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Pharmacology is a branch of biomedical science, encompassing clinical pharmacology, that is concerned with the effects of drugs/pharmaceuticals and other xenobiotics on living systems, as well as their development and chemical properties.

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Introduction to Pharmaceutical Analysis

• First Online: 18 December 2019

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• Muhammad Sajid Hamid Akash 3 &
• Kanwal Rehman 4  

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Pharmaceutical analysis is a broader term which can be defined in many ways. It is the series of processes that are used for identification, determination, separation, purification, and structure elucidation of the given compound used in the formulation of pharmaceutical products. The components, to which the pharmaceutical analysis is done, are normally active pharmaceutical ingredients, pharmaceutical excipients, contaminants present in pharmaceutical products, or drug metabolites. In pharmaceutical analysis, the samples are typically finished pharmaceutical products, biological samples, impurities, contaminants, and pharmaceutical raw materials. Pharmaceutical analysis can be done using various analytical techniques. This chapter discusses in details the fundamentals of pharmaceutical analysis including its types and its associated important terminologies.

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Further Reading

Beckett AH, Stenlake JB Text book of practical pharmaceutical chemistry, Vol. I and II. A&C Black, London

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Connors KA (2007) A textbook of pharmaceutical analysis. Wiley, Hoboken

Görög S (2007) The changing face of pharmaceutical analysis. TrAC Trends Anal Chem 26(1):12–17

Article   Google Scholar  

Griffin JP, O’Grady J (2006) The textbook of pharmaceutical medicine. Wiley, Hoboken

Hansen SH, Pedersen-Bjergaard S, Rasmussen K (2011) Introduction to pharmaceutical chemical analysis. Wiley, Hoboken

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Kar A (2005) Pharmaceutical drug analysis. New Age International, Chennai

LibreTexts™. Pharmaceutical analysis. Accessed 2 May 2019

Ohannesian L, Streeter AJ (2002) Handbook of pharmaceutical analysis. Marcel Dekker, New York

Sudha PC (2012) Pharmaceutical analysis. Pearson Education India, Chennai

Valcárcel M (2012) Principles of analytical chemistry: a textbook. Springer, Berlin

Waters Corporation. Pharmaceutical analysis. Accessed 11 June 2019

Watson DG (2015) Pharmaceutical analysis E-book: a textbook for pharmacy students and pharmaceutical chemists. Elsevier Health Sciences, Amsterdam

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Department of Pharmaceutical Chemistry, Government College University, Faisalabad, Pakistan

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Akash, M.S.H., Rehman, K. (2020). Introduction to Pharmaceutical Analysis. In: Essentials of Pharmaceutical Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-15-1547-7_1

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Explore Top 10 Pharmaceutical Industry Trends in 2024

Delve into our data-driven analysis of 1700+ pharma startups, revealing significant pharmaceutical industry trends. Our research highlights the impact of AI, precision medicine, 3D printing, and blockchain on treatment innovation and industry standards. Explore these key trends in-depth and understand their role in shaping the future of healthcare.

The pharmaceutical industry is undergoing a significant transformation, driven by the rapid integration of technologies like artificial intelligence (AI), additive manufacturing, and blockchain. These key pharmaceutical industry trends are catalyzed by substantial investments, the maturation of industry technologies, and the expiration of crucial patents. Enhanced inter-organizational collaboration and a supportive regulatory framework are further accelerating innovation and reshaping the pharmaceutical landscape.

This article was published in October 2020 and updated in January 2024.

Innovation Map outlines the 10 Current Trends in Pharmaceutical Industry & 20 Promising Startups

For this in-depth research on the Top Pharma Industry Trends & Startups, we analyzed a sample of 1745 global startups & scaleups. This data-driven research provides innovation intelligence that helps you improve strategic decision-making by giving you an overview of emerging technologies in the pharma industry. In the Pharma Industry Innovation Map, you get a comprehensive overview of the innovation trends & startups that impact your company.

What are the Top Pharmaceutical Industry Trends (2024)?

• AI in Pharmaceuticals
• Big Data & Analytics
• Flexible Production
• Precision Medicine

Additive Manufacturing

• Extended Reality
• Real-World Data (RWD)
• Digital Therapeutics
• Curative Therapies

Click to download

These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform , covering 3 790 000+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant technologies and industry trends quickly & exhaustively.

Tree Map reveals the Impact of the Top 10 Pharma Industry Trends in 2024

The Tree Map below illustrates the top 10 pharmaceutical trends that will impact companies in 2024. From drug discovery and development to medical imaging and patient engagement, artificial intelligence occupies a prominent position in the industry. Along with big data and analytics, more than a third of pharma startups are working on software solutions for the industry. There is still a lack of access to basic medicines in many regions of the world, prompting demand for flexible pharmaceutical manufacturing.

The use of real-world data to collect accurate patient experiences, blockchain to securely transact and manage patient records, and augmented, virtual, and mixed reality (AR, VR & MR) solutions also find a place in the top 10 trends in the pharmaceutical industry. However, these technology trends in the pharmaceutical industry cover only a small fraction of the breadth of innovation in the industry. Based on your specific criteria, your top trends can look quite different.

Global Startup Heat Map covers 1745 Pharma Startups & Scaleups

The Global Startup Heat Map below highlights the global distribution of the 1745 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform , the Heat Map reveals that the United States is home to most of these companies while we also observe increased activity in India as well as Europe, particularly in the UK and France.

Below, you get to meet 20 out of these 1700+ promising startups & scaleups as well as the solutions they develop. These 20 startups were hand-picked based on criteria such as founding year, location, funding raised, and more. Depending on your specific needs, your top picks might look entirely different.

Interested to explore all 1700+ pharma startups & scaleups?

10 Key Trends in Pharmaceutical Industry (2024)

1. ai in pharma.

The use of artificial intelligence (AI) is accelerating drug discovery and development processes. Innovative startups are leveraging AI to tackle industry challenges, streamlining manufacturing processes, and devising efficient marketing strategies for post-launch success. In the critical realm of patient selection for clinical trials, AI enhances the precision of eligibility criteria, streamlines patient inclusion, and accelerates cohort identification.

This not only expedites the entire clinical trial process but also reduces associated costs significantly. Furthermore, AI’s predictive analytics are instrumental in identifying potential participants, ensuring a more targeted and effective trial phase, and consequently, a swifter route to market for new drugs. The global AI in drug discovery market is expected to grow at a CAGR of 28.2% from 2024 to 2030 , highlighting its increasing impact on the pharmaceutical industry.

Pangaea Data – Patient Cohort Identification

Pangaea Data is a British startup that uses unsupervised AI algorithms to identify patient cohorts for drug discovery, clinical trials, and real-world evidence (RWE) studies. The machine learning-based software scans through electronic health records (EHR) and unstructured doctors’ notes. Pangaea Data’s solution enables clinicians to find the right patients based on phenotypes. The startup also develops a library of AI models for different disease areas.

Valence Discovery – Drug Discovery

Canadian startup Valence Discovery develops novel algorithms for drug discovery. The startup uses different machine learning approaches, such as few-shot learning, reinforcement learning, active learning, and representation learning, to aid the drug discovery process. Its deep learning solution uses small and noisy datasets to predict and optimize potential drug candidates, further eliminating the need for large datasets.

2. Big Data & Analytics

The pharma industry requires high-performance systems to analyze the large volumes of data generated during the drug discovery and development process. Pharmaceutical companies use third parties to share data with collaborators, making data management a crucial area of focus. The advancement in analytical techniques is also turning historical and real-time data available with pharmaceutical companies into valuable assets for predictive, diagnostic, prescriptive, and descriptive analytics.

Moreover, these pharmaceutical analytics techniques are used on almost all types of medical data from patient records, medical imaging, and hospital data, to name a few. The pharmaceutical analytical testing market is expected to reach USD 8.98 billion in 2024 and grow at a CAGR of 8.41% to reach USD 13.43 billion by 2029 , highlighting the growing importance of analytics in the industry.

Baixing AI Lab – Medical AI Research Platform

Baixing AI Lab is a US-based startup that creates AI Pharma Bx , a SaaS medical AI research platform. It combines proprietary data sourced from RWD, clinical data, and more with multimodal fusion, machine learning, and natural language processing (NLP).

With this integrated approach, the startup eliminates data silos in pharmaceutical research. AI Pharma Bx thus benefits pharmaceutical companies, doctors, researchers in basic medicine and drug mechanisms, and other aspects of research.

Pomicell – In-Silico Modeling

Israeli startup Pomicell offers software tools for big data analytics in pharmaceutical research and development (R&D). The startup utilizes machine learning techniques to analyze and aid in the development of in-silico models. The startup further builds customized drug development road-maps by augmenting the available data, analysis, and insights through matching and in-silico modeling.

3. Flexible Production

The pharma industry is adapting its manufacturing to meet new market needs, like producing smaller batches for precision medicine. Single-use bioreactors are becoming popular for their efficiency, cutting downtime by simplifying cleaning and validation.

Additionally, new bioreactor technologies and continuous manufacturing are key in biopharmaceutical production. They minimize downtime, use less energy, boost productivity, and reduce waste. These advancements also ensure consistent product quality, comply with strict regulations, and allow quicker market response.

Cellexus – Single-Use Bioreactor

Cellexus is a Scottish startup that makes single-use airlift bioreactor systems. The startup’s patented airlift technology uses bubbles instead of mechanical mixing to move cells and nutrients. The reactor comes with disposable bioreactor bags and an integrated heater.

The startup also offers precise regulation of biochemical parameters such as pH, dissolved oxygen, and temperature. The single-use system is used for a variety of cell cultures and fermentation and has achieved the growth of bacteria, yeast, microalgae, and bacteriophage amplification.

Secoya Technologies – Continuous Manufacturing Optimisation

Belgian startup Secoya Technologies offers novel technologies for production processes by tailoring the continuous manufacturing process equipment down to the ideal scale. Microstructured elements such as microfluidic droplet generators are used for optimizing manufacturing processes. The startup’s solutions find use in intensified chemical synthesis, crystallization, pervaporation, and micro-encapsulation.

4. Precision Medicine

Precision medicine stems from the concept of tailoring treatment to the distinct characteristics of each patient. Progress in omics and data analytics is shedding light on the human body’s drug response mechanisms. This understanding, combined with innovative production techniques like additive manufacturing, is bringing personalized medicine closer to fruition.

Drug exposure models play a crucial role in precision medicine by assessing the pharmacokinetic and pharmacodynamic attributes of drugs. These models aid in determining the optimal drug dosage by considering factors such as age, gender, comorbidities, and other clinical variables. The global precision medicine market size is expected to reach USD 168.3 billion by 2032 , growing at a CAGR of 9.1% during 2024-2032.

ExactCure – Drug Exposure Model

ExactCure is a French pharmaceutical technology company that offers a software solution for simulating the effects of drugs in a patient’s body based on personal characteristics. The startup utilizes population pharmacokinetics, as well as scientific literature data, for real-time prediction of efficacy and drug interaction. ExactCure is developing drug-specific exposure models for drugs under investigation for the treatment of COVID-19.

GenomicDAO – Decentralized Science (DeSci) for Precision Medicine

GenomicDAO is a Singaporean decentralized autonomous organization (DAO) that develops an AI-powered DeSci platform for precision medicine. It allows members to contribute to active DAOs and receive proprietary tokens in return.

The startup then recruits participants to join genetic tests and the revenue from the test is shared back to DAO members. Using its drug response study, the startup provides gene-based drug recommendations for patients, advancing precision medicine.

5. Additive Manufacturing

The demand for precision medicine is driving pharmaceutical companies to revolutionize their production techniques. Significant research is dedicated to evolving advanced 3D printers that print tissues or cells . Within the pharmaceutical sector, 3D printing finds utility in the realms of drug formulation, organ fabrication, and regenerative therapy.

Consequently, additive manufacturing enables the crafting of medical formulations tailored to individual age or physiological profiles, as well as the creation of precision dosage pills. Additionally, bioprinters are pivotal in advancing the fields of bioinks, tissue scaffolding, and microfluidics .

FabRx – Printed Pill

FabRx is a UK-based startup that manufactures M3DIMAKER , a 3D printer for personalized pills. M3DIMAKER uses proprietary technology for direct powder extrusion. The single-step printing process uses a single screw extruder for the extrusion of powdered material.

The startup manufactures pills with properties such as sustained or delayed doses, and multidrug combination pills (polypills). The printer also enables small batch production for clinical trials and precise personalized dosage forms for individuals.

Frontier Bio – Tissue Bioprinter

The US-based startup Frontier Bio offers FLUX-1 , a 3D bioprinter for making human tissues. The startup prints tissues as an effective way to test new pharmaceuticals. Frontier Bio addresses the limitations in making tissues have desirable structures and features with a good cell survival rate. FLUX-1 employs the electro-hydrodynamic printing (EHDP) technique to deliver tissues with micro and nano-scale features, as well as higher cell survival rates.

6. Blockchain

Blockchain technology is crucial at every stage of drug production and distribution in the pharmaceutical industry. Stakeholders typically guard their data closely due to its sensitive nature. Furthermore, blockchain is being examined as a reliable tool to tackle the use of counterfeit medicines and substandard drugs , which compromise the pharmaceutical supply chain and result in many patient deaths each year. The shift towards digital transactions makes blockchain a viable solution for improving the tracking and safety of the pharmaceutical transaction landscape.

PharmaTrace – Smart Contracts

PharmaTrace , a German startup offers a blockchain-based ecosystem to secure data and deploy smart contracts in the pharmaceutical industry. The ecosystem provides a secure system for sharing crucial and sensitive information between stakeholders in the pharmaceutical marketplace. PharmaTrace leverages smart contracts implemented in Hyperledger Fabric to address this trust deficit. Moreover, the network provides precise control and security over the information being shared.

