Sample management and workflow execution with Xavo Lab Logistics

Transcription

1 Sample management and workflow execution with Xavo Lab Logistics Thomas Frech Markus Nenniger 2015 Xavo Systems AG Reinach, Switzerland, all rights reserved Abstract The last decade has seen significant changes in the way service laboratories work. Steadily growing numbers of computerized devices handle more and more samples. This places increased demands on the organizational infrastructure from sample management to the organization of assay execution to data storage and analysis. Xavo Lab Logistics is a software solution for sample management and workflow execution. It supports the placement, planning and fulfillment of requests for delivery, analysis or use in experiments of any type of sample. It integrates with existing IT systems and automated hardware platforms to provide users with a continuous workflow, and it serves as a central hub for information exchange and the planning and monitoring of experiments. Its modular design makes XLL adaptable to different environments from compound management and screening to large scale cell culture. Functionality can be added or removed as needed by the customer. For data analysis requirements, Xavo provides integrated solutions with partner companies. Software. Dedicated to People.

2 About This whitepaper will give an overview of the Xavo Lab Logistics (XLL) software. It will showcase XLL s functionality in its areas of application and provide a high level technical overview. For more information about this document and our product Xavo Lab Logistics please contact Thomas Frech (thomas.frech@xavo.com) or Yannick Hofmann (yannick.hofmann@xavo.com). August Xavo Systems AG Reinach, Switzerland, all rights reserved Table of Content 1 Challenges Working with standalone equipment Consequences of lab automation The next logical step: integrated laboratories How Xavo Lab Logistics meets the lab integration challenge Areas of application Software structure LabRequest LabPlanning LabFulfillment LabCore LabInventory Technical details XLL Client XLL Server XLL Database Deployment options System prerequisites XLL Client XLL Server XLL Database Limitations Further reading... 8 Software. Dedicated to People. 2

3 1 Challenges 1.1 Working with standalone equipment Larger laboratories usually accumulate large amounts of equipment from relatively simple devices such as freezers, incubators, PCR machines, dispensers, etc. to highly complex ones such as liquid handlers, mass spectrometers, flow cytometers or even whole platforms such as life cell imaging systems with integrated incubation and image analysis. Even sophisticated devices are rarely connected to the lab IT infrastructure, let alone to each other and become silos of excellence. This causes several problems: there is the inevitable loss of time by having to manually move documents and data between the right places and keeping them in sync. Then again there is the possible loss of data from recordings that have been misplaced, misnamed or cannot be assigned to a specific 1.2 Consequences of lab automation On the one hand, automation decreases the visible complexity of research by assigning repetitive tasks to machines. In addition to saving operator and technician hours, this generates if not better, at least more consistent results which in turn lead to increased efficiency of the research process. On the other hand, lab automation itself creates more complexity. This is for several reasons: first, it greatly increases the amount of data produced, second, it requires careful (and time consuming) setup and third, with increases in the numbers of automated and interdependent devices the arising flood of information (and physical items) has to be managed. Figure 2: Typical communication flow: specifically optimized, but inflexible Figure 1: Simple lab setup with personnel, equipment and software experiment anymore. Should a project require rigorous documentation, generating audit trails that are compliant with regulations is costly and a challenge in itself. The generated data and documentation are also not readily visible to researchers and lab heads, and it can be time-consuming to put them into context across different projects. The desire to increase throughput and efficiency often leads to the idea to automate parts of some frequently performed workflows. Here again a lack of connective IT infrastructure can hinder the steps towards automation, since the problems described above are aggravated with increased automation in the laboratory. The integration of different automated systems can be difficult since the software, even if available, used for planning, execution and analysis is rarely the same and even more rarely, products from different vendors do communicate. This can potentially lead to confusion, loss or unintentional modification of data and less than optimal utilization of the equipment. It can also cause significant organizational overhead as described in the previous section and lead to problems in regulated environments. In short: while relieving humans from repetitive error-prone mechanical tasks, lab automation without proper integration will burden other humans with repetitive error-prone organizational tasks to achieve data availability and consistency. 1.3 The next logical step: integrated laboratories As a consequence of the above, even in laboratories with extensive automation there is usually efficiency and quality to be gained. Key for efficiency gains is the effective connection and interaction of already existing processes - both automated and manually executed - to remove obstructions in the workflow. Software. Dedicated to People. 3

