Find out about new approaches and technologies being applied to the search for future therapeutics.
This free to attend, one day symposium, is aimed at scientists from pharmaceutical, biotechnology, agrochemical, flavor and fragrance organizations, not-for-profits and academia who wish to get a broad appreciation of the latest advances in drug discovery delivered by key scientists and thought leaders from leading organizations.
If you are unable to attend this symposium, you or your colleagues may be interested in the events taking place on:
|10:00||Welcome from meeting chairman||Matt Danielson, Lilly|
|10:05||Keynote: In Silico and In Vitro Assessment of OATP1B1 Inhibition in Drug Discovery||Matt Danielson, Lilly|
|10:50||Bigfoot, the Loch Ness Monster, and Halogen Bonds: Separating Rumors from Reality in Molecular Design||Dave Lawson, Takeda|
|11:50||Capturing and Applying Knowledge to Guide Compound Optimization||Matthew Segall, Optibrium|
|12:20||Imputation of Protein Activity Data Using Deep Learning||Tom Whitehead, Intellegens|
|13:45||Using Water Swap to Assess Target Druggability||Adam Kallel, Retrophin|
|14:15||Electrostatic Complementarity™ as a New Approach to Visualize and Predict Activity||Sylvie Sciammetta, Cresset|
|15:15||Managing External Chemistry for Effective Support of Drug Projects||David Hollinshead, Elixir Software and Andrew Griffin, Praxis Precision Medicines|
|15:45||The CFL: Maximizing Fragment-Target Interaction Space by Design with a Minimal Screening Set||Warren Wade, BioBlocks|
|16:15||Closing remarks||Matt Danielson, Lilly|
The organic anion-transporting polypeptide 1B1 transporter belongs to the solute carrier superfamily and is highly expressed at the basolateral membrane of hepatocytes. Several clinical studies showed drug-drug interactions involving OATP1B1 thereby prompting the International Transporter Consortium to label OATP1B1 as a critical transporter that can influence a compound’s disposition. To examine OATP1B1 inhibition early in the drug discovery process, we established a medium-throughput concentration-dependent OATP1B1 assay. In order to create an in silico OATP1B1 inhibition model, deliberate in vitro assay enrichment was performed with publically known OATP1B1 inhibitors, non-inhibitors, and compounds from our own internal chemistry.
In this presentation the strategy and results of assay enrichment, along with the performance of subsequent QSAR model(s) will be discussed. In addition to the in silico OATP1B1 inhibition QSAR models, physicochemical trends were also examined to provide structure activity relationship guidance to early discovery teams.
Structure-based drug design (SBDD) allows for fast, hypothesis-driven potency optimization during the drug discovery process. Hydrogen bonds, van der Waals interactions, and solvent displacement have long been considered the cornerstones for creating potent molecules via SBDD and are relatively well understood. Recently, however, several protein-ligand interactions – such as halogen ‘bonds’, Dunitz interactions, and sulfur-sulfur interactions – have gained notoriety disproportional to their contribution to binding energy. While these lower energy interactions can sometimes lead to modest potency gains, the nature of the interactions are not intuitive and are often inaccurately portrayed in the literature. A proper understanding of these interactions facilitates better decision making when considering molecules for synthesis in structurally-enabled drug discovery projects.
Compound design requires a combination of knowledge and expertise from different perspectives: Understanding of structure-activity relationships (SAR), based on data from previously studied compounds; expertise from diverse fields to define the multi-parameter optimization (MPO) objectives of a project; and knowledge of synthetic strategies that may be applicable to create the next rounds of compounds for investigation. All of these forms of knowledge can be captured and applied computationally: Machine learning methods can generate quantitative structure-activity relationship (QSAR) models to predict the properties of novel, virtual compounds; MPO methods capture the desired property criteria for a successful compound for a specific project and rigorously prioritize ideas for consideration; and, optimization strategies can be captured as structural transformations that reflect steps made in previous chemistry projects[3,4].
In this presentation, we will describe these methods and illustrate how they can be seamlessly combined to rigorously explore new, relevant compound ideas and prioritize those most likely to achieve a project objective. This approach can help to stimulate the search for new optimization strategies and explore a much broader range of compounds than could be achieved based on a single chemist’s or even a project team’s experience. Example applications include the optimization of compounds with a desired polypharmacology or selectivity profile and exploration of lead hopping strategies to overcome pharmacokinetic issues, while maintaining target potency.
