The course describes SBDD workflows in drug discovery projects and encompasses a range of topics from pharmacophore query generation to protein-ligand interaction fingerprints. More specifically, the course will cover the application of pharmacophores in the context of protein-ligand docking, scaffold replacement and R-group screening. A method for querying a 3D project database will also be presented along with the generation and analysis of protein- ligand interaction fingerprints (PLIF).
Ligand-Based Drug Design and SAR Analysis R-Group Profiles and Analysis / MOEsaic / MMP Analysis / Descriptor Calculations / Conformational Searching / Molecular Alignments / Pharmacophore Modeling and Searching / Diversity Analysis
The course covers essential in silico methods needed for guiding drug discovery projects in the absence of a protein structure. Analysis of SAR through R-group profiling and matched molecular pairs (MMP) analysis using MOEsaic to determine relationships among a chemical series are examined. Molecular descriptor calculations and their application for determining property correlations along with diversity analysis are described. Molecular alignments and conformational analyses of a congeneric series are explored to assess the impact of ligand substituents. An approach for developing pharmacophore queries is discussed. Management and manipulation of MOE databases are also covered.
Registration (check-in and badge pick-up)
Opening Remarks Alain Deschenes, Director of Scientific Services, Chemical Computing Group
Of solvated pockets, magic methyl groups & compchem methods to tackle them
Torsten Herbertz, Director, Computational Chemistry, FORMA Therapeutics
The presentation will relate key learnings from a BET bromodomain inhibitor project. Examples discussed include subtleties when key SAR is or is not transferable to other scaffolds, when water molecules are part of protein and not in play for ligand design & which computational methods are suitable to predict this qualitatively/quantitatively. In addition, an example will be presented that highlights how simple substitution can challenge conformational sampling techniques.
Discovery of a GPR40 Superagonist – the Impact of α-Fluorination on the Aryl Propionic Acid
Hui Huang, Senior Principal Scientist, Janssen
GPR40 is a G-protein-coupled receptor which mediates fatty acid-induced glucose-stimulated insulin secretion from pancre-atic beta cells and incretion release from enteroendocrine cells of the small intestine. GPR40 full agonists exhibit superior glucose lowering compared to partial agonists in pre-clinical species due to increased insulin and GLP-1 secretion, with the added benefit of promoting weight loss. In our search for potent GPR40 full agonists, we discovered a superagonist which displayed excellent in vitro potency and superior efficacy in the Gαs-mediated signaling pathway. With a methyl group and a fluorine atom substituted at the α-C of the carboxylic acid group, our superagonist is not only highly efficacious in lowering glucose and body weight in rodent models, but also has a low DILI risk due to its stable acylglucuronide metabolite.
Structure Based Optimization Of TYK2 Pseudokinases Inhibitors From A DNA Encoded Library
Gang Yao Drug Design and Selection, Medicinal Science & Technology, GlaxoSmithKline
200 Cambridge Park Drive, Cambridge, Massachusetts, 02140, USA
The JAK-family kinases, including TYK2, are unique in that they possess an ATP-binding JH2 domain in addition to a functional JH1 domain. JH2 ligand are found to negatively regulate activation of JH1 kinase domain. Inhibition of the JH2 domain may offer an unique opportunity to modulate the TYK2 kinase activity with high kinome selectivity.
This talk will describe the structure based optimization of a TYK2 pseudokinase inhibitor identified from a DNA encoded library. The binding mode of the hit molecule was confirmed by X-ray crystallography. Optimization of the initial hit led to the identification of a chemical series with exceptional kinome selectivity, inhibiting the pro-inflammatory cytokines IL-12 and IL-23 whilst sparing JAK2-mediated EPO signaling.
