Building a Smart Laboratory 2018 - An introduction to the integrated lab - Scientific Computing World
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Building a Smart
Laboratory 2018
An introduction to the integrated lab
From the publishers of
From the publishers of
www.scientific-computing.com/BASL2018
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Contents
WELCOME TO THE SMART LABORATORY
T
his year’s edition of Building a Smart
Laboratory discusses the importance
of developing a robust strategy for
the deployment of paperless lab
technology. As the article on page 6 discusses,
in order to gain the most insight and value
from paperless technology there needs to be a
consistent and comprehensive approach that
covers the four most important pillars; connect,
manage, decide, archive.
As laboratories seek to drive more value
and to move from a cost centre to being a value
An introduction to building Knowledge: Document proposition for an organisation, it is important
4 a smart laboratory 2018 26 management that all knowledge can be used effectively to
generate the largest return on investment. The
This introduction sets out procedures to help lab How the smart laboratory contributes to the
requirements of a knowledge eco-system, and the only way to truly achieve this is to adopt smart
users implement paperless technologies in the lab
– with a particular focus on data-intensive science practical consequences of delivering access and laboratory technology.
and new trends preservation of knowledge that was traditionally This is a consistent theme throughout
stored in paper archives the entire publication. Building a smart
laboratory can provide huge benefits to an
The key layers to a organisation in terms of increased productivity
6 paperless strategy or value generation, but also through collection
30 Beyond the lab management and archiving of data. However,
Developing a robust strategy is a key concern in order to make the most of the investment
when deploying paperless laboratory technology, How the smart laboratory can help to improve
your business, through greater productivity in ‘smart’ technologies, it is imperative that a
write Isabel Muñoz Willery and Roberto strategy is devised that can look at the needs
Castelonovo of NL42 Consulting and efficiency, better integration with existing
systems, better regulatory compliance, data to the lab and its users to properly adapt and
integrity and authenticity configure the technology accordingly.
Technology will not do the thinking for us,
10 Dealing with data but if properly constructed a smart laboratory
Informatics providers share their experiences Practical considerations in can add considerable value. While this guide
on the importance of using the latest laboratory 36 specifying and building the cannot provide all the answers, it does provide an
technology smart laboratory introduction to everyone that faces the challenge
of increasing productivity and data integrity for
This chapter focuses on how to go about building the modern laboratory workflow.
a smart laboratory with information relating to
12 Smart laboratories approaches to take, and potential roadblocks
An introduction to the concept of a ‘smart’ The authors of the guide are:
laboratory, based on the data/information/ Peter Boogaard
knowledge triangle 40 Knowledge: Data analytics Industrial Lab Automation
Siri Segalstad
Taking the theme of knowledge management Segalstad Consulting AS
beyond document handling into the analysis of Joe Liscouski
14 Data: Instrumentation data to help develop new products or improve Institute for Laboratory Automation
existing ones Charlie Sodano
eOrganizedWorld
We look at the latest progress towards truly
digital laboratories, with a focus on the types of John Trigg
laboratory instruments and their capabilities 41 Summary phaseFour Informatics Ltd
Isabel Muñoz-Willery
Pulling together the various threads on how to NL42 Consulting SL
make the laboratory ‘smart’ this chapter hopes to Roberto Castelnovo
NL42 Consulting SL
Information: Laboratory lay out the most important factors that must be
19 informatics tools considered
Cover image and all other images: Shutterstock.com
Building a Smart Laboratory is published by Europa Science, the
An overview of laboratory informatics tools publishers of Scientific Computing World (ISSN 1356-7853). ©2018
– LIMS, ELN, LES and – how convergence is 42 References and further reading Europa Science Ltd. 4 Signet Court, Cambridge, CB5 8LA, UK.
All images Shutterstock.com
changing the informatics market
Design: Zöe Andrews
Tel: +44 (0)1223 221033. Fax: +44 (0)1223 213385.
www.scientific-computing.com/BASL2018
www.scientific-computing.com/BASL2018 3
An introduction to: Building a Smart Laboratory 2018 Building a Smart Laboratory 2018
AN INTRODUCTION TO
Building a Smart Laboratory 2018
It’s rare for a company to start with
sharing, the barriers to implementing successful Paperless or less paper?
a clean slate when making decisions
electronic integrated processes often remain a
about laboratory automation
bridge too far. Data-intensive science is becoming far more
T
mainstream; however, going digital in the
The informatics journey laboratory has been a relatively slow process. More
his chapter serves as an introduction than 75 per cent of laboratory analysis starts with
to this guide Building a Smart The journey starts with data capture, data a manual process such as weighing; the majority
Laboratory 2018. We hope to highlight processing, and laboratory automation. When of results of these measurements are still written
the importance of adopting smart samples are being analysed, several types of down or re-typed.
laboratory technology but also to guide users scientific data are being created. They can be There are exceptions: probably the best
through some of the challenges and pitfalls when categorised in three different classes. example of integrated laboratory automation
designing and running the latest technologies in Raw data refers to all data on which decisions can be found in how chromatography data
the lab. are based. Raw data is created in real-time from an handling systems (CDS) operate in modern
For any laboratory a cost/benefit analysis instrument or in real-time from a sensor device. laboratories. The characteristics of such a system
needs to consider the functionality already Metadata is ‘data about the data’ and it is used include repeatable, often standardised, automated
provided by legacy applications – as well as for cataloguing, describing, and tagging data processes that create a significant amount of raw
business justifications. This guide will help you resources. It adds basic information, knowledge, and processed data.
