Drug-drug interactions of plant alkaloids derived from herbal medicines on the phase II UGT enzymes: an introductory review.

Herbal medicines are widely used as alternative or complementary therapies to treat and prevent chronic diseases. However, these can lead to drug-drug interactions (DDIs) that affect the glucuronidation reaction of UDP glucuronosyltransferases (UGTs), which convert drugs into metabolites. Plant extracts derived from medicinal herbs contain a diverse array of compounds categorized into different functional groups. While numerous studies have examined the inhibition of UGT enzymes by various herbal compounds, it remains unclear which group of compounds exerts the most significant impact on DDIs in the glucuronidation reaction. Recently, alkaloids derived from medicinal herbs, including kratom (Mitragyna speciosa), have gained attention due to their diverse pharmacological properties. This review primarily focuses on the DDIs of plant alkaloids from medicinal herbs, including kratom on the phase II UGT enzymes. Kratom is a new emerging herbal product in Western countries that is often used to self-treat chronic pain, opioid withdrawal, or as a replacement for prescription and non-prescription opioids. Kratom is well-known for its psychoactive alkaloids, which have a variety of psychopharmacological effects. However, the metabolism mechanism of kratom alkaloids, particularly on the phase II pathway, is still poorly understood. Simultaneously using kratom or other herbal products containing alkaloids with prescribed medicines may have an impact on the drug metabolism involving the phase II UGT enzymes. To ensure the safety and efficacy of treatments, gaining a better understanding of the DDIs when using herbal products with conventional medicine is crucial.

Effects of three flavonoids on the metabolism of lenvatinib.

Lenvatinib is a first-line therapy for the treatment of hepatocellular carcinoma (HCC), an active multi-target tyrosine kinase inhibitor (TKI). The interaction between Traditional Chinese Medicine (TCM) and chemicals has increasingly become a research hotspot. The objective of this study was to pinpoint the effects of three flavonoids on the metabolism of lenvatinib. Enzyme reaction system was established and optimized in vitro, and in vivo experiments were conducted in Sprague-Dawley (SD) rats, where the analytes were detected by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). We found that among three flavonoids, luteolin and myricetin had strong inhibitory effects on lenvatinib metabolism, with half-maximal inhibitory concentration (IC50) values of 11.36 ± 0.46 µM and 11.21 ± 0.81 µM in rat liver microsomes (RLM), respectively, and 6.89 ± 0.43 µM and 12.32 ± 1.21 µM in human liver microsomes (HLM), respectively. In Sprague-Dawley rats, the combined administration of lenvatinib and luteolin obviously expanded the exposure to lenvatinib; however, co-administered with myricetin did not have any changes, which may be due to the poor bioavailability of myricetin in vivo. Furthermore, the inhibitory type of luteolin on lenvatinib showed an un-competitive in RLM and a mixed in HLM. Collectively, flavonoids with liver protection, especially luteolin, may inhibit lenvatinib metabolism in vitro and in vivo.

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.

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.

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases.

The purpose of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model combined with an EGFR occupancy (EO) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and the intracranial time-course of EGFR (T790M and L858R mutants) engagement in patient populations. The PBPK model was also used to investigate the key factors affecting OSI pharmacokinetics (PK) and intracranial EGFR engagement, analyze resistance to the target mutation C797S, and determine optimal dosing regimens when used alone and in drug-drug interactions (DDIs). A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using nine clinical PK studies, observed EO study, and two clinical DDI studies. The PBPK-EO model demonstrated good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.7 to 1.3 for plasma AUC, Cmax, Ctrough and intracranial free concentration. The simulated time-course of C797S occupancy by the PBPK model was much lower than T790M and L858R occupancy, providing an explanation for OSI on-target resistance to the C797S mutation. The PBPK model identified ABCB1 CLint,u, albumin level, and EGFR expression as key factors affecting plasma Ctrough and intracranial EO for OSI. Additionally, PBPK-EO simulations indicated that the optimal dosing regimen for OSI in patients with brain metastases is either 80 mg once daily (OD) or 160 mg OD, or 40 mg or 80 mg twice daily (BID). When used concomitantly with CYP enzyme perpetrators, the PBPK-EO model suggested appropriate dosing regimens of 80 mg OD with fluvoxamine (FLUV) itraconazole (ITR) or fluvoxamine (FLUC) for co-administration and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). In conclusion, the PBPK-EO model has been shown to be capable of simulating the pharmacokinetic concentration-time profiles and the time-course of EGFR engagement for OSI, as well as determining the optimum dosing in various clinical situations.

