Chemical and Biological Properties of Frequent Screening Hits

Emerging Direct Targeting β-Catenin Agents

Aberrant accumulation of β-catenin in the cell nucleus as a result of deregulation of the Wnt/β-catenin pathway is found in various types of cancer. Direct β-catenin targeting agents are being researched despite obstacles; however, specific β-catenin drugs for clinical treatments have not been approved so far. We focused on direct β-catenin targeting of potential therapeutic value as anticancer agents. This review provides recent advances on small molecule β-catenin agents. Structure-activity relationships and biological activities of reported inhibitors are discussed. This work provides useful knowledge in the discovery of β-catenin agents.

Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

Modeling Tanimoto Similarity Value Distributions and Predicting Search Results

Similarity searching using molecular fingerprints has a long history in chemoinformatics and continues to be a popular approach for virtual screening. Typically, known active reference molecules are used to search databases for new active compounds. However, this search has black box character because similarity value distributions are dependent on fingerprints and compound classes. Consequently, no generally applicable similarity threshold values are available as reliable indicators of activity relationships between reference and database compounds. Therefore, it is generally uncertain where new active compounds might appear in database rankings, if at all. In this contribution, methods are discussed for modeling similarity value distributions of fingerprint search calculations using Tanimoto coefficients and estimating rank positions of active compounds. To our knowledge, these are the first approaches for predicting the results of fingerprint-based similarity searching.

Pharmacological Chaperoning: A Potential Treatment for PMM2-CDG

The congenital disorder of glycosylation (CDG) due to phosphomannomutase 2 deficiency (PMM2-CDG), the most common N-glycosylation disorder, is a multisystem disease for which no effective treatment is available. The recent functional characterization of disease-causing mutations described in patients with PMM2-CDG led to the idea of a therapeutic strategy involving pharmacological chaperones (PC) to rescue PMM2 loss-of-function mutations. The present work describes the high-throughput screening, by differential scanning fluorimetry, of 10,000 low-molecular-weight compounds from a commercial library, to search for possible PCs for the enzyme PMM2. This exercise identified eight compounds that increased the thermal stability of PMM2. Of these, four compounds functioned as potential PCs that significantly increased the stability of several destabilizing and oligomerization mutants and also increased PMM activity in a disease model of cells overexpressing PMM2 mutations. Structural analysis revealed one of these compounds to provide an excellent starting point for chemical optimization since it passed tests based on a number of pharmacochemical quality filters. The present results provide the first proof-of-concept of a possible treatment for PMM2-CDG and describe a promising chemical structure as a starting point for the development of new therapeutic agents for this severe orphan disease.

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.

Novel approaches to pulmonary arterial hypertension drug discovery

INTRODUCTION

Pulmonary arterial hypertension (PAH) is a rare disorder associated with abnormally elevated pulmonary pressures that, if untreated, leads to right heart failure and premature death. The goal of drug development for PAH is to develop effective therapies that halt, or ideally, reverse the obliterative vasculopathy that results in vessel loss and obstruction of blood flow to the lungs.

AREAS COVERED

This review summarizes the current approach to candidate discovery in PAH and discusses the currently available drug discovery methods that should be implemented to prioritize targets and obtain a comprehensive pharmacological profile of promising compounds with well-defined mechanisms.

EXPERT OPINION

To improve the successful identification of leading drug candidates, it is necessary that traditional pre-clinical studies are combined with drug screening strategies that maximize the characterization of biological activity and identify relevant off-target effects that could hinder the clinical efficacy of the compound when tested in human subjects. A successful drug discovery strategy in PAH will require collaboration of clinician scientists with medicinal chemists and pharmacologists who can identify compounds with an adequate safety profile and biological activity against relevant disease mechanisms.

Non-stoichiometric inhibition in integrated lead finding - a literature review

INTRODUCTION

Non-stoichiometric inhibition summarizes different mechanisms by which low-molecular weight compounds can reproducibly inhibit high-throughput screening (HTS) and other lead finding assays without binding to a structurally defined site on their molecular target. This disqualifies such molecules from optimization by medicinal chemistry, and therefore their rapid elimination from screening hit lists is essential for productive and effective drug discovery.

AREAS COVERED

This review covers recent literature that either investigates the various mechanisms behind non-stoichiometric inhibition or suggests assays and readouts to identify them. In addition, combination of the various methods to distill promising molecules out of raw primary hit lists step-by-step is considered. Emerging technologies to demonstrate target engagement in cells are also discussed.

EXPERT OPINION

Over the last few years, awareness of non-stoichiometric inhibitors within screening libraries and HTS hit lists has considerably increased, not only in the pharmaceutical industry but also in the academic drug discovery community. This has resulted in a variety of methods to detect and handle such compounds. These range from in silico approaches to flag suspicious compounds, and counterassays to measure non-stoichiometric inhibition, to biophysical methods that positively demonstrate stoichiometric binding. In addition, novel technologies to verify target engagement within cells are becoming available. While still a time- and resource-consuming nuisance, non-stoichiometric inhibitors therefore do not fundamentally jeopardize the discovery of low molecular weight lead and drug candidates. Rather, they should be viewed as a manageable issue that with appropriate expertise can be overcome through integration of the above-mentioned approaches.

Discovery of Selective Small-Molecule Inhibitors for the β-Catenin/T-Cell Factor Protein-Protein Interaction through the Optimization of the Acyl Hydrazone Moiety

Acyl hydrazone is an important functional group for the discovery of bioactive small molecules. This functional group is also recognized as a pan assay interference structure. In this study, a new small-molecule inhibitor for the β-catenin/Tcf protein-protein interaction (PPI), ZINC02092166, was identified through AlphaScreen and FP assays. This compound contains an acyl hydrazone group and exhibits higher inhibitory activities in cell-based assays than biochemical assays. Inhibitor optimization resulted in chemically stable derivatives that disrupt the β-catenin/Tcf PPI. The binding mode of new inhibitors was characterized by site-directed mutagenesis and structure-activity relationship studies. This series of inhibitors with a new scaffold exhibits dual selectivity for β-catenin/Tcf over β-catenin/cadherin and β-catenin/APC PPIs. One derivative of this series suppresses canonical Wnt signaling, downregulates the expression of Wnt target genes, and inhibits the growth of cancer cells. This compound represents a solid starting point for the development of potent and selective β-catenin/Tcf inhibitors.

A brief review of recent Charcot-Marie-Tooth research and priorities

This brief review of current research progress on Charcot-Marie-Tooth (CMT) disease is a summary of discussions initiated at the Hereditary Neuropathy Foundation (HNF) scientific advisory board meeting on November 7, 2014. It covers recent published and unpublished in vitro and in vivo research. We discuss recent promising preclinical work for CMT1A, the development of new biomarkers, the characterization of different animal models, and the analysis of the frequency of gene mutations in patients with CMT. We also describe how progress in related fields may benefit CMT therapeutic development, including the potential of gene therapy and stem cell research. We also discuss the potential to assess and improve the quality of life of CMT patients. This summary of CMT research identifies some of the gaps which may have an impact on upcoming clinical trials. We provide some priorities for CMT research and areas which HNF can support. The goal of this review is to inform the scientific community about ongoing research and to avoid unnecessary overlap, while also highlighting areas ripe for further investigation. The general collaborative approach we have taken may be useful for other rare neurological diseases.

Pharmacophore Modeling, Ensemble Docking, Virtual Screening, and Biological Evaluation on Glycogen Synthase Kinase-3β

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. GSK-3β, also known as tau protein kinase I (TPK-I), is one of the most important kinases implicated in the hyperphosphorylation of tau that leads to neurodegenerative diseases. Hence, GSK-3β has emerged as an important therapeutic target. To identify compounds that are structurally novel and diverse compared to previously reported ATP-competitive GSK-3β inhibitors, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to induced-fit effects were explored. An enrichment study was employed to verify the robustness of ensemble docking, using 13 X-ray structures of GSK-3β, compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening underwent biological validation. The bioassay results showed that 15 out of the 24 hit compounds are indeed GSK-3β inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization.

Exploring the biological promiscuity of high-throughput screening hits through DFT calculations

The goal of this study is the understanding of biologically promiscuous compounds (frequent hitters) in HTS outcomes through their chemical behavior estimated via reactivity descriptors. Chemical reactivity is often an undesirable property due to the lack in biological selectivity of compounds comprised in HTS libraries. In this study the reactivity indexes have been computed within the DFT formalism, at different levels of theory, for two classes of representative compounds compiled from PubChem database, one comprising frequent hitters and the second one comprising rare hitters (biologically more selective compounds). We found that frequent hitters exert increased reactivity, mainly due to their electrophilic character, compared to the more selective class of compounds.

Screening and evaluation of small organic molecules as ClpB inhibitors and potential antimicrobials

Inhibition of ClpB, the bacterial representative of the heat-shock protein 100 family that is associated with virulence of several pathogens, could be an effective strategy to develop new antimicrobial agents. Using a high-throughput screening method, we have identified several compounds that bind to different conformations of ClpB and analyzed their effect on the ATPase and chaperone activities of the protein. Two of them inhibit these functional properties as well as the growth of Gram negative bacteria (E. coli), displaying antimicrobial activity under thermal or oxidative stress conditions. This activity is abolished upon deletion of ClpB, indicating that the action of these compounds is related to the stress cellular response in which ClpB is involved. Moreover, their moderate toxicity in human cell lines suggests that they might provide promising leads against bacterial growth.

