Differential effects of two phosphodiesterase 4 inhibitors against lipopolysaccharide-induced neuroinflammation in mice.

BACKGROUND: Several phosphodiesterase 4 (PDE4) inhibitors have emerged as potential therapeutics for central nervous system (CNS) diseases. This study investigated the pharmacological effects of two selective PDE4 inhibitors, roflumilast and zatolmilast, against lipopolysaccharide-induced neuroinflammation. RESULTS: In BV-2 cells, the PDE4 inhibitor roflumilast reduced the production of nitric oxide and tumor necrosis factor-α (TNF-α) by inhibiting NF-κB phosphorylation. Moreover, mice administered roflumilast had significantly reduced TNF-α, interleukin-1ß (IL-1ß), and IL-6 levels in plasma and brain tissues. By contrast, zatolmilast, a PDE4D inhibitor, showed no anti-neuroinflammatory effects in vitro or in vivo. Next, in vitro and in vivo pharmacokinetic studies of these compounds in the brain were performed. The apparent permeability coefficients of 3 µM roflumilast and zatolmilast were high (> 23 × 10-6 cm/s) and moderate (3.72-7.18 × 10-6 cm/s), respectively, and increased in a concentration-dependent manner in the MDR1-MDCK monolayer. The efflux ratios were < 1.92, suggesting that these compounds are not P-glycoprotein substrates. Following oral administration, both roflumilast and zatolmilast were slowly absorbed and eliminated, with time-to-peak drug concentrations of 2-2.3 h and terminal half-lives of 7-20 h. Assessment of their brain dispositions revealed the unbound brain-to-plasma partition coefficients of roflumilast and zatolmilast to be 0.17 and 0.18, respectively. CONCLUSIONS: These findings suggest that roflumilast, but not zatolmilast, has the potential for use as a therapeutic agent against neuroinflammatory diseases.

Effect of methylphenidate on height in pediatric attention-deficit hyperactivity disorder patients: a systematic review and meta-analysis.

Methylphenidate (MPH), a first-line treatment for attention-deficit hyperactivity disorder (ADHD) management, has been the focus of debate for decades regarding its effect on growth. The aim of this PRISMA meta-analysis was to determine the effect of MPH on height in children/adolescents with ADHD and its predictive factors based on literature reports. Available full-text articles were systematically reviewed to identify clinical studies of pediatric ADHD patients with height Z-score (HZS) data for monotherapy MPH-treated and non-treated groups. We estimated standardized mean differences (SMDs) of HZS or its changes from baseline (ΔHZS) between groups, then identified associated factors through subgroup analyses and meta-regression. For before-after treatment studies, the paired standard errors of ΔHZS were re-estimated to demonstrate in the forest plot. Risk of bias was analyzed using the Newcastle-Ottawa Scale. Among the 29 eligible studies, 26 reported ΔHZS with self-control groups, and ΔHZS or absolute HZS were compared to other external controls in 11 studies. A significant reduction was observed between post-MHP and pre-MPH use, with high heterogeneity (SMD = - 0.40; 95% confidence interval = [ - 0.54, - 0.27]; I2 = 91%). The study region, ADHD subtype, and stimulant-naïve status of patients at baseline may modify the effect on HZS. Because of the high clinical heterogeneity in observational studies, clinicians should consider the negative effect of MPH on height in ADHD patients by determining whether patients fulfill appropriate high-risk criteria. Further well-designed longitudinal studies are required to better quantify this effect, especially with prolonged treatment.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates.

AIM: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS: A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Pharmacokinetics of eperisone following oral administration in healthy Korean volunteers.