understand what informatics processes are and meaning. Metadata helps organise electronic The paper versus paperless discussion is as
needed in laboratories, and why the laboratory resources, provide digital identification, and old as the existence of commercial computers. In
should not merely be seen as a necessary cost helps support archiving and preservation of the the 1970s, just after the introduction of the first
centre. resource. personal computer, Scelbi (Scientific, Electronic
Only by becoming smart – as this guide Secondary or processed data describes how and Biological), Business Week predicted that
outlines – can lab managers change that mind-set raw data is transformed by using scientific computer records would soon completely replace
and generate true value for their organisation. methodologies to create results. To maintain paper. It took at least 35 years before paperless
Many laboratory operations are still data integrity, altering methods to reprocess will operations were accepted and successfully adopted
predominantly paper-based. Even with the require a secured audit trail functionality, data in many work operations. Although they have
enormous potential to reduce data integrity and access security. If metadata is not captured, been accepted in banking, airlines, healthcare, and
for compliance, to make global efficiency gains the ability to find and re-use previous knowledge retail, they lag behind in science.
in manufacturing and to increase knowledge from scientific experiments is eliminated. The journey from paper to electronic begins
4 www.scientific-computing.com/BASL2018
Building a Smart Laboratory 2018 An introduction to: Building a Smart Laboratory 2018
with the transition from paper to digital, which changing to a new model based upon a ‘pay-as- mainstream adoption. The acceptance of tablets
includes both the transfer of paper-based you-go’ or philosophy (OPEX). CRM applications and mobile devices will expand exponentially in
processes to ‘glass’ and the identification and such as SalesForce.com started this business the laboratory.
adoption of information and process standards to model in the traditional enterprise business Laboratories will need to manage the
harmonise data exchange. software segment. Popular applications such as challenges presented by new consumers of
Photoshop, Microsoft Office 365 and Amazon scientific data outside traditional laboratory
Think exponential are following these trends rapidly. It is expected operations. Non-invasive, end-to-end strategies
that scientific software suppliers will be forced will connect science to operational excellence.
Traditional mainstream LIMS will face challenges. to follow the same model in the years to come. Technology will be critical, but our ability
LIMS has been a brilliant tool to manage Community collaboration and social networking to change our mind-set to enable this cross-
predictable, repeatable planned sample, test is changing the value of traditional vendor help functional collaboration will be the real
and study data flows, creating structured data desks. challenge. n
generated by laboratories. In R&D environments,
unpredictable workflows creating massive Reduce and simplify workflow complexities
amounts of unstructured data showed that current The need to simplify our scientific processes Adapting to change
LIMS systems lack the capability effectively to will have a significant impact on reducing data
manage this throughput. ELNs are great tools to integrity challenges. For example, balance and Much of the change that drives new
capture and share complex scientific experiments, titrator instruments may store approved and processes or methods in the laboratory is
while an underlying scientific data management pre-validated methods and industry best practice based on regulation from that aims to more
system (SDMS) is used to manage large volumes workflows in their firmware. tightly control the way in which data is
of data seamlessly. collected, stored and handled.
Adopt and use industry standards and processes Many laboratory users will be aware
Data consumer vs data creator examples Initiatives such as the Allotrope Foundation are of previous regulations such as Title 21
working hard to apply common standards. The CFR Part 11, part of Title 21 of the Code
For the researcher, the ability to record data, make Allotrope Foundation is an international not-for- of Federal Regulations that establishes the
observations, describe procedures, include images, profit association of biotech and pharmaceutical United States Food and Drug Administration
drawings and diagrams and collaborate with companies, building a common laboratory (FDA) regulations on electronic records and
others to find chemical compounds, biological information framework for an interoperable electronic signatures (ERES).[1]
structures – without any limitation – requires a means of generating, storing, retrieving, Part 11, of the document, as it is
flexible user interface. For the QA/QC analyst transmitting, analysing and archiving laboratory commonly called, defines the criteria under
or operator, the requirements for an integrated data and higher-level business objects. which electronic records and electronic
laboratory are quite different. A simple, natural signatures are considered trustworthy and
language-based platform to ensure that proper Consolidation and equivalent to paper records.
procedures are followed will be well received. harmonisation of systems However new regulation around General
Product innovation and formulators will Data Protection Regulation (GDPR)
need the capability to mine data across projects, Most laboratories already depend on an and data integrity are new standards that
analytical methods or formulations to create informatics hub comprising one or more of the laboratory users must now familiarise
valuable insights. Transforming unstructured major tools: laboratory information management themselves with. For many users GDPR will
scientific experimental data into a structured systems (LIMS); electronic laboratory notebooks not be applicable as it only relates patient
equivalent will be mandatory to perform these (ELN); scientific data management systems data or companies that hold data of EU
tasks. (SDMS); chromatography data-handling systems citizens. However, if in a clinical setting
Organisations with a strong consumer (CDS) and laboratory execution systems (LES). GDPR could have a huge effect on the way
marketing focus deal with data mining techniques The trend over recent years has been towards that you store patient data. [2]
providing clear pictures of products sold, price, convergence, applying best practice industry In addition to GDPR lab managers must
competition and customer demographics. standard processes to harmonise multisite also familiarise themselves with pending
deployments. Cost reduction to interface regulation on Data Integrity (DI) which
New trends harmonised processes to ERP (SAP), MES and hopes to improve completeness, consistency,
CAPA results in lower maintenance and validation and accuracy of data recorded by
The power of life cycle process improvement costs with a significant overall higher system laboratories [3]. In simple terms this means
The scientist is no longer in the laboratory, but availability for end-users. abiding by principles such as ALCOA
integrated in the overall quality process. Quality (attributable, legible, contemporaneous,
should be built into the design throughout the Mobile computing original, and accurate). However it is advised
specification, design, and verification process. that lab managers and users explore the
Performance metrics on non-conformance While many other industries are implementing ramifications of this new regulation to see
tracking are mandated and monitored by modern tools to connect equipment wirelessly, how it might affect daily workflows.