Metabolism characterization and toxicity of N-hydap, a marine candidate drug for lung cancer therapy by LC-MS method.

N-Hydroxyapiosporamide (N-hydap), a marine product derived from a sponge-associated fungus, has shown promising inhibitory effects on small cell lung cancer (SCLC). However, there is limited understanding of its metabolic pathways and characteristics. This study explored the in vitro metabolic profiles of N-hydap in human recombinant cytochrome P450s (CYPs) and UDP-glucuronosyltransferases (UGTs), as well as human/rat/mice microsomes, and also the pharmacokinetic properties by HPLC-MS/MS. Additionally, the cocktail probe method was used to investigate the potential to create drug-drug interactions (DDIs). N-Hydap was metabolically unstable in various microsomes after 1 h, with about 50% and 70% of it being eliminated by CYPs and UGTs, respectively. UGT1A3 was the main enzyme involved in glucuronidation (over 80%), making glucuronide the primary metabolite. Despite low bioavailability (0.024%), N-hydap exhibited a higher distribution in the lungs (26.26%), accounting for its efficacy against SCLC. Administering N-hydap to mice at normal doses via gavage did not result in significant toxicity. Furthermore, N-hydap was found to affect the catalytic activity of drug metabolic enzymes (DMEs), particularly increasing the activity of UGT1A3, suggesting potential for DDIs. Understanding the metabolic pathways and properties of N-hydap should improve our knowledge of its drug efficacy, toxicity, and potential for DDIs.

Evaluation of the inhibitory effects of antigout drugs on human carboxylesterases in vitro.

Gout is an immune-metabolic disease that frequently coexists with multiple comorbidities such as chronic kidney disease, cardiovascular disease and metabolic syndrome, therefore, it is often treated in combination with these complications. The present study aimed to evaluate the inhibitory effect of antigout drugs (allopurinol, febuxostat, topiroxostat, benzbromarone, lesinurad and probenecid) on the activity of the crucial phase I drug-metabolizing enzymes, carboxylesterases (CESs). 2-(2-benzoyl-3-methoxyphenyl) benzothiazole (BMBT) and fluorescein diacetate (FD) were utilized as the probe reactions to determine the activity of CES1 and CES2, respectively, through in vitro culturing with human liver microsomes. Benzbromarone and lesinurad exhibited strong inhibition towards CESs with Ki values of 2.16 and 5.15 µM for benzbromarone towards CES1 and CES2, respectively, and 2.94 µM for lesinurad towards CES2. In vitro-in vivo extrapolation (IVIVE) indicated that benzbromarone and lesinurad might disturb the metabolic hydrolysis of clinical drugs in vivo by inhibiting CESs. In silico docking showed that hydrogen bonds and hydrophobic interactions contributed to the intermolecular interactions of antigout drugs on CESs. Therefore, vigilant monitoring of potential drug-drug interactions (DDIs) is imperative when co-administering antigout drugs in clinical practice.

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.

A Drug Safety Briefing (II) in Transplantation from Real-World Individual Pharmacotherapy Management to Prevent Patient and Graft from Polypharmacy Risks at the Very Earliest Stage.

For early and long-term patient and graft survival, drug therapy in solid organ and hematopoietic stem cell transplantation inevitably involves polypharmacy in patients with widely varying and even abruptly changing conditions. In this second part, relevant medication briefing is provided, in addition to the scores defined in the previously published first part on the design of the Individual Pharmacotherapy Management (IPM). The focus is on the growing spectrum of contemporary polypharmacy in transplant patients, including early and long-term follow-up medications. 1. Unlike the available drug-drug interaction (DDI) tables, for the first time, this methodological all-in-one device refers to the entire risks, including contraindications, special warnings, adverse drug reactions (ADRs), and DDIs. The selection of 65 common critical drugs results from 10 years of daily IPM with real-world evidence from more than 60,800 IPM inpatient and outpatient medication analyses. It includes immunosuppressants and typical critical antimicrobials, analgesics, antihypertensives, oral anticoagulants, antiarrhythmics, antilipids, antidepressants, antipsychotics, antipropulsives, antiemetics, propulsives, proton pump inhibitors (PPIs), sedatives, antineoplastics, and protein kinase inhibitors. As a guide for the attending physician, the drug-related risks are presented in an alphabetical overview based on the Summaries of Product Characteristics (SmPCs) and the literature. 2. Further briefing refers to own proven clinical measures to manage unavoidable drug-related high-risk situations. Drug-induced injuries to the vulnerable graft and the immunosuppressed comorbid patient require such standardized, intensive IPM and the comprehensive preventive briefing toolset to optimize the outcomes in the polypharmacy setting.

