of macrolides on CYP450 are focused on CYP3A, but a few exist on other enzymes and drug combinations, such as telithromycin, which can decrease the activity of hepatic CYP1A2 and CYP3A2. This article summarizes some published applications of the influence of macrolides on CYP450 and the DDIs of macrolides caused by CYP450. And the article may subsequently guide the rational use of drugs in clinical trials. To a certain extent, poisoning caused by adverse drug interactions can be avoided. Unreasonable use of macrolide antibiotics may enable the presence of residue of macrolide antibiotics in animal-origin food. It is unhealthy for people to eat food with macrolide antibiotic residues. So it is of great significance to guarantee food safety and protect the health of consumers by the rational use of macrolides. This review gives a detailed description of the influence of macrolides on CYP450 and the DDIs of macrolides caused by CYP450. Moreover, it offers a perspective for researchers to further explore in this area.

Prevalence and nature of potential drug-drug interactions among hospitalized HIV patients presenting with suspected meningitis in Uganda

Background

Management of co-infections including cryptococcal meningitis, tuberculosis and other opportunistic infections in persons living with HIV can lead to complex polypharmacotherapy and increased susceptibility to drug-drug interactions (DDIs). Here we characterize the frequency and types of potential DDIs (pDDIs) in hospitalized HIV patients presenting with suspected cryptococcal or tuberculous meningitis.

Methods

In a retrospective review of three cryptococcal meningitis trials between 2010 and 2017 in Kampala, Uganda, medications received over hospitalization were documented and pDDI events were assessed. IBM Micromedex DRUGDEX® online drug reference system was used to identify and describe potential interactions as either contraindicated, major, moderate or minor. For antiretroviral DDIs, the Liverpool Drug Interactions Checker from the University of Liverpool was also used to further describe interactions observed.

Results

In 1074 patients with suspected meningitis, pDDIs were present in 959 (overall prevalence = 89.3%) during the analyzed 30 day window. In total, 278 unique interacting drug pairs were identified resulting in 4582 pDDI events. Of all patients included in this study there was a mean frequency of 4.27 pDDIs per patient. Of the 4582 pDDI events, 11.3% contraindicated, 66.4% major, 17.4% moderate and 5% minor pDDIs were observed. Among all pDDIs identified, the most prevalent drugs implicated were fluconazole (58.4%), co-trimoxazole (25.7%), efavirenz (15.6%) and rifampin (10.2%). Twenty-one percent of the contraindicated pDDIs and 27% of the major ones involved an antiretroviral drug. Increased likelihood of QT interval prolongation was the most frequent potential clinical outcome. Dissonance in drug interaction checkers was noted requiring clinicians to consult more than one database in making clinical decisions about drug combinations.

Conclusions

The overall prevalence of pDDIs in this population is high. An understanding of drug combinations likely to result in undesired clinical outcomes, such as QT interval prolongation, is paramount. This is especially important in resource limited settings where availability of therapeutic drug monitoring and laboratory follow-up are inconsistent. Adequate quantification of the increased likelihood of adverse clinical outcomes from multiple drug-drug interactions of the same kind in a single patient is needed to aid clinical decisions in this setting.

CYPs-mediated drug-drug interactions on psoralidin, isobavachalcone, neobavaisoflavone and daidzein in rats liver microsomes

