Promiscuous Compounds Research Articles

De-risking Drug Discovery of Intracellular Targeting Peptides: Screening Strategies to Eliminate False-Positive Hits.

Nonspecific promiscuous compounds can mislead researchers and waste significant resources. This phenomenon, though well-documented for small molecules, has not been widely explored for the peptide modality. Here we demonstrate that two purported peptide-based KRas inhibitors, SAH-SOS1 A and cyclorasin 9A5, exemplify false-positive molecules-in terms of both their binding affinities and cellular activities. Through multiple gold-standard biophysical techniques, we unambiguously show that both peptides lack specific binding to KRas and instead induce protein unfolding. Although these peptides inhibited cellular proliferation, the activities appeared to be off-target on the basis of a counterscreen with KRas-independent cell lines. We further demonstrate that their cellular activities are derived from membrane disruption. Accordingly, we propose that to de-risk false-positive molecules, orthogonal binding assays and cellular counterscreens are indispensable.

Biological Activity Profiles of Multitarget Ligands from X-ray Structures.

In pharmaceutical research, compounds with multitarget activity receive increasing attention. Such promiscuous chemical entities are prime candidates for polypharmacology, but also prone to causing undesired side effects. In addition, understanding the molecular basis and magnitude of multitarget activity is a stimulating topic for exploratory research. Computationally, compound promiscuity can be estimated through large-scale analysis of activity data. To these ends, it is critically important to take data confidence criteria and data consistency across different sources into consideration. Especially the consistency aspect has thus far only been little investigated. Therefore, we have systematically determined activity annotations and profiles of known multitarget ligands (MTLs) on the basis of activity data from different sources. All MTLs used were confirmed by X-ray crystallography of complexes with multiple targets. One of the key questions underlying our analysis has been how MTLs act in biological screens. The results of our analysis revealed significant variations of MTL activity profiles originating from different data sources. Such variations must be carefully considered in promiscuity analysis. Our study raises awareness of these issues and provides guidance for large-scale activity data analysis.

Promiscuity analysis of a kinase panel screen with designated p38 alpha inhibitors

Protein phosphorylation by kinases is of critical importance for the regulation of many cellular functions. When kinases are deregulated numerous biological processes are affected, which may cause a variety of diseases. Therefore, kinase inhibition plays an important role for therapeutic intervention. A number of kinase inhibitors have been approved as drugs, initially in oncology where promiscuous (multi-kinase) inhibitors were most efficacious. Exploring kinase inhibitor selectivity and promiscuity for therapy is among the most challenging aspects of kinase drug discovery. Herein, we thoroughly analyze a kinase profiling experiment in which 637 designated inhibitors of p38α MAP kinase (p38α) were tested against a panel of 60 kinases distributed across the human kinome. In this experiment, only 19% of the inhibitors were found to be promiscuous when the median p38α inhibition level was applied as an activity threshold. Promiscuous inhibitors had a median value of two targets per compound, and many of these inhibitors were only active against the p38α and closely related JNK3 enzymes. Promiscuity cliffs were identified and analyzed in a network representation revealing structural modifications that were implicated in triggering compound promiscuity. Taken together, the findings revealed a high degree of selectivity of designated p38α directed inhibitors although they target the ATP binding site that is largely conserved across the human kinome.

Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome.

Small molecules with multi-target activity, also termed promiscuous compounds, are increasingly considered for pharmaceutical applications. The use of promiscuous chemical entities represents a departure from the compound specificity paradigm, one of the pillars of modern drug discovery. The popularity of promiscuous compounds is due to the concept of polypharmacology; another more recent drug discovery paradigm. It refers to insights that the efficacy of drugs often depends on interactions with multiple targets. Views concerning the extent to which small molecules might form well-defined interactions with multiple targets often differ, but comprehensive experimental investigations of promiscuity are currently rare. On the other hand, large volumes of active compounds and experimental measurements are becoming available and enable data-driven analyses of compound selectivity versus promiscuity. In this perspective, we discuss computational methods and data structures designed for promiscuity analysis. In addition, findings from large-scale exploration of activity profiles of inhibitors covering the human kinome are summarized. Although many kinase inhibitors are expected to be promiscuous, they are frequently found to be selective, which provides opportunities for target-directed drug discovery (rather than polypharmacology). We also discuss that machine learning yields evidence for the existence of structure-promiscuity relationships.