regulatory authorities. Integrating laboratory many laboratories still write scientific results
systems will add significant value by decreasing on a piece of paper, or re-type them into a References
non-conformance. computer or tablet. Many modern ELN and
1. https://www.fda.gov/regulatoryinformation
LES systems allow electronic connection to a guidances/ucm125067.htm
New budgeting and licensing models (wireless) network. However, to integrate simple 2. https://www.ncbi.nlm.nih.gov/pmc/articles
Managing operating budgets will be redefined instruments like a pH balance, titration and PMC5346164
3. https://www.fda.gov/downloads/drugs/guidances
in the next decade. The days of purchasing Karl-Fischer instruments to mobile devices, a ucm495891.pdf
software as a capital investment (CAPEX) are simpler approach is required in order to achieve
www.scientific-computing.com/BASL2018 5
Planning your lab Building a Smart Laboratory 2018
‘eConnect, eDecide, eManage, eArchive’
The key layers of a laboratory
paperless strategy
Isabel Muñoz-Willery and Roberto will be discussed in detail at the Paperless Lab manual transcriptions. The goal is to reduce
Castelnovo, of NL42 Consulting, Academy 2018. The annual European event aims the manual documentation, the risk of human
highlight the importance of developing to become a learning platform for anyone looking errors, and more importantly, to maintain the
a robust strategy for the adoption to consolidate, integrate or simplify their data information about the source that has generated
paperless laboratory operations management systems. the raw data.
The raw data may be a critical part of the
I
‘eConnect’: effective workflows based activities performed in the systems of the upper
on self-documenting data capture layers. Data management and the creation of
n the new era of the internet of things strategies meaningful information and decisions should
and artificial intelligence, the majority of be always taken with the possibility to go back to
laboratories still have a long way to move Even if data integrity is a critical aspect of the the original data from the system in which it was
from paper-based processes to paperless entire data life cycle, data capture requires a generated.
ones. strong focus from both the inspectors and Finally, while in this first stage of collecting
The electronic data life cycle, as it is auditors. Most lab instruments are now offered data we should not obviate the ones coming
described in several regulations and documents with intelligent software embedded into them. from collaborators. Collaborators are generators
used in paperless projects, can be divided in Labware and sensors are beginning to embrace of data and potential sources of information.
four layers of data, information and activities: the internet of things, ensuring the collection of If external organisations such as academic
eConnect; eManage; eDecide and eArchive. the raw data and the related metadata which can contributors or outsourced services from CRO
These keywords refer to initial capture of then be transferred to the next phase of the data and CMO are generating the data, it can create
data, the data management to create useful life cycle. immediate security concerns. With the latest
information, the decisions taken based on Several laboratories are using instruments GDPR considerations, we need to incorporate
information and data available in the lower which are not able to connect the current data protection assessment at least on the most
layers and, finally, the electronic data archiving platforms. While searching for the business vulnerable data. By May 2018, companies
to ensure long-term availability of the justification for their replacement, intermediate will need to design their processes and also
information and the related data. solutions should be considered to generate digital include serious considerations on cybersecurity
Those are the four-main streams that inputs and reduce paper-based processes and protection to avoid any risk in losing data.
6 www.scientific-computing.com/BASL2018
LABWARE 7
LIMS and ELN together in a single
integrated software platform.
A laboratory automation solution
for the entire enterprise.
Offices worldwide supporting customers
in more than 100 countries
www.labware.com
Planning your lab Building a Smart Laboratory 2018
‘eManage’: generation of meaningful ‘eDecide’: Rapid decisions taken their fingertips. Moreover, these tools are able
information from trusted data from meaningful information to dig into the underlying systems to view the
information and related raw data, when needed.
The ‘translation’ from data to information is the In the everyday activities of a laboratory, we are We will finally see one single screen open on
key principle of this layer of activities typically getting used to perform them very rapidly and the computers of the managers instead of multiple
performed in the most well-known systems decisions should be taken in short time. Little windows jumping from one system to another
The real challenge in the new era of Internet of remains available for data review, approval of data in order to desperately collect all the necessary
Lab Things (IoLT) is not about picking up the and creation of related documents. The request information required in a given moment for a
right acronym for the lab. The challenge lies coming from laboratory’s customers, both given decision to be taken urgently.
in identifying the right solutions that provide internal and external is a prompt answer.
answers to a series of requirements: secured The removal of manual processes, of paper- ‘eArchive’: essentials to secure long-
connectivity without large investment; usability based activities and mix of information sitting term multi-departmental archiving
with limited customisation; ability to share in different systems is essential for taking faster
information using the newest technologies; decisions. Only paperless processes shorten A key objective in operating with efficient
mobile devices and web-access without the periods of review of the information and archival approach is to reduce the challenge of
performing complex platform implementations; ease rapid decision-making which can then finding the right data. Considering the growing
and the possibility to use the software as a service. be communicated immediately to the relevant digital universe, archiving can no longer be left
We are observing a large market stakeholders. New approaches like the review by behind in a project and considered only once it is
transformation in this area. The presence exception are helping to increase the efficiency of too late. Nowadays, we often hear about concerns
of systems which are offering a large set of this process. on legibility and format consistency along the
functionalities and product offerings based on The laboratories that are able to respond to time for a given retention time that might end up
new technologies. these requests on time and with the adequate requiring access to obsolete technologies.
Multiple software modules adapted to specific level of quality will transform from cost centers to Archiving should be approached and
laboratory activities and software platforms allow value generators. designed to reduce multiple types of risk:
the creation of personalised solutions with no
need to customise but rather configure the system
to the needs of the user.
