Abstract
Laboratory Automation and High-Throughput Chemistry
Rationalizing the Chemical Space of Protein—Protein Interaction Inhibitors
O. Sperandio et al. claim that protein—protein interactions (PPIs) might be one of the next major classes of therapeutic targets, although they are too intricate to tackle with standard approaches. This could be attributed, in part, to the inadequacy of today's chemical libraries. However, the emergence of a growing number of experimentally validated inhibitors of PPIs (i-PPIs) allows drug designers to use chemoinformatics and machine-learning technologies to unravel the nature of the chemical space covered by the reported compounds. Key characteristics of i-PPIs can then be revealed, and they highlight the importance of specific shapes and aromatic bonds, enabling the design of i-PPI-enriched focused libraries and of cost-effective screening strategies (Drug Discov. Today
Solid-Phase Peptide Synthesis of Endothelin Receptor Antagonists on Novel Flexible, Styrene-Acryloyloxyhydroxypropyl Methacrylate-Tripropyleneglycol Diacrylate Resin
Novel cross-linked polymeric support by the copolymerization of styrene and 3-(acryloyloxy)-2-hydroxypropyl methacrylate with Tri(propyleneglycol) diacrylate (SAT) for solid-phase peptide synthesis is reported by G. S. Vinod Kumar et al. The synthesis of SAT is based on the cross-linking of 3-(acryloyloxy)-2-hydroxypropyl methacrylate with styrene by free-radical suspension polymerization, consisting of an ester and a secondary hydroxyl group. An additional cross-linker tri(propyleneglycol) diacrylate provides a hydrophilic environment throughout the resin, which enhances the physico-chemical properties of the resin toward organic synthesis. The resins are synthesized in various cross-linking densities to check the swelling property, mechanical stability, and functional loading capacity. The efficiency of SAT support is proven by synthesizing the challenging peptide sequence of acyl carrier protein and compared with commercially available Merrifield resin (J. Comb. Chem.
Nano-Combinatorial Chemistry Strategy for Nanotechnology Research
Nanotechnology refers to the creation of functional materials, devices, and systems, through the control of matter on the nanometer scale, and the exploitation of novel phenomena and properties at that scale. The discovery and optimization of novel nanomaterials with unique properties require time-consuming research efforts. Parallel reactions and screenings are deemed to be more efficient than conventional linear operations. Combinatorial chemistry has already revolutionized drug discovery and the discovery of materials, catalysts, polymers, and pesticides. It recently also has made a significant impact on nanotechnology. G. Su and B. Yan review diverse applications of combinatorial and high-throughput approaches in nanotechnology research (J. Comb. Chem.
Microfluidic Chip Technology and Micro Reactor Technology
Fully Automated Continuous-Flow Synthesis of Highly Functionalized Imidazo[1,2-a] Heterocycles
The first continuous-flow synthesis of imidazo[1,2-a]pyridine-2-carboxylic acids directly from 2-aminopyridines and bromopyruvic acid has been developed by N. D. P. Cosford et al., representing a significant advance over the corresponding in-flask method. The process is applied to the multistep synthesis of imidazo[1,2-a]pyridine-2-carboxamides, including a Mur ligase inhibitor, using a two-microreactor, multistep continuous-flow process without the isolation of intermediates (Org. Lett.
High-Throughput Analytics
Preformulation Designed to Enable Discovery and Assess Developability
Physicochemical properties of drug molecules impact many aspects of both in vivo and in vitro behavior. Poor physicochemical properties can often create a significant impediment in establishing reliable structure-activity relationships (SAR), establishing proof of principle-type studies using in vivo models, and eventually leading to added performance variability and costs throughout the development life cycle—in the worst-case scenario, even preventing the execution of the desired development plan. Understanding the fundamental physicochemical properties provides the basis to dissect and deconvolute experimental observations in such a way that modification or mitigation of poor molecular properties can be impacted at the design phase, ensuring design and selection of a molecule that has a high probability of making it through the development cycle. M. J. Hageman describes in a review the key physicochemical properties and how they can be assessed and how they are implicated in both discovery enablement and in final product developability of the selected candidate (Comb. Chem. High Throughput Screen.