Eperisone is an oral muscle relaxant used to treat musculoskeletal diseases, which exhibits high pharmacokinetic (PK) variability in bioequivalence studies. The aim of this study was to characterize the PKs of eperisone following its oral administration to Korean volunteers through the conduct of a noncompartmental and population analysis. A total of 360 concentration-time measurements collected on two separate occasions from 15 healthy volunteers during a bioequivalent study of eperisone 50 mg (Murex® ) were used in the PK analysis. Noncompartmental analysis was performed using WinNonLinTM and population analysis was performed using NONMEM® . The possible influence of thirty demographic and pathophysiological characteristics on the PKs of eperisone were explored. Based on noncompartmental analysis mean eperisone elimination half-life, apparent clearance (CL/F), and apparent volume of distribution were estimated to be 3.81 h, 39.24 × 103  l/h × 103  L, respectively. During population PK modeling a two-compartment model with first-order absorption rate constant (typical population K a  = 1.5 h-1 ) and first-order elimination (typical population CL/F and apparent volume of distribution in the central compartment [V c /F] = 30.8 × 103  l/h and 86.2 × 103  l, respectively) best described the PKs of eperisone. Interindividual variability in CL/F and V c /F were estimated to be 87.9% and 130.3%, respectively and interoccasion variability in CL/F and V c /F were estimated to be 23.8% and 30.8%, respectively. Aspartate aminotransferase level and smoking status were identified as potential covariates that may influence the CL/F of eperisone. This is the first study to develop a disposition model for eperisone and investigate the potential influence of covariate factors on it PK variability.

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

Duloxetine (DLX) is a potent drug investigated for the treatment of depression and urinary incontinence. DLX is extensively metabolized in the liver by two P450 isozymes, CYP2D6 and CYP1A2. Propolis (PPL) is one of the popular functional foods known to have effects on activities of CYPs, including CYP1A2. Due to the high probability of using DLX and PPL simultaneously, the present study was designed to investigate the potent effect of PPL on pharmacokinetics (PKs) of DLX after co-administration in humans. A PK study was first conducted in 18 rats (n = 6/group), in which the plasma concentration of DLX and its major metabolite 4-hydroxy duloxetine (4-HD) with or without administration of PPL was recorded. Population PKs and potential effects of PPL were then analyzed using NONMEM software. Lastly, these results were extrapolated from rats to humans using the allometric scaling and the liver blood flow method. PPL (15,000 mg/day) exerts a statistically significant increase in DLX exposures at steady state, with a 20.2% and 24.6% increase in DLX C m a x , s s and the same 28.0% increase in DLX A U C s s when DLX (40 or 60 mg) was administered once or twice daily, respectively. In conclusion, safety issues are required to be attended to when individuals simultaneously use DLX and PPL at high doses, and the possibility of interactions between DLX and PPL might be noted.

Association of mitochondrial DNA content in peripheral blood with cancer-related fatigue and chemotherapy-related cognitive impairment in early-stage breast cancer patients: a prospective cohort study.

PURPOSE: Cancer-related fatigue (CRF) and chemotherapy-related cognitive impairment (CRCI) are reported to be associated with mitochondrial dysfunction. Hence, mitochondrial DNA (mtDNA) content, a biomarker of mitochondrial dysfunction, is hypothesized to correlate with the onset of CRF and CRCI. This study aims to evaluate the association between peripheral blood mtDNA content reduction and severity of CRF and CRCI in patients receiving chemotherapy. METHODS: This was a prospective cohort study. Early-stage breast cancer patients receiving anthracycline- or taxane-based chemotherapy were recruited. CRF was assessed using MFSI-SF, and CRCI was assessed using FACT-Cog and CANTAB at two timepoints: baseline (T1; prior to treatment) and 6 weeks after initiation of treatment (T2). mtDNA content was measured at both timepoints using real-time quantitative polymerase chain reaction. Multiple logistic regression was utilized to evaluate the association between mtDNA reduction and worsening of CRF and CRCI, adjusting for age, anxiety, insomnia, plasma cytokines concentrations, and other clinically important covariates. RESULTS: A total of 108 patients (age 52.0 ± 9.2 years; 82.4% Chinese; 64.8% receiving anthracycline-based chemotherapy) were recruited. Proportions of patients with worsening of CRF increased from the lower to the upper quartiles of mtDNA reduction (22.2, 33.3, 55.6, and 63.0% in quartiles 1, 2, 3, and 4, respectively, p = 0.001 for trend). Reduction of mtDNA content was significantly greater among those with worsening of CRF and CRCI compared to those without CRF [mean reduction (± SD): 36.5 (46.1) vs. 9.4 (34.5), p < 0.001]. After adjusting for covariates, every 1-unit reduction of the mtDNA content was associated with a 4% increased risk for worsening of CRF (95% CI, 1-6%; p = 0.009). CONCLUSIONS: This is the first study to show that the reduction of mtDNA content in peripheral blood is associated with the onset of CRF in patients receiving chemotherapy. Further validation studies are required to confirm the findings.