Veratrak – Pharma Supply Chain

Veratrak is a UK-based startup offering a blockchain-based document collaboration and workflow management platform for the pharmaceutical supply chain. The platform enables secure document sharing across supply chain partners with immutable audit logs. Good automated manufacturing practice 5 (GAMP 5)-compliant cloud-based software further lets stakeholders, within and outside organizations, collaborate at various stages of the pharma supply chain.

7. Extended Reality (XR)

Mixed reality (MR), virtual reality (VR), and augmented reality (AR) are transforming visualizations in unparalleled ways. Pharma startups are investigating the potential of extended reality technologies in the realms of pharmaceutical research and manufacturing.

These extended reality tools enable data-rich and meaningful interactions among research teams in real time, regardless of geographical location. Startups are bringing human augmentation in the pharmaceutical sector to life through innovative extended reality wearables and devices.

Nanome – VR Collaboration Tool

Nanome , a US-based startup, offers VR collaboration tools for atomic, molecular, and protein visualization. The VR-based molecular visualization tool by Nanome imports molecular data from public databases or custom inputs. It lets researchers design proteins, iterate 3D structures, and also work in a virtual workspace with global team members.

Goodly Innovations – AR Suite

German startup Goodly Innovations develops OptiworX , an AR suite for pharma and biopharma manufacturing. The solution enables technicians and line operators to increase their productivity and efficiency by prompting various tasks in real-time, in an AR environment.

The startup’s modular design allows for both standalone and connected systems, enabling two-way data flow. Additionally, this suite supports all shop floor processes like manufacturing, filling, primary packaging, and secondary packaging.

8. Real-World Data

Real-world data (RWD) and real-world evidence (RWE) are reshaping innovation in the pharmaceutical sector. RWD encompasses information about patient health, treatments, and routine health reports. Given its research-driven essence, the pharmaceutical industry must ensure the reliability and significance of the data it utilizes. The accessibility of real-world data, facilitated by the Internet of Things (IoT), sensors, and wearable technology, is restructuring the operational dynamics of the pharmaceutical industry.

Graticule – Unstructured Patient Data

The US-based startup Graticule creates structured data sets from unstructured RWD sources. The startup offers data subscriptions and on-demand data collaborations to pharma clients for uncovering value in RWD. Graticule makes use of clinical notes, free text, and images, as well as non-clinical data for data completeness.

OncoChain – Oncological Data

Romanian startup OncoChain offers a research platform based on a de-identified real-world oncological patient database. The startup’s solution enables early detection and timely intervention for cancer detection, treatment, and cure. The OncoChain Analytics tool also provides real-world evidence insights for regulatory decision-making, clinical trial design, and multi-center studies.

9. Digital Therapeutics

Digital therapeutics deliver evidence-based therapeutic interventions using software , providing non-drug, technology-centric solutions to prevent, manage, or treat a wide array of physical, mental, and behavioral conditions. These interventions function independently or in conjunction with medications, devices, or other therapies. Digital therapeutics empower individuals by granting them enhanced control over their health and treatment outcomes.

MINDCURE Health – Psychedelic Therapy

MINDCURE Health is a Canadian startup that offers iSTRYM , a software application to deliver data-driven support for psychedelic therapies. It collects real-world data before, during, and after therapy sessions and then applies AI to analyze the data. The application also provides clinicians with data-proofed protocols and integration plans as well as real-time plans for personalized plans. This, in turn, improves clinical outcomes and patient convenience.

Dopavision – Eye Treatment

German startup Dopavision is making a smartphone-based digital therapeutic for myopia. The startup’s solution aims to slow down the progression of myopia in the young population, especially children. The solution achieves the activation of dopamine, a neurotransmitter that plays an important role in eye growth regulation. Dopavision is currently undertaking pre-clinical trials of the digital therapeutic.

10. Curative Therapies

A fundamental shift is occurring in the approach to treating illnesses, moving from disease management to achieving complete cures. Curative treatments, including cell and gene therapies, are transforming the treatment landscape for chronic and complex conditions by negating the necessity for prolonged therapy.

Gene therapy involves the introduction of genetic material into cells to counteract defective genes or to produce a beneficial protein. Viruses that have been genetically modified typically serve as the primary vectors in gene therapy applications.

Mogrify – Cell Therapy

Mogrify is a British startup that develops a proprietary direct cellular conversion platform to transmogrify any mature human cells. The platform technology identifies the transcription factors or small molecules required to convert any mature cell into any other mature cell type by analyzing sequencing data and regulatory networks. Mogrify develops novel cell therapies for musculoskeletal, auto-immune, and cancer immunotherapy, as well as ocular and respiratory diseases.

Lacerta Therapeutics – Gene Therapy

The US-based Lacerta Therapeutics is a clinical-stage gene therapy startup working on cures for the central nervous system and lysosomal storage diseases. The startup’s proprietary adeno-associated virus (AAV) vector technology platform develops novel AAV vectors with improved transduction, tissue- or cell subtype-selectivity, and immune escape profiles. Lacerta further offers novel capsid variants and a scalable vector manufacturing platform with limited production components.

Discover all Pharma Technologies & Startups

The COVID-19 pandemic has forced companies to reevaluate various facets of their operations, including manufacturing and supply chains, a trend equally pertinent for pharmaceutical firms. The crisis underscored the necessity to enhance the rapidity and precision in the discovery, mass production, and distribution of new drugs, treatment methodologies, and vaccines. Moreover, entities at the nexus of life sciences research, biotechnology, and pharmaceuticals are unveiling novel cellular and molecular properties, paving the way for groundbreaking industry solutions.

The trends and startup innovations in the pharmaceutical sector highlighted in this report represent just a fraction of the developments uncovered through our comprehensive research. Emerging technologies such as low-volume production, nanotechnology, and mRNA vaccine technologies are set to redefine the industry landscape. Proactively identifying and integrating these emerging opportunities and technologies into your business strategy is crucial for securing a competitive edge. Contact us to seamlessly and thoroughly explore relevant technologies and startups that align with your objectives.

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Research Designs and Methodologies Related to Pharmacy Practice

The need for evidence to inform policy and practice in pharmacy is becoming increasingly important. In parallel, clinical pharmacy and practice research is evolving. Research evidence should be used to identify new areas for improved health service delivery and rigorously evaluate new services in pharmacy. The generation of such evidence through practice-based research should be predicated on appropriate use of robust and rigorous methodologies. In addition to the quantitative and qualitative approaches used in pharmacy practice research, mixed methods and other novel approaches are increasingly being applied in pharmacy practice research. Approaches such as discrete choice experiments, Delphi techniques, and simulated client technique are now commonly used in pharmacy practice research. Therefore, pharmacy practice researchers need to be competent in the selection, application, and interpretation of these methodological and analytical approaches. This chapter focuses on introducing traditional and novel study designs and methodologies that are particularly pertinent to contemporary clinical pharmacy and practice research. This chapter will introduce the fundamentals and structures of these methodologies, but more details regarding the different approaches may be found within the Encyclopedia.