Solubility and Permeability Measurement and Applications in Drug Discovery
Solubility and cellular permeability are two of the most important biopharmaceutical properties impacting the successful development of drug substances. Given the importance of these properties, most pharmaceutical companies have invested in medium- to high-throughput technologies for early evaluation of these characteristics in the drug discovery funnel to select, prioritize, or eliminate compounds with unfavorable solubility and permeability. However, these technologies require physical samples of the substances to be tested. To facilitate the early stages of drug discovery, such as defining compound collection composition, designing combinatorial libraries, and in hit expansion or lead optimization, models for predicting aqueous solubility and permeability in the absence of physical sample are increasingly being used. P. S. Burton and J. T. Goodwin give an overview of solubility and permeability experimental and computational methods and interrelate them in physiologically relevant models for predicting in vivo performance (Comb. Chem. High Throughput Screen.
Bioautomation and Screening
Positive Fluorescent Selection Permits Precise, Rapid, and In-Depth Overexpression Analysis in Plant Protoplasts
The use of high-throughput cell-based screening methods in the study of regulatory networks has become a conventional and effective approach in animal systems. Cytometric analyses and fluorescence-activated cell sorting (FACS) have also been much more widespread and prolific in animal or microbiology research than in plant research. B. O. R. Bargmann and K. D. Birnbaum combine the advantages of a fluorescent selectable marker for transient plant protoplast transformation with flow cytometric analysis and FACS. Transient protoplast transformation is a widely used tool in plant research because of its easy and fast procedure compared with conventional Agrobacterium-mediated gene transfer. In two model studies of the analysis of auxin signaling, the authors make use of a stably transformed Arabidopsis line with auxin-responsive DR5::GFP. Protoplasts derived from this line are transiently transformed with different genes of interest coupled with monomeric red fluorescent protein (mRFP). The notable advantage of this system is that it allows for the exclusive analysis of the transformed cells and facilitates high-throughput dual-color analysis. Because of its red emission spectrum, the mRFP marker easily can be used in combination with the commonly used GFP. In these model studies, the effects of dominant negative auxin-signaling components on DR5::GFP are quantified. Moreover, genome-wide transcriptional changes in a specific cell type are determined. The results show that positive selection of transformed plant protoplasts together with the mRFP/GFP dual-labeling capability opens novel opportunities for in-depth high-throughput expression and interaction analyses in plants. Especially, this wide range of possibilities will raise the demand for automated cell-sorting solutions in plant sciences (Bargmann, B. O. R.; Birnbaum, K. D. Plant Physiol.
A Rapid and Nondestructive Screenable Marker, Fluorescence-Accumulating Technology, for Identifying Transformed Seeds of Arabidopsis thaliana
The progress made in the last two decades in plant sciences and breeding relied mostly on the generation of transgenic plants. But identification of transgenic lines and establishment of homozygous lines is still a slow process. A major obstacle in this process lies in the analysis of the seeds. Depending on the transformation procedure, either a great number of seeds must be screened because of the small fraction of transgenic seeds in the T1 generation, or subsequent generations must be analyzed for nonsegregating lines. In all cases, transgene identification in these seeds is a laborious and tedious task. To circumvent these and other difficulties, it would be desirable to identify transgenic seeds just at a glance. With the FAST, the authors present a novel technology toward this ideal. The technology is based on the expression of a fluorescent codominant screenable marker under the control of a seed-specific promoter. The FAST marker harbors a fusion gene encoding either GFP or RFP with oleosin, an oil body membrane protein that is prominent in seeds. As a result of this manipulation, dry seeds of transgenic plants emit green fluorescence under a fluorescence stereomicroscope, which suggests that the FAST marker can be used as a visible marker of transformation. The authors show that the expression of oleosin in Arabidopsis thaliana is confined to the seeds, does not affect germination and seedling growth, and that the false-discovery rate is less than 1%. This codominant marker technology saves time, eliminates the need for antibiotics and herbicides, and is cheap to produce and use. Especially, the application of fluorescence opens new ways for automated identification of transgenic individuals by simple image analysis. A prerequisite for further spreading of this rapid and nondestructive method is its applicability in other plant species than Arabidopsis. In this case, this technology may be another major step toward an automated production and establishment of transgenic crop lines (Shimada, T. L.; Shimada, T.; Hara-Nishimura, I., Plant J.
