High‐throughput cell quantification assays for use in cell purification development – enabling technologies for cell production

High‐throughput screening (HTS) technology is gaining increasing importance in downstream process development of cell‐based products. The development of such HTS‐technologies, however, is highly dependent on the availability of robust, accurate, and sensitive high‐throughput cell quantification methods. In this article, we compare state‐of‐the‐art cell quantification methods with focus on their applicability in HTS‐platforms for downstream processing of cell‐based products. Sensitivity, dynamic range, and precision were evaluated for four methods that differ in their respective mechanism. In addition, we evaluated the performance of these methods over a range of buffer compositions, medium densities, and viscosities, representing conditions found in many downstream processing methods. We found that CellTiter‐Glo? and flow cytometry are excellent tools for high‐throughput cell quantification. Both methods have broad working ranges (3–4 log) and performed well over a wide range of buffer compositions. In comparison, CyQuant® Direct and CellTracker? had smaller working ranges and were more sensitive to changes in buffer composition. For fast and sensitive quantification of a single cell type, CellTiter‐Glo? performed best, while for more complex cell mixtures flow cytometry is the method of choice. Our analysis will facilitate the selection of the most suitable method for a specific application and provides a benchmark for future HTS development in downstream processing of cell‐based products.     

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.     

Automated assay optimization with integrated statistics and smart robotics

The transition from manual to robotic high throughput screening (HTS) in the last few years has made it feasible to screen hundreds of thousands of chemical entities against a biological target in less than a month. This rate of HTS has increased the visibility of bottlenecks, one of which is assay optimization. In many organizations, experimental methods are generated by therapeutic teams associated with specific targets and passed on to the HTS group. The resulting assays frequently need to be further optimized to withstand the rigors and time frames inherent in robotic handling. Issues such as protein aggregation, ligand instability, and cellular viability are common variables in the optimization process. The availability of robotics capable of performing rapid random access tasks has made it possible to design optimization experiments that would be either very difficult or impossible for a person to carry out. Our approach to reducing the assay optimization bottleneck has been to unify the highly specific fields of statistics, biochemistry, and robotics. The product of these endeavors is a process we have named automated assay optimization (AAO). This has enabled us to determine final optimized assay conditions, which are often a composite of variables that we would not have arrived at by examining each variable independently. We have applied this approach to both radioligand binding and enzymatic assays and have realized benefits in both time and performance that we would not have predicted a priori. The fully developed AAO process encompasses the ability to download information to a robot and have liquid handling methods automatically created. This evolution in smart robotics has proven to be an invaluable tool for maintaining high-quality data in the context of increasing HTS demands.     

Systematic error detection in experimental high-throughput screening

Background  

High-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measured in order to identify potential drug candidates (i.e., hits). Many technical, procedural or environmental factors can cause systematic measurement error or inequalities in the conditions in which the measurements are taken. Such systematic error has the potential to critically affect the hit selection process. Several error correction methods and software have been developed to address this issue in the context of experimental HTS [1–7]. Despite their power to reduce the impact of systematic error when applied to error perturbed datasets, those methods also have one disadvantage - they introduce a bias when applied to data not containing any systematic error [6]. Hence, we need first to assess the presence of systematic error in a given HTS assay and then carry out systematic error correction method if and only if the presence of systematic error has been confirmed by statistical tests.     

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.     

High throughput screening setup of a scale-down device for membrane chromatography-aggregate removal of monoclonal antibodies

In biopharmaceutical process development, resin-based high throughput screening (HTS) is well known for overcoming experimental limitations by permitting automated parallel processing at miniaturized scale, which results in fast data generation and reduced feed consumption. For membrane adsorber (MA), HTS solutions have so far only been available to a partial extent. Three case studies were performed with the aim of aligning HTS applications for MAs with those established for column chromatography: Process parameter range determination, mechanistic modeling (MM), and scalability. In order to exploit the MA typically features, such as high mass transfer and easy scalability, for scalable high throughput process development, a scale-down device (SDD) for MA was developed. Its applicability is confirmed for a monoclonal antibody aggregate removal step. The first case study explores the experimental application of the SDD developed. It uses bind and elute mode and variations of pH and salt concentration to obtain process operation windows for ion-exchange MAs Sartobind® S and Q. In the second case study, we successfully developed a mechanistic model based on parameters obtained from the SDD–HTS setup. The results proved to validate the use of the SDD developed for parameter estimation and thus model-based process development. The third case study shows the transferability and scalability of data from the SDD–HTS setup using both a direct scale factor and MM. Both approaches show good applicability with a deviation below 20% in the prediction of 10% dynamic breakthrough capacity and reliable scale-up from 0.42 to 800 ml.     

