Population pharmacokinetic and pharmacodynamic modeling of epinephrine administered using a mobile inhaler

Pharmacokinetic-Pharmacodynamic Modeling and Simulation

Personalisation strategies of ovarian stimulation for in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments using exogenous follicle-stimulating hormone (FSH) have been extensively studied over the past 20 years. This research aimed to develop a FSH population pharmacokinetic (PPK) model taking into account the contribution of gene polymorphisms in Chinese reproductive-age women. Data from 173 patients undergoing GnRH agonist down-regulation long protocols of IVF/ICSI treatment were collected. PPK analysis was subsequently conducted using the nonlinear mixed-effect model (NONMEM) software. Several covariates, including 18 single nucleotide polymorphisms, demographic factors and biological characteristics, were evaluated. The final PPK model was extensively validated using bootstrapping and normalised prediction error distribution, as well as external validation on an independent group of 35 patients. FSH PPK was accurately described by a one-compartment model with first-order absorption. The typical population value of apparent clearance was estimated to be 0.81 L/h [relative standard errors (RSE) 5.3%] with an inter-individual variability (IIV) of 16.0%. The typical apparent distribution volume was 8.36 L (RSE 9.7%, 59.7% IIV), and the absorption rate constant was estimated to be 0.0444 h-1 (RSE 9.1%). Body weight, basal prolactin concentration and the gene ADIPOQ (rs1501299) showed a significant covariate effect on the FSH clearance rate and exposure concentration. Genotypes of THADA (rs12478601) significantly influenced the distribution volume. Simulation results indicated that patients with the TT genotype of THADA (rs12478601) required a longer time to reach steady state and had less fluctuation in FSH levels. Model evaluations showed that the final model accurately and precisely described the observed data and demonstrated effective prediction performance. PPK models of FSH have been developed, which could potentially be used for FSH dosage individualisation in the clinical setting. This study has been registered with the Chinese Clinical Trials Registry (ChiCTR2100049142).

Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients. Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into development (n = 136) and validation (n = 36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performance for GFR estimation was evaluated in the validation set. A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9h exhibiting a relative mean prediction error (MPE) (RMSE) = -3.7% (14.3%) and better performance than the Bröchner-Mortensen formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients (MPE (RMSE) = 3.7% (18.8%)) while the best performances were obtained for renal transplant patients (MPE (RMSE) = 1.0% (5.8%)). All LSS that included samples before 9hours for the third sample were associated with an increased imprecision. A single MAP-BE of iohexol based on a three-sample LSS for four heterogeneous populations was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients.

Diltiazem is a benzothiazepine calcium blocker and widely used in renal transplant patients since it improves the level of tacrolimus or cyclosporine A concentration. Several population pharmacokinetic (PopPK) models had been established for cyclosporine A and tacrolimus but no specific PopPK model was established for diltiazem. The aim of the study is to develop a PopPK model for diltiazem in renal transplant recipients and provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study. Patients received tacrolimus as primary immunosuppressant agent after renal transplant and started administration of diltiazem 90mg twice daily on 5th day. The concentration of diltiazem at 0, 0.5, 1, 2, 8, and 12h was measured by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Genotyping for CYP3A4*1G, CYP3A5*3, and MDR1 3435 was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). 25 covariates were considered in the stepwise covariate model (SCM) building procedure. One-compartment structural pharmacokinetic model with first-order absorption and elimination was used to describe the pharmacokinetic characteristics of diltiazem. Total bilirubin (TBIL) influenced apparent volume of distribution (V/F) of diltiazem in the forward selection. The absorption rate constant (K a), V/F, and apparent oral clearance (CL/F) of the final population pharmacokinetic (PopPK) model of diltiazem were 1.96/h, 3550 L, and 92.4 L/h, respectively. A PopPK model of diltiazem is established in Chinese renal transplant recipients and it will provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study.

