Introduction of an acyl group to the 3-O-position of erythromycin A derivatives instead of L-cladinose led to a novel class of macrolide antibiotics that we named "acylides". The 3-O-nitrophenylacetyl derivative TEA0777 showed significantly potent activity against not only erythromycin-susceptible Gram-positive pathogens but also inducibly macrolides-lincosamides-streptogramin B (MLS(B))-resistant Staphylococcus aureus and efflux-resistant Streptococcus pneumoniae. These results indicated that acylides have potential as next-generation macrolide antibiotics.

A novel series of acylides, 3-O-(aryl)acetylerythromycin A derivatives, were synthesized and evaluated. These compounds have significant potent antibacterial activity against not only Gram-positive pathogens, including inducibly macrolide-lincosamide-streptogramin B (MLS(B))-resistant and efflux-resistant strains, but also Gram-negative pathogens, such as H. influenzae. 6,9:11,12-dicarbonate acylide 47 (FMA0122) was twice as active against H. influenzae than azithromycin, whereas it showed only moderate in vivo efficacy in mouse protection tests. However, the 11,12-carbamate acylide 19 (TEA0929), which showed potent antibacterial activity against almost all of the main causative pathogens of community-acquired pneumonia tested, exhibited excellent in vivo efficacy comparable to those of second-generation macrolides.

Diversity-oriented organic synthesis (DOS) is a strategy to make compound collections to probe biological systems [1][2][3][4][5][6][7] . Designing better DOS libraries requires having methods to assess the consequences of different synthesis decisions on the biological performance of resulting library members 8 . Since we are particularly interested in how stereochemistry affects performance in biological assays, we prepared a disaccharide library containing systematic stereochemical variations, assayed the library for different biological effects, and developed methods to assess the similarity of performance between members across multiple assays. These methods allow us to ask which subsets of stereochemical features best predict similarity in patterns of biological performance between individual members and which features produce the greatest variation of outcomes. We anticipate that the data-analysis approach presented here can be generalized to other sets of biological assays and other chemical descriptors. Methods to assess which structural features of library members produce the greatest similarity in performance for a given set of biological assays should help prioritize synthesis decisions in second-generation library development targeting the underlying cellbiological processes. Methods to assess which structural features of library members produce the greatest variation in performance should help guide decisions about what synthetic methods need to be developed to make optimal small-molecule screening collections.