Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics

Identifying optimal dosing strategies for meropenem in the paediatric intensive care unit through modelling and simulation

BACKGROUND

Meropenem, a β-lactam antibiotic commonly prescribed for severe infections, poses dosing challenges in critically ill patients due to highly variable pharmacokinetics.

OBJECTIVES

We sought to develop a population pharmacokinetic model of meropenem for critically ill paediatric and young adult patients.

PATIENTS AND METHODS

Paediatric intensive care unit patients receiving meropenem 20-40 mg/kg every 8 h as a 30 min infusion were prospectively followed for clinical data collection and scavenged opportunistic plasma sampling. Nonlinear mixed effects modelling was conducted using Monolix®. Monte Carlo simulations were performed to provide dosing recommendations against susceptible pathogens (MIC ≤ 2 mg/L).

RESULTS

Data from 48 patients, aged 1 month to 30 years, with 296 samples, were described using a two-compartment model with first-order elimination. Allometric body weight scaling accounted for body size differences. Creatinine clearance and percentage of fluid balance were identified as covariates on clearance and central volume of distribution, respectively. A maturation function for renal clearance was included. Monte Carlo simulations suggested that for a target of 40% fT > MIC, the most effective dosing regimen is 20 mg/kg every 8 h with a 3 h infusion. If higher PD targets are considered, only continuous infusion regimens ensure target attainment against susceptible pathogens, ranging from 60 mg/kg/day to 120 mg/kg/day.

CONCLUSIONS

We successfully developed a population pharmacokinetic model of meropenem using real-world data from critically ill paediatric and young adult patients with an opportunistic sampling strategy and provided dosing recommendations based on the patients' renal function and fluid status.

Oral docetaxel plus encequidar - A pharmacokinetic model and evaluation against IV docetaxel

The development of optimized dosing regimens plays a crucial role in oncology drug development. This study focused on the population pharmacokinetic modelling and simulation of docetaxel, comparing the pharmacokinetic exposure of oral docetaxel plus encequidar (oDox + E) with the standard of care intravenous (IV) docetaxel regimen. The aim was to evaluate the feasibility of oDox + E as a potential alternative to IV docetaxel. The article demonstrates an approach which aligns with the FDA's Project Optimus which aims to improve oncology drug development through model informed drug development (MIDD). The key question answered by this study was whether a feasible regimen of oDox + E existed. The purpose of this question was to provide an early GO / NO-GO decision point to guide drug development and improve development efficiency.

METHODS

 A stepwise approach was employed to develop a population pharmacokinetic model for total and unbound docetaxel plasma concentrations after IV docetaxel and oDox + E administration. Simulations were performed from the final model to assess the probability of target attainment (PTA) for different oDox + E dose regimens (including multiple dose regimens) in relation to IV docetaxel using AUC over effective concentration (AUCOEC) metric across a range of effective concentrations (EC). A Go / No-Go framework was defined-the first part of the framework assessed whether a feasible oDox + E regimen existed (i.e., a PTA ≥ 80%), and the second part defined the conditions to proceed with a Go decision.

RESULTS

 The overall population pharmacokinetic model consisted of a 3-compartment model with linear elimination, constant bioavailability, constant binding mechanics, and a combined error model. Simulations revealed that single dose oDox + E regimens did not achieve a PTA greater than 80%. However, two- and three-dose regimens at 600 mg achieved PTAs exceeding 80% for certain EC levels.

CONCLUSION

 The study demonstrates the benefits of MIDD using oDox + E as a motivating example. A population pharmacokinetic model was developed for the total and unbound concentration in plasma of docetaxel after administration of IV docetaxel and oDox + E. The model was used to simulate oDox + E dose regimens which were compared to the current standard of care IV docetaxel regimen. A GO / NO-GO framework was applied to determine whether oDox + E should progress to the next phase of drug development and whether any conditions should apply. A two or three-dose regimen of oDox + E at 600 mg was able to achieve non-inferior pharmacokinetic exposure to current standard of care IV docetaxel in simulations. A Conditional GO decision was made based on this result and further quantification of the "effective concentration" would improve the ability to optimise the dose regimen.

