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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (p0.05) 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 RegistrationClinicalTrials.gov, identifier NCT04899960.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug–gene interactions (DGIs), drug–drug interactions (DDIs) and drug–drug–gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies. [ABSTRACT FROM AUTHOR]

Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug–drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim® Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (Cmax) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type, and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral bioavailability and is susceptible to drug–drug interactions (DDIs) with CYP3A4 perpetrators. This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) parent–metabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The model was developed in PK-Sim® and MoBi® using 49 clinical studies (94 plasma concentration–time profiles in total) that investigated different doses (1–40 mg) of the intravenous and oral administration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included, using an Emax function to describe the felodipine concentration–effect relationship. The model was tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4 perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values. The model will be freely available in the Open Systems Pharmacology model repository and can be applied in DDI predictions as a CYP3A4 victim drug. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • CESs were strongly inhibited by benzbromarone and lesinurad. • Benzbromarone and lesinurad might induce drug-drug interactions by inhibiting CESs. • Intermolecular interactions contributed to the inhibition of antigout drugs on CESs. • Potential DDIs should be monitored when co-administering antigout drugs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug–drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim® Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (Cmax) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug–gene interactions (DGIs), drug–drug interactions (DDIs) and drug–drug–gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Background The potentially inappropriate medications (PIMs) and drug-drug interactions (DDIs) can significantly affect patient safety in the elderly, especially at transition of health care. Objective The aim of this study is to evaluate PIMs involved in potentially clinically significant DDIs in prescribed pharmacotherapy of elderly patients at hospital discharge. Setting Internal Medicine Clinic of University Hospital Dubrava, Zagreb, Croatia. Method During a 16-month period, the pharmacotherapy data were assessed using Lexicomp Online screening software to identify category C (monitor drug therapy), D (consider therapy modification) and X (avoid combination) DDIs. The European Union (EU)(7)-PIM criteria were applied to detect inappropriately prescribed medications involved in DDIs. Clinical pharmacists obtained data from patients' medical records and patient/caregiver interviews. Main outcome measure The incidence of PIMs involved in potentially clinically significant DDIs. Results A total of 364 consecutive elderly patients were enrolled in the study. The mean number of prescription medications at discharge was 9.3. Overall, 2833 potentially clinically significant DDIs were identified: 2445 (86.3%) of them were category C, 347 (12.3%) category D and 41 (1.4%) were category X interactions. A total of 1164 PIMs were involved in 31.2% of category C interactions, 60.2% of category D interactions and 43.9% of category X interactions. The most frequent PIMs involved in potentially clinically significant DDIs were tramadol, benzodiazepines, moxonidine, vildagliptin and metoclopramide. Conclusion A very high incidence of DDIs in elderly patients and a high incidence of PIMs involved in DDIs was determined at hospital discharge. [ABSTRACT FROM AUTHOR]

• A physiologically-based pharmacokinetic (PBPK) model was developed to explore potential drug-drug interactions between lenvatinib and the traditional chinese medicine compounds schisantherin a (STA) and schisandrin a (SIA). • Simulations using the PBPK model predicted minimal increases (up to 1.09-fold) in lenvatinib exposure when co-administered with STA or SIA. • The limited pharmacokinetic interactions suggest STA and SIA are unlikely to synergistically enhance lenvatinib efficacy through pharmacokinetic changes, but may enhance efficacy through inherent pharmacological properties. • PBPK modeling provides a useful approach to prospectively evaluate interactions between new targeted cancer therapies and traditional medicines. 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. 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. Following single-dose administration, the predicted area under the plasma concentration-time curve (AUC) and maximum plasma concentrations (C max) 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 AUC 0-t and C max increasing up to 1.09-fold and 1.02-fold, respectively. 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. [Display omitted] [ABSTRACT FROM AUTHOR]

The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type, and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral bioavailability and is susceptible to drug–drug interactions (DDIs) with CYP3A4 perpetrators. This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) parent–metabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The model was developed in PK-Sim® and MoBi® using 49 clinical studies (94 plasma concentration–time profiles in total) that investigated different doses (1–40 mg) of the intravenous and oral administration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included, using an Emax function to describe the felodipine concentration–effect relationship. The model was tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4 perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values. The model will be freely available in the Open Systems Pharmacology model repository and can be applied in DDI predictions as a CYP3A4 victim drug.

