High-Throughput Miniaturized Synthesis of PROTAC-Like Molecules.

The development of miniaturized high-throughput in situ screening platforms capable of handling the entire process of drug synthesis to final screening is essential for advancing drug discovery in the future. In this study, an approach based on combinatorial solid-phase synthesis, enabling the efficient synthesis of libraries of proteolysis targeting chimeras (PROTACs) in an array format is presented. This on-chip platform allows direct biological screening without the need for transfer steps.  UV-induced release of target molecules into individual droplets facilitates further on-chip experimentation. Utilizing a mitogen-activated protein kinase kinases (MEK1/2) degrader as a template, a series of 132 novel PROTAC-like molecules is synthesized using solid-phase Ugi reaction. These compounds are further characterized using various methods, including matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) imaging, while consuming only a few milligrams of starting materials in total. Furthermore, the feasibility of culturing cancer cells on the modified spots and quantifying the effect of MEK suppression is demonstrated. The miniaturized synthesis platform lays a foundation for high-throughput in situ biological screening of potent PROTACs for potential anticancer activity and offers the potential for accelerating the drug discovery process by integrating miniaturized synthesis and biological steps on the same array.

High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels.

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.

Wavelength Orthogonal Photodynamic Networks.

The ability of light to remotely control the properties of soft matter materials in a dynamic fashion has fascinated material scientists and photochemists for decades. However, only recently has our ability to map photochemical reactivity in a finely wavelength resolved fashion allowed for different colors of light to independently control the material properties of polymer networks with high precision, driven by monochromatic irradiation enabling orthogonal reaction control. The current concept article highlights the progress in visible light-induced photochemistry and explores how it has enabled the design of polymer networks with dynamically adjustable properties. We will explore current applications ranging from dynamic hydrogel design to the light-driven adaptation of 3D printed structures on the macro- and micro-scale. While the alternation of mechanical properties via remote control is largely reality for soft matter materials, we herein propose the next frontiers for adaptive properties, including remote switching between conductive and non-conductive properties, hydrophobic and hydrophilic surfaces, fluorescent or non-fluorescent, and cell adhesive vs. cell repellent properties.