Search Results (11)

The aim of the present study was to develop and evaluate the performance of a methodology to estimate the population pharmacokinetic (PK) parameters along with the inter-individual variabilities (IIVs) from patients’ reported aggregate concentration–time data, in particular, mean plasma concentrations and their standard deviations (SDs) versus time, such as those often found in published graphs. This method was applied to the published data of gevokizumab, a novel monoclonal anti-interleukin-1β antibody, in order to estimate the drug’s population pharmacokinetic (PopPK) parameters of a second-generation minimal physiologically based pharmacokinetic (mPBPK) model. Assuming this mPBPK model, a mixed effects approach was utilized to allow accounting for the random inter-group variability (IGV) that was assumed among different dosage groups. The entire analysis was performed using R software (Rstudio) and the Bayesian software tool RStan was used for the application of Bayesian priors on the parameters. Conclusively, the proposed method could be applied to monoclonal antibodies for which the second-generation mPBPK model has been proposed as well as to other drugs with different PK models when only a published graph with aggregate concentration–time data is available. In addition, the method could be used when multiple aggregate datasets from different sources need to be combined in a meta-analysis approach in order to estimate the PopPK parameters of a drug. Full article

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia. Full article

Empagliflozin is a sodium–glucose cotransporter 2 (SGLT2) inhibitor that is commonly used for the treatment of type 2 diabetes mellitus (T2DM). CKD-370 was newly developed as a cocrystal formulation of empagliflozin with co-former L-proline, which has been confirmed to be bioequivalent in South Korea. This study aimed to quantify the differences in the absorption phase and pharmacokinetic (PK) parameters of two empagliflozin formulations in healthy subjects by using population PK analysis. The plasma concentration data of empagliflozin were obtained from two randomized, open-label, crossover, phase 1 clinical studies in healthy Korean subjects after a single-dose administration. A population PK model was constructed by using a nonlinear mixed-effects (NLME) approach (Monolix Suite 2021R1). Interindividual variability (IIV) and interoccasion variability (IOV) were investigated. The final model was evaluated by goodness-of-fit (GOF) diagnostic plots, visual predictive checks (VPCs), prediction errors, and bootstrapping. The PK of empagliflozin was adequately described with a two-compartment combined transit compartment model with first-order absorption and elimination. Log-transformed body weight significantly influenced systemic clearance (CL) and the volume of distribution in the peripheral compartment (V2) of empagliflozin. GOF plots, VPCs, prediction errors, and the bootstrapping of the final model suggested that the proposed model was adequate and robust, with good precision at different dose strengths. The cocrystal form did not affect the absorption phase of the drug, and the PK parameters were not affected by the different treatments. Full article

High interindividual variability (IIV) of the clinical response to epidermal growth factor receptor (EGFR) inhibitors such as osimertinib in non-small-cell lung cancer (NSCLC) might be related to the IIV in plasma exposure. The aim of this study was to evaluate the exposure–response relationship for toxicity and efficacy of osimertinib in unselected patients with advanced EGFR-mutant NSCLC. This retrospective analysis included 87 patients treated with osimertinib. Exposure–toxicity analysis was performed in the entire cohort and survival analysis only in second-line patients (n = 45). No significant relationship between occurrence of dose-limiting toxicity and plasma exposure was observed in the entire cohort (p = 0.23, n = 86). The median overall survival (OS) was approximately two-fold shorter in the 4th quartile (Q4) of osimertinib trough plasma concentration (>235 ng/mL) than in the Q1–Q3 group (12.2 months [CI95% = 8.0–not reached (NR)] vs. 22.7 months [CI95% = 17.1–34.1]), but the difference was not statistically significant (p = 0.15). To refine this result, the exposure–survival relationship was explored in a cohort of 41 NSCLC patients treated with erlotinib. The Q4 erlotinib exposure group (>1728 ng/mL) exhibited a six-fold shorter median OS than the Q1–Q3 group (4.8 months [CI95% = 3.3-NR] vs. 22.8 months (CI95% = 10.6–37.4), p = 0.00011). These results suggest that high exposure to EGFR inhibitors might be related to worse survival in NSCLC patients. Full article

