Discovery of potent azaindazole leucine-rich repeat kinase 2 (LRRK2) inhibitors possessing a key intramolecular hydrogen bond - Part 2.

The discovery of disease-modifying therapies for Parkinson's Disease (PD) represents a critical need in neurodegenerative medicine. Genetic mutations in LRRK2 are risk factors for the development of PD, and some of these mutations have been linked to increased LRRK2 kinase activity and neuronal toxicity in cellular and animal models. As such, research towards brain-permeable kinase inhibitors of LRRK2 has received much attention. In the course of a program to identify structurally diverse inhibitors of LRRK2 kinase activity, a 5-azaindazole series was optimized for potency, metabolic stability and brain penetration. A key design element involved the incorporation of an intramolecular hydrogen bond to increase permeability and potency against LRRK2. This communication will outline the structure-activity relationships of this matched pair series including the challenge of obtaining a desirable balance between metabolic stability and brain penetration.

Development of a series of aryl pyrimidine kynurenine monooxygenase inhibitors as potential therapeutic agents for the treatment of Huntington's disease.

We report on the development of a series of pyrimidine carboxylic acids that are potent and selective inhibitors of kynurenine monooxygenase and competitive for kynurenine. We describe the SAR for this novel series and report on their inhibition of KMO activity in biochemical and cellular assays and their selectivity against other kynurenine pathway enzymes. We describe the optimization process that led to the identification of a program lead compound with a suitable ADME/PK profile for therapeutic development. We demonstrate that systemic inhibition of KMO in vivo with this lead compound provides pharmacodynamic evidence for modulation of kynurenine pathway metabolites both in the periphery and in the central nervous system.

Discovery of highly potent, selective, and brain-penetrant aminopyrazole leucine-rich repeat kinase 2 (LRRK2) small molecule inhibitors.

Leucine-rich repeat kinase 2 (LRRK2) has drawn significant interest in the neuroscience research community because it is one of the most compelling targets for a potential disease-modifying Parkinson's disease therapy. Herein, we disclose structurally diverse small molecule inhibitors suitable for assessing the implications of sustained in vivo LRRK2 inhibition. Using previously reported aminopyrazole 2 as a lead molecule, we were able to engineer structural modifications in the solvent-exposed region of the ATP-binding site that significantly improve human hepatocyte stability, rat free brain exposure, and CYP inhibition and induction liabilities. Disciplined application of established optimal CNS design parameters culminated in the rapid identification of GNE-0877 (11) and GNE-9605 (20) as highly potent and selective LRRK2 inhibitors. The demonstrated metabolic stability, brain penetration across multiple species, and selectivity of these inhibitors support their use in preclinical efficacy and safety studies.

Discovery of a Highly Selective, Brain-Penetrant Aminopyrazole LRRK2 Inhibitor.

The modulation of LRRK2 kinase activity by a selective small molecule inhibitor has been proposed as a potentially viable treatment for Parkinson's disease. By using aminopyrazoles as aniline bioisosteres, we discovered a novel series of LRRK2 inhibitors. Herein, we describe our optimization effort that resulted in the identification of a highly potent, brain-penetrant aminopyrazole LRRK2 inhibitor (18) that addressed the liabilities (e.g., poor solubility and metabolic soft spots) of our previously disclosed anilino-aminopyrimidine inhibitors. In in vivo rodent PKPD studies, 18 demonstrated good brain exposure and engendered significant reduction in brain pLRRK2 levels post-ip administration. The strategies of bioisosteric substitution of aminopyrazoles for anilines and attenuation of CYP1A2 inhibition described herein have potential applications to other drug discovery programs.

Discovery of highly potent, selective, and brain-penetrable leucine-rich repeat kinase 2 (LRRK2) small molecule inhibitors.

There is a high demand for potent, selective, and brain-penetrant small molecule inhibitors of leucine-rich repeat kinase 2 (LRRK2) to test whether inhibition of LRRK2 kinase activity is a potentially viable treatment option for Parkinson's disease patients. Herein we disclose the use of property and structure-based drug design for the optimization of highly ligand efficient aminopyrimidine lead compounds. High throughput in vivo rodent cassette pharmacokinetic studies enabled rapid validation of in vitro-in vivo correlations. Guided by this data, optimal design parameters were established. Effective incorporation of these guidelines into our molecular design process resulted in the discovery of small molecule inhibitors such as GNE-7915 (18) and 19, which possess an ideal balance of LRRK2 cellular potency, broad kinase selectivity, metabolic stability, and brain penetration across multiple species. Advancement of GNE-7915 into rodent and higher species toxicity studies enabled risk assessment for early development.

Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery.

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of in silico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether in silico, in vitro or in vivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structure-activity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.

The design and preparation of metabolically protected new arylpiperazine 5-HT1A ligands.

New arylpiperazines related to buspirone, gepirone and NAN-190 were designed and screened in silico for their 5-HT(1A) affinity and potential sites of metabolism by human cytochrome p450 (CYP3A4). Modifications to these structures were assessed in silico for their influence on both 5HT(1A) affinity and metabolism. Selected new molecules were synthesized and purified in a parallel chemistry approach to determine structure activity relationships (SARs). The resulting molecules were assessed in vitro for their 5HT(1A) affinity and half-life in a heterologously expressed human CYP3A4 assay. Molecular features responsible for 5-HT(1A) affinity and CYP3A4 stability are described.

In silico prediction of ADME properties: are we making progress?

The use of computational models in the prediction of ADME properties of compounds is growing rapidly in drug discovery as the benefits they provide in throughput and early application in drug design are realized. In addition, there is an increasing range of models available, as model builders have advanced from the first-generation' models, which were predominantly focused on solubility, absorption and metabolism, to include models of other optimization factors such as HERG, glucuronyl transferase and drug transport proteins. This widening interest is now driving demand for developments in the component elements of model building, namely higher quality datasets, better molecular descriptors and more computational power, and the quality of models is improving rapidly as a consequence. Models generally have very high throughput and can be used with virtual structures. As a consequence, they can generate large quantities of data on large numbers of compounds. Thus, one consequence of the wider choice of models, coupled with their high throughput, is a growing need to integrate their output into collective analyses of molecules against pre-set criteria. This article comments on some of the recent developments in ADME models, and highlights the importance of integrating the data to aid compound selection in drug discovery projects.

The emerging importance of predictive ADME simulation in drug discovery.

Absorption, distribution, metabolism and excretion (ADME) studies, are widely used in drug discovery to optimize the balance of properties necessary to convert leads into good medicines. However, throughput using traditional methods is now too low to support recent developments in combinatorial and library chemistry, which have generated many more molecules of interest. To the more enlightened practitioners of ADME science, this situation is generating both the problem and the solution: an opportunity is now forming, with the use of higher throughput ADME screens and computational models, to access this wide chemical diversity and to dissect out the rules that dictate a pharmacokinetic or metabolic profile. In the future we could see ADME properties designed-in from the first principles in drug design.