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.

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions.

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

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.

In Vitro Investigations into the Potential Drug Interactions of Pseudoginsenoside DQ Mediated by Cytochrome P450 and Human Drug Transporters.

Pseudoginsenoside DQ (PDQ), an ocotillol-type ginsenoside, is synthesized with protopanaxadiol through oxidative cyclization. PDQ exhibits good anti-arrhythmia activity. However, the inhibitory effect of PDQ on the cytochrome 450 (CYP450) enzymes and major drug transporters is still unclear. Inhibition of CYP450 and drug transporters may affect the efficacy of the drugs being used together with PDQ. These potential drug-drug interactions (DDIs) are essential for the clinical usage of drugs. In this study, we investigated the inhibitory effect of PDQ on seven CYP450 enzymes and seven drug transporters with in vitro models. PDQ has a significant inhibitory effect on CYP2C19 and P-glycoprotein (P-gp) with a half-inhibitory concentration (IC50) of 0.698 and 0.41 µM, respectively. The inhibition of CYP3A4 and breast cancer-resistant protein (BCRP) is less potent, with IC50 equal to 2.02-6.79 and 1.08 µM, respectively.

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.

Complexity and clinical significance of drug-drug interactions (DDIs) in oncology: challenging issues in the care of patients regarding cancer-associated thrombosis (CAT).

Cancer patients have an increased risk of developing venous thromboembolic events. Anticoagulation management includes prophylactic or therapeutic doses of low molecular weight heparins (LMWHs) or direct oral anticoagulants (DOACs). However, the management of thrombosis in patients with cancer is complex due to various individual and disease-related factors, including drug-drug interactions (DDIs). Furthermore, DDIs may impact both, cancer and venous thrombosis, treatment effectiveness and safety; their relevance is highlighted by the advances in cancer therapeutics. Given that these new oncology drugs are extensively used, more attention should be given to monitoring potential DDIs to minimize risks. Recognition of DDIs is of utmost importance in an era of rapid developments in cancer treatments and introduction of novel treatments and protocols. When managing cancer-associated thrombosis (CAT), the concomitant use of a DOAC and a moderate or strong modulator (inhibitor or inducer) of CYP3A4 or a P-glycoprotein (P-gp) is most likely to be associated with significant DDIs. Therefore, LMWHs remain the first-line option for the long-term management of CAT under these circumstances and physicians must consider utilizing LMWHs as first line. This review describes the risk of DDIs and their potential impact and outcomes in patients with cancer associated thrombosis (CAT) receiving anticoagulation.

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.

Elevated INR in a COVID-19 patient after concomitant administration of favipiravir and warfarin: A case report.

WHAT IS KNOWN AND OBJECTIVE: Favipiravir is a promising treatment candidate for managing coronavirus disease 2019 (COVID-19). Warfarin has many drug interactions, but no interactions with favipiravir have been reported. CASE SUMMARY: Our patient was taking warfarin for deep vein thrombosis. The international normalized ratio (INR) was stable (1.65 to 2.0); however, it increased to 4.63 after administering favipiravir. The patient had no other factors justifying this change. WHAT IS NEW AND CONCLUSION: Favipiravir and warfarin might have previously unidentified drug interactions that elevated the INR. Therefore, INR must be closely monitored when they are concomitantly administered in COVID-19 patients.

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.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions.

PURPOSE: To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). METHODS: The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002-80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. RESULTS: The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. CONCLUSIONS: A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

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.

Clinically Relevant OATP2B1 Inhibitors in Marketed Drug Space.

