Basic concepts in population modeling, simulation, and model-based drug development-part 2: introduction to pharmacokinetic modeling methods

Population Pharmacokinetic Modeling of Adavosertib (AZD1775) in Patients with Solid Tumors

Adavosertib (AZD1775) is a potent small-molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed-effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3-week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state: maximum concentration during a 21-day cycle, area under the curve (AUC) during a 21-day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2-compartment model with lag time into the dosing compartment and first-order absorption into the central compartment, time-varying CL, and random effects on all model parameters. VPCs and steady-state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation-based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug-exposure metrics.

A population pharmacokinetic model for sertraline in women during the perinatal period-A contribution from the ConcePTION project

AIMS

Sertraline is frequently prescribed for mental health conditions in both pregnant and breastfeeding women. According to the limited available data, only small amounts of sertraline are transferred into human milk, yet with a large amount of unexplained interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model to describe the pharmacokinetics of sertraline during the perinatal period and explain interindividual variability.

METHODS

Pregnant women treated with sertraline were enrolled in the multicenter prospective cohort SSRI-Breast Milk study. A popPK model for sertraline maternal plasma and breast milk concentrations was developed and allowed estimating the milk-to-plasma ratio (MPR). An additional fetal compartment allowed cord blood concentrations to be described. Several covariates were tested for significance. Ultimately, model-based simulations allowed infant drug exposure through placenta and breast milk under various conditions to be predicted.

RESULTS

Thirty-eight women treated with sertraline were included in the study and provided 89 maternal plasma, 29 cord blood and 107 breast milk samples. Sertraline clearance was reduced by 42% in CYP2C19 poor metabolizers compared to other phenotypes. Doubling milk fat content increased the MPR by 95%. Simulations suggested a median daily infant dosage of 6.9 μg kg-1 after a 50 mg maternal daily dose, representing 0.95% of the weight-adjusted maternal dose. Median cord blood concentrations could range from 3.29 to 33.23 ng mL-1 after maternal daily doses between 25 and 150 mg.

CONCLUSIONS

Infant exposure to sertraline, influenced by CYP2C19 phenotype and breast milk fat content, remains low, providing reassurance regarding the use of sertraline during pregnancy and breastfeeding.

Pharmacokinetic-pharmacodynamic modelling and simulation of methotrexate dosing in patients with rheumatoid arthritis

AIMS

To develop a non-linear mixed-effects population pharmacokinetic and pharmacodynamic (PK-PD) model describing the change in the concentration of methotrexate polyglutamates in erythrocytes (ery-MTX-PGn with "n" number of glutamate, representing PK component) and how this relates to modified 28-joint Disease Activity Score incorporating erythrocyte sedimentation rate (DAS-28-3) for rheumatoid arthritis (RA), representing PD component.

METHODS

An existing PK model was fitted to data from a study consisting of 117 RA patients. The estimation of population PK-PD parameters was performed using stochastic approximation expectation maximisation algorithm in Monolix 2021R2. The model was used to perform Monte Carlo simulations of a loading dose regimen (50mg subcutaneous methotrexate as loading doses, then 20mg weekly oral methotrexate) compared to a standard dosing regimen (10mg weekly oral methotrexate for 2 weeks, then 20mg weekly oral methotrexate).

RESULTS

Every 40 nmol/L increase in ery-MTX-PG3-5 total concentration correlated with 1-unit reduction in DAS-28-3. Significant covariate effects on the therapeutic response of methotrexate included the use of prednisolone in the first 4 weeks (positive use correlated with 25% reduction in DAS-28-3 when other variables were constant) and patient age (every 10-year increase in age correlated with 3.4% increase in DAS-28-3 when other variables were constant). 4 methotrexate loading doses led to a higher percentage of patients achieving a good/moderate response compared to the standard regimen (Week 4: 87.6% vs. 39.8%; Week 10: 64.7% vs. 57.0%).

CONCLUSIONS

A loading dose regimen was more likely to achieve higher ery-MTX-PG concentration and better therapeutic response after 4 weeks of methotrexate treatment.

Desmopressin dosing in children using real-world data and pharmacokinetic/pharmacodynamic model simulations

BACKGROUND

Variability in pediatric dosing of desmopressin (ddAVP) in AVP-deficiency (AVP-D) is well-documented but dosing recommendations are limited. This study evaluates and optimizes ddAVP dosing regimens in children with AVP-D using pharmacokinetic and pharmacodynamic (PK/PD) simulations.

METHODS

Retrospective electronic health record review was done to identify children (<18 years) with AVP-D on ddAVP evaluated in the outpatient setting using ICD 9 and 10 codes. A previously developed PK/PD model from Michelet et al was used to simulate ddAVP concentrations and urine rates based on a child's age and ddAVP dose. The effects of demographic characteristics (age, weight, etc.) on dose and urine rate were investigated through simulations to optimize doses of ddAVP for children who were wet overnight.

RESULT

A total of 276 dosing records were identified among 53 children with AVP-D. Simulations indicated that in children under 5 years of age who were wet overnight, increasing the outpatient dose to 50 mcg was predicted to decrease urine rate to a pattern similar to those who remained dry.

CONCLUSION

An initial outpatient dose of at least 50 mcg for children between 1 and 5 years of age would improve efficacy of ddAVP.

IMPACT

50 mcg is likely a safe initial outpatient dose of oral desmopressin tablet for young children 1-5 yrs of age with central Diabetes Insipidus/AVP-Deficiency. We confirmed that desmopressin doses vary greatly in children with central Diabetes Insipidus/AVP-deficiency. Real-world clinical data can be leveraged to improve medication dosing in rare diseases.

Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes

The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed-effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed-effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed-effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed-effects model. As an example, we use a simulated clinical trial with within-subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained.

Population Pharmacokinetic Model of Intravenous Immunoglobulin in Patients Treated for Various Immune System Disorders

PURPOSE

Intravenous immunoglobulin (IVIG) is used to treat various immune system disorders, but the factors influencing its disposition are not well understood. This study aimed to estimate the population pharmacokinetic parameters of IVIG and to investigate the effect of genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor (FcRn) and clinical variability on the pharmacokinetic properties of IVIG in patients with immune system disorders.

METHODS

Patients were recruited from 4 hospitals in Malaysia. Clinical data were recorded, and blood samples were taken for pharmacokinetic and genetic studies. Population pharmacokinetic parameters were estimated by nonlinear mixed-effects modeling in Monolix. Age, weight, baseline immunoglobulin G concentration, ethnicity, sex, genotype, disease type, and comorbidity were investigated as potential covariates. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive checks, and bootstrap analysis.

FINDINGS

A total of 292 blood samples were analyzed from 79 patients. The IVIG concentrations were best described by a 2-compartment model with linear elimination. Weight was found to be an important covariate for volume of distribution in the central compartment (Vc), volume of distribution in the peripheral compartment (Vp), and clearance in the central compartment, whereas disease type was found to be an important covariate for Vp. Goodness-of-fit plots indicated that the model fit the data adequately. Genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor did not affect the pharmacokinetic properties of IVIG.