 O. Obrezanova, M.D. Segall, J. Chem. Inf. Model. (2010) 50 (6), pp. 1053-1061
 M.D. Segall, Curr. Pharm. Des. (2012) 18(9) pp. 1292-1310
 M.D. Segall et al., J. Chem. Inf. Model. (2011) 51(11) pp. 2967-2976
 I. Ujváry, J. Hayward, In N. Brown ed., Bioisosteres in Medicinal Chemistry (2012)
The knowledge of compound bioactivity data against drug targets underpins the discovery of new drugs. However, databases are currently sparse; for example, the ChEMBL dataset is just 0.05% compete and the sparsity of data in proprietary pharma databases is similar. We will describe a novel deep learning algorithm to capture correlations within protein activity data, as well as between molecular descriptors and protein activities, to impute the missing activities. Unlike many deep learning methods, this approach is capable of being trained using sparse and variable data, typical of those available in drug discovery. We will present examples illustrating the application of these deep learning networks to impute missing activities in the sparse input data, as well as to make predictions for new compounds based on molecular descriptors alone. The results will be compared with conventional machine learning methods such as random forests and Gaussian processes.
Only 2% of human proteins interact with approved drugs. It is also estimated that only about 15% of human proteins are disease modifying and only about 12% are druggable (with no correlation between the two sets). Consequently, only about 2% of disease modifying proteins would be druggable. I used Water Swap to assess a number of proteins that fell in to the category of having ligands that advanced into clinically evaluated drugs and those that had been studied and were not able to advance preclinical ligands into clinical evaluation. Proteins that had clinically advanced ligands were found to have pockets that had a favorable DG for ligand binding as opposed to water. Those Proteins that had not yielded clinically advanced ligands had pockets that favored water rather that ligand. Water Swap is a good method to evaluate protein druggability.
Electrostatic interactions between small molecules and their respective receptors are a key contributor to the free energy of binding. Assessing the electrostatic match between ligands and binding pockets provides therefore important insights into ligand binding and molecule design.
The polarizable XED force field is an excellent base for calculating electrostatic properties due to its description of anisotropic atomic charge distributions and relatively modest computational costs. By computing electrostatic potentials for both ligand and protein with XED, the Electrostatic Complementarity™ (EC) of complexes can be calculated and translated into a simple coloring scheme on the ligand and protein surface.
We present the theoretical background of our EC calculations and discuss their application to mGluR5, XIAP, and other selected targets. We show how using EC can inform SAR interpretation and new molecule design.
The pharmaceutical and biotechnology R&D model for in-house drug projects, and their chemistry components, has evolved over recent decades. The drivers for conducting projects through collaborations using dispersed global teams are readily apparent. Yet various challenges in managing the essential, iterative design – make – test – analyze cycle, have persisted.
This joint presentation highlights the nature of the challenges faced, and provides a case history of how iterative design – make – test – analyze cycles can be efficiently delivered through an intuitive, real-time software platform which enables a co-ordinated chemistry contribution to drug projects.
As part of our effort to better address target-ligand interaction space with fragments, BioBlocks has developed the Comprehensive Fragment Library (CFL). This library of small, rigid, medicinally interesting fragments was selected to provide multiple independent starting points from each hit. Using advanced clustering methods developed in a collaboration with Cresset, the design of the CFL connects each fragment hit to thousands of potential analogs, most of which are contained within our proprietary virtual compound database. These connections enable maximal representation of fragment structure space by a small physical library of non-redundant chemotypes. Based on this analysis, the CFL represents the vast majority of commercially available fragment motifs in a single 96-well screening plate. Chemical synthesis of library members is ongoing to cover underutilized target interaction motifs, where each synthetic sequence represents a new fragment family.
Screening the CFL against multiple targets produced fragment hit sets with hit rates near 5% and minimal overlap between target classes, consistent with CFL design goals. Hit sets generally include both commercially available and synthetic fragments. Connections from each fragment hit to the whole CFL generated fragment analogs, which in turn led to compounds active in biochemical assays. A series from our first project has now progressed to lead optimization, with submicromolar cellular activity and promising IP.
Details of the library design, results and hit set overlap between target classes will be discussed. We will propose the estimated minimal library size required to evaluate target druggability.
Matt Danielson earned his PhD in Medicinal Chemistry and Molecular Pharmacology from Dr. Markus Lill’s group at Purdue University where he studied molecular dynamics and docking in CYP enzymes. After completing his PhD, Matt joined Dr. Michel Sanner and the AutoDock group at the Scripps Research Institute for his postdoc and worked on incorporating protein flexibility into molecular docking. In 2013 Matt joined the computational ADME group at Eli Lilly where he has supported drug discovery efforts in several therapeutic areas.