OATP2B1 is an intestinal and hepatic drug uptake transporter. Intestinal OATP2B1 has been elucidated as the mechanism of unexpected clinical drug-drug interactions (DDIs), where drug exposure was unexpectedly decreased with unchanged half-life. Hepatic OATP2B1 may be an understudied clinical DDI mechanism. The aim of the present work was to understand the prevalence of clinically relevant intestinal and hepatic OATP2B1 inhibitors in marketed drug space. HEK293 cells stably overexpressing human OATP2B1 or vector control were generated and cultured for 72 h in a 96-well format. OATP2B1-mediated uptake of dibromofluorescein (DBF) was found to be optimal at 10 µM concentration and 30 min incubation time. A total of 294 drugs (top 300 marketed drugs, excluding biologics and restricted drugs, supplemented with ∼100 small-molecule drugs) were screened for OATP2B1 inhibition at 10 µM. Drugs demonstrating ≥50% inhibition in this screen were advanced for IC50 determination, which was extrapolated to clinical intestinal and hepatic OATP2B1 inhibition as per 2017 FDA DDI guidance. Of the 294 drugs screened, 67 elicited ≥50% inhibition of OATP2B1-mediated DBF uptake at 10 µM screening concentration. For the 67 drugs flagged in the single-concentration inhibition screen, upon evaluation of a full concentration range, IC50 values could be determined for 58 drugs. OATP2B1 IC50 values established for these 58 drugs were extrapolated as potentially clinically relevant at the intestinal level for 38 orally administered drugs (Igut/IC50 ≥ 10), and 17 were flagged as potential clinical inhibitors of hepatic OATP2B1 uptake (1 + Iin,max,u/IC50 ≥ 1.1). This analysis of 294 drugs demonstrated prevalence of clinically relevant intestinal and hepatic OATP2B1 inhibitors to be 13 and 6%, respectively. As OATP2B1-inhibitor drugs are not exceedingly rare, these results suggest that clinical OATP2B1 DDIs have been rarely observed because OATP2B1 is uncommonly the predominant determinant of drug disposition.

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.

Drug-drug interaction of acetaminophen and roxithromycin with the cocktail of cytochrome P450 and hepatotoxicity in rats.

Acetaminophen (APAP) and roxithromycin (ROX) are often used in combination in clinical practice. To evaluate their drug-drug interactions (DDIs) and the hepatotoxicity of co-administration, rats were randomly separated into four groups: Control, APAP (50 mg/kg), ROX (5.5 mg/kg) and APAP-ROX (50 mg/kg and 5.5 mg/kg, respectively). The pharmacokinetic parameters between APAP and ROX were assayed by HPLC, and a cocktail method was used to evaluate the activities of cytochrome (CYP) 450. The liver microsome CYP2E1 protein was detected using Western blot. The levels of plasma parameters, mRNA levels of inflammatory factors (TNF-α, INF-γ, VCAM-1, CXCL-1 and STAT-3) and antioxidant factors (Nrf-2, GSTA, GCLC-1, HO-1 and NQO1) were determined using real-time PCR, along with the observation on histopathological changes in the liver tissue. APAP and ROX co-treatment significantly increased CYP2E1 activity, decreased CYP2D6 activity and prolonged APAP and ROX clearance. Co-treatment increased mRNA expressions of TNF-α, NQO1 and MDA while decreasing GPX and SOD levels. Histopathological evidence showed the changes of liver tissues in terms of structure, size and tight arrangement. This study confirmed that a combination of APAP and ROX inhibited APAP metabolism and that the peak concentration of ROX was delayed. The resulting high level of CYP2E1 may induce oxidative stress and cause liver damage.

De-Identification of Radiomics Data Retaining Longitudinal Temporal Information.

We propose a de-identification system which runs in a standalone mode. The system takes care of the de-identification of radiation oncology patient's clinical and annotated imaging data including RTSTRUCT, RTPLAN, and RTDOSE. The clinical data consists of diagnosis, stages, outcome, and treatment information of the patient. The imaging data could be the diagnostic, therapy planning, and verification images. Archival of the longitudinal radiation oncology verification images like cone beam CT scans along with the initial imaging and clinical data are preserved in the process. During the de-identification, the system keeps the reference of original data identity in encrypted form. These could be useful for the re-identification if necessary.

Comparison of the inhibition potential of parthenolide and micheliolide on various UDP-glucuronosyltransferase isoforms.

