Cambridge Healthtech Instituteの第5回年次会議
Encoded Libraries for Drug Discovery
2023年4月12 - 13日
Registration Open12:00 pm
Dessert Break in the Exhibit Hall with Poster Viewing12:45 pm
Welcome Remarks1:30 pm
DNA-ENCODED LIBRARY INNOVATIONS
FEATURED PRESENTATION: Accelerated Screening and Optimization of DEL-Hits: Cleavable Linker Platform (CLiP)
In recent years, we have seen an increase in demand for screening our DNA-Encoded Libraries (DELs) for hit identification across the Pfizer portfolio. To increase efficiency of these campaigns, we optimized our DEL-hit follow-up platform to enable both on-DNA and off-DNA hit confirmation in a seamless manner using cleavable linkers (DEL-CLiP). This talk will detail the operation and application of DEL-CLiP highlighting its impact on expediting the hit-to-lead optimization process.
Activity-Based and Cellular Analysis Technology for Encoded Libraries
I provide updates on activity-based DEL technology, which interfaces solid-phase “OBOC” libraries with HTS-style activity assay. Our recent efforts focus on three thematic areas: 1) the discovery of translation modulators as a general strategy for interrogating the proteome via one universal biochemical activity assay, 2) development of pharmacokinetic assays for analyzing “beyond Rule of 5” libraries, and 3) novel 3D tissue culture strategies to enable cell-based DEL screening.
Refreshment Break in the Exhibit Hall with Poster Viewing3:10 pm
Using DNA-Encoded Libraries to Find Potent and Selective Inhibitors to Phosphodiesterases
Phosphodiesterases (PDEs) play critical roles in cellular signaling, making them desirable therapeutic targets. Finding isoform-specific inhibitors can be essential to limiting adverse side effects in drug discovery. DEL platforms allow for simultaneous interrogation of numerous conditions in parallel making it an ideal platform to find selectivity. This presentation will highlight how we leveraged the Valo DEL platform to find potent and selective inhibitors for a PDE program.
Expanding the Scope of DNA-Encoded Libraries
At Anagenex, we are successfully developing methods via both machine learning and in-lab experimental design. Herein, we will discuss how our new methods have enabled us to leverage both active and inactive molecules towards the drug discovery pipeline as well as establish binding compounds for a wide range of protein classes including DNA-binding proteins.
In-Person Group Discussions5:00 pm
Deploying DEL in Lead Discovery
- Improving “off-DNA” hit confirmation rates-or are they good enough?
- Machine learning with DEL data: is it living up to the hype?
- Could more be happening in the pre-competitive space?
- DEL libraries: partnering vs. in-house
Close of Day5:45 pm
Registration Open7:15 am
Diversity in Chemistry Breakfast Discussion (Sponsorship Opportunity Available)7:45 am
Diversity in Chemistry beyond Molecules: Gender and More
We encourage all to attend this moderated, audience-interactive discussion session. When it comes to increasing diversity among scientists, there continues to be a drop-off as one moves higher in leadership. Where do systemic challenges remain, what is your experience, and how can we continue to equalize the system?
Topics may include below, but will be guided by audience input:
- Where does the 'drop-off' of women in the chemistry career progression pipeline occur and why?
- How did the pandemic and other sea changes in the past three years bring us closer to or further from equality?
- What issues arose that you thought were solved?
- Diversity in life paths should include us all - how are men and nonbinary scientists being included?
- Intersectionality and equality - what is the experience of women of color, first-generation women scientists, and others?
PLENARY KEYNOTE SESSION
Plenary Keynote Introduction (Sponsorship Opportunity Available)8:35 am
Reflections on a Career as a Medicinal Chemist in Drug Discovery
A successful drug candidate depends on many factors: creativity of scientists involved, effective collaboration and commitment by the team, and the quality of the compound advanced. I reflect on a 40-year career pursuing the discovery of drug candidates designed to address unmet medical need in the cardiovascular, CNS, and viral diseases therapeutic areas and share undervalued strategies and other synthetic chemistry approaches for overcoming specific medicinal chemistry challenges.
