Resistance Suppression for P. Aeruginosa using Novel Combination Therapy Modeling

描述（由申请方提供）：铜绿假单胞菌是ICU发病和死亡的主要原因，尤其是在呼吸机相关肺炎患者中。许多分离株是多重耐药的，有些分离株对所有现有的抗感染药物都有耐药性。目前临床开发（人体）中没有具有抗假单胞菌新作用机制的新抗生素。考虑到新药开发的周期时间，很可能在5 - 7年内不会出现对这种病原体具有新作用机制的抗生素。因此，我们必须产生关于如何最好地抑制这种病原体的抗生素耐药性的新知识。这将有助于在我们等待新药物的同时保存我们现有的药物。此外，当具有新作用机制的药物变得可用时，它们可以以最佳方式开发用于耐药性抑制，无论是作为单药治疗还是联合治疗。在本申请中（具体目标#1），我们假设，我们可以通过在我们的中空纤维感染模型（HALLOW）中研究该病原体并将大型数学模型拟合到HALLOW数据以确定这些剂量和时间表，来确定用于耐药性抑制的单药治疗的最佳剂量和给药时间表。我们进一步假设，不同的耐药机制将改变最佳剂量和时间表。我们建议研究野生型铜绿假单胞菌PAO-1分离株的同基因突变体，每种突变体都含有确定的耐药机制。这些发现将通过使用蒙特卡罗模拟（MCS）与人类联系起来。在具体目标#2中，我们建议检查联合化疗中的药物。我们已经开发了一个全新的数学模型，可以描述两种药物联合化疗对铜绿假单胞菌分离株的影响。该模型是一种混合模型，允许将模型拟合到两种药物的浓度-时间过程，以及将模型拟合到药物组合对存在的敏感和不太敏感的微生物群体造成的随时间的不同变化。该系统参数的稳健识别将允许计算用于耐药性抑制的最佳组合方案。如上所述，此类方案将通过使用MCS桥接至人类。HPLIFICATION是一种体外系统。在具体目标3中，我们还将在血小板减少小鼠肺炎模型中验证这些最佳和非最佳方案，采用与目标1和目标2中体外研究的相同分离株。在检查这一点时，我们将使用“人源化”给药方案，以便小鼠和人药代动力学之间的差异不会导致不适当的推断。这将对单药治疗和联合治疗进行。将设计并进行前瞻性验证实验。结果将与体外研究结果进行比较，并与人体进行比较。这样，将为抑制耐药突变群体扩增的药物方案定义强有力的原则。铜绿假单胞菌是重症监护病房中的一种重要病原体，通常对我们治疗设备中的许多甚至所有药物具有耐药性。由于预期至少7年内没有具有独特作用机制的药物对假单胞菌有效，因此必须了解如何以抑制耐药性出现并使这些药物对患者保持活性的方式使用我们目前可用的药物。我们计划通过以下方式来实现这一点：1）了解在我们的中空纤维感染模型（HPLOIS）中给予假单胞菌活性药物以抑制耐药性的最佳方式，并描述不同耐药机制对该过程的影响; 2）了解如何在HPLOIS中联合给予这些药物以最佳抑制耐药性的出现; 3）在铜绿假单胞菌肺炎的小鼠模型中验证了来自HSP 70的体外发现。

DESCRIPTION (provided by applicant): Pseudomonas aeruginosa is a major cause of morbidity and mortality in the ICU, particularly among patients with Ventilator-Associated Pneumonia. Many isolates are multi-drug resistant and some isolates are resistant to all currently extant anti-infective agents. There are currently no new antibiotics in clinical development (in man) with novel mechanisms of action against Pseudomonas. Given the cycle time associated with new drug development, it is likely that no antibiotics with new mechanisms of action for this pathogen will arise for 5 - 7 years. Thus, we must generate new knowledge about how best to suppress antibiotic resistance for this pathogen. This will help preserve our current drugs while we await new agents. Also, when agents with new mechanisms of action become available they can be developed in an optimal fashion for resistance suppression, both as monotherapy and in combination. In this application (Specific Aim #1), we hypothesize that we can identify optimal doses and schedules of administration for monotherapy for resistance suppression by studying this pathogen in our Hollow Fiber Infection Model (HFIM) and fitting a large mathematical model to the HFIM data to identify these doses and schedules. We further hypothesize that different resistance mechanisms will alter optimal doses and schedules. We propose to study isogenic mutants of the wild-type P. aeruginosa PAO-1 isolate, each containing a defined resistance mechanism. These findings will be bridged to man through use of Monte Carlo simulation (MCS). In Specific Aim #2, we propose to examine drugs in combination chemotherapy. We have developed a completely novel mathematical model that allows description of the impact of two drug combination chemotherapy on isolates of P. aeruginosa. This model is a mixture model and allows the fitting of the model to the concentration-time course of both agents as well as to fit the model to the disparate changes over time wrought by the combination of agents on the susceptible and less-susceptible populations of organisms present. Robust identification of the parameters of this system will allow calculation of optimal combination regimens for resistance suppression. Such regimens will be bridged to man through the use of MCS, as above. The HFIM is an in vitro system. In Specific Aim #3, we will also validate these optimal and non-optimal regimens in a neutropenic mouse pneumonia model, employing the same isolates studied in vitro in Aims #1 and #2. In examining this, we will use "humanized" dosing for the regimens, so that differences between mouse and human pharmacokinetics will not drive an improper inference. This will be done for both mono- and combination therapy. Prospective validation experiments will be designed and carried out. Results will be compared with the in vitro findings and also bridged to man. In so doing robust principles will be defined for drug regimens that will suppress amplification of resistant mutant populations. Pseudomonas aeruginosa is a pathogen of major importance in the Intensive Care Unit and is often resistant to many or even all of the drugs in our therapeutic armamentarium. As no agents with a unique mechanism of action active against Pseudomonas are expected for at least 7 years, it is imperative to learn how to use our currently available agents in a way that suppresses emergence of resistance and keeps these agents active for our patients. We plan to do this by 1) understanding the optimal way to dose Pseudomonas-active drugs in our hollow fiber infection model (HFIM) to suppress resistance and delineate the impact of different resistance mechanisms on this process 2) understand how to administer these drugs in combination in the HFIM to optimally suppress resistance emergence 3) validate the in vitro findings from the HFIM in a mouse model of Pseudomonas aeruginosa pneumonia.