Introduction: Coronavirus disease 2019 (COVID-19) is an emerging viral infection without any approved treatment. Investigational therapies for COVID-19 may cause clinically important drug-drug interactions (DDIs). We aimed to study drug-drug interactions (DDIs) and their risk factors in hospitalised COVID-19 patients. Methods: We conducted a retrospective study in a tertiary care hospital dedicated to COVID-19 patients. The Lexi-Interact database was used to investigate clinically important DDIs. The database output, including interacting drug pairs, risk rating, reliability rating, mechanism, and management, was evaluated. Results: Medical records of 200 COVID-19 patients were analysed. All patients had at least one clinically important DDI. More than half of interactions were associated with hydroxychloroquine and azithromycin, the most commonly prescribed medications for the management of COVID-19. Concomitant drugs for comorbid conditions leading to polypharmacy were significantly associated with the occurrence of this. Conclusion: There is a higher chance of DDI, which necessitates ongoing care evaluation and therapy adjustment. Drugs used to treat COVID-19 should be carefully selected. [ABSTRACT FROM AUTHOR]

Backround: We aimed at assessing the prevalence of polypharmacy and potential drug-drug interactions (DDIs) with clinical relevance in elderly patient on Emilia Romagna area. Both outpatients and residents in nursing homes were assessed, with only partially overlapping strategies.Methods: We defined a list of 190 pairs of potentially interacting drugs, based on literature appraisal and availability of therapeutic alternatives. January-June 2018 data on drug use in patients over 65 years-old were collected from nine Local Health Authorities of Emilia Romagna: data on community-dwelling subjects were extracted from archives of reimbursed prescriptions, while drug use in a sample of nursing homes was recorded from clinical charts in one index day within the same semester. The frequency of polypharmacy (at least five or at least 10 concurrent drugs) and of each DDI was calculated.Results: In line with different rates of polypharmacy (80% vs 16%), the risk of exposure to at least one interaction was 53.7% in nursing homes and 26.4% in outpatients. Among DDIs, in nursing homes antidepressants—anxiolytics (11.9%) ranked first, followed by antidepressants—aspirin (7.4%). In outpatients, ACE-inhibitors—non-steroidal anti-inflammatory drugs (NSAIDs) reached 7.2% followed by the calcium channel blockers—α-blockers (2.4%).Discussion: Polypharmacy and risk of DDIs appeared very different in the two settings, due to both technical and clinical reasons. In order to reduce use of benzodiazepines, NSAIDs, antidepressants and relevant DDIs, 1) defining alternative options for pain relief in elderly outpatients, and 2) implementing non-pharmacological management of insomnia and anxiety in nursing homes should be prioritized.

Background: Prolongation of the QTc interval may lead to life threatening arrhythmias. QTc prolongation is common in intensive care unit (ICU) patients. The objectives of this study were to identify the role of drug-drug interactions (DDIs) and other predictors (age, sex, cardiovascular diseases, and electrolyte abnormalities) in life threatening QTc prolongation in patients admitted to medical (M), surgical (S) and emergency (E) ICUs. Methods: This prospective, observational study included patients above the age of 18 years who were admitted to SICU, EICU, and MICU at a tertiary respiratory referral center. Electrocardiogram (ECG) monitoring was performed during the first 5 days of ICU admission. Risk factors and DDIs which were anticipated to be associated with the prolongation of the QTc interval were assessed for all patients. Results: Two hundred patients were included in the study. QTc prolongation occurred in 10.7% of patients and the majority of patients presenting with QTc prolongation had creatinine levels above 1.3 mg/dL during their 5 days of ICU admission. Incidence of pharmacodynamic (PD) DDIs was significantly higher in patients with QTc prolongation vs. other patients. Creatinine levels above 1.3 mg/dL and PD DDIs were associated with QTc prolongation during 5 days of ICU admission. Conclusions: High serum creatinine and PD DDIs can increase the risk of QTc prolongation in patients admitted to the ICU. QTc interval measurements should be performed prior to initiation or after starting any drug that is associated with QT prolongation, specifically in patients with the known risk factors. [ABSTRACT FROM AUTHOR]

