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Antibiotics
Special Issues
Clinical Pharmacokinetics, Pharmacodynamics, and/or TDM of Antimicrobial Agents
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Clinical Pharmacokinetics, Pharmacodynamics, and/or TDM of Antimicrobial Agents

A special issue of Antibiotics (ISSN 2079-6382).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1905

Special Issue Editor

Prof. Dr. Yukihiro Hamada

E-Mail Website
Guest Editor
Department of Pharmacy, Kochi Medical School Hospital, Nankoku, Kochi, Japan
Interests: clinical PK/PD; TDM; pharmacometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the increase in antimicrobial-resistant bacteria has become a global threat. While we look forward to the development of new antimicrobial agents, it is important to maximize the use of existing agents. To this end, successful utilization of PK/PD and TDM can be expected to improve clinical outcomes. Therefore, the main theme of this Special Issue is all approaches to AMR control in the areas of PK/PD, TDM, application, and AST activities. Manuscripts on other approaches to the proper use of antimicrobial agents for AMR control are also welcome.

Prof. Dr. Yukihiro Hamada
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pharmacokinetics/pharmacodynamics
  • therapeutic drug monitoring
  • antimicrobial resistant
  • AI
  • antimicrobial stewardship

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Published Papers (2 papers)

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Research

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12 pages, 1643 KiB  
Article
Association between Extended Meropenem Regimen and Achievement of Aggressive PK/PD in Patients Receiving Continuous Renal Replacement Therapy for Septic AKI
by Shinya Chihara, Tomoyuki Ishigo, Satoshi Kazuma, Kana Matsumoto, Kunihiko Morita and Yoshiki Masuda
Antibiotics 2024, 13(8), 755; https://doi.org/10.3390/antibiotics13080755 - 11 Aug 2024
Viewed by 1103
Abstract
Aggressive pharmacokinetic (PK)/pharmacodynamic (PD) targets have shown better microbiological eradication rates and a lower propensity to develop resistant strains than conservative targets. We investigated whether meropenem blood levels, including aggressive PK/PD, were acceptable in terms of efficacy and safety using a meropenem regimen [...] Read more.
Aggressive pharmacokinetic (PK)/pharmacodynamic (PD) targets have shown better microbiological eradication rates and a lower propensity to develop resistant strains than conservative targets. We investigated whether meropenem blood levels, including aggressive PK/PD, were acceptable in terms of efficacy and safety using a meropenem regimen of 1 g infusion every 8 h over 3 h in patients undergoing continuous renal replacement therapy (CRRT) for septic acute kidney injury (AKI). Aggressive PK/PD targets were defined as the percentage of time that the free concentration (%fT) > 4 × minimal inhibitory concentration (MIC), the toxicity threshold was defined as a trough concentration >45 mg/L, and the percentage of achievement at each MIC was evaluated. The 100% fT > 4 × MIC for a pathogen with an MIC of 0.5 mg/L was 89%, and that for a pathogen with an MIC of 2 mg/L was 56%. The mean steady-state trough concentration of meropenem was 11.9 ± 9.0 mg/L and the maximum steady-state trough concentration was 29.2 mg/L. Simulations using Bayesian estimation showed the probability of achieving 100% fT > 4 × MIC for up to an MIC of 2 mg/L for the administered administration via continuous infusion at 3 g/24 h. We found that an aggressive PK/PD could be achieved up to an MIC of 0.5 mg/L with a meropenem regimen of 1 g infused every 8 h over 3 h for patients receiving CRRT for septic AKI. In addition, the risk of reaching the toxicity range with this regimen is low. In addition, if the MIC was 1–2 mg/L, the simulation results indicated that aggressive PK/PD can be achieved by continuous infusion at 3 g/24 h without increasing the daily dose. Full article
(This article belongs to the Special Issue Clinical Pharmacokinetics, Pharmacodynamics, and/or TDM of Antimicrobial Agents)
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Review

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20 pages, 1048 KiB  
Review
Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review
by Iria Varela-Rey, Enrique Bandín-Vilar, Francisco José Toja-Camba, Antonio Cañizo-Outeiriño, Francisco Cajade-Pascual, Marcos Ortega-Hortas, Víctor Mangas-Sanjuan, Miguel González-Barcia, Irene Zarra-Ferro, Cristina Mondelo-García and Anxo Fernández-Ferreiro
Antibiotics 2024, 13(12), 1203; https://doi.org/10.3390/antibiotics13121203 - 10 Dec 2024
Viewed by 410
Abstract
Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to [...] Read more.
Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time, and allows more factors to be considered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of antibiotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, for dose prediction and analyze their results in comparison with the results obtained by classical methods. Furthermore, in the review, the techniques employed and the metrics to evaluate the precision are described to improve the compression of the results. Methods: A systematic search was carried out in the EMBASE, OVID, and PubMed databases and the results obtained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in seven and none of the studies were performed on new antibiotics. The most used techniques were XGBoost and neural networks. Comparisons were conducted in most cases against population pharmacokinetic models. Conclusions: AI techniques offer promising results. However, the diversity in terms of the statistical metrics used and the low power of some of the articles make the overall assessment difficult. For now, AI-based ML techniques should be used in addition to classical population pharmacokinetic models in clinical practice. Full article
(This article belongs to the Special Issue Clinical Pharmacokinetics, Pharmacodynamics, and/or TDM of Antimicrobial Agents)
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