Parthenolide (PTL) and micheliolide (MCL) are sesquiterpene lactones with similar structures, and both of them have been reported to exhibit multiple biochemical and pharmacological activities. This study aims to investigate the inhibition of these two compounds on the activity of UDP-glucuronosyltransferases (UGTs). In vitro incubation mixture for recombinant UGTs-catalyzed glucuronidation metabolism of 4-methylumbelliferone (4-MU) was utilized to investigate the inhibition potential. Inhibition kinetics (including inhibition type and parameters) were determined, and in silico docking was employed to elucidate the inhibition difference between PTL and MCL on UGT1A1. MCL showed no inhibition toward all the UGT isoforms, and PTL showed strong inhibition toward UGT1A1. The half-maximal inhibitory concentration (IC50) of PTL on the activity of UGT1A1 was determined to be 64.4 µM. Inhibition kinetics determination showed that PTL exerted noncompetitive inhibition toward UGT1A1, and the inhibition kinetic constant (Ki) was determined to be 12.1 µM. In silico docking method has been employed to show that hydrogen bonds between PTL and the activity cavity of UGT1A1 contributed to the stronger inhibition of PTL on the activity of UGT1A1 than MCL. In conclusion, PTL can more easily induce drug-drug interaction (DDI) with clinical drugs mainly undergoing UGT1A1-catalyzed glucuronidation.

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.

Differential Diagnosis between Anomalous Experiences and Dissociation Disorder Using the Dissociative Disorders Interview Schedule (DDIS).

BACKGROUND: It is important to understand the most diverse cultural aspects related to religiosity. Scientifically, it is important to understand religious manifestations and their relation to health, and to differentiate them from psychopathological manifestations. OBJECTIVE: To evaluate the mental health of a group of mediums and compare it with that of a control group from the same religious context who do not manifest mediumship, using the Dissociative Disorders Interview Schedule (DDIS). METHODS: This was a cross-sectional study, evaluating 47 mediums (Group 1) and comparing them with 22 non-medium volunteers from the same religious context (Group 2) using the DDIS questionnaire. All results were matched with historical data from patients with dissociative identity disorder (DID) who answered the DDIS. RESULTS: Scores obtained from the DDIS were similar in both groups. The number of positive symptoms was comparable in a wide range of analyzed areas, involving but not being restricted to somatization disorder, major depressive episode, borderline personality disorder, extrasensory/paranormal experiences, physical/sexual abuse and five dissociative disorders. There were considerable differences when we compared these results with historical data from patients with DID. CONCLUSION: In agreement with the extant literature, these results showed that mediumship can be considered a non-pathological form of dissociative phenomena.

Clinically relevant drug-drug interactions among elderly people with dementia.

PURPOSE: Increased numbers of drugs and changes in pharmacokinetic and pharmacodynamic parameters among elderly people contribute to increased prevalence of adverse drug reactions. Drug-drug interactions (DDIs) are an important reason for admission to hospital and elderly people with dementia are particularly vulnerable. The aims of the present study were to assess the occurrence and characteristics of clinically relevant DDIs and to investigate potential risk factors associated with DDIs among elderly people with dementia. METHODS: People ≥ 65 years with dementia, admitted to two hospitals in Northern Sweden, were included. The medical records of 458 patients were reviewed. Clinically relevant DDIs were identified using the Janusmed interactions database. Pharmacological classification was conducted using Stockley's classification system. RESULTS: A total of 401 DDIs were identified among 43.2% of the study population, of which 98.5% had interactions that may require dose adjustment and 7.6% had drug combinations that should be avoided. Pharmacodynamic interactions were most common, of which furosemide-citalopram (n = 35) were most frequently observed. Omeprazol-citalopram (n = 25) was the most common drug combination among pharmacokinetic interactions. Citalopram and warfarin were the most commonly involved drug substances. An association was found between a higher number of medications being prescribed and having at least one DDI. CONCLUSION: Clinically relevant drug-drug interactions are prevalent among elderly people with dementia living in Northern Sweden. Drug-drug interactions should be identified in order to manage and prevent adverse outcomes. This is particularly important among this group of people especially when multiple medications are being prescribed.

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 (Km = 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.