Chemotherapy-associated cognitive impairments in Korean cancer patients: Risk factors and functional outcome.

OBJECTIVE: To identify those experiencing significant self-reported cognitive decline over 2 time points during chemotherapy, examine the risk factors for cognitive decline, and examine differences between those with and without significant decline in functional limitations. METHODS: This secondary analysis used data from 163 cancer patients, collected from a Korean University hospital. Significant decline was determined by 15% or more reduction from baseline in the Functional Assessment of Cancer Therapy-Cognitive Function. Multivariate logistic regression was performed to estimate risk factors. Repeated-measures ANOVA and t tests tested differences in groups with and without cognitive decline in cognitive impairment and functional limitation. RESULTS: About 31% (n = 51) experienced significant cognitive decline. Groups with and without decline significantly differed in cognitive-impairment changes over time (F = 238.49, P < .001) and in functional limitations at follow-up (t test, P < .01). Those experiencing increased fatigue over time (odds = 0.94, P < .05) and those who underwent 2 or more cycles between time 1 and 2 (odds = 2.61; P < .05) had higher risk of significant decline over time during chemotherapy. CONCLUSION: Significant cognitive decline occurred during active chemotherapy; attention to cognitive impairment should be given in the early phase of chemotherapy.

Psychometric properties and measurement equivalence of the Multidimensional Fatigue Syndrome Inventory- Short Form (MFSI-SF) amongst breast cancer and lymphoma patients in Singapore.

BACKGROUND: Currently, several fatigue measurement instruments are available to evaluate and measure cancer-related fatigue. Amongst them, Multidimensional Fatigue Syndrome Inventory-Short Form (MFSI-SF) is a self-reported instrument and a multidimensional scale that aims to capture the global, somatic, affective, cognitive and behavioural symptoms of fatigue. This study examines the psychometric properties and measurement equivalence of the English and Chinese versions of MFSI-SF in breast cancer and lymphoma patients in Singapore. METHODS: Patients were recruited from National Cancer Centre Singapore. Validity, reliability and responsiveness of MFSI-SF were evaluated in this study. Convergent validity was evaluated by correlating total and subscales of MFSI-SF to known related constructs in EORTC QLQ-C30. Known group validity was assessed based on patients' cancer stage, pain, insomnia and depression symptoms. Reliability was evaluated by Cronbach's α. Responsiveness analyses were performed with patients who have undergone at least one cycle of chemotherapy. Multiple regression was used to compare the total and subscale scores of MSFI-SF between the two language versions. RESULTS: Data from 246 (160 English and 86 Chinese version) breast cancer and lymphoma patients were included in the study. Moderate to high correlations were observed between correlated MFSI-SF subscales and EORTC QLQ-C30 domains (|r| = 0.524 to 0.774) except for a poor correlation (r = 0.394) observed between MFSI-SF vigour subscale and EORTC QLQ-C30 role functioning subscale. Total MFSI-SF scores could differentiate between patients with higher depression, pain and insomnia status. Internal consistency of MFSI-SF was also high (α = 0.749 to 0.944). Moderate correlation was observed between change in total MFSI-SF score and change in fatigue symptom scale score and global QoL score on EORTC QLQ-C30 (|r| = 0.478 and 0.404 respectively). Poor correlations were observed between change in scores of hypothesised subscales (|r| = 0.202 to 0.361) except for a moderate correlation between change in MFSI-SF emotional fatigue score and change in EORTC QLQ-C30 emotional functioning domain score. Measurement equivalence was established for all subscales and total MFSI-SF score except for the emotional and vigour subscales. CONCLUSIONS: This study supports the use of MFSI-SF as a reasonably valid scale with good internal consistency for measuring fatigue levels in the Singapore cancer population.

