(15f) Closed-Loop Reaction Optimization in Microscale Oscillating Droplets: An MINLP Algorithm Applied to Suzuki-Miyaura Coupling Catalyst Selection | AIChE

(15f) Closed-Loop Reaction Optimization in Microscale Oscillating Droplets: An MINLP Algorithm Applied to Suzuki-Miyaura Coupling Catalyst Selection


Baumgartner, L. M., Massachusetts Institutes of Technology
Gao, K. W., University of California, Berkeley
Reizman, B., Eli Lilly and Company
Jensen, K. F., Massachusetts Institute of Technology
Among the many challenges of the notoriously-expensive drug development process is scaling up small molecule synthesis. Promising drug candidates must be produced in significantly larger quantities for preclinical toxicological and pharmacokinetic studies (10s of grams) compared to primary assays during discovery and lead optimization (sub-100 mg). While purity and speed is the primary concern during discovery, synthesis at the multigram scale warrants additional consideration of yield and cost.

The optimization of reaction conditions can help achieve this goal, but the inherent combinatorial nature of selecting reaction conditions (e.g., catalyst identity, concentration, reaction time, temperature) means that performing an exhaustive screen is counter to the goal of efficiency, both in terms of material usage and time. More focused experimentation is possible through the use of optimization algorithms that propose experiments based on accumulated data, so that only useful experiments need be performed.

Building on previous work in the group [1], here we report a mixed-integer nonlinear program (MINLP) algorithm for the simultaneous optimization of both discrete and continuous variables. The algorithm consists of two phases (1) an initial D-optimal design, which proposes a diverse range of experiments to regress a quadratic response surface model, and (2) an iterative branch-and-bound approach to discrete variable selection, where G-optimal experiments are proposed to minimize the variance at predicted optima. The algorithm is designed to optimize catalyst turnover number (TON) subject to a minimum yield constraint.

We first validate its performance using a suite of five simulated test cases representing variations on a catalytic bimolecular reaction. Next, we validate its performance experimentally using the exemplary Suzuki-Miyaura cross-coupling of 3-chloropyridine and 2-fluoropyridine-3-boronic acid pinacol ester. Experimental validation was conducted using an automated chemistry platform that performs reactions sequentially at the 20 microliter scale [2,3]. Yield and TON are calculated via online HPLC/MS and automatic peak integration. This closed-loop optimization proceeds without any human intervention. The new algorithm exhibits significantly faster convergence than the previous version and is found to be robust to measurement error.

[1] B. J. Reizman, K. F. Jensen, Accounts of Chemical Research 2016, 49, 1786-1796.

[2] Y.-J. Hwang*, C. W. Coley*, M. Abolhasani, A. L. Marzinzik, G. Koch, C. Spanka, H. Lehmann and K. F. Jensen, Chemical Communications, 2017, 53, 6649–6652.

[3] C. W. Coley, M. Abolhasani, H. Lin and K. F. Jensen, Angewandte Chemie, 2017, 129, 9979–9982.