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Clean Synthetic Strategies to Biologically Active Molecules from Lignin: A Green Path to Drug Discovery**

AbstractDeriving active pharmaceutical agents from renewable resources is crucial to increasing the economic feasibility of modern biorefineries and promises to alleviate critical supply‐chain dependencies in pharma manufacturing. Our multidisciplinary approach combines research in lignin‐first biorefining, sustainable catalysis, and alternative solvents with bioactivity screening, an in vivo efficacy study, and a structural‐similarity search. The resulting sustainable path to novel anti‐infective, anti‐inflammatory, and anticancer molecules enabled the rapid identification of frontrunners for key therapeutic indications, including an anti‐infective against the priority pathogen Streptococcus pneumoniae with efficacy in vivo and promising plasma and metabolic stability. Our catalytic methods provided straightforward access, inspired by the innate structural features of lignin, to synthetically challenging biologically active molecules with the core structure of dopamine, namely, tetrahydroisoquinolines, quinazolinones, 3‐arylindoles and the natural product tetrahydropapaveroline. Our diverse array of atom‐economic transformations produces only harmless side products and uses benign reaction media, such as tunable deep eutectic solvents for modulating reactivity in challenging cyclization steps.
- Hannover Medical School Germany
- University of Graz Austria
- University of Graz
- Helmholtz Centre for Infection Research Germany
- Helmholtz Center for Information Security Germany
ddc:500, Green Chemistry, Tunable Deep Eutectic Solvents, Dopamine, Drug Discovery, Solvents, 500, Biomass, Lignin, Catalysis
ddc:500, Green Chemistry, Tunable Deep Eutectic Solvents, Dopamine, Drug Discovery, Solvents, 500, Biomass, Lignin, Catalysis
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).15 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