Congenital hyperinsulinism (HI) is the most common cause of persistent hypoglycaemia in infants and children. Exendin-(9-39), an inverse glucagon-like peptide 1 (GLP-1) agonist, is a novel therapeutic agent for HI that has demonstrated glucose-raising effect. We report the first population pharmacokinetic (PopPK) model of the exendin-(9-39) in patients with HI and propose the optimal dosing regimen for future clinical trials in neonates with HI. A total of 182 pharmacokinetic (PK) observations from 26 subjects in three clinical studies were included for constructing the PopPK model using first order conditional estimation (FOCE) with interaction method in nonlinear mixed-effects modelling (NONMEM). Exposure metrics (area under the curve [AUC] and maximum plasma concentration [Cmax ]) at no observed adverse effect levels (NOAELs) in rats and dogs were determined in toxicology studies. Observed concentration-time profiles of exendin-(9-39) were described by a linear two-compartmental PK model. Following allometric scaling of PK parameters, age and creatinine clearance did not significantly affect clearance. The calculated clearance and elimination half-life for adult subjects with median weight of 69kg were 11.8lh-1 and 1.81h, respectively. The maximum recommended starting dose determined from modelling and simulation based on the AUC0-last at the NOAEL and predicted AUC0-inf using the PopPK model was 27mgkg-1 day-1 intravenously. This is the first study to investigate the PopPK of exendin-(9-39) in humans. The final PopPK model was successfully used with preclinical toxicology findings to propose the optimal dosing regimen of exendin-(9-39) for clinical studies in neonates with HI, allowing for a more targeted dosing approach to achieve desired glycaemic response.

Background: Clozapine is a key antipsychotic drug for treatment-resistant schizophrenia but exhibits highly variable pharmacokinetics and a propensity for serious adverse effects. Currently, these challenges are addressed using therapeutic drug monitoring (TDM). This study primarily sought to (i) verify the importance of covariates identified in a prior clozapine population pharmacokinetic (popPK) model in the absence of environmental covariates using physiologically based pharmacokinetic (PBPK) modelling, and then to (ii) evaluate the performance of the popPK model as an adjunct or alternative to TDM-guided dosing in an active TDM population. Methods: A popPK model incorporating age, metabolic activity, sex, smoking status and weight was applied to predict clozapine trough concentrations (Cmin) in a PBPK-simulated population and an active TDM population comprising 142 patients dosed to steady state at Flinders Medical Centre in Adelaide, South Australia. Post hoc analyses were performed to deconvolute the impact of physiological and environmental covariates in the TDM population. Results: Analysis of PBPK simulations confirmed age, cytochrome P450 1A2 activity, sex and weight as physiological covariates associated with variability in clozapine Cmin (R2 = 0.7698; p = 0.0002). Prediction of clozapine Cmin using a popPK model based on these covariates accounted for <5% of inter-individual variability in the TDM population. Post hoc analyses confirmed that environmental covariates accounted for a greater proportion of the variability in clozapine Cmin in the TDM population. Conclusions: Variability in clozapine exposure was primarily driven by environmental covariates in an active TDM population. Pharmacokinetic modelling can be used as an adjunct to TDM to deconvolute sources of variability in clozapine exposure.

ObjectiveMethotrexate (MTX) serves as a cornerstone therapy for primary central nervous system lymphoma (PCNSL). However, the considerable intra- and inter-individual variability in its pharmacokinetic and therapeutic efficacy poses significant challenges to clinical application. This study aims to employ population pharmacokinetic (PPK) models to investigate the pharmacokinetics of MTX in Chinese patients with PCNSL, thereby facilitating personalized therapeutic strategies for these patients.MethodA retrospective dataset comprising 6074 MTX plasma concentrations from 752 adult patients with PCNSL receiving high-dose methotrexate (HD-MTX) therapy was employed to construct the PPK model, utilizing the nonlinear mixed-effects modeling approach. The pharmacokinetics of MTX were characterized using a three-compartment model in conjunction with a proportional residual model. Covariate effects on model parameters were evaluated using forward addition and backward elimination approaches. Model performance was assessed through goodness-of-fit, bootstrap analysis, and visual predictive checks.ResultIn the final PPK models, the estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), alanine aminotransferase (ALT), and a combined genotype of ABCC-ABCG-ADORA2A were identified as significant covariates impacting the clearance (CL) of MTX. Additionally, total protein (TP) was found to be a significant covariate influencing inter-compartmental clearance (Q). The relationship between pharmacokinetic parameters and covariates was quantified as follows: CL (L/h) = 8.45×(eGFR⁄101.8)0.67×(BUN⁄4.6)−0.08×(ALT⁄25)0.03×a (a = 0.91 for gene-model if ABCC-ABCG-ADORA2A mutation, otherwise a = 1); Q1 (L/h) = 0.04×(TP⁄58)b (b = −1.68 for nongene-model and b = −1.72 for gene-model). Bootstrap analysis and visual predictive checks demonstrated the stability and adequate predictive capacity of the final PPK models.ConclusionIn managing HD-MTX therapy for PCNSL patients, it is essential to consider pharmacokinetic factors such as eGFR, BUN, ALT, TP, and genetic polymorphisms. The PPK models developed will aid in optimizing and personalizing HD-MTX treatment for PCNSL patients.