Learning Objectives

• • Discuss the value of pharmacy practice research to evidence-based practice and policy.
• • Describe the classifications and types of study designs commonly used in pharmacy practice research.
• • Discuss the concepts and structure of common study designs used in pharmacy practice research including experimental, quasi-experimental, observational, qualitative, and mixed method designs.
• • Discuss the important considerations for conducting pharmacy practice research in terms of study design, data collection, data analyses, and ethical considerations.

Introduction to Research Methodologies Used in Pharmacy Practice

The mission of pharmacy profession and the role of pharmacists in healthcare have evolved toward patient-centered care in the last few decades. Pharmacists with their expertise in drug therapy and accessibility to the public have unprecedented opportunities to assume increasing responsibility for direct patient care ( Bond, 2006 ). New cognitive pharmaceutical services and new roles for pharmacists continue to emerge.

In the era of evidence-based practice and health services, it is not just adequate to propose those new pharmacy services or new roles without evidence of their benefit ( Awaisu and Alsalimy, 2015 , Bond, 2006 ). New pharmacy services and new roles must be proven to be feasible, acceptable, cost-effective, and increase health outcomes. Pharmacy practice research provides such evidence and can confirm the value of a new service, inform policy, and result in practice changes ( Bond, 2006 , Chen and Hughes, 2016 ). Research evidence should be used to identify new areas for improved health service delivery and rigorously evaluate new services. The research used to generate such evidence should be grounded in robust and rigorous methodologies ( Chen and Hughes, 2016 ). Traditionally, common quantitative and qualitative methods such as randomized controlled trials, cohort study, case control study, questionnaire-based surveys, and phenomenology using qualitative interviews have been used in pharmacy. However, in recent years, novel and more complex methods are being developed and utilized. Pharmacy practice researchers need to know how these old and new methodological approaches should be selected, applied, and interpreted in addressing research problems.

Various study designs, including, but not limited to experimental, quasi-experimental, observational, qualitative, and mixed method designs, have been used in pharmacy practice research. Furthermore, different classification systems (e.g., quantitative vs. qualitative, experimental vs. observational, descriptive vs. analytical study designs) have been used in the literature. The choice of a study design to answer a research question in pharmacy practice research is driven by several factors, including the type of the research question or the research hypothesis, expertise of the investigator, availability of data, and funding opportunities. Pharmacy practice researchers need to be competent in the selection, design, application, and interpretation of these methodological and analytical approaches. Today, many of the research methods used in pharmacy practice research have been adapted from fields such as sociology, anthropology, psychology, economics, and other disciplines. This paradigm shift has led to a greater emphasis on the appropriate choice of a specific research design or method to answer a specific research question ( Chen and Hughes, 2016 ). Consequently, pharmacy practice researchers should place an emphasis on the reliability of the methods selected, the correct interpretation of their findings, the testing of a specific hypothesis, and the internal validity of their data, among other considerations. Novice and early career researchers should be familiar and have sound foundation in a variety of methods applied in pharmacy practice research, which will be covered in this chapter and other chapters in this Encyclopedia. We do believe that more experienced researchers should focus on certain methods in order to advance research in our discipline.

Core Quantitative and Qualitative Approaches Used in Pharmacy Practice Research

Traditionally, core quantitative approaches used in pharmacy practice research include nonexperiments, quasi-experimental designs, and true experimental designs such as prospective randomized controlled intervention trials. Nonexperiments also include observational study designs that are often described as pharmacoepidemiologic study designs such as case–control study, cohort study, nested case–control study, and cross-sectional study ( Etminan, 2004 , Etminan and Samii, 2004 ). In recent years, conventional qualitative approaches and their philosophical paradigms are increasingly been used in pharmacy. These include the five qualitative approaches to inquiry: narrative research, phenomenology, grounded theory, ethnography, and case study. These qualitative methods are often difficult for pharmacy practice researchers to comprehend, and researchers tend to describe the methods of data collection such as individual interviews and focus group discussions as qualitative methods of inquiry. These data collection methods are briefly described later in this chapter, among others. Furthermore, there is an increasing importance on the appropriate selection and use of mixed method approach ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b ), which are often designed and applied wrongly. Finally, it is worthwhile to be familiar with novel research methodologies such as discrete choice experiments, Delphi techniques, simulated client technique, and nominal group techniques, which fall between quantitative and qualitative approaches, often with no clear differentiation on where they belong. Although called “novel” in the context of this chapter, these methods are not new in other relevant disciplines, but new and not commonly used in pharmacy practice research.

Research Question and Selection of Study Design

Pharmacy practice researchers begin by conception of a research idea or identifying a research question and defining a hypothesis based on the question. The researcher then selects a study design that will be suitable to answer the research question. The study design should be appropriately selected prior to initiation of any research investigation. Selecting an inappropriate study design may potentially undermine the validity of a study in its entirety. Investigators are encouraged to critically think about the possible study designs to ensure that the research question is adequately addressed and should be able to adequately justify their choice. These study designs have been variously classified and one common classification system is quantitative vs. qualitative study designs. Study designs play a major role in determining the scientific value of research studies. Inappropriate choice of a study design is impossible to correct after completion of the study. Therefore, thorough planning is required to avoid unconvincing results and invalid conclusions. Good understanding of basic study design concepts will aid researchers in conducting robust and rigorous practice-based research. This chapter introduces the structure and the fundamentals of common study designs used in pharmacy practice research and discusses the important considerations for conducting pharmacy practice research in terms of study design, data collection, data analyses, and ethical considerations.

Classification of Research Methodologies Used in Pharmacy Practice

Various classifications for research designs and methods used in pharmacy practice have been used in the literature. The following are some of the approaches for the classification of research designs:

Case example: Investigators were looking for the association between acute myocardial infarction and smoking status, type of tobacco, amount of smoke, etc. ( Teo et al., 2006 ). Another example of a case–control study from published literature is the study investigating the association between the use of phenylpropanolamine and the risk of hemorrhagic stroke ( Kernan et al., 2000 ).

Case example: Investigators were interested to determine the long-term effectiveness of influenza vaccines in elderly people; they recruited cohorts of vaccinated and unvaccinated community-dwelling elderly ( Nichol et al., 2007 ).

Case example: A case report was written by a physician who contracted Severe Acute Respiratory Syndrome (SARS) during an outbreak in Hong Kong ( Wu and Sung, 2003 ). Another example is an ecological study examining diet and sunlight as risks for prostate cancer mortality ( Colli and Colli, 2006 ). Chim et al. conducted a large population-based survey in Australia to determine what community members think about the factors that do and should influence government spending on prescribed medicines ( Chim et al., 2017 ).

Case example: A group of investigators carried out a study to establish an association between the use of traditional eye medicines (TEM) and corneal ulcers. In this case, both case–control and cohort study designs are applicable. In an example of a case control study, Archibugi et al. aimed to investigate the association between aspirin and statin exclusive and combined and pancreatic ductal adenocarcinoma occurrence ( Archibugi et al., 2017 ). Another example of a cohort study is a study carried out by Wei et al. in which they investigated whether or not acid-suppression medicines increased the risk of bacterial gastroenteritis ( Wei et al., 2017 ).

Case examples: Investigators conducted a study about the newer versus older antihypertensive agents in African hypertensive patients (NOAAH) trial (nct01030458) to compare the efficacy of single-pill combinations of newer versus older antihypertensive agents (i.e., a single-pill combination of newer drugs, not involving a diuretic, with a combination of older drugs including a diuretic) ( Odili et al., 2012 ). In a crossover design, a group of investigators evaluated the effect of spironolactone on nonresolving central serous chorioretinopathy ( Bousquet et al., 2015 ).

Case examples: Prashanth et al. aimed to understand if (and how) a package of interventions targeting primary health centers and community participation platforms affect utilization and access to generic medicines for people with noncommunicable diseases using quasi-experimental design approach ( Prashanth et al., 2016 ).

• c. Observational design—It involves only observation of natural phenomena and does not involve investigator intervention. Typically, this study design investigates associations and not causation. Examples include cohort study and case–control study. These studies can explore an association between a pharmacologic agent and a disease of interest. Case examples: Please see previous examples of these.

Case examples: Please see experimental studies, and case–control and cohort study designs.

Case examples: Investigators in Canada explored the lived experiences of youth who are prescribed antipsychotics by conducting interpretative phenomenology study ( Murphy et al., 2015 ).

Case examples: Shiyanbola et al. combined focus group discussion with a survey tool to investigate patients' perceived value and use of quality measures in evaluating and choosing community pharmacies ( Shiyanbola and Mort, 2015 ).

Below is a brief description of traditional and novel pharmacoepidemiologic study designs. Several examples of pharmacoepidemiologic study designs are provided above. Some descriptive studies including case reports, case series, and ecological studies will not be described in this chapter.

• a. Case–control studies—In this design, patients (those who develop the disease or outcome of interest) are identified and control patients (those who do not develop the disease or outcome of interest) are sampled at random from the original cohort that gives rise to the cases ( Etminan and Samii, 2004 , Newman et al., 2013 ). The distribution of exposure to certain risk factors between the cases and the controls is then explored, and an odds ratio (OR) is calculated.
• b. Cohort studies—This can be described as a study in which a group of exposed subjects and a group of unexposed subjects are followed over time and the incidence of the disease or outcome of interest in the exposed group is compared with that in the unexposed group ( Etminan and Samii, 2004 , Hulley et al., 2013 ).
• c. Case-crossover studies—The case-crossover may be considered comparable to a crossover randomized controlled trial in which the patients act as their own control ( Etminan and Samii, 2004 ). Pattern of exposure among the cases is compared between event time and control time. The between-patient confounding that occurs in a classic case-control study is circumvented in this design. Tubiana et al. evaluated the role of antibiotic prophylaxis and assessed the relation between invasive dental procedures and oral streptococcal infective endocarditis, using a nationwide population-based cohort and a case-crossover study design ( Tubiana et al., 2017 ).
• d. Case–time control studies—This design is an extension of the case-crossover design, but includes a control group ( Etminan and Samii, 2004 ). A group of researchers assessed medication-related hospitalization. They used the case–time control study design to investigate the associations between 12 high risk medication categories (e.g., antidiabetic agents, diuretics, benzodiazepine hypnotics) and unplanned hospitalizations ( Lin et al., 2017 ).
• e. Nested case–control studies—In this design, a cohort of individuals is followed during certain time periods until a certain outcome is reached and the analysis is conducted as a case–control study in which cases are matched to only a sample of control subjects ( Etminan, 2004 ). de Jong et al. examined the association between interferon-β (IFN-β) and potential adverse events using population-based health administrative data in Canada ( De Jong et al., 2017 ).
• f. Cross-sectional studies—In this type of study, the investigator measures the outcome of interest and the exposures among the study participants at the same time ( Hulley et al., 2013 , Setia, 2016b ). It provides a snapshot of a situation for a particular period.

Quantitative Research Designs in Pharmacy Practice

A wide range of quantitative methods are commonly applied in pharmacy practice research. These methods are widely used in published pharmacy practice literature to explore appropriateness of medicines use, appropriateness and quality of prescribing, and medication safety, through analyzing existing datasets, direct observation, or self-report ( Green and Norris, 2015 ). Pharmacy practice research questions also seek to determine the knowledge, behaviors, attitudes, and practices of pharmacists, other healthcare providers, patients, policy-makers, regulators, and the general public. Quantitative methods are also used in evaluating the effect of new pharmacy services and interventions to improve medicines use. These practice research projects provide valuable insights about how medicines are used, and how to maximize their benefits and minimize their harmful effects. In the context of this chapter, quantitative study designs will be broadly classified into three: (1) observational, (2) experimental and quasi experimental, and (3) other designs.

Observational Study Designs

Pharmacoepidemiology is a “relatively new science that explores drug efficacy or toxicity using large observational study designs” ( Etminan, 2004 , Etminan and Samii, 2004 ). These study designs explore drug use studies that usually cannot be answered using randomized controlled trials or other experimental designs. In several instances, experimental study designs may not be suitable or feasible; in such circumstances, observational study designs are applied ( Cummings et al., 2013 ). As the name implies, observational studies involve merely observing the subjects in a noncontrolled setting, without investigator intervention or manipulating other aspects of the study. Therefore, observational studies are nonexperimental. The observation of the variables of interest can be prospective, retrospective, or current depending on the type of the observational study.

In pharmacoepidemiology and other areas of pharmacy practice, researchers are often interested in measuring the relationships between exposure to a drug and its efficacy, toxicity, or other outcomes of interest using observational study designs. It is worthwhile to note that observational study designs investigate association, but, in most cases, not causation. Here, we provide descriptions of some commonly used study designs in pharmacoepidemiology and pharmacy practice research in general.

Case–Control Studies

Case–control study design is used to determine association between risk factors or exposures and outcomes. It is a useful design to study exposures in rare diseases or diseases that take long time to develop ( Newman et al., 2013 ). It investigates exposures in individuals with and those without the outcome of interest. Nevertheless, case–control studies can help to identify harmful or beneficial exposures. Furthermore, the outcome of interest can be undesirable (e.g., mortality) or desirable (e.g., microbiological cure). As the name suggests, in a case–control study design, there are two groups of subjects: (1) cases (individuals with the outcome of interest) and (2) controls (individuals without the outcome of interest) ( Newman et al., 2013 ). Cases are randomly selected based on prespecified eligibility criteria from a population of interest. Appropriate representative controls for the cases selected are then identified. The researchers then retrospectively investigate possible exposures to the risk factor. Fig. 1 represents a schematic diagram of a case–control study.

Case–control study design.

Case–control studies are relatively inexpensive, less time-consuming to conduct, allow investigation of several possible exposures or associations, and are suitable for rare diseases. Selection of the control group is a critical component of case–control studies. Case–control studies have several drawbacks: confounding must be controlled, subject to recall, observation, and selection biases.

OR is the measure of association used for the analysis of case–control studies. This is defined as the odds of exposure to a factor in those with a condition or disease compared with those who do not have the condition or disease.

