Jon A. Erickson

The Potent BACE1 Inhibitor LY2886721 Elicits Robust Central Aβ Pharmacodynamic Responses in Mice, Dogs, and Humans

BACE1 is a key protease controlling the formation of amyloid β, a peptide hypothesized to play a significant role in the pathogenesis of Alzheimers disease (AD). Therefore, the development of potent and selective inhibitors of BACE1 has been a focus of many drug discovery efforts in academia and industry. Herein, we report the nonclinical and early clinical development of LY2886721, a BACE1 active site inhibitor that reached phase 2 clinical trials in AD. LY2886721 has high selectivity against key off-target proteases, which efficiently translates in vitro activity into robust in vivo amyloid β lowering in nonclinical animal models. Similar potent and persistent amyloid β lowering was observed in plasma and lumbar CSF when single and multiple doses of LY2886721 were administered to healthy human subjects. Collectively, these data add support for BACE1 inhibition as an effective means of amyloid lowering and as an attractive target for potential disease modification therapy in AD.

Structure-guided expansion of kinase fragment libraries driven by support vector machine models

This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amount of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addition, a broad diversity of X-ray structure information is needed for binding mode prediction. This is important for scaffold and substituent site selection. Borrowing from FBDD, the process involves fragmentation of known actives, proposition of binding mode hypotheses for the fragments, and model-driven recombination using a pharmacophore derived from known kinase inhibitor structures. The support vector machine method, using Merck atom pair derived fingerprint descriptors, was used to build models from activity from 6 kinase assays. These models were qualified prospectively by selecting and testing compounds from the internal compound collection. Overall hit and enrichment rates of 82% and 2.5%, respectively, qualified the models for use in library design. Using the process, 7 novel libraries were designed, synthesized and tested against these same 6 kinases. The results showed excellent results, yielding a 92% hit rate for the 179 compounds that made up the 7 libraries. The results of one library designed to include known literature compounds, as well as an analysis of overall substituent frequency, are discussed.

Biochemical and kinetic characterization of BACE1: investigation into the putative species-specificity for β- and β′-cleavage sites by human and murine BACE1

β‐amyloid peptides (Aβ) are produced by a sequential cleavage of amyloid precursor protein (APP) by β‐ and γ‐secretases. The lack of Aβ production in beta‐APP cleaving enzyme (BACE1)–/– mice suggests that BACE1 is the principal β‐secretase in mammalian neurons. Transfection of human APP and BACE1 into neurons derived from wild‐type and BACE1–/– mice supports cleavage of APP at the canonical β‐secretase site. However, these studies also revealed an alternative BACE1 cleavage site in APP, designated as β′, resulting in Aβ peptides starting at Glu11. The apparent inability of human BACE1 to make this β′‐cleavage in murine APP, and vice versa, led to the hypothesis that this alternative cleavage was species‐specific. In contrast, the results from human BACE1 transgenic mice demonstrated that the human BACE1 is able to cleave the endogenous murine APP at the β′‐cleavage site. To address this discrepancy, we designed fluorescent resonance energy transfer peptide substrates containing the β‐ and β′‐cleavage sites within human and murine APP to compare: (i) the enzymatic efficiency; (ii) binding kinetics of a BACE1 active site inhibitor LY2039911; and (iii) the pharmacological profiles for human and murine recombinant BACE1. Both BACE1 orthologs were able to cleave APP at the β‐ and β′‐sites, although with different efficiencies. Moreover, the inhibitory potency of LY2039911 toward recombinant human and native BACE1 from mouse or guinea pig was indistinguishable. In summary, we have demonstrated, for the first time, that recombinant BACE1 can recognize and cleave APP peptide substrates at the postulated β′‐cleavage site. It does not appear to be a significant species specificity to this cleavage.

A comparative molecular field analysis study of obtusifoliol 14α-methyl demethylase inhibitors †

This report describes the development of a Comparative Molecular Field Analysis (CoMFA) model from a set of obtusifoliol 14α-methyl demethylase (DM) inhibitors to aid in the design of herbicides targeting sterol biosynthesis. CoMFA is a three-dimensional (3-D) quantitative structure–activity relationship (QSAR) method that is useful in the probing of receptor binding sites when experimental structure data are unavailable. Conformational analysis and SAR of some rigid and active analogs were used to build the initial model using the active analog hypothesis. The model was subsequently used to design compounds that retain the active site shape requirements, but incorporate physical properties that favor soil-applied herbicidal action. In addition, a second-generation CoMFA model incorporating the newly designed inhibitors was developed and represents the current understanding of the DM binding site. This model was derived from a pharmacophore developed from two methods, the active analog approach as well as from the Catalyst program. The fact that two independent methods produced a similar pharmacophore strengthens the validity of the model. © 1999 Society of Chemical Industry

Predicting the Accuracy of Ligand Overlay Methods with Random Forest Models

The accuracy of binding mode prediction using standard molecular overlay methods (ROCS, FlexS, Phase, and FieldCompare) is studied. Previous work has shown that simple decision tree modeling can be used to improve accuracy by selection of the best overlay template. This concept is extended to the use of Random Forest (RF) modeling for template and algorithm selection. An extensive data set of 815 ligand-bound X-ray structures representing 5 gene families was used for generating ca. 70,000 overlays using four programs. RF models, trained using standard measures of ligand and protein similarity and Lipinski-related descriptors, are used for automatically selecting the reference ligand and overlay method maximizing the probability of reproducing the overlay deduced from X-ray structures (i.e., using rmsd < or = 2 A as the criteria for success). RF model scores are highly predictive of overlay accuracy, and their use in template and method selection produces correct overlays in 57% of cases for 349 overlay ligands not used for training RF models. The inclusion in the models of protein sequence similarity enables the use of templates bound to related protein structures, yielding useful results even for proteins having no available X-ray structures.

Fragment-based design of kinase inhibitors: a practical guide.

Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.

Theoretical Study of Protein–Ligand Interactions Using the Molecules-in-Molecules Fragmentation-Based Method

We have recently significantly expanded the applicability of our Molecules-in-Molecules (MIM) fragmentation method to large proteins by developing a three-layer model (MIM3) in which an accurate quantum-mechanical method is used in conjunction with a cost-effective, dispersion-corrected semiempirical model to overcome previous computational bottlenecks. In this work, we develop MIM3 as a structure-based drug design tool by application of the methodology for the accurate calculation of protein-ligand interaction energies. A systematic protocol is derived for the determination of the geometries of the protein-ligand complexes and to calculate their accurate interaction energies in the gas phase using MIM3. We also derive a simple and affordable procedure based on implicit solvation models and the ligand solvent-accessible surface area to approximate the ligand desolvation penalty in gas-phase interaction energy calculations. We have carefully assessed how closely such interaction energies, which are based on a single protein-ligand conformation, display correlations with the experimentally determined binding affinities. The performance of MIM3 was evaluated on a total of seven data sets comprising 89 protein-ligand complexes, all with experimentally known binding affinities, using a binding pocket involving a quantum region ranging in size from 250 to 600 atoms. The dispersion-corrected B97-D3BJ density functional, previously known to perform accurately for calculations involving non-covalent interactions, was used as the target level of theory for this work, with dispersion-corrected PM6-D3 as the semiempirical low level to incorporate the long-range interactions. Comparing directly to the experimental binding potencies, we obtain impressive correlations over all seven test sets, with an R2 range of 0.74-0.93 and a Spearman rank correlation coefficient (ρ) range of 0.83-0.93. Our results suggest that protein-ligand interaction energies are useful in predicting binding potency trends and validate the potential of MIM3 as a quantum-chemical structure-based drug design tool.