Simultaneous targeting of PD-1 and IL-2Rßγ with radiation therapy inhibits pancreatic cancer growth and metastasis.

In pancreatic ductal adenocarcinoma (PDAC) patients, we show that response to radiation therapy (RT) is characterized by increased IL-2Rß and IL-2Rγ along with decreased IL-2Rα expression. The bispecific PD1-IL2v is a PD-1-targeted IL-2 variant (IL-2v) immunocytokine with engineered IL-2 cis targeted to PD-1 and abolished IL-2Rα binding, which enhances tumor-antigen-specific T cell activation while reducing regulatory T cell (Treg) suppression. Using PD1-IL2v in orthotopic PDAC KPC-driven tumor models, we show marked improvement in local and metastatic survival, along with a profound increase in tumor-infiltrating CD8+ T cell subsets with a transcriptionally and metabolically active phenotype and preferential activation of antigen-specific CD8+ T cells. In combination with single-dose RT, PD1-IL2v treatment results in a robust, durable expansion of polyfunctional CD8+ T cells, T cell stemness, tumor-specific memory immune response, natural killer (NK) cell activation, and decreased Tregs. These data show that PD1-IL2v leads to profound local and distant response in PDAC.

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.