Evaluation of Drug Transporter Interactions in Drug Discovery and Development
Drug transporters play an important role in the absorption, distribution, excretion, and toxicity of both endogenous and exogenous compounds. Transporters may act as physiological gatekeepers in the regulation of the pharmacological and toxicological effects of drugs by limiting distribution to tissues responsible for their effect or toxicity. Y. Lai et al. provide a brief outline of the characteristics of membrane-bound drug transporter families and their respective roles in regulating drug pharmacokinetics (PK). This background then provides the context for a discussion of the characterization of a drug candidate as a substrate, inhibitor, or inducer of drug transporters, followed by an assessment of the in vitro and in vivo preclinical methods used in drug discovery and development for screening molecules to identify potential transporter interactions. Specific examples of the translation of in vitro findings to the in vivo effects are discussed to link the current understanding of the impact of drug transporters on clinical pharmacology (Comb. Chem. High Throughput Screen.
Enzyme Induction: Translating Multiple Approaches, Assays, Endpoints, and Opinions into a Valuable Induction-Screening Strategy
Drug-metabolizing enzyme induction, or the process of generating excess drug-metabolizing enzyme in important tissues of drug disposition, such as liver and intestine, can give rise to PK situations, whereby drug interactions occur. There are two major concerns associated with enzyme induction. First is the potential loss of efficacy because of more rapid metabolism, and second is the risk of an increase in the formation of a potential reactive and toxic metabolite. Because of this, pharmaceutical companies consider enzyme induction as an undesired drug property for their potential drug candidates. As a consequence, the number of tools and models to evaluate the induction of drug-metabolizing enzymes has increased tremendously over the past decade. As often is the case, every assay and approach has its own advantages and disadvantages, and unfortunately, no single tool is currently available to predict the induction potential of drug candidates in humans. A. Fretland and M. Monshouwer outline the screening tools currently available for determining the induction potential of new chemical entities and how these tools translate into a valuable screening strategy, covering aspects, such as induction liability assessment, SAR, induction risk assessment, and clinical relevance (Comb. Chem. High Throughput Screen.
Application of Cytochrome P450 Drug Interaction Screening in Drug Discovery
Advances in drug interaction screening have resulted in reduced compound attrition rates because of unfavorable cytochrom-mediated drug interactions in clinical trials and improved patient safety. A major driver for the success in predicting drug interactions is a better understanding of the biological, chemical, or mechanical factors that can impact the prediction of drug interactions in vitro. The enzyme source, probe substrate, accessory proteins, and pharmacogenetics can all have profound effects on the robustness and relevance of data generated with in vitro drug—drug interaction assays. Furthermore, the use of in silico techniques can potentially afford a priori knowledge of drug interaction potential, thus reducing the time and cost associated with drug interaction screening. In a recent review, J. L. Wahlstrom et al. focus on recent advances in in vitro, in silico, and bioanalytical techniques, and demonstrate how these tools are currently used to provide effective cytochrom drug interaction screening in a discovery setting (Comb. Chem. High Throughput Screen.
Scaling in Vivo Pharmacokinetics from in Vitro Metabolic Stability Data in Drug Discovery
W. Klopf and P. Worboys discuss the current approach for predicting hepatic clearance from in vitro metabolic systems along with a survey of current industry practice. The definitive method of determining intrinsic clearance remains the measurement of Michaelis-Menten parameters derived from metabolite formation rate data. However, in drug discovery, this method has limitations that result in the method most commonly applied being the half-life method using a single, low substrate concentration. Additionally, the importance of correcting in vitro intrinsic clearance values for futile binding within the incubation has been accepted (Comb. Chem. High Throughput Screen.
Plasma Protein Binding in Drug Discovery and Development
M. A. McLean et al. describe methods for quantifying the binding of small-molecule drug candidates to plasma proteins and the application of these methods in drug discovery and development. Particular attention is devoted to methods amenable to medium- to high-throughput analysis and those well suited for measurement of compounds that are highly protein bound. The methods reviewed include the conventional techniques of equilibrium dialysis, ultrafiltration, and ultracentrifugation, as well as more novel approaches using micropartitioning and biosensor-based analysis. Additional concepts discussed include plasma protein structure, enantioselective protein binding, drug displacement, the effect of patient demographics and disease states on free (unbound) drug levels, and the influence of protein binding on drug candidate PK and pharmacodynamics (PD). Practical considerations pertaining to the evaluation of highly protein-bound drug candidates are also highlighted (Comb. Chem. High Throughput Screen.
Building a Tiered Approach to in Vitro Predictive Toxicity Screening: A Focus on Assays with in Vivo Relevance
One of the greatest challenges facing the pharmaceutical industry today is the failure of promising new drug candidates because of unanticipated adverse effects discovered during preclinical animal safety studies and clinical trials. Late-stage attrition increases the time required to bring a new drug to market, inflates development costs, and represents a major source of inefficiency in the drug discovery and development process. It is generally recognized that the early evaluation of new drug candidates is necessary to improve the process. Building in vitro data sets that can accurately predict adverse effects in vivo would allow compounds with high-risk profiles to be deprioritized, whereas those that possess the requisite drug attributes and a lower-risk profile are brought forward. In vitro cytotoxicity assays have been used for decades as a tool to understand hypotheses-driven questions regarding mechanisms of toxicity. However, when used in a prospective manner, they have not been highly predictive of in vivo toxicity. Therefore, the issue may not be how to collect in vitro toxicity data but rather how to translate in vitro toxicity data into meaningful in vivo effects. J. M. McKim Jr. describes the development of an in vitro toxicity screening strategy that is based on a tiered approach to data collection combined with data interpretation (Comb. Chem. High Throughput Screen.
Application of in Vivo Animal Models to Characterize the Pharmacokinetic and Pharmacodynamic Properties of Drug Candidates in Discovery Settings
A goal of preclinical discovery is the identification of drug candidates suitable for clinical testing. Successful integration of in vitro and in vivo experimental data sets can afford projections of human dose regimens anticipated to be safe and therapeutically beneficial. Although in vitro experiments guide new chemical syntheses and are essential in understanding drug action and disposition, in vivo characterizations provide unique insight into complex biological systems that control concentrations at the site of action and the pharmacological response. PK/PD concepts underlying drug disposition and response provide a quantitative framework with which potential clinical candidates could be identified. To improve throughput in earlier stages of drug discovery, in vivo PK study designs, such as cassette dosing and sparse sampling schemes, are used. In later stages of discovery, PK studies using chemical inhibitors or surgical and genetic animal models are used to characterize the underlying determinants of drug disposition. In a complimentary fashion, modeling of in vivo PD effects may quantitatively link biomarkers to pharmacological response, validate in vitro to in vivo correlations, and underwrite predictions of efficacious exposure targets. When applied to in vivo discovery data, the PK/PD models aid in understanding the mechanisms of pharmacological response, such as receptor theory in the central nervous system and cell turnover concepts in infectious disease and oncology. B. M. Amore et al. consider the role of in vivo testing in understanding the PK and PD attributes of lead candidates in drug discovery (Comb. Chem. High Throughput Screen.
Automation Systems and Software
Access of Enterprise Resource Planning (ERP) to Field Level of Automation Systems
Companies in the manufacturing industry are increasingly challenged to continuously improve their efficiency in terms of economy, operating and process safety, product quality, and environmental protection. In this context, the vertical and horizontal integration of all subprocesses has become an indispensable task. The levels of automation are merging more and more, and the full continuity of data and information on all levels and in both directions has become a current challenge. The exchange of information between the field level with its actuators and sensors and management for the optimization of all subprocesses of a production plant is very important. The Enterprise Resource Planning (ERP) system needs data from the field level (measurement values, trends and threshold information, diagnostic messages, deviant values …) to optimize transactions. In addition, effective quality management should be able to become directly involved in the process. These authors present a solution for a direct communication link between field level and ERP systems SAP R/3 with an industrial PC and an embedded software interface to SAP R/3 (WAGO Kontakttechnik GmbH & Co KG, Minden, Germany). The software module of the example system, Wago I/O 758 IPC (WAGO Kontakttechnik GmbH & Co KG, Minden, Germany), uses a standard Ethernet file transfer protocol (FTP) server for the transmission of the data. The SAP system requires no changes. The Wago system has a variety of different modules for capturing (wireless or wired), transformation, and processing of various signals. Specific requirements on the part of the SAP system exceeding raw data require functional enhancements that are realized at the automation level of the IPC. The transmission of data to the SAP is assumed by the SAP communication module, which also is arranged at the control level and uses a standard Ethernet FTP server. Thus, the exchange of information between field level and ERP is directly and in accordance with the requirements of the ERP. Installation of additional computers can be made for data collection drops. Application examples refer to an advanced quality management in a pharmaceutical company, the securing of the process workflow and product quality for dosing in chemical manufacturing and the automation of the billing systems for tank refills. This Wago I/O 758 IPC system example makes processes transparent to the manufacturing plant, so that optimization concepts are implemented quickly and easily. The potential of this system lies in quality control and the fault management (Laufmann, W.; Theuerzeit, W. Prozesstechnik Automation
Visualization of Temporal Trend Relationships
Almost all scientific simulations and investigations produce multivariate results. When analyzing a multivariate data set, one important task is to understand the correlation of the variables and their underlying cause—effect relationship. When the data set is time varying, each variable will exhibit certain temporal trends at different time intervals. By looking at how the trends evolve over time and how the trends from different variables are related to each other, scientists can gain better insight into the data and formulate additional hypotheses. This article presents a new algorithm to explore and visualize multivariate time-varying data sets. The main focus is to understand the temporal relationships among the variables. Important trend relationships among the variables are identified based on how the value of the variables change over time and how those changes are related to each other in different spatial regions and time intervals. Displaying the relationship among important temporal trends provides an opportunity for the scientist to identify correlations across multiple variables and search for salient features from the data set. The existing techniques for multivariate data, such as parallel coordinates or scatter plots, show the correlation among the scalars but not necessarily the temporal trends, and the existing time-series—based algorithms are not explicitly tailored after multivariate data sets. A new algorithm identifies the temporal trends from local regions. It estimates when a trend appears and vanishes in a given time series. Based on the beginning and ending times of the trends, their temporal relationships can be modeled as a state machine representing the trend sequence. These trend sequences highlight the correlations among different variables. Because a scientific data set usually contains millions of data points, an algorithm to extract important trend relationships in linear time complexity is proposed. The data analysis pipeline consists of these stages: trend specification; trend estimation; trend sequence modeling; trend sequence clustering, visualization, and exploration. Several visualization user interfaces explore trend relationships to visualize temporal characteristics and display spatial distributions (Lee, T.-Y. IEEE Trans. Vis. Comp. Graph.
