DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds. We train models using only DEL selection data and apply automated or automatable filters with chemist review restricted to the removal of molecules with potential for instability or reactivity. We validate this approach with a large prospective study (nearly 2000 compounds tested) across three diverse protein targets: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). The approach is effective, with an overall hit rate of ∼30% at 30 µM and discovery of potent compounds (IC50 <10 nM) for every target. The model makes useful predictions even for molecules dissimilar to the original DEL and the compounds identified are diverse, predominantly drug-like, and different from known ligands. Collectively, the quality and quantity of DEL selection data; the power of modern machine learning methods; and access to large, inexpensive, commercially-available libraries creates a powerful new approach for hit finding.
A chemical ligation method for construction of DNA-encoded small-molecule libraries has been developed. Taking advantage of the ability of the Klenow fragment of DNA polymerase to accept templates with triazole linkages in place of phosphodiesters, we have designed a strategy for chemically ligating oligonucleotide tags using cycloaddition chemistry. We have utilized this strategy in the construction and selection of a small molecule library, and successfully identified inhibitors of the enzyme soluble epoxide hydrolase.
Antisense oligonucleotides (ASOs) and small interfering RNA (siRNA) promise specific correction of disease-causing gene expression. Therapeutic implementation, however, has been forestalled by poor delivery to the appropriate tissue, cell type, and subcellular compartment. Topical administration is considered to circumvent these issues. The availability of inhalation devices and unmet medical need in lung disease has focused efforts in this tissue. We report the development of a novel cell sorting method for quantitative, cell type-specific analysis of siRNA, and locked nucleic acid (LNA) ASO uptake and efficacy after intratracheal (i.t.) administration in mice. Through fluorescent dye labeling, we compare the utility of this approach to whole animal and whole tissue analysis, and examine the extent of tissue distribution. We detail rapid systemic access and renal clearance for both therapeutic classes and lack of efficacy at the protein level in lung macrophages, epithelia, or other cell types. We nevertheless observe efficient redirection of i.t. administered phosphorothioate (PS) LNA ASO to the liver and kidney leading to targeted gene knockdown. These data suggest delivery remains a key obstacle to topically administered, naked oligonucleotide efficacy in the lung and introduce inhalation as a potentially viable alternative to injection for antisense administration to the liver and kidneys.
We have identified and characterized novel potent inhibitors of Bruton's tyrosine kinase (BTK) from a single DNA-encoded library of over 110 million compounds by using multiple parallel selection conditions, including variation in target concentration and addition of known binders to provide competition information. Distinct binding profiles were observed by comparing enrichments of library building block combinations under these conditions; one enriched only at high concentrations of BTK and was competitive with ATP, and another enriched at both high and low concentrations of BTK and was not competitive with ATP. A compound representing the latter profile showed low nanomolar potency in biochemical and cellular BTK assays. Results from kinetic mechanism of action studies were consistent with the selection profiles. Analysis of the co-crystal structure of the most potent compound demonstrated a novel binding mode that revealed a new pocket in BTK. Our results demonstrate that profile-based selection strategies using DNA-encoded libraries form the basis of a new methodology to rapidly identify small molecule inhibitors with novel binding modes to clinically relevant targets.
Bispecific degraders (PROTACs) of ERα are expected to be advantageous over current inhibitors of ERα signaling (aromatase inhibitors/SERMs/SERDs) used to treat ER+ breast cancer. Information from DNA-encoded chemical library (DECL) screening provides a method to identify novel PROTAC binding features as the linker positioning, and binding elements are determined directly from the screen. After screening ∼120 billion DNA-encoded molecules with ERα WT and 3 gain-of-function (GOF) mutants, with and without estradiol to identify features that enrich ERα competitively, the off-DNA synthesized small molecule exemplar 7 exhibited nanomolar ERα binding, antagonism, and degradation. Click chemistry synthesis on an alkyne E3 ligase engagers panel and an azide variant of 7 rapidly generated bispecific nanomolar degraders of ERα, with PROTACs 18 and 21 inhibiting ER+ MCF7 tumor growth in a mouse xenograft model of breast cancer. This study validates this approach toward identifying novel bispecific degrader leads from DECL screening with minimal optimization.
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