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Journals
Pharmaceuticals
Special Issues
Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies
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Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 25 August 2024 | Viewed by 1142

Special Issue Editors

Dr. Silvia De Francia

E-Mail Website
Guest Editor
Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
Interests: pharmacology; sex and gender medicine; pharmacokinetics; pharmacodynamics; pharmacogenomics; personalized therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Sarah Allegra

E-Mail Website
Guest Editor
Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
Interests: sex and gender pharmacology; gender medicine; pharmacokinetics; pharmacogenomics; personalized therapy.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding the safety and effectiveness of any drug depends, in large, upon pharmacokinetics and pharmacodynamics. Pharmacokinetic and pharmacodynamic modelling and simulation approaches, such as population analyses, are employed in order to understand the characteristics of drugs and how they behave in diverse patient populations.

Drug developers apply the insights gained from pharmacokinetic and pharmacodynamic analyses in order to design enhanced clinical studies. Then, clinicians use the information obtained from pharmacokinetic and pharmacodynamic analyses to treat various types of patients. Pharmacokinetic and pharmacodynamic analyses and modelling are important tools in the development and approval of every drug.

In this Special Issue, we aim to present preclinical and clinical research from experts in the field of pharmaceuticals that highlights the application of therapeutic agents and clinical strategies focused on tailored populations. We welcome the submission of articles presenting disaggregated data pertaining to research on new and old drugs in order to identify future directions and subsequently design inclusive trials that everyone can benefit from.

Dr. Silvia De Francia
Dr. Sarah Allegra
Guest Editors

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. Pharmaceuticals 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

  • drugs
  • kinetics
  • dynamics
  • preclinical
  • clinical
  • research
  • interaction

Published Papers (1 paper)

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Research

11 pages, 786 KiB  
Article
Logistic Regression Is Non-Inferior to the Response Surface Model in Patient Response Prediction of Video-Assisted Thoracoscopic Surgery
by Hui-Yu Huang, Shih-Pin Lin, Hsin-Yi Wang, Jing-Yang Liou, Wen-Kuei Chang and Chien-Kun Ting
Pharmaceuticals 2024, 17(1), 95; https://doi.org/10.3390/ph17010095 - 10 Jan 2024
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Abstract
Response surface models (RSMs) are a new trend in modern anesthesia. RSMs have demonstrated significant applicability in the field of anesthesia. However, the comparative analysis between RSMs and logistic regression (LR) in different surgeries remains relatively limited in the current literature. We hypothesized [...] Read more.
Response surface models (RSMs) are a new trend in modern anesthesia. RSMs have demonstrated significant applicability in the field of anesthesia. However, the comparative analysis between RSMs and logistic regression (LR) in different surgeries remains relatively limited in the current literature. We hypothesized that using a total intravenous anesthesia (TIVA) technique with the response surface model (RSM) and logistic regression (LR) would predict the emergence from anesthesia in patients undergoing video-assisted thoracotomy surgery (VATS). This study aimed to prove that LR, like the RSM, can be used to improve patient safety and achieve enhanced recovery after surgery (ERAS). This was a prospective, observational study with data reanalysis. Twenty-nine patients (American Society of Anesthesiologists (ASA) class II and III) who underwent VATS for elective pulmonary or mediastinal surgery under TIVA were enrolled. We monitored the emergence from anesthesia, and the precise time point of regained response (RR) was noted. The influence of varying concentrations was examined and incorporated into both the RSM and LR. The receiver operating characteristic (ROC) curve area for Greco and LR models was 0.979 (confidence interval: 0.987 to 0.990) and 0.989 (confidence interval: 0.989 to 0.990), respectively. The two models had no significant differences in predicting the probability of regaining response. In conclusion, the LR model was effective and can be applied to patients undergoing VATS or other procedures of similar modalities. Furthermore, the RSM is significantly more sophisticated and has an accuracy similar to that of the LR model; however, the LR model is more accessible. Therefore, the LR model is a simpler tool for predicting arousal in patients undergoing VATS under TIVA with Remifentanil and Propofol. Full article
(This article belongs to the Special Issue Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies)
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