This revolution will generate large benefits
for the laboratories because the selection of the The removal of manual processes, of paper-based activities
solutions will be based on the needs rather than and mix of information sitting in different systems is essential for
the capabilities. taking faster decisions
“
These modules should respond to a few
critical requirements in order to become part of
the ‘solution’: easily connectable to the ‘eConnect’
layer; easily connectable to modules of the
‘eManage’ layer; easily accessible from browsers
and mobile devices; and easily accessible from the Decisions should be taken according to the knowledge limited to one critical person, security
‘eDecide’ layer. available information. Today many software and loss of data.
providers offer simple tools presenting the A comprehensive archiving protocol should
What is the end goal? information in a graphical view, showing the eliminate the struggle to find the data to the point
outliers, highlighting the areas of attention, of desperately looking for the person owning the
On one hand, the final goal should be to interface allowing the ‘drill-down’ approach when needed. knowledge of where it is.
the ‘solution’ with the multiple generators of raw Fact is that solutions providers, integrators A corporate master data management and
data in order to enable the review directly at the and customers are joining efforts in organisations vocabulary model should support a correct
source at any time. Additionally, the possibility to like the allotrope foundation, Pistoia Alliance, management and archival, facilitating a flawless
exchange information between the modules of the SiLA consortium to consolidate outputs and track record of the data.
‘eManage’ layer, in a flawless manner, should allow tools, that could one day lead to the creation During the Paperless Lab Academy 2018,
the access and interpretation of all data to generate of one single user interface, one single way several presentations will focus on this item that
meaningful information. of showing the information in a unique and too often is approached too late in a ‘paperless’
The possibility to access the ‘modules’ from personalised dashboard. project. The archiving strategy requires a clear
any remote location or even from mobile devices Simple reports created automatically definition of the business requirements and, also
in order to manage all the information in the overnight and available in the ‘eDecide layer’ first the potential technical challenges.
shortest period of time. The possibility to provide thing in the morning. A new ‘control room’ of the The ability to archive and then retrieve
aggregated information to the next layer of laboratory where decisions are taken to correct unstructured data is becoming an urgent need
systems where decisions are taken. situations not in line with the expectations, where which must be solved in R&D laboratories.
Is this real? Absolutely. The technology has scheduling changes are adjusted to ensure that Solutions providers are dedicating resources
evolved to the level that all these goals could be the activities are completed on time, on budget to this matter and positioning their data
reached. and according to the customer expectations. management software to address the need for
Numerous solutions are already Is this real? Yes, again. Great reporting and better archiving and retrieval. Above all, the
implemented in various markets where they are business intelligence tools are now available ‘eArchive’ strategy is one that requires stronger
using the newest technologies. The laboratory to integrate the information coming from alignment within the whole company in order
informatics systems will have to be ready for this different systems and present in a simple and to build up a reference master data management
new era too. graphical way. All what the managers need at strategy at an enterprise-level. n
8 www.scientific-computing.com/BASL2018
Dealing with data Building a Smart Laboratory 2018
Dealing with data
Informatics experts share their and they want to know what we could do using
experiences on the implementing new that layer,’ said Gonzalez. ‘That is the technical
technologies and manging change in question that we get the most from our existing
the modern laboratory users. They don’t tend to ask about cloud because
they have a running system. The IT department
might be interested in moving to the cloud but
since they have the system already running and
they are not likely to want to change that in the
short term,’ Gonzalez added.
Gonzalez noted that mobile technology as a
solution for laboratory users ‘is a solution that
Mark Gonzalez needs to solve real-life problems’.
Technical director at Labware ‘What we want to do is solve the right
problems we don’t want to just throw out a bunch
of technology that doesn’t really solve anything of
What technologies are requested by any business value.’
laboratory users? One example that he noted was the ability
to use mobile devices in untethered mode. This
Mark highlighted that there are clear divisions could allow users to perform actions such as
between the two primary groups of existing entering data without a continuous connection
customers and potential users. to the LIMS server. Once the connection is
‘In terms of technology the question that re-established the data can be automatically
existing users are asking about most often is sent to the LIMS system. ‘One value of mobile
mobile. That is not to say that they have a clear technology is that people could work remotely to
plan on how to use the technology but they have collect data, even if they don’t have a connection
smart phones and tablets in their personal lives to the LIMS server,’ concluded Gonzalez
10 www.scientific-computing.com/BASL2018Building a Smart Laboratory 2018 Dealing with data
and objectives in order to increase efficiency effort and money, so they aren’t eager to move.
and productivity and finally it must facilitate When a Lab needs to be compliant to GMP, GLP,
collaboration between scientists. etc it has many other points to manage: change
‘The main issue is to handle data, not only control, system validation, certification and audits.’
to store it but also to be able to use that data All of these aspects can make a move more
effectively,’ stressed Acker. ‘Laboratories are challenging, but ultimately choosing not to
producing and accumulating more and more upgrade impacts agility – and the speed and
data from experiments, analysis, bibliography and quality of further laboratory operations.
other areas. For instance, one screening campaign Another aspect that AgiLab was keen to stress
could generate hundreds of thousands of results, a was that cloud deployments are increasingly seen
query on a citation source like Pubmed can report as a good choice for many laboratories. However,
Renaud Acker thousands of references. the move to cloud based informatics requires a
Chief operating officer at Agilab ‘The challenge is to centralise data, to manage user to change their mindset as they move from
and gather information, to generate knowledge silos of data to a more fluid model of shared data
from data – and to keep track of what has been sets and collaboration.