UDP-glucuronosyltransferase 1A1 (UGT1A1) plays a key role in detoxification of many potentially harmful compounds and drugs. UGT1A1 inhibition may bring risks of drug–drug interactions (DDIs), hyperbilirubinemia and drug-induced liver injury. This study aimed to investigate and compare the inhibitory effects of icotinib and erlotinib against UGT1A1, as well as to evaluate their potential DDI risks via UGT1A1 inhibition. The results demonstrated that both icotinib and erlotinib are UGT1A1 inhibitors, but the inhibitory effect of icotinib on UGT1A1 is weaker than that of erlotinib. The IC50 values of icotinib and erlotinib against UGT1A1-mediated NCHN-O-glucuronidation in human liver microsomes (HLMs) were 5.15 and 0.68 μmol/L, respectively. Inhibition kinetic analyses demonstrated that both icotinib and erlotinib were non-competitive inhibitors against UGT1A1-mediated glucuronidation of NCHN in HLMs, with the Ki values of 8.55 and 1.23 μmol/L, respectively. Furthermore, their potential DDI risks via UGT1A1 inhibition were quantitatively predicted by the ratio of the areas under the concentration–time curve (AUC) of NCHN. These findings are helpful for the medicinal chemists to design and develop next generation tyrosine kinase inhibitors with improved safety, as well as to guide reasonable applications of icotinib and erlotinib in clinic, especially for avoiding their potential DDI risks via UGT1A1 inhibition.

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 (f t ) 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., Competing Interests: Declaration of competing interest The authors have declared no conflicts of interest., (© 2023 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.)

Cabozantinib (CAB) is a receptor tyrosine kinase inhibitor approved for the treatment of several cancer types. Enterohepatic recirculation (EHC) of the substance is assumed but has not been further investigated yet. CAB is mainly metabolized via CYP3A4 and is susceptible for drug–drug interactions (DDI). The goal of this work was to develop a physiologically based pharmacokinetic (PBPK) model to investigate EHC, to simulate DDI with Rifampin and to simulate subjects with hepatic impairment. The model was established using PK-Sim® and six human clinical studies. The inclusion of an EHC process into the model led to the most accurate description of the pharmacokinetic behavior of CAB. The model was able to predict plasma concentrations with low bias and good precision. Ninety-seven percent of all simulated plasma concentrations fell within 2-fold of the corresponding concentration observed. Maximum plasma concentration (Cmax) and area under the curve (AUC) were predicted correctly (predicted/observed ratio of 0.9–1.2 for AUC and 0.8–1.1 for Cmax). DDI with Rifampin led to a reduction in predicted AUC by 77%. Several physiological parameters were adapted to simulate hepatic impairment correctly. This is the first CAB model used to simulate DDI with Rifampin and hepatic impairment including EHC, which can serve as a starting point for further simulations with regard to special populations.

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. [ABSTRACT FROM AUTHOR]

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent−metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug–drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration−time profiles (dosing range 50–800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or Ctrough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

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. [ABSTRACT FROM AUTHOR]

