Sensing of Enantiomeric Excess in Chiral Carboxylates

Chirality of organic compounds plays a crucial role in human and veterinary medicine. More specifically, chiral carboxylates are extensively utilized in drug development processes, and a number of them are commercialized as drugs. Due to distinct pharmacological properties of enantiomers of chiral drugs, asymmetric synthesis became an essential tool to synthesize enantiopure compounds. While combinatorial libraries enable to screen catalysts, auxiliaries, and conditions rapidly, the enormous amount of resulting samples cannot be tested simultaneously. In this dissertation, we have reported two optical methods to determine the ee values of the chiral carboxylates in a high-throughput fashion. In the first method, we have employed cinchona alkaloids, known as Corey-Lygo catalysts, as chemosensors for ee analysis of chiral carboxylates. N-alkylated 9-chloromethylanthracenyl cinchona alkaloids form complexes with carboxylates by means of electrostatic interactions and hydrogen bonds. Using characteristic changes in fluorescence intensities of the chemosensors upon addition of analytes, we were able to perform the successful qualitative and quantitative ee analysis of enantiomers of chiral carboxylates in aqueous media. Using the sensor array comprising four N-alkylated cinchona alkaloids, we were able to achieve 100 % correct classification of chiral carboxylates, including several enantiomers of non-steroidal anti-inflammatory drugs (NSAIDs) successfully. Furthermore, quantitative ee analysis of (S)-Ibuprofen, (S)-Naproxen, and (S)-Ketoprofen shows prediction errors as low as 3%. The second method involves fluorescent macrocyclic sensors containing the chiral BINOL moiety, and H-bond donors in the pocket to form complexes with carboxylates. The intrinsic structure of the pocket of macrocycles displays an enantioselective behavior for chiral carboxylates, while the substituents at the 3,3’-positions of the BINOL moiety allow for tuning the shape and size of the cavity to enhance the recognition performance of the macrocycles. The resulting macrocycles show distinct responses for a number of carboxylates, including the enantiomers of ibuprofen, ketoprofen, 2-phenylpropanoate, mandelate, and phenylalanine. The fingerprint-like responses of the macrocycles, for structurally similar analytes, enabled to perform a qualitative analysis of 12 carboxylates with 100 % correct classification. Quantitative ee analysis of ibuprofen, ketoprofen, and phenylalanine shows that the sensors correctly identify the ee values of the unknown samples with an error of prediction \u3c 3% even in the presence of impurities