Adding Model-Informed Precision Dosing to Precision Medicine to Improve Patient Drug Treatment Outcomes

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 10109

Special Issue Editors


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Guest Editor
Department of Urologic Sciences, Faculty of Medicine, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
Interests: pharmaceutics; drug delivery; formulation; drug development; translational pharmacotherapy; lipid and lipoprotein metabolism; pharmacokinetics
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Guest Editor
College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
Interests: precision medicine; precision dosing; targeted therapies; pharmacokinetics; clinical translation; therapeutic drug monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dept. of Clinical Pharmacy & Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstr. 31, 12169 Berlin, Germany
Interests: model-based precision dosing; pharmacometrics; translational research; nonlinear mixed-effects modelling; physiologically based pharmacokinetic modelling

Special Issue Information

Dear Colleagues,

Providing the right treatment at the right dose to the right patient is the ultimate goal of individualized (personalised) medicine. Although innovative breakthroughs in personalised medicine have led to the identification of mostly genetic biomarkers, improving treatment selection (the right treatment for the right patient), this process often stops without providing optimised personalised dosing (right dose). Thus, a large research gap remains.

Despite the complex pharmacokinetics of many clinically used drugs, and known physiological and pharmacological variabilities, fixed dosing, as determined by tightly controlled clinical trials, is still used. This often results in large variability in drug exposure between individuals and can lead to a significant proportion of patients experiencing toxicity or therapeutic failure, leading to poor patient outcome and a significant health and economic burden.

Model-informed precision dosing is an advanced mathematical approach, which integrates existing clinical and individual patient characteristics to determine the optimal dose. This approach has been shown to reduce the large inter-individual variability in drug exposure observed with many clinically used drugs. At present, patients are in an advantageous position to benefit from their treatment due to the advances in treatment selection, but the commonly used “one dose fits all” approach needs to be replaced by model-informed precision dosing, putting patients in an ideal position to receive the optimal treatment together with the optimal dose. The need to combine model-informed precision dosing with precision medicine is undeniable.

This Special Issue's theme focuses on using pharmacokinetic, pharmacodynamic, pharmacogenetic or any biomarker knowledge in a model-informed precision-dosing strategy to further enhance precision medicine for therapeutic agents by improving toxicity, efficacy, or survival.

Prof. Dr. Kishor Wasan
Dr. Madele Dyk
Dr. Robin Michelet
Guest Editors

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Keywords

  • model-informed precision dosing
  • therapeutic drug monitoring
  • personalized medicine
  • pharmacometrics
  • pharmacokinetics
  • inter-individual variability

Published Papers (4 papers)

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Research

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18 pages, 1944 KiB  
Article
Evaluation and Validation of the Limited Sampling Strategy of Polymyxin B in Patients with Multidrug-Resistant Gram-Negative Infection
by Xueyong Li, Bingqing Zhang, Yu Cheng, Maohua Chen, Hailing Lin, Binglin Huang, Wancai Que, Maobai Liu, Lili Zhou, Qinyong Weng, Hui Zhang and Hongqiang Qiu
Pharmaceutics 2022, 14(11), 2323; https://doi.org/10.3390/pharmaceutics14112323 - 28 Oct 2022
Cited by 2 | Viewed by 1396
Abstract
Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration–time curve across 24 h at a steady state (AUCss,24h) of 50–100 mg·h/L. The limited sampling strategy (LSS) is [...] Read more.
Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration–time curve across 24 h at a steady state (AUCss,24h) of 50–100 mg·h/L. The limited sampling strategy (LSS) is useful for predicting AUC values. However, establishing an LSS is a time-consuming process requiring a relatively dense sampling of patients. Further, given the variability among different centers, the predictability of LSSs is frequently questioned when it is extrapolated to other clinical centers. Currently, limited data are available on a reliable PMB LSS for estimating AUCss,24h. This study assessed and validated the practicability of LSSs established in the literature based on data from our center to provide reliable and ready-made PMB LSSs for laboratories performing therapeutic drug monitoring (TDM) of PMB. The influence of infusion and sampling time errors on predictability was also explored to obtain the optimal time points for routine PMB TDM. Using multiple regression analysis, PMB LSSs were generated from a model group of 20 patients. A validation group (10 patients) was used to validate the established LSSs. PMB LSSs from two published studies were validated using a dataset of 30 patients from our center. A population pharmacokinetic model was established to simulate the individual plasma concentration profiles for each infusion and sampling time error regimen. Pharmacokinetic data obtained from the 30 patients were fitted to a two-compartment model. Infusion and sampling time errors observed in real-world clinical practice could considerably affect the predictability of PMB LSSs. Moreover, we identified specific LSSs to be superior in predicting PMB AUCss,24h based on different infusion times. We also discovered that sampling time error should be controlled within −10 to 15 min to obtain better predictability. The present study provides validated PMB LSSs that can more accurately predict PMB AUCss,24h in routine clinical practice, facilitating PMB TDM in other laboratories and pharmacokinetics/pharmacodynamics-based clinical studies in the future. Full article
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11 pages, 955 KiB  
Article
Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients
by Simon Kallee, Christina Scharf, Lea Marie Schatz, Michael Paal, Michael Vogeser, Michael Irlbeck, Johannes Zander, Michael Zoller and Uwe Liebchen
Pharmaceutics 2022, 14(9), 1920; https://doi.org/10.3390/pharmaceutics14091920 - 10 Sep 2022
Cited by 1 | Viewed by 1329
Abstract
Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK [...] Read more.
Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use. Full article
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20 pages, 2979 KiB  
Article
Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach
by Qingxia Liu, Huiping Huang, Baohua Xu, Dandan Li, Maobai Liu, Imam H. Shaik and Xuemei Wu
Pharmaceutics 2022, 14(5), 1004; https://doi.org/10.3390/pharmaceutics14051004 - 07 May 2022
Cited by 2 | Viewed by 3701
Abstract
The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation [...] Read more.
The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice. Full article
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Review

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23 pages, 1917 KiB  
Review
Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management
by Rannissa Puspita Jayanti, Nguyen Phuoc Long, Nguyen Ky Phat, Yong-Soon Cho and Jae-Gook Shin
Pharmaceutics 2022, 14(5), 990; https://doi.org/10.3390/pharmaceutics14050990 - 05 May 2022
Cited by 8 | Viewed by 2715
Abstract
Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with [...] Read more.
Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management. Full article
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