For patients with myelofibrosis, allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the only curative treatment to date. Busulfan-based conditioning regimens are commonly used, although high inter-individual variability (IIV) in busulfan drug exposure makes individual dose selection challenging. Since data regarding the IIV in patients with myelofibrosis are sparse, this study aimed to develop a population pharmacokinetic (PopPK) model of busulfan and its metabolite sulfolane in patients with myelofibrosis. The influence of patient-specific covariates on the pharmacokinetics of drug and metabolite was assessed using non-linear mixed effects modeling in NONMEM^®. We obtained 523 plasma concentrations of busulfan and its metabolite sulfolane from 37 patients with myelofibrosis. The final model showed a population clearance (CL) and volume of distribution (V_d) of 0.217 L/h/kg and 0.82 L/kg for busulfan and 0.021 L/h/kg and 0.65 L/kg for its metabolite. Total body weight (TBW) and a single-nucleotide polymorphism of glutathione-S-transferase A1 (GSTA1 SNP) displayed a significant impact on volume of distribution and metabolite clearance, respectively. This is the first PopPK-model developed to describe busulfan’s pharmacokinetics in patients with myelofibrosis. Incorporating its metabolite sulfolane into the model not only allowed the characterization of the covariate relationship between GSTA1 and the clearance of the metabolite but also improved the understanding of busulfan’s metabolic pathway. Full article

Torsemide is a diuretic drug used for several cardiovascular and chronic diseases. With regard to the clinical application of torsemide, studies on individualized pharmacotherapy and modeling that take variability in pharmacokinetics (PKs) within a population into account have been rarely reported. Thus, the objective of this study was to perform population pharmacokinetic (Pop-PK) modeling and to identify effective covariates that could explain the inter-individual variability (IIV) of torsemide PK. Pop-PK modeling for torsemide was performed based on serum concentration data obtained from 112 healthy Korean males and analysis of various genetic and physicochemical parameters. Modeling was performed with nonlinear mixed-effects (NLME) using Phoenix NLME. The finally developed model was fully verified. The model was also reconfirmed using NONMEM software. As a basic model, the PKs of torsemide within the population were well described by a two-compartment model reflecting the lag-time on oral absorption. According to the genetic polymorphisms of OATP1B1 and CYP2C9, significant associations were found in the V/F, CL/F, and CL₂/F of torsemide. These were reflected as effective covariates in the final Pop-PK model of torsemide, resulting in an approximately 5–10% improvement in the model parameter IIV values. Considering that torsemide is a substrate for CYP2C9 and OATP1B1, it was important to search for genetic polymorphisms in CYP2C9 and OATP1B1 as covariates to explain the PK diversity of torsemide between individuals. The differences in CL/F and CL₂/F between the phenotypes of CYP2C9 were approximately 36.5–51%. The difference in V/F between the phenotypes of OATP1B1 was approximately 41–64.6%. These results suggested that the phenotypes of CYP2C9 and OATP1B1 produced significant differences in torsemide PKs. Considering that CYP2C9 and OATP1B1 phenotypes as covariates affected different PK parameters of torsemide, it could be inferred that torsemide’s cell membrane permeation process by OATP1B1 and the metabolic process by CYP2C9 could independently affect each other in vivo without interplay. There was no significant difference in the parameter estimates between modeling software (Phoenix NLME vs. NONMEM). In this study, the torsemide PK variability between individuals was largely explained. In the future, individualized effective drug therapy of torsemide taking individual patient’s genotypes into account might become possible. Full article

One of the major concerns for all in vivo experiments is intra- and inter-subject variability, which can be a great source of inaccuracy. The aim of this study is, therefore, to estimate the ability of parallel vs. cross-over design studies in order to describe the relative pharmacokinetic performance of the studied drug formulations. We analyzed the data from a drug development program that examined the performance of innovative abiraterone acetate formulations against the identical reference product in three stages. In stages 1–3, groups A–F were dosed with the reference product once in a parallel manner. Stage 4 was performed to evaluate the intra-individual variability (IIV) by repeated administration of the reference product to the same animals. Although the geometric mean (90% CI) values of abiraterone AUC_last in groups A–F were similar to the IIV group (24.36 (23.79–41.00) vs. 26.29 (20.56–47.00) mg/mL·min·g), the results generated in the isolated parallel groups provided imprecise estimates of the true AUC_last values ranging from 9.62 to 44.62 mg/mL·min·g due to chance. Notably, in 4 out of 15 possible pair comparisons between the parallel groups, the confidence intervals did not include 100%, which is the true ratio for all comparisons tested after identical formulation administration to all groups. A cross-over design can significantly improve the methodology in short-term comparative pre-clinical pharmacokinetic studies, and can provide more precise and accurate results in comparison to more traditional pre-clinical study designs. Full article

