An improved beta-lactamase reporter assay: multiplexing with a cytotoxicity readout for enhanced accuracy of hit identification

A problem inherent to the use of cellular assays for drug discovery is their sensitivity to cytotoxic compounds, which can result in false hits from certain compound screens. To alleviate the need to follow-up hits from a reporter assay with a separate cytotoxicity assay, the authors have developed a multiplexed assay that combines the readout of a beta-lactamase reporter with that of a homogeneous cytotoxicity indicator. Important aspects to the development of the multiplexed format are addressed, including results that demonstrate that the IC(50) values of 40 select compounds in a beta-lactamase reporter assay for nuclear factor kappa B and SIE pathway antagonists are not affected by the addition of the cytotoxicity indicator. To demonstrate the improvement in hit confirmation, the multiplexed assay was used to perform a small-library screen (7728 compounds) for serotonin 5HT1A receptor antagonists. Hits identified from analysis of the beta-lactamase reporter data alone were compared to those hits determined when the reporter and cytotoxicity data generated from the multiplexed assay were combined. Confirmation rates were determined from compound follow-up using dose-response analysis of the potential antagonist hits identified by the initial screen. In this representative screen, the multiplexed assay approach yielded a 19% reduction in the number of compounds flagged for follow-up, with a 37% decrease in the number of false hits, demonstrating that multiplexing a beta-lactamase reporter assay with a cytotoxicity readout is a highly effective strategy for reducing false hit rates in cell-based compound screening assays.     

Statistical analysis of systematic errors in high-throughput screening

High-throughput screening (HTS) is an efficient technology for drug discovery. It allows for screening of more than 100,000 compounds a day per screen and requires effective procedures for quality control. The authors have developed a method for evaluating a background surface of an HTS assay; it can be used to correct raw HTS data. This correction is necessary to take into account systematic errors that may affect the procedure of hit selection. The described method allows one to analyze experimental HTS data and determine trends and local fluctuations of the corresponding background surfaces. For an assay with a large number of plates, the deviations of the background surface from a plane are caused by systematic errors. Their influence can be minimized by the subtraction of the systematic background from the raw data. Two experimental HTS assays from the ChemBank database are examined in this article. The systematic error present in these data was estimated and removed from them. It enabled the authors to correct the hit selection procedure for both assays.     

Median absolute deviation to improve hit selection for genome-scale RNAi screens

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)-based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results.     

Identifying actives from HTS data sets: practical approaches for the selection of an appropriate HTS data-processing method and quality control review

High-throughput screening (HTS) has achieved a dominant role in drug discovery over the past 2 decades. The goal of HTS is to identify active compounds (hits) by screening large numbers of diverse chemical compounds against selected targets and/or cellular phenotypes. The HTS process consists of multiple automated steps involving compound handling, liquid transfers, and assay signal capture, all of which unavoidably contribute to systematic variation in the screening data. The challenge is to distinguish biologically active compounds from assay variability. Traditional plate controls-based and non-controls-based statistical methods have been widely used for HTS data processing and active identification by both the pharmaceutical industry and academic sectors. More recently, improved robust statistical methods have been introduced, reducing the impact of systematic row/column effects in HTS data. To apply such robust methods effectively and properly, we need to understand their necessity and functionality. Data from 6 HTS case histories are presented to illustrate that robust statistical methods may sometimes be misleading and can result in more, rather than less, false positives or false negatives. In practice, no single method is the best hit detection method for every HTS data set. However, to aid the selection of the most appropriate HTS data-processing and active identification methods, the authors developed a 3-step statistical decision methodology. Step 1 is to determine the most appropriate HTS data-processing method and establish criteria for quality control review and active identification from 3-day assay signal window and DMSO validation tests. Step 2 is to perform a multilevel statistical and graphical review of the screening data to exclude data that fall outside the quality control criteria. Step 3 is to apply the established active criterion to the quality-assured data to identify the active compounds.     

Streamlining lead discovery by aligning in silico and high-throughput screening

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.     

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.     

