Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA.

To elicit optimal immune responses, messenger RNA vaccines require intracellular delivery of the mRNA and the careful use of adjuvants. Here we report a multiply adjuvanted mRNA vaccine consisting of lipid nanoparticles encapsulating an mRNA-encoded antigen, optimized for efficient mRNA delivery and for the enhanced activation of innate and adaptive responses. We optimized the vaccine by screening a library of 480 biodegradable ionizable lipids with headgroups adjuvanted with cyclic amines and by adjuvanting the mRNA-encoded antigen by fusing it with a natural adjuvant derived from the C3 complement protein. In mice, intramuscular or intranasal administration of nanoparticles with the lead ionizable lipid and with mRNA encoding for the fusion protein (either the spike protein or the receptor-binding domain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) increased the titres of antibodies against SARS-CoV-2 tenfold with respect to the vaccine encoding for the unadjuvanted antigen. Multiply adjuvanted mRNA vaccines may improve the efficacy, safety and ease of administration of mRNA-based immunization.

Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents.

The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.

Recent advances in the synthesis and reactivity of azetidines: strain-driven character of the four-membered heterocycle.

Azetidines represent one of the most important four-membered heterocycles used in organic synthesis and medicinal chemistry. The reactivity of azetidines is driven by a considerable ring strain, while at the same the ring is significantly more stable than that of related aziridines, which translates into both facile handling and unique reactivity that can be triggered under appropriate reaction conditions. Recently, remarkable advances in the chemistry and reactivity of azetidines have been reported. In this review, we provide an overview of the synthesis, reactivity and application of azetidines that have been published in the last years with a focus on the most recent advances, trends and future directions. The review is organized by the methods of synthesis of azetidines and the reaction type used for functionalization of azetidines. Finally, recent examples of using azetidines as motifs in drug discovery, polymerization and chiral templates are discussed.

Random Forest Model Prediction of Compound Oral Exposure in the Mouse.

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.