How Do We Use Drug Concentration Data to Improve the Treatment of Overdose Patients?

2010 ◽  
Vol 32 (3) ◽  
pp. 300-304 ◽  
Author(s):  
Geoffrey K Isbister
Keyword(s):  
Drug Concentration ◽  
Concentration Data

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.

scholarly journals Pharmacokinetics and Pharmacodynamics of Once-Daily versus Twice-Daily Raltegravir in Treatment-Naïve HIV-Infected Patients

2012 ◽  
Vol 56 (6) ◽  
pp. 3101-3106 ◽  
Author(s):  
Matthew L. Rizk ◽  
Yaming Hang ◽  
Wen-Lin Luo ◽  
Jing Su ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Geometric Mean ◽  
Virologic Response ◽  
Threshold Concentration ◽  
Phase Iii ◽  
Concentration Data ◽  
Time Curve ◽  
Once Daily ◽  
Hiv Rna ◽  
Treatment Naïve

ABSTRACTQDMRK was a phase III clinical trial of raltegravir given once daily (QD) (800-mg dose) versus twice daily (BID) (400 mg per dose), each in combination with once-daily coformulated tenofovir-emtricitabine, in treatment-naive HIV-infected patients. Pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) analyses were conducted using a 2-step approach: individual non-model-based PK parameters from observed sparse concentration data were determined, followed by statistical analysis of potential relationships between PK and efficacy response parameters after 48 weeks of treatment. Sparse PK sampling was performed for all patients (QD,n= 380; BID,n= 384); selected sites performed an intensive PK evaluation at week 4 (QD,n= 22; BID,n= 20). In the intensive PK subgroup, daily exposures (area under the concentration-time curve from 0 to 24 h [AUC0–24]) were similar between the two regimens, but patients on 800 mg QD experienced ∼4-fold-higher maximum drug concentration in plasma (Cmax) values and ∼6-fold-lower trough drug concentration (Ctrough) values than those on 400 mg BID. Geometric mean (GM)Ctroughvalues were similarly lower in the sparse PK analysis. With BID dosing, there was no indication of any significant PK/PD association over the range of tested PK parameters. With QD dosing,Ctroughvalues correlated with the likelihood of virologic response. Failure to achieve an HIV RNA level of <50 copies/ml appeared predominantly at high baseline HIV RNA levels in both treatment arms and was associated with lower values of GMCtroughin the 800-mg-QD arm, though other possible drivers of efficacy, such as time above a threshold concentration, could not be evaluated due to the sparse sampling scheme. Together, these findings emphasize the importance of the shape of the plasma concentration-versus-time curve for long-term efficacy.

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