GDF15 is a distant TGF-β family member that induces anorexia and weight loss. Due to its function, GDF15 has attracted attention as a potential therapeutic for the treatment of obesity and its associated metabolic diseases. However, the pharmacokinetic and physicochemical properties of GDF15 present several challenges for its development as a therapeutic, including a short half-life, high aggregation propensity, and protease susceptibility in serum. Here, we report the design, characterization and optimization of GDF15 in an Fc-fusion protein format with improved therapeutic properties. Using a structure-based engineering approach, we combined knob-into-hole Fc technology and N-linked glycosylation site mutagenesis for half-life extension, improved solubility and protease resistance. In addition, we identified a set of mutations at the receptor binding site of GDF15 that show increased GFRAL binding affinity and led to significant half-life extension. We also identified a single point mutation that increases p-ERK signaling activity and results in improved weight loss efficacy in vivo. Taken together, our findings allowed us to develop GDF15 in a new therapeutic format that demonstrates better efficacy and potential for improved manufacturability.

Motivation: Drug re-positioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying drugs whose impact on expression in a collection of cells most closely reverses the disease's impact on expression in disease-relevant tissues. The high throughput LINCS project has expanded the universe of compounds, cellular perturbations, and cell types for which data are available, but even with this effort, many potentially clinically useful combinations are missing. To evaluate the possibility of finding disease-relevant drug connectivity despite missing data, we compared methods using cross-validation on a complete subset of the LINCS data. Results: Modified recommender systems with either neighborhood-based or SVD imputation methods were compared to autoencoders and two naive methods. All were evaluated for accuracy in prediction of both expression signatures and connectivity query responses. We demonstrate that cellular context is important, and that it is possible to predict cell-specific drug responses with improved accuracy over naive approaches. Neighborhood-based collaborative filtering was the most successful, improving prediction accuracy in all tested cells. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify drugs that reverse the expression signatures observed in disease.

Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease’s impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.