Evaluation of plasma brain-derived neurotrophic factor levels and self-perceived cognitive impairment post-chemotherapy: a longitudinal study.

BACKGROUND: Preliminary evidence suggests that changes in plasma brain-derived neurotrophic factor (BDNF) levels may contribute to the occurrence of chemotherapy-associated cognitive impairment (CACI), and a previous study suggested that carriers of the BDNF Met homozygous genotype are protected from CACI. METHODS: This multicenter, prospective cohort study involved chemotherapy-receiving early-stage breast cancer (ESBC) patients. Self-perceived cognitive function was longitudinally assessed using the validated FACT-Cog (ver. 3) across three time points: Prior to chemotherapy (T1), during chemotherapy (T2), and at the end of chemotherapy (T3). Plasma BDNF levels were quantified using enzyme-linked immunosorbent assay. Genotyping was performed using Sanger Sequencing. RESULTS: A total of 51 chemotherapy-receiving ESBC patients (mean age: 52.6 ± 9.5 years) were recruited, and 11 patients (21.6%) reported subjective cognitive impairment post-chemotherapy. Overall, there was a reduction in median plasma BDNF levels over time (T1: 5423.0 pg/ml; T2: 5313.6 pg/ml; T3: 4050.3 pg/ml; p < 0.01). After adjusting for confounding factors, longitudinal analysis revealed that BDNF levels were associated with self-reported concentration deficit (p = 0.032). Carriers of Val/Val (p = 0.011) and Val/Met (p = 0.003) BDNF genotypes demonstrated a significant reduction in plasma BDNF levels over time; however, plasma BDNF levels were similar across all time points among Met homozygous carriers (p = 0.107). CONCLUSION: There was a statistically significant change in BDNF levels post-chemotherapy in ESBC patients, and plasma BDNF levels were associated with self-perceived concentration deficit in patients receiving chemotherapy.

Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects.

BACKGROUND: Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. METHODS: In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2 ), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. RESULTS: The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. CONCLUSIONS: The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.

Development of a pharmacokinetic/pharmacodynamic/disease progression model in NC/Nga mice for development of novel anti-atopic dermatitis drugs.

1. JHL45, a novel immune modulator against atopic dermatitis (AD), was synthesized from decursin isolated from Angelica gigas. The goal is to evaluate the lead compound using quantitative modeling approaches to novel anti-AD drug development. 2. We tested the anti-inflammatory effect of JHL45 by in vitro screening, characterized its in vitro pharmacokinetic (PK) properties. The dose-dependent efficacy of JHL45 was developed using a pharmacokinetics/pharmacodynamics/disease progression (PK/PD/DIS) model in NC/Nga mice. 3. JHL45 has drug-like properties and pharmacological effects when administered orally to treat atopic dermatitis. The developed PK/PD/DIS model described well the rapid metabolism of JHL45, double-peak phenomenon in the PK of decursinol and inhibition of IgE generation by compounds in NC/Nga mice. Also, a quantitative model was developed and used to elucidate the complex interactions between serum IgE concentration and atopic dermatitis symptoms. 4. Our findings indicate that JHL45 has good physicochemical properties and powerful pharmacological effects when administered orally for treatment of AD in rodents.

Development of a population pharmacokinetic model to describe olmesartan medoxomil/ hydrochlorothiazide (20/12.5 mg) FDC tablet in male healthy South Korean subjects.

AIM: The objective of the present study was to develop population pharmacokinetic models for olmesartan medoxomil and hydrochlorothiazide and to investigate the influence of demographic factors on these population pharmacokinetics. METHODS: Plasma concentrations of olmesartan medoxomil and hydrochlorothiazide were measured in 41 healthy volunteers enrolled in our bioequivalence study by LC-MS/MS following oral administration of an olmesartan medoxomil/hydrochlorothiazide (20/12.5 mg) fixed-dose combination tablet. This data and covariates were subjected to nonlinear mixed-effect modeling analysis using the NONMEM software. Evaluation featured a visual predicted check and bootstrapping. RESULTS: The distributions of olmesartan medoxomil and hydrochlorothiazide were best fitted using a two-compartment model with no lag time and first-order elimination. When analyzing hydrochlorothiazide kinetics, we found that TCHO and CL/F were correlated, while. HB and Ka influenced olmesartan medoxomil modeling. All evaluations indicated that the pharmacokinetic profiles of olmesartan medoxomil and hydrochlorothiazide were adequately described using our PPK model. CONCLUSIONS: This study indicates that demographic factors influence the inter-individual variability in the disposition of the combination drug, and it might be more useful to apply it to the PK of olmesartan medoxomil/hydrochlorothiazide (20/12.5 mg) FDC tablets administered to patients with hypertension. *These two authors contributed equally to this work.

