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Purpose: Minzasolmin (UCB0599) is an orally administered, small molecule inhibitor of ASYN misfolding in development as a potential disease-modifying therapy for Parkinson's disease. Here we describe the preclinical development of a radiolabeled tracer and results from a phase 1 study using the tracer to investigate the brain distribution of minzasolmin., Procedures: In the preclinical study, two radiolabeling positions were investigated on the S-enantiomer of minzasolmin (UCB2713): [ 11 C]methylamine UCB2713 ([ 11 C-N-CH 3 ]UCB2713) and [ 11 C]carbonyl UCB2713 ([ 11 C-CO]UCB2713). Male C57 black 6 mice (N = 10) received intravenous [ 11 C-N-CH 3 ]UCB2713; brain homogenates were assessed for radioactivity and plasma samples analyzed by high-performance liquid chromatography. Positron emission tomography-computed tomography (PET-CT) was used to image brains in a subset of mice (n = 3). In the open-label, phase 1 study, healthy volunteers were scanned twice with PET-CT following injection with [ 11 C]minzasolmin radiotracer (≤ 10 µg), first without, then with oral dosing with non-radiolabeled minzasolmin 360 mg., Primary Objective: to determine biodistribution of minzasolmin in the human brain; secondary objectives included minzasolmin safety/tolerability., Results: Preclinical data supported the use of [ 11 C]minzasolmin in clinical studies. In the phase 1 study, PET data showed substantial drug signal in the brain of healthy volunteers (N = 4). The mean estimated whole brain total distribution volume (V T ) at equilibrium across all regions of interest was 0.512 mL/cm 3 , no difference in V T was observed following administration of minzasolmin 360 mg. Treatment-emergent adverse events (TEAEs) were reported by 75% (n = 3) of participants. No drug-related TEAEs, deaths, serious adverse events, or discontinuations were reported., Conclusion: Following positive preclinical results with the N-methyl labeled PET tracer, [ 11 C]minzasolmin was used in the phase 1 study, which demonstrated that minzasolmin readily crossed the blood-brain barrier and was well distributed throughout the brain. Safety and pharmacokinetic findings were consistent with previous early-phase studies (such as UP0077, NCT04875962)., (© 2023. The Author(s).)

Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions., Results: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants' solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE., Conclusions: The SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs' annotation solutions in comparison to the SSC-I.