Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

In silico studies, nitric oxide, and cholinesterases inhibition activities of pyrazole and pyrazoline analogs of diarylpentanoids

A new series of pyrazole, phenylpyrazole, and pyrazoline analogs of diarylpentanoids (excluding compounds 3a, 4a, 5a, and 5b) was pan-assay interference compounds-filtered and synthesized via the reaction of diarylpentanoids with hydrazine monohydrate and phenylhydrazine. Each analog was evaluated for its anti-inflammatory ability via the suppression of nitric oxide (NO) on IFN-γ/LPS-activated RAW264.7 macrophage cells. The compounds were also investigated for their inhibitory capability toward acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), using a modification of Ellman's spectrophotometric method. The most potent NO inhibitor was found to be phenylpyrazole analog 4c, followed by 4e, when compared with curcumin. In contrast, pyrazole 3a and pyrazoline 5a were found to be the most selective and effective BChE inhibitors over AChE. The data collected from the single-crystal X-ray diffraction analysis of compound 5a were then applied in a docking simulation to determine the potential binding interactions that were responsible for the anti-BChE activity. The results obtained signify the potential of these pyrazole and pyrazoline scaffolds to be developed as therapeutic agents against inflammatory conditions and Alzheimer's disease.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de.

Series of screening compounds with high hit rates for the exploration of multi-target activities and assay interference

Aim:

Generation of a database of analog series (ASs) with high assay hit rates for the exploration of assay interference and multi-target activities of compounds.

Methodology:

ASs were computationally extracted from extensively tested screening compounds with high hit rates.

Data:

A total of 6941 ASs were assembled comprising 14,646 unique compounds that were tested in a total of 1241 different assays covering 426 specified targets. These ASs were organized and prioritized on the basis of different activity and assay frequency criteria. All ASs and associated information are made available in an open access deposition.

Next steps:

The large set of ASs will be further analyzed computationally and from a chemical perspective to identify assay interference compounds and candidates for exploring target promiscuity.

In drug discovery, compounds are tested in high-throughput screening assays for desired biological activities. Unfortunately, some test compounds that are not specifically active give false-positive (artificial) activity signals in assays, which are difficult to recognize. However, other compounds can bind specifically to multiple targets and hence give true activity signals in multiple assays. To help distinguish between artifacts and true activities, we have systematically identified series of analogs (i.e., structurally closely related compounds) that are active with unusually high frequency across biological assays. Herein, we describe these data and make them freely available for follow-up analysis.