A simple pharmacokinetic model of alendronate developed using plasma concentration and urine excretion data from healthy men.

The study of pharmacokinetics of alendronate has been hampered by difficulties in accurately and reproducibly determining their concentrations in serum and urine. Thus, pharmacokinetic characteristics of alendronate have been described in many reports based on urinary excretion data; and plasma pharmacokinetics and the simultaneous pharmacokinetic models of alendronate in plasma and urine are not available. The aims of this study were to measure alendronate concentration in plasma and excretion in urine concurrently and to develop compartmental pharmacokinetic model using urine data. In open-label, single-dose pharmacokinetic study, 10 healthy male volunteers received oral dose of alendronate (70 mg tablet). Blood and urine alendronate concentrations were determined using validated high-performance liquid chromatography method. Non-compartmental analysis was performed using WinNonlin program (Pharsight Inc., Apex, NC). A one-compartment pharmacokinetic model was applied to describe pharmacokinetics of alendronate. A peak plasma alendronate concentration of 33.10 ± 14.32 ng/mL was attained after 1.00 ± 0.16 h. The cumulative amount of alendronate excreted in urine and peak excretion rate were 731.28 ± 654.57 µg and 314.68 ± 395.43 µg/h, respectively. The model, which included first-order absorption rate for oral dosing, showed good fit to alendronate data obtained from plasma and urine. The absorption rate constant was 2.68 ± 0.95 h(-1). The elimination rate constants Kurine and Knon-ur were 0.005 ± 0.004 h(-1) and 0.42 ± 0.08 h(-1), respectively. The pharmacokinetics of alendronate in plasma and urine of healthy men can be predicted using one-compartment model, and thus the behavior of drug in plasma can be estimated from urinary excretion data.

Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power-law based pharmacokinetic modeling.

Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID-19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long-acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose-exposure relationship, a hamster model was selected, given its utility in previous COVID-19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power-law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness-of-fit and visual predictive checks, were employed to assess the models. The power-law-based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power-law-based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Pharmacokinetic and Pharmacodynamic Interaction of Finerenone with Diltiazem, Fluconazole, and Ritonavir in Rats.

BACKGROUND AND OBJECTIVES: Finerenone, a novel selective non-steroidal mineralocorticoid receptor antagonist, has been indicated in chronic kidney disease associated with type 2 diabetes mellitus. Considering the potential complications of diabetes, finerenone can be co-administered with various drugs, including fluconazole, diltiazem, and ritonavir. Given that finerenone is a substrate of cytochrome P450 (CYP) 3A4, the concurrent administration of finerenone with CYP3A4 inhibitors (diltiazem or fluconazole or ritonavir) could potentially lead to drug interactions, which may cause adverse events such as hyperkalemia. No studies have investigated interactions between finerenone and diltiazem or fluconazole or ritonavir. Therefore, this study aims to investigate the pharmacokinetic interaction of finerenone with diltiazem or fluconazole or ritonavir and to evaluate the impact of fluconazole on the pharmacodynamics of finerenone. METHODS: The pharmacokinetic study included four rat groups (n = 8 rats/group), including a control group (finerenone alone) and test groups (finerenone pretreated with diltiazem or fluconazole or ritonavir) using both non-compartment analysis (NCA) and population pharmacokinetic (pop-PK) modeling. The pop-PK model was developed using non-linear mixed-effects modeling in NONMEM® (version 7.5.0). In the pharmacodynamic study, serum potassium (K+) levels were measured to assess the effects of fluconazole on finerenone-induced hyperkalemia. RESULTS: The NCA results indicated that the area under the plasma concentration-time curve (AUC) of finerenone increased by 1.86- and 1.95-fold when coadministered with fluconazole and ritonavir, respectively. In contrast, diltiazem did not affect the pharmacokinetics of finerenone. The pharmacokinetic profiles of finerenone were best described by a one-compartment disposition with first-order elimination and dual first-order absorption kinetics. The pop-PK modeling results demonstrated that the apparent clearance of finerenone decreased by 50.3% and 49.2% owing to the effects of fluconazole and ritonavir, respectively. Additionally, the slow absorption rate, which represents the absorption in the distal intestinal tract of finerenone, increased by 55.7% due to the effect of ritonavir. Simultaneously, a pharmacodynamic study revealed that finerenone in the presence of fluconazole caused a significant increase in K+ levels compared with finerenone alone. CONCLUSIONS: Coadministration of finerenone with fluconazole or ritonavir increased finerenone exposure in rats. Additionally, the administration of finerenone in the presence of fluconazole resulted in elevated K+ levels in rats. Further clinical studies are required to validate these findings.

