In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

No abstract

No abstract

In

Traits offer a fine-grained mechanism to compose classes from reusable components while avoiding problems of fragility brought by multiple inheritance and mixins. Traits as originally proposed are stateless, that is, they contain only methods, but no instance variables. State can only be accessed within stateless traits by accessors, which become required methods of the trait. Although this approach works reasonably well in practice, it means that many traits, viewed as software components, are artificially incomplete, and classes that use such traits may contain significant amounts of boilerplate glue code. We present an approach to stateful traits that is faithful to the guiding principle of stateless traits: the client retains control of the composition. Stateful traits consist of a minimal extension to stateless traits in which instance variables are purely local to the scope of a trait, unless they are explicitly made accessible by the composing client of a trait. We demonstrate by means of a formal object calculus that adding state to traits preserves the flattening property: traits contained in a program can be compiled away. We discuss and compare two implementation strategies, and briefly present a case study in which stateful traits have been used to refactor the trait-based version of the Smalltalk collection hierarchy.