The study aimed to establish a population pharmacokinetic (PPK) model of tacrolimus for Chinese patients with nephrotic syndrome using the patient's genotype and Wuzhi capsule dosage as the main test factors. Ninety-six adult patients with nephrotic syndrome, who were receiving tacrolimus treatment, were enrolled. A nonlinear mixed-effects model was used to determine the influencing factors of interindividual tacrolimus metabolism variation and establish a PPK model. To optimize the tacrolimus dosage, 10,000 Monte Carlo simulations were performed. The 1-chamber model of first-order absorption and elimination was the most suitable model for the data in this study. The typical population tacrolimus clearance (CL/F) value was 16.9 L/h. The percent relative standard error (RSE%) of CL/F was 12%. Increased Wuzhi capsule and albumin doses both decreased the tacrolimus CL/F. In CYP3A5 homozygous mutation carriers, the CL/F was 39% lower than that of carriers of the wild-type and heterozygous mutation. The tacrolimus CL/F in patients who were coadministered glucocorticoids was 1.23-fold higher than that of the control. According to the patient genotype and combined use of glucocorticoids, 26 combinations of Wuzhi capsule and tacrolimus doses were matched. The Monte Carlo simulation identified the most suitable combination scheme. An improved tacrolimus PPK model for patients with nephrotic syndrome was established, and the most suitable combination of Wuzhi capsule and tacrolimus doses was identified, thus, facilitating the selection of a more economical and safe administration regimen.

Background and ObjectiveWhen eye diseases are treated by topical administration, the success of treatment lies in the effective drug concentration in the target tissue. This is why the drug’s pharmacokinetic, in the different substructures of the eye, needs to be explored more accurately during drug development. The aim of the present analysis was to describe by rabbit model, the distribution of a drug after ocular instillation in the selected eye tissues and fluids.MethodsBy a top-down population approach, we developed and validated a population pharmacokinetics (PopPK) model, using tissue concentrations (tear, naso-lacrymal duct, cornea and aqueous humor) of a new src tyrosine kinase inhibitor (FV-60165) in each anterior segment’s tissue and fluid of the rabbit eye. Inter-individual variability was estimated and the impact of the formulation (solution or nanosuspension) was evaluated.ResultsThe model structure selected for the eye is a 4-compartment model with the formulation as a significant covariate on the first-order rate constant between tears and the naso-lacrymal duct. The model showed a good predictive performance and may be used to estimate the concentration–time profiles after single or repeated administration, in each substructure of the eye for each animal included in the analysis.ConclusionsThis analysis allowed describing the distribution of a drug in the different selected tissues and fluids in the rabbit’s eyes after instillation of the prodrug as a solution or nanosuspension.

In this study, we aimed to improve upon a published population pharmacokinetic (PK) model for venlafaxine (VEN) in the treatment of depression in older adults, then investigate whether CYP2D6 metabolizer status affected model-estimated PK parameters of VEN and its active metabolite O-desmethylvenlafaxine. The model included 325 participants from a clinical trial in which older adults with depression were treated with open-label VEN (maximum 300 mg/day) for 12 weeks and plasma levels of VEN and O-desmethylvenlafaxine were assessed at weeks 4 and 12. We fitted a nonlinear mixed-effect PK model using NONMEM to estimate PK parameters for VEN and O-desmethylvenlafaxine adjusted for CYP2D6 metabolizer status and age. At both lower doses (up to 150 mg/day) and higher doses (up to 300 mg/day), CYP2D6 metabolizers impacted PK model-estimated VEN clearance, VEN exposure, and active moiety (VEN + O-desmethylvenlafaxine) exposure. Specifically, compared with CYP2D6 normal metabolizers, (i) CYP2D6 ultra-rapid metabolizers had higher VEN clearance; (ii) CYP2D6 intermediate metabolizers had lower VEN clearance; (iii) CYP2D6 poor metabolizers had lower VEN clearance, higher VEN exposure, and higher active moiety exposure. Overall, our study showed that including a pharmacogenetic factor in a population PK model could increase model fit, and this improved model demonstrated how CYP2D6 metabolizer status affected VEN-related PK parameters, highlighting the importance of genetic factors in personalized medicine.