4 Increasing overall transparency and the accessibility of information to those who require it, will help in several ways. - Impasses arising from missing resources such as culture medium, labware or required compounds or lack of trained personnel can be avoided when the person planning the experiment is aware of the currently available resources. - Workflows competing for resources can be planned in a way that minimizes interference. - Complete information about previous experiments from audit trails will greatly facilitate their reproduction. - The ability to directly provide task lists to operators decreases the likelihood of human error and makes it easier to pinpoint the source of errors. - Immediate access to measured data creates shorter feedback loops for corrections and helps to improve quality of the produced data. - A notification system that informs users when a specific request needs to go through an approval flow and that notifies the approver(s) immediately about pending requests helps to accelerate the process and make it transparent. - A single point of contact for relevant documents reduces organizational overhead. - A highly interconnected system with a comprehensive search function makes planning easier and prevents the user from missing important points. Efficiency gains will be visible in the form of an overall decrease in organizational overhead, particularly as a decrease in the number and duration of interruptions due to miscommunication, and in the higher reliability of planning. Ultimately, this also improves the overall quality of the processes and services of the laboratory. Another consequence of lab automation is the drastically increased amount of collected data. The task of analyzing data is highly complex and varies considerably between different types of experiments. While the integrated lab still has to face this challenge, the gathered data is accurately referenced and annotated with process metadata to make it easier to put it into context. 1.4 How Xavo Lab Logistics meets the lab integration challenge XLL s approach is based on three key concepts that complement each other: Communication: XLL aims to connect users to all services they may require, whether they are part of the application or from external resources. To do this, XLL frequently exchanges information with external systems. This can happen via an adapter that, in its simplest form, consists of file import or file export to and from XLL. More sophisticated adapter solutions use a direct connection to the external systems database or interface, as is the case for SiLA 1 compliant equipment. It is possible to have direct integration into XLL s applications view, so the external application behaves as if it was part of XLL. This is for example realized in a collaboration that introduces an AnIML 2 data viewer into the application. In this way, data analysis gets closer to the processes in which the data are recorded and thus provides a way to handle large amounts of sensitive and project specific data in the context of lab integration. Simplicity: Users are only shown the parts of the application that are needed for completion of their tasks. This is obviously necessary from a security point of view, but usability is an equally important consideration. Reducing visible complexity is a big benefit since it makes the system usable by nonexpert computer users and reduces the probability of user error. Scalability: XLL addresses equipment integration with an evolutionary approach that is possible due to the modular application concept: solutions for Figure 3: Simplified information exchange in research context specifics aspects can be implemented with immediate benefit and a stepwise completion is possible with increasing benefits along the way. For the same reason, XLL integrates into the existing IT landscape and supplements the customers solutions rather than replaces them. 1 Standardization in Lab Automation 2 Analytical Information Markup Language Software. Dedicated to People. 4

5 2 Areas of application XLL s origins lie in compound screening for drug discovery. However, since the underlying workflows generalize well, it is able to cover the needs of most centralized service facilities for CROs, academia and pharma. This includes for example compound and sample management and large scale cell culture as well as QC analysis. Depending on the use case and user role within the application, there is different emphasis placed on XLL s individual functions. Figure 4:Role-based data flow paradigm in XLL On one hand, XLL provides comprehensive sample management complete with search function and the ability to store metadata such as QC results for any type of sample. In XLL, sample is used in a very wide sense to include for example cultured cells. The applications' requesting functionality then allows samples to be used in workflows which are executed by a number of different people and devices. Coordinating this via one application reduces overhead and provides more control over the activities in the laboratory, resulting in more overall efficiency. Consider a simple example of a compound screening performed with XLL integration: a scientist places a request to screen a number of compounds in an assay. As soon as the request is released, it becomes visible to the lab planner who can check if the desired compounds are available. Operators then start to prepare assay plates or reorder compounds. Other operators see which assay plates are ready to be used, load them to screening platforms and initiate testing. At any time, lab planners and scientists can bring themselves up to date on the progress of the screen and as soon as any assays are completed, the resulting data is available to the scientist. Most benefits for this scenario lie in efficient inventory management and the ability to communicate instructions in order to drive the workflow. As another example, fulfilling multiple parallel requests for regular deliveries of cells creates different demands: here inventory management is relatively simple while efficient planning with multiple constraints poses the real challenge. Academic core facilities Lab automation and system integration used to be the hallmark of large screening facilities in major pharmaceutical companies, but with the increasing complexity of biological experiments and data there is also a growing trend in academia to use core facilities. This reflects need to share equipment that is both expensive and complicated to operate. Using planning tools that consider availability of equipment, setup times, consumables and qualified personnel can increase their utilization while making it easier for facility customers to find a time slot for an experiment. 3 Software structure XLL is modular by design and therefore allows flexible, scalable and cost-effective solutions. It is possible to connect XLL to existing IT infrastructure to enhance the system or systems already in place. Such a system can later be upgraded to include additional XLL modules or to replace in-house solutions when the situation requires it. Except LabCore, none of XLL s modules are prerequisites for other modules and can be freely exchanged for a company s in-house or other commercial solutions. Figure 5: LabCore provides the foundation for all other XLL modules, which can then be added as needed. Software. Dedicated to People. 5

