Periodic Reporting for period 1 - SELECTFLOW (Selective Alkylation of Complex Molecules in Flow)
Periodo di rendicontazione: 2022-09-01 al 2024-08-31
SELECTFLOW overall objectives are:
1) Development of selective, sustainable and predictable synthetic methodologies for the modification of drug-like small molecules.
2) Valorisation of gaseous chemicals, and convert them into higher added-value compounds, thus providing a sustainable solution for environmental pollution and contributing towards a circular economy.
3) Promote environmentally-friendly chemical processes that minimize waste and resource consumption, in line with the European Commission MSCA Green Charter and Horizon Europe missions: the adaptation to climate change to become climate resilient by 2030.
To succeed in these objectives, SELECTFLOW presents a multidisciplinary project that lies in the intersection of synthetic photoredox catalysis with microfluidic technologies, with the objective of delivering a selective, sustainable and predictable methodology for the modification of biologically-active molecules using gaseous reagents as raw materials. The combination of multiphasic flow chemistry with the design of photocatalytic synthetic methodologies will open new and more efficient routes for the production of fine chemicals. The use of standardized continuous-flow reactors will enable researchers from various fields to carry out transformations in a reproducible and scalable fashion and will streamline the transition from academia to industrial applications.
For the second work package, we decided to turn our attention toward the use of gaseous reagents for the modification of drug-like compounds. To achieve this, we studied the regioselective installation of carbon monoxide gas and light and heavy alkanes into organic compounds. Due to the advantages of working under microfluidic conditions, the safe handling of hazardous CO and gaseous alkanes could be achieved. By employing a photocatalytic methodology, we developed an unprecedented method for the upgrading of light hydrocarbons at ambient temperature, thereby opening the door to the use of these readily available feedstocks as coupling partners.
Finally, we targeted the third and last work package of SELECTFLOW: the development of environmentally-friendly photochemical processes that minimize waste and resource consumption. Key challenges developing efficient photochemical reactions include optimization, replication and scale-up. These challenges arise from non-reproducible light absorption and experimental variability and are responsible for large use of resources and produce waste. In response to the need for efficient optimization of complex photocatalytic reaction conditions, we developed a robotic platform that iteratively determines optimal, substrate-specific reaction conditions for photochemical processes in a scalable, flow-based architecture. A machine learning algorithm enabled the development of a closed-loop optimization process, describing tailored reaction conditions for each substrate across different reactivities without human intervention. Operating autonomously, the platform eliminates the need for extensive expertise in photocatalysis or scaling processes to achieve optimal results. This makes this platform a valuable collaborative robotic setup suitable for any synthetic organic chemistry laboratory, irrespective of users’ specific familiarity with photocatalysis.
In the second work package of SELECTFLOW, we introduced a novel methodology for the upcycling of gaseous reagents, specifically, toxic and harmful gaseous alkanes and carbon monoxide. We showcased the unparalleled advantage that microfluidic setups provide in handling these chemicals safely and efficiently. Consequently, we paved the way for the development of further photochemical transformations harnessing otherwise elusive gaseous chemicals, potentially leading to new synthetic routes towards drug development.
Finally, in the third work package of SELECTFLOW, we provided an efficient solution for optimizing complex photocatalytic reactions, reducing resource use and waste. To achieve this, we developed a robotic platform for the hands-off, flow-based, iterative optimization of chemical reactions. The implementation of an artificial intelligence algorithm allowed for a closed-loop optimization, significantly enhancing the sustainability of the photochemical process by dramatically reducing the number of experiments needed to the optimization process.