IMPLICATIONS

This study supports the use of dosage based on weight as per current practice. The study findings highlight that Vp is significantly influenced by the type of disease being treated with IVIG. This relationship suggests that different disease types, particularly inflammatory and autoimmune conditions, may alter tissue permeability and fluid distribution due to varying degrees of inflammation. Increased inflammation can lead to enhanced permeability and retention of IVIG in peripheral tissues, reflecting higher Vp values.

A fully automatic tool for development of population pharmacokinetic models

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape

Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentration, [MIC]) necessitate personalised approaches. Dose individualisation strategies aim to address this challenge, improving treatment outcomes and minimising the risk of toxicity and antimicrobial resistance. Therapeutic drug monitoring (TDM), with the application of population pharmacokinetic (popPK) models, enables model-informed precision dosing (MIPD). PopPK models mathematically describe drug behaviour across populations and can be combined with patient-specific TDM data to optimise dosing regimens. The integration of machine learning (ML) techniques promises to further enhance dose individualisation by identifying complex patterns within extensive datasets. Implementing these approaches involves challenges, including rigorous model selection and validation to ensure suitability for target populations. Understanding the relationship between drug exposure and clinical outcomes is crucial, as is striking a balance between model complexity and clinical usability. Additionally, regulatory compliance, outcome measurement, and practical considerations for software implementation will be addressed. Emerging technologies, such as real-time biosensors, hold the potential for revolutionising TDM by enabling continuous monitoring, immediate and frequent dose adjustments, and near patient testing. The ongoing integration of TDM, advanced modelling techniques, and ML within the evolving digital health care landscape offers a potential for enhancing antimicrobial therapy. Careful attention to model development, validation, and ethical considerations of the applied techniques is paramount for successfully optimising antimicrobial treatment for the individual patient.

Do Chinese HIV-infected adult patients with altered renal function need to adjust tenofovir disoproxil fumarate dosage? A population pharmacokinetics analysis

Tenofovir disoproxil fumarate (TDF), a prodrug of tenofovir (TFV), is an effective drug in treating patients infected with human immunodeficiency virus (HIV). Previous population pharmacokinetics (PPK) studies have showed the large variabilities in PK of TFV. Furthermore, limited information was known in Chinese populations. Therefore, the aim of this study was to characterize PPK of TDF in Chinese and identify factors that may affect its PK. TFV concentrations (n = 552) from 30 healthy subjects and 162 HIV-infected Chinese adult patients were pooled for PPK analysis by a nonlinear mixed-effects method. The PK of TFV was adequately described as a two-compartment model with first order absorption and elimination. The typical apparent clearance (CL/F) of TFV in 70-kg adults was 137 L/h, higher than that reported in Caucasians and Blacks (45.8-93 L/h). Estimated glomerular filtration rate was identified to be a significant factor influencing CL/F. Monte Carlo simulation showed that the exposure of standard dosing regimen of TDF 300 mg every 24 h in Chinese people with mild renal impairment (60 to 90 ml/min/1.73 m2) was close to that in individuals with normal renal function (90 mL/min). Dose adjustment is not required for patients with mild renal impairment. Our study might offer new clues for optimal dosing strategies in Chinese patients with HIV-infected.

Time-course and dose-effect of omalizumab in treating chronic idiopathic urticaria/chronic spontaneous urticaria

PURPOSE

Several studies have shown that subcutaneous injections of omalizumab can treat chronic idiopathic/spontaneous urticaria (CIU/CSU) patients by only assessing the efficacy on specific endpoints. This study aimed to quantitatively analyze different doses of omalizumab in CIU/CSU and compare it with ligelizumab.

METHODS

Literature searches were performed in PubMed, Embase, and Web of Science databases. A model-based meta-analysis (MBMA) was utilized to develop a model incorporating time since the initiation of treatment and dose for omalizumab, with the change from baseline in Urticaria Activity Score (CFB-UAS7) as the primary efficacy endpoint. The time-course and dose-effect relationship throughout the omalizumab treatment period was analyzed, and the findings were compared with those of the investigational ligelizumab.

RESULTS

The model equation for the CFB-UAS7 was established as E = -Emax × time/(ET50 + time) × (b0 + b1 × dose). The estimated values of the model parameters E max ,ET 50 , b 0 , and b 1 were -1.16, 1.26 weeks, -9.90, and -0.0361 mg-1, respectively. At week 12 after the first dose, the model-predicted CFB-UAS7 for 150 mg and 300 mg of omalizumab were -16.0 (95% CI, -17.2 to -14.8) and -21.7 (95% CI, -22.9 to -20.5), respectively. In the PEARL-1 trial, the CFB-UAS7 for 72 mg and 120 mg of ligelizumab were -19.4 (95% CI, -20.7 to -18.1) and -19.3 (95% CI, -20.6 to -18.0), respectively. In the PEARL-2 trial, these values were -19.2 (95% CI, -20.5 to -17.9) and -20.3 (95% CI, -21.6 to -19.0), respectively.

CONCLUSION

Omalizumab showed a significant dose-dependent effect in the treatment of CSU. Both 72 mg and 120 mg ligelizumab might have the potential to outperform 150 mg (but not 300 mg) omalizumab.

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 Pharmacokinetics of Sertraline in Psychiatric and Substance Use Disorders

This study aimed to characterize the population pharmacokinetics of sertraline in Mexican patients with psychiatric and substance use disorders. Fifty-nine patients (13 to 76 years old) treated with doses of sertraline between 12.5 and 100 mg/day were included. Plasma sertraline concentrations were determined in blood samples and five of the main substances of abuse were determined by rapid tests in urine samples. Demographic, clinical, and pharmacogenetic factors were also evaluated. Population pharmacokinetic analysis was performed using NONMEM software with first-order conditional estimation method. A one-compartment model with proportional residual error adequately described the sertraline concentrations versus time. CYP2D6*2 polymorphism and CYP2C19 phenotypes significantly influenced sertraline clearance, which had a population mean value of 66 L/h in the final model. The absorption constant and volume of distribution were fixed at 0.855 1/h and 20.2 L/kg, respectively. The model explained 11.3% of the interindividual variability in sertraline clearance. The presence of the CYP2D6*2 polymorphism caused a 23.1% decrease in sertraline clearance, whereas patients with intermediate and poor phenotype of CYP2C19 showed 19.06% and 48.26% decreases in sertraline clearance, respectively. The model was internally validated by bootstrap and visual predictive check. Finally, stochastic simulations were performed to propose dosing regimens to achieve therapeutic levels that contribute to improving treatment response.

Population Pharmacokinetics of Dolutegravir in African Children: Results From the CHAPAS-4 Trial

We characterized population pharmacokinetics in 42 African children receiving once-daily 25 mg (14 to <20 kg) or 50 mg (>20 kg) dolutegravir. Coadministration with emtricitabine and tenofovir alafenamide reduced dolutegravir bioavailability by 19.6% (95% confidence interval: 8.13%-30.8%) compared with zidovudine or abacavir with lamivudine. Nevertheless, concentrations remained above efficacy targets, confirming current dosing recommendations.