Impact of CYP2D6*2, CYP2D6*35, rs5758550, and related haplotypes on risperidone clearance in vivo

PURPOSE

The CYP2D6 gene exhibits significant polymorphism, contributing to variability in responses to drugs metabolized by CYP2D6. While CYP2D6*2 and CYP2D6*35 are presently designated as alleles encoding normal metabolism, this classification is based on moderate level evidence. Additionally, the role of the formerly called "enhancer" single nucleotide polymorphism (SNP) rs5758550 is unclear. In this study, the impacts of CYP2D6*2, CYP2D6*35 and rs5758550 on CYP2D6 activity were investigated using risperidone clearance as CYP2D6 activity marker.

METHODS

A joint parent-metabolite population pharmacokinetic model was used to describe 1,565 serum concentration measurements of risperidone and 9-hydroxyrisperidone in 512 subjects. Risperidone population clearance was modeled as the sum of a CYP2D6-independent clearance term and the partial clearances contributed from each individually expressed CYP2D6 allele or haplotype. In addition to the well-characterized CYP2D6 alleles (*3-*6, *9, *10 and *41), *2, *35 and two haplotypes assigned as CYP2D6*2-rs5758550G and CYP2D6*2-rs5758550A were evaluated.

RESULTS

Each evaluated CYP2D6 allele was associated with significantly lower risperidone clearance than the reference normal function allele CYP2D6*1 (p < 0.001). Further, rs5758550 differentiated the effect of CYP2D6*2 (p = 0.005). The haplotype-specific clearances for CYP2D6*2-rs5758550A, CYP2D6*2-rs5758550G and CYP2D6*35 were estimated to 30%, 66% and 57%, respectively, relative to the clearance for CYP2D6*1. Notably, rs5758550 is in high linkage disequilibrium (R2 > 0.85) with at least 24 other SNPs and cannot be assigned as a functional SNP.

CONCLUSION

CYP2D6*2 and CYP2D6*35 encode reduced risperidone clearance, and the extent of reduction for CYP2D6*2 is differentiated by rs5758550. Genotyping of these haplotypes might improve the precision of genotype-guided prediction of CYP2D6-mediated clearance.

Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

BACKGROUND AND OBJECTIVES

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

METHODS

Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

RESULTS

Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

CONCLUSIONS

This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

A Population Pharmacokinetic Assessment of the Effect of Food on Selumetinib in Patients with Neurofibromatosis Type 1-Related Plexiform Neurofibromas and Healthy Volunteers

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.

Toward Personalized Salbutamol Therapy: Validating Virtual Patient-Derived Population Pharmacokinetic Model with Real-World Data

Interindividual variability, influenced by patient-specific factors including age, weight, gender, race, and genetics, among others, contributes to variations in therapeutic response. Population pharmacokinetic (popPK) modeling is an essential tool for pinpointing measurable factors affecting dose-concentration relationships and tailoring dosage regimens to individual patients. Herein, we developed a popPK model for salbutamol, a short-acting β2-agonist (SABA) used in asthma treatment, to identify key patient characteristics that influence treatment response. To do so, synthetic data from physiologically-based pharmacokinetic (PBPK) models was employed, followed by an external validation using real patient data derived from an equivalent study. Thirty-two virtual patients were included in this study. A two-compartment model, with first-order absorption (no delay), and linear elimination best fitted our data, according to diagnostic plots and selection criteria. External validation demonstrated a strong agreement between individual predicted and observed values. The incorporation of covariates into the basic structural model identified a significant impact of age on clearance (Cl) and intercompartmental clearance (Q); gender on Cl and the constant rate of absorption (ka); race on Cl; and weight on Cl in the volume of distribution of the peripheral compartment (V2). This study addresses critical challenges in popPK modeling, particularly data scarcity, incompleteness, and homogeneity, in traditional clinical trials, by leveraging synthetic data from PBPK modeling. Significant associations between individual characteristics and salbutamol's PK parameters, here uncovered, highlight the importance of personalized therapeutic regimens for optimal treatment outcomes.