6 3.1 LabRequest The starting point for any workflow in XLL is a request that specifies the parameters of its execution. This can be a request for a flask containing cells of a certain cell line, plates with a specific concentration of different substances or mixtures or the request to screen a list of hundreds of thousands of compounds in a specific assay. All request parameters, such as delivery date (or dates), labware layout, sample concentrations, etc. can be entered in a graphical user. New workflows can be imported e.g. from the LabFulfillment module. Depending on the chosen solution, issued requests are then forwarded to a person in charge or to a planning system and progress information is eventually fed back to the requester. 3.2 LabPlanning Any formal request can be forwarded to LabPlanning. The module will then check if all necessary prerequisites for the requests fulfilment are met: this can be the availability of a sufficient amount of samples or cells, but can also be the availability of personnel and time slots on the required devices. The latter is a particularly important consideration when handling multiple requests which require the same equipment and trained operators. The planned duration is either passed directly from the request, from an external source or acquired from the LabFulfillment module. Requests are then scheduled to optimize equipment and personnel usage and thus minimize time to fulfilment. At any time, incoming changes, such as downtimes, maintenance, etc., can be factored into the planning, leading to a new optimal solution. For fully automated environments this allows for real time monitoring and reaction on conflict. To the user, LabPlanning presents itself as a Ganttchart style GUI that provides a visualization of the timing of the different steps of the requests. It will also highlight conflicts and allow necessary adjustments to be made from within this GUI. In this way even large requests can be planned with little effort. XLL s planning algorithms are adopted from Xavo Plant Scheduling, a software solution for real-time planning and monitoring in automated manufacturing that has been in service since LabFulfillment LabFulfilment handles the execution of the workflows underlying requests. In XLL, a workflow is defined as a series of activities, performed in a certain order. Its processing can be completely manual, e.g. when an operator performs the required task and manually confirms the execution of the individual steps (or groups of steps). Fulfilment can also be partially or completely automated with the exchange of process data between XLL and connected devices. Workflows can be defined and edited in LabFulfillment s workflow editor. By providing information about workflows, the LabFulfilment module can also assist LabPlanning: the scheduling of requests requires information about the required material, personnel and equipment and an estimation of the duration of the steps involved. Information about durations are taken from the specification of the workflow or queried from connected automated devices and then passed to LabPlanning. The fulfillment of requests requires instructions to be provided to the people and devices involved. In both cases, the information is derived from the combination of the workflow underlying the request and the parameters specified when the request was issued. For the execution of a workflow, instructions are communicated to operators in the form of task lists or directly to connected devices, depending on the degree of automation. Feedback to the application may also be given manually or via a direct interface between application and device. However, instructions can also be provided for all activities without the need to be part of a workflow. 3.4 LabCore All XLL functionality is grounded in this module. It contains basic features such as user rights management, configuration options for equipment, labware, sample and substance types. It also provides the means to make comparisons between lists: It is possible to compare nominal and actual inventory for freezers in a screening lab or to compare a request for samples with the list of samples which are presently available in a ready to use format. The object browser is another entry point to XLL, since it allows to search for any type of XLL object: prescriptions for cell cultivation, screening requests, substances, samples, etc. Furthermore it lets you check if there are e.g. samples with more than a specific number of freeze/thaw cycles or will soon exceed their shelf life. Additionally, a history viewer shows each objects past uses (and locations where applicable) and a genealogy viewer will reveal the objects connection to other objects, e.g. when working with samples which are mixtures of other samples or there are multiple additions of stimulants, etc. in a cell based assay. Software. Dedicated to People. 6

