Rapid Discovery of a Novel Series of Abl Kinase Inhibitors by Application of an Integrated Microfluidic Synthesis and Screening Platform

Abstract

Drug discovery faces economic and scientific imperatives to deliver lead molecules rapidly and efficiently. Using traditional paradigms the molecular design, synthesis, and screening loops enforce a significant time delay leading to inefficient use of data in the iterative molecular design process. Here, we report the application of a flow technology platform integrating the key elements of structure-activity relationship (SAR) generation to the discovery of novel Abl kinase inhibitors. The platform utilizes flow chemistry for rapid in-line synthesis, automated purification, and analysis coupled with bioassay. The combination of activity prediction using Random-Forest regression with chemical space sampling algorithms allows the construction of an activity model that refines itself after every iteration of synthesis and biological result. Within just 21 compounds, the automated process identified a novel template and hinge binding motif with pIC50 > 8 against Abl kinase--both wild type and clinically relevant mutants. Integrated microfluidic synthesis and screening coupled with machine learning design have the potential to greatly reduce the time and cost of drug discovery within the hit-to-lead and lead optimization phases.

Active Learning for Computational Chemogenomics

“…As a number of recent studies have indeed validated that the computational chemogenomic concept can lead to prospective discovery of interactions [36][37][87][88], we anticipate that actively learned models will be capable of similar novel discovery [48,49,51] . Given the increasing applicability of chemogenomics to uncover untested ligand-target pairs, many different exciting applications come to mind.…”

Small Random Forest Models for Effective Chemogenomic Active Learning

“…To date, several pro-and retrospective studies report successful applications of AL strategies throughout different fields of research [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. In the area of drug discovery, active learning has been shown to efficiently derive high-performance prediction models based on small subsets of input data [32,34,39,46].…”

“…Furthermore, actively trained models not only reached significantly higher hit rates compared to experimental standards which frequently remain below 1 % in cases of unbiased chemical libraries [34,39,40,[47][48][49], but such models also contributed to successful identification of novel bioactive compounds [33,36,42] and cancer rescue mutants of p53 [31]. Overall, AL approaches bear the potential to improve drug discovery processes by increasing hit rates, reducing the amount of time-and cost-intensive experimentation, and accelerate hit-to-lead processes through integration into a feedback-driven experimentation workflow [33,36,42,43,50].…”

An efficient one-pot synthesis of 2-bromo-6-aryl[5H]pyrrolo[2,3-b]pyrazines

“…[1][2][3][4][5] During a recent medicinal chemistry program we became interested in the synthesis of 2-bromo-6-phenyl[5H]pyrrolo [2,3-b]pyrazine (1) (Figure 1) as a key building block for further elaboration. Whilst the synthesis of 6-phenyl[5H]pyrrolo [2,3-b]pyrazines has been reported, this approach did not allow inclusion of the bromine atom that was required for the convergent synthesis of our target molecules.…”

“…Reagents and conditions: Pd 2 (dba)3 (0.05 equiv), S-Phos (0.05 equiv), Et 3 SiH (5 equiv), Et 3 N (2 equiv), 1,4-dioxane, 89% yield.…”