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.

Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions.

Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

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

BACKGROUND: Lenvatinib'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. METHODS: To 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. RESULTS: Following 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. CONCLUSION: Our 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.

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.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model.

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin.

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Management of Patients Treated with Direct Oral Anticoagulants in Clinical Practice and Challenging Scenarios.

It is well established that direct oral anticoagulants (DOACs) are the cornerstone of anticoagulant strategy in atrial fibrillation (AF) and venous thromboembolism (VTE) and should be preferred over vitamin K antagonists (VKAs) since they are superior or non-inferior to VKAs in reducing thromboembolic risk and are associated with a lower risk of intracranial hemorrhage (IH). In addition, many factors, such as fewer pharmacokinetic interactions and less need for monitoring, contribute to the favor of this therapeutic strategy. Although DOACs represent a more suitable option, several issues should be considered in clinical practice, including drug-drug interactions (DDIs), switching to other antithrombotic therapies, preprocedural and postprocedural periods, and the use in patients with chronic renal and liver failure and in those with cancer. Furthermore, adherence to DOACs appears to remain suboptimal. This narrative review aims to provide a practical guide for DOAC prescription and address challenging scenarios.

A Drug Safety Concept (I) to Avoid Polypharmacy Risks in Transplantation by Individual Pharmacotherapy Management in Therapeutic Drug Monitoring of Immunosuppressants.

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety.

Impact of clinical pharmacist-led intervention for drug-related problems in neonatal intensive care unit a randomized controlled trial.

Introduction: Drug-related problems (DRPs) incidence is higher in neonatal intensive care units (NICUs), compared to other pediatric wards due to aspects like off-label medications, pharmacokinetic/dynamic variability, or organ dysfunction/immaturity. This study aimed to determine whether and to what extent a clinical pharmacist intervention improves medication safety and prevents DRPs [medication errors (MEs), adverse drug reactions (ADRs), drug-drug interactions (DDIs)]. Methods: A prospective, randomized, double blind, controlled study in NICU-admitted neonates was conducted. NICU patients were randomly assigned to the intervention (clinical pharmacist-led) (IG) or control group (standard care such as clinical diagnosis, pharmacotherapy) (CG). The clinical pharmacist was involved in the IG to identify-prevent-intervene MEs, or identify and monitor ADRs and DDIs. The primary outcome was the number of neonates who developed at least one DRP compared with those seen across IG and CG. Secondary outcomes included length of hospital stay, total number of drugs or DRP type. Results: Neonates were randomly assigned to CG (n = 52) or IG (n = 48). In total, 45%, 42%, and 16% of patients had at least 1 MEs, ADRs, and clinically significant DDIs, respectively. The number of patients with at least 1 ME was 28 (53%) and 17 (35%) in the CG and IG (p>0.05). The median (range) number of ME was higher in CG [1 (0-7)] than in IG [0 (0-4)] (p = 0.003). Applying regression analysis, the CG had 2.849 times more MEs than the IG (p<0.001). Furthermore, the number of patients (CG to IG) with at least one detected ADR or clinical DDI was 19 (36%) to 23 (47%) (p>0.05) and 4 (7%) to 12 (25%), respectively (p = 0.028). Conclusion: Clinical pharmacist availability to systematically and standardized identify, prevent and resolve DRPs among NICU patients is effective. Daily detailed clinical pharmacist observations and interventions enables prevention and monitoring of DRPs. Clinical Trial Registration ClinicalTrials.gov, identifier NCT04899960.

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.

