BACKGROUND: Loxoprofen has been actively used clinically to relieve musculoskeletal pain and inflammatory symptoms. However, there are few reports on quantitative pharmacokinetic (PK) prediction tools and diversity analyzes for loxoprofen within populations.
OBJECTIVE: The aim of this study was to identify effective covariates associated with explaining inter-individual PK variability through a population pharmacokinetic (Pop-PK) modeling approach for loxoprofen, and to provide a starting point for establishing scientific dosing regimens.
METHODS: The bioequivalence PK results of loxoprofen performed on 52 healthy Korean men and the physiological and biochemical parameters derived from each individual were used as base data for the development of a Pop-PK model of loxoprofen. In order to simultaneously predict the PKs of the active form according to loxoprofen exposure, previously reported PK results of trans-alcohol loxoprofen, an active metabolite of loxoprofen, were used to expand the model.
RESULTS: The Pop-PK profiles of loxoprofen were described in terms of the basic structure of a non-sequential two absorption with 2-disposition compartment, and for inter-individual PK variations, peripheral compartment volume of distribution could be correlated with body surface area (BSA), and central compartment clearance with creatinine clearance (CrCL) and albumin levels. As a result of the model simulation, the concentrations of loxoprofen and its alcoholic metabolites in plasma significantly decreased as CrCL and albumin levels increased and decreased, respectively. On the other hand, it was confirmed that the higher the BSA, the greater the distribution of loxoprofen to the periphery, and the minimum concentrations of loxoprofen and alcoholic metabolites in plasma in steady-state increased by approximately 1.78-2 times, while the fluctuation between maximum and minimum concentrations decreased. The results suggest that patients with large BSA, impaired renal function, and high serum albumin levels may have significantly higher plasma exposure to loxoprofen and trans-alcohol loxoprofen. It was also suggested that the potential side effects in the gastrointestinal system and various tissues and the level of exposure in plasma due to long-term application of loxoprofen in this patient group could be causally explained.
CONCLUSIONS: This study provides a very useful starting point for a scientific precision medicine approach to loxoprofen by discovering effective covariates and establishing a quantitative model that can explain the diversity of loxoprofen PKs within the population.
BACKGROUND: The clinical study protocol used in this study was thoroughly reviewed and approved by the Institutional Review Board of the Institute of Bioequivalence and Bridging Study, Chonnam National University, Gwangju, Republic of Korea. The bioequivalence study permit numbers are as follows: 041113; 10.15.2004.

Acute graft-versus-host disease (GVHD) is a common life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT), ranking as the second leading cause of death among recipients, surpassed only by disease relapse. Tacrolimus is commonly used for GVHD prophylaxis, but achieving therapeutic blood levels is challenging, particularly in pediatrics, due to the narrow therapeutic window and the high interindividual variability. The retrospective study conducted at IRCCS \"Burlo Garofolo\" in Italy aimed to assess the impact of early post-HSCT tacrolimus levels on transplant-related outcomes in pediatric recipients. The population pharmacokinetic model (POP/PK) was set up to describe tacrolimus pharmacokinetics. Elevated tacrolimus (>12-15 ng/ml) levels within the initial weeks post-HSCT are associated with reduced post-transplant infections (p < 0.0001) and decreased incidence of early transplant-related events (p < 0.01), including a lower incidence of acute GVHD (p < 0.05 on day 0). High tacrolimus exposure can lead to an increased risk of chronic GVHD (p < 0.0001) and reduced overall survival (p < 0.01). Personalized dosing and therapeutic monitoring of tacrolimus are crucial to ensure optimal outcomes. POP/PK could help achieve this goal, giving us a model by which we can balance immunosuppression while looking at the patient\'s general well-being and providing the necessary treatment.

Intractable lymphatic malformations (iLM) pose a significant threat to affected children, demonstrating limited responses to conventional treatments. Sirolimus, effectively inhibiting endothelial cell proliferation in lymphatic vessels, plays a crucial role in iLM treatment. However, the drug\'s narrow therapeutic window and substantial interindividual variability necessitate customized dosing strategies. This study aims to establish a Population Pharmacokinetic Model (PopPK model) for sirolimus in pediatric iLM patients, identifying quantitative relationships between covariates and sirolimus clearance and volume of distribution. Initial dosages are recommended based on a target concentration range of 5-15 ng/mL. Retrospective data from our institution, encompassing 53 pediatric patients with 275 blood concentration results over the past five years (average age: 4.64 ± 4.19 years), constituted the foundation of this analysis. The final model, adopting a first-order absorption and elimination single-compartment model, retained age as the sole covariate. Results indicated a robust correlation between apparent clearance (CL/F) at 5.56 L/h, apparent volume of distribution (V/F) at 292.57 L, and age. Monte Carlo simulation guided initial dosages for patients aged 0-18 years within the target concentration range. This study presents the first PopPK model using a large Therapeutic Drug Monitoring (TDM) database to describe personalized sirolimus dosing for pediatric iLM patients, contributing to pharmacokinetic guidance and potentially improving long-term clinical outcomes.

