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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: closed (25 August 2024) | Viewed by 7341

Special Issue Editors

Dr. Silvia De Francia

E-Mail Website
Guest Editor
Department of Clinical and Biological Sciences, University of Turin, 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

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Published Papers (4 papers)

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Research

12 pages, 870 KiB  
Article
Hydroxyurea Pharmacokinetic Evaluation in Patients with Sickle Cell Disease
by Daniela Di Grazia, Cristina Mirabella, Francesco Chiara, Maura Caudana, Francesco Maximillian Anthony Shelton Agar, Marina Zanatta, Sarah Allegra, Jenni Bertello, Vincenzo Voi, Giovanni Battista Ferrero, Giuliana Abbadessa and Silvia De Francia
Pharmaceuticals 2024, 17(10), 1386; https://doi.org/10.3390/ph17101386 - 17 Oct 2024
Viewed by 1662
Abstract
Background: Hydroxyurea (HU), also known as hydroxycarbamide, is an oral ribonucleotide reductase inhibitor. In 1999, the United States Food and Drug Administration (FDA) approved HU for the treatment of sickle cell disease (SCD). Since then, it has become the cornerstone in the management [...] Read more.
Background: Hydroxyurea (HU), also known as hydroxycarbamide, is an oral ribonucleotide reductase inhibitor. In 1999, the United States Food and Drug Administration (FDA) approved HU for the treatment of sickle cell disease (SCD). Since then, it has become the cornerstone in the management of SCD patients, helping to reduce vaso-occlusive crises, acute chest syndrome, the need for blood transfusions, hospitalizations and mortality. There is considerable variability among individuals in HU pharmacokinetic (Pk) parameters that can influence treatment efficacy and toxicity. The objective of this work is part of a clinical study aimed at investigating HU Pk and determining the optimal sampling time to estimate the Area Under the Curve (AUC) in SCD patients. Methods: HU plasma concentration in 80 patients at various time points (2, 4, 6, 24 h) following a 48-h drug washout was quantified using High-Pressure Liquid Chromatography (HPLC) coupled with an ultraviolet (UV) detection method previously described in the literature and adapted to new conditions with partial modifications. Results: The mean HU administered dose was 19.5 ± 5.1 mg/kg (range: 7.7–37.5 mg/kg). The median AUC quantified in plasma patients was 101.3 mg/L/h (Interquartile Range (IQR): 72.5–355.9) and it was not influenced by the weight-based dose. However, there was a strong positive correlation between AUC and Body Mass Index (BMI) as well as dose per Body Surface Area (BSA). Along with a three-point approach for AUC determination present in the literature, we show results obtained from a four-point sampling strategy, which is more useful and effective for better optimizing dose escalation to the maximum tolerated dose (MTD). Moreover, we observed that most patients achieved the maximum HU plasma concentration two hours after drug administration, regardless of age differences. Conclusions: HU treatment, which represents a milestone in the treatment of SCD due to its ability to reduce disease complications and improve patients’ quality of life, requires careful monitoring to optimize the individual dose for saving potential side effects and/or adverse events. Full article
(This article belongs to the Special Issue Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies)
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25 pages, 9316 KiB  
Article
Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone
by Andreas D. Meid, Camilo Scherkl, Michael Metzner, David Czock and Hanna M. Seidling
Pharmaceuticals 2024, 17(8), 1041; https://doi.org/10.3390/ph17081041 - 7 Aug 2024
Viewed by 1216
Abstract
Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was [...] Read more.
Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia. Full article
(This article belongs to the Special Issue Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies)
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14 pages, 2518 KiB  
Article
Population Pharmacokinetics of Dasatinib in Healthy Subjects
by Walaa B. Hassouneh, Mutasim A. Al-Ghazawi, Mohammad I. Saleh and Naji Najib
Pharmaceuticals 2024, 17(6), 671; https://doi.org/10.3390/ph17060671 - 23 May 2024
Cited by 2 | Viewed by 2127
Abstract
Background and Objectives: Dasatinib is one of the tyrosine kinase inhibitors. The main use of these agents is inhibition of cancerous cell proliferation. The therapeutic importance of tyrosine kinase inhibitors raises the necessity of many types of investigations, especially the pharmacokinetic analysis of [...] Read more.
Background and Objectives: Dasatinib is one of the tyrosine kinase inhibitors. The main use of these agents is inhibition of cancerous cell proliferation. The therapeutic importance of tyrosine kinase inhibitors raises the necessity of many types of investigations, especially the pharmacokinetic analysis of these drugs in humans. This analysis, along with other investigations and clinical research, will contribute to the overall knowledge of the drug. This study focused on the population pharmacokinetics of dasatinib. The objective of the study was to investigate the sources of the variability of dasatinib in a population pharmacokinetics study in healthy participants. Methods: We utilized 4180 plasma observations from 110 subjects who were administered SPRYCEL® on two separate occasions under fasting conditions; data from 20% of the subjects (22 subjects) were extracted for the purpose of internal model evaluation and data from 88 subjects were used in modeling. The model was evaluated by visual predictive check of three different datasets. A two-compartmental model with first order absorption and transit compartment was considered the simplest base model to describe the data based on the corrected Bayesian information criterion evaluation. Covariates were tested through conditional sampling for the stepwise approach-screening procedure in Monolix 2020R1 version. Conditional sampling for the stepwise approach was used to include the correlated covariates within the base model in the forward inclusion step and then to eliminate them backwardly to ensure that the key covariates were kept in the model at the final stage. Results: The effect of body mass index on the absorption rate constant was considered as significant covariate in the final established model. Visual predictive check for simulations, 20% of the original dataset (internal dataset) and an external dataset demonstrated the appropriateness of the final model. Conclusions: Population pharmacokinetic modeling was performed to describe dasatinib pharmacokinetics in healthy subjects. Body mass index was considered as a factor that might be used in the future along with studies on patients to adjust the dosing regimens. Key Points: Dasatinib is classified as a highly variable drug; this variability was demonstrated in the study by the effect of body mass index on the absorption rate constant. Full article
(This article belongs to the Special Issue Population Pharmacokinetic and Pharmacodynamic and Clinical Strategies)
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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
Cited by 1 | Viewed by 1501
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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