7 3.5 LabInventory This component manages facility inventories. It contains a model of the physical inventory with storages, devices, robotic systems, etc. and specification of all used labware types. The module allows tracking labware and its content in this model by allowing to document all operations in the laboratory by assigning unique IDs to all (labware) units. It is possible to record movements of individual units or batch wise operations such as loading and unloading of freezer content. Through XLL s SiLA support, compatible devices will update the inventory automatically. When new labware is introduced, its properties (i.e. type and content of labware) can be registered either manually, through file import or via a connection to an external database. In this way, it is also possible to work with anonymous labware when confidentiality is required. In cell culture applications this includes tracking of culture plates and flasks as well as necessary reagents while in screening and compound management, sample location and state will be of primary interest. Benefits of modularity: scaling up Consider the following case: A biotech startup uses XLL as a compound management solution but, because of its relatively small size, prefers to handle and process orders manually. As the company grows, so do the demands placed on its compound management infrastructure: orders become larger and more frequent and it is no longer feasible to process everything manually. The modular nature of XLL now allows to add the LabRequest module so requestors can place their order in XLL and the compound management staff will automatically be informed, complete with task lists which substances have to be dissolved and transferred to which labware. In the long run this could scale up to a fully automated service center with automated storages and liquid handlers which are used to their full potential by the LabPlanning and LabFulfillment modules. 4 Technical details 4.1 XLL Client Since there are already complex processes and applications such as LIMS in place at an average HTS facility, XLL was designed to provide an intuitive user experience. As a rich client application, the GUI enhances the responsiveness and overall performance of the application, because the parts of the required data can be cached on the client. Importing lists of labware IDs / barcodes for inventory updates or feedback from the screening progress supports commonly used formats. The installation in the company s internal network uses Microsoft s Click- Once technology and therefore does not require local administrator privileges on the user s PC. 4.2 XLL Server The XLL server performs user authentication and processes complex business operations in the background (module functions such as comparisons or importing and reformatting of data from corporate databases or web services). Interfaces for the integration of corporate data are designed to keep the required implementation effort to a minimum. The XLL server also notifies all connected clients once interesting data has been updated. 4.3 XLL Database The XLL database is the data storage for all XLL relevant data (inventories, requests, samples, etc.). With the implemented constraints for uniqueness, null ability of table fields and relations between the data table entries, the XLL database ensures that only valid data is stored. 4.4 Deployment options A typical installation for one site consists of one server with multiple clients. Depending on customers requirements, XLL can also be deployed to multiple sites with the option to either use a central server with clients connection from the different sites or to have one server for each site with clients connecting only to their sites server. Communication between servers ensures that the database is in sync at all times. 5 System prerequisites 5.1 XLL Client Recommended Requirements: Supported OS: Microsoft Windows XP, Vista, 7, 8, 8.1 Installed components: Microsoft.NET Framework Network: Online access to the XLL Server User management: Default: Windows domain user accounts, others on request Software. Dedicated to People. 7

8 5.2 XLL Server Recommended Requirements: Supported OS: Microsoft Windows Server 2008/2012 R2 x64 Installed components: Microsoft.NET Framework Database connection tools (e.g. Oracle Client for Oracle database) Network: Online access to XLL Database and all automated data import and export modules CPU: 4 2GHz Memory: 4 GB Required disk space: 500MB XLL Server instance: Windows Service (requires a specific domain/system user) For example, company data like names and details of solutions, samples and compounds might be classified and access restricted. In this case XLL will only access, store and display the unique identifier of every solution in every well. The user then has to look up the solution, sample and compound information on a separate system by taking the unique identifier. 7 Further reading Xavo Lab Logistics SiLA - Standardization in Lab Automation - AniML - Analytical Markup Language Access rights granted to XLL Server: Corporate databases: Importing physical plates and template info from corporate databases Active directory: Accessing user authentication and authorization information 5.3 XLL Database Supported databases: Microsoft SQL Server 2008/2012, Oracle 11g Required disk space: min. 100GB / year (strongly depends on number of systems, samples, etc.) Encryption: Default: No database encryption. Encryption on request. 6 Limitations XLL focuses on logistics related aspects of research and on interconnecting applications and devices. Lab automation in the sense of directly controlling devices and in-depth data analysis are not in XLL s scope. For this, specialized software by other vendors is connected with XLL to provide the desired functionality. Some XLL functions require access detailed data for full functionality. However, some companies have rules to restrict access to data like compounds, solutions and samples. XLL respects those restrictions and offers customizable views and data tables according to the corporate regulations and security guidelines. It only accesses and displays information that are granted. Software. Dedicated to People. 8