Cohort Studies

Similar to case–control studies, cohort studies determine an association between exposures/factors and development of an outcome of interest. As previously described, a cohort study is a study in which a group of exposed subjects and a group of unexposed subjects are followed over time to measure and compare the rate of a disease or an outcome of interest in both groups ( Etminan and Samii, 2004 , Hulley et al., 2013 ). A cohort study can be prospective (most common) or retrospective. While a case–control study begins with patients with and those without the outcome of interest (e.g., diseased and nondiseased patients), a cohort study begins with exposed and unexposed patients (e.g., patients with and those without certain risk factor) ( Hulley et al., 2013 , Setia, 2016a ). In a cohort study, both the exposed and the unexposed subjects are members of a larger cohort in which subjects may enter and exit the cohort at different periods in time ( Etminan and Samii, 2004 , Hulley et al., 2013 ).

Typically, a cohort study should have a defined time zero, which is defined as the time of entry into the cohort ( Etminan and Samii, 2004 ). The cohort (a group of exposed and unexposed subjects, who are free of the outcome at time zero) is followed for a certain period until the outcome of interest occurs. In addition, information or data related to all potential confounders or covariates should also be collected as failure to account for these can bias the results and over- or underestimates the risk estimate. There are two types of cohort studies: retrospective cohort and prospective cohort studies.

Retrospective cohort study, also known as historical cohort study, begins and ends in the present, while looking backward to collect information about exposure that occurred in the past ( Fig. 2 ). Historical cohort studies are relatively less time-consuming and less expensive than prospective cohort studies ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016a ). In addition, there is no loss to follow-up and researchers can investigate issues not amenable to intervention study designs. However, these studies are only as good as the data available, the investigator has limited control of confounding variables, and it is prone to recall bias.

Retrospective (historical) cohort study design.

On the other hand, prospective cohort study, also known as longitudinal cohort study, begins in the present and progresses forward, collecting data from enrolled subjects whose outcomes fall in the future ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016a ) ( Fig. 3 ). Prospective cohort studies are easier to plan for data collection, have low recall bias, and the researcher has a better control of confounding factors. On the other hand, it is difficult to study rare conditions; they are more prone to selection bias, more time-consuming, expensive, and loss of subjects to follow-up is common.

Prospective (longitudinal) cohort study design.

Relative risk (RR) is the measure of association used for the analysis of a cohort study. This is defined as the risk of an event or development of an event relative to exposure (i.e., the risk of subjects developing a condition when exposed to a risk factor compared with subjects who have not been exposed to the risk factor).

Case-Crossover Studies

This is a relatively new design in the field of epidemiology in which the patients act as their own controls ( Maclure, 1991 ). In this design, there is a case and a control element both of which come from the same subject. In other words, each case serves as its own control. It can be considered equivalent to a crossover RCT with a washout period ( Etminan and Samii, 2004 ). Pattern of exposure to the risk factor is compared between the event time and the control time ( Etminan and Samii, 2004 ). Case-crossover study design is useful to investigate triggers within an individual. For instance, it is applicable when studying a transient exposure or risk factor. However, determination of the period of the control and case components is a crucial and challenging aspect of a case-crossover study design. Since the patients serve as their own controls, the interindividual variability that is inherent in classic case–control studies is eliminated. This is important in studies involving progressive disease states in which disease severity may differ between patients such as multiple sclerosis. OR is estimated using techniques such as Mantel–Haenszel statistics and logistic regression.

Cross-Sectional Studies

Cross-sectional studies also known as prevalence studies identify the prevalence or characteristics of a condition in a group of individuals. This design provides a snapshot of the prevalence or the characteristics of the study subjects in a single time point. The study investigator measures the outcomes and the exposures in the study subjects simultaneously ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016b ). Hence, cross-sectional studies do not follow up patients to observe outcomes or exposures of interest. Data are often collected through surveys. Cross-sectional design cannot provide cause and effect relationships between certain exposures and outcomes of interest.

Experimental and Quasi-Experimental Study Designs

In a typical experimental study design, the investigator assigns subjects to the intervention and control/comparison groups in an effort to determine the effects of the intervention ( Cummings et al., 2013 ). Since the investigator has the opportunity to control various aspects of the experiment, this allows the researcher to determine the causal link between exposure to the intervention and outcome of interest. The researcher either randomly or conveniently assigns the subjects to an experimental group and a control group. When the investigator performs randomization, the study is considered a true experiment (see Fig. 4 ). On the other hand, if subjects are assigned into groups without randomization, the study is considered a quasi-experiment (refer to Fig. 5 ). As with experimental designs, quasi-experimental designs also attempt to demonstrate a causal link between the intervention and the outcome of interest. Due to the challenges of conducting a true experimental design, the quasi-experimental study designs have been consistently used in pharmacist intervention research.

True experimental study design.

Quasi experimental study design.

RCTs are considered the gold standard of experimental study designs in pharmacy practice and evidence-based research ( Cummings et al., 2013 ). The investigator randomly assigns a representative sample of the study population into an experimental group and a control group ( Fig. 4 ). Randomization in RCT is to minimize confounding and selection bias; it enables attainment of similar experimental and control groups, thereby isolating the effect of the intervention. The experimental group receives the treatment or intervention (e.g., a new drug or pharmaceutical care for treatment of a certain disease), while the control group receives a placebo treatment, no treatment, or usual care treatment depending on the objective of the study ( Cummings et al., 2013 ). These groups are then followed prospectively over time to observe the outcomes of interest that are hypothesized to be affected by the treatment or intervention. The result of the study is considered to have high internal validity if significant changes on the outcome variable occur in the experimental group, but not the control group. The investigator can infer that the treatment or intervention is the most probable cause of the changes observed in the intervention group. The unit of randomization in RCTs is usually the patient, but can sometimes be clusters to circumvent the drawbacks of contamination.

RCTs are very challenging to undertake and pharmacy practice researchers should ensure design of robust experiments, while considering all essential elements and adhering to best practices. For instance, to determine the impact of a cognitive pharmaceutical service, the selection of a representative sample of the population is a prime consideration in an RCT. Moreover, RCTs are expensive, labor-intensive, and highly prone to attrition bias or loss to follow-up.

In pharmacy practice research, it is often difficult to comply with the stringent requirements of true experimental designs such as RCTs, due to logistic reasons and/or ethical considerations ( Grady et al., 2013 , Krass, 2016 ). Whenever true experimental models are not feasible to be applied in pharmacy practice research, the researcher should endeavor to use a more robust quasi-experimental design. For instance, when randomization is not feasible, the researcher can choose from a range of quasi-experimental designs that are non-randomized and often noncontrolled ( Grady et al., 2013 , Krass, 2016 ). Quasi-experimental studies used in pharmacy literature may be classified into five major categories: (1) quasi-experimental design without control groups (i.e., one group pre–posttest design); (2) quasi-experimental design that use control groups with no pretest; (3) quasi-experimental design that use control groups and pretests (i.e., nonequivalent control group design with dependent pretests and posttests) (see Fig. 5 ); (4) interrupted time series and; (5) stepped wedge designs ( Brown and Lilford, 2006 , Grady et al., 2013 , Harris et al., 2006 ).

The one group pretest posttest design and the nonequivalent control group design ( Fig. 5 ) are the most commonly applied quasi-experimental designs in practice-based research literature. These designs have been commonly used to evaluate the effect of pharmacist interventions in medications management in general and specific disease states management. The lack of randomization and/or the lack of control group is a major weakness and a threat to internal validity in quasi-experimental designs ( Grady et al., 2013 ). The observed changes could be due to some effects other than the treatment.

Other Quantitative Study Designs

In addition to the common observational, experimental, and quasi-experimental designs described above, there are other designs that are used in pharmacy. These research methods include, but are not limited to, simulated client technique, discrete choice experiments, and Delphi techniques. These methods, which are considered relatively new to pharmacy, are now commonly used in pharmacy practice research. In this chapter, we briefly describe these methods and their application in pharmacy. However, a more detailed description of their components and the nitty gritty of their application in pharmacy practice are available elsewhere within this textbook.

Simulated Client Method

The use of simulated client or simulated patient (mystery shopper) method to assess practices or behaviors in pharmacy practice has received much attention in recent times ( Watson et al., 2004 , Watson et al., 2006 ). “A simulated patient is an individual who is trained to visit a pharmacy (or drug store) to enact a scenario that tests a specific behavior of the pharmacist or pharmacy staff” ( Watson et al., 2006 ). A review by Watson et al. demonstrated the versatility and applicability of this method to pharmacy practice research in both developing and developed countries ( Watson et al., 2006 ). The investigators also identified some important characteristics that should be taken into consideration in designing studies that use this technique.

This method can be used to assess wide range of cognitive pharmacy services including counseling and advice provision, treatment of minor ailments, provision of nonprescription medicines, and public health pharmacy, among other things. This method can be a robust and rigorous method of assessing pharmacy practice if used appropriately ( Watson et al., 2006 , Xu et al., 2012 ). More recent developments have documented that the simulated patient methods have been used to provide formative feedback in addition to assessing practice behavior of pharmacists and their staff ( Xu et al., 2012 ).

In a case example, a group of investigators evaluated Qatari pharmacists' prescribing, labeling, dispensing, and counseling practices in response to acute community-acquired gastroenteritis ( Ibrahim et al., 2016 ). In another example, the investigators documented the state of insomnia management at community pharmacies in Pakistan ( Hussain et al., 2013 ).

Discrete Choice Experiments

Evidence in healthcare suggests that understanding consumers' preferences can help policy-makers to design services to match their views and preferences ( Ryan, 2004 ). Traditionally, studies to understand patients' and consumers' preferences for pharmaceutical services used opinion or satisfaction survey instruments. Nevertheless, such satisfaction surveys lack the ability to identify the drivers of satisfaction or the relative importance of the different characteristics of the service ( Vass et al., 2016 ). Discrete choice experiments are a novel survey-based method in pharmacy that are predicated on economic theories that allow systematic quantification of preferences to help identify which attributes of a good or service consumers like, the relative value of each attribute, and the balance between the different attributes ( Naik Panvelkar et al., 2010 , Ryan, 2004 , Vass et al., 2016 ). In-depth description of this method and its essential elements are described in another chapter in the Encyclopedia.

Qualitative Research Designs in Pharmacy Practice

Qualitative research methodology is applied to investigate a problem that has unmeasurable variables, to get a comprehensive understanding of the topic, through discussing it with the involved individuals, and to recognize the natural context in which the investigated issue takes place ( Creswell, 2013 ). The use of qualitative research methodology is becoming increasingly common across diverse health-related disciplines, including pharmacy practice. This is because of its ability to describe social processes and behaviors associated with patients or healthcare professionals, which strengthen the research impact ( McLaughlin et al., 2016 ). Therefore, pharmacy researchers and practitioners need to be better oriented to qualitative research methods ( Behar-Horenstein et al., 2018 ).

In the following section, interpretative frameworks and philosophical orientations, methodologies, data collection and analysis methods, approaches to ensure rigor, and ethical considerations in qualitative research are briefly discussed ( Cohen et al., 2013 , Creswell, 2013 ).

Interpretative Framework and Philosophical Assumptions of Qualitative Research

Interpretative frameworks.

Interpretative frameworks are the conceptual structures for comprehension, which form researcher's reasoning and views of truth and knowledge ( Babbie, 2015 ). Different scholars have categorized qualitative research paradigms or interpretative frameworks differently. The following are examples of interpretative framework categories that are used in health science research based on the categorization of Creswell (2013) : (1) social constructivism (interpretivism) framework; (2) post-positivism framework; (3) transformative, feminist, critical frameworks and disabilities theories; (4) postmodern frameworks; (5) pragmatism frameworks.

Philosophical Assumptions

Philosophical assumptions are theories and perspectives about ontology, epistemology, axiology, and methodology, which underpin the interpretative frameworks selected by qualitative researchers ( Cohen et al., 2013 ). As with interpretative framework, there are numerous means to categorize the philosophical assumptions that are folded within interpretative framework. The following are explanations of philosophical assumptions based on the categorization of Creswell (2013) :

• 1. Ontological assumptions, which define the nature of reality
• 2. Epistemological assumptions, which clarify means for knowing reality
• 3. Axiological assumptions, which explain the role and influence of researcher values
• 4. Methodological assumptions, which identify approaches to inquiry

It is important that a qualitative researcher understands how interpretative frameworks (e.g., social constructivism, post-positivism, and pragmatic interpretative frameworks) are differentiated because of their underpinning philosophical assumptions (i.e., ontological, epistemological, axiological, and methodological assumptions).

Approaches to Inquiry (Methodology)

It is important that qualitative researchers understand the differences between the characteristics of the five qualitative approaches to inquiry, in order to select an approach to inquiry and attain methodological congruence ( Creswell, 2013 ). The five approaches to qualitative research inquiry are:

• a. Narrative research: Describes participants' written and spoken stories about their experiences with a phenomenon being investigated, while considering the chronological connection of the phenomenon's series of events ( Anderson and Kirkpatrick, 2016 , Creswell, 2013 , Czarniawska, 2004 ).
• b. Phenomenological research: Describes the essence of participants' common experiences of a phenomenon, so that the description is a general essence rather than an individual experience ( Creswell, 2013 , Giorgi, 1997 , Moustakas, 1994 ).
• c. Grounded theory research: Aims to generate a theory grounded in participants' data that conceptually explain a social phenomenon, which could involve social processes, or actions or interactions ( Creswell, 2013 , Strauss and Corbin, 1990 , Woods et al., 2016 ).
• d. Ethnographic research: Involves describing the shared patterns of values, behaviors, and beliefs of culture-sharing participants ( Creswell, 2013 , Harris, 1968 , Rosenfeld et al., 2017 ).
• e. Case study research: Provides an in-depth examination of a real-life contemporary phenomenon that researchers cannot change over time, to illustrate the significance of another general topic ( Baker, 2011 , Creswell, 2013 , de León-Castañeda et al., 2018 , Mukhalalati, 2016 , Yin, 2014 ).

