abstract
BACKGROUNDLevodopa is a precursor of several neurotransmitters, such as dopamine, and is used in the treatment of Parkinson's disease. In this work, an alternative strategy was studied to separate levodopa from similar biomolecules using aqueous two-phase systems (ATPS). RESULTSTernary ATPS composed of polyethylene glycol (PEG) 400 or ionic liquids (ILs), citrate buffer (K3C6H5O7/C6H8O7) at pH 7.0 and water, and quaternary ATPS composed of PEG 400, K3C6H5O7/C6H8O7 at pH 7.0, water and the same ILs at 5 wt%, were studied. The respective liquid-liquid phase diagrams were determined at 298 K to appraise the mixture compositions required to form two-phase systems, followed by studies of the partition of levodopa and structurally similar biomolecules (dopamine, L-phenylalanine, and L-tyrosine). Their partition coefficients and extraction efficiencies have been determined, and the selectivity of the ATPS to separate levodopa from the remaining biomolecules evaluated. CONCLUSIONThe results obtained indicated that PEG-based ATPS were the most effective to separate levodopa from L-phenylalanine while the separation from the other biomolecules was better using IL-based ATPS, in particular those based on [P-4444]Cl and [N-4444]Cl, with extraction efficiencies of levodopa to the salt-rich phase ranging between 62.7 and 74.0%, and of the remaining biomolecules to polymer/IL-rich phase up to 91.5%. (c) 2017 Society of Chemical Industry
keywords
IONIC LIQUIDS; BIPHASIC SYSTEMS; MUCUNA-PRURIENS; EXTRACTION; QUANTIFICATION; ADJUVANTS; DISEASE; SEEDS; WATER; RAT
subject category
Biotechnology & Applied Microbiology; Chemistry; Engineering
authors
Sousa, RDS; Neves, CMSS; Pereira, MM; Freire, MG; Coutinho, JAP
our authors
Catarina Maia Seco Seiça Neves
Assistant Researcher
Mara Guadalupe Freire Martins
Coordinating Researcher
Matheus M. Pereira
Researcher
Rita de Cássia Superbi de Sousa
Post-doc FellowshipGroups
G4 - Renewable Materials and Circular Economy
G5 - Biomimetic, Biological and Living Materials
G6 - Virtual Materials and Artificial Intelligence
acknowledgements
This work was developed in the scope of the project CICECO-Aveiro Institute of Materials (Ref. FCT UID/CTM/50011/2013), financed by national funds through the FCT/MEC and co-financed by FEDER under the PT2020 Partnership Agreement. RCS Sousa acknowledges the post-doctoral grant (200833/2015-4/PDE) and financial support from Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq. CMSS Neves thanks FCT for the postdoctoral grant SFRH/BPD/109057/2015. MM Pereira acknowledges the PhD grant (2740-13-3) and financial support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Capes. MG Freire thanks the European Research Council (ERC) for the Starting Grant ECR-2013-StG-337753.