Palbociclib is an oral CDK4/6 inhibitor indicated in HR+/HER2- advanced or metastatic breast cancer in combination with hormonotherapy. Its main toxicity is neutropenia. The aim of our study was to describe the kinetics of circulating neutrophils from real-life palbociclib-treated patients. A population pharmacokinetic (popPK) model was first constructed to describe palbociclib pharmacokinetic (PK). Individual PK parameters obtained were then used in the pharmacokinetic/pharmacodynamic (PK/PD) model to depict the relation between palbociclib concentrations and absolute neutrophil counts (ANC). The models were built with a population of 143 patients. Palbociclib samples were routinely collected during therapeutic drug monitoring, whereas ANC were retrospectively retrieved from the patient files. The optimal popPK model was a mono-compartmental model with a first-order absorption constant of 0.187 h⁻¹ and an apparent clearance Cl/F of 57.09 L (32.8% of inter individuality variability (IIV)). The apparent volume of distribution (1580 L) and the lag-time (T_lag: 0.658 h) were fixed to values from the literature. An increase in creatinine clearance and a decrease in alkaline phosphatase led to an increase in palbociclib Cl/F. To describe ANC kinetics during treatment, Friberg’s PK/PD model, with linear drug effect, was used. Parameters estimated were Base (2.92 G/L; 29.6% IIV), Slope (0.0011 L/µg; 28.8% IIV), Mean Transit Time (MTT; 5.29 days; 17.9% IIV) and γ (0.102). The only significant covariate was age on the initial ANC (Base), with lower ANC in younger patients. PK/PD model-based simulations show that the higher the estimated C_ressSS (trough concentration at steady state), the higher the risk of developing neutropenia. In order to present a risk lower than 20% to developing a grade 4 neutropenia, the patient should show an estimated C_ressSS lower than 100 µg/L. Full article

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials. Full article

Tamoxifen displays wide inter-individual variability (IIV) in its pharmacokinetics and treatment outcome. Data on tamoxifen pharmacokinetics and pharmacogenetics from black African breast cancer patient populations is lacking. We investigated the pharmacokinetic and pharmacogenetic profile of tamoxifen and its major active metabolite, endoxifen, in Ethiopian breast cancer patients. A total of 81 female breast cancer patients on adjuvant tamoxifen therapy were enrolled. Tamoxifen (Tam) and its major metabolites, N-desmethyltamoxifen (NDM), 4-hydroxy-tamoxifen (4-HT), and (Z)-endoxifen (E) were quantified using LC-MS/MS. Genotyping for CYP2D6, CYP2C9, CYP2C19, CYP3A5, POR, and ABCB1 and UGT2B15 and copy number variation for CYP2D6 were done. The proportion of patients with low endoxifen level (<5.9 ng/mL) was 35.8% (median concentration 7.94 ng/mL). The allele frequency of CYP2D6 gene deletion (*5) and duplication (*1×N or *2×N) was 4.3% and 14.8%, respectively. Twenty-six percent of the patients carried duplicated or multiplicated CYP2D6 gene. An increase in CYP2D6 activity score was associated with increased endoxifen concentration and MR_E/NDM (p < 0.001). The IIV in endoxifen concentration and MR_E/NDM was 74.6% and 59%, respectively. CYP2D6 diplotype explained 28.2% and 44% of the variability in absolute endoxifen concentration and MR_E/NDM, respectively. The explanatory power of CYP2D6 diplotype was improved among ABCB1c.4036G carriers (43% and 65.2%, respectively for endoxifen concentration and MR_E/NDM) compared to A/A genotype. CYP2C9, CYP2C19, and CYP3A5 genotypes had no significant influence on endoxifen concentration or MR_E/NDM. In conclusion, we report a high rate of low endoxifen level as well as large IIV in tamoxifen and its metabolite concentrations. CYP2D6 is significant predictor of plasma endoxifen level in a gene-dose dependent manner. Full article

Successful prospective memory (PM) performance relies on executive functions, including inhibition. However, PM and inhibition are usually assessed in separate tasks, and analytically the focus is either on group differences or at most on interindividual differences. Conjoint measures of PM and inhibition performance that take into account intraindividual variability (IIV) are thus missing. In the present study, we assessed healthy older adults’ level of performance and IIV in both inhibition and PM using a classical Go/NoGo task. We also created a prospective Go/NoGo version that embeds a PM component into the task. Using dynamic structural equation modeling, we assessed the joint effects of mean level (μ), an indicator of amplitude of fluctuations in IIV (or net IIV; intraindividual standard deviation, iSD), and an indicator of time dependency in IIV (the autoregressive parameter ϕ) in reaction times (RTs) on inhibition and PM performance. Results indicate that higher inhibition failure, but not IIV, predicted PM errors, corroborating the current literature on the involvement of prepotent response inhibition in PM processes. In turn, fastest RT latency (μ) and increased net IIV (iSD) were consistently associated with prepotent response inhibition failure, while coherence in RT pattern (ϕ) was beneficial to inhibition performance when the task was novel. Time-dependent IIV (ϕ) appears to reflect an adaptive exploration of strategies to attain optimal performance, whereas increased net IIV (iSD) may indicate inefficient sustained cognitive processes when performance is high. We discuss trade-off processes between competing tasks. Full article