Comparing Community and Online Pharmacies: Investigating Potential Interactions Between Cancer and Immune-Modulating Drugs with Over-the-Counter Medications, and the Importance of Patient Awareness and Healthcare Professional Guidance in Minimizing Adverse Effects and Maintaining Treatment Efficacy Modern anticancer and immune-modulating drugs have the advantage of often being taken orally, but they present other challenges in daily use. Unlike intravenously administered drugs, these are usually not administered by a physician but taken by the patient at home. In these cases, patients may be more likely to buy and take self-medicating drugs over-the-counter (OTC) without consulting a healthcare professional. This study aimed to investigate whether there is a different risk of drug interactions between cancer or immune-modulating drugs and OTC drugs when bought in a community pharmacy versus an online pharmacy. Therefore, we looked at the most common cancer and immune-modulating drugs purchased in 14 local community pharmacies in Munich and identified which OTC drugs could cause problems when used simultaneously. Additionally, we analyzed the sales data from 11 local and 3 online pharmacies to determine if people were more likely to buy different OTC drugs from the two types of pharmacies. As a result, this study showed 10 relevant OTC drug types that potentially cause problems and influence effectiveness when used with cancer or immune-modulating drugs. Furthermore, we observed that some of these OTC drugs were purchased more often online than in community pharmacies and thus are more distant from the control of a physician or pharmacist. It is therefore essential for patients to be aware of the risks associated with easily accessible OTC drugs in combination with their cancer or immune-modulating medication, as serious side effects or decreased efficacy may develop. Patients should remember to consult their doctor or pharmacist if there is any uncertainty about potential drug interactions. At the same time, healthcare professionals should proactively draw their patients' attention to these potential risks, especially when purchasing online.

Underdiagnosed CKD in Geriatric Trauma Patients and Potent Prevention of Renal Impairment from Polypharmacy Risks through Individual Pharmacotherapy Management (IPM-III).

The aging global patient population with multimorbidity and concomitant polypharmacy is at increased risk for acute and chronic kidney disease, particularly with severe additional disease states or invasive surgical procedures. Because from the expertise of more than 58,600 self-reviewed medications, adverse drug reactions, drug interactions, inadequate dosing, and contraindications all proved to cause or exacerbate the worsening of renal function, we analyzed the association of an electronic patient record- and Summaries of Product Characteristics (SmPCs)-based comprehensive individual pharmacotherapy management (IPM) in the setting of 14 daily interdisciplinary patient visits with the outcome: further renal impairment with reduction of eGFR ≥ 20 mL/min (redGFR) in hospitalized trauma patients ≥ 70 years of age. The retrospective clinical study of 404 trauma patients comparing the historical control group (CG) before IPM with the IPM intervention group (IG) revealed a group-match in terms of potential confounders such as age, sex, BMI, arterial hypertension, diabetes mellitus, and injury patterns. Preexisting chronic kidney disease (CKD) > stage 2 diagnosed as eGFR < 60 mL/min/1.73 m2 on hospital admission was 42% in the CG versus 50% in the IG, although in each group only less than 50% of this was coded as an ICD diagnosis in the patients' discharge letters (19% in CG and 21% in IG). IPM revealed an absolute risk reduction in redGFR of 5.5% (11 of 199 CG patients) to 0% in the IPM visit IG, a relative risk reduction of 100%, NNT 18, indicating high efficacy of IPM and benefit in improving outcomes. There even remained an additive superimposed significant association that included patients in the IPM group before/beyond the 14 daily IPM interventions, with a relative redGFR risk reduction of 0.55 (55%) to 2.5% (5 of 204 patients), OR 0.48 [95% CI 0.438-0.538] (p < 0.001). Bacteriuria, loop diuretics, allopurinol, eGFR ≥ 60 mL/min/1.73 m2, eGFR < 60 mL/min/1.73 m2, and CKD 3b were significantly associated with redGFR; of the latter, 10.5% developed redGFR. Further multivariable regression analysis adjusting for these and established risk factors revealed an additive, superimposed IPM effect on redGFR with an OR 0.238 [95% CI 0.06-0.91], relative risk reduction of 76.2%, regression coefficient -1.437 including patients not yet visited in the IPM period. As consequences of the IPM procedure, the IG differed from the CG by a significant reduction of NSAIDs (p < 0.001), HCT (p = 0.028) and Würzburger pain drip (p < 0.001), and significantly increased prescription rate of antibiotics (p = 0.004). In conclusion, (1) more than 50% of CKD in geriatric patients was not pre-recognized and underdiagnosed, and (2) the electronic patient records-based IPM interdisciplinary networking strategy was associated with effective prevention of further periinterventional renal impairment and requires obligatory implementation in all elderly patients to urgently improve patient and drug safety.