Structure-Based Predictions of CYP Selectivity, Reactivity, and Regioselectivity
Michael Drummond, Scientific Applications Manager, Chemical Computing Group
Cytochrome P450 oxidases (CYPs) are a class of well-known heme-containing enzymes that are responsible for clearing xenobiotics, including drug molecules, through oxidative metabolism. Understanding the interactions between drug molecules and CYPs is therefore critical for evaluating drug efficacy, clearance, toxicity, and drug-drug interactions. Although dozens of crystal structures of the five predominant CYP isoforms have been solved, most modeling tools that predict drug-CYP interactions completely neglect this structural information. Understanding the shape, flexibility, and electrostatic nature of the CYP binding pockets can lead to improved modeling of CYP metabolism vis-à-vis approaches that only consider properties of the small molecules themselves. In this work, both 2D methods and 3D methods are used to predict the isoform selectivity, small molecule reactivity, and regioselectivity of CYPs. The 2D-based methods developed herein are parsimonious yet accurate, and can be used to quickly evaluate selectivity and reactivity. The 3D approach is based on a pharmacophore framework, which provides a rapid and flexible way to predict CYP isoform selectivity and regioselectivity. The modular components of the pharmacophore afford a straightforward means to tailor for a particular problem of interest. Moreover, directly incorporating 3D CYP structures into the models confers unique advantages over 2D-based approaches, such as the ability to distinguish reactivity differences among stereoisomers. Finally, predicted results can be readily visualized in a CYP pocket, and thus potential CYP liabilities are not merely flagged in a binary fashion, but can also be designed against in a structure-based design context – a clear improvement over the pass/fail filtering paradigm prevalent in CYP modeling efforts to date.
MicroCycle: Re-investigating Early Medicinal Chemistry Jonathan Grob, Scientist II, Novartis
The Discovery of an IRAK4 Clinical Candidate from Fragment-Based Drug Design
Frank Lovering, Director, Computational Chemistry, Pfizer
Interleukin-1 Receptor Associated Kinase 4 (IRAK4), is a serine/threonine kinase in the IL-1 receptor and toll-like receptor pathways and is recognized as important in innate immunity. Inhibition of IRAK4 is predicted to be beneficial in the treatment of a number of inflammatory diseases which has led to intense effort across the pharmaceutical industry to identify potent and selective inhibitors suitable for clinical study. The presentation will describe the discovery, structure and profile of clinical candidate PF-06650833 from Pfizer’s IRAK4 project.
Clinical candidate PF-06650833 arose from hits from an NMR-based fragment screen of the Pfizer Global Fragment library. Lead optimization of weakly active fragment hits, by applying structure- and property-based medicinal chemistry design strategies, led to a significant increase in IRAK4 potency and the discovery of a potent, efficient series of IRAK4 inhibitors with broad kinome selectivity and good pharmacokinetic properties. This work culminated in the discovery of PF-06650833 as a potential first-in-class IRAK4 inhibitor for the treatment of inflammatory disease.
Discovery of a Macrocyclic Peptide Inhibitor (BMS-986189) of Programmed Death-Ligand 1 (PD-L1)
Paul Scola, Research Fellow, Bristol Myers Squibb
Macrocyclic peptides were identified as inhibitors of PD-L1 through mRNA display, an in vitro selection technique. These screening leads demonstrated modest in vitro activity in PD-L1 binding assays, while they proved inactive in functional assays. Co-crystal structures of selected analogues with PD-L1 provided insight into the nonbonding interactions between these macrocyclic peptides and the PD-L1 protein. The structure-based insights gleaned enabled the rapid optimization of these macrocycles with respect to PD-L1 inhibitory activity and the mitigation of off-target liabilities identified in early leads. This rational drug design approach led to the discovery of a macrocyclic peptide (BMS-986189) with activity in binding and functional assays comparable to a PD-L1 antibody. Details of these discoveries will be discussed.
Importance of Making Early Dose Predictions and Half-Life Optimization Strategy
Hakan Gunaydin, Principal Scientist, Relay Therapeutics
Preclinical optimization of compounds towards viable drug candidates requires an integrated understanding of properties that impact predictions of the clinically efficacious dose. The importance of optimizing half-life, unbound clearance and potency and how they impact dose predictions will be discussed. Modest half-life improvements for short half-life compounds can significantly lower the efficacious dose. The relationship between dose and half-life is non-linear whereas the relationship between dose and unbound clearance is linear. Due to this difference, dose is more sensitive to changes in half-life than changes in unbound clearance when half-lives are shorter than 2 hours. Matched molecular pair analyses of Merck internal data showed that the strategic introduction of halogens is likely to increase half-life and lower projected human dose despite the increased lipophilicity.