Coffee Break in the Exhibit Hall with Poster Viewing9:30 am
NEW DNA-ENCODED LIBRARY APPLICATIONS
Utilizing DEL as a Primary Discovery Engine for Targeted Protein Degradation
Targeted protein degradation has emerged as a promising new modality for addressing previously undruggable targets. DNA-Encoded Libraries offer significant and specific advantages for identifying ligands for bifunctional degraders. Nurix has designed and deployed an integrated DEL discovery pipeline to harness novel ligases and degrade both traditional and challenging targets.
DEL Selections for Molecular Glues
Molecular glues are an established modality for therapeutic intervention. There is high interest in identifying putative molecular glues to modulate protein-protein interactions involved in protein degradation, cellular localization, or activity. DNA-encoded libraries allow for the screening of billions of small molecules as potential molecular glues. These molecular glues are identified via binding enrichment in the presence of both proteins over-enrichment in selections with a single protein.
1859 is transforming how the Life Science industry discovers new medicines by combining custom chemistry capabilities, miniaturized screening technology, and machine learning to generate novel, diverse, and bioactive Lead molecules through iterative drug discovery. With our ability to predict new chemical starting points, our platform can address new therapeutic targets, or find novel matter for those previously explored, to bring new medicines to patients in need.
DNA-Encoded Libraries for the Identification of Covalent Inhibitors
We have designed and synthesized a series of covalent libraries for the identification of the covalent inhibitors. We describe three different proteins for which we identified inhibitors by heterocycle scaffold-based DELs (https://pubs.acs.org/doi/abs/10.1021/acsmedchemlett.2c00127).
Presentation to be Announced12:10 pm
Transition to Lunch12:40 pm
DNA-Encoded library (DEL) screening is now commonly used in the pharmaceutical industry to find novel chemical matter that modulates protein targets of interest. Point-by-point I will discuss the primary challenges and limitations of DEL screening, and concisely state the current abilities of the technology and then its potential.
Refreshment Break in the Exhibit Hall with Poster Awards Announced1:20 pm
Poster Award (Sponsorship Opportunity Available)
ENCODED LIBRARIES FOR ORAL MACROCYCLICS
Discovery and Optimization of an Oral PCSK9 Macrocyclic Inhibitor from mRNA Display Screening
This talk will highlight the discovery efforts from mRNA display screening hits to a tricyclic peptide PCSK9 inhibitor drug candidate, which demonstrated its pharmacodynamic effects similar to the FDA-approved, parenterally dosed anti-PCSK9 mAb, with the advantage of oral administration using lipidic dosing vehicle Labrasol.
Improved DNA-Encoded Libraries for Macrocyclic Peptides
Libraries and Display Selection for Macrocycles with Better Membrane Permeability
Networking Refreshment Break3:35 pm
CHOOSING DEL's GREATEST HITS
Facilitating DEL Hit Triage: Estimating Data Noise Level via Selection Replicate Samples
An approach to estimating data noise level has been developed by including replicate samples in DEL selections. The noise level is seen to be dependent on sequencing depth and specific selection conditions. The estimated noise cutoff can be used to remove compounds with low sequence reads for DEL hit triage, which greatly reduces (>100-fold) challenges encountered in DEL data analysis without impacting interpretation of the results.
Transforming DNA-Encoded Library Screening from Qualitative to Quantitative Outcomes
DEL screening has typically classified enriched compounds as binder/not binder, with a highly variable confirmation rate after off-DNA synthesis. A better understanding of the impacts of selection variables on the output of DEL could improve off-DNA confirmation rates and provide more useful datasets for ML. Here, tool compounds of known different affinities are used to validate selection techniques to improve the quantitative prediction of on-DNA affinity from DEL selections.
Applying Machine Learning to DEL Hit Selection
Automated structure-activity relationship (SAR) analysis has historically relied on clustering, Quantitative-SAR models, or machine learning to identify potent medicines from high-throughput screening assays. We propose a new straightforward scaffold-based approach for identifying enriched chemical series. Our technique picks chemotypes by breaking down hits into a network of scaffold and scaffold fragments, then uses rank-choice voting to select which scaffolds best represent their exemplars and confer enrichment.
Close of Conference5:25 pm