Optimization of Vancomycin Initial Dosing Regimen in Neonates Using an Externally Evaluated Population Pharmacokinetic Model

BACKGROUND

Vancomycin therapeutic monitoring guidelines were revised in March 2020, and a population pharmacokinetics-guided Bayesian approach to estimate the 24-hour area under the concentration-time curve to the minimum inhibitory concentration ratio has since been recommended instead of trough concentrations. To comply with these latest guidelines, we evaluated published population pharmacokinetic models of vancomycin using an external dataset of neonatal patients and selected the most predictive model to develop a new initial dosing regimen.

METHODS

The models were identified from the literature and tested using a retrospective dataset of Canadian neonates. Their predictive performance was assessed using prediction- and simulation-based diagnostics. Monte Carlo simulations were performed to develop the initial dosing regimen with the highest probability of therapeutic target attainment.

RESULTS

A total of 144 vancomycin concentrations were derived from 63 neonates in the external population. Five of the 28 models retained for evaluation were found predictive with a bias of 15% and an imprecision of 30%. Overall, the Grimsley and Thomson model performed best, with a bias of -0.8% and an imprecision of 20.9%; therefore, it was applied in the simulations. A novel initial dosing regimen of 15 mg/kg, followed by 11 mg/kg every 8 hours should favor therapeutic target attainment.

CONCLUSIONS

A predictive population pharmacokinetic model of vancomycin was identified after an external evaluation and used to recommend a novel initial dosing regimen. The implementation of these model-based tools may guide physicians in selecting the most appropriate initial vancomycin dose, leading to improved clinical outcomes.

Genetic and clinical predictors of rifapentine and isoniazid pharmacokinetics in paediatrics with tuberculosis infection

OBJECTIVES

Twelve weekly doses of rifapentine and isoniazid (3HP regimen) are recommended for TB preventive therapy in children with TB infection. However, they present with variability in the pharmacokinetic profiles. The current study aimed to develop a pharmacokinetic model of rifapentine and isoniazid in 12 children with TB infection using NONMEM.

METHODS

Ninety plasma and 41 urine samples were collected at Week 4 of treatment. Drug concentrations were measured using a validated HPLC-UV method. MassARRAY® SNP genotyping was used to investigate genetic factors, including P-glycoprotein (ABCB1), solute carrier organic anion transporter B1 (SLCO1B1), arylacetamide deacetylase (AADAC) and N-acetyl transferase (NAT2). Clinically relevant covariates were also analysed.

RESULTS

A two-compartment model for isoniazid and a one-compartment model for rifapentine with transit compartment absorption and first-order elimination were the best models for describing plasma and urine data. The estimated (relative standard error, RSE) of isoniazid non-renal clearance was 3.52 L·h-1 (23.1%), 2.91 L·h-1 (19.6%), and 2.58 L·h-1 (20.0%) in NAT2 rapid, intermediate and slow acetylators. A significant proportion of the unchanged isoniazid was cleared renally (2.7 L·h-1; 8.0%), while the unchanged rifapentine was cleared primarily through non-renal routes (0.681 L·h-1; 3.6%). Participants with the ABCB1 mutant allele had lower bioavailability of rifapentine, while food prolonged the mean transit time of isoniazid.

CONCLUSIONS

ABCB1 mutant allele carriers may require higher rifapentine doses; however, this must be confirmed in larger trials. Food did not affect overall exposure to isoniazid and only delayed absorption time.

Trilaciclib dosage in Chinese patients with extensive-stage small cell lung cancer: a pooled pharmacometrics analysis

This study aimed to analyze potential ethnic disparities in the dose-exposure-response relationships of trilaciclib, a first-in-class intravenous cyclin-dependent kinase 4/6 inhibitor for treating chemotherapy-induced myelosuppression in patients with extensive-stage small cell lung cancer (ES-SCLC). This investigation focused on characterizing these relationships in both Chinese and non-Chinese patients to further refine the dosing regimen for trilaciclib in Chinese patients with ES-SCLC. Population pharmacokinetic (PopPK) and exposure-response (E-R) analyses were conducted using pooled data from four randomized phase 2/3 trials involving Chinese and non-Chinese patients with ES-SCLC. PopPK analysis revealed that trilaciclib clearance in Chinese patients was approximately 17% higher than that in non-Chinese patients with ES-SCLC. Sex and body surface area influenced trilaciclib pharmacokinetics in both populations but did not exert a significant clinical impact. E-R analysis demonstrated that trilaciclib exposure increased with a dosage escalation from 200 to 280 mg/m2, without notable changes in myeloprotective or antitumor efficacy. However, the incidence of infusion site reactions, headaches, and phlebitis/thrombophlebitis rose with increasing trilaciclib exposure in both Chinese and non-Chinese patients with ES-SCLC. These findings suggest no substantial ethnic disparities in the dose-exposure-response relationship between Chinese and non-Chinese patients. They support the adoption of a 240-mg/m2 intravenous 3-day or 5-day dosing regimen for trilaciclib in Chinese patients with ES-SCLC.

Population Pharmacokinetic and Pharmacodynamic Modeling of Romiplostim Biosimilar GP40141 and Reference Product in Healthy Volunteers to Evaluate Biosimilarity

GP40141 is a romiplostim biosimilar. A Phase 1 clinical trial was previously conducted in healthy volunteers to evaluate the pharmacodynamics (PD), pharmacokinetics (PK), and safety of GP40141 compared to the reference romiplostim (NCT05652595). Using noncompartmental analysis, the biosimilarity of PD end points was determined according to the classical criterion (0.8-1.25). PK end points were also in good agreement between GP40141 and the reference romiplostim; however, the confidence interval for the area under concentration-time curve from time 0 to the time of last measurement was slightly out of the bioequivalence range (0.91-1.29). Population PK/PD was used in the present study to characterize the individual PK and PD data of 56 healthy subjects in 2 cross-over periods of the Phase 1 clinical trial. Body weight and neutralizing antibodies to romiplostim were found to be important predictors of apparent volume of distribution and linear elimination constant, respectively. Within the framework of the conducted modeling, population estimates of PK/PD parameters were obtained, which were in agreement with literature data for the reference romiplostim. Additionally, values of intersubject variability, previously unreported for romiplostim in a healthy subject population, were derived. Covariate analysis, conducted during model development, as well as visual diagnostics and model-based simulations, demonstrated the absence of significant differences in PK and PD between GP40141 and romiplostim-ref.

Is Higher Docetaxel Clearance in Prostate Cancer Patients Explained by Higher CYP3A? An In Vivo Phenotyping Study with Midazolam

Patients with prostate cancer (PCa) have a lower docetaxel exposure for both intravenous (1.8-fold) and oral administration (2.4-fold) than patients with other solid cancers, which could influence efficacy and toxicity. An altered metabolism by cytochrome P450 3A (CYP3A) due to castration status might explain the observed difference in docetaxel pharmacokinetics. In this in vivo phenotyping, pharmacokinetic study, CYP3A activity defined by midazolam clearance (CL) was compared between patients with PCa and male patients with other solid tumors. All patients with solid tumors who did not use CYP3A-modulating drugs were eligible for participation. Patients received 2 mg midazolam orally and 1 mg midazolam intravenously on 2 consecutive days. Plasma concentrations were measured with a validated liquid chromatography-tandem mass spectrometry method. Genotyping was performed for CYP3A4 and CYP3A5. Nine patients were included in each group. Oral midazolam CL was 1.26-fold higher in patients with PCa compared to patients with other solid tumors (geometric mean [coefficient of variation], 94.1 [33.5%] L/h vs 74.4 [39.1%] L/h, respectively; P = .08). Intravenous midazolam CL did not significantly differ between the 2 groups (P = .93). Moreover, the metabolic ratio of midazolam to 1'-hydroxy midazolam did not differ between the 2 groups for both oral administration (P = .67) and intravenous administration (P = .26). CYP3A4 and CYP3A5 genotypes did not influence midazolam pharmacokinetics. The observed difference in docetaxel pharmacokinetics between both patient groups therefore appears to be explained neither by a difference in midazolam CL nor by a difference in metabolic conversion rate of midazolam.

Topiramate dosage optimization for effective antiseizure management via population pharmacokinetic modeling

OBJECTIVE

Despite the suggested topiramate serum level of 5-20 mg/L, numerous institutions have observed substantial drug response at lower levels. We aim to investigate the correlation between topiramate serum levels, drug responsiveness, and adverse events to establish a more accurate and tailored therapeutic range.

