Pharmacokinetic assessment of vancomycin in critically ill patients and nephrotoxicity prediction using individualized pharmacokinetic parameters

Ghasemiyeh, Parisa; Vazin, Afsaneh; Zand, Farid; Haem, Elham; Karimzadeh, Iman; Azadi, Amir; Masjedi, Milad; Sabetian, Golnar; Nikandish, Reza; Mohammadi‐Samani, Soliman

doi:10.3389/fphar.2022.912202

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…Hydrogel‐based therapeutics can be utilized for a range of routes of drug administration, such as wound, oral, brain, ocular, and rectal. [ 81,82 ] Controllable drug delivery through various administration routes can maintain a steady and consistent level of medication, thus minimizing variations and reducing side effects. [ 82 ]…”

Section: Routes Of Administrationmentioning

confidence: 99%

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Advances in Stimuli‐Responsive Chitosan Hydrogels for Drug Delivery Systems

Xia,

Ma,

et al. 2023

Macromolecular Bioscience

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Sustainable and controllable drug transport is one of the most efficient ways of disease treatment. Due to high biocompatibility, good biodegradability, and low costs, chitosan and its derivatives are widely used in biomedical fields. Specifically, chitosan hydrogel enables drugs to pass through biological barriers because of their abundant amino and hydroxyl groups that can interact with human tissues. Moreover, the multi‐responsive nature (pH, temperature, ions strength, and magnetic field, etc.) of chitosan hydrogels makes precise drug release a possibility. Here, the synthesis methods, modification strategies, stimuli‐responsive mechanisms of chitosan‐based hydrogels, and their recent progress in drug delivery are summarized. Chitosan hydrogels that carry and release drugs through subcutaneous (dealing with wound dressing), oral (dealing with gastrointestinal tract), and facial (dealing with ophthalmic, ear, and brain) are reviewed. Finally, challenges toward clinic application and the future prospects of stimuli‐responsive chitosan‐based hydrogels are indicated.

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Section: Routes Of Administrationmentioning

confidence: 99%

“…[ 81,82 ] Controllable drug delivery through various administration routes can maintain a steady and consistent level of medication, thus minimizing variations and reducing side effects. [ 82 ]…”

Section: Routes Of Administrationmentioning

confidence: 99%

Advances in Stimuli‐Responsive Chitosan Hydrogels for Drug Delivery Systems

Xia,

Ma,

et al. 2023

Macromolecular Bioscience

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“…Polymer gels are three‐dimensional networks of polymer chains that are swollen with a solvent. They exhibit a unique combination of solid and liquid‐like properties, making them useful in various applications, such as drug delivery, tissue engineering, and sensors 10 …”

Section: Biomedical Applications Of Polymer Gelsmentioning

confidence: 99%

Advancing biomedicine with gel‐based materials and composites: A comprehensive review

Sajjadi,

Gholizadeh‐Hashjin,

Shafizadeh

et al. 2023

J of Applied Polymer Sci

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Polymer gels are an important class of soft materials with growing interest for biomedical applications due to their distinctive properties. These three‐dimensional polymer networks are capable of imbibing large amounts of solvent while maintaining solid‐like behavior. Polymer gels can be classified into hydrogels, aerogels, and cryogels based on their structural features. Their highly porous structure, swelling capacity, and biocompatibility make them well‐suited for diverse uses in biomedicine. Recent advances have enabled the engineering of polymer gels with tunable architectures and smart functionality responsive to stimuli. These developments have expanded the potential of gels in areas like tissue engineering, drug delivery, wound healing, and biosensing. However, the toxicity of polymer gels remains an important consideration for biomedical use. Careful design and toxicological evaluation is essential to ensure safe clinical application. Overall, polymer gels offer new possibilities in biomedicine through continued research on synthesizing biocompatible gels and elucidating structure–function relationships. This review provides an updated perspective on the progress and promise of tailoring polymer gels to advance biomedicine, while also considering the critical aspect of biocompatibility.

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A Deep Learning–Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness

Kim,

Choi,

Lee

et al. 2024

JMIR Form Res

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Background Vancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning–based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. Objective This study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. Methods We used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care–IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. Results Our JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. Conclusions Our JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.

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Pharmacokinetic assessment of vancomycin in critically ill patients and nephrotoxicity prediction using individualized pharmacokinetic parameters

Cited by 3 publications

References 47 publications

Advances in Stimuli‐Responsive Chitosan Hydrogels for Drug Delivery Systems

Advances in Stimuli‐Responsive Chitosan Hydrogels for Drug Delivery Systems

Advancing biomedicine with gel‐based materials and composites: A comprehensive review

A Deep Learning–Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness

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