Deconvolution of 1D NMR spectra: A deep learning-based approach.

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.

Population Modeling of Selexipag Pharmacokinetics and Clinical Response Parameters in Patients With Pulmonary Arterial Hypertension.

Selexipag (Uptravi) is an oral selective IP prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension (PAH). The pivotal GRIPHON study was the largest clinical study ever conducted in PAH patients, providing long-term data from 1,156 patients. PAH comedication did not affect exposure to selexipag, while exposure to its active metabolite ACT-333679 was reduced by 30% when taken in combination, clinically not relevant in the context of individual dose up-titration. Using log-linear regression models linking model-predicted steady-state exposure to pharmacodynamics (PD), exposure to selexipag and ACT-333679 showed some statistically significant, albeit not clinically relevant, effects on exercise capacity, laboratory values, and the occurrence of prostacyclin-related adverse events, but not on vital signs or adverse events denoting hemorrhage. Using suitable modeling techniques, the GRIPHON study yielded clinically relevant data with limited burden of pharmacokinetics (PK) blood sampling, demonstrating that PK/PD modeling enables firm conclusions even with sparse PK and PD sampling.

Incidence of and risk factors for severe hypoglycaemia in treated type 2 diabetes mellitus patients in the UK--a nested case-control analysis.

AIMS: To assess incidence rates (IRs) of and identify risk factors for incident severe hypoglycaemia in patients with type 2 diabetes newly treated with antidiabetic drugs. METHODS: Using the UK-based General Practice Research Database, we performed a retrospective cohort study between 1994 and 2011 and a nested case-control analysis. Ten controls from the population at risk were matched to each case with a recorded severe hypoglycaemia during follow-up on general practice, years of history in the database and calendar time. Using multivariate conditional logistic regression analyses, we adjusted for potential confounders. RESULTS: Of 130,761 patients with newly treated type 2 diabetes (mean age 61.7 ± 13.0 years), 690 (0.5%) had an incident episode of severe hypoglycaemia recorded [estimated IR 11.97 (95% confidence interval, CI, 11.11-12.90) per 10,000 person-years (PYs)]. The IR was markedly higher in insulin users [49.64 (95% CI, 44.08-55.89) per 10,000 PYs] than in patients not using insulin [8.03 (95% CI, 7.30-8.84) per 10,000 PYs]. Based on results of the nested case-control analysis increasing age [≥ 75 vs. 20-59 years; adjusted odds ratio (OR), 2.27; 95% CI, 1.65-3.12], cognitive impairment/dementia (adjusted OR, 2.00; 95% CI, 1.37-2.91), renal failure (adjusted OR, 1.34; 95% CI, 1.04-1.71), current use of sulphonylureas (adjusted OR, 4.45; 95% CI, 3.53-5.60) and current insulin use (adjusted OR, 11.83; 95% CI, 9.00-15.54) were all associated with an increased risk of severe hypoglycaemia. CONCLUSIONS: Severe hypoglycaemia was recorded in 12 cases per 10,000 PYs. Risk factors for severe hypoglycaemia included increasing age, renal failure, cognitive impairment/dementia, and current use of insulin or sulphonylureas.

Phosphorylation of splicing factor SF1 on Ser20 by cGMP-dependent protein kinase regulates spliceosome assembly.

Splicing factor 1 (SF1) functions at early stages of pre-mRNA splicing and contributes to splice site recognition by interacting with the essential splicing factor U2AF65 and binding to the intron branch site. We have identified an 80 kDa substrate of cGMP-dependent protein kinase-I (PKG-I) isolated from rat brain, which is identical to SF1. PKG phosphorylates SF1 at Ser20, which inhibits the SF1-U2AF65 interaction leading to a block of pre-spliceosome assembly. Mutation of Ser20 to Ala or Thr also inhibits the interaction with U2AF65, indicating that Ser20 is essential for binding. SF1 is phosphorylated in vitro by PKG, but not by cAMP-dependent protein kinase A (PKA). Phosphorylation of SF1 also occurs in cultured neuronal cells and is increased on Ser20 in response to a cGMP analogue. These results suggest a new role for PKG in mammalian pre-mRNA splicing by regulating in a phosphorylation-dependent manner the association of SF1 with U2AF65 and spliceosome assembly.