Cheminformatics at the interface of medicinal chemistry and proteomics

Multiple factors have to be optimized in the course of a drug discovery project. Traditionally this includes potency on a single target, eventually specificity as well as the pharmacokinetic, physicochemical and the safety profile. Recently an additional dimension has been added by realizing that the therapeutic outcome of a drug is often determined not only by its activity on a single target but also by its activity profile across a variety of biological targets. To address the polypharmacology of drug candidates many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. To extract useful information computational methods at the interface of proteomics and cheminformatics are indispensable. This review will focus on some recent developments in this field. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Locating sweet spots for screening hits and evaluating pan-assay interference filters from the performance analysis of two lead-like libraries

The efficiency of automated compound screening is heavily influenced by the design and the quality of the screening libraries used. We recently reported on the assembly of one diverse and one target-focused lead-like screening library. Using data from 15 enzyme-based screenings conducted using these libraries, their performance was investigated. Both libraries delivered screening hits across a range of targets, with the hits distributed across the entire chemical space represented by both libraries. On closer inspection, however, hit distribution was uneven across the chemical space, with enrichments observed in octants characterized by compounds at the higher end of the molecular weight and lipophilicity spectrum for lead-like compounds, while polar and sp³-carbon atom rich compounds were underrepresented among the screening hits. Based on these observations, we propose that screening libraries should not be evenly distributed in lead-like chemical space but be enriched in polar, aliphatic compounds. In conjunction with variable concentration screening, this could lead to more balanced hit rates across the chemical space and screening hits of higher ligand efficiency will be captured. Apart from chemical diversity, both screening libraries were shown to be clean from any pan-assay interference (PAINS) behavior. Even though some compounds were flagged to contain PAINS structural motifs, some of these motifs were demonstrated to be less problematic than previously suggested. To maximize the diversity of the chemical space sampled in a screening campaign, we therefore consider it justifiable to retain compounds containing PAINS structural motifs that were apparently clean in this analysis when assembling screening libraries.

Discovery of a Potent Anti-tumor Agent through Regioselective Mono-N-acylation of 7H-Pyrrolo[3,2-f]quinazoline-1,3-diamine

7H-Pyrrolo[3,2-f]quinazoline-1,3-diamine (1) is a privileged chemical scaffold with significant biological activities. However, the currently accessible chemical space derived from 1 is rather limited. Here we expanded the chemical space related to 1 by developing efficient methods for regioselective monoacylation at N¹ , N³ and N⁷ , respectively. With this novel methodology, a focused library of mono-N-acylated pyrroloquinazoline-1,3-diamines were prepared and screened for anti-breast cancer activity. The structure-activity relationship (SAR) results showed that N³ -acylated compounds were in general more potent than N¹ -acylated compounds while N⁷ -acylation significantly reduced their solubility. Among the compounds evaluated, 7f possessed 8-fold more potent activity than 1 in MDA-MB-468 cells. More importantly, 7f was not toxic to normal human cells. These results suggest that 7f is a novel compound as a potential anti-breast cancer agent without harming normal cells.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization

INTRODUCTION

The most desirable chemical starting point in drug discovery is a hit or lead with a good overall profile, and where there may be issues; a clear SAR strategy should be identifiable to minimize the issue. Filtering based on drug-likeness concepts are a first step, but more accurate theoretical methods are needed to i) estimate the biological profile of molecule in question and ii) based on the underlying structure-activity relationships used by the model, estimate whether it is likely that the molecule in question can be altered to remove these liabilities.

AREAS COVERED

In this paper, the authors discuss the generation of ADMET models and their practical use in decision making. They discuss the issues surrounding data collation, experimental errors, the model assessment and validation steps, as well as the different types of descriptors and statistical models that can be used. This is followed by a discussion on how the model accuracy will dictate when and where it can be used in the drug discovery process. The authors also discuss how models can be developed to more effectively enable multiple parameter optimization.

EXPERT OPINION

Models can be applied in lead generation and lead optimization steps to i) rank order a collection of hits, ii) prioritize the experimental assays needed for different hit series, iii) assess the likelihood of resolving a problem that might be present in a particular series in lead optimization and iv) screen a virtual library based on a hit or lead series to assess the impact of diverse structural changes on the predicted properties.