Exploring structure-promiscuity relationships using dual-site promiscuity cliffs and corresponding single-site analogs

Currently available volumes of compounds and biological activity data enable large-scale analyses of compound promiscuity (multi-target activity). To aid in the exploration of structure-promiscuity relationships, promiscuity cliffs (PCs) were introduced previously. In analogy to activity cliffs, PCs were defined as pairs of structurally analogous compounds with large differences in the number of targets they are active against. Hence, PCs reveal small chemical modifications that are implicated in promiscuity. As introduced originally, PCs were identified by applying the matched molecular pair formalism and were thus confined to changes at a single substitution site. Herein, PCs with multiple substitution sites are introduced and a pilot study on a large collection of protein kinase inhibitors is reported, which provide excellent test cases for promiscuity analysis. For dual-site PCs (dsPCs), which dominated the distribution of multi-site PCs, an extended data structure was generated comprising a dsPC and two single-site analogs accounting for individual substitutions. Using a canonical representation, extended dsPCs are intuitive and easy to interpret from a chemical perspective. The analog quartet representing an extended dsPC is rich in structure-promiscuity relationship information and makes it possible to evaluate the potential interplay of chemical modifications implicated in promiscuity. Furthermore, extended dsPCs provide insights into possible experimental causes of apparent differences in analog promiscuity such as varying test frequencies. Hence, the newly introduced PC format should be of interest for exploring origins of compound promiscuity in medicinal chemistry and for formulating experimentally testable target hypotheses for analogs.

Identifying Promiscuous Compounds with Activity against Different Target Classes.

Compounds with multitarget activity are of high interest for polypharmacological drug discovery. Such promiscuous compounds might be active against closely related target proteins from the same family or against distantly related or unrelated targets. Compounds with activity against distinct targets are not only of interest for polypharmacology but also to better understand how small molecules might form specific interactions in different binding site environments. We have aimed to identify compounds with activity against drug targets from different classes. To these ends, a systematic analysis of public biological screening data was carried out. Care was taken to exclude compounds from further consideration that were prone to experimental artifacts and false positive activity readouts. Extensively assayed compounds were identified and found to contain molecules that were consistently inactive in all assays, active against a single target, or promiscuous. The latter included more than 1000 compounds that were active against 10 or more targets from different classes. These multiclass ligands were further analyzed and exemplary compounds were found in X-ray structures of complexes with distinct targets. Our collection of multiclass ligands should be of interest for pharmaceutical applications and further exploration of binding characteristics at the molecular level. Therefore, these highly promiscuous compounds are made publicly available.

Small-Scale Panel Comprising Diverse Gene Family Targets To Evaluate Compound Promiscuity.

Despite the recent advances in the life sciences and the remarkable investment in drug discovery research, the success rate of small-molecule drug development remains low. Safety is the second most influential factor of drug attrition in clinical studies; thus, the selection of compounds with fewer toxicity concerns is crucial to increase the success rate of drug discovery. Compounds that promiscuously bind to multiple targets are likely to cause unexpected pharmacological activity that may lead to adverse effects. Therefore, avoiding such compounds during early research stages would contribute to identifying compounds with a higher chance of success in the clinic. To evaluate the interaction profile against a wide variety of targets, we constructed a small-scale promiscuity panel (PP) consisting of eight targets (ROCK1, PDE4D2, GR, PPARγ, 5-HT2B, adenosine A3, M1, and GABAA) that were selected from diverse gene families. The validity of this panel was confirmed by comparison with the promiscuity index evaluated from larger-scale panels. Analysis of data from the PP revealed that both lipophilicity and basicity are likely to increase promiscuity, while the molecular weight does not significantly contribute. Additionally, the promiscuity assessed using our PP correlated with the occurrence of both in vitro cytotoxicity and in vivo toxicity, suggesting that the PP is useful to identify compounds with fewer toxicity concerns. In summary, this small-scale and cost-effective PP can contribute to the identification of safer compounds that would lead to a reduction in drug attrition due to safety issues.