High Throughput Screen for Escherichia coli Twin Arginine Translocation (Tat) Inhibitors

The twin arginine translocation (Tat) pathway transports fully-folded and assembled proteins in bacteria, archaea and plant thylakoids. The Tat pathway contributes to the virulence of numerous bacterial pathogens that cause disease in humans, cattle and poultry. Thus, the Tat pathway has the potential to be a novel therapeutic target. Deciphering the Tat protein transport mechanism has been challenging since the active translocon only assembles transiently in the presence of substrate and a proton motive force. To identify inhibitors of Tat transport that could be used as biochemical tools and possibly as drug development leads, we developed a high throughput screen (HTS) to assay the effects of compounds in chemical libraries against protein export by the Escherichia coli Tat pathway. The primary screen is a live cell assay based on a fluorescent Tat substrate that becomes degraded in the cytoplasm when Tat transport is inhibited. Consequently, low fluorescence in the presence of a putative Tat inhibitor was scored as a hit. Two diverse chemical libraries were screened, yielding average Z''-factors of 0.74 and 0.44, and hit rates of ~0.5% and 0.04%, respectively. Hits were evaluated by a series of secondary screens. Electric field gradient (Δψ) measurements were particularly important since the bacterial Tat transport requires a Δψ. Seven low IC₅₀ hits were eliminated by Δψ assays, suggesting ionophore activity. As Δψ collapse is generally toxic to animal cells and efficient membrane permeability is generally favored during the selection of library compounds, these results suggest that secondary screening of hits against electrochemical effects should be done early during hit validation. Though none of the short-listed compounds inhibited Tat transport directly, the screening and follow-up assays developed provide a roadmap to pursue Tat transport inhibitors.     

High‐throughput screening of a small molecule library for promoters and inhibitors of mesenchymal stem cell osteogenic differentiation

The use of high‐throughput screening (HTS) techniques has long been employed by the pharmaceutical industry to increase discovery rates for new drugs that could be useful for disease treatment, yet this technology has only been minimally applied in other applications such as in tissue regeneration. In this work, an assay for the osteogenic differentiation of human mesenchymal stem cells (hMSCs) was developed and used to screen a library of small molecules for their potential as either promoters or inhibitors of osteogenesis, based on levels of alkaline phosphatase activity and cellular viability. From a library of 1,040 molecules, 36 promoters, and 20 inhibitors were identified as hits based on statistical criteria. Osteopromoters from this library were further investigated using standard culture techniques and a wider range of outcomes to verify that these compounds drive cellular differentiation. Several hits led to some improvement in the expression of alkaline phosphatase, osteogenic gene expression, and matrix mineralization by hMSCs when compared to the standard dexamethasone supplemented media and one molecule was investigated in combination with a recently identified biodegradable and osteoconductive polymer. This work illustrates the ability of HTS to more rapidly identify potential molecules to control stem cell differentiation. Biotechnol. Bioeng. 2011; 108:163–174. © 2010 Wiley Periodicals, Inc.     

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.     

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.     

High‐throughput downstream process development for cell‐based products using aqueous two‐phase systems (ATPS) – A case study

The availability of preparative‐scale downstream processing strategies for cell‐based products presents a critical juncture between fundamental research and clinical development. Aqueous two‐phase systems (ATPS) present a gentle, scalable, label‐free, and cost‐effective method for cell purification, and are thus a promising tool for downstream processing of cell‐based therapeutics. Here, the application of a previously developed robotic screening platform that enables high‐throughput cell partitioning analysis in ATPS is reported. In the present case study a purification strategy for two model cell lines based on high‐throughput screening (HTS)‐data and countercurrent distribution (CCD)‐modeling, and validated the CCD‐model experimentally is designed. The obtained data are shown an excellent congruence between CCD‐model and experimental data, indicating that CCD‐models in combination with HTS‐data are a powerful tool in downstream process development. Finally, the authors are shown that while cell cycle phase significantly influences cell partitioning, cell type specific differences in surface properties are the main driving force in charge‐dependent separation of HL‐60 and L929 cells. In order to design a highly robust purification process it is, however, advisable to maintain constant growth conditions.     

