A promising emerging area for the treatment of obesity and diabetes is combinatorial hormone therapy, where single-molecule peptides are rationally designed to integrate the complementary actions of multiple endogenous metabolically-related hormones. We describe here a proof-of-concept study on developing unimolecular polypharmacy agents through the use of selection methods based on phage-displayed peptide libraries (PDL). Co-agonists of the glucagon (GCG) and GLP-1 receptors were identified from a PDL sequentially selected on GCGR- and GLP1R-overexpressing cells. After two or three rounds of selection, 7.5% of randomly picked clones were GLP1R/GCGR co-agonists, and a further 1.53% were agonists of a single receptor. The phages were sequenced and 35 corresponding peptides were synthesized. 18 peptides were potent co-agonists, 8 of whom showed EC50 ≤ 30 pM on each receptor, comparable to the best rationally designed co-agonists reported in the literature. Based on literature examples, two sequences were engineered to stabilize against dipeptidyl peptidase IV cleavage and prolong the in vivo half-life: the engineered peptides were comparably potent to the parent peptides on both receptors, highlighting the potential use of phage-derived peptides as therapeutic agents. The strategy described here appears of general value for the discovery of optimized polypharmacology paradigms across several metabolically-related hormones.

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of research can enhance the transfer of knowledge from simulations to real-world environments and can increase the responsiveness and safety of UAV systems.