Leveraging Atropisomerism to Obtain a Selective Inhibitor of RET Kinase with Secondary Activities toward EGFR Mutants.

Unstable atropisomerism is innate in many common scaffolds in drug discovery, commonly existing as freely rotating aryl-aryl bonds. Such compounds can access the majority of dihedral conformations around the bond axis; however, most small molecules bind their target within a narrow range of these available conformations. The remaining accessible conformations can interact with other proteins leading to compound promiscuity. Herein, we leverage atropisomerism to restrict the accessible low-energy dihedral conformations available to a promiscuous kinase inhibitor and achieve highly selective and potent inhibitors of the oncogenic target rearranged during transfection (RET) kinase. We then evaluate our lead inhibitor against kinases that were predicted to bind compounds in a similar conformational window to RET, discovering a potent inhibitor of drug-resistant epidermal growth factor receptor (EGFR) mutants including L858R/T790M/C797S EGFR. Leveraging atropisomerism to restrict accessible conformational space should be a generally applicable strategy due to the prevalence of unstable atropisomerism in drug discovery.

Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome.

Aim:A large collection of promiscuity cliffs (PCs), PC pathways (PCPs) and promiscuity hubs (PHs) formed by inhibitors of human kinases is made freely available.Methodology:Inhibitor PCs were systematically identified and organized in network representations, from which PCPs were extracted. PH compounds were classified and their neighborhoods analyzed.Data & exemplary results:Nearly 16,000 PCs covering the human kinome were identified, which yielded more than 600 PC clusters and 8900 PCPs. Moreover, 520 PHs were obtained.Limitations & next steps:PC and PCP data structures capture structure–promiscuity relationships. Promiscuity assessment is also affected by data sparseness. Given the rapid growth of kinase inhibitor data, the relevance of PC/PCP/PH information for medicinal chemistry and chemical biology applications will further increase.

An Electrophilic Natural Product Provides a Safe and Robust Odor Neutralization Approach To Counteract Malodorous Organosulfur Metabolites Encountered in Skunk Spray.

The anal secretions of skunks comprise several types of malodorous organosulfur compounds. The pungent metabolites are used defensively by skunks to repel threats posed by predators, and in many parts of the world, those perceived threats include humans and their pets. The extremely low thresholds for detection of the organosulfur metabolites make efforts to "de-skunk" people, animals, and clothing a process fraught with many challenges. The fungal-derived metabolite pericosine A (4) is a promiscuous yet stabile electrophilic compound that we propose is used by some fungi as a novel form of chemical defense. Our investigations have indicated that pericosine A readily reacts with skunk-spray secretions to transform them into odorless products. Mechanistic and computational studies suggested that pericosine A and its synthetic analogues react via SN2'-type mechanisms with thiols and thioacetates under aqueous conditions to generate stable thioethers. Testing revealed that pericosine A did not cause skin or eye irritation and was highly effective at deodorizing skunk anal gland secretions when formulated to include adjunctive cosmetic ingredients.

