Mechanism of Slow Onset Enzyme Inhibition and Drug Target Residence Time

描述（申请人提供）：许多候选药物在II期临床试验中因缺乏疗效而失败。这种失败发生在所有治疗领域，主要源于体内有效性以及缺乏安全性（毒性）。我们假设使用药物靶向停留时间（tR）测量，以及其他热力学估计化合物的效力，将提高预测药物疗效的能力在体内。由于我们对tR重要性的认识是基于许多药物与其靶点缓慢解离的观察，因此当结合药物药代动力学知识时，证明tR与特定化合物系列内的体内药物活性之间的相关性将允许创建预测药物药效学的数学模型。这一目标是创新的，并将创造一个范式转变，如何信息的相互作用的先导化合物（抑制剂，激动剂，拮抗剂）与他们的目标是既 收集和使用。为了实现这一目标，我们将使用X射线晶体学，定点诱变，化学合成和计算方法的组合。特别是，与时间无关的分子动力学（MD）模拟将有助于解开结合动力学测量所探测的特定原子级相互作用，并将提供动态信息，以填补晶体结构中观察到的稳定状态之间的时间间隙。这将使用来自结核分枝杆菌（mtFabI）和金黄色葡萄球菌（saFabI）的FabI酶来实现，这两者都是临床相关的药物靶标。此外，虽然这两种酶抑制的二苯醚化合物类，和第二个相关系列的基础上吡啶酮，通过相同的两步慢发作诱导配合动力学机制，伴随酶抑制的结构变化不同。因此，我们的目标是确定我们是否可以首先理解，然后合理地调节在两种不同的酶的停留时间，同时保持化合物类恒定。这将提供一个平台，将我们的知识转化为其他系统。在目的1中，我们将阐明mtFabI的时间依赖性抑制的机制。时间依赖性MD模拟和X射线晶体学将用于确定导致最终酶-β-内酰胺酶复合物（E-I *）的过渡态结构，并确定对时间依赖性抑制至关重要的关键相互作用。将设计具有增加的tR值的抑制剂。这将提供一个详细的理解诱导适合绑定机制。在目的2中，我们将确定saFabI的时间依赖性抑制的结构基础。这将使用动力学和结构方法来完成。将合成其他类似物，以询问我们对慢发作saFabI抑制的理解。在目标3中，我们将描述tR，抗生素后效应（PAE）和体内活性之间的关系。PAE是药物暴露和清除后对微生物生长的持续抑制，是微生物学中众所周知且经常观察到的现象，对抗菌药物药代动力学和改进给药方案的开发具有广泛的影响。将在S.金黄色。对活细胞的PAE测量将在药物活性的体外和体内估计之间提供桥梁。这些研究将为在药物发现中使用停留时间提供基础。在更广泛的层面上，我们的研究将深入了解蛋白质构象变化的时间依赖性以及这些变化如何直接与蛋白质功能相关，并将为探索其他系统中时间依赖性酶抑制的结构基础提供平台。证明tR的重要性将导致先导化合物优化的范式转变。 公共卫生相关性：我们假设，除了测量热力学亲和力外，还应使用药物-靶点停留时间（tR），以便沿着药物发现管道推进化合物。这是因为药物浓度在体内不是恒定的。在目前的建议中，我们将阐明调节细菌靶标的药物靶向寿命的分子因素。这些研究将为在药物发现中使用停留时间提供基础。在更广泛的层面上，我们的研究将深入了解蛋白质构象变化的时间依赖性以及这些变化如何直接与蛋白质功能相关，并将为探索其他系统中时间依赖性酶抑制的结构基础提供平台。证明药物-靶标停留时间的重要性将导致先导化合物优化的范式转变。

DESCRIPTION (provided by applicant): Many drug candidates fail due to lack of efficacy in Phase II clinical trials. This failure occurs in all therapeutic areas and primarily stems from poo in vivo efficacy as well as lack of safety (toxicity). We hypothesize that the use of drug-target residence time (tR) measurements, together with other thermodynamic estimates of compound potency, would improve the ability to predict drug efficacy in vivo. Since much of our appreciation for the importance of tR is anecdotally based on the observation that many drugs dissociate slowly from their targets, demonstration of correlations between tR and in vivo drug activity within specific compound series would, when coupled with knowledge of drug pharmacokinetics, allow mathematical models to be created that predict drug pharmacodynamics. This goal is innovative and will create a paradigm shift in how information on the interaction of lead compounds (inhibitors, agonists, antagonists) with their targets is both gathered and used. To meet this goal, we will use a combination of X-ray crystallography, site-directed mutagenesis, chemical synthesis, and computational methods. In particular, time-independent molecular dynamics (MD) simulations will help unravel the specific atomic-level interactions that are probed by the binding kinetics measurements, and will provide dynamic information to fill in the gaps in time between the stable states observed in the crystal structure. This will be accomplished using the FabI enzymes from Mycobacterium tuberculosis (mtFabI) and Staphylococcus aureus (saFabI), both of which are clinically relevant drug targets. In addition, while both enzymes are inhibited by the diphenyl ether compound class, and a second related series based on pyridones, through the same two-step slow-onset induced-fit kinetic mechanism, the structural changes that accompany enzyme inhibition differ. Thus our goal is to determine whether we can first understand and then rationally modulate residence time in two distinct enzymes whilst keeping the compound class constant. This will provide a platform for translating our knowledge to other systems. In Aim 1 we will elucidate the mechanism for the time-dependent inhibition of mtFabI. Time-indendent MD simulations and X-ray crystallography will be used to determine the structure of the transition state leading to the final enzyme-inhibitr complex (E-I*) and to identify key interactions critical for time-dependent inhibition. Inhibitors with increased tR values will be designed. This will provide a detailed understanding of an induced-fit binding mechanism. In Aim 2 we will determine the structural basis for the time-dependent inhibition of saFabI. This will be accomplished using kinetic and structural approaches. Additional analogues will be synthesized to interrogate our understanding of slow-onset saFabI inhibition. In Aim 3 we will delineate the relationship between tR, post-antibiotic effect (PAE) and in vivo activity. The PAE is the persistent suppression of microbial growth following drug exposure and removal, and is a well-known and frequently observed phenomenon in microbiology with widely stated implications for antimicrobial pharmacokinetics and the development of improved dosing regimens. The contribution of tR to PAE and, ultimately, in vivo antibacterial activity will be evaluated in S. aureus. The PAE measurements on live cells will provide a bridge between in vitro and in vivo estimates of drug activity. These studies will provide a foundation for using residence time in drug discovery. At a broader level, our studies will provide insight into the time dependence of conformational changes in proteins and how these relate directly to protein function, and will provide a platform for exploring the structural basis for time-dependent enzyme inhibition in other systems. Demonstrating the importance of tR will lead to a paradigm shift in lead compound optimization. PUBLIC HEALTH RELEVANCE: We hypothesize that drug-target residence time (tR) should be used in addition to measurements of thermodynamic affinity for advancing compounds along the drug discovery pipeline. This is because drug concentration is not constant in vivo. In the current proposal we will elucidate the molecular factors that modulate drug-target lifetime for a bacterial target. These studies will provide a foundation for using residence time in drug discovery. At a broader level, our studies will provide insight into the time dependence of conformational changes in proteins and how these relate directly to protein function, and will provide a platform for exploring the structural basis for time-dependent enzyme inhibition in other systems. Demonstrating the importance of drug-target residence time will lead to a paradigm shift in lead compound optimization.