Following a Ph.D with Tim Gallagher and postdoctoral studies with Professor Stephen Hanessian, Andrew moved to the AstraZeneca (Montreal) where he held the position of Associate Director in Medicinal Chemistry. He led multiple chemistry teams within AZ and with external chemistry partners, delivering lead generation projects and clinical candidates. Throughout his career Andrew has maintained a keen interest in using informatics to aid drug discovery and was member of the AZ Predictive Chemistry Network as well as leading a global initiative that provided a platform to facilitate external chemistry and testing. Since 2012 he has become a medicinal chemistry consultant and has been working with Praxis Precision Medicines since its creation.
David is Technical Director at Elixir Software Ltd with a primary focus customizing software applications matched to customer needs. Before Elixir was founded in 2012, David spent 28 years within AstraZeneca, initially as a medicinal chemist in the Infection, Cardiovascular & Metabolism Research Areas, then subsequently building technology platforms for Discovery and subsequently Global Process R&D. His roles at AstraZeneca have predicated upon external collaboration for successful project delivery.
Adam brings over 25 years of computational chemistry experience to Retrophin. He received his Ph.D. in theoretical organic chemistry from UCLA, working with noted theoretical organic chemist Kendall N. Houk. He has authored multiple patents, 10 at Vertex Pharmaceuticals, with more being written. Adam has also supported many basic research programs that have led to pre-clinical and clinical programs. At Ligand Pharmaceuticals he led the Thromcytopenia program leading to LGD4665(Totrombopag). His expertise includes drug discovery in women’s health, anti-infective, metabolic diseases, neurological diseases, oncology and hematopoietic disorders. Adam is also considered to be an expert in the statistical analysis and graphical display of biological data.
David Lawson is a computational chemist at Takeda California. While at Takeda, he has specialized in enabling and leading SBDD and FBDD projects from the target hunting phase through to the clinic. Dave is a recognized expert in protein kinases with an extensive list of patents and publications in the field. Prior to joining Takeda, Dave was a computational chemist at Vitae Pharmaceuticals helping to build their proprietary computational drug discovery platform, Contour. Before entering the pharmaceutical industry, Dave completed his Ph.D. degree in Ralph Yount’s lab at Washington State University investigating chemomechanical transduction of the motor protein myosin and held a postdoctoral position with Keith Dunker studying the functional role of protein disorder.
Following a PhD in organic chemistry at the University of Leeds in the UK, Sylvie took a postdoctoral research fellowship at the University of Geneva in Switzerland. In 2000 Sylvie joined BioFocus (now part of Charles River Laboratories) where she led and advanced multiple medicinal chemistry projects on GPCRs to successful collaboration milestones. Sylvie joined Cresset in 2017 where, as an application scientist, she provides scientific and technical support to customers in North America.
Matt is CEO of Optibrium. He has a Master of Science in computation from the University of Oxford and a Ph.D. in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium, which develops software for small molecule design, optimisation and data analysis. Matt has published over 30 peer-reviewed papers and book chapters on computational chemistry, cheminformatics and drug discovery.
Dr. Wade joined the management team at BioBlocks, Inc. in April 2008 to manage BioBlocks’ Lead Optimization collaborations. Dr. Wade is the chief architect of Bioblocks’ Leap-to-Lead™ platform and brings lead chemistry innovation, cheminformatics and the application of new technologies for lead discovery and optimization. Previously, he spent 7 years at Neurocrine Biosciences as a Director of Medicinal Chemistry where he advanced two small molecule candidates for Investigational New Drug (IND) filings. Prior to this, Dr. Wade spent 8 years Abbott where he was involved in developing the Company’s combinatorial chemistry program to maturity, and applied parallel synthesis and solid phase synthesis for medicinal chemistry. Dr. Wade is an author of over 25 publications and a co-inventor on more than 15 issued US patents. He has Ph.D. in Bioorganic Chemistry from the California Institute of Technology (Caltech) and a BA in Chemistry from Cornell University.
Tom joined Intellegens straight from his PhD in theoretical physics at the University of Cambridge, and is now leading the application of Intellegens’ novel deep learning approaches to a wide variety of industrial applications. Tom is interested in developing machine learning approaches to solve previously intractable problems, in drug discovery and elsewhere.
The Alexandria, 10996 Torreyana Road, San Diego, CA 92121