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy

BACKGROUND

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

METHODS

A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

RESULTS

The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

CONCLUSIONS

Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Population pharmacokinetic study in children with vascular anomalies: body weight as a key variable in predicting the initial dose and dosing frequency of sirolimus

Background

The main challenges faced when using sirolimus in children with vascular anomalies (VAs) still include significant pharmacokinetic (PK) variability, uncertainty in the target concentration range, as well as inconsistencies in initial dosing and dosing frequency. The aim of this study is to establish a new population pharmacokinetic (PPK) model for children with VAs to guide the individualized use of sirolimus.

Methods

A PPK study was performed using data from children with VAs who received sirolimus between July 2017 and April 2022. A nonlinear mixed-effect modeling with a one-compartment model structure was applied. Monte Carlo simulation was employed to propose specific dosing recommendations to achieve the target trough concentrations (C trough) of 5-15 ng/mL.

Results

In total, 134 blood concentrations from 49 pediatric patients were used to characterize the sirolimus pharmacokinetics. Covariate analysis identified body weight (BW) as a significant factor affecting clearance (CL) in the final PPK model. The typical clearance rate and distribution volume, standardized to a BW of 16 kg, were 4.06 L/h (4% relative standard error, RSE) and 155 L (26% RSE), respectively. Optimal dosing regimens were simulated for different BWs. For a twice-daily regimen, the recommended doses were 0.05, 0.06, 0.07, and 0.08 mg/kg/day for BW of <10, 10-20, 20-40, and ≥40 kg, respectively; for a once-daily regimen, the recommended doses were 0.06, 0.07, 0.08, and 0.09 mg/kg/day for BW of <10, 10-30, 30-50, and ≥50 kg, respectively. Notably, sirolimus C trough could be maintained between 5-15 ng/mL across various dosing frequencies based on the recommended dosing regimen.

Conclusion

We established a PPK model of sirolimus for children with VAs and proposed an initial dosing strategy. Integrating initial dose and medication frequency recommendations into sirolimus' guidelines will broaden its clinical options and simplify the clinical management for childhood VAs.

The Effect of Gastrointestinal Graft-Versus-Host Disease and Diarrhea on the Pharmacokinetic Profile of Sotrovimab in Hematopoietic Stem Cell Transplant Recipients

BACKGROUND

Monoclonal antibodies (mAbs) are utilized broadly to treat cancer and infectious diseases, and mAb exposure (serum concentration over time) is one predictor of overall treatment efficacy. Herein, we present findings from a clinical trial evaluating the pharmacokinetics of the long-acting mAb sotrovimab targeting severe acute respiratory syndrome coronavirus 2 in hematopoietic cell transplant (HCT) recipients.

METHODS

All participants received an intravenous infusion of sotrovimab within 1 week prior to initiating the pretransplant preparative regimen. The serum concentration of sotrovimab was measured longitudinally for up to 24 weeks posttransplant.

RESULTS

Compared to non-HCT participants, we found that mAb clearance was 10% and 26% higher in autologous and allogeneic HCT recipients, respectively. Overall sotrovimab exposure was approximately 15% lower in HCT recipients compared to non-HCT recipients. Exposure was significantly reduced in HCT recipients who developed diarrhea and lower gastrointestinal graft-versus-host disease (GVHD) posttransplant.

CONCLUSIONS

These data show that sotrovimab exposure may be reduced in HCT recipients, possibly related to increased gastrointestinal clearance in patients with GVHD. This phenomenon has implications for dose selection and duration of efficacy with sotrovimab and potentially other mAbs in this vulnerable patient population. Thus, mAb dose regimens developed in non-HCT populations may have to be optimized when applied to HCT populations.

Rerouting cardiovascular management following gastric bypass surgery: Dose optimization of carvedilol using population-based analysis

AIMS

A population-based pharmacokinetic (PK) modeling approach (PopPK) was used to investigate the impact of Roux-en-Y gastric bypass (RYGB) on the PK of (R)- and (S)-carvedilol. We aimed to optimize carvedilol dosing for these patients utilizing a pharmacokinetic/pharmacodynamic (PK/PD) link model.

METHODS

PopPK models were developed utilizing data from 52 subjects, including nonobese, obese, and post- RYGB patients who received rac- carvedilol orally. Covariate analysis included anthropometric and laboratory data, history of RYGB surgery, CYP2D6 and CYP3A4 in vivo activity, and relative intestinal abundance of major drug- metabolizing enzymes and transporters. A direct effect inhibitory Emax pharmacodynamic model was linked to the PK model of (S)- carvedilol to simulate the changes in exercise- induced heart rate.

RESULTS

A 2-compartmental model with linear elimination and parallel first-order absorptions best described (S)-carvedilol PK. RYGB led to a twofold reduction in relative oral bioavailability compared to nonoperated subjects, along with delayed absorption of both enantiomers. The intestinal ABCC2 mRNA expression increases the time to reach the maximum plasma concentration. The reduced exposure (AUC) of (S)-carvedilol post-RYGB corresponded to a 33% decrease in the predicted area under the effect curve (AUEC) for the 24-hour β-blocker response. Simulation results suggested that a 50-mg daily dose in post-RYGB patients achieved comparable AUC and AUEC to 25-mg dose in nonoperated subjects.

CONCLUSION

Integrated PK/PD modeling indicated that standard dosage regimens for nonoperated subjects do not provide equivalent β-blocking activity in RYGB patients. This study highlights the importance of personalized dosing strategies to attain desired therapeutic outcomes in this patient cohort.

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Population pharmacokinetics of TLD-1, a novel liposomal doxorubicin, in a phase I trial

STUDY OBJECTIVES

TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling.

METHODS

The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored.

RESULTS

Medians  ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0-∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate.

CONCLUSION

The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov-NCT03387917-January 2, 2018.

Model-Informed Assessment of Probability of Phase 3 Success for Ritlecitinib in Patients with Moderate-to-Severe Ulcerative Colitis

Ritlecitinib, an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor, was evaluated in patients with ulcerative colitis (UC) in a phase 2b trial. Model-informed drug development strategies were applied to bridge observations from phase 2b to predictions for a proposed phase 3 study design to assess the probability of achieving the target efficacy outcome. A longitudinal exposure-response model of the time course of the 4 Mayo subscores (rectal bleeding, stool frequency, physician's global assessment, and endoscopic subscore) in patients with UC receiving placebo or ritlecitinib was developed using population modeling approaches and an item response theory framework. The quantitative relationships between the 4 Mayo subscores accommodated the prediction of composite endpoints such as total Mayo score and partial Mayo score (key endpoints from phase 2b), and modified clinical remission and endoscopic remission (proposed phase 3 endpoints). Clinical trial simulations using the final model assessed the probability of candidate ritlecitinib dosing regimens (including those tested in phase 2b and alternative) and phase 3 study designs for achieving target efficacy outcomes benchmarked against an approved treatment for moderate-to-severe UC. The probabilities of achieving target modified clinical remission and endoscopic improvement outcomes at both weeks 8 and 52 for ritlecitinib 100 mg once daily was 74.8%. Model-based assessment mitigated some of the risk associated with proceeding to pivotal phase 3 trials with dosing regimens of which there was limited clinical experience.