Background and Aims The phase 2 SANCTUARY study was a randomized, double-blind, placebo-controlled trial (NCT04564339; EudraCT No. 2020-001537-13) which evaluated treatment with the second-generation complement C5 inhibitor, ravulizumab, in adults with IgA nephropathy (IgAN). Rapid and clinically meaningful proteinuria reduction was observed. Change from baseline to week 26 in 24-hour urine protein was 41.9% with ravulizumab and 16.8% with placebo (30.1% treatment effect; P = 0.005) [1]. The aim of this analysis was to develop a population pharmacokinetic (PopPK) model to characterize the pharmacokinetics (PK) of ravulizumab in patients with IgAN and determine the impact of potential covariates (intrinsic and extrinsic factors) on exposure. Furthermore, the developed PopPK model was used to perform dose simulations to confirm dosing regimens for the phase 3 I CAN trial (NCT06291376; EU CT 2023-507851-31-00). Method The phase 2 trial enrolled 66 patients with biopsy-proven primary IgAN. Patients were randomized 2:1 to ravulizumab (n = 43) or placebo (n = 23) during the 26-week initial evaluation period. All patients received ravulizumab during the subsequent 24-week extension period. Patients received weight-based dosing, with loading dose on Day 1 followed by maintenance dosing on Day 15 and every 8 weeks thereafter. A previously developed PopPK model for atypical hemolytic uremic syndrome was used as the base model for developing an IgAN-specific model. The NONMEM® software (ICON Development Solutions, Hanover, MD) was used to perform nonlinear mixed effects regression analyses to assess if potential covariates (demographics, laboratory assessments, and concomitant medications) identify PK parameter source and magnitude of inter-patient variability in drug exposure. Qualification of the PopPK model was performed by comparing observed and predicted concentration-time profiles using a Visual Predictive Check (VPC). Results In total, 926 blood samples were obtained from 61 of 66 patients with IgAN at baseline and in follow-up visits (female 44.3%, age ranged from 21 to 64 years, body weight ranged from 48 to 129 kg). Inhibition of terminal complement was assessed by free C5 levels and showed immediate, complete, and sustained C5 inhibition following dosing with ravulizumab: a precipitous drop in free C5 from ∼100 µg/mL at baseline to &amp;lt;0.5 µg/mL following the end of infusion after the first dose. For PK characterization, a two-compartment model with linear clearance (CL) described the serum ravulizumab concentration-time data. A covariate analysis identified body mass index (BMI), hemoglobin, and proteinuria as significant factors that helped explain inter-patient variability, thus reducing overall variability in the dose-concentration relationship among patients. The observed median, 5th, and 95th percentiles of ravulizumab concentrations were consistent with model-predicted values. For the final PopPK model, the ravulizumab systemic CL, intercompartmental CL (Q), apparent volume of distribution in the central compartment (Vc), and apparent volume of distribution in the peripheral compartment (Vp) were 0.00388 L/h, 0.0087 L/h, 3.37 L, and 1.44 L, respectively, given a typical 70-kg patient with a BMI of 25.5 kg/m2, hemoglobin of 134 g/dL, and proteinuria of 2.3 g/day. Relative standard error for CL, Q, Vc, and Vp were 2.37%, 31.9%, 2.11%, and 6.88%, respectively. The inter-patient variability of CL and Vc were 15.9% and 9.98%, respectively. The mean (standard deviation) terminal elimination half-life of ravulizumab was 38.1 (7.7) days. Conclusion A two-compartment linear PK model described the dose-concentration relationship in adults with IgAN. Our findings describe the PK/PD characteristics of ravulizumab and support the rationale for dose selection in the ongoing phase 3 trial in IgAN (NCT06291376, EU CT 2023-507851-31-00).

Background and ImportanceTeicoplanin is a widely used antibiotic in patients with haematologic malignancies (HM); however, a population pharmacokinetic (popPK) model for these patients is not available.Aim and ObjectivesTo develop a...

Population pharmacokinetic (PPK) model is capable of identifying the factors that influence the variability of pharmacokinetic (PK) profiles and the degree of their influence, effectively reduces unexplained variability, and demonstrates excellent predictive ability. PPK model has been successfully constructed in several populations for a variety of drugs. However, no study has yet conducted a bibliometric analysis of publications related to the PPK model. This study aims to provide a comprehensive overview of the research progress and hotspots in the field of PPK model research through bibliometric methods. A systematic search of the Web of Science database was conducted to collect articles and reviews related to the PPK model between 2000 and 2024. We then analyzed the data using Bibliometrix R package, Microsoft Office Excel, CiteSpace and VOSviewers. Between 2000 and 2024, we identified a total of 6,125 papers and 128,856 citations. The average annual growth rate of the relevant publications was 10.35%, showing continued growth momentum. These research outputs are concentrated in North America, Western Europe, and East Asia, with USA leading the way with 2,340 publications and having the highest H-index (93) and total citations (54,965). Uppsala University and British Journal of Clinical Pharmacology are the institutions with the highest publication output and the most influential journals, respectively. Most of the funding agencies are from the USA and the subject categories for most publications are Pharmacology Pharmacy. In terms of author contributions, professor Karlsson MO is the leader in the field with 149 publications. In addition, wo found that "critically ill patients," "tacrolimus," "machine learning," "external evaluation," "polymyxin b," "voriconazole," "extracorporeal membrane oxygenation," "dose optimization" and "model-informed precision dosing" are current research hotspots and future research trends. This study is the first comprehensive overview of the development of PPK model and research hotspots using bibliometric methods. Our findings provide researchers, especially beginners, with insights into the application area of PPK model, helping them to grasp key information in the field.