Safety screening in early drug discovery: An optimized assay panel

BackgroundSeveral factors contribute to the development failure of novel pharmaceuticals, one of the most important being adverse effects in pre-clinical and clinical studies. Early identification of off-target compound activity can reduce safety-related attrition in development. In vitro profiling of drug candidates against a broad range of targets is an important part of the compound selection process. Many compounds are synthesized during early drug discovery, making it necessary to assess poly-pharmacology at a limited number of targets. This paper describes how a rational, statistical-ranking approach was used to generate a cost-effective, optimized panel of assays that allows selectivity focused structure-activity relationships to be explored for many molecules.This panel of 50 targets has been used to routinely screen Roche small molecules generated across a diverse range of therapeutic targets. Target hit rates from the Bioprint® database and internal Roche compounds are discussed. We further describe an example of how this panel was used within an anti-infective project to reduce in vivo testing. MethodTo select the optimized panel of targets, IC50 values of compounds in the BioPrint® database were used to identify assay “hits” i.e. IC50 ≤ 1 μM in 123 different in vitro pharmacological assays. If groups of compounds hit the same targets, the target with the higher hit rate was selected, while others were considered redundant. Using a step-wise analysis, an assay panel was identified to maximize diversity and minimize redundancy. Over a five-year period, this panel of 50 off-targets was used to screen ≈1200 compounds synthesized for Roche drug discovery programs. Compounds were initially tested at 10 μM and hit rates generated are reported. Within one project, the number of hits was used to refine the choice of compounds being assessed in vivo. Results95% of compounds from the BioPrint® panel were identified within the top 47-ranked assays. Based on this analytical approach and the addition of three targets with established safety concerns, a Roche panel was created for external screening. hERG is screened internally and not included in this analysis. Screening at 10 μM in the Roche panel identified that adenosine A3 and 5HT2B receptors had the highest hit rates (~30%), with 50% of the targets having a hit rate of ≤4%. An anti-infective program identified that a high number of hits in the Roche panel was associated with mortality in 19 mouse tolerability studies. To reduce the severity and number of such studies, future compound selections integrated the panel hit score into the selection process for in vivo studies. It was identified that compounds which hit less targets in the panel and had free plasma exposures of ~2 μM were generally better tolerated. DiscussionThis paper describes how an optimized panel of 50 assays was selected on the basis of hit similarity at 123 targets. This reduced panel, provides a cost-effective screening panel for assessing compound promiscuity, whilst also including many safety-relevant targets. Frequent use of the panel in early drug discovery has provided promiscuity and safety-relevant information to inform pre-clinical drug development at Roche.

Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures.

The Toxicology in the 21st Century (Tox21) project seeks to develop and test methods for high-throughput examination of the effect certain chemical compounds have on biological systems. Although primary and toxicity assay data were readily available for multiple reporter gene modified cell lines, extensive annotation and curation was required to improve these datasets with respect to how FAIR (Findable, Accessible, Interoperable, and Reusable) they are. In this study, we fully annotated the Tox21 published data with relevant and accepted controlled vocabularies. After removing unreliable data points, we aggregated the results and created three sets of signatures reflecting activity in the reporter gene assays, cytotoxicity, and selective reporter gene activity, respectively. We benchmarked these signatures using the chemical structures of the tested compounds and obtained generally high receiver operating characteristic (ROC) scores, suggesting good quality and utility of these signatures and the underlying data. We analyzed the results to identify promiscuous individual compounds and chemotypes for the three signature categories and interpreted the results to illustrate the utility and re-usability of the datasets. With this study, we aimed to demonstrate the importance of data standards in reporting screening results and high-quality annotations to enable re-use and interpretation of these data. To improve the data with respect to all FAIR criteria, all assay annotations, cleaned and aggregate datasets, and signatures were made available as standardized dataset packages (Aggregated Tox21 bioactivity data, 2019).

Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis

The ability of compounds to interact with multiple targets is also referred to as promiscuity. Multitarget activity of pharmaceutically relevant compounds provides the foundation of polypharmacolog...

