Combinatorial Chemistry & High Throughput Screening - Volume 14, Issue 9, 2011

I was asked to serve as the guest editor to assemble a selection of manuscripts to fill a complete issue of “Combinatorial Chemistry & High Throughput Screening” that would focus on Laboratory Information Management Systems (LIMS) and data management. This process started in the Spring of 2010 and several laboratories, experts and vendors were invited to participate with submissions. At first, it appeared that we would end up with more articles than a single issue could handle as 17 different authors had agreed to submit a manuscript. However, I quickly learned that many authors would drop out along the way. People of all industries are very busy and even though there is credibility in having peer-reviewed articles published, for many, the time required to do this is just not available. By the time we reached our first deadline for submissions, 9 of the authors had dropped out. During the next few months, we conducted a double blind peer review process where 3 more authors dropped out or were eliminated by their peers. The 5 articles that were finally approved have gone through a rigorous referee process and have been deemed worthy for publishing. LIMS and data management means something different to nearly every laboratory manager which is why there are so many different types of LIMS solutions in the marketplace. Change is the one thing that has been a constant over the past 18 years that I have been involved in laboratory automation. No matter what your laboratory is doing today, very likely, you will be doing something different in a few months down the road. One laboratory that I worked with went through a rigorous compliance audit for accreditation in their industry. The audit went very well and they were pleased with the assessment. About 6 months later I was talking to the system administrator and asked him how much of his system had evolved and changed since the audit. He told me that about 80% of the system had been tweaked, extended, changed, revised or optimized - in just 6 months! I knew that change was constant but I did not realize how big of a factor it was. When seeking for the right LIMS for your lab, the best approach is to talk to many peers and colleagues to find out what is working for them and what is not. While the industry is full of horror stories of implementations that were never successful or way over budget, there are also many success stories of laboratories that are being well-served by their systems....

The Miami Project to Cure Paralysis, part of the University of Miami Miller School of Medicine, includes a laboratory devoted to High Content Analysis (HCA) of neurons. The goal of the laboratory is to uncover signaling pathways, genes, compounds, or drugs that can be used to promote nerve growth. HCA permits the quantification of neuronal morphology, including the lengths and numbers of axons. HCA of various libraries on primary neurons requires a team-based approach, a variety of process steps and complex manipulations of cells and libraries to obtain meaningful results. HCA itself produces vast amounts of information including images, well-based data and cell-based phenotypic measures. Documenting and integrating the experimental workflows, library data and extensive experimental results is challenging. For academic laboratories generating large data sets from experiments involving thousands of perturbagens, a Laboratory Information Management System (LIMS) is the data tracking solution of choice. With both productivity and efficiency as driving rationales, the Miami Project has equipped its HCA laboratory with an On Demand or Software As A Service (SaaS) LIMS to ensure the quality of its experiments and workflows. The article discusses how the system was selected and integrated into the laboratory. The advantages of a SaaS based LIMS over a client-server based system are described.

The availability of high-throughput techniques combined with more exploratory and confirmatory studies in small-molecule science (e.g., probe- and drug-discovery) creates a significant need for structured approaches to data management. The probe- and drug-discovery scientific processes start and end with lower-throughput experiments, connected often by high-throughput cheminformatics, screening, and small-molecule profiling experiments. A rigorous and disciplined approach to data management ensures that data can be used to ask complex questions of assay results, and allows many questions to be answered computationally, without the need for significant manual effort. A structured approach to recording scientific experimental design and observations involves using a consistently maintained set of ‘master data’ or ‘metadata’. Master data include sets of tightly controlled terminology used to describe an experiment, including both materials and methods. Master data can be used at the level of an individual laboratory or with a scope as extensive as a whole community of scientists. Consistent use of master data increases experimental power by allowing data analysis to connect all parts of the discovery life cycle, across experiments performed by different researchers and from different laboratories, thus decreasing the opportunity cost for making novel connections between results. Despite the promise of this increased experimental power, challenges remain in implementation and consistent use of master data management (MDM) techniques in the laboratory. In this paper, we discuss how specific MDM techniques can enhance the quality and utility of scientific data at a project, laboratory, and institutional level. We present a model for storage and exploitation of master data, practical applications of these techniques in the research context of small-molecule science, and specific benefits of MDM to small-molecule screening aimed at probe- and drug-discovery.

High throughput screening (HTS) has emerged as an important technique for allowing researchers to rapidly profile very large numbers of chemicals against drug targets. As recent and future advances make HTS cheaper to perform on even larger scales, the amount of data that has to be processed, analyzed, and searched will only grow larger in size and harder for researchers to manually sift through. It is therefore an unavoidable requirement that institutions utilizing HTS technology will need to begin looking for effective solutions in the maturing area of laboratory information management systems like many other types of labs have already done. K-Screen is one such solution. Our initial goal with K-Screen was to have an integrated application environment that supported data analysis, management, and presentation so we could efficiently perform client requested screens and searches as well as generate detailed reports on the results of those. Previously, we had attempted but failed to locate an existing software suite that sufficiently addressed all our requirements. K-Screen is a web accessible application that offers the ability to host a large chemical structure library, process and store single-dose (primary) and dose response (secondary) screening data, perform searches based on screening results, plate coordinates, and structure, substructure and structure similarity. It uses heat maps and histograms to visualize screen or plate level statistics. Interfaces to external searches against PubChem and ZINC databases are also provided. We feel that these features make K-Screen a practical and effective alternative to other commercial or academic HTS LIMS systems.

In recent years high throughput screening operations have become a critical application in functional and translational research. Although a seemingly unmanageable amount of data is generated by these high-throughput, largescale techniques, through careful planning, an effective Laboratory Information Management System (LIMS) can be developed and implemented in order to streamline all phases of a workflow. Just as important as data mining and analysis procedures at the end of complex processes is the tracking of individual steps of applications that generate such data. Ultimately, the use of a customized LIMS will enable users to extract meaningful results from large datasets while trusting the robustness of their assays. To illustrate the design of a custom LIMS, this practical example is provided to highlight the important aspects of the design of a LIMS to effectively modulate all aspects of an siRNA screening service. This system incorporates inventory management, control of workflow, data handling and interaction with investigators, statisticians and administrators. All these modules are regulated in a synchronous manner within the LIMS.

Every laboratory has a need to track and control the variables that drive the quality of the results. However, each laboratory is unique and what one organization deems to be a critical process to track and control will likely differ from other organizations. Furthermore, there is more than just the end product or result that needs to be tracked and controlled. All of the intermediate products and resources play a significant role in producing the final product and each of these needs to be included in the LIMS. At a high level, this article will present ideas and opinions on the following topics in relation to implementing a LIMS process tracking and control system in a laboratory: The difference between tracking and controlling processes; What to track and control in the lab; The “product” of the laboratory; Preventing mistakes in a laboratory; Comprehensive software platform options; The value of seeing a system as opposed to imagining it; The use of barcodes in the laboratory; and an assessment on using the Risk Based Approach in deciding what to include in the tracking system.

Mr. Hull is the vice president of sales for UNIConnect, a LIMS and process management software company based in Sandy, Utah. He received a degree from Brigham Young University in Provo, Utah from the Marriott School of Management, (B.S. in Business Management, 1992). He worked for 12 years as a product and account manager for Computype, a bar code label automation company focused on laboratory applications, among other markets. During this time Mr. Hull consulted with many laboratories and learned first-hand the challenges that labs face in managing data, samples, supplies, resources and equipment. He recognized that even though many labs appeared to be similar at first glance, they all had very unique needs in tracking and controlling samples and data. He designed specialized labeling sets for various types of tubes and vials and created complex serialization schemes to assure unique identification of samples. He also developed a colored label scheme for laboratory technicians to quickly identify the sample type by sight rather than scanning. He design complex donation number sets for blood label used in the collection of blood by multiple blood centers throughout the intermountain west. In 2006 Mr. Hull joined UNIConnect, an emerging software company with empowering process management technology for laboratories. His background in bar code label automation gives him a unique perspective on bringing all the process management pieces together, including software systems to manage data and processes, and bar code label systems which become the entry point into the software system.

The reaction of isocyanides with electron deficient alkynes has been first reported in 1969, about ten years after the first reports on the famous Ugi four component reaction. However it took about thirty years to realise that the zwitterionic intermediate originating from interaction of the two species could be trapped by a third component, thus giving the start to a novel class of isocyanide-based multicomponent reactions. From that first report dated 1996 there has been an ongrowing interest that has produced, so far, about 150 distinct scientific papers. This review is aimed at rationalising and cathegorising these reports and at offering an overview of all the possible applications of this novel methodology.

Metabolite identification study plays an important role in determining the sites of metabolic liability of new chemical entities (NCEs) in drug discovery for lead optimization. Here we compare the two predictive software, MetaSite and StarDrop, available for this purpose. They work very differently but are used to predict the site of oxidation by major human cytochrome P450 (CYP) isoforms. Neither software can predict non-CYP catalyzed metabolism nor the rates of metabolism. For the purpose of comparing the two software packages, we tested known probe substrate for these enzymes, which included 12 substrates of CYP3A4 and 18 substrates of CYP2C9 and CYP2D6 were analyzed by each software and the results were compared. It is possible that these known substrates were part of the training set but we are not aware of it. To assess the performance of each software we assigned a point system for each correct prediction. The total points assigned for each CYP isoform experimentally were compared as a percentage of the total points assigned theoretically for the first choice prediction for all substrates for each isoform. Our results show that MetaSite and StarDrop are similar in predicting the correct site of metabolism by CYP3A4 (78% vs 83%, respectively). StarDrop appears to do slightly better in predicting the correct site of metabolism by CYP2C9 and CYP2D6 metabolism (89% and 93%, respectively) compared to MetaSite (63% and 70%, respectively). The sites of metabolism (SOM) from 34 in-house NCEs incubated in human liver microsomes or human hepatocytes were also evaluated using two prediction software packages and the results showed comparable SOM predictions. What makes this comparison challenging is that the contribution of each isoform to the intrinsic clearance (Clint) is not known. Overall the software were comparable except for MetaSite performing better for CYP2D6 and that MetaSite has a liver model that is absent in StarDrop that predicted with 82% accuracy.

An efficient synthesis of 2-(dialkylamino)-4-phenyl)-1,3-thiazol-5-yl)(phenyl)methanone using acid chlorides, secondary amines, 2-bromoacethophenone and ammonium thiocyanate is described.