An efficient method for the detection and elimination of systematic error in high-throughput screening

MOTIVATION: High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Probing the primary screening efficiency by multiple replicate testing: a quantitative analysis of hit confirmation and false screening results of a biochemical assay

Despite a large body of references on assay development, assay optimization, strategies, and methodologies for high-throughput screening (HTS), there have been few reports on investigations of the efficiency of primary screening in a systematic and quantitative manner for a typical HTS process. Recently, the authors investigated the primary hit comparison and the effect of measurement variability by screening a library of approximately 25,000 random compounds in multiple replicate tests in a nuclear receptor recruitment assay with 2 different assay detection technologies. In this report, we utilized these sets of multiple replicate screening data from a different perspective and conducted a systematic data analysis in order to gain some insights into the hit-finding efficiency of a typical primary screening process. Specifically, hit confirmation, false-positive (declaration) rates, and false-negative rates at different hit cutoff limits were explored and calculated from the 2 different assay formats. Results and analyses provided some quantitative estimation regarding the reliability and efficiency of the primary screening process. For the 2 assay formats tested in this report, the confirmation rate (activity repeated at or above a certain hit limit) was found to be 65% or above. It was also suggested that, at least in this case, applying some hit-selection strategies, it is possible to decrease the number of false-negative or false-positive hits without significantly increasing the efforts in primary screening.     

High-throughput siRNA-based functional target validation

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.     

Identification of novel Kv1.3 blockers using a fluorescent cell-based ion channel assay

A functional cell-based assay was developed using a generic proprietary assay protocol, based on a membrane-potential sensitive dye, for the identification of small-molecule antagonists against the Kv1.3 potassium ion channel. A high-throughput screen (HTS) was subsequently performed with 20,000 compounds from the Evotec library, preselected using known small molecule antagonists for both sodium and potassium ion channels. Following data analysis, the hit rate was measured at 1.72%, and subsequent dose-response analysis of selected hits showed a high hit confirmation rate yielding approximately 50 compounds with an apparent IC50 value lower than 10 microM. Subsequent electrophysiological characterization of selected hits confirmed the initial activity and potency of the identified hits on the Kv1.3 target and also selectivity toward Kv1.3 through measurements on HERG as well as Kv1.3-expressing cell lines. Follow-up structure-activity relationship analysis revealed a variety of different clusters distributed throughout the library as well as several singlicates. In comparison to known Kv1.3 blockers, new chemical entities and scaffolds showing potency and selectivity against the Kv1.3 ion channel were detected. In addition, a screening strategy for ion channel drug discovery HTS, medicinal chemistry, and electrophysiology is presented.     

On the prediction of statistical parameters in high-throughput screening using resampling techniques

A severe drawback in the high-throughput screening (HTS) process is the unintentional (random) presence of false positives and negatives. Their rates depend, among others, on the screening process being applied and the target class. Although false positives can be sorted out in subsequent process steps, their occurrence can lead to increased project cost. More fundamentally, it is not possible to rescue false nonhits. In this article, we investigate the prediction of the primary hit rate, hit confirmation rate, and false-positive and false-negative rates. Results for approximately 2800 compounds are considered that are tested as a pilot screen ahead of the primary screening work. This pilot screen is done at several concentrations and in replicates. The rates are predicted as a function of the proposed hit threshold by having the replicates serve as each other's confirmers, and confidence limits to the prediction are attached by means of a resampling scheme. A comparison of the rates resulting from the resampling with the primary hit rate and the confirmation rates obtained during the screening campaign shows how accurate this method is. Hence, the "optimal" compound concentration for the screen as well as the optimal hit threshold corresponding to low false rates can be determined prior to starting the subsequent screening campaign.     