Leveraging Compound Promiscuity to Identify Targetable Cysteines within the Kinome

Covalent kinase inhibitors, which typically target cysteine residues, represent an important class of clinically relevant compounds. Approximately 215 kinases are known to have potentially targetable cysteines distributed across 18 spatially distinct locations proximal to the ATP-binding pocket. However, only 40 kinases have been covalently targeted, with certain cysteine sites being the primary focus. To address this disparity, we havedeveloped a strategy that combines the useof a multi-targeted acrylamide-modified inhibitor, SM1-71, with a suite of complementary chemoproteomic and cellular approaches to identifyadditional targetable cysteines. Using this singlemulti-targeted compound, we successfully identified 23 kinases that are amenable to covalentinhibition including MKNK2, MAP2K1/2/3/4/6/7, GAK, AAK1, BMP2K, MAP3K7, MAPKAPK5, GSK3A/B, MAPK1/3, SRC, YES1, FGFR1, ZAK (MLTK), MAP3K1, LIMK1, and RSK2. The identification of nine of these kinases previously not targeted by a covalent inhibitor increases the number of targetable kinases and highlights opportunities for covalent kinase inhibitor development.

Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.

The ability of a small molecule to interact with multiple target proteins provides the molecular basis of polypharmacology. So-defined compound promiscuity is intensely investigated in drug discovery. For example, for kinase inhibitors, the interplay between target selectivity and promiscuity plays a decisive role for different therapeutic applications. The "promiscuity cliff" (PC) concept was introduced previously to aid in promiscuity analysis. A PC is defined as a pair of structurally similar compounds with a large difference in promiscuity. Accordingly, PCs can reveal small structural modifications that might be responsible for selectivity or multi-target activity. In network representations, PCs form clusters of varying size and complexity that are difficult to analyze interactively. Herein, we introduce a computational method to systematically identify PC pathways, which are particularly rich in structure-promiscuity information, and extract them from PC clusters. PC pathways provide informative templates for experimental design. In a proof-of-concept investigation, we have applied the new computational approach to systematically identify pathways in more than 600 PC clusters formed by inhibitors of the human kinome, demonstrating the utility of the method and revealing many interesting promiscuity patterns.

Recent Progress in Structure-Based Evaluation of Compound Promiscuity

In chemistry and drug discovery, compound promiscuity is a controversial issue and often viewed differently. On the one hand, promiscuity has a clear-cut negative connotation, as promiscuous behavior of small molecules is often associated with nonspecific binding or assay artifacts. On the other hand, it is also well-established that compounds frequently interact with multiple targets including distantly related or unrelated proteins. In drug discovery, multitarget activity of small molecules receives increasing attention because it is therapeutically relevant, giving rise to desired or undesired pharmacological effects. Exploration of compound promiscuity is far from being simple because true and artificial activities are often difficult to discern. In light of these complications, X-ray structures of ligand–target complexes provide a wealth of information about molecular promiscuity, which is just beginning to be recognized and explored. Systematic analysis of structurally confirmed binding events invol...

Promiscuous Ligands from Experimentally Determined Structures, Binding Conformations, and Protein Family-Dependent Interaction Hotspots.

Compound promiscuity is often attributed to nonspecific binding or assay artifacts. On the other hand, it is well-known that many pharmaceutically relevant compounds are capable of engaging multiple targets in vivo, giving rise to polypharmacology. To explore and better understand promiscuous binding characteristics of small molecules, we have searched X-ray structures (and very few qualifying solution structures) for ligands that bind to multiple distantly related or unrelated target proteins. Experimental structures of a given ligand bound to different targets represent high-confidence data for exploring promiscuous binding events. A total of 192 ligands were identified that formed crystallographic complexes with proteins from different families and for which activity data were available. These “multifamily” compounds included endogenous ligands and were often more polar than other bound compounds and active in the submicromolar range. Unexpectedly, many promiscuous ligands displayed conserved or similar binding conformations in different active sites. Others were found to conformationally adjust to binding sites of different architectures. A comprehensive analysis of ligand–target interactions revealed that multifamily ligands frequently formed different interaction hotspots in binding sites, even if their bound conformations were similar, thus providing a rationale for promiscuous binding events at the molecular level of detail. As a part of this work, all multifamily ligands we have identified and associated activity data are made freely available.

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds", "bad actors", or "nuisance compounds". These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on "privileged scaffolds" to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose-response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de , not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (−)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.