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ASSIGNMENT # 2
Critical Appraisal of Research Articles
Pragati
Lakehead University
EDUC-5113-WDF
Prof. Helen
March 11th, 2023
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Introduction
In this Literature article, I would discuss the concern or issues that I have found in a interview
with DEV, working as a QC chemist in a pharmaceutical company related to time management
and appropriate technology or software to manage and organise the data for testing of chemicals
and drug development. In the interview, the interviewee mentioned the LIMS software which is
used at his workplace but there are still some barriers while implementing the software for
analysis and creating the data and reports. In this paper, I will discuss and evaluate the four
research literature by eminent researcher in the current field and critically analyse the study’s
finding with other scholarly sources.
Article 1: “Laboratory information and management system: A tool to increase laboratory
productivity”
The article “Laboratory information and management system: A tool to increase laboratory
productivity” by Dubey et al. (2012) highlights the components of LIMS (Laboratory
information and management system), how it works, its application, and approach of validation
of LIMS was summarizes in an easy-to-understand way in the introduction part. As Connell
Meehan, (1999) (cited in Coughlan, 2007) said that the title as well as the purpose of any study
should be clearly identifiable by the readers, this article meets that requirement perfectly.
The purpose of the study was clearly mentioned in the introductory section, as they should
usually be (Creswell & Creswell, 2018; Ryan et al., 2007). The article is based on a research study
conducted in various journals based on various title and abstract. A Qualitative study was done for
gathering the informative data for the article. The article contains a very clear topic, which
demonstrates the main issue addressed in his study. The author has conceptualised the crucial
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problem in the introduction section by describing basic information about the LIMS and how it
works, application of LIMS like keep track of the due dates for re-sampling, generating reports
like quality control, Validations of LIMS at various sites, response to errors. The importance of
using a qualitative research technique for this study is not explained, however, explicitly stated in
the abstract or introductory section. Because Connell & Meehan (1999) (cited in Ryan, 2007)
emphasised that the researcher should provide a clear explanation for using a qualitative approach
for that specific study, no such justification was provided for this publication.
The study concludes the necessary factors related to LIMS as well other studies includes
in the articles are also beneficial to understand the selection procedure. Dubey et al. (2012)
suggested about the most important and frequently missed criteria for selection in two parts
which are “WPE” (Work process evaluation) and “Purchase process” in the article. Additionally,
the author(s) pay emphasis on the customization and validation process at various sites as it is
easy to find the error(s).
The data included in the article is appropriately linked with many literatures to support the
research work and findings. The author(s) in conclusion stated that LIMS is a critical component
of successful commercial laboratory in quality control, process control, and R&D environment.
However, in the conclusion, researchers have not discussed whether or how the findings can be
transferred to other populations or considered other ways the research may be used in future.
(CASP checklists, 2023).
Critical analysis
The author(s) in the introduction highlight the concern of my interviewee that “With the
advent of new technologies, both the quality and quantity of information is increasing
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exponentially. This increase of data can cause significant problems and methods are needed to
manage it. One such method used is a Laboratory Information Management System (LIMS)”
(Dubey et al., 2012, p. 46). The study explained that the level of data availability for all project
stakeholders can be significantly increased, and significant time savings achieved with the use of
LIMS. A LIMS can prove to be quite helpful in such situation in pharmaceutical filed.
Moreover, the article explained how LIMS (Laboratory Information Management
System) is an application designed basically to administer samples, gather, and modify data, and
communicate results via a database. The process for sampling, analyzing, and reporting are
automated in LIMS. Additionally, the study by Dubey et al., (2012) well explain how LIMS
works to manage the data and creating a report in the article as “While carrying out the actual
testing, the readings and observations are noted down in the work sheet. After completing the
tests all data are fed into the LIMS package. It automatically calculates the results, compares the
findings with the standards, and also decides about the compliance with the standards” (p. 48).
The main concern of the person interviewee for Assignment 2 was the time constrain to complete
the testing and generation of reports including the quality control, productivity report, analyst
performance report, samples pending for the sampling which is addressed by the author in this
article.
In the article author(s) also discussed “How can a LIMS be created to meet this
combination of many requirements?” The solution is also discussed in the article, which is to
provide the users with a LIMS, which they themselves can customize and alter to their own ends.
