Fragment-based virtual screening identifies a first-in-class preclinical drug candidate for Huntington's disease.

Currently, there are no therapies available to modify the disease progression of Huntington's disease (HD). Recent clinical trial failures of antisense oligonucleotide candidates in HD have demonstrated the need for new therapeutic approaches. Here, we developed a novel in-silico fragment scanning approach across the surface of mutant huntingtin (mHTT) polyQ and predicted four hit compounds. Two rounds of compound analoging using a strategy of testing structurally similar compounds in an affinity assay rapidly identified GLYN122. In vitro, GLYN122 directly binds and reduces mHTT and induces autophagy in neurons. In vivo, our results confirm that GLYN122 can reduce mHTT in the cortex and striatum of the R/2 mouse model of Huntington's disease and subsequently improve motor symptoms. Thus, the in-vivo pharmacology profile of GLYN122 is a potential new preclinical candidate for the treatment of HD.

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure.

Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.