Back

Azithromycin Derivatives to Mitigate Off-Target Inhibition of Autophagy and Retain Beneficial Host Directed Effects

Doan, D. Q.; Liu, S. W.; Clark, S.; Sapula, S. A.; Crowhurst, T.; Reynolds, P. N.; Kopp, B. T.; Blencowe, A.; Roscioli, E.

2026-09-02 cell biology
10.64898/2026.09.01.748460 bioRxiv
Show abstract

Azithromycin (AZM) is central for the treatment of chronic respiratory diseases (CRD) but has divergent off-target effects. We synthesised AZM Derivatives 1 and 2 (D1/D2) that were predicted to permit autophagy and preserve AZM's anti-inflammatory effect. The 16HBE14o- airway epithelial cell model was exposed to AZM, D1 and D2 for 16 h and assessed for autophagy flux via LC3B-II:p62/SQSTM1 abundance (Western blot). Necrosis was quantified via lactate dehydrogenase release. Inflammation (IL-6 secretion) was assessed in the THP-1 macrophage model exposed to 10 ng/mL lipopolysaccharide vs co-treatment with AZM and the derivatives for 18 h. AZM-derivative antibacterial activity (vs AZM) was determined via the minimum inhibition concentration (MIC) method using methicillin sensitive Staphylococcus aureus (MSSA). Autophagy (LC3B-II and p62/SQSTM1 abundance) was not altered by the two derivatives and was indistinguishable from the control exposure (P> 0.05 for D1 and D2, each 10 and 50 ug/mL, vs control). D2 elicited a significant decrease in LPS-induced IL-6 secretion vs the LPS-only exposure (58.22 pg/ml, n=3, 95% +/- CI [6.521-109.9]). Importantly, D2 caused a similar reduction in LPS-induced IL-6 secretion, as observed for AZM (-10.30 pg/mL, n=3, 95% CI [-62.00 to 41.39]). The MIC of AZM for MSSA growth was 0.5 ug/ml, where as D1 and D2 were 1.0 and 8.0 ug/mL, respectively (P<0.05). We show for the first time that AZM can be redesigned to mitigate its potent arrest of autophagy while preserving its anti-inflammatory activity, to counter the generation of further AZM resistant strains.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.