As a result, the overall potential functionality for the system is greatly increased. The author(s)
also analysis one more factor related to the technology in the study that is a selection of the types
of LIMS according to the requirement of the workplace as LIMS is a management tool because
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selection and implementation of the LIMS is a complex process. Moreover, additional research
literature is also included by the author to support the findings and conclusion.
Article 2: 20th Anniversary Special Feature: The transformation and future trends of
laboratory data management.
The article “20th Anniversary Special Feature: The transformation and future trends of laboratory”
by Leitham et al. (2008) sheds light on the data management system from their earliest forms and
how they are being used today in pharmaceutical industry. Moreover, the paper also forecasts
future technological advancements that will affect the pharmaceutical sector. The article makes an
important point on the evolution of laboratory data management software and the solution to cope
with the strict regulatory requirements for complex operation of pharmaceutical laboratories that
no longer requires customization, validation, and high cost of maintenance.
As Connell &Meehan (1999) (cited in Ryan, 2007) has put emphasised on the fact
that the researcher should justify the use of a qualitative approach for that study, no clear
justification was mentioned for this article. However, the literature is based on the descriptive
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study as well as no appropriate methodology is used to collect the data for the literature. The
information in the article was organised from introduction part the author(s) explain the
emergence of LIMS, then its emergence in Pharma. The author draws his reader’s attention by
providing the brief description of past trends in technology as well as highlighting the limitations
of generic LIMS.
As per Ryan et al. (2007) “Qualitative Research Criteria” this paper has a detailed
discussion part of the final outcomes. Future recommendation for further research is also
mentioned clearly. The author(s) stated as the technology has changed drastically, there is need to
look for more concise alternative for the pharmaceutical companies as the increasing amount of
data is generated and more pressure is created on the companies to automate and integrate the
system. The author(s) of the article also discussed the necessity of LIMS integration with
Microsoft and Oracle to guarantee that information sharing via LIMS will be possible on a
worldwide scale across various locations and disciplines.
Overall, the author(s) has used the qualitative method for description and no additional
researcher literature are used to support the study for the article.
Critical Analysis
The article “20th Anniversary Special Feature: The transformation and future trends of
laboratory” meets the need of my interviewee’s concern. Dev, a quality control chemist find it
difficult to manage the data, and using LIMS 7, particularly at his workplace. The author(s) has
given the clear explanation of why LIMS emerged in Pharma and limitations of generic LIMS to
fulfill the needs of the user? Additionally, it is discussed by the author why the LIMS vendor
focused on developing highly adaptable generic systems that could suit various industry
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requirements. As these solutions were provided with customization tools design, to extend the
system capabilities to meet end user needs. However, customization was expensive, time
consuming and resource intensive as it requires specialized IT expert skilled programmers.
Moreover, the author(s) also answered in the article about the need of alternative solution like
Purpose-built LIMS for the pharmaceutical value chain that can help the QC and R&D
department.
In the nutshell, the article somehow addresses the concern of my interview related to type
of LIMS in pharmaceutical that can organise the data collecting from testing like purpose-built
LIMS. I believe this should be a topic of concern for the researchers, because there is not much
research done in this area. Moreover, I have found some non-peer reviewed article that support
the study done by the author(s).
Article 3: “An overview of LIMS in the pharmaceutical industry”
The article “An overview of LIMS in the pharmaceutical industry” by Wagner Richard seeks to
address “The challenges facing pharmaceutical companies, including large pharma, biotech and
generics manufacturers are sometimes overwhelming” (Wagner, 2005, p. 54). In the article,
author concludes about the unique problems of pharmaceutical laboratories, types of LIMS
(Laboratory Information Management System), advantages of using customized LIMS according
to the needs of the user or company and limitations of all solutions.
The author addressed the challenge of using LIMS in pharmaceutical companies for
evaluating laboratories informatics system used in the various laboratories. The objective of the
article is clear, although no appropriate research methodology is used to collect the data. The
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research does not interpret the actions or experiences of the QC (Quality control) chemists and
other people working within the same field. However, the whole article is well written, and
author has briefly explained each type of LIMS and its advantages and disadvantages according
to the end user needs.