What are the main challenges that done, how it has been done, if it has worked or ‘Labs are still working in silos,’ added Acker.
your users face when deploying digital not. Big data technologies will be very useful to ‘New R&D processes should break this logic in
informatics technology? annotate, explore and exploit the whole set of order not only to exchange data but mostly to
data generated in labs and gathered from external anticipate issues by gathering scientists working
Renaud Acker explains that, for many of AgiLab’s public sources. on a project. Collaboration is essential for R&D
customers, ‘change control’ is the main challenge: While there are clear benefits to using the project success. Cloud applications could help to
‘Processes have changed by using a new latest software, cost of investment can be a big exchange data and ideas between labs in different
generation of software. Users must be trained, issue that prevents companies from replacing locations, between industrial, partners and
standard operating procedures (SOPs) must be legacy infrastructure – but it is not the only academics.’
adapted, data handling and traceability must be reason, as Acker explains. Acker concluded that cloud-based laboratory
managed in a different way. ‘There are at least two main reasons why labs informatics is growing due to a number of factors
‘This means that lab software should be don’t move easily to new lab software. Many including their robust security, the potential for
user-friendly for daily use. Screens must be clear companies and labs have spent fortunes in their hosting management of services off-premise and
with adapted vocabulary,’ stated Acker. ‘However, first generation of lab software. Secondly, they the use of cloud subscription models that can
it must also be adapted to laboratory processes have customised these products with considerable reduce initial investment and running costs.
that iVention is managing in Europe that is solution was hosted for the client by iVention.
consolidating as many as seven individual ‘I don’t think there are many of those rollouts
implementations with their own custom software, completed successfully with a conventional LIMS
with additional software connected to it. system,’ said Kox.
‘They cannot upgrade everything all at once,’ ‘They are a big company with their own
he said. IT department and we are hosting it for them
The presence of custom software in each because we have all the technology in place to
implementation means that each installation is automate everything, so all the upgrades can be
essentially a new piece of software. done automatically.’ He explained the success of
‘Now if you compare this to the capabilities of this rollout has meant this company is now using
Oscar Kox a web-based system you can rollout to all of those iVention as a strategic partner for much larger
Business delopment manager at Ivention sites without custom software – there is a big rollouts in the future.
benefit,’ said Kox. Kox said: ‘I have seen organisations with
‘If there is a LIMS project that people who are very old software, which can be costly and time
now looking for a new LIMS or ELN, the decision consuming to maintain and upgrade. Some IT
How important are digital technologies they make now will affect them for the coming directors would say the upgrade would cost more
to the modern laboratory? five to 10 years, because that is the investment than the original installation, so they either try and
that you are looking at.’ run for a few more years or select a new system.’
‘There is a lot of innovation available in the Kox stressed customers should ask He said one of the main challenges when
market but I don’t think many labs are picking it themselves: will this big conventional LIMS dealing with legacy LIMS or ELN systems is a lack
up as early adopters,’ said Kox. vendor help me to innovate? ‘That is where the of maintenance and upgradability: ‘The biggest
‘People should ask themselves how important gap comes in. There’s a lot of innovation out thing I see is customers paying maintenance and
is it adopt new technologies – to innovate in the there but can I adopt it right now, because of the they cannot upgrade. Support cannot help them
lab. Having worked in this industry for more than systems I have in place?’ because they have an old version and in many
20 years – of course it is important. You want to He explained that iVention has installed cases this support money is wasted because the
see new technology getting into the laboratory systems across very large organisations. He gave system is too old to be properly supported.
either because you want to reduce FTE, you want an example of a pharma client who wanted to roll ‘I would strongly recommend firms look at
to increase throughput or improve quality.’ out a system for 300 users across seven countries, their maintenance contacts and ask themselves
Kox gave an example of large implementation over eight months. Cox also mentioned that this “what are we getting back from it?”’ n
www.scientific-computing.com/BASL2018 11The smart laboratory Building a Smart Laboratory 2018
The smart laboratory
T
This chapter discusses what we mean
oday the landscape for laboratory a common problem facing many laboratories –
by a ‘smart laboratory’ and its role in
technologies is broad and varied. This data generated through ‘dumb’ instrumentation
an integrated business. We also look
is true purely in terms of the variation such as pH meter or weighing scales. Instruments
at the development of computerised
of management systems and other that are not connected directly to a (Laboratory
laboratory data and information
software packages but also due to the proliferation Informatics Management System) LIMS or
management; the relationships
of additional technology such as cloud, mobile Electronic Laboratory Notebook (ELN) type
between laboratory instruments
technologies and more recently the IoT. management system present opportunities to
and automation (data acquisition);
There is no specific definition of a ‘smart introduce error through human data entry but
laboratory informatics systems
laboratory’. The term is often used in different there are multiple ways to solve this problem.
(information management); and higher-
contexts to imply either that a laboratory is One would be to buy new scales for example.
level enterprise systems and how they
designed to optimise its physical layout, that it Purchasing a new instrument with smart
align with knowledge management
incorporates the latest technology to control the capabilities could feed that data directly into the
initiatives.
laboratory environment, or that the laboratory is LIMS reducing the chance for error. Another
using the latest technology to manage its scientific approach would be the use of mobile devices
The progressive ‘digitisation’ of the
activities. For the purposes of this publication, it is which could be used to capture the data at the
laboratory offers an unprecedented
the latter definition that applies. bench another would be to use a raspberry Pi like
opportunity not only to increase
Using technology to manage scientific device connected to the internet to take the result
laboratory efficiency and productivity,
endeavours is conceptually a straightforward and feed it into the LIMS. The choice around
but also to move towards ‘predictive
task but the subtlety lies in choosing the right whether mobile, IoT or new instruments is one
science’, where accumulated explicit
combination of technologies that can be adapted that can only be answered on a case by case basis
knowledge and computer algorithms
to suit the use case of a specific laboratory which – there is no one size fits all solution for every
can be exploited to bring about greater
may be dictated by geography and personnel laboratory.
understanding of materials, products,
as much as it is driven by the availability of The introduction of industrial R&D
and processes
technology. As such the right answer to setting laboratories heralded a new era of innovation and
up a smart laboratory is not to adopt all possible development dependent on the skills, knowledge
technological features but to identify which and creativity of individual scientists. The
areas of the laboratory need to be accelerated or evolution has continued into the ‘information
improved upon. age’ with a growing dependence on information
A simple example of this could be found in technology, both as an integral part of the
12 www.scientific-computing.com/BASL2018Building a Smart Laboratory 2018 The smart Laboratory
FIG 1 Information structure The two primary areas of technology that apply
to a smart laboratory can be broadly categorised as
laboratory automation and laboratory informatics.
In general, laboratory automation refers to the use
of technology to streamline or substitute manual
manipulation of equipment and processes. The
field of laboratory automation comprises many
different automated laboratory instruments,
Programmes devices, software algorithms, and methodologies
Document
used to enable, expedite, and increase the
management
efficiency and effectiveness of scientific research
Projects in labs. Laboratory informatics generally refers to
Project management the application of information technology to the
handling of laboratory data and information, and
Experiments optimising laboratory operations.
Laboratory notebook In practice, it is difficult to define a
boundary between the two ‘technologies’ but,
Interpreted/processed data in the context of this publication, chapter three
SDM/LIMS (Data) will provide an overview of laboratory
instrumentation and automation, predominantly
Raw data data capture.
Laboratory instrumentation Chapter four (Information) will look at
the four major multi-user tools that fall into
scientific process, and as a means of managing model (see Figure 1) that defines the conceptual, the ‘informatics’ category, identifying their
scientific information and knowledge. multi-layered relationship between data, similarities, differences and the relationship
Laboratory information has traditionally information, and knowledge. between them. Chapters three and four, therefore,
been managed on paper, typically in the form The triangle represents the different layers of focus on the acquisition and management of
of the paper laboratory notebook, worksheets abstraction that exist in laboratory workflows. data and information, whereas chapter five
and reports. This provided a simple and These are almost always handled by different (Knowledge) will provide guidance about the
portable means of recording ideas, hypotheses, systems. The ‘experiment’ level is the focal point long-term retention and accessibility of laboratory
descriptions of laboratory apparatus and for cross-disciplinary collaboration: the point knowledge through online storage and search
laboratory procedures, results, observations, and at which the scientific work is collated and algorithms that aim to offer additional benefits
conclusions. As such, the lab notebook served as traditionally handled by the paper laboratory through the re-use of existing information, the
both a scientific and business record. However, notebook. avoidance of repeating work, and enhancing the
the introduction of digital technologies to the Above the experimental layer is a management ability to communicate and collaborate.
laboratory has brought about significant change. context that is handled by established groupware The underlying purpose of laboratory
From the basic application of computational and document management tools at the automation and laboratory informatics is to
power to undertake scientific calculations at ‘programme’ level, and by standard ‘office’ tools increase productivity, improve data quality, to
unprecedented speeds, to the current situation at the project level. Below the experiment level reduce laboratory process cycle times, and to
of extensive and sophisticated laboratory there is an increasing specialisation of data types facilitate laboratory data acquisition and data
automation, black box measurement devices, and tools, typically encompassing laboratory processing techniques that otherwise would be
and multiuser information management instrumentation and multi-user sample and test impossible. Laboratory work is, however, just one
systems, technology is causing glassware and management systems. The triangle also represents step in a broader business process – and therefore,
paper notebooks to become increasingly rare the transformation of data to knowledge, the in order to realise full benefit from being ‘smart’,
in the laboratory landscape. The evolution of journey from data capture to usable and reusable it is essential that the laboratory workflow is
sophisticated lab instrumentation, data and knowledge that is at the heart of the smart consistent with business requirements and is
information management systems, and electronic laboratory. integrated into the business infrastructure in order
record keeping has brought about a revolution The introduction of ELNs therefore opens up for the business to achieve timely progress and
in the process of acquiring and managing the possibility of a more strategic approach, which, remain competitive.
laboratory data and information. However, the in theory at least, offers the opportunity for an Chapter seven (Beyond the laboratory) will
underlying principles of the scientific method integrated and ‘smart’ solution. examine the relationship between laboratory
are unchanged, supporting the formulation, A frequently articulated fear about the processes and workflows with key business
testing, and modification of hypotheses by relentless incorporation of technology in issues such as regulatory compliance and
means of systematic observation, measurement, scientific processes is the extent to which it can patent evidence creation, and will also address
and experimentation. In our context, a smart de-humanise laboratory activities and reduce productivity and business efficiency.
laboratory seeks to deploy modern tools and the demand for intellectual input, or indeed, any Chapter eight (Practical considerations in
technologies to improve the efficiency of fundamental knowledge about the science and specifying and building the smart laboratory)
the scientific method by providing seamless technology processes that are in use. The objective is therefore devoted to the process of making
integration of systems, searchable repositories of this publication is to present a basic guide to the the laboratory ‘smart’, taking into account the
of data of proven integrity, authenticity and most common components of a ‘smart laboratory’, functional needs and technology considerations
reliability, and the elimination of mindless and to give some general background to the benefits to meet the requirements of the business, and
unproductive paper-based processes. they deliver, and to provide some guidance to how addressing the impact of change on laboratory
At the heart of the smart laboratory is a simple to go about building a smart laboratory. workers. n
www.scientific-computing.com/BASL2018 13Data: Instrumentation Building a Smart Laboratory 2018
DATA
Instrumentation
This chapter will consider the
D
Simple laboratory instruments technologies in instrumentation significantly
different classes of instruments and improves both their utility and the labs’ workflow.
