Abbott Physicochemical Tiering (APT)--a unified approach to HTS triage

The selection of the highest quality chemical matter from high throughput screening (HTS) is the ultimate aim of any triage process. Typically there are many hundreds or thousands of hits capable of modulating a given biological target in HTS with a wide range of physicochemical properties that should be taken into consideration during triage. Given the multitude of physicochemical properties that define drug-like space, a system needs to be in place that allows for a rapid selection of chemical matter based on a prioritized range of these properties. With this goal in mind, we have developed a tool, coined Abbott Physicochemical Tiering (APT) that enables hit prioritization based on ranges of these important physicochemical properties. This tool is now used routinely at Abbott to help prioritize hits out of HTS during the triage process. Herein we describe how this tool was developed and validated using Abbott internal high throughput ADME data (HT-ADME).     

Using clustering techniques to improve hit selection in high-throughput screening

A typical modern high-throughput screening (HTS) operation consists of testing thousands of chemical compounds to select active ones for future detailed examination. The authors describe 3 clustering techniques that can be used to improve the selection of active compounds (i.e., hits). They are designed to identify quality hits in the observed HTS measurements. The considered clustering techniques were first tested on simulated data and then applied to analyze the assay inhibiting Escherichia coli dihydrofo-late reductase produced at the HTS laboratory of McMaster University.     

Diverse heterocyclic scaffolds as dCTP pyrophosphatase 1 inhibitors. Part 1: Triazoles,triazolopyrimidines, triazinoindoles,quinoline hydrazones and arylpiperazines

A high-throughput screening campaign using a commercial compound library (ChemBridge DiverSET) revealed diverse chemotypes as inhibitors of the human dCTP pyrophosphatase 1 (dCTPase). Triazole, triazolopyrimidine, triazinoindole, quinoline hydrazone and arylpiperazine hits were clustered, confirmed by IC₅₀ determinations, and their preliminary structure-activity-relationships (SAR) and ligand efficiency scores are discussed in this letter.     

A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays

The ability to identify active compounds (3hits2) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the 3suitability2 of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called 3Z-factor,2 is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.     

The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.     

High-throughput determination of mode of inhibition in lead identification and optimization

After finishing the primary high-throughput screening, the screening team is often faced with thousands of hits to be evaluated further. Effective filtering of these hits is crucial in identifying leads. Mode of inhibition (MOI) study is extremely useful in validating whether the observed compound activity is specific to the biological target. In this article, the authors describe a high-throughput MOI determination method for evaluating thousands of compounds using an existing screening infrastructure. Based on enzyme or receptor kinetics theory, the authors developed the method by measuring the ratio of IC(50) or percent inhibition at 2 carefully chosen substrate or ligand concentrations to define an inhibitor as competitive, uncompetitive, or noncompetitive. This not only facilitates binning of HTS hits according to their MOI but also greatly expands HTS utility in support of the medicinal chemistry team's lead optimization practice. Three case studies are presented to demonstrate how the method was applied successfully in 3 discovery programs targeting either an enzyme or a G-protein-coupled receptor.     

Use of mixture distributions to deconvolute the behavior of "hits" and controls in high-throughput screening data

The stochastic nature of high-throughput screening (HTS) data indicates that information may be gleaned by applying statistical methods to HTS data. A foundation of parametric statistics is the study and elucidation of population distributions, which can be modeled using modern spreadsheet software. The methods and results described here use fundamental concepts of statistical population distributions analyzed using a spreadsheet to provide tools in a developing armamentarium for extracting information from HTS data. Specific examples using two HTS kinase assays are analyzed. The analyses use normal and gamma distributions, which combine to form mixture distributions. HTS data were found to be described well using such mixture distributions, and deconvolution of the mixtures to the constituent gamma and normal parts provided insight into how the assays performed. In particular, the proportion of hits confirmed was predicted from the original HTS data and used to assess screening assay performance. The analyses also provide a method for determining how hit thresholds--values used to separate active from inactive compounds--affect the proportion of compounds verified as active and how the threshold can be chosen to optimize the selection process.     