Multidimensional profiling of CSF1R screening hits and inhibitors: assessing cellular activity, target residence time, and selectivity in a higher throughput way

Over the past years, improvements in high-throughput screening (HTS) technology and compound libraries have resulted in a dramatic increase in the amounts of good-quality screening hits, and there is a growing need for follow-on hit profiling assays with medium throughput to further triage hits. Here the authors present such assays for the colony-stimulating factor 1 receptor (CSF1R, Fms), including tests for cellular activity and a homogeneous assay to measure affinity for inactive CSF1R. They also present a high-throughput assay to measure target residence time, which is based on competitive binding kinetics. To better fit k(off) rates, they present a modified mathematical model for competitive kinetics. In all assays, they profiled eight reference inhibitors (imatinib, sorafenib, sunitinib, tandutinib, dasatinib, GW2580, Ki20227, and J&J's pyrido[2,3-d]pyrimidin-5-one). Using the known biochemical selectivities of these inhibitors, which can be quantified using metrics such as the selectivity entropy, the authors have determined which assay readout best predicts hit selectivity. Their profiling shows surprisingly that imatinib has a preference for the active form of CSF1R and that Ki20227 has an unusually slow target dissociation rate. This confirms that follow-on hit profiling is essential to ensure that the best hits are selected for lead optimization.     

A versatile Rapture (RAD‐Capture) platform for genotyping marine turtles

Advances in high‐throughput sequencing (HTS) technologies coupled with increased interdisciplinary collaboration are rapidly expanding capacity in the scope and scale of wildlife genetic studies. While existing HTS methods can be directly applied to address some evolutionary and ecological questions, certain research goals necessitate tailoring methods to specific study organisms, such as high‐throughput genotyping of the same loci that are comparable over large spatial and temporal scales. These needs are particularly common for studies of highly mobile species of conservation concern like marine turtles, where life history traits, limited financial resources and other constraints require affordable, adaptable methods for HTS genotyping to meet a variety of study goals. Here, we present a versatile marine turtle HTS targeted enrichment platform adapted from the recently developed Rapture (RAD‐Capture) method specifically designed to meet these research needs. Our results demonstrate consistent enrichment of targeted regions throughout the genome and discovery of candidate variants in all species examined for use in various conservation genetics applications. Accurate species identification confirmed the ability of our platform to genotype over 1,000 multiplexed samples and identified areas for future methodological improvement such as optimization for low initial concentration samples. Finally, analyses within green turtles supported the ability of this platform to identify informative SNPs for stock structure, population assignment and other applications over a broad geographic range of interest to management. This platform provides an additional tool for marine turtle genetic studies and broadens capacity for future large‐scale initiatives such as collaborative global marine turtle genetic databases.     

Identification of novel GPR81 agonist lead series for target biology evaluation

GPR81 is a novel drug target that is implicated in the control of glucose and lipid metabolism. The lack of potent GPR81 modulators suitable for in vivo studies has limited the pharmacological characterization of this lactate sensing receptor. We performed a high throughput screen (HTS) and identified a GPR81 agonist chemical series containing a central acyl urea scaffold linker. During SAR exploration two additional new series were evolved, one containing cyclic acyl urea bioisosteres and another a central amide bond. These three series provide different selectivity and physicochemical properties suitable for in-vivo studies.     

High throughput screening for small molecule therapy for Gaucher disease using patient tissue as the source of mutant glucocerebrosidase

Gaucher disease (GD), the most common lysosomal storage disorder, results from the inherited deficiency of the lysosomal enzyme glucocerebrosidase (GCase). Previously, wildtype GCase was used for high throughput screening (HTS) of large collections of compounds to identify small molecule chaperones that could be developed as new therapies for GD. However, the compounds identified from HTS usually showed reduced potency later in confirmatory cell-based assays. An alternate strategy is to perform HTS on mutant enzyme to identify different lead compounds, including those enhancing mutant enzyme activities. We developed a new screening assay using enzyme extract prepared from the spleen of a patient with Gaucher disease with genotype N370S/N370S. In tissue extracts, GCase is in a more native physiological environment, and is present with the native activator saposin C and other potential cofactors. Using this assay, we screened a library of 250,000 compounds and identified novel modulators of mutant GCase including 14 new lead inhibitors and 30 lead activators. The activities of some of the primary hits were confirmed in subsequent cell-based assays using patient-derived fibroblasts. These results suggest that primary screening assays using enzyme extracted from tissues is an alternative approach to identify high quality, physiologically relevant lead compounds for drug development.     

Identification and optimisation of novel sulfonamide, selective vasopressin V1B receptor antagonists

The synthesis and preliminary structure-activity relationships (SAR) of a novel class of vasopressin V(1B) receptor antagonists are described. Hit compound 5, identified via high throughput screening of the corporate collection, showed good activity in a V(1B) binding assay (K(i) 63 nM) but did not possess the lead-like physicochemical properties typically required in a hit compound. A 'deletion approach' on the HTS hit 5 was performed, with the focus on improvement of physicochemical properties, yielding the selective V(1B) antagonist 9f (K(i) 190 nM), with improved druglike characteristics.     

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.