The core features and outcomes of a specialised camp programme for children with life-limiting conditions and their families: A qualitative multi-perspective approach.

Previous research has reported that the families of children with enduring and life-limiting health conditions are at risk of negative psychosocial effects. Adjunct to medical interventions, specialist camp programmes have been developed to promote familial adjustment. However, limited research has been carried out in this area. The aim of this study was to describe the core features and outcomes of a specialised camp programme for children with life-limiting conditions (LLC) and their family. Semi-structured interviews were conducted with four professionals, three volunteers involved in facilitating the programme and two mothers representing families that attended the programme. Multiple perspectives were sought to gain a detailed understanding of the programme and outcomes. Data were analysed through an inductive thematic approach. There was considerable overlap among participant groups on the core features and outcomes of the programme. Thematically, core features are described in terms of familial togetherness, peer interaction, safety and positive experiences. Noted outcomes include lasting memories, continued peer relations for parents and siblings and enhancement of relationships between family members and professionals. Findings suggest that specialised camp programmes may provide families of children with LLC with positive experiences that support adjustment, although further research is required.

High-Throughput Agonist Shift Assay Development for the Analysis of M1-Positive Allosteric Modulators.

Agonist shift assays feature cross-titrations of allosteric modulators and orthosteric ligands. Information generated in agonist shift assays can include a modulator's effect on the orthosteric agonist's potency (alpha) and efficacy (beta), as well as direct agonist activity of the allosteric ligand (tauB) and the intrinsic binding affinity of the modulator to the unoccupied receptor (KB). Because of the heavy resource demand and complex data handling, these allosteric parameters are determined infrequently during the course of a drug discovery program and on a relatively small subset of compounds. Automation of agonist shift assays enables this data-rich analysis to evaluate a larger number of compounds, offering the potential to differentiate compound classes earlier and prospectively prioritize based on desired molecular pharmacology. A high-throughput calcium-imaging agonist shift assay was pursued to determine the allosteric parameters of over 1000 positive allosteric modulator (PAM) molecules for the human muscarinic acetylcholine receptor 1 (M1). Control compounds were run repeatedly to demonstrate internal consistency. Comparisons between potency measurements and the allosteric parameter results demonstrate that these different types of measurements do not necessarily correlate, highlighting the importance of fully characterizing and understanding the allosteric properties of leads.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.