Prediction of the Intra-T Lymphocyte Tacrolimus Concentration after Kidney Transplantation with Population Pharmacokinetic Modeling

The intracellular tacrolimus concentration in CD3+ T lymphocytes is proposed to be a better representative of the active component of tacrolimus than the whole blood concentration. However, intracellular measurements are complicated. Therefore, the aim of this study was to describe the relationship between intracellular and whole blood tacrolimus concentrations in a population pharmacokinetic model. Twenty-eight de novo kidney transplant recipients, treated with a once-daily oral extended-release tacrolimus formulation, were followed during the first-month post-transplantation. Additional whole blood and intracellular tacrolimus concentrations were measured at day 6 ± 1 (pre-dose, 4 and 8 hours post-dose) and day 14 ± 3 (pre-dose) post-transplantation. Pharmacokinetic analysis was performed using nonlinear mixed effects modeling software (NONMEM). The ratio between intracellular (n = 109) and whole blood (n = 248) concentrations was best described by a two-compartment whole blood model with an additional intracellular compartment without mass transfer from the central compartment. The ratio remained stable over time. Prednisolone dose influenced the absorption rate of tacrolimus, while hemoglobin, CYP3A4*22 allele carrier, and CYP3A5 expresser status were associated with the oral clearance of tacrolimus (P-value < 0.001). Furthermore, the intracellular tacrolimus concentrations were correlated with the intracellular production of interleukin-2 (P-value 0.015). The intracellular tacrolimus concentration can be predicted from a measured whole blood concentration using this model, without the need for repeated intracellular measurements. This knowledge is particularly important when the intracellular concentration is ready to be implemented into clinical practice, to overcome the complexities of cell isolation and analytical methods.

Redefining Statin Dosage Post-Gastric Bypass: Insights from a Population Pharmacokinetics-Pharmacodynamics Link Approach

Roux-en-Y gastric bypass (RYGB) involves creating a small stomach pouch, bypassing part of the small intestine, and rerouting the digestive tract. These alterations can potentially change the drug exposure and response. Our primary aim was to assess the impact of RYGB on the pharmacokinetics of simvastatin lactone (SV) and its active metabolite, simvastatin hydroxy acid (SVA). Ultimately, we aimed to optimize dosing for this understudied population by employing a population pharmacokinetic-pharmacodynamic link approach. The study comprised patients who had undergone RYGB surgery and individuals without a previous history of RYGB. All participants received a single oral dose of simvastatin. Plasma concentration data were analyzed with a nonlinear mixed-effect modeling approach. A parent-metabolite model with first-order absorption, 2-compartments for SV and 1-compartment for SVA, linear elimination, and enterohepatic circulation best described the data. The model was linked to the turnover pharmacodynamic model to describe the SVA inhibition on LDL-cholesterol production. Our simulations indicated that following RYGB surgery, the exposure to SV and SVA decreased by 40%. Consequently, for low-intensity statin patients, we recommend increasing the dose from 10 to 20 mg in post-RYGB patients to maintain a comparable response to that of non-operated subjects. Moderate-intensity statin patients should require increasing doses to 40 or 60 mg or the addition of a non-statin medication to achieve similar therapeutic outcomes. In conclusion, individuals post-RYGB exhibit diminished exposure to SV and may benefit from increasing the dose or adjunctive therapy with non-statin drugs to attain equivalent responses and mitigate potential adverse events.

Population Pharmacokinetic of the Diterpenes ent-Polyalthic Acid and Dihydro-ent-Agathic Acid from Copaifera Duckei Oil Resin in Rats

Copaifera duckei oleoresin is a plant product extensively used by the Brazilian population for multiple purposes, such as medicinal and cosmetic. Despite its ethnopharmacological relevance, there is no pharmacokinetic data on this important medicinal plant. Due to this, we determined the pharmacokinetic profile of the major nonvolatile compounds of C. duckei oleoresin. The diterpenes ent-polyalthic acid and dihydro-ent-agathic acid correspond to approximately 40% of the total oleoresin. Quantification was performed using LC-MS/MS, and the validated analytical method showed to be precise, accurate, robust, reliable, and linear between 0.57 and 114.74 µg/mL plasma and 0.09 to 18.85 µg/mL plasma, respectively, for ent-polyalthic acid and dihydro-ent-agathic acid, making it suitable for application in preclinical pharmacokinetic studies. Wistar rats received a single 200 mg/kg oral dose (gavage) of C. duckei oleoresin, and blood was collected from their caudal vein through 48 h. Population pharmacokinetics analysis of ent-polyalthic and dihydro-ent-agathic acids in rats was evaluated using nonlinear mixed-effects modeling conducted in NONMEN software. The pharmacokinetic parameters of ent-polyalthic acid were absorption constant rate = 0.47 h-1, central and peripheral apparent volume of distribution = 0.04 L and 2.48 L, respectively, apparent clearance = 0.15 L/h, and elimination half-life = 11.60 h. For dihydro-ent-agathic acid, absorption constant rate = 0.28 h-1, central and peripheral apparent volume of distribution = 0.01 L and 0.18 L, respectively, apparent clearance = 0.04 L/h, and elimination half-life = 3.49 h. The apparent clearance, central apparent volume of distribution, and peripheral apparent volume of distribution of ent-polyalthic acid were approximately 3.75, 4.00-, and 13.78-folds higher than those of dihydro-ent-agathic.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

High-Dose Isoniazid Lacks EARLY Bactericidal Activity against Isoniazid-resistant Tuberculosis Mediated by katG Mutations: A Randomized Phase II Clinical Trial

Rationale: Observational studies suggest that high-dose isoniazid may be efficacious in treating multidrug-resistant tuberculosis. However, its activity against Mycobacterium tuberculosis (M.tb) with katG mutations (which typically confer high-level resistance) is not established. Objectives: To characterize the early bactericidal activity (EBA) of high-dose isoniazid in patients with tuberculosis caused by katG-mutated M.tb. Methods: A5312 was a phase IIA randomized, open-label trial. Participants with tuberculosis caused by katG-mutated M.tb were randomized to receive 15 or 20 mg/kg isoniazid daily for 7 days. Daily sputum samples were collected for quantitative culture. Intensive pharmacokinetic sampling was performed on Day 6. Data were pooled across all A5312 participants for analysis (drug-sensitive, inhA-mutated, and katG-mutated M.tb). EBA was determined using nonlinear mixed-effects modeling. Measurements and Main Results: Of 80 treated participants, 21 had katG-mutated M.tb. Isoniazid pharmacokinetics were best described by a two-compartment model with an effect of NAT2 acetylator phenotype on clearance. Model-derived maximum concentration and area under the concentration-time curve in the 15 and 20 mg/kg groups were 15.0 and 22.1 mg/L and 57.6 and 76.8 mg ⋅ h/L, respectively. Isoniazid bacterial kill was described using an effect compartment and a sigmoidal maximum efficacy relationship. Isoniazid potency against katG-mutated M.tb was approximately 10-fold lower than in inhA-mutated M.tb. The highest dose of 20 mg/kg did not demonstrate measurable EBA, except against a subset of slow NAT2 acetylators (who experienced the highest concentrations). There were no grade 3 or higher drug-related adverse events. Conclusions: This study found negligible bactericidal activity of high-dose isoniazid (15-20 mg/kg) in the majority of participants with tuberculosis caused by katG-mutated M.tb. Clinical trial registered with www.clinicaltrials.gov (NCT01936831).

