Problems that Can Occur when Assaying Extracts to Pure Compounds in Biological Systems

For a significant number of years, scientists of many persuasions have assayed natural product materials ranging from crude extracts to pure compounds, in a multitude of assays causally related to some biological processes. However, in a very significant number of submitted papers and published articles, what may be considered as canned biological assays were used, and if a positive effect was observed, then the authors would claim that the material assayed was a potential drug lead. This also occurred with pure synthetic compounds and compounds derived from natural products by simple chemical modifications.

However, what has now become quite obvious—with all such classes of materials—is that there are many promiscuous players with multiple bioactivities. These can range from relatively crude extracts, pure compounds from natural products, synthetic processes that produce natural product derivatives, and even compounds that are truly synthetic in origin. There is also a potential problem with the data from crude to purified extracts being used to claim some form of beneficial activities for such materials, to sell that particular mixture to the lay public, by very careful descriptions of its possible uses due to legal hurdles.

With the advent of artificial intelligence and very large compound databases, some of which may well contain impure materials, scientists from a variety of backgrounds have begun to utilize such listings to obtain compounds for their low to high throughput biological screens, without realizing that there are very significant numbers of active compounds (eg, pan assay interference compounds and invalid metabolic panaceas), that will hit in many different screens for a variety of reasons, thus leading to significant wasted efforts and published scientific articles that have incorrect results.

This commentary gives some of the history of such materials but is designed to be used as a warning to both researchers and in particular, journal editors, and reviewers, that reports of biological results that are claimed to be the result of the compounds used, need to be very carefully screened for results due to such promiscuous compounds, irrespective of their nominal source(s).

All literature searches were made by the author and the background knowledge has come from more than 55 years of research in industry and governmental laboratories in both the United Kingdom and the United States, for enzyme inhibitors/activators as well as antimicrobial and antitumor lead compounds mainly from natural product sources.

The conclusion that I came up with as a result is this: Caveat emptor. (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX)

© 2021 Elsevier HS Journals, Inc.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds

BACKGROUND

Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection.

CONCLUSION

ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies

A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.

Nuisance Compounds, PAINS Filters, and Dark Chemical Matter in the GSK HTS Collection

High-throughput screening (HTS) hits include compounds with undesirable properties. Many filters have been described to identify such hits. Notably, pan-assay interference compounds (PAINS) has been adopted by the community as the standard term to refer to such filters, and very useful guidelines have been adopted by the American Chemical Society (ACS) and subsequently triggered a healthy scientific debate about the pitfalls of draconian use of filters. Using an inhibitory frequency index, we have analyzed in detail the promiscuity profile of the whole GlaxoSmithKline (GSK) HTS collection comprising more than 2 million unique compounds that have been tested in hundreds of screening assays. We provide a comprehensive analysis of many previously published filters and newly described classes of nuisance structures that may serve as a useful source of empirical information to guide the design or growth of HTS collections and hit triaging strategies.

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.

Activation of Yap-Directed Transcription by Knockdown of Conserved Cellular Functions

The Yap-Hippo pathway has a significant role in regulating cell proliferation and growth, thus controlling organ size and regeneration. The Hippo pathway regulates two highly conserved, transcription coactivators, YAP and TAZ. The upstream regulators of the Yap-Hippo pathway have not been fully characterized. The aim of this study was to use a siRNA screen, in a liver biliary cell line, to identify regulators of the Yap-Hippo pathway that allow activation of the YAP transcription coactivator at high cell density. Activation of the YAP transcription coactivator was monitored using a high-content, image-based assay that measured the intracellular localization of native YAP protein. Active siRNAs were identified and further validated by quantification of CYR61 mRNA levels (a known YAP target gene). The effect of compounds targeting the putative gene targets identified as hits was also used for further validation. A number of validated hits reveal basic aspects of Yap-Hippo biology, such as components of the nuclear pore, by which YAP cytoplasmic-nuclear shuttling occurs, or how proteasomal degradation regulates intracellular YAP concentrations, which then alter YAP localization and transcription. Such results highlight how targeting conserved cellular functions can lead to validated activity in phenotypic assays.