Quantitative determination of duloxetine and its metabolite in rat plasma by HPLC-MS/MS.

The major metabolite of duloxetine is a glucuronide conjugate of 4-hydroxy duloxetine (4-HD). However, interestingly, there have been no reports determining concentrations of 4-HD and no fully validated method has been established for measuring duloxetine and 4-HD in rat plasma. We developed a method for the simultaneous quantification of duloxetine and its metabolite in rat plasma using high-performance liquid chromatography tandem mass spectrometry. Duloxetine and 4-HD were analyzed on a reverse-phase C18 analytical column after protein precipitation of the plasma sample with methanol, using carbamazepine as an internal standard. The isocratic mobile phase of 5 mm ammonium acetate-methanol (4:6, v/v) was eluted at 0.4 mL/min. Quantification was performed on a triple-quadrupole mass spectrometer using electrospray ionization, and the ion transition monitored in selective reaction monitoring mode. The coefficient of variation for assay precision was <18.0%, and the accuracy was 84.0-118.0%. This method was successfully used to measure the concentrations of duloxetine and its metabolite in plasma following the oral administration of a single 40 mg/kg dose in rats.

Biopharmaceutical characterization of decursin and their derivatives for drug discovery.

Angelica gigas Nakai and its components are known to have neuroprotective, antiplatelet, and anticancer activities. The present study evaluated the in vitro and in vivo biopharmaceutical characterization of Angelica gigas component substances, including decursin (the main substance), decursinol angelate (decursin isomer), JH714 (ether form of decursin) and epoxide decursin (epoxide form of decursin). Decursin, decursinol angelate and JH714 exhibited acceptable metabolic stability (>50%) in liver microsomes from human and higher bound fraction (>90%) in human plasma operating ultrafiltration. Decursin and decursinol angelate in CYP1A2 and CYP2C19 indicated less than 50% CYP activity, suggesting inhibition of the CYP isoforms using Vivid® CYP screening kit. JH714 only showed an apparent permeability coefficient of <10 × 10⁻6 cm/s in MDCK cells, suggesting that it is poorly absorbed. Blood brain barrier permeability was examined after oral administration to male Sprague-Dawley (SD) rats, and pharmacokinetic studies were performed after oral and intravenous administration of 10 mg/kg compounds. Decursin, decursinol angelate and JH714 showed ratios of compound concentration in brain with respect to plasma (Cbrain/Cplasma) of >1.5, suggesting good brain/plasma ratio at 0.5, 1, 3, and 5 h. In contrast, Cbrain/Cplasma was <0.5 for epoxide decursin. For all test compounds, >1.5% of the dose remained in GI tract after 8 h, and the excretion rate in urine was <0.5% which suggests that gastro intestinal tract may be major site of disposition following oral administration. Finally, these results may be useful for the design of dosage regimens of decursin and its derivatives.

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans.

Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUCinf and Cmax of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.