This study described a pH-gradient dissolution method combined with flux measurements as an in vitro tool for assessing the risk of bioavailability reduction due to drug-drug interactions (DDI) caused by acid reducing agents (ARAs). The device incorporates absorption chambers into USP II dissolution vessels, with fiber optic UV-probes monitoring concentration in situ. Dosage forms of Genentech BCS class II drugs, GDC-0810, GDC-0941, and compound A, were tested by starting the dissolution in either pH 1.6 or pH 4.0 media then converting to FaSSIF after 30 min. GDC-0810 showed no significant difference in flux between the two conversion experiments. A supersaturation phase was observed for GDC-0941 in the pH 1.6 experiments after media conversion to FaSSIF; however, it did not appear to occur in the pH 4.0 experiment due to low drug solubility at pH 4.0, resulting in a 95% decrease in flux compared to pH 1.6 experiment. The extent of flux reduction and the total accumulated API mass in the absorption chamber agreed well with the 89% reduction in mean Cmax and the 82% reduction in mean AUC from dog PK study between animals treated with pentagastrin and famotidine. Testing of the compound A optimized formulation tablets showed a 25% reduction in flux and in vitro absorbed amount by changing pH 1.6 to 4.0, correlating well with the AUC decrease in clinical studies. Good correlation between in vitro data and in vivo PK data demonstrated the applicability of the method for formulators to develop drug products mitigating DDI from ARAs. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to evaluate the occurrence of drug-drug interactions (DDIs) in patients on cancer chemotherapy, with the identification of risk factors for these DDIs. Methods: This was a cross-sectional, descriptive study carried out at the Department of Onco-Radiation at Guru Gobind Singh Medical College, Faridkot, Punjab. The DDIs were recorded with the help of a drug interaction/interplay information software. Results: In total, 354 interactions were identified from 283 patient records. The mean age of the patients in the study was 49.05 ± 14.35 years. According to the mechanism of interaction, 306 (86.44%) drug interactions were classified as pharmacokinetic and 48 (13.56%) as pharmacodynamic in nature. Conclusions: Sensitization of the treating oncologist and the establishment of alerts, such as electronic alerts or a novel fully digital computerized technology that gives a warning when a health expert enters a patient's prescription orders into the electronic medical documentation, can be helpful in controlling DDIs. [ABSTRACT FROM AUTHOR]

UDP-glucuronosyltransferase 1A1 (UGT1A1) plays a key role in detoxification of many potentially harmful compounds and drugs. UGT1A1 inhibition may bring risks of drug–drug interactions (DDIs), hyperbilirubinemia and drug-induced liver injury. This study aimed to investigate and compare the inhibitory effects of icotinib and erlotinib against UGT1A1, as well as to evaluate their potential DDI risks via UGT1A1 inhibition. The results demonstrated that both icotinib and erlotinib are UGT1A1 inhibitors, but the inhibitory effect of icotinib on UGT1A1 is weaker than that of erlotinib. The IC 50 values of icotinib and erlotinib against UGT1A1-mediated NCHN- O -glucuronidation in human liver microsomes (HLMs) were 5.15 and 0.68 μmol/L, respectively. Inhibition kinetic analyses demonstrated that both icotinib and erlotinib were non-competitive inhibitors against UGT1A1-mediated glucuronidation of NCHN in HLMs, with the K i values of 8.55 and 1.23 μmol/L, respectively. Furthermore, their potential DDI risks via UGT1A1 inhibition were quantitatively predicted by the ratio of the areas under the concentration–time curve (AUC) of NCHN. These findings are helpful for the medicinal chemists to design and develop next generation tyrosine kinase inhibitors with improved safety, as well as to guide reasonable applications of icotinib and erlotinib in clinic, especially for avoiding their potential DDI risks via UGT1A1 inhibition. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interactions (DDIs) are factors of adverse drug reactions and are more common in elderly patients. Identifying potential DDIs can prevent the related risks. Fewer studies of potential DDIs in prescribing for elderly patients in outpatient clinics. This study aimed to investigate the prevalence and associated factors with potential DDIs and potentially clinically significant DDIs (csDDIs) among elderly outpatients based on 3 DDIs databases., Methods: A cross-sectional study was carried out on outpatients (≥65 years old) of a tertiary care hospital in China between January and March 2022. Patients' prescriptions, including at least 1 systemic drug, were consecutively collected. The potential DDIs were identified by Lexicomp ® , Micromedex ® , and DDInter. Patient-related clinical parameter recorded at the prescriptions and DDIs with higher risk rating was analyzed. Variables showing association in univariate analysis (P<0.2) were included in logistic regression analysis. Weighted kappa analysis was used to analyze the consistencies of different databases., Results: A total of 19,991 elderly outpatients were involved in the study, among whom 21,527 drug combinations including 486 drugs occurred. Lexicomp ® , Micromedex ® , and DDInter respectively identified 32.22%, 32.93%, and 22.62% of patients have at least one potential DDIs, meanwhile, 9.16%, 14.53%, and 4.56% of patients have at least one potential csDDIs. Under any evaluation criteria, polypharmacy and neurology visits were risk factors for csDDIs. Lexicomp ® has the highest coverage rate (87.86%) for drugs. Micromedex ® identified the most csDDIs (740 drug combinations). Drugs used in diabetes and psycholeptics were frequently found in the csDDIs of 2 commercial databases. The consistency between Lexicomp ® and Micromedex ® was moderate (weighted kappa 0.473). DDInter had fair consistencies with the other databases., Conclusions: This study showed the prevalence of potential DDIs is high in elderly outpatients and potential csDDIs were prevalent. Considering the relative risk, pre-warning of potential DDIs before outpatient prescribing is necessary. As the consistencies among identification criteria are not good, more research is needed to focus on actual adverse outcomes to promote accurate prevention of csDDIs., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-5463/coif). The authors have no conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