Perampanel exhibits substantial interindividual variability, and pharmacokinetic data in pediatric patients are scarce. The aim of this study was to develop a population pharmacokinetic (PPK) model to optimize the dosing of perampanel in children with epilepsy. The PPK model was developed via a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 454 plasma concentrations of perampanel obtained from 151 pediatric patients with epilepsy, 120 (79.5%) of whom were aged < 12 years. Goodness-of-fit plots and bootstrap analysis were employed to evaluate the final model. Monte Carlo simulations were utilized to suggest perampanel dosing strategies using a reference plasma concentration range of 100-1000 ng/mL. In the final PPK models of perampanel, linear centralized age, coadministration of oxcarbazepine (OXC), carbamazepine (CBZ), and valproic acid (VPA) were covariates of clearance (CL/F), and log-transformed body weight was a covariate of the apparent distribution volume (V). The CL/F was estimated via the formula CL/F=0.177*((age+10)/8.8)1.31*1.51OXC*0.745VPA*1.88CBZ. The relative standard errors (RSEs) for each fixed effect parameter were 15.2%, 14.2%, 12.0%, 7.92%, and 16.3%, respectively. The V was estimated via the formula V=227*LGBW with an RSE of 14.1%. The model demonstrated good robustness according to goodness-of-fit plots and bootstrap analysis. The simulation analysis resulted in a dosing regimen stratified by covariates. A reliable perampanel PPK model for pediatric patients was successfully developed. This result could be helpful for dosing optimization in pediatric patients receiving perampanel, especially those aged under 12 years.

BackgroundTrastuzumab-DM1 (T-DM1) is a first-in-class HER2 antibody–drug conjugate (ADC) in development for the treatment of HER2-postive MBC. T-DM1 is composed of trastuzumab (Herceptin®), DM1, an inhibitor of tubulin polymerization derived from maytansine, and the stable MCC linker that conjugates DM1 and trastuzumab. T-DM1 has been tested at multiple dose levels in a Phase I trial: q3w (0.3–4.8 mg/kg) and weekly (1.2–2.9 mg/kg), and in two subsequent Phase II trials with T-DM1 administered as a single agent at 3.6 mg/kg q3w. A population pharmacokinetic (PK) model for T-DM1 conjugate was developed using pooled PK data from these trials to estimate typical PK parameter values and inter-patient variability in patients with HER2-positive MBC. The model was further developed to explore and quantify the effect of body size and pathophysiologic covariates on the pharmacokinetics of T-DM1 to better understand the clinical factors that might affect safety for individual patients.MethodsFor the current analysis, 2935 T-DM1 serum concentration–time datapoints from 167 patients were simultaneously fitted using NONMEM® software. T-DM1 concentration-time data to date are best described using a two-compartment linear model. All relevant and plausible covariates likely to have an effect on T-DM1 systemic exposure, e.g., body size covariates (body weight [BW], body surface area [BSA], and body mass index [BMI]), albumin, liver enzymes, creatinine clearance, tumor burden, baseline trastuzumab levels, HER2ECD, concomitant medications, etc., will be explored for possible correlation with the parameters in the model. In addition, the effect of selected covariates on the pharmacokinetics of DM1 is also explored.ResultsEstimated PK parameters for T-DM1 from the base population PK model with data available to date are: clearance (CL) 0.75 L/day, central distribution volume (V1) 3.37 L, peripheral volume (V2) 1.04 L, and inter-compartmental clearance (Q) 0.92 L/day. Inter-individual variability on CL, V1, and V2 was 26%, 17%, and 66%, respectively. Preliminary univariate covariate analysis suggests that body size metric (BSA, WT, BMI) has an influence on exposure of T-DM1. Albumin and tumor burden also appear to influence the clearance of T-DM1. Additional covariate analysis is ongoing. Results of the updated PK model and covariate analysis based on ongoing Phase II trials will be presented at the meeting.ConclusionsModerate inter-individual variability for the estimated PK parameters was observed. The model incorporating all available PK data from Phase I and II trials will help identify clinical factors, from the covariates studied, that affect the pharmacokinetics of T-DM1 and thus potentially, the safety of T-DM1 in individual patients. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5099.