Although the classical antibiotic spectinomycin is a potent bacterial protein synthesis inhibitor, poor antimycobacterial activity limits its clinical application for treating tuberculosis. Using structure-based design, a novel semisynthetic series of spectinomycin analogs was generated with selective ribosomal inhibition and excellent narrow-spectrum antitubercular activity. In multiple murine infection models, these spectinamides were well tolerated, significantly reduced lung mycobacterial burden and increased survival. In vitro studies demonstrated a lack of cross-resistance with existing tuberculosis therapeutics, activity against MDR/XDR-tuberculosis, and an excellent pharmacological profile. Key to their potent antitubercular properties was their structural modification to evade the Rv1258c efflux pump, which is upregulated in MDR strains and is implicated in macrophage induced drug tolerance. The antitubercular efficacy of spectinamides demonstrates that synthetic modifications to classical antibiotics can overcome the challenge of intrinsic efflux pump-mediated resistance and expands opportunities for target based tuberculosis drug discovery.

Whereas the PROTAC approach to target protein degradation greatly benefits from rational design, the discovery of small-molecule degraders relies mostly on phenotypic screening and retrospective target identification efforts. Here, we describe the design, synthesis, and screening of a large diverse library of thalidomide analogues against a panel of patient-derived leukemia and medulloblastoma cell lines. These efforts led to the discovery of potent and novel GSPT1/2 degraders displaying selectivity over classical IMiD neosubstrates, such as IKZF1/3, and high oral bioavailability in mice. Taken together, this study offers compound 6 (SJ6986) as a valuable chemical probe for studying the role of GSPT1/2 in vitro and in vivo , and it supports the utility of a diverse library of CRBN binders in the pursuit of targeting undruggable oncoproteins.

Targeting cereblon (CRBN) is currently one of the most frequently reported proteolysis-targeting chimera (PRO-TAC) approaches,o wingt of avorable drug-like properties of CRBN ligands,i mmunomodulatory imide drugs (IMiDs). However,I MiDs are knownt ob ei nherently unstable,r eadily undergoing hydrolysis in body fluids.H ere we showt hat IMiDs and IMiD-based PROTACs rapidly hydrolyze in commonly utilized cell media, which significantly affects their cell efficacy.W ed esigned novel CRBN binders,p henyl glutarimide (PG) analogues,a nd showed that they retained affinity for CRBN with high ligand efficiency (LE > 0.48) and displayed improved chemical stability.O ur efforts led to the discovery of PG PROTAC 4c (SJ995973), au niquely potent degrader of bromodomain and extra-terminal (BET) proteins that inhibited the viability of human acute myeloid leukemia MV4-11 cells at lowp icomolar concentrations (IC 50 = 3pM; BRD4 DC 50 = 0.87 nM). These findings strongly support the utility of PG derivatives in the design of CRBN-directed PROTACs.

Histone lysine demethylases facilitate the activity of oncogenic transcription factors including possibly MYC. Here we show that multiple histone demethylases influence the viability and poor prognosis of neuroblastoma cells where MYC is often overexpressed. We also identified the approved small molecule antifungal agent ciclopirox as a novel pan-histone demethylase inhibitor. Ciclopirox targeted several histone demethylases including KDM4B implicated in MYC function. Accordingly, ciclopirox inhibited Myc signaling in parallel with mitochondrial oxidative phosphorylation, resulting in suppression of neuroblastoma cell viability and inhibition of tumor growth associated with an induction of differentiation. Our findings provide new insights into epigenetic regulation of MYC function and suggest a novel pharmacologic basis to target histone demethylases as an indirect MYC targeting approach for cancer therapy.

We report the use of the molecular signatures known as "Property-Encoded Shape Distributions" (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore -a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously.