Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health Assay Guidance Manual Guidelines

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Discovery of Novel Small-Molecule Scaffolds for the Inhibition and Activation of WIP1 Phosphatase from a RapidFire Mass Spectrometry High-Throughput Screen

Wild-type P53-induced phosphatase 1 (WIP1), also known as PPM1D or PP2Cδ, is a serine/threonine protein phosphatase induced by P53 after genotoxic stress. WIP1 inhibition has been proposed as a therapeutic strategy for P53 wild-type cancers in which it is overexpressed, but this approach would be ineffective in P53-negative cancers. Furthermore, there are several cancers with mutated P53 where WIP1 acts as a tumor suppressor. Therefore, activating WIP1 phosphatase might also be a therapeutic strategy, depending on the P53 status. To date, no specific, potent WIP1 inhibitors with appropriate pharmacokinetic properties have been reported, nor have WIP1-specific activators. Here, we report the discovery of new WIP1 modulators from a high-throughput screen (HTS) using previously described orthogonal biochemical assays suitable for identifying both inhibitors and activators. The primary HTS was performed against a library of 102 277 compounds at a single concentration using a RapidFire mass spectrometry assay. Hits were further evaluated over a range of 11 concentrations with both the RapidFire MS assay and an orthogonal fluorescence-based assay. Further biophysical, biochemical, and cell-based studies of confirmed hits revealed a WIP1 activator and two inhibitors, one competitive and one uncompetitive. These new scaffolds are prime candidates for optimization which might enable inhibitors with improved pharmacokinetics and a first-in-class WIP1 activator.

Updated Prediction of Aggregators and Assay-Interfering Substructures in Food Compounds

Positive outcomes in biochemical and biological assays of food compounds may appear due to the well-described capacity of some compounds to form colloidal aggregates that adsorb proteins, resulting in their denaturation and loss of function. This phenomenon can lead to wrongly ascribing mechanisms of biological action for these compounds (false positives) as the effect is nonspecific and promiscuous. Similar false positives can show up due to chemical (photo)reactivity, redox cycling, metal chelation, interferences with the assay technology, membrane disruption, etc., which are more frequently observed when the tested molecule has some definite interfering substructures. Although discarding false positives can be achieved experimentally, it would be very useful to have in advance a prognostic value for possible aggregation and/or interference based only in the chemical structure of the compound tested in order to be aware of possible issues, help in prioritization of compounds to test, design of appropriate assays, etc. Previously, we applied cheminformatic tools derived from the drug discovery field to identify putative aggregators and interfering substructures in a database of food compounds, the FooDB, comprising 26,457 molecules at that time. Here, we provide an updated account of that analysis based on a current, much-expanded version of the FooDB, comprising a total of 70,855 compounds. In addition, we also apply a novel machine learning model (SCAM Detective) to predict aggregators with 46-53% increased accuracies over previous models. In this way, we expect to provide the researchers in the mode of action of food compounds with a much improved, robust, and widened set of putative aggregators and interfering substructures of food compounds.