HTS promiscuity analyses for accelerating decision making

Frequent hitters are compounds that are detected as a "hit" in multiple high-throughput screening (HTS) assays. Such behavior is specific (e.g., target family related) or unspecific (e.g., reactive compounds) or can result from a combination of such behaviors. Detecting such hits while predicting the underlying reason behind their promiscuous behavior is desirable because it provides valuable information not only about the compounds themselves but also about the assay methodology and target classes at hand. This information can also greatly reduce cost and time during HTS hit profiling. The present study exemplifies how to mine large HTS data repositories, such as the one at Boehringer Ingelheim, to identify frequent hitters, gain further insights into the causes of promiscuous behavior, and generate models for predicting promiscuous compounds. Applications of this approach are demonstrated using two recent large-scale HTS assays. The authors believe this analysis and its concrete applications are valuable tools for streamlining and accelerating decision-making processes during the course of hit discovery.     

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.     

High-throughput identification of phage-derived imaging agents

The use of phage-displayed peptide libraries is a powerful method for selecting peptides with desired binding properties. However, the validation and prioritization of "hits" obtained from this screening approach remains challenging. Here, we describe the development and testing of a new analysis method to identify and display hits from phage-display experiments and high-throughput enzyme-linked immunosorbent assay screens. We test the method using a phage screen against activated macrophages to develop imaging agents with higher specificity for active disease processes. The new methodology should be useful in identifying phage hits and is extendable to other library screening methods such as small-molecule and nanoparticle libraries.     

A simple, no-wash cell adhesion-based high-throughput assay for the discovery of small-molecule regulators of the integrin CD11b/CD18

The leukocyte-specific integrin CD11b/CD18 plays a key role in the biological function of these cells and represents a validated therapeutic target for inflammatory diseases. Currently, the low affinity interaction between CD11b/CD18 integrin and its respective ligand poses a challenge in the development of cell-based adhesion assays for the high-throughput screening (HTS) environment. Here the authors describe a simple cell-based adhesion assay that can be readily used for HTS for the discovery of functional regulators of CD11b/CD18. The assay consistently produces acceptable Z' values (> 0.5) for HTS. After testing the assay using 2 established blocking antibodies as reference biologicals, the authors performed a proof-of-concept primary screen using a library of 6612 compounds and identified both agonist and antagonist hits.     

Discovery of novel benzoquinazolinones and thiazoloimidazoles, inhibitors of influenza H5N1 and H1N1 viruses, from a cell-based high-throughput screen

A highly reproducible and robust cell-based high-throughput screening (HTS) assay was adapted for screening of small molecules for antiviral activity against influenza virus strain A/Vietnam/1203/2004 (H5N1). The NIH Molecular Libraries Small Molecule Repository (MLSMR) Molecular Libraries Screening Centers Network (MLSCN) 100,000-compound library was screened at 50 μM. The "hit" rate (>25% inhibition of the viral cytopathic effect) from the single-dose screen was 0.32%. The hits were evaluated for their antiviral activity, cell toxicity, and selectivity in dose-response experiments. The screen yielded 5 active compounds (SI value >3). One compound showed an SI(50) value of greater than 3, 3 compounds had SI values ranging from greater than 14 to 34, and the most active compound displayed an SI value of 94. The active compounds represent 2 different classes of molecules, benzoquinazolinones and thiazoloimidazoles, which have not been previously identified as having antiviral/anti-influenza activity. These molecules were also effective against influenza A/California/04/2009 virus (H1N1) and other H1N1 and H5N1 virus strains in vitro but not H3N2 strains. Real-time qRT-PCR results reveal that these chemotypes significantly reduced M1 RNA levels as compared to the no-drug influenza-infected Madin Darby canine kidney cells.     

Stability Challenges in Drug Discovery

Stability is one of the most important properties of drug candidates. Instable compounds can lead to false positive high‐throughput screening (HTS) hits, incorrect bioassay results, erroneous structure–activity relationships (SAR), low oral bioavailability, drug withdrawal, toxic reactions from degradation products, and difficult formulation development. Screening of stability has been implemented early in drug discovery to identify labile chemotypes and guide structural modification. The most commonly applied stability studies in drug discovery are stability–pH profile, stability in gastrointestinal fluids, stability in bioassay media, excipient compatibility, and prodrug screening. The strategy enhances the quality of drug development candidates and reduces the risks.     

Early phase drug discovery: Cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.     

