Using the BioAssay Ontology for analyzing high-throughput screening data.

High-throughput screening (HTS) is the main starting point for hit identification in drug discovery programs. This has led to a rapid increase of available screening data both within pharmaceutical companies and the public domain. We have used the BioAssay Ontology (BAO) 2.0 for assay annotation within AstraZeneca to enable comparison with external HTS methods. The annotated assays have been analyzed to identify technology gaps, evaluate new methods, verify active hits, and compare compound activity between in-house and PubChem assays. As an example, the binding of a fluorescent ligand to formyl peptide receptor 1 (FPR1, involved in inflammation, for example) in an in-house HTS was measured by fluorescence intensity. In total, 155 active compounds were also tested in an external ligand binding flow cytometry assay, a method not used for in-house HTS detection. Twelve percent of the 155 compounds were found active in both assays. By the annotation of assay protocols using BAO terms, internal and external assays can easily be identified and method comparison facilitated. They can be used to evaluate the effectiveness of different assay methods, design appropriate confirmatory and counterassays, and analyze the activity of compounds for identification of technology artifacts.

Assessing HTS performance using BioAssay Ontology: screening and analysis of a bacterial phospho-N-acetylmuramoyl-pentapeptide translocase campaign.

With the public availability of biochemical assays and screening data constantly increasing, new applications for data mining and method analysis are evolving in parallel. One example is BioAssay Ontology (BAO) for systematic classification of assays based on screening setup and metadata annotations. In this article we report a high-throughput screening (HTS) against phospho-N-acetylmuramoyl-pentapeptide translocase (MraY), an attractive antibacterial drug target involved in peptidoglycan synthesis. The screen resulted in novel chemistry identification using a fluorescence resonance energy transfer assay. To address a subset of the false positive hits, a frequent hitter analysis was performed using an approach in which MraY hits were compared with hits from similar assays, previously used for HTS. The MraY assay was annotated according to BAO and three internal reference assays, using a similar assay design and detection technology, were identified. Analyzing the assays retrospectively, it was clear that both MraY and the three reference assays all showed a high false positive rate in the primary HTS assays. In the case of MraY, false positives were efficiently identified by applying a method to correct for compound interference at the hit-confirmation stage. Frequent hitter analysis based on the three reference assays with similar assay method identified additional false actives in the primary MraY assay as frequent hitters. This article demonstrates how assays annotated using BAO terms can be used to identify closely related reference assays, and that analysis based on these assays clearly can provide useful data to influence assay design, technology, and screening strategy.

Transporter assays and assay ontologies: useful tools for drug discovery.

Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g. transport activity, strength of ligand­protein interaction), but also due to heterogeneous assay setups used by different research groups. Efforts to systematically organize this (divergent) bioassay data have large potential impact in Public-Private partnership and conventional commercial drug discovery. In this short review, we highlight some of the frequently used high-throughput assays for transport proteins, and we discuss emerging assay ontologies and their application to this field. Focusing on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), we exemplify how annotation of bioassay data per target class could improve and add to existing ontologies, and we propose to include an additional layer of metadata supporting data fusion across different bioassays.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors.