abstract
State-of-the-art delivery systems currently rely on chemical synthesis routes for drug encapsulation. However, these methods have inherent drawbacks concerning toxicity, selectivity, and the potential for burst release. To overcome these limitations, the enzymatic synthesis of polymer-drug conjugates emerges as a promising alternative to promote eco-friendliness and safety in production and grants precise control over the resulting structures. In this study, we delved into the reactivity of Pseudozyma (Candida) antarctica lipase B for polyesters' capping with small molecules. For that purpose, Quantum Mechanics/Molecular Mechanics simulations were used to predict the conjugation reactions. These predictions were translated to the laboratory, where the enzymatic reactions were replicated, subsequently benchmarking them against metal oxide-catalysed reactions. The outcome of our experiments was the successful generation of end-capped oligo/polycaprolactone, with their molecular weights spanning from 540 to 2600 g mol-1 with an enzymatic approach, and slightly higher when applying conventional catalysis, and with isolation yields up to 68.4 %. This work underscores the potential of enzyme-driven strategies in fabricating precisely engineered drug delivery systems, marking a significant stride toward more efficient and controlled therapeutics synthesis.
keywords
RING-OPENING POLYMERIZATION; CATALYZED SYNTHESIS; SIDE-CHAIN; DYNAMICS; POLYESTERS; PARAMETERS; DEPENDENCE; SYSTEM; EWALD
subject category
Chemistry; Materials Science
authors
Figueiredo, PR; Silvestre, AJD; Sousa, AF; Carvalho, ATP
our authors
Andreia Fernandes de Sousa
Principal Researcher
Armando Jorge Domingues Silvestre
Full professor
Pedro R. Figueiredo
PhD StudentProjects
Collaboratory for Emerging Technologies, CoLab (EMERGING TECHNOLOGIES)
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
CICECO - Aveiro Institute of Materials (UIDP/50011/2020)
Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)
acknowledgements
This work was sponsored through: grants SFRH/BD/144303/2019 and IF/01272/2015; AFS acknowledge FCT-Foundation for Science and Technology for the research contract CEECIND/02322/2020 (DOI 10.54499/2020.02322.CEECIND/CP1589/CT0008) ; and projects CNC - Center for Neurosciences and Cell Biology UIDB/04539/2020, UIDP/04539/2020 and LA/P/0058/2020 and CICECO - Aveiro Institute of Materials UIDB/50011/2020, UIDP/50011/2020 and LA/P/0006/2020, financed by Portuguese National funds via FCT, Regional Opera-tional Program (CENTRO2020) under the Portuguese Partnership Agreement 2020 by the European Regional Development Fund (ERDF) , and FCT/MCTES (PIDDAC) . The authors acknowledge the computing resources made available by the Laboratory for Advanced Computing of the University of Coimbra (LCA-UC) funded by FCT and FEDER under the Advanced Computing Project CPCA/A1/447491/2021.