High-throughput identification of inhibitors of human mitochondrial peptide deformylase

The human mitochondrial peptide deformylase (HsPDF) provides a potential new target for broadly acting antiproliferative agents. To identify novel nonpeptidomimetic and nonhydroxamic acid-based inhibitors of HsPDF, the authors have developed a high-throughput screening (HTS) strategy using a fluorescence polarization (FP)-based binding assay as the primary assay for screening chemical libraries, followed by an enzymatic-based assay to confirm hits, prior to characterization of their antiproliferative activity against established tumor cell lines. The authors present the results and performance of the established strategy tested in a pilot screen of 2880 compounds and the identification of the 1st inhibitors. Two common scaffolds were identified within the hits. Furthermore, cytotoxicity studies revealed that most of the confirmed hits have antiproliferative activity. These findings demonstrate that the designed strategy can identify novel functional inhibitors and provide a powerful alternative to the use of functional assays in HTS and support the hypothesis that HsPDF inhibitors may constitute a new class of antiproliferative agent.     

Automated nano-electrospray mass spectrometry for protein-ligand screening by noncovalent interaction applied to human H-FABP and A-FABP

A method for ligand screening by automated nano-electrospray ionization mass spectrometry (nano-ESI/MS) is described. The core of the system consisted of a chip-based platform for automated sample delivery from a 96-well plate and subsequent analysis based on noncovalent interactions. Human fatty acid binding protein, H-FABP (heart) and A-FABP (adipose), with small potential ligands was analyzed. The technique has been compared with a previously reported method based on nuclear magnetic resonance (NMR), and excellent correlation with the found hits was obtained. In the current MS screening method, the cycle time per sample was 1.1 min, which is approximately 50 times faster than NMR for single compounds and approximately 5 times faster for compound mixtures. High reproducibility was achieved, and the protein consumption was in the range of 88 to 100 picomoles per sample. Futhermore, a novel protocol for preparation of A-FABP without the natural ligand is presented. The described screening approach is suitable for ligand screening very early in the drug discovery process before conventional high-throughput screens (HTS) are developed and/or used as a secondary screening for ligands identified by HTS.     

A new method with flexible and balanced control of false negatives and false positives for hit selection in RNA interference high-throughput screening assays

The z-score method and its variants for testing mean difference are commonly used for hit selection in high-throughput screening (HTS) assays. Strictly standardized mean difference (SSMD) offers a way to measure and classify the short interfering RNA (siRNA) effects. In this article, based on SSMD, the authors propose a new testing method for hit selection in RNA interference (RNAi) HTS assays. This SSMD-based method allows the differentiation between siRNAs with large and small effects on the assay output and maintains flexible and balanced control of both the false-negative rate, in which the siRNAs with strong effects are not selected as hits, and the restricted false-positive rate, in which the siRNAs with weak or no effects are selected as hits. This method directly addresses the size of siRNA effects represented by the strength of difference between an siRNA and a negative reference, whereas the classic z-score method and t-test of testing no mean difference address whether the mean of an siRNA is exactly the same as the mean of a negative reference. This method can readily control the false-negative rate, whereas it is nontrivial for the classic z-score method and t-test to control the false-negative rate. Therefore, theoretically, the SSMD-based method offers better control of the sizes of siRNA effects and the associated false-positive and false-negative rates than the commonly used z-score method and t-test for hit selection in HTS assays. The SSMD-based method should generally be applicable to any assay in which the end point is a difference in signal compared to a reference sample, including those for RNAi, receptor, enzyme, and cellular function.     

Kinesin spindle protein (KSP) inhibitors. Part 1: The discovery of 3,5-diaryl-4,5-dihydropyrazoles as potent and selective inhibitors of the mitotic kinesin KSP

Optimization of high-throughput screening (HTS) hits resulted in the discovery of 3,5-diaryl-4,5-dihydropyrazoles as potent and selective inhibitors of KSP. Dihydropyrazole 15 is a potent, cell-active KSP inhibitor that induces apoptosis and generates aberrant mitotic spindles in human ovarian carcinoma cells at low nanomolar concentrations. X-ray crystallographic evidence is presented which demonstrates that these inhibitors bind in an allosteric pocket of KSP distant from the nucleotide and microtubule binding sites.