Virtual screening strategies in drug discovery

The identification of novel therapeutic targets and characterization of their 3D structures is increasing at a dramatic rate. Computational screening methods continue to be developed and improved as credible and complementary alternatives to high-throughput biochemical compound screening (HTS). While the majority of drug candidates currently being developed have been found using HTS methods, high-throughput docking and pharmacophore-based searching algorithms are gaining acceptance and becoming a major source of lead molecules in drug discovery. Refinements and optimization of high-throughput docking methods have lead to improvements in reproducing experimental data and in hit rates obtained, validating their use in hit identification. In parallel with virtual screening methods, concomitant developments in cheminformatics including identification, design and manipulation of drug-like small molecule libraries have been achieved. Herein, currently used in silico screening techniques and their utility on a comparative and target dependent basis is discussed.     

Evaluation of the InteraX system technology in a high-throughput screening environment

The authors have developed a cell-based high-throughput screening (HTS)-compatible assay to measure EGFR dimerization using the InteraX enzyme complementation technology of Applied Biosystems. The cells contain 2 chimeric proteins with complementing deletion mutants of the beta galactosidase enzyme, each fused to the extracellular and transmembrane part of EGFR. On binding of EGF, EGF receptor dimerizes and an active beta galactosidase is built. The authors used this homogeneous 384-well assay to screen about 20,000 diverse compounds. From 2 independent primary screen runs 239 hits were identified. For run 1, a mean S/B ratio of 4.26 and a mean Z' factor of 0.74 were obtained, for run 2 a mean S/B ratio of 3.88 and a mean Z' factor of 0.71 were obtained. After hit confirmation, repeated 4 times, 112 hits remained with a confirmation rate of 48.9%. Thirty of the 112 could be identified as cytotoxic. Fifty-one of the remaining 82 compounds could be shown to be inhibitors of the beta galactosidase enzyme itself. In summary, 31 compounds remained as potential EGFR dimerization or EGF stimulation inhibitors. The authors conclude that the InteraX system technology is HTS capable and can detect small molecule inhibitors capable of inhibiting protein-protein interactions.     

Statistical and graphical methods for quality control determination of high-throughput screening data

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server HTS application, a custom-developed software tool built from the commercially available S-PLUS and Stat Server statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.     

Discovery and optimization of Lu AF58801, a novel,selective and brain penetrant positive allosteric modulator of alpha-7 nicotinic acetylcholine receptors: Attenuation of subchronic phencyclidine (PCP)-induced cognitive deficits in rats following oral administration

In this Letter, we describe a chemical lead optimization campaign starting from a novel, weak α7 nicotinic acetylcholine receptor positive allosteric modulator (PAM) hit from a HTS screen. Exploration of the structure–activity relationships for α7 PAM potency, intrinsic hepatic clearance, the structure–property relationships for lipophilicity, and thermodynamic solubility, led to the identification of Lu AF58801: a potent, orally available, brain penetrant PAM of the α7 nicotinic acetylcholine receptor, showing efficacy in a novel object recognition task in rats treated subchronically with phencyclidine (PCP).     

Identification of novel drug scaffolds for inhibition of SARS-CoV 3-Chymotrypsin-like protease using virtual and high-throughput screenings

We have used a combination of virtual screening (VS) and high-throughput screening (HTS) techniques to identify novel, non-peptidic small molecule inhibitors against human SARS-CoV 3CLpro. A structure-based VS approach integrating docking and pharmacophore based methods was employed to computationally screen 621,000 compounds from the ZINC library. The screening protocol was validated using known 3CLpro inhibitors and was optimized for speed, improved selectivity, and for accommodating receptor flexibility. Subsequently, a fluorescence-based enzymatic HTS assay was developed and optimized to experimentally screen approximately 41,000 compounds from four structurally diverse libraries chosen mainly based on the VS results. False positives from initial HTS hits were eliminated by a secondary orthogonal binding analysis using surface plasmon resonance (SPR). The campaign identified a reversible small molecule inhibitor exhibiting mixed-type inhibition with a K_i value of 11.1 μM. Together, these results validate our protocols as suitable approaches to screen virtual and chemical libraries, and the newly identified compound reported in our study represents a promising structural scaffold to pursue for further SARS-CoV 3CLpro inhibitor development.     

Whole organism high-content screening by label-free, image-based bayesian classification for parasitic diseases

Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on in vitro cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping.     

Statistical practice in high-throughput screening data analysis

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.