Sustained remission of symptoms and improved health-related quality of life in patients with cryopyrin-associated periodic syndrome treated with canakinumab: results of a double-blind placebo-controlled randomized withdrawal study

Abstract Introduction To assess the effect of canakinumab, a fully human anti-interleukin-1β antibody, on symptoms and health-related quality of life (HRQoL) in patients with cryopyrin-associated periodic syndrome (CAPS). Methods In this 48-week, phase 3 study, patients with CAPS received canakinumab 150 mg subcutaneously at 8-week intervals. All patients (n = 35) received canakinumab during weeks 1 through 8; weeks 9 through 24 constituted a double-blind placebo-controlled withdrawal phase, and weeks 24 through 48 constituted an open-label phase in which all patients received canakinumab. Patient and physician assessments of symptoms, levels of inflammatory markers, and HRQoL were performed. Results Rapid symptom remission was achieved, with 89% of patients having no or minimal disease activity on day 8. Responses were sustained in patients receiving 8-weekly canakinumab. Responses were lost during the placebo-controlled phase in the placebo group and were regained on resuming canakinumab therapy in the open-label phase. Clinical responses were accompanied by decreases in serum levels of C-reactive protein, serum amyloid A protein, and interleukin-6. HRQoL scores at baseline were considerably below those of the general population. Improvements in all 36-item Short-Form Health Survey (SF-36) domain scores were evident by day 8. Scores approached or exceeded those of the general U.S. population by week 8 and remained stable during canakinumab therapy. Improvements in bodily pain and role-physical were particularly marked, increasing by more than 25 points from baseline to week 8. Therapy was generally well tolerated. Conclusions Canakinumab, 150 mg, 8-weekly, induced rapid and sustained remission of symptoms in patients with CAPS, accompanied by substantial improvements in HRQoL. Trial registration Clintrials.gov NCT0046598

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality