The Clinical Interpretation and Application of Drug Concentration Data

1981 ◽  
Vol 28 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Ralph E. Kauffman
Keyword(s):  
Drug Concentration ◽  
Concentration Data ◽  
Clinical Interpretation

scholarly journals Traditional, unfairly forgotten, rarely used and promising drug delivery methods in ophthalmology: a clinical interpretation (part 1)

2019 ◽  
Vol 12 (2) ◽  
pp. 83-95 ◽  
Author(s):  
A. V. Kuroyedov ◽  
V. V. Brzhesky ◽  
E. A. Krinitsyna
Keyword(s):  
Drug Delivery ◽  
Side Effects ◽  
Drug Concentration ◽  
Targeted Drug Delivery ◽  
Anatomical Structure ◽  
Delivery Methods ◽  
Clinical Interpretation ◽  
Physiological Properties ◽  
Challenging Tasks ◽  
Targeted Drug

Ocular targeted drug delivery is one of the most challenging tasks for pharmaceutical researchers and practical ophthalmologists. The possibilities of drug delivery to the eye are naturally determined by the anatomical structure of the eye and its physiological properties, which restrict the period when therapeutically required drug concentration could be maintained. Combined drug delivery schemes may, potentially, improve the patient’s acceptance of treatment, reduce side effects, increase efficacy, and eventually preserve vision.

scholarly journals vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Vi Ngoc-Nha Tran ◽  
Alireza Shams ◽  
Sinan Ascioglu ◽  
Antal Martinecz ◽  
Jingyi Liang ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Computational Models ◽  
Treatment Strategies ◽  
Extended Model ◽  
Concentration Data ◽  
Bacterial Populations ◽  
Web Based ◽  
Antibiotic Target ◽  
Dosing Regimens ◽  
Target Binding

Abstract Background As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

scholarly journals REGEN-COV® antibody cocktail bioanalytical strategy: comparison of LC-MRM-MS and immunoassay methods for drug quantification

Bioanalysis ◽  
2021 ◽  
Author(s):  
Susan C Irvin ◽  
Samit Ganguly ◽  
Rachel Weiss ◽  
Chinnasamy Elango ◽  
Xuefei Zhong ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Multiple Reaction Monitoring ◽  
Pharmacokinetic Data ◽  
Sample Analysis ◽  
Concentration Data ◽  
Phase Study ◽  
The Usa ◽  
Linear Correlations ◽  
Antibody Cocktail

Aim: In response to the COVID-19 pandemic, Regeneron developed the anti-SARS-CoV-2 monoclonal antibody cocktail, REGEN-COV® (RONAPREVE® outside the USA). Drug concentration data was important for determination of dose, so a two-part bioanalytical strategy was implemented to ensure the therapy was rapidly available for use. Results & methodology: Initially, a liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) assay, was used to analyze early-phase study samples. Subsequently, a validated electrochemiluminescence (ECL) immunoassay was implemented for high throughput sample analysis for all samples. A comparison of drug concentration data from the methods was performed which identified strong linear correlations and for Bland-Altman, small bias. In addition, pharmacokinetic data from both methods produced similar profiles and parameters. Discussion & conclusion: This novel bioanalytical strategy successfully supported swift development of a critical targeted therapy during the COVID-19 public health emergency.

scholarly journals vCOMBAT: a Novel Tool to Create and Visualize a COmputational Model of Bacterial Antibiotic Target-binding

2020 ◽  
Author(s):  
Vi Ngoc-Nha Tran ◽  
Alireza Shams ◽  
Sinan Ascioglu ◽  
Antal Martinecz ◽  
Jingyi Liang ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Computational Models ◽  
Treatment Strategies ◽  
Extended Model ◽  
Concentration Data ◽  
Bacterial Populations ◽  
Web Based ◽  
Antibiotic Target ◽  
Dosing Regimens ◽  
Target Binding

AbstractMotivationAs antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens to from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allows non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria.ResultsIn this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis (TB) bacteria using our vCOMBAT tool.AvailabilityvCOMBAT online tool is publicly available at https://combat-bacteria.org/.

scholarly journals Practical unidentifiability of receptor density in target mediated drug disposition models can lead to over-interpretation of drug concentration data

2017 ◽  
Author(s):  
Andrew M Stein
Keyword(s):  
Monoclonal Antibodies ◽  
Drug Development ◽  
Drug Concentration ◽  
Drug Disposition ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Receptor Density ◽  
Concentration Data ◽  
Human Dose ◽  
Target Mediated Drug Disposition

AbstractFor monoclonal antibodies, mathematical models of target mediated drug disposition (TMDD) are often fit to data in order to estimate key physiological parameters of the system. These parameter estimates can then be used to support drug development by assisting with the assessment of whether the target is druggable and what the first in human dose should be. The TMDD model is almost always over-parameterized given the available data, resulting in the practical unidentifiability of some of the model parameters, including the target receptor density. In particular, when only PK data is available, the receptor density is almost always practically unidentifiable. However, because practical identifiability is not regularly assessed, incorrect interpretation of model fits to the data can be made. This issue is illustrated using two case studies from the literature.

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