Division of Pharmaceutics and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Division of Pharmaceutics and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Int J Antimicrob Agents. 2024 Apr;63(4):107100. doi: 10.1016/j.ijantimicag.2024.107100. Epub 2024 Jan 26.
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 (R = 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.
在过去的十年中，人们越来越认识到使用体外和体内感染模型来生成强大且信息丰富的非临床 PK/PD 数据，以加速治疗方案的临床转化。本研究的目的是开发一种基于模型的“学习和确认”方法，以帮助使用体外感染模型设计联合治疗方案，从而优化现有抗生素的临床应用。静态浓度时间杀伤研究用于评估多粘菌素 B (PMB) 和美罗培南对两种碳青霉烯类耐药肺炎克雷伯菌 (CRKP) 分离株 BAA2146 (PMB 敏感) 和 BRKP67 (PMB 耐药) 的 PD 活性。建立了一个基于机制的模型 (MBM) 来量化 PMB 和美罗培南的联合活性。使用临床相关 PK 暴露谱进行计算机模拟，预测细菌杀伤的时间过程。通过使用单室 PK/PD 体外动态感染模型 (IVDIM) 验证模型预测来进一步评估模型的预测准确性。MBM 很好地捕获了在 BAA2146 和 BRKP67 分离株中细菌负荷的减少和再生长 (R = 0.900 和 0.940)。MBM 预测的细菌杀伤和再生长与 IVDIM 中的观察结果一致：对 BAA2146 持续有效，BRKP67 分离株完全再生长。PD 活性的差异表明，在 PMB 耐药分离株中，进一步优化剂量可能是有益的。本文提出的基于模型的方法证明了 MBM 作为从静态到动态体外系统的转化工具的实用性，可有效地进行模型指导的药物优化。