Translational pharmacokinetic/pharmacodynamic model for mRNA-0184, an investigational therapeutic for the treatment of heart failure

Heart failure (HF) is a complex, progressive disorder that is associated with substantial morbidity and mortality on a global scale. Relaxin-2 is a naturally occurring hormone that may have potential therapeutic benefit for patients with HF. To investigate the therapeutic potential of relaxin in the treatment of patients with HF, mRNA-0184, a novel, investigational, lipid nanoparticle (LNP)-encapsulated mRNA therapy that encodes for human relaxin-2 fused to variable light chain kappa (Rel2-vlk) was developed. A translational semi-mechanistic population pharmacokinetic (PK)/pharmacodynamic (PD) model was developed using data from non-human primates at dose levels ranging from 0.15 to 1 mg/kg. The PK/PD model was able to describe the PK of Rel2-vlk mRNA and translated Rel2-vlk protein in non-human primates adequately with relatively precise estimates. The preclinical PK/PD model was then scaled allometrically to determine the human mRNA-0184 dose that would achieve therapeutic levels of Rel2-vlk protein expression in patients with stable HF with reduced ejection fraction. Model-based simulations derived from the scaled PK/PD model support the selection of 0.025 mg/kg as an appropriate starting human dose of mRNA-0184 to achieve average trough relaxin levels between 1 and 2.5 ng/mL, which is the potential exposure for cardioprotective action of relaxin.

Population-based model selection for an accurate estimation of time-integrated activity using non-linear mixed-effects modelling

PURPOSE

Personalized treatment planning in Molecular Radiotherapy (MRT) with accurately determining the absorbed dose is highly desirable. The absorbed dose is calculated based on the Time-Integrated Activity (TIA) and the dose conversion factor. A crucial unresolved issue in MRT dosimetry is which fit function to use for the TIA calculation. A data-driven population-based fitting function selection could help solve this problem. Therefore, this project aims to develop and evaluate a method for accurately determining TIAs in MRT, which performs a Population-Based Model Selection within the framework of the Non-Linear Mixed-Effects (NLME-PBMS) model.

METHODS

Biokinetic data of a radioligand for the Prostate-Specific Membrane Antigen (PSMA) for cancer treatment were used. Eleven fit functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted (in the NLME framework) to the biokinetic data of all patients. The goodness of fit was assumed acceptable based on the visual inspection of the fitted curves and the coefficients of variation of the fitted fixed effects. The Akaike weight, the probability that the model is the best among the whole set of considered models, was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. NLME-PBMS Model Averaging (MA) was performed with all functions having acceptable goodness of fit. The Root-Mean-Square Error (RMSE) of the calculated TIAs from individual-based model selection (IBMS), a shared-parameter population-based model selection (SP-PBMS) reported in the literature, and the functions from NLME-PBMS method to the TIAs from MA were calculated and analysed. The NLME-PBMS (MA) model was used as the reference as this model considers all relevant functions with corresponding Akaike weights.

RESULTS

The function [Formula: see text] was selected as the function most supported by the data with an Akaike weight of (54 ± 11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS (f3a) methods are 7.4%, 8.8%, and 2.4%, respectively.

CONCLUSION

A procedure including fitting function selection in a population-based method was developed to determine the best fit function for calculating TIAs in MRT for a given radiopharmaceutical, organ and set of biokinetic data. The technique combines standard practice approaches in pharmacokinetics, i.e. an Akaike-weight-based model selection and the NLME model framework.

Effect of Interindividual Variability in Metabolic Clearance and Relative Bioavailability on Rifampicin Exposure in Tuberculosis Patients with and without HIV Co-Infection: Does Formulation Quality Matter?

The present study aims to characterise the pharmacokinetics of rifampicin (RIF) in tuberculosis (TB) patients with and without HIV co-infection, considering the formation of 25-O-desacetyl-rifampicin (desRIF). It is hypothesised that the metabolite formation, HIV co-infection and drug formulation may further explain the interindividual variation in the exposure to RIF. Pharmacokinetic, clinical, and demographic data from TB patients with (TB-HIV+ group; n = 18) or without HIV (TB-HIV- group; n = 15) who were receiving RIF as part of a four-drug fixed-dose combination (FDC) regimen (RIF, isoniazid, pyrazinamide, and ethambutol) were analysed, along with the published literature data on the relative bioavailability of different formulations. A population pharmacokinetic model, including the formation of desRIF, was developed and compared to a model based solely on the parent drug. HIV co-infection does not alter the plasma exposure to RIF and the desRIF formation does not contribute to the observed variability in the RIF disposition. The relative bioavailability and RIF plasma exposure were significantly lower than previously reported for the standard regimen with FDC tablets. Furthermore, participants weighting less than 50 kg do not reach the same RIF plasma exposure as compared to those weighting >50 kg. In conclusion, as no covariate was identified other than body weight on CL/F and Vd/F, low systemic exposure to RIF is likely to be caused by the low bioavailability of the formulation.

Inter-Antibody Variability in the Clinical Pharmacokinetics of Monoclonal Antibodies Characterized Using Population Physiologically Based Pharmacokinetic Modeling

The objective of this work was to develop a population physiologically based pharmacokinetic (popPBPK) model to characterize the variability in the clinical PK of monoclonal antibodies (mAbs) following intravenous (IV) and subcutaneous (SC) administration. An extensive literature search was conducted and clinical PK data for FDA-approved as well as non-approved mAbs were collected. Training and validation datasets of 44 and 9 mAbs exhibiting linear pharmacokinetics were used for model development. The variability in antibody PK was captured by accounting for different rate constants of pinocytosis (CLup) and intracellular degradation (kdeg) for different mAbs. Typical values for CLup and kdeg and their respective inter-antibody variabilities (ωClup, ωKdeg) were estimated to be 0.32 L/h/L and 26.1 h-1 (73% and 46%). Varied absorption profiles following SC dosing were characterized by incorporating inter-antibody variability in local degradation (kSC) and rate of lymphatic uptake (S_Lu) of mAbs. Estimates for typical kSC and S_Lu values, and ωKsc,ωS_Lu, were found to be 0.0015 h-1 and 0.54 (193%, and 49%). FDA-approved mAbs showed less local degradation (0.0014 h-1 vs. 0.0038 h-1) compared with other clinically tested mAbs, whereas no substantial differences in physiological processes involved in disposition were observed. To evaluate the generalizability of estimated PK parameters and model validation, the final popPBPK model was used to simulate the range of expected PK for mAbs following SC administration of nine different mAbs that were not used for model-building purposes. The predicted PK of all nine mAbs was within the expected range specified a priori. Thus, the popPBPK model presented here may serve as a tool to predict the clinical PK of mAbs with linear disposition before administering them to humans. The model may also support preclinical-to-clinical translation and 'first-in-human' dose determination for mAbs.