computerised instrument systems to evices such as analytical balances
be found in laboratories and the role and pH meters use low-level Computerised instrument systems
they play in computerised experiments processing to carry out basic
and sample processing – and the functions that make them easier to The improvement in workflow becomes more
steady progress towards all-electronic work with. The tare function on a balance avoids evident as the level of sophistication of the
laboratories. a subtraction step and makes it much easier to software increases. It is rare to find commercial
However, the choice of best-of-breed weigh out a specific quantity of material. instrumentation that doesn’t have processing
laboratory instruments and instrument Connecting them to an electronic lab capability either within the instruments’
systems can present challenges when notebook (ELN), a laboratory information packaging or, through a connection to an
it comes to getting everything to work management system (LIMS), a lab execution external computer system.
together in a seamless way. The final system (LES), or a robot, adds computer- The choice of dedicated computer-instrument
part of this chapter will look at the issue controlled sensing capability that can significantly combinations vs. multi-user, multi-instrument
of standard data interchange formats, off-load manual work. Accessing that balance packages is worth careful consideration. The most
the extent of the challenge, and some of through an ELN or LES permits direct insertion common example is chromatography, which has
the initiatives to address them of the measurement into the database and avoids options from both instrument vendors and third-
the risk of transcription errors. In addition, the party suppliers.
informatics software can catch errors and carry One of the major differences is data access
out calculations that might be needed in later and management. In a dedicated format, each
steps of the procedure. computer’s data system is independent and has
The connection between the instrument and to be managed individually, including backups to
computer system may be as simple as an RS-232 servers.
connection or USB. Direct Ethernet connections It also means that searching for data may
or connections through serial-to-Ethernet be more difficult. With multi-user/instrument
converters can offer more flexibility by permitting systems there is only one database that needs to
access to the device from different software be searched and managed.
systems and users. The inclusion of smart If you are considering connecting the systems
14 www.scientific-computing.com/BASL2018Standardize Analytical Data
across techniques and vendors in a single informatics platform
Process, review, and store data in context Assure data integrity
Provide live, on-demand access Enhance regulatory compliance
Simplify and expedite knowledge sharing Drive innovation
ACD/Spectrus Platform
Integrate live, standardized analytical data into your IT stack
www.acdlabs.com/Standardize
IVENTION
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ivention.nlData: Instrumentation Building a Smart Laboratory 2018
to a LIMS or ELN, make the connections as FIG 2 Analogue data acquisition
simple as possible. If an instrument supported by
the software needs to be replaced, changing the
connection will be simpler.
Display
Licence costs are also a factor. Dedicated
formats require a licence for each system. Shared-
access systems have more flexible licensing Electrical
Property
circuit
considerations. Some have a cost per user and to be Control
converting A/D Communications
measured processor
connected instrument; others have a cost per (detector)
properly to
active user/instrument schedules. voltage
In the latter case, there are eight instruments
and four analysts, of which only half may be Product Digital I/0
simultaneously active, licenses for only four (switches, LEDs, etc.)
packaging
instruments and two users are needed.
One factor that needs attention is the
education of laboratory staff in the use of
computer-instrument systems.
While instrument software systems are Instrument data management are created – and transfer the format to that
capable of doing a great deal, their ability to needed by the instrument. Robotic arms – still
function is often governed by user-defined The issue of instrument data management is appropriate for many applications – have been
parameters that affect, at least in chromatography, a significant one and requires considerable replaced with components more suitable to the
baseline-corrections, area allocation for planning. Connecting instruments to a LIMS task, particularly where liquid handling is the
unresolved peaks, etc. Carefully adjusted and or ELN is a common practice, though often dominant activity, as in life science applications.
tuned parameters will yield good results, but not an easy one if the informatics vendor Success in automating sample preparation
problems can occur if they are not managed and hasn’t provided a mechanism for interfacing depends heavily on thoroughly analysing the
checked for each run. equipment. Depending on how things are set process in question and determining:
up, only a portion of the information in the n Whether or not the process is well
instrument data system is transferred to the documented and understood (no
Type of A/D Capability
informatics system. undocumented short-cuts or workarounds
Successive These are general- If the transfer is the result of a worklist that are critical to success), and whether
approximation purpose devices suitable execution of a quantitative analysis, only the improvements or changes can be made
for a wide range of final result may be transferred – the reference without adversely impacting the underlying
applications. They
have limited resolution,
data still resides on the instrument system. The science;
but have amplifiers for result is a distributed data structure. In regulated n Suitability for automation: whether or not
low-level signals, and environments, this means that links to the backup there are any significant barriers (equipment,
can sequentially access information have to be maintained within the etc.) to automation and whether they can be
multiple input channels. LIMS or ELN, so that it can be traced back to the resolved;
Their resolutions are
up to 18 bits (262,144 original analysis. n That the return in investment is acceptable
steps) and sampling The situation becomes more interesting and that automation is superior to other
speeds of up to five when instrument data systems change or are alternatives such as outsourcing, particularly
million samples per retired. Access still has to be maintained to for shorter-term applications; and
second (sps). The higher
the resolution, the slower
the data those systems hold. One approach is n That the people implementing the project
the sampling speed. virtualising the instrument data system so that the have the technical and project management
operating system, instrument support software, skills appropriate for the work.