METHODS

We retrospectively analyzed clinical data collected between January 2017 and January 2022 at Seoul National University Hospital. Drug responses to topiramate were categorized as "insufficient" or "sufficient" by reduction in seizure frequency ≥ 50%. A population pharmacokinetic model estimated serum levels from spot measurements. ROC curve analysis determined the optimal cutoff values.

RESULTS

A total of 389 epilepsy patients were reviewed having a mean dose of 178.4 ± 117.9 mg/day and the serum level, 3.9 ± 2.8 mg/L. Only 5.6% samples exhibited insufficient response, with a mean serum level of 3.6 ± 2.5 mg/L while 94.4% demonstrated sufficient response, with a mean 4.0 ± 2.8 mg/L, having no statistical significance. Among the 69 reported adverse events, logistic regression analysis identified a significant association between ataxia and serum concentration (p = 0.04), with an optimal cutoff value of 6.5 mg/L.

INTERPRETATION

This study proposed an optimal therapeutic concentration for topiramate based on patients' responsiveness to the drug and the incidence of adverse effects. We recommended serum levels below 6.5 mg/L to mitigate the risk of ataxia-related side effects while dose elevation was found unnecessary for suboptimal responders, as the drug's effectiveness plateaus at minimal doses.

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Development of a population pharmacokinetic model to characterize the pharmacokinetics of intrathecally administered tominersen in cerebrospinal fluid and plasma

Tominersen is an intrathecally administered antisense oligonucleotide targeting huntingtin mRNA which leads to a dose-dependent, reversible lowering of cerebrospinal fluid (CSF) mutant huntingtin protein concentration in individuals with Huntington's disease. Nonlinear mixed-effect population pharmacokinetic (PopPK) modeling was conducted to characterize the CSF and plasma pharmacokinetics (PK) of tominersen, and to identify and quantify the covariates that affect tominersen PKs. A total of 750 participants from five clinical studies with a dose range from 10 to 120 mg contributed CSF (n = 6302) and plasma (n = 5454) PK samples. CSF PK was adequately described by a three-compartment model with first-order transfer from CSF to plasma. Plasma PK was adequately described by a three-compartment model with first-order elimination from plasma. Baseline total CSF protein, age, and antidrug antibodies (ADAs) were the significant covariates for CSF clearance. Body weight was a significant covariate for clearances and volumes in plasma. ADAs and sex were significant covariates for plasma clearance. The developed PopPK model was able to describe tominersen PK in plasma and CSF after intrathecal administration across a range of dose levels, and relevant covariate relationships were identified. This model has been applied to guide dose selection for future clinical trials of tominersen in patients with Huntington's disease.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Plasma and Cerebrospinal Fluid Population Pharmacokinetics of Vancomycin in Patients with External Ventricular Drain

Vancomycin is a commonly used antibacterial agent in patients with primary central nervous system (CNS) infection. This study aims to examine predictors of vancomycin penetration into cerebrospinal fluid (CSF) in patients with external ventricular drainage and the feasibility of CSF sampling from the distal drainage port for therapeutic drug monitoring. Fourteen adult patients (9 with primary CNS infection) were treated with vancomycin intravenously. The vancomycin concentrations in blood and CSF (from proximal [CSF_P] and distal [CSF_D] drainage ports) were evaluated by population pharmacokinetics. Model-based simulations were conducted to compare various infusion modes. A three-compartment model with first-order elimination best described the vancomycin data. Estimated parameters included clearance (CL, 4.53 L/h), central compartment volume (Vc, 24.0 L), apparent CSF compartment volume (VCSF, 0.445 L), and clearance between central and CSF compartments (QCSF, 0.00322 L/h and 0.00135 L/h for patients with and without primary CNS infection, respectively). Creatinine clearance was a significant covariate on vancomycin CL. CSF protein was the primary covariate to explain the variability of QCSF. There was no detectable difference between the data for sampling from the proximal and the distal port. Intermittent infusion and continuous infusion with a loading dose reached the CSF target concentration faster than continuous infusion only. All infusion schedules reached similar CSF trough concentrations. Beyond adjusting doses according to renal function, starting treatment with a loading dose in patients with primary CSF infection is recommended. Occasionally, very high and possibly toxic doses would be required to achieve adequate CSF concentrations, which calls for more investigation of direct intraventricular administration of vancomycin. (This study has been registered at ClinicalTrials.gov under registration no. NCT04426383).

Investigating the contribution of residual unexplained variability components on bias and imprecision of parameter estimates in population pharmacokinetic mixed-effects modeling

In a nonlinear mixed-effects modeling (NLMEM) approach of pharmacokinetic (PK) and pharmacodynamic (PD) data, two levels of random effects are generally modeled: between-subject variability (BSV) and residual unexplained variability (RUV). The goal of this simulation-estimation study was to investigate the extent to which PK and RUV model misspecification, errors in recording dosing and sampling times, and variability in drug content uniformity contribute to the estimated magnitude of RUV and PK parameter bias. A two-compartment model with first-order absorption and linear elimination was simulated as a true model. PK parameters were clearance 5.0 L/h; central volume of distribution 35 L; inter-compartmental clearance 50 L/h; peripheral volume of distribution 50 L. All parameters were assumed to have a 30% coefficient of variation (CV). One hundred in-silico subjects were administered a labeled dose of 120 mg under 4 sample collection designs. PK and RUV model misspecifications were associated with relatively larger increases in the magnitude of RUV compared to other sources for all levels of sampling design. The contribution of dose and dosing time misspecifications have negligible effects on RUV but result in higher bias in PK parameter estimates. Inaccurate sampling time data results in biased RUV and increases with the magnitude of perturbations. Combined perturbation scenarios in the studied sources will propagate the variability and accumulate in RUV magnitude and bias in PK parameter estimates. This work provides insight into the potential contributions of many factors that comprise RUV and bias in PK parameters.

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients

Brepocitinib is a tyrosine kinase 2 and Janus kinase 1 inhibitor in development for treatment of inflammatory autoimmune diseases. This analysis aimed to add to the pharmacokinetic knowledge of the medication, through development of a population pharmacokinetic model and identification of factors that affect drug disposition. Plasma samples from 5 clinical trials were collated, composed of healthy volunteers, patients with psoriasis and patients with alopecia areata taking oral brepocitinib. NONMEM was used to develop a population pharmacokinetic model, and patient demographics were tested as covariates. The final model was a 1-compartment model with first-order absorption. The typical values for apparent clearance and apparent volume of distribution were 18.7 L/h (78% coefficient of variation [CV]) and 136 L (60.5% CV), respectively. Absorption was rapid with an absorption constant of 3.46 h, with an absorption lag of 0.24 hours observed with the oral tablet formulation. The proportional residual error was found to be 52.7% CV in healthy volunteers and 87.5% CV in patients. High-fat meals were associated with a reduction in both the rate (69.9% lower) and extent (28.3% lower) of absorption, while Asian populations had reduced clearance (24.3% lower). Nonlinear pharmacokinetics were observed at doses of 175 mg and above, with a 35.1% higher relative bioavailability at these doses. There were insufficient data to describe this nonlinearity as a continuous relationship. This initial description of the population pharmacokinetics will act as a foundation for the model-informed drug development of brepocitinib and will facilitate future modeling of this medicine. ClinicalTrials.gov numbers NCT02310750 NCT03236493 NCT03656952 NCT02969018 NCT02974868.

Overestimation of the effect of (fos)aprepitant on intravenous dexamethasone pharmacokinetics requires adaptation of the guidelines for children with chemotherapy-induced nausea and vomiting

Purpose

Chemotherapy-induced nausea and vomiting (CINV) are common side effects in pediatric oncology treatment. Besides 5-HT₃-antagonists, both dexamethasone and aprepitant are cornerstone drugs in controlling these side effects. Based on results of adult studies, the dexamethasone dose is reduced by 50% when combined with aprepitant, because of a drug-drug interaction, even though data on the interaction in children is lacking. The current study was developed to investigate the effect of aprepitant on dexamethasone clearance (CL) in children, in order to assess if dexamethasone dose reduction for concomitant use of aprepitant is appropriate in the current antiemetic regimen.

Methods

In total, 65 children (0.6–17.9 years), receiving intravenous or oral antiemetic therapy (dexamethasone ± aprepitant) as standard of care, were included. 305 dexamethasone plasma concentrations were determined using LC–MS/MS. An integrated dexamethasone and aprepitant pharmacokinetic model was developed using non-linear mixed effects modelling in order to investigate the effect of aprepitant administration on dexamethasone CL.

