Lara Marques (1, 2, 3), Nuno Vale (1, 3, 4)
(1) PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), (2) Faculty of Medicine, University of Porto, (3) Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, (4) CINTESIS@RISE, Faculty of Medicine, University of Porto
Objectives: The variability in patients’ therapeutic responses underscores the importance of understanding pharmacokinetics (PK) and pharmacodynamics (PD) for effective drug regimens [1]. Individual factors such as age, weight, body mass index (BMI), gender, race, genetics, smoking, and dietary habits, can all contribute to variations in drug disposition [1,2]. With the advancement of pharmacometrics in precision medicine, there is growing interest in exploring the impact of these factors on drug PK profiles. Population pharmacokinetic (popPK) modeling is acknowledged as an essential tool for accurately identifying measurable factors affecting dose-concentration relationships, and tailoring dosage regimens to individual patients, thereby leading to improved therapeutic outcomes [3]. Salbutamol, a short-acting β2-agonist (SABA), used in asthma treatment, is still prescribed despite not being currently recommended as monotherapy [4,5]. In this context, studying variations in salbutamol PK becomes essential to clarify its therapeutic landscape beyond the molecular level. Our study aimed to develop a popPK model for salbutamol delivered via dry-powder inhaler (DPI), identifying key patient characteristics that influence treatment response. Additionally, we sought to demonstrate the feasibility of using synthetic data from physiologically-based pharmacokinetic (PBPK) models to develop the popPK model, as PK data of salbutamol is scarce. External validation using real patient data tested the model’s predictive performance, highlighting the potential of virtual patient modeling in advancing this field.
Methods: The physicochemical and PK properties of salbutamol were estimated using ADMET Predictor® (Version 10.4). Virtual patient PK data was generated through PBPK models in GastroPlus software (Version 9.8.3) based on ADMET Predictor® predictions. All patients, with different individual characteristics, were treated with 600 μg of salbutamol DPI (3 successive inhalations of 200 μg). Non-compartmental analysis (NCA) was performed to establish initial PK metrics, followed by popPK modeling using Monolix Suite 2023R1. Structural and statistical models were evaluated, incorporating covariates such as age, weight, height, gender, and race. External validation used clinical data from an equivalent study in healthy volunteers. The popPK model’s accuracy and reliability were assessed using evaluative metrics.
Results: Developing precise popPK models faces significant challenges due to limited access to PK data and personal information, alongside data missing and inaccuracies in clinical trials. To address this, a synthetic dataset was generated to mimic a diverse set of patients. Thirty-two virtual subjects were included in this study (18 males and 14 females; median age: 20.0 years; median BMI: 21.3 kg/m2; 38% American and 62% Asian patients). The plasma concentration-time profiles of salbutamol DPI were best described by a two-compartment model, with first-order absorption (no lag time), and linear elimination. A –2 loglikelihood (LL) of –6910.06 and a corrected Bayesian Information Criterion (BICc) of –6808.94 were obtained. Although the literature reports a one-compartment model to describe salbutamol PK [6], the inclusion of two compartments may be logical since there is a significant portion of dose that is swallowed [7]. All structure parameters were estimated with good precision, with relative standard error (RSE) < 30% for fixed effects. This model was further validated using real-world data, demonstrating a good fit when analyzing diagnostic plots. The proportion of outliers was 4.62% and the visual predictive check (VPC) revealed a strong agreement between individual predicted and observed values. The incorporation of covariates in the base structural model has identified a significant impact of age, gender, race, and weight on clearance (Cl). Moreover, age showed great influence on intercompartmental clearance (Q); gender on absorption constant rate (ka), and weight on Q and the volume of distribution of peripheral compartment (V2). A subsequent compartmental analysis demonstrated great consistency with popPK model’s findings.
Conclusions:
Our study addresses critical challenges in popPK modeling, particularly regarding data scarcity, incompleteness, and homogeneity in traditional clinical trials. By leveraging synthetic data derived from PBPK modeling, we have overcome these limitations and provided insights into salbutamol PK profile across diverse patient populations. We have identified significant associations between individual characteristics and salbutamol’s PK parameters, highlighting the importance of personalized therapeutic regimens in achieving optimal treatment outcomes.
References:
[1] Lin, Y.S.; Thummel, K.E.; Thompson, B.D.; Totah, R.A.; Cho, C.W. Sources of Interindividual Variability. Methods Mol. Biol. 2021, 2342, 481–550, doi:10.1007/978-1-0716-1554-6_17.
[2] Lin, J. Pharmacokinetic and Pharmacodynamic Variability: A Daunting Challenge in Drug Therapy. Curr. Drug Metab. 2007, 8, 109–136, doi:10.2174/138920007779816002.
[3] Mould, D.R.; Upton, R.N. Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development – Part 2: Introduction to Pharmacokinetic Modeling Methods. CPT Pharmacometrics Syst. Pharmacol. 2013, 2, doi:10.1038/psp.2013.14.
[4] Salbutamol: Uses, Interactions, Mechanism of Action | DrugBank Online Available online: https://go.drugbank.com/drugs/DB01001 (accessed on 14 January 2023).
[5] Reddel, H.K.; FitzGerald, J.M.; Bateman, E.D.; Bacharier, L.B.; Becker, A.; Brusselle, G.; Buhl, R.; Cruz, A.A.; Fleming, L.; Inoue, H.; et al. GINA 2019: A Fundamental Change in Asthma Management: Treatment of Asthma with Short-Acting Bronchodilators Alone Is No Longer Recommended for Adults and Adolescents. Eur. Respir. J. 2019, 53, doi:10.1183/13993003.01046-2019.
[6] Courlet, P.; Buclin, T.; Biollaz, J.; Mazzoni, I.; Rabin, O.; Guidi, M. Model-Based Meta-Analysis of Salbutamol Pharmacokinetics and Practical Implications for Doping Control. CPT Pharmacometrics Syst. Pharmacol. 2022, 11, 469–481, doi:10.1002/psp4.12773.
[7] Skoner, D.P. Pharmacokinetics, Pharmacodynamics, and the Delivery of Pediatric Bronchodilator Therapy. J. Allergy Clin. Immunol. 2000, 106, 158–164, doi:10.1067/mai.2000.109422.
Reference: PAGE 32 (2024) Abstr 11197 [www.page-meeting.org/?abstract=11197]
Poster: Methodology - Covariate/Variability Models