The incubation system of CYP2E1 and CYP3A4 enzymes in rat liver microsomes was established to investigate the effects of psoralidin, isobavachalcone, neobavaisoflavone and daidzein from Fructus Psoraleae in vitro. The relevant metabolites were measured by the method of high performance liquid chromatography (HPLC), after probe substrates of 4-nitrophenol, testosterone and the drugs at different concentrations were added to the incubation systems. In addition, real-time RT-PCR was performed to determine the effect of psoralidin, neobavaisoflavone and daidzein on the mRNA expression of CYP3A4 in rat liver. The results suggested that psoralidin, isobavachalcone and neobavaisoflavone were Medium-intensity inhibitors of CYP2E1 with K_i values of 2.58, 1.28 and 19.07 μM, respectively, which could inhibit the increase of CYP2E1 and reduce diseases caused by lipid peroxidation. Isobavachalcone (K_i = 37.52 μM) showed a weak competitive inhibition on CYP3A4. Psoralidin and neobavaisoflavone showed obvious induction effects on CYP3A4 in the expression level of mRNA, which could accelerate the effects of drug metabolism and lead to the risk of inducing DDIs and serious adverse reactions. The results could be used for guideline of Fructus Psoraleae in clinic, which aimed to calculate the drug toxicity by studying the drug-drug interactions caused by the induction and inhibition of CYP450.

Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.

The involvement of multidrug and toxin extrusion protein 1 in the distribution and excretion of berberine

1. Berberine (BBR), an isoquinoline alkaloid, has demonstrated multiple clinical pharmacological actions. As a substrate of multiple transporters in the liver, BBR is rarely excreted into the bile but can be found in the urine. The purpose of the present study was to investigate the role of multidrug and toxin extrusion protein 1 (MATE1) in the transport of BBR in the liver and kidney. 2. Using human MATE1 (hMATE1)-transfected HEK293 cells, BBR was shown to be a substrate of hMATE1 (K_m = 4.28 ± 2.18 μM). In primary rat hepatocytes, pH-dependent uptake and efflux studies suggested that the transport of BBR was driven by the exchange of H⁺ and involved Mate1. In rats, we found that pyrimethamine (PYR), an inhibitor of Mate1, increased hepatic and renal distribution of BBR and decreased systematic excretion of BBR. 3. These findings indicated that BBR is a substrate of MATE1 and that hepatic and renal Mate1 promote excretion of BBR into bile and urine, respectively. In conclusion, Mate1 plays a key role in the distribution and excretion of BBR, and we speculate that drug-drug interactions (DDIs) caused by MATE1 may occur between BBR and other co-administered drugs.

Evaluation of a novel PXR-knockout in HepaRG™ cells

The nuclear pregnane X receptor (PXR) regulates the expression of genes involved in the metabolism, hepatobiliary disposition, and toxicity of drugs and endogenous compounds. PXR is a promiscuous nuclear hormone receptor (NHR) with significant ligand and DNA‐binding crosstalk with the constitutive androstane receptor (CAR); hence, defining the precise role of PXR in gene regulation is challenging. Here, utilising a novel PXR‐knockout (KO) HepaRG cell line, real‐time PCR analysis was conducted to determine PXR involvement for a range of inducers. The selective PXR agonist rifampicin, a selective CAR activator, 6‐(4‐chlorophenyl)imidazo[2,1‐b][1,3]thiazole‐5‐carbaldehyde O‐(3,4‐dichlorobenzyl)oxime (CITCO), and dual activators of CAR and PXR including phenobarbital (PB) were analyzed. HepaRG control cells (5F clone) were responsive to prototypical inducers of CYP2B6 and CYP3A4. No response was observed in the PXR‐KO cells treated with rifampicin. Induction of CYP3A4 by PB, artemisinin, and phenytoin was also much reduced in PXR‐KO cells, while the response to CITCO was maintained. This finding is in agreement with the abolition of functional PXR expression. The apparent EC₅₀ values for PB were in agreement between the cell lines; however, CITCO was ~threefold (0.3 μmol/L vs. 1 μmol/L) lower in the PXR‐KO cells compared with the 5F cells for CYP2B6 induction. Results presented support the application of the novel PXR‐KO cells in the definitive assignment of PXR‐mediated CYP2B6 and CYP3A4 induction. Utilization of such cell lines will allow advancement in composing structure activity relationships rather than relying predominantly on pharmacological manipulations and provide in‐depth mechanistic evaluation.