BACKGROUND: There is an unmet need for prediction of treatment outcome or patient selection for [177Lu]Lu-PSMA therapy in patients with metastatic castration-resistant prostate cancer (mCRPC). Quantification of the tumor exposure-response relationship is pivotal for further treatment optimization. Therefore, a population pharmacokinetic (PK) model was developed for [177Lu]Lu-PSMA-I&T using SPECT/CT data and, subsequently, related to prostate-specific antigen (PSA) dynamics after therapy in patients with mCRPC using a pharmacokinetic/pharmacodynamic (PKPD) modelling approach.
METHODS: A population PK model was developed using quantitative SPECT/CT data (406 scans) of 76 patients who received multiple cycles [177Lu]Lu-PSMA-I&T (± 7.4 GBq with either two- or six-week interval). The PK model consisted of five compartments; central, salivary glands, kidneys, tumors and combined remaining tissues. Covariates (tumor volume, renal function and cycle number) were tested to explain inter-individual variability on uptake into organs and tumors. The final PK model was expanded with a PD compartment (sequential fitting approach) representing PSA dynamics during and after treatment. To explore the presence of a exposure-response relationship, individually estimated [177Lu]Lu-PSMA-I&T tumor concentrations were related to PSA changes over time.
RESULTS: The population PK model adequately described observed data in all compartments (based on visual inspection of goodness-of-fit plots) with adequate precision of parameters estimates (< 36.1% relative standard error (RSE)). A significant declining uptake in tumors (k14) during later cycles was identified (uptake decreased to 73%, 50% and 44% in cycle 2, 3 and 4-7, respectively, compared to cycle 1). Tumor growth (defined by PSA increase) was described with an exponential growth rate (0.000408 h-1 (14.2% RSE)). Therapy-induced PSA decrease was related to estimated tumor concentrations (MBq/L) using both a direct and delayed drug effect. The final model adequately captured individual PSA concentrations after treatment (based on goodness-of-fit plots). Simulation based on the final PKPD model showed no evident differences in response for the two different dosing regimens currently used.
CONCLUSIONS: Our population PK model accurately described observed [177Lu]Lu-PSMA-I&T uptake in salivary glands, kidneys and tumors and revealed a clear declining tumor uptake over treatment cycles. The PKPD model adequately captured individual PSA observations and identified population response rates for the two dosing regimens. Hence, a PKPD modelling approach can guide prediction of treatment response and thus identify patients in whom radioligand therapy is likely to fail.

Selumetinib is clinically used for pediatric patients with neurofibromatosis type 1 and symptomatic, inoperable plexiform neurofibromas. Until recently, selumetinib had to be taken twice daily, after 2 hours of fasting and followed by 1 hour of fasting, which could be inconvenient. This population analysis evaluated the effect of low- and high-fat meals on the pharmacokinetic (PK) parameters of selumetinib and its active metabolite N-desmethyl selumetinib. The dataset comprised 511 subjects from 15 clinical trials who received ≥1 dose of selumetinib and provided ≥1 measurable postdose concentration of selumetinib and N-desmethyl selumetinib. A 2-compartment model with sequential 0- and 1st-order delayed absorption and 1st-order elimination adequately described selumetinib PK characteristics. A 1-compartment model reasonably described N-desmethyl selumetinib PK characteristics over time simultaneously with selumetinib. Selumetinib geometric mean area under the concentration-time curve ratio (1-sided 90% confidence interval [CI] lower bound) was 76.9% (73.3%) with a low-fat meal and 79.3% (76.3%) with a high-fat meal versus fasting. The lower bound of the 1-sided 90% CI demonstrated a difference of <30% between fed and fasted states. Considering the flat exposure-response relationship within the dose range (20-30 mg/m2), the observed range of exposure, and the variability in the SPRINT trial, this was not considered clinically relevant.

Background: ORIN1001, a first-in-class oral IRE1-α endoribonuclease inhibitor to block the activation of XBP1, is currently in clinical development for inhibiting tumor growth and enhancing the effect of chemical or targeted therapy. Early establishment of a population pharmacokinetic (PopPK) model could characterize the pharmacokinetics (PK) of ORIN1001 and evaluate the effects of individual-specific factors on PK, which will facilitate the future development of this investigational drug. Methods: Non-linear mixed effect model was constructed by Phoenix NLME software, utilizing the information from Chinese patients with advanced solid tumors in a phase I clinical trial (Register No. NCT05154201). Statistically significant PK covariates were screened out by a stepwise process. The final model, after validating by the goodness-of-fit plots, non-parametric bootstrap, visual predictive check and test of normalized prediction distribution errors, was further applied to simulate and evaluate the impact of covariates on ORIN1001 exposure at steady state up to 900 mg per day as a single agent. Results: A two-compartment model with first-order absorption (with lag-time)/elimination was selected as the best structural model. Total bilirubin (TBIL) and lean body weight (LBW) were considered as the statistically significant covariates on clearance (CL/F) of ORIN1001. They were also confirmed to exert clinically significant effects on ORIN1001 steady-state exposure after model simulation. The necessity of dose adjustments based on these two covariates remains to be validated in a larger population. Conclusion: The first PopPK model of ORIN1001 was successfully constructed, which may provide some important references for future research.

OBJECTIVE: Chronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram.
METHODS: A systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions.
RESULTS: Eight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models.
CONCLUSIONS: Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Currently, model-informed precision dosing uses one population pharmacokinetic model that best fits the target population. We aimed to develop a subgroup identification-based model selection approach to improve the predictive performance of individualized dosing, using vancomycin in neonates/infants as a test case. Data from neonates/infants with at least one vancomycin concentration was randomly divided into training and test dataset. Population predictions from published vancomycin population pharmacokinetic models were calculated. The single best-performing model based on various performance metrics, including median absolute percentage error (APE) and percentage of predictions within 20% (P20) or 60% (P60) of measurement, were determined. Clustering based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm was used to group neonates/infants according to their best-performing model. Subsequently, classification trees to predict the best-performing model using clinical and demographic characteristics were developed. A total of 208 vancomycin treatment episodes in training and 88 in test dataset was included. Of 30 identified models from the literature, the single best-performing model for training dataset had P20 26.2-42.6% in test dataset. The best-performing clustering approach based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm had P20 44.1-45.5% in test dataset, whereas P60 was comparable. Our proof-of-concept study shows that the prediction of the best-performing model for each patient according to the proposed model selection approaches has the potential to improve the predictive performance of model-informed precision dosing compared with the single best-performing model approach.

BACKGROUND: There is great interindividual difference in the plasma concentration of quetiapine, and optimizing quetiapine therapy to achieve a balance between efficacy and safety is still a challenge. In our study, a population pharmacokinetic (PPK) model considering genetic information was developed with the expectation of comprehensively explaining this observation in Chinese patients with bipolar disorder.
METHODS: Patients who were dispensed quetiapine and underwent the therapeutic drug monitoring (TDM) were included. The genotypes of CYP3A5*3, CYP2D6*10, and ABCB1 C3435T/G2677T were analyzed. Finally, a multivariable linear regression model was applied to describe the PPK of quetiapine considering the covariates weight, height and genotype information.
RESULTS: A total of 175 TDM points from 107 patients were adopted for PPK model development. Resultantly, the CL/F of quetiapine in CYP3A5 expressers was 81.1 CL/h, whereas it was 43.6 CL/h in CYP3A5 nonexpressers. The interindividual variability in CL/F was 47.7 %. However, neither the ABCB1 nor CYP2D6 genotype was significantly associated with the predictor of quetiapine clearance in our study.
CONCLUSIONS: Only trough concentrations were collected, and the span between different points was relatively wide, impeding the application of the typical nonlinear compartment model for PPK analysis. In addition, this was a single-center study which limited the sample of wild-type CYP3A5 carriers.
CONCLUSIONS: The currently established PPK model of quetiapine considering the contribution of the CYP3A5 genotype could efficiently predict the population and individual pharmacokinetic parameters of Chinese bipolar disorder patients, which could better guide the personalized therapy with quetiapine, thus to achieve the best clinical response.

BACKGROUND: RYKINDO® (Rykindo) is a novel, long-acting injectable risperidone formulation administered biweekly (Q2W) through intramuscular gluteal injection for the treatment of schizophrenia in adult patients. This analysis was conducted to demonstrate that the clinical outcomes of Rykindo are equivalent to those of RISPERDAL CONSTA® (Consta; Q2W), and to establish a dosing methodology to switch from Consta to Rykindo, as well as to introduce Rykindo to patients who are currently on oral RISPERDAL® (Risperdal).
METHODS: Population pharmacokinetic (PK) models for Rykindo and Consta were developed using a nonlinear mixed-effects model with the data from phase 1 studies. A model-based simulation was also conducted using NONMEM.
RESULTS: The PK profiles of Rykindo and Consta were adequately represented by a one-compartment model with an immediate release followed by an intermediate and third main release. Drug release of Rykindo was faster than for Consta, reaching steady state approximately 2-3 weeks earlier. The exposures of the active moiety of Rykindo and Consta were comparable at steady state. Model-based simulation indicated that switching from Consta to Rykindo requires administration of the first Rykindo injection within 4-5 weeks following the last Consta injection. For patients taking Risperdal, introducing Rykindo with 1 week of Risperdal supplemental for once-daily dosing (QD) can achieve comparable or superior exposure to that of Consta with 3 weeks of oral QD supplements. A dosing window of ± 3 days for Rykindo was recommended.
CONCLUSIONS: This established approach provides guidance to physicians to initiate Rykindo therapy in adult patients with schizophrenia.
BACKGROUND: ClinicalTrials.gov identifier, NCT02055287, NCT02186769 and NCT02091388.