The role of the informatics framework in early lead discovery

Screening_mgmt: a Python module for managing screening data

High-throughput screening is an established technique in drug discovery and, as such, has also found its way into academia. High-throughput screening generates a considerable amount of data, which is why specific software is used for its analysis and management. The commercially available software packages are often beyond the financial limits of small-scale academic laboratories and, furthermore, lack the flexibility to fulfill certain user-specific requirements. We have developed a Python module, screening_mgmt, which is a lightweight tool for flexible data retrieval, analysis, and storage for different screening assays in one central database. The module reads custom-made analysis scripts and plotting instructions, and it offers a graphical user interface to import, modify, and display the data in a uniform manner. During the test phase, we used this module for the management of 10,000 data points of various origins. It has provided a practical, user-friendly tool for sharing and exchanging information between researchers.

Data-driven medicinal chemistry in the era of big data

Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.

Workflow-Based Software Environment for Large-Scale Biological Experiments

High-content screening (HCS) technologies are becoming increasingly used in both large-scale drug discovery and basic research programs. These automated imaging and analysis technologies enable the researcher to elucidate the complex biology that underlies the functions of genes, proteins, and other biomolecules at the cellular level. HCS combines the power of automated digital microscopy and advanced software-based image analysis algorithms to detect and quantify biological changes in cells and tissues. This technology is a particularly powerful tool when used to interrogate the cellular effects of exogenously applied agents such as RNAi and/or small molecules. HCS allows for the evaluation of cellular perturbations that occur both at the level of the single cell and within cellular populations. In a multivariate approach, multiple cellular parameters are collected, allowing for more complex analysis. However, in these scenarios, data flow and management still represent substantial bottlenecks in HCS projects. HCS data include a diversity of information from multiple sources such as details pertaining to screening libraries (e.g., siRNA and small molecules), image stacks acquired from automated microscopes (of which there may be up to several million), and the image analysis data. From this, postprocessing algorithms are required to generate statistical, quality control bioinformatic information and ultimately a final hit list. To accomplish these individual tasks, numerous tools can be used to perform each analytical step; however, management of the entire information flow currently requires the use of commercially available proprietary software, the scope of which is often limited, or bespoke customized scripts. In this article, the authors introduce an open-source research tool that allows for the management of the entire data flow of the HCS data chain, by handling and linking information and providing many powerful postprocessing and visualization tools.

A profiling platform for the characterization of transglutaminase 2 (TG2) inhibitors

Huntington's disease (HD) is associated with increased expression levels and activity of tissue transglutaminase (TG2), an enzyme primarily known for its cross-linking of proteins. To validate TG2 as a therapeutic target for HD in transgenic models and for eventual clinical development, a selective and brain-permeable inhibitor is required. Here, a comprehensive profiling platform of biochemical and cellular assays is presented which has been established to evaluate the potency, cellular efficacy, subtype selectivity and the mechanism-of-action of known and novel TG2 inhibitors. Several classes of inhibitors have been characterized including: the commonly used pseudo-substrate inhibitors, cystamine and putrescine (which are generally nonspecific for TG2 and therefore not practical for drug development), the various peptidic inhibitors that target the active site cysteine residue (which display excellent selectivity but in general have poor cellular activity), and the allosteric reversible small-molecule hydrazides (which show poor selectivity and a lack of cellular activity and could not be improved despite considerable medicinal chemistry efforts). In addition, a set of inhibitors identified from a collection of pharmacologically active compounds was found to be unselective for TG2. Moreover, inhibition at the guanosine triphosphate binding site has been examined, but apart from guanine nucleotides, no such inhibitors have been identified. In addition, the promising pharmacological profile of a TG2 inhibitor is presented which is currently in lead optimization to be developed as a tool compound.

A Primer on Screening Data Management

A drug discovery startup company or academic lab entering the screening arena faces numerous challenges as it tries to manage the large quantity of data generated by a typical drug discovery screening campaign. Although there are sophisticated off-the-shelf software solutions available, their use requires substantial forethought and attention to detail if the data they capture are to be of sufficient quality to serve the various purposes to which it will be put. For newcomers to the field of screening data management in particular, the problem is compounded by a lack of literature covering the practical aspects of managing screening data. The authors provide some practical advice based on their experience of using a commercially available software suite. They discuss issues ranging from the organizational aspects to examples of how the form and content of metadata can have a big impact on whether results can be easily queried, pivoted, and reported. It is also hoped that their experiences might provide an opportunity for reflection to data management practitioners operating in established environments.

Connecting chemosensitivity, gene expression and disease

Omics-based investigations offer potentially powerful readouts that might be useful for probing the underlying biology of normal and diseased states, identifying novel therapeutic targets and proposing relevant markers for designing treatment strategies. A vital component of these investigations involves a systematic analysis of gene expression and chemosensitivity data in the context of disease states and small molecule probes into the function of targets responsible for a disease phenotype. Systematic analysis of chemical and pharmacogenetics data offers a possible means to identify novel, small-molecule, potentially therapeutic, agents that affect the phenotype of a particular target. Elegantly simple in concept, the covariation of genetic and chemosensitivity readouts provide a hypothetical link for relating compounds through genomic expression profiles to underlying biology.