Moreover, the author does a great job by directly outlining the significance of the problem, “Why
there is a need of selecting an appropriate LIMS in pharma?” To support this author,
acknowledge “In general, the industry is rapidly acknowledging that one tool cannot possibly
meet the needs of these diverse users” (Wagner, 2005, p. 54). Wagner highly emphasises that
solutions for these laboratories should be designed with an understanding of the study protocol.
“It does not make sense to try to apply a tool designed for QC testing to a protocol-driven
clinical bioanalysis laboratory” (Wagner, 2005, p. 55).
Additionally, the author also demonstrated the need of LIMS as it is playing a pivotal role in the
evolution towards paperless laboratories work which ultimately saves the time and cost of the
pharmaceutical companies. The author well-structured the article by describing the role of three
various solutions, their initial cost, limitations related to the samples and advantages of the
system. However, there is no best design or data that show accountability to the theory laid by
the author.
Critical Analysis
The article “An overview of LIMS in the pharmaceutical industry” by Wagner Richard
described the concern of my interviewee related to time constrain as well as types of LIMS that
can be used in various pharmaceutical companies according to the need of the end user.
According to Wagner (2005), “In general, the industry is rapidly acknowledging that one tool
cannot possibly meet the needs of these diverse users” (p. 54). The author suggested the various
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types of LIMS and their drawbacks. However, no futhur future research recommendations are
discussed by author in the article.
Overall, in the article, the limitation is neither the appropriate methodology nor the
qualitative surveys or analysis is included. There is no research in the traditional sense for this
article. There are many terms in the article which are not supported by any research references
like why COTS (Commercial off-the-shelf) and generic LIMS are better solution for the pharma
companies to deal with their problems.
A new, more concise research data, methodology is required if the author want more
people to be interested and read the entire piece. Moreover, the author well described the
problems of time constrain and data entry in the pharmaceutical field. I believe research should
be done on such area and researcher and developer should look for new solutions that can
eliminate the problem on validation and customization of LIMS.
Article 4: “Artificial intelligence and machine learning in drug discovery and development”.
https://doi.org/10.1016/j.imed.2021.10.001
Objectives Relating to Interviewee’s Needs
The title of the article “Artificial intelligence and machine learning in drug discovery and
development” by Veer Patel and Manan Shah clearly fulfill the needs of the interviewee related to
technology in testing API (Active pharmaceutical ingredients) in pharmaceutical company. The
purpose of the study was clearly mentioned in the introductory section, as they should usually be
(Creswell & Creswell, 2018; Ryan et al., 2007). The objective of work highlighted the roles of
machine learning and artificial intelligence in facilitating drug development and discovery
procedures, making them more affordable or eliminating the need for clinical trials, especially to
the capacity to conduct simulations using these technologies.
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Rationale of Study
The rationale of the study is:
Demonstrate the prevalent application of machine learning and artificial intelligence methods in
drug discovery and indicate a promising future for these technologies; these results should enable
researchers, students, and pharmaceutical industry to dive deeper into machine learning and
artificial intelligence in a drug discovery and development context” (Patel & Shah, 2022, p. 134).
These statements clearly and compellingly help readers to understand the roles of machine learning
and artificial intelligence in facilitating drug development and discovery processes, making them
more cost-effective or altogether eliminating the need for clinical trials, owing to the ability to
conduct simulations using these technologies. (CASP checklists, 2023; Ryan et al., 2007).
Design and Methodology
A thorough analysis of studies was done; these were chosen based on prior expertise of the authors
and a keyword search in readily accessible databases that were filtered according to associated
context, abstract, approach, and full text. (Ryan et al., 2007). A total of 36 studies were taken into
consideration for the literature evaluation. The author arranged the whole article in a wellstructured way and used easy to understand language. Overall, the qualitative research techniques
appeared to be extremely relevant, since they correctly identified and assessed the factors listed at
the beginning of the article.
Critical Analysis
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The article contains a very clear topic, which demonstrates the main issue addressed in the study.
The literature review part was very well organized and focused on the research issue particularly.
According to Creswell & Creswell (2018), the “literature sections in proposals are generally
shaped from the larger problem to the narrower issue[s]”, which has been done properly in this
article.