Integrating Good for low speed and the data are archived together on a server. The tools available for successfully implementing
sampling (14
(Virtualisation is, in part, a process of making a a process are clearly superior to what was
bits, single channel copy of everything on a computer so that it can available in the past. Rather than having a robot
inputs, with good noise be stored on a server as a file or ‘virtual container’ adapt to equipment that was made for people
rejection. Often used in and then executed on the server without the to work with, equipment has been designed
chromatography. need for the original hardware. It can be backed for automation – a major advance. In the life
Sigma-Delta Up to 24 bits of up or archived, (so that it is protected from loss). sciences, the adoption of the microplate as a
A/D resolution, single channel In the smart laboratory, system management is standard format multi-sample holder (typically
input – may not be a significant function – one that may be new to 96 wells, but can have 384 or 1,536 wells – denser
efficient for multi-channel many facilities. The benefits of doing it smartly forms have been manufactured) has fostered
inputs, low speed, may
replace integrating A/Ds. are significant. the commercial availability of readers, shakers,
washers, handlers, stackers, and liquid additions
Flash Single channel input, Computer-controlled experiments and systems, which makes the design of preparation
8-bit conversion, sample processing and analysis systems easier. Rather than
approximately 1 billion
SPS. Good for very processing samples one at a time, as was done in
high-speed applications, Adding intelligence to lab operations isn’t limited early technologies, parallel processing of multiple
where low resolution is to processing instrument data, it extends to an samples is performed to increase productivity.
not a problem. You can earlier phase of the analysis: sample preparation. Another area of development is the ability to
digitise electrical noise.
Robotic systems can take samples – as they centralise sample preparation and then distribute
16 www.scientific-computing.com/BASL2018Building a Smart Laboratory 2018 Data: Instrumentation
the samples to instrumentation outside the processed by the instrument would wait until examples of integration methods that enabled
sample prep area through pneumatic tubes. the data system told it to go ahead. The LIMS the user to extend the basic capability and have
This technology offers increased efficiency by has the expected range for valid results and the ready access to a third-party market of useful
putting the preparation phase in one place so that acceptable limits. If a result exceeded the range, components. It also allowed the computer
solvents and preparation equipment can be several things could happen: vendors to concentrate on their core product and
easily managed, with analysis taking place n The analyst would be notified; satisfy end-user needs through partnerships; each
elsewhere. This is particularly useful if safety is n The analysis system would be notified that vendor could concentrate on what they did best
an issue. the test should be repeated; and the resulting synergy gave the users what they
Across the landscape of laboratory types n If confirmed, standards would be run needed.
and industries, the application of sample to confirm that the system was operating Now these traditional methods are being
preparation robotics is patchy at best. Success and properly; and surpassed by the IOT or wireless connected
commercial interest have favoured areas where n If the system were not operating according devices but the argument for connecting devices
standardisation in sample formats has taken to SOPs, the system would stop to avoid still remains the same – is the value added worth
place. wasting material and notify the analyst. the investment? The answer depends on the
The development of microplate sample The introduction of a feedback facility would instrument, but generally it is more effective to
formats, including variations such as tape systems significantly improve productivity. connect the most widely used instruments such
that maintain the same sample cell organisation At the end of the analysis, any results that are as PH meters and weighing scales.
in life sciences, and standard sample vials for outside expected limits would have been checked Connections are only part of the issue. The
more significant factor is the structure of the data
that is being exchanged: how it is formatted; and
the organisation of the content. In the examples
Building a smart laboratory needs to look beyond commonplace above, that is managed by the use of standard
approaches and make better use of the potential that exists in device drivers or, when called for, specialised
informatics technologies device handlers that are loaded once by the user.
“
In short, hardware and software are
designed for integration, otherwise vendors find
themselves at a disadvantage in the marketplace.
Laboratory software comes with a different
auto-samplers, are common examples. Standard and the systems integrity verified. Making this mindset. Instrument support software was
sample geometries give vendors a basis for happen depends on connectivity and the ability designed first and foremost to support the
successful product development if those products to integrate components. vendor’s instrument and provide facilities that
can have wider use rather than being limited to weren’t part of the device, such as data analysis.
niche markets. Instrument integration Integration with other systems wasn’t a factor.
That is changing. The increasing demand
Putting the pieces together In order for the example described above to for higher productivity and better return
work, components must be connected in a way on investment has resulted in the need for
It’s not enough to consider in isolation sample that permits change without rebuilding the systems integration to get overall better systems
preparation, the introduction of samples into entire processing train from scratch. Information performance; part of that measure is to reduce
instruments, the instruments themselves, and technology has learned those lessons repeatedly the need for human interaction with the system.
the data systems that support them. Linking as computing moved from proprietary products Integration should result in:
them together provides a train of tasks that can and components to user friendly consumer Ease-of-use: integrated systems are
lead to an automated sample processin systems. expected to take less effort to get things done;
system as shown in Figure 3. Consumer level systems aren’t any less capable Improved productivity, streamlined
The control/response link is needed to than the earlier private-brand-only systems, they operations: the number of steps needed to
synchronise sample introduction and data are just easier to manage and smarter in design. accomplish a task should be reduced;
acquisition. Depending on the nature of the Small Computer Systems Interconnect, nA voiding duplicate data: no need to look
work, that link can extend to sample preparation. Firewire and Universal Serial Bus are a few in multiple places;
The end result is a system that not only provides
higher productivity than manual methods, but
does so with reduced operating costs (after the
initial development investment). FIG 3 An automated sample processing system
However, building a smart laboratory needs
to look beyond commonplace approaches and Control/response
make better use of the potential that exists in
informatics technologies. Extending that train
of elements to include a LIMS, for example, has
additional benefits. The initial diagram above
would result in a worklist of samples with the Instrument
test results that would be sent to a LIMS for
incorporation into its database.
Suppose there was a working link between
a LIMS and the data system that would send Sample Sample Data acquisition, analysis,
sample results individually, and that each sample preparation introduction reporting, storage, etc
www.scientific-computing.com/BASL2018 17You can also read