Understanding adefovir pharmacokinetics as a component of a transporter phenotyping cocktail

PURPOSE

Adefovir (as dipivoxil) was selected as a probe drug in a previous transporter cocktail phenotyping study to assess renal organic anion transporter 1 (OAT1), with renal clearance (CLR) as the primary parameter describing renal elimination. An approximately 20% higher systemic exposure of adefovir was observed when combined with other cocktail components (metformin, sitagliptin, pitavastatin, and digoxin) compared to sole administration. The present evaluation applied a population pharmacokinetic (popPK) modeling approach to describe adefovir pharmacokinetics as a cocktail component in more detail.

METHODS

Data from 24 healthy subjects were reanalyzed. After establishing a base model, covariate effects, including the impact of co-administered drugs, were assessed using forward inclusion then backward elimination.

RESULTS

A one-compartment model with first-order absorption (including lag time) and a combination of nonlinear renal and linear nonrenal elimination best described the data. A significantly higher apparent bioavailability (73.6% vs. 59.0%) and a lower apparent absorption rate constant (2.29 h-1 vs. 5.18 h-1) were identified in the combined period compared to the sole administration period, while no difference was seen in renal elimination. The population estimate for the Michaelis-Menten constant (Km) of the nonlinear renal elimination was 170 nmol/L, exceeding the observed range of adefovir plasma maximum concentration, while the maximum rate (Vmax) of nonlinear renal elimination was 2.40 µmol/h at the median absolute estimated glomerular filtration rate of 105 mL/min.

CONCLUSION

The popPK modeling approach indicated that the co-administration primarily affected the apparent absorption and/or prodrug conversion of adefovir dipivoxil, resulting in the minor drug-drug interaction observed for adefovir as a victim. However, renal elimination remained unaffected. The high Km value suggests that assessing renal OAT1 activity by CLR has no relevant misspecification error with the cocktail doses used.

Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients

BACKGROUND AND AIM

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.

CONCLUSION

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.

Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

Distribution of Bevacizumab into the Cerebrospinal Fluid of Children and Adolescents with Recurrent Brain Tumors

BACKGROUND

To date, evidence has been lacking regarding bevacizumab pharmacokinetics in the cerebrospinal fluid (CSF).

OBJECTIVE

This study assessed the penetration of bevacizumab, as part of a metronomic antiangiogenic treatment regimen, into the CSF of children, adolescents, and young adults with recurrent brain tumors.

PATIENTS AND METHODS

Serum and CSF concentrations, malignant cells, and vascular endothelial growth factor A (VEGF-A) were analyzed in 12 patients (5-27 years) following 10 mg/kg bevacizumab intravenous biweekly administration (EudraCT number 2009-013024-23). A population pharmacokinetic model including body weight, albumin, and tumor type as influential factors was extended to quantify the CSF penetration of bevacizumab.

RESULTS

Apart from in serum (minimum concentration/maximum concentration [Cmin/Cmax] 77.0-305/267-612 mg/L, median 144/417 mg/L), bevacizumab could be quantified in the CSF (0.01-2.26 mg/L, median 0.35 mg/L). The CSF/serum ratio was 0.16 and highly variable between patients. Malignant cells could be detected in CSF before initiation of treatment in five of 12 patients; after treatment, the CSF was cleared in all patients. VEGF-A was detected in three patients before treatment (mean ± SD: 20 ± 11 pg/mL), and was still measurable in one of these patients despite treatment (16 pg/mL).

CONCLUSIONS

This pharmacokinetic pilot study indicated penetration of bevacizumab into the CSF in a population of children, adolescents, and young adults with recurrent brain tumors.

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.

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.

Population Pharmacokinetics and Absolute Oral Bioavailability of Lasalocid after Single Intravenous and Intracrop Administration in Laying Hens

Lasalocid sodium is a polyether carboxylic ionophore agent authorized by the EU for use as a coccidiostat in broilers, turkeys, and pullets up to 16 weeks of age, except for laying hens. However, laying hens are the most common nontarget species exposed to lasalocid sodium, mainly due to cross-contamination from feed mills. This exposure may result in potential drug residue deposition in eggs, which could potentially expose consumers to the drug. The breeds commonly used for commercial egg production in Poland are Isa Brown and Green-legged Partridge hens, which have been found to significantly differ in egg-laying performance. This variability may also affect the pharmacokinetics of lasalocid. Data on lasalocid plasma pharmacokinetics in laying hens are lacking. In this study, we aimed to determine typical population pharmacokinetic parameters, absolute oral bioavailability, and how breed may influence the pharmacokinetics of lasalocid. Twenty-layer hens of the two breeds were used in this study. Lasalocid was administered orally at a single dose of either 1 mg or 5 mg/kg body weight or intravenously at a dose of 1 mg/kg body weight, in a crossover design with a three-week washout period between study periods. Blood samples were collected for 72 h, and lasalocid concentrations were measured using high-performance liquid chromatography with fluorescence detection. A population pharmacokinetic analysis was conducted using nonlinear mixed effects modeling. Standard numerical and graphical criteria were used to select the best model, and a stepwise covariate modeling approach was used to determine any influencing factors. The best model was a three-compartment mammillary model with first-order absorption, transit compartments, and linear elimination. The estimated absolute oral bioavailability was low (36%). It was found that breed significantly influenced not only absorption but also the elimination of lasalocid. This study revealed that lasalocid absorption and elimination varied between the two breeds. This variability in pharmacokinetics may result in breed-related differences in drug residue accumulation in eggs, and ultimately, the risk associated with consumer exposure to drug residues may also vary.

Combination Therapy With Guselkumab and Golimumab in Patients With Moderately to Severely Active Ulcerative Colitis: Pharmacokinetics, Immunogenicity and Drug-Drug Interactions

A proof-of-concept study with the combination of guselkumab and golimumab in patients with ulcerative colitis (UC) has shown that the combination therapy resulted in greater efficacy than the individual monotherapies. The current analysis evaluated the pharmacokinetics (PK) and immunogenicity of guselkumab and golimumab in both the combination therapy and individual monotherapies. Blood samples were collected to evaluate serum concentrations and immunogenicity of guselkumab and golimumab. Population PK (PopPK) models were developed to assess the effects of combination therapy and other potential covariates on the PK of guselkumab and golimumab. The guselkumab PK was comparable between monotherapy and combination therapy, whereas golimumab concentrations were slightly higher with combination therapy. The anti-guselkumab antibody incidence was low with both monotherapy and combination therapy, and guselkumab immunogenicity did not impact the clearance. Conversely, the anti-golimumab antibody incidence with combination therapy was lower than that for monotherapy. PopPK analysis suggested that the slightly higher golimumab concentrations with combination therapy were partially due to lower immunogenicity and thus lower clearance with combination therapy. C-reactive protein (CRP) was also a significant covariate on golimumab clearance. The greater improvement of inflammation with combination therapy, as shown by reductions in CRP, may have also contributed to the higher golimumab concentrations. Combination therapy slightly decreased the clearance of golimumab, but not guselkumab clearance, in patients with UC. Lower immunogenicity and greater improvement of inflammation with combination therapy were potential mechanisms for slightly increased golimumab concentrations with combination therapy as compared with golimumab monotherapy.