The author(s) address the need to compile a thorough article examining AI (Artificial
Intelligence), Machine learning, and big data's role in drug discovery and development. This article
covers the most recent developments, advancements, and original research conducted by
researchers in this field as well as what the pharmaceutical industry can expect in the future as it
applies AI to real-world applications. The author also explained in the article “why AI enabled
technology is being adopted by many pharmaceutical companies?” According to the author(s),
“AI-enabled technology is actively being adopted to tackle minor yet crucial concerning issues in
drug and development industries due to the advancements, growth and the adaption of surplus
amounts of data available for generating meaningful insights” (Patel & Shah,2022).
The author also discussed the case study done by Margulis and colleagues to prove that AI
technology can be used as substitute of animal testing. The authors gave an example of research
conducted by Raschka that showed how machine learning might be used into many diverse fields
(like as multimedia, healthcare, the petroleum trade, etc.), highlighting its potential for the
development of new drugs. Similarly, the article also includes the study done by Turki and Taguchi,
which showed the time required to determine a medicine by machine learning (ML) was just 46
days, much less time than needed when utilising conventional methods. Overall, the author(s) talk
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about various case studies and research done by several researcher to prove the importance of AI
in drug detection.
The article also includes the limitations of AI technology and machine learning briefly. One
drawback of using AI technology and ML is inefficient data integration. Secondly, immobility in
terms of profession, and skills is another problem faced by people while integrating the AI
technology and ML in pharmaceutical field.
Overall, Patel and Shah (2022) well explained:
The integration of AI and machine learning is likely to revolutionise drug development in time,
but there are still a number of barriers like clearing of unstructured and heterogeneous dataset,
occasional in- competency of the computing device etc. may impede this. Once these barriers are
removed, advancements in AI and machine learning can be more widely implemented and
improved, signaling the start of a new age for the pharmaceutical industry. (p.
As per Ryan et al. (2007) “Qualitative Research Criteria” this paper has a detailed discussion part
of the final outcomes, Limitation and future recommendation for further research is also mentioned
clearly.
Conclusion
The main issue identified in the interview of my interviewee was time constrain for drug testing
as well as technology/ software used to do analysis and management of big data. I acknowledge
that there has been a lot of research done in the pharmaceutical field, but there are still certain
problems or concerns that necessitate attention from many researchers, such as finding ways to
manage the information about QC testing, reports, and data analysis in the pharmaceutical
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companies because traditional data storage techniques are becoming less effective as data volume
grows. The articles shortlisted for critical appraisal addressed the problem of my interviewee;
however, additional research literature is also required to completely eradicate the problem.
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References:
Artificial intelligence and machine learning in drug discovery and development. (2021,
November 11). Artificial Intelligence and Machine Learning in Drug Discovery and
Development - ScienceDirect. https://doi.org/10.1016/j.imed.2021.10.001
CASP checklists - critical appraisal skills programme. CASP. (2023).
https://casp-uk.net/casp-tools-checklists/
Creswell_Research_Design_ qualitative, quantitative, and mixed methods approaches (2018) 5th
ed.pdf. DocDroid. (n.d.). Retrieved March 4, 2023, from
https://www.docdroid.net/XAQ0IXz/creswell-research-design-qualitative-quantitative-andmixed-methods-approaches-2018-5th-ed-pdf
Ryan, F., Coughlan, M., & Cronin, P. (2007). Step-by-step guide to critiquing research. Part 2:
Qualitative research. British journal of nursing (Mark Allen Publishing), 16(12), 738–744.
https://doi.org/10.12968/bjon.2007.16.12.23726
Kolluri, S., Lin, J., Liu, R., Zhang, Y., & Zhang, W. (2022). Machine Learning and Artificial
Intelligence in Pharmaceutical Research and Development: a Review. The AAPS Journal, 24(1). ).
https://doi.org/10.1208/s12248-021-00644-3
Laboratory Information Management Systems (LIMS) - researchgate. (n.d.). Retrieved March 9,
2023, from
https://www.researchgate.net/profile/KyleBoyar/publication/349942149_Laboratory_Informatio
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n_Management_Systems_LIMS/links/6062967692851cd8ce762003/Laboratory-InformationManagement-Systems-LIMS.pdf?origin=publication_detail