Intracellular PD Modelling (<i>PD</i>_i) for the Prediction of Clinical Activity of Increased Rifampicin Dosing

• Main Authors: Ghaith Aljayyoussi (Author), Samantha Donnellan (Author), Stephen A. Ward (Author), Giancarlo A. Biagini (Author)
• Format: Book
• Published: MDPI AG, 2019-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1999-4923; 10.3390/pharmaceutics11060278
• Keywords: pharmacokinetic/pharmacodynamic modelling; rifampicin; high dose; tuberculosis; infectious diseases; <i>Mycobacterium tuberculosis</i>; Pharmacy and materia medica; RS1-441
• Source: Pharmaceutics, Vol 11, Iss 6, p 278 (2019)

Increasing rifampicin (RIF) dosages could significantly reduce tuberculosis (TB) treatment durations. Understanding the pharmacokinetic-pharmacodynamics (PK&#8722;PD) of increasing RIF dosages could inform clinical regimen selection. We used intracellular PD modelling (<i>PD</i>_i) to predict clinical outcomes, primarily time to culture conversion, of increasing RIF dosages. <i>PD</i>_i modelling utilizes in vitro-derived measurements of intracellular (macrophage) and extracellular <i>Mycobacterium tuberculosis</i> sterilization rates to predict the clinical outcomes of RIF at increasing doses. We evaluated <i>PD</i>_i simulations against recent clinical data from a high dose (35 mg/kg per day) RIF phase II clinical trial. <i>PD</i>_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, <i>PD</i>_i modelling was less predictive of culture conversion status at 26 weeks for high-dosage RIF (99% predicted vs. 81% observed). <i>PD</i>_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 <i>PD</i>_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.