Development of LB244, an Irreversible STING Antagonist.

The cGMP-AMP Synthase (cGAS)-Stimulator of Interferon Genes (STING) pathway plays a critical role in sensing dsDNA localized to the cytosol, resulting in the activation of a robust inflammatory response. While cGAS-STING signaling is essential for antiviral immunity, aberrant STING activation is observed in amyotrophic lateral sclerosis (ALS), lupus, and autoinflammatory diseases such as Aicardi-Goutières syndrome (AGS) and STING associated vasculopathy with onset in infancy (SAVI). Significant efforts have therefore focused on the development of STING inhibitors. In a concurrent submission, we reported that BB-Cl-amidine inhibits STING-dependent signaling in the nanomolar range, both in vitro and in vivo. Considering this discovery, we sought to generate analogs with higher potency and proteome-wide selectivity. Herein, we report the development of LB244, which displays nanomolar potency and inhibits STING signaling with markedly enhanced proteome-wide selectivity. Moreover, LB244 mirrored the efficacy of BB-Cl-amidine in vivo. In summary, our data identify novel chemical entities that inhibit STING signaling and provide a scaffold for the development of therapeutics for treating STING-dependent inflammatory diseases.

Model based strategies towards protein A resin lifetime optimization and supervision.

The high cost of protein A resins drives the biopharmaceutical industry to maximize its lifetime, which is limited by several processes, usually referred to as resin aging. In this work, two model based strategies are presented, aiming to control and improve the resin lifetime. The first approach, purely statistical, enables qualitative monitoring of the column state and prediction of column performance indicators (e.g. yield, purity and dynamic binding capacity) from chromatographic on-line data (e.g. UV signal). The second one, referred to as hybrid modeling, is based on a lumped kinetic model, which includes two aging parameters fitted on several resin cycling experimental campaigns with varying cleaning procedures (CP). The first aging parameter accounts for binding capacity deterioration (caused by ligand degradation, leaching, and pore occlusion), while the second accounts for a decreased mass transfer rate (mainly caused by fouling). The hybrid model provides important insights into the prevailing aging mechanism as a function of the different CPs. In addition, it can be applied to model based CP optimization and yield forecasting with the capability of state estimation corrections based on on-line process information. Both approaches show promising results, which could help to significantly extend the resin lifetime. This comes along with increased understanding, reduced experimental effort, decreased cost of goods, and improved process robustness.