Purpose: To evaluate the function of multidrug and toxin extrusion proteins (MATEs) using C-labeled metformin ([C]metformin) by positron emission tomography (PET). Methods: PET was performed by intravenous bolus injection of [C]metformin. Pyrimethamine at 0.5 and 5 mg/kg was intravenously administered to mice 30 min prior to the scan. Integration plot analysis was conducted for calculating liver (CL), kidney (CL) tissue uptake, intrinsic biliary (CL) and urinary (CL) excretion clearances of [C]metformin. Results: Visualization by PET showed that pyrimethamine increased concentrations of [C]metformin in the liver and kidneys, and decreased the concentrations in the urinary bladder without changing the blood profiles. Pyrimethamine had no effect on the CL and CL, which were similar to the blood-flow rate. CL with regard to the liver concentration was unable to be determined, but administration of 0.5 and 5 mg/kg of pyrimethamine increased the liver-to-blood ratio to 1.6 and 2.3-fold, respectively, indicating that pyrimethamine inhibited the efflux of [C]metformin from the liver. CL with regard to the corticomedullary region concentrations was decreased 37 and 68% of the control by administration of 0.5 and 5 mg/kg of pyrimethamine, respectively ( P < 0.05). Conclusions: Tissue concentration based investigations using [C]metformin by PET enables the functional analysis of MATEs in the liver and kidneys. [ABSTRACT FROM AUTHOR]

1. The purpose of this study was to clarify species differences in the heteroactivation of CYP3A substrates by efavirenz, which is known from clinical studies to activate midazolam 1′-hydroxylation, and to assess the feasibility of an animal model. 2. In monkey and human liver microsomes, efavirenz activated CYP3A-mediated midazolam 1′-hydroxylation, but had no effect in rat liver microsomes. The activating effect of efavirenz was also observed with recombinant human CYP3A4 and CYP3A5. Midazolam 4-hydroxylation, testosterone 6β-hydroxylation and the oxidation of nifedipine were not activated by efavirenz in any of the microsomes. 3. In an in vivo study using monkeys, the AUC ratio of midazolam/1′-hydroxymidazolam was reduced from 0.85 to 0.30 by efavirenz treatment, which was comparable to that obtained in clinical studies. However, the AUC changes of midazolam caused by efavirenz were smaller than those observed in clinical results, therefore the effect of efavirenz on monkeys was not completely consistent with that seen in humans. 4. In conclusion, this is the first report that efavirenz specifically activates midazolam 1′-hydroxylation only in monkey and human liver microsomes, revealing marked species differences and high substrate specificity in the heteroactivation. A further study is required to clarify whether this in vitro result reflects the in vivo situation. [ABSTRACT FROM AUTHOR]