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

Translational kinetic-pharmacodynamics of mRNA-6231, an investigational mRNA therapeutic encoding mutein interleukin-2

Regulatory T cells (Tregs) are essential for maintaining immune homeostasis by serving as negative regulators of adaptive immune system effector cell responses. Reduced production or function of Tregs has been implicated in several human autoimmune diseases. The cytokine interleukin 2 plays a central role in promoting Treg differentiation, survival, and function in vivo and may therefore have therapeutic benefits for autoimmune diseases. mRNA-6231 is an investigational, lipid nanoparticle-encapsulated, mRNA-based therapy that encodes a modified human interleukin 2 mutein fused to human serum albumin (HSA-IL2m). Herein, we report the development of a semi-mechanistic kinetic-pharmacodynamic model to quantify the relationship between subcutaneous dose(s) of mRNA-6231, HSA-IL2m protein expression, and Treg expansion in nonhuman primates. The nonclinical kinetic-pharmacodynamic model was extrapolated to humans using allometric scaling principles and the physiological basis of pharmacological mechanisms to predict the clinical response to therapy a priori. Model-based simulations were used to inform the dose selection and design of the first-in-human clinical study (NCT04916431). The modeling approach used to predict human responses was validated when data became available from the phase I clinical study. This validation indicates that the approach is valuable in informing clinical decision-making.

Population pharmacokinetics of vancomycin in term neonates with perinatal asphyxia treated with therapeutic hypothermia

AIMS

Little is known about the population pharmacokinetics (PPK) of vancomycin in neonates with perinatal asphyxia treated with therapeutic hypothermia (TH). We aimed to describe the PPK of vancomycin and propose an initial dosing regimen for the first 48 h of treatment with pharmacokinetic/pharmacodynamic target attainment.

METHODS

Neonates with perinatal asphyxia treated with TH were included from birth until Day 6 in a multicentre prospective cohort study. A vancomycin PPK model was constructed using nonlinear mixed-effects modelling. The model was used to evaluate published dosing guidelines with regard to pharmacokinetic/pharmacodynamic target attainment. The area under the curve/minimal inhibitory concentration ratio of 400-600 mg*h/L was used as target range.

RESULTS

Sixteen patients received vancomycin (median gestational age: 41 [range: 38-42] weeks, postnatal age: 4.4 [2.5-5.5] days, birth weight: 3.5 [2.3-4.7] kg), and 112 vancomycin plasma concentrations were available. Most samples (79%) were collected during the rewarming and normothermic phase, as vancomycin was rarely initiated during the hypothermic phase due to its nonempirical use. An allometrically scaled 1-compartment model showed the best fit. Vancomycin clearance was 0.17 L/h, lower than literature values for term neonates of 3.5 kg without perinatal asphyxia (range: 0.20-0.32 L/h). Volume of distribution was similar. Published dosing regimens led to overexposure within 24 h of treatment. A loading dose of 10 mg/kg followed by 24 mg/kg/day in 4 doses resulted in target attainment.

CONCLUSION

Results of this study suggest that vancomycin clearance is reduced in term neonates with perinatal asphyxia treated with TH. Lower dosing regimens should be considered followed by model-informed precision dosing.

Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations.

Comparative Analysis of Posiphen Pharmacokinetics across Different Species-Similar Absorption and Metabolism in Mouse, Rat, Dog and Human

Posiphen is a small molecule that exhibits neuroprotective properties by targeting multiple neurotoxic proteins involved in axonal transport, synaptic transmission, neuroinflammation, and cell death. Its broad-spectrum effects make it a promising candidate for treating neurodegenerative conditions, including Alzheimer's and Parkinson's diseases. Despite extensive investigation with animal models and human subjects, a comprehensive comparative analysis of Posiphen's pharmacokinetics across studies remains elusive. Here, we address this gap by examining the metabolic profiles of Posiphen and its breakdown into two primary metabolites-N1 and N8-across species by measuring their concentrations in plasma, brain, and CSF using the LC-MS/MS method. While all three compounds effectively inhibit neurotoxic proteins, the N1 metabolite is associated with adverse effects. Our findings reveal the species-specific behavior of Posiphen, with both Posiphen and N8 being predominant in various species, while N1 remains a minor constituent, supporting the drug's safety. Moreover, in plasma, Posiphen consistently showed fast clearance of all metabolites within 8 h in animal models and in human subjects, whereas in CSF or brain, the compound has an extended half-life of over 12 h. Combining all our human data and analyzing them by population pharmacokinetics showed that there are no differences between healthy volunteers, Alzheimer's, and Parkinson's patients. It also showed that Posiphen is absorbed and metabolized in a similar fashion across all animal species and human groups tested. These observations have critical implications for understanding the drug's safety, therapeutic effect, and clinical translation.

Population pharmacokinetics, dosing optimization and clinical outcomes of biapenem in patients with sepsis

Introduction: Biapenem is a carbapenem antibiotic widely used in Asia, can be used for the treatment of adults and children with infections due to susceptible bacteria. Although biapenem is utilized in the treatment of a diverse range of bacterial infections, current pharmacokinetic data in the context of septic populations remain limited. Consequently, our research aims to evaluate the pharmacokinetics and efficacy of biapenem within a septic population to optimize biapenem therapy. Methods: In this study, we characterized the pharmacokinetics of biapenem in septic patients using a population pharmacokinetic (PPK) approach. The clinical PK data to develop the PPK model were obtained from 317 septic patients admitted to Nanjing Drum Tower Hospital between 2018 and 2022. All patients were randomized to the modeling and validation cohorts at a 3:1 ratio, with PPK modeling and validation performed utilizing the NONMEM software. Results: The model found to best describe the available data was a two-compartment PPK model with first-order elimination characterized by the parameters clearance (CL), central volume (V1), peripheral volume (V2), and intercompartmental clearance (Q). A covariate analysis identified that creatinine clearance (CLCR) was a significant covariate influencing biapenem CL, while blood urea nitrogen (BUN) was a significant covariate influencing biapenem Q. Accoding to the clinical outcome analyses, 70% of the time that the free antimicrobial drug concentration exceeds the MIC (fT >MIC) is associated with favourable clinical outcomes. The PPK model was then used to perform Monte Carlo simulations to evaluate the probability of attaining 70% fT >MIC. Conclusions: A final PPK model of biapenem was established for patients with sepsis. The current daily dosage regimen of 1.2 g may insufficient to achieve 70% fT >MIC in septic patients. The dosage regimen of 600 mg every 6 h appears to be the optimal choice.

Assessing potential drug-drug interactions between clofazimine and other frequently used agents to treat drug-resistant tuberculosis

Clofazimine is included in drug regimens to treat rifampicin/drug-resistant tuberculosis (DR-TB), but there is little information about its interaction with other drugs in DR-TB regimens. We evaluated the pharmacokinetic interaction between clofazimine and isoniazid, linezolid, levofloxacin, and cycloserine, dosed as terizidone. Newly diagnosed adults with DR-TB at Klerksdorp/Tshepong Hospital, South Africa, were started on the then-standard treatment with clofazimine temporarily excluded for the initial 2 weeks. Pharmacokinetic sampling was done immediately before and 3 weeks after starting clofazimine, and drug concentrations were determined using validated liquid chromatography-tandem mass spectrometry assays. The data were interpreted with population pharmacokinetics in NONMEM v7.5.1 to explore the impact of clofazimine co-administration and other relevant covariates on the pharmacokinetics of isoniazid, linezolid, levofloxacin, and cycloserine. Clofazimine, isoniazid, linezolid, levofloxacin, and cycloserine data were available for 16, 27, 21, 21, and 6 participants, respectively. The median age and weight for the full cohort were 39 years and 52 kg, respectively. Clofazimine exposures were in the expected range, and its addition to the regimen did not significantly affect the pharmacokinetics of the other drugs except levofloxacin, for which it caused a 15% reduction in clearance. A posteriori power size calculations predicted that our sample sizes had 97%, 90%, and 87% power at P < 0.05 to detect a 30% change in clearance of isoniazid, linezolid, and cycloserine, respectively. Although clofazimine increased the area under the curve of levofloxacin by 19%, this is unlikely to be of great clinical significance, and the lack of interaction with other drugs tested is reassuring.

Dose regimen optimization of cephalothin for surgical prophylaxis against Staphylococcus aureus and coagulase negative staphylococci in dogs by pharmacokinetic/pharmacodynamic modeling

First generation cephalosporins such cephalothin of cefazolin are indicated for antimicrobial prophylaxis for clean and clean contaminated surgical procedures because its antimicrobial spectrum, relative low toxicity and cost. Anesthesia and surgery could alter the pharmacokinetic behavior of different drugs administered perioperative by many mechanisms that affect distribution, metabolism or excretion processes. Intravenous administration of the antimicrobial within 30 and 60 min before incision is recommended in order to reach therapeutic serum and tissue concentrations and redosing is recommended if the duration of the procedure exceeds two half-life of the antimicrobial. To the author's knowledge there are no pharmacokinetic studies of cephalothin in dogs under anesthesia/surgery conditions. The aim of this study was (1) to evaluate the pharmacokinetics of cephalothin in anesthetized dogs undergoing ovariohysterectomy by a nonlinear mixed-effects model and to determine the effect of anesthesia/surgery and other individual covariates on its pharmacokinetic behavior; (2) to determine the MIC and conduct a pharmacodynamic modeling of time kill curves assay of cephalothin against isolates of Staphylococcus spp. isolated from the skin of dogs; (3) to conduct a PK/PD analysis by integration of the obtained nonlinear mixed-effects models in order to evaluate the antimicrobial effect of changing concentrations on simulated bacterial count; and (4) to determine the PK/PD endpoints and PK/PDco values in order to predict the optimal dose regimen of cephalothin for antimicrobial prophylaxis in dogs. Anesthesia/surgery significantly reduced cephalothin clearance by 18.78%. Based on the results of this study, a cephalothin dose regimen of 25 mg/kg q6h by intravenous administration showed to be effective against Staphylococcus spp. isolates with MIC values ≤2 μg/mL and could be recommended for antimicrobial prophylaxis for clean surgery in healthy dogs.

Covariate modeling in pharmacometrics: General points for consideration

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Understanding hemoglobin contribution to high-dose methotrexate disposition-population pharmacokinetics in pediatric patients with hematological malignancies

PURPOSE

The aim of the present study was to develop a population pharmacokinetic model for methotrexate (MTX) during high-dose treatment (HDMTX) in pediatric patients with acute lymphoblastic leukemia (ALL) and non-Hodgkin's lymphoma (NHL) and to describe the influence of variability factors.

METHODS

The study included 50 patients of both sexes (aged 1-18 years) who received 3 or 5 g/m2 of HDMTX. A nonlinear mixed effect modeling approach was applied for data analysis. Parameter estimation was performed by first-order conditional estimation method with interaction (FOCEI), whereas stepwise covariate modeling was used to assess variability factors.

RESULTS

The final model is a two-compartment model that incorporates the effect of body surface area and the influence of hemoglobin and serum creatinine on MTX clearance (CL). Population pharmacokinetic values for a typical subject were estimated at 5.75 L/h/m2 for clearance (CL), 21.3 L/m2 for volume of the central compartment (V1), 8.2 L/m2 for volume of the peripheral compartment (V2), and 0.087 L/h/m2 for intercompartmental clearance (Q). According to the final model, MTX CL decreases with increasing serum creatinine, whereas a positive effect was captured for hemoglobin. A difference of almost 32% in MTX CL was observed among patients' hemoglobin values reported in the study.

CONCLUSION

The developed population pharmacokinetic model can contribute to the therapy optimization during HDMTX in pediatric patients with ALL and NHL. In addition to renal function and body weight, it describes the influence of hemoglobin on CL, allowing better understanding of its contribution to the disposition of HDMTX.

Population pharmacokinetic-pharmacodynamic model of subcutaneous bupivacaine in a novel extended-release microparticle formulation

The objective of this study was to develop a population pharmacokinetic-pharmacodynamic model of subcutaneously administered bupivacaine in a novel extended-release microparticle formulation for postoperative pain management. Bupivacaine was administered subcutaneously in the lower leg to 28 healthy male subjects in doses from 150 to 600 mg in a phase 1 randomized, placebo-controlled, double-blind, dose-ascending study with two different microparticle formulations, LIQ865A and LIQ865B. Warmth detection threshold was used as a surrogate pharmacodynamic endpoint. Population pharmacokinetic-pharmacodynamic models were fitted to plasma concentration-effect-time data using non-linear mixed-effects modelling. The pharmacokinetics were best described by a two-compartment model with biphasic absorption as two parallel absorption processes: a fast, zero-order process and a slower, first-order process with two transit compartments. The slow absorption process was found to be dose-dependent and rate-limiting for elimination at higher doses. Apparent bupivacaine clearance and the transit rate constant describing the slow absorption process both appeared to decrease with increasing doses following a power function with a shared covariate effect. The pharmacokinetic-pharmacodynamic relationship between plasma concentrations and effect was best described by a linear function. This model gives new insight into the pharmacokinetics and pharmacodynamics of microparticle formulations of bupivacaine and the biphasic absorption seen for several local anaesthetics.

CYP2D6 Phenotype Influences Pharmacokinetic Parameters of Venlafaxine: Results from a Population Pharmacokinetic Model in Older Adults with Depression

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.