Results

In this population, dexamethasone CL in patients with concomitant administration of aprepitant was reduced by approximately 30% of the uninhibited CL (23.3 L/h (95% confidence interval 20.4–26.0)). This result is not consistent with the results of adult studies (50% reduction). This difference was not age dependent, but might be related to the route of administration of dexamethasone. Future studies are needed to assess the difference in oral/intravenous dexamethasone.

Conclusion

When dexamethasone is given intravenously as a component of triple therapy to prevent CINV in children, we advise to reduce the dexamethasone dose by 30% instead of 50%.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-07423-6.

External evaluation of published population pharmacokinetic models of posaconazole

Population pharmacokinetic (PopPK) models of posaconazole have been established to promote the precision dosing. However, the performance of these models extrapolated to other centers has not been evaluated. This study aimed to conduct an external evaluation of published posaconazole PopPK models to evaluate their predictive performance. Posaconazole PopPK models screened from the PubMed and MEDLINE databases were evaluated using an external dataset of 213 trough concentration samples collected from 97 patients. Their predictive performance was evaluated by prediction-based diagnosis (prediction error), simulation-based diagnosis (visual predictive check), and Bayesian forecasting. In addition, external cohorts with and without proton pump inhibitor were used to evaluate the models respectively. Ten models suitable for the external dataset were finally included into the study. In prediction-based diagnostics, none of the models met pre-determined criteria for predictive indexes. Only M4, M6, and M10 demonstrated favorable simulations in visual predictive check. The prediction performance of M5, M7, M8, and M9 evaluated using the cohort without proton pump inhibitor showed a significant improvement compared to that evaluated using the whole cohort. Consistent with our expectations, Bayesian forecasting significantly improved the predictive per-formance of the models with two or three prior observations. In general, the applicability of these published posaconazole PopPK models extrapolated to our center was unsatisfactory. Prospective studies combined with therapeutic drug monitoring are needed to establish a PopPK model for posaconazole in the Chinese population to promote individualized dosing.

Impact of pharmacogenetics on variability in exposure to oral vinorelbine among pediatric patients: a model-based population pharmacokinetic analysis

PURPOSE

Better understanding of pharmacokinetics of oral vinorelbine (VNR) in children would help predicting drug exposure and, beyond, clinical outcome. Here, we have characterized the population pharmacokinetics of oral VNR and studied the factors likely to explain the variability observed in VNR exposure among young patients.

DESIGN/METHODS

We collected blood samples from 36 patients (mean age 11.6 years) of the OVIMA multicentric phase II study in children with recurrent/progressive low-grade glioma. Patients received 60 mg/m² of oral VNR on days 1, 8, and 15 during the first 28-day treatment cycle and 80 mg/m², unless contraindicated, from cycle 2-12. Population pharmacokinetic analysis was performed using nonlinear mixed-effects modeling within the Monolix^® software. Fifty SNPs of pharmacokinetic-related genes were genotyped. The influence of demographic, biological, and pharmacogenetic covariates on pharmacokinetic parameters was investigated using a stepwise multivariate procedure.

RESULTS

A three-compartment model, with a delayed double zero-order absorption and a first-order elimination, best described VNR pharmacokinetics in children. Typical population estimates for the apparent central volume of distribution (V_c/F) and elimination rate constant were 803 L and 0.60 h^-1, respectively. Following covariate analysis, BSA, leukocytes count, and drug transport ABCB1-rs2032582 SNP showed a dramatic impact on V_c/F. Conversely, age and sex had no significant effect on VNR pharmacokinetics.

CONCLUSION

Beyond canonical BSA and leukocytes, ABCB1-rs2032582 polymorphism showed a meaningful impact on VNR systemic exposure. Simulations showed that the identified covariates could have an impact on both efficacy and toxicity outcomes. Thus, a personalized dosing strategy, using those covariates, could help to optimize the efficacy/toxicity balance of VNR in children.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia

AIMS

Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions.

METHODS

A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis.

RESULTS

Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions.

CONCLUSIONS

Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non-data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).