Cargando…

Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing

Increasing rifampicin (RIF) dosages could significantly reduce tuberculosis (TB) treatment durations. Understanding the pharmacokinetic-pharmacodynamics (PK–PD) of increasing RIF dosages could inform clinical regimen selection. We used intracellular PD modelling (PD(i)) to predict clinical outcomes,...

Descripción completa

Detalles Bibliográficos
Autores principales: Aljayyoussi, Ghaith, Donnellan, Samantha, Ward, Stephen A., Biagini, Giancarlo A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630509/
https://www.ncbi.nlm.nih.gov/pubmed/31200534
http://dx.doi.org/10.3390/pharmaceutics11060278
_version_ 1783435318736715776
author Aljayyoussi, Ghaith
Donnellan, Samantha
Ward, Stephen A.
Biagini, Giancarlo A.
author_facet Aljayyoussi, Ghaith
Donnellan, Samantha
Ward, Stephen A.
Biagini, Giancarlo A.
author_sort Aljayyoussi, Ghaith
collection PubMed
description Increasing rifampicin (RIF) dosages could significantly reduce tuberculosis (TB) treatment durations. Understanding the pharmacokinetic-pharmacodynamics (PK–PD) of increasing RIF dosages could inform clinical regimen selection. We used intracellular PD modelling (PD(i)) to predict clinical outcomes, primarily time to culture conversion, of increasing RIF dosages. PD(i) modelling utilizes in vitro-derived measurements of intracellular (macrophage) and extracellular Mycobacterium tuberculosis sterilization rates to predict the clinical outcomes of RIF at increasing doses. We evaluated PD(i) simulations against recent clinical data from a high dose (35 mg/kg per day) RIF phase II clinical trial. PD(i)-based simulations closely predicted the observed time-to-patient culture conversion status at eight weeks (hazard ratio: 2.04 (predicted) vs. 2.06 (observed)) for high dose RIF-based treatments. However, PD(i) modelling was less predictive of culture conversion status at 26 weeks for high-dosage RIF (99% predicted vs. 81% observed). PD(i)-based simulations indicate that increasing RIF beyond 35 mg/kg/day is unlikely to significantly improve culture conversion rates, however, improvements to other clinical outcomes (e.g., relapse rates) cannot be ruled out. This study supports the value of translational PD(i)-based modelling in predicting culture conversion rates for antitubercular therapies and highlights the potential value of this platform for the improved design of future clinical trials.
format Online
Article
Text
id pubmed-6630509
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-66305092019-08-19 Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing Aljayyoussi, Ghaith Donnellan, Samantha Ward, Stephen A. Biagini, Giancarlo A. Pharmaceutics Communication Increasing rifampicin (RIF) dosages could significantly reduce tuberculosis (TB) treatment durations. Understanding the pharmacokinetic-pharmacodynamics (PK–PD) of increasing RIF dosages could inform clinical regimen selection. We used intracellular PD modelling (PD(i)) to predict clinical outcomes, primarily time to culture conversion, of increasing RIF dosages. PD(i) modelling utilizes in vitro-derived measurements of intracellular (macrophage) and extracellular Mycobacterium tuberculosis sterilization rates to predict the clinical outcomes of RIF at increasing doses. We evaluated PD(i) simulations against recent clinical data from a high dose (35 mg/kg per day) RIF phase II clinical trial. PD(i)-based simulations closely predicted the observed time-to-patient culture conversion status at eight weeks (hazard ratio: 2.04 (predicted) vs. 2.06 (observed)) for high dose RIF-based treatments. However, PD(i) modelling was less predictive of culture conversion status at 26 weeks for high-dosage RIF (99% predicted vs. 81% observed). PD(i)-based simulations indicate that increasing RIF beyond 35 mg/kg/day is unlikely to significantly improve culture conversion rates, however, improvements to other clinical outcomes (e.g., relapse rates) cannot be ruled out. This study supports the value of translational PD(i)-based modelling in predicting culture conversion rates for antitubercular therapies and highlights the potential value of this platform for the improved design of future clinical trials. MDPI 2019-06-13 /pmc/articles/PMC6630509/ /pubmed/31200534 http://dx.doi.org/10.3390/pharmaceutics11060278 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Aljayyoussi, Ghaith
Donnellan, Samantha
Ward, Stephen A.
Biagini, Giancarlo A.
Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title_full Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title_fullStr Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title_full_unstemmed Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title_short Intracellular PD Modelling (PD(i)) for the Prediction of Clinical Activity of Increased Rifampicin Dosing
title_sort intracellular pd modelling (pd(i)) for the prediction of clinical activity of increased rifampicin dosing
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630509/
https://www.ncbi.nlm.nih.gov/pubmed/31200534
http://dx.doi.org/10.3390/pharmaceutics11060278
work_keys_str_mv AT aljayyoussighaith intracellularpdmodellingpdiforthepredictionofclinicalactivityofincreasedrifampicindosing
AT donnellansamantha intracellularpdmodellingpdiforthepredictionofclinicalactivityofincreasedrifampicindosing
AT wardstephena intracellularpdmodellingpdiforthepredictionofclinicalactivityofincreasedrifampicindosing
AT biaginigiancarloa intracellularpdmodellingpdiforthepredictionofclinicalactivityofincreasedrifampicindosing