Visual Analysis of Complex Scientific Data
Simulation is used in science and engineering to study a wide range of problems and to understand underlying models and investigated phenomena. Interactive visual analysis helps professionals explore simulation results and understand and explain data. Multiple independent variables are considered, and large and complex data are computed, especially in the case of a multirun simulation. One important motivation for the study of multirun simulation data is to perform a sensitivity analysis of the computation. Data from multiple simulation runs are very complex to study compared with conventional simulations where just time and/or space are considered as independent variables. Classical visualization techniques deal with two-dimensional (2D) or 3D data and also with time-dependent data. For multirun simulations, new ways of visual data exploration and analysis are needed. This article focuses on how to deal with the complexity of the data. A million data items can easily be depicted when using a scatter plot, but only about 10,000 items using parallel coordinates and only a handful of surfaces when using 3D surface view. The authors present an advanced visual analysis approach that enables a thorough investigation of families of data surfaces. Those are data sets with respect to pairs of independent dimensions. Although it is almost trivial to visualize one such data surface, the visual exploration and analysis of many such data surfaces is a grand challenge, stressing the user's perception and cognition. The proposed approach integrates different projections and aggregations of the data surfaces at different levels. The authors show the necessity for a flexible visual analysis system that allows integrating a large number of different and linked views for making sense of this highly complex data and propose new interaction and analysis techniques that make it possible to deal well with complex data. Its usefulness is demonstrated in the context of a multirun simulation data case and in the context of the engineering domain (Matkovic, K.; Gracanin, D.; Klarin, B.; Hauser, H. IEEE Trans. Vis. Comp. Graph.
Laboratory Information Management System and Workflow
The Hybrid Lab
This article considers united manual workplaces, automation, logistics, and information technologies for the laboratory of future. The approach of a Hybrid Lab means an integration of manual workplaces and laboratory automation. The ideas reflect the large number of tedious activities that require personnel time in addition to conducting actual experimentation, such as the manual process of planning, process documentation, evaluation of experiments, or retrieval and return of material. Modern virtual workspaces for both humans and machines in changing laboratory environments are discussed. A new information infrastructure (LIMS as a Synapse Framework) or better human-machine interfaces are able to reduce time killers in laboratories of the future. Several projects and interdisciplinary works are introduced as overview information. One of the projects aims to provide a semiautonomous transportation system that interfaces automated and manual workplaces. Different LIMS controlled tasks for the transport of the substances between laboratory desks, devices and storage facilities are included in the system. This article represents concepts and researching results of the Fraunhofer Institute for Manufacturing Engineering and Automation (Fraunhofer IPA), Germany (Schöning, S.; Knoch, S. Bioforum Eur.
A Workflow-Driven Software for Experimental Protocol Development, Data Acquisition, and Analysis
This author addresses the requirements and solutions of information systems used by the genome biology community. Typical problems are complex data structures, heterogeneous data, and a consistent storage of both raw data and processed data. LIMS must fulfill these primary requirements. Electronic laboratory notebooks (ELN) offer a second important information technology (IT) Infrastructure focused on mobile work, scientific data documentation, and information sharing. The authors discuss an in-house development named Laboratory Data Management, Analysis and Protocol Development. Laboratory Data Management, Analysis and Protocol Development covers LIMS and ELN functionalities. Some of the requirements of the system development are an integrated data management for workflow definitions and workflow processing, including data acquisition up to HTS scenarios. The properties of the IT platform show a best-practice case (combinatorial multiple fluorescence in situ hybridization -m-FISH- protocol and 3D-image reconstruction). The IT solution is freely available under the open-source license AGPL from http://genome.tugraz.at/ilab/ (Stocker, G. et al., BMC Bioinform.
Proteomer: A Workflow-Optimized Laboratory Information Management System for Two-Dimensional Electrophoresis-Centered Proteomics
This article considers IT laboratory applications in the field of functional genomics. The problem of data analysis in environments of heterogeneous mass data pools of different analytical platforms and over different experiments is addressed. Typical data-mining methods are not able to deliver results needed in the bio applications presented; hence, a proteomic database custom tailored to connect MS protein identification information to two-dimensional polyacrylamide gel electrophoresis-derived protein expression profiles is developed. One impact is that automatic data evaluation of single experiments and multiple two-dimensional polyacrylamide gel electrophoresis experiments with MS data on different levels generate a comprehensive network of proteomics data. PRO-TEOMER is a database with high cross-referencing opportunities for a wide range of experimental data. The developed IT system is demonstrated by two examples of practice, from process data to biological knowledge (Nebrich, G. et al. Proteomics