Efficacy and safety of adjunctive padsevonil in adults with drug-resistant focal epilepsy: Results from two double-blind, randomized, placebo-controlled trials.

OBJECTIVE: To characterize efficacy, safety/tolerability, and pharmacokinetics of padsevonil (PSL) administered concomitantly with ≤3 antiseizure medications (ASMs) for observable focal seizures in adults with drug-resistant epilepsy in two multicenter, randomized, double-blind, placebo-controlled, parallel-group trials. METHODS: The phase 2b dose-finding trial (EP0091/NCT03373383) randomized patients 1:1:1:1:1 to PSL 50/100/200/400 mg or placebo twice daily (b.i.d.). The phase 3 efficacy trial (EP0092/NCT03739840) randomized patients 1:1:1:1 to PSL 100/200/400 mg or placebo b.i.d. Patients with observable (focal aware with motor symptoms, focal impaired awareness, focal to bilateral tonic-clonic) focal seizures for ≥3 years, experiencing them ≥4 times per 28 days including during the 4-week baseline period despite treatment with ≥4 lifetime ASMs including current ASMs, were enrolled. RESULTS: In EP0091 and EP0092, 410 and 231 patients, respectively, were randomized and received at least one dose of trial medication. In patients in EP0091 on PSL 50/100/200/400 mg b.i.d. (n = 80/82/81/81, respectively) versus placebo (n = 81), outcomes included percentage reductions over placebo in observable focal seizure frequency during the 12-week maintenance period: 17.2%, 19.1% (p = 0.128), 19.2% (p = 0.128), 12.4% (p = 0.248); 75% responder rates (p-values for odds ratios): 13.8%, 12.2% (p = 0.192), 11.1% (p = 0.192), 16.0% (p = 0.124) versus 6.2%; 50% responder rates: 33.8% (p = 0.045), 31.7% (p = 0.079), 25.9% (p = 0.338), 32.1% (p = 0.087), versus 21.0%; TEAEs were reported by 82.7% (67/81), 78.3% (65/83), 74.4% (61/82), 90.1% (73/81) versus 78.3% (65/83). In patients in EP0092 on PSL 100/200/400 mg b.i.d. (n = 60/56/56, respectively) versus placebo (n = 54), outcomes included percentage reductions over placebo: -5.6% (p = 0.687), 6.5% (p = 0.687), 6.3% (p = 0.687); 75% responder rates: 15.3% (p = 0.989), 12.5% (p = 0.989), 14.3% (p = 0.989) versus 13.0%; 50% responder rates: 35.6% (p = 0.425), 33.9% (p = 0.625), and 42.9% (p = 0.125) versus 27.8%; TEAEs were reported by 80.0% (48/60), 78.9% (45/57), 83.1% (49/59) versus 67.3% (37/55). SIGNIFICANCE: In both trials, the primary outcomes did not reach statistical significance in any PSL dose group compared with placebo. PSL was generally well tolerated, and no new safety signals were identified.

Developing integrated crop knowledge networks to advance candidate gene discovery.

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

LAILAPS: the plant science search engine.

With the number of sequenced plant genomes growing, the number of predicted genes and functional annotations is also increasing. The association between genes and phenotypic traits is currently of great interest. Unfortunately, the information available today is widely scattered over a number of different databases. Information retrieval (IR) has become an all-encompassing bioinformatics methodology for extracting knowledge from complex, heterogeneous and distributed databases, and therefore can be a useful tool for obtaining a comprehensive view of plant genomics, from genes to traits. Here we describe LAILAPS (http://lailaps.ipk-gatersleben.de), an IR system designed to link plant genomic data in the context of phenotypic attributes for a detailed forward genetic research. LAILAPS comprises around 65 million indexed documents, encompassing >13 major life science databases with around 80 million links to plant genomic resources. The LAILAPS search engine allows fuzzy querying for candidate genes linked to specific traits over a loosely integrated system of indexed and interlinked genome databases. Query assistance and an evidence-based annotation system enable time-efficient and comprehensive information retrieval. An artificial neural network incorporating user feedback and behavior tracking allows relevance sorting of results. We fully describe LAILAPS's functionality and capabilities by comparing this system's performance with other widely used systems and by reporting both a validation in maize and a knowledge discovery use-case focusing on candidate genes in barley.

A query suggestion workflow for life science IR-systems.

Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.