Lara Marques1,2,3, Nuno Vale1,2,3
1PerMed Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450, 2CINTESIS@RISE, Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200-319, 3Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto
Introduction: Salbutamol, a short-acting ß2-agonist widely used in asthma treatment [1], is available in multiple formulations, including inhalers, nebulizers, oral tablets, and parenteral routes (intravenous, intramuscular, and subcutaneous) [2]. Each formulation exhibits distinct pharmacokinetic (PK) and pharmacodynamic (PD) profiles, influencing therapeutic outcomes and adverse effects. Although asthma management predominantly relies on inhaled salbutamol [2], understanding how these formulations interact with patient-specific characteristics could improve personalized medicine approaches, potentially uncovering the therapeutic benefits of alternative formulations for an individual patient. Herein, this study aims to analyze how covariates – such as age, weight, gender, body surface area (BSA), cytochrome P450 (CYP) expression, race, and health status – affect the therapeutic regimen of orally administered salbutamol using population PK (popPK) modeling. The final model serves as a tool to guide formulation and dose selection on individual patient characteristics. Methods: A whole-body physiologically based PK (PBPK) model was implemented in the GastroPlus software (Version 9.9) to generate a dataset of 40 virtual patients. The PK profile of orally administered salbutamol (4 mg every 6 hours) was modelled in a virtual population with specific individual characteristics using the GastroPlus’ Population Simulator interface, where random population estimates were generated for age-related (PEAR) physiologies. Notably, our prior research has demonstrated the reliability of virtual data in exploring interindividual variability (IIV) in PK parameters [4]. Subsequently, population PK analysis was performed using a nonlinear mixed-effects (NLME) approach in Monolix (Version 2024R1). Several structural models were initially fitted to the concentration-time data without any covariates. After selecting the most appropriate structural model, eight potential covariates were evaluated for their effects on the PK parameters: age, weight, BSA, CYP2D6 and CYP2C19 expression, gender, race, and health status. The covariate screening process was conducted through two different approaches. First, a statistical assessment using Pearson and Spearman’s correlations for continuous covariates and ANOVA tests for categorical variables, applying a threshold of p < 0.05. Then, collinearity was assessed before incorporating significant covariates into the model through a combined stepwise forward selection and backward elimination approach. In parallel, the automated covariate selection algorithm COSSAC (Conditional Sampling use for Stepwise Approach based on Correlation tests) in Monolix was employed [5]. To further validate the model, a visual predictive check (VPC) using a 90% prediction interval was performed to graphically assess potential misspecifications in structural, variability, and covariate models. Additionally, a visual inspection of the goodness-of-fit (GOF) plots was conducted to evaluate the model’s predictive performance, alongside with the lowest corrected Bayesian Information Criterion (BICc) and acceptable relative standard errors (RSE). Results: A two-compartment model with first-order elimination and absorption, with a transit compartment, best described the plasma concentration-time profile (BICc = –11,936.81 and –2LL = –12,022.77). The model was parameterized in terms of ka, transit compartments (mean transit time, Mtt, and transit rate, Ktr) to describe the delay in drug absorption onset, apparent clearance (Cl/F), apparent intercompartmental clearance (Q/F), and apparent volumes of distribution for the central (V1) and peripheral (V2) compartments. Relationships were identified between gender and mean transit time (Mtt) and clearance (Cl), as well as the effects of weight and BSA on the volume of distribution of the central compartment (V1) and Cl, and a significant impact of health status on Cl. Conclusions: Despite current contraindications for oral salbutamol [6], our findings suggest that certain individuals, particularly children, may benefit from oral treatment over inhalation. We also identified individual characteristics associated with increased salbutamol toxicity risk, indicating the need for dose adjustment or alternative administration. This study further highlights the potential of popPK modeling in personalized therapy through a fully in silico approach.
[1] Craig, S.; Tuszynski, M.; Armstrong, D. It Is Time to Stop Prescribing Oral Salbutamol. Aust. Fam. Physician 2016, 45, 245–247. [2] Ahrens, R.C.; Smith, G.D.; Pharm, D. Albuterol: An Adrenergic Agent for Use in the Treatment of Asthma Pharmacology, Pharmacokinetics and Clinical Use. Pharmacotherapy 1984, 4, 105–120. [3] Marques, L.; Vale, N. Toward Personalized Salbutamol Therapy: Validating Virtual Patient-Derived Population Pharmacokinetic Model with Real-World Data. Pharmaceutics 2024, 16, 881. [5] Monolix Suite. Automatic Covariate Model Building. Available online: https://monolix.lixoft.com/model-building/automatic-covariate-model-building/ (accessed on 20 October 2024). [5] Craig, S.; Tuszynski, M.; Armstrong, D. It Is Time to Stop Prescribing Oral Salbutamol. Aust. Fam. Physician 2016, 45, 245–247.
Reference: PAGE 33 (2025) Abstr 11564 [www.page-meeting.org/?abstract=11564]
Poster: Clinical Applications