Model-based learn and confirm: designing effective treatment regimens against multidrug resistant Gram-negative pathogens

• Published in: International journal of antimicrobial agents 2024-04, Vol.63 (4), p.107100-107100, Article 107100
• Main Authors: Garcia, Estefany, Diep, John K., Sharma, Rajnikant, Rao, Gauri G.
• Format: Article
• Language: English
• Subjects: computer simulation; In vitro dynamic infection model; Individualized antimicrobial therapies; Klebsiella pneumoniae; Learn-confirm; Mechanism-based model; meropenem; microbial load; Model-informed drug optimization; multiple drug resistance; polymyxin B; Polymyxin-based combination therapy; regrowth
• ISSN: 0924-8579; 1872-7913
• DOI: 10.1016/j.ijantimicag.2024.107100

•An antibiotic resistance research framework advocating the use of PK/PD approaches.•Leveraging low-cost, high-yield preclinical studies for model-informed drug optimisation.•Combination therapy against PMB-susceptible and PMB-resistant CRKP isolates. Over the last decade, there has been a growing appreciation for the use of in vitro and in vivo infection models to generate robust and informative nonclinical PK/PD data to accelerate the clinical translation of treatment regimens. The objective of this study was to develop a model-based “learn and confirm” approach to help with the design of combination regimens using in vitro infection models to optimise the clinical utility of existing antibiotics. Static concentration time-kill studies were used to evaluate the PD activity of polymyxin B (PMB) and meropenem against two carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates; BAA2146 (PMB-susceptible) and BRKP67 (PMB-resistant). A mechanism-based model (MBM) was developed to quantify the joint activity of PMB and meropenem. In silico simulations were used to predict the time-course of bacterial killing using clinically-relevant PK exposure profiles. The predictive accuracy of the model was further evaluated by validating the model predictions using a one-compartment PK/PD in vitro dynamic infection model (IVDIM). The MBM captured the reduction in bacterial burden and regrowth well in both the BAA2146 and BRKP67 isolate (R2 = 0.900 and 0.940, respectively). The bacterial killing and regrowth predicted by the MBM were consistent with observations in the IVDIM: sustained activity against BAA2146 and complete regrowth of the BRKP67 isolate. Differences observed in PD activity suggest that additional dose optimisation might be beneficial in PMB-resistant isolates. The model-based approach presented here demonstrates the utility of the MBM as a translational tool from static to dynamic in vitro systems to effectively perform model-informed drug optimisation.