Data Collection and Analysis Methods in Qualitative Research

Data collection tools in qualitative research can be categorized into the following fundamental categories ( Creswell, 2013 ):

• a. Observation
• b. Documents
• c. Individual semi-structured interviews
• d. Focus groups (FGs)
• e. Audio-visual materials
• f. Emails chat rooms, weblogs, social media, and instant messaging.
• a. Topic guides: Topic guides guide the discussions in focus groups and individual interviews, and contain open-ended questions and probes, to enable the researcher to understand the complete picture, based on participant views and experiences. They are developed based on the literature review, aim and objectives, research questions, and propositions ( Kleiber, 2004 ).
• b. Audio recording of FGs and interviews: Audio recording of discussions that take place in interviews and FGs is essential for managing and analyzing data, and for increasing the accuracy of data collection and analysis, and ultimately enhancing the dependability and credibility of the research ( Rosenthal, 2016 , Tuckett, 2005 ).
• c. Transcription of FGs and interviews recording: Verbatim transcription refers to the word-for-word conversion of oral words from an audio-recorded format into a scripted text format. Transcribing data is considered as the first data reduction step because it generates texts that can be examined and rechecked ( Miles et al., 2014 , Grossoehme, 2014 ).

Data analysis comprises several fundamental steps, including reading the transcribed text, arranging data, coding data deductively based on prefigured themes or inductively to produce emergent themes, and then summarizing the codes into themes, and finally presenting the analyzed data as results ( Cohen et al., 2013 , Crabtree and Miller, 1999 , Pope et al., 2000 ).

The most commonly used data analysis methods in health science research are:

Thematic analysis is characterized by identifying, analyzing, and reporting themes that are available in the data ( Braun and Clarke, 2006 , Castleberry and Nolen, 2018 ).

Content analysis comprises systematic coding followed by quantification of the analyzed data in a logical and unbiased way ( Berelson, 1952 , Vaismoradi et al., 2013 ).

Discourse analysis emphasizes the core format and the structure of texts to examine the assumptions and concealed aspirations behind discourses ( Brown and Yule, 1983 , Gee, 2004 ).

Quality Perspectives in Qualitative Research

Qualitative research validation involves ensuring the rigor of the utilized data collection, management, and analysis methods, by utilizing approaches to ensure the quality. In pharmacy practice research, Hadi and Closs, 2016a , Hadi and Closs, 2016b argued that quality in qualitative research topic has not been discussed widely in the literature, and therefore Hadi and Closs, 2016a , Hadi and Closs, 2016b suggested using several trustworthiness criteria to ensure the rigor of qualitative study. The trustworthiness criteria for ensuring quality in qualitative research ( Lincoln and Guba, 1985 ) are:

This criterion aims to ensure that the results are true and increases the possibility that the conclusions are credible ( Cohen and Crabtree, 2008 ).

This criterion aims to indicate that the research results are repeatable and consistent, in order to support the conclusions of the research ( Cohen and Crabtree, 2008 ).

This criterion aims to confirm the neutrality in interpretation by ensuring that the perspectives of participants, not the bias of researchers, influence the results ( Krefting, 1991 ).

This criterion involves identifying the contexts to which the study results can be generalized, and indicating if the study conclusions can be applied in similar setting ( Yin, 2014 ).

Reflexivity implies revealing and evaluating the effect and biases that researchers can possibly bring to research process, by explaining the researcher's opinion, feelings, and experience with the phenomenon in question, and explaining the influence of this experience on research methods, findings, and write-ups ( Creswell, 2013 , Krefting, 1991 , Lincoln and Guba, 1985 ).

Ethical Considerations

Obtaining an ethical approval from the Institutional Review Board (IRB) is required before conducting the qualitative research ( Creswell, 2013 ). The key ethical issues that need to be considered are:

Informed consent refers to the decision taken by a competent individual to voluntarily participate in a research, after adequately understanding the research. Participant information leaflet is usually distributed to participants before they consent to participate in the research to clarify them the voluntary nature of research participation, the aim and objectives of the research, the rights of the respondents and the potential risks and harms, the data collection, management and storage conditions, and the right of participants to withdraw from the research ( Jefford and Moore, 2008 ).

The anonymity is usually ensured by not disclosing names of participants and by utilizing a code system to identify them during data collection, management, analysis, and in the writing up of the research. The confidentiality of participants and data is ensured by using a code system to identify participants, and by storing all data in a locked cabinet and a password-protected computer for a specified period of time ( Creswell, 2013 ).

Power imbalance is caused by the fact that participants have the experience about the investigated phenomenon, and researchers need to obtain information about these experiences. The power imbalance is usually associated with interaction between the researcher and participants during recruitment stage, and during data collection, analysis, interpretation, and validation stages. Hence, researchers should take suitable measures at each stage to decrease the influence of possible power imbalance, and should enhance trust with participants ( Karnieli-Miller et al., 2009 , Yardley, 2000 ).

Mixed Methods in Pharmacy Practice Research

Research studies in pharmacy practice usually utilize single-method research designs. However, often these report numerous limitations and may not adequately answer the research question. Therefore, the combination of more than one research method to answer certain research questions has become increasingly common in pharmacy practice research ( Ryan et al., 2015 ). Mixed methods research design is now a popular and widely used research paradigm in pharmacy practice research fields ( Hadi et al., 2013 , Hadi et al., 2014 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Ryan et al., 2015 ). Mixed methods research allows the expansion of the scope of research to offset the weaknesses of using either quantitative or qualitative approach alone ( Creswell et al., 2004 , Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ). Typically, qualitative and quantitative data are collected concurrently or sequentially in order to increase the validity and the comprehensiveness of the study findings ( Creswell et al., 2004 , Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 , Ryan et al., 2015 ). The mixed method approach provides an expanded understanding of phenomenon under investigation through the comparison between qualitative and quantitative data ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ).

This section provides an overview and application of mixed method research in pharmacy practice. However, considerations in selecting, designing, and analyzing mixed methods research studies as well as the various typologies of mixed methods research are discussed elsewhere. Johnson et al. (2007) proposed the following definition for mixed methods research: “The type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purpose of breadth and depth of understanding and corroboration.”

Mixed methods design allows the viewpoints of participants to be reflected, enables methodological flexibility, and promotes multidisciplinary teamwork ( Ryan et al., 2015 ). Furthermore, the approach allows a more holistic understanding of the research question. However, its major limitations include: need for wide range of research expertise across the research team members, highly labor-intensive, and the complexity of data integration.

Scholars believe that it is challenging to provide researchers with a step-by-step guide on how to undertake a mixed methods study and that this is driven by the specific research question ( Ryan et al., 2015 ). Nevertheless, the investigator should precisely determine the type of qualitative and quantitative methods to be employed, the order of data collection to be undertaken, the data collection instruments to be used, and the method of data analysis ( Ryan et al., 2015 ). This approach encompasses a synthesis of findings from both quantitative and qualitative components, which is achieved through integration of the findings from each approach ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ).

Different models or typologies for mixed methods research have been described in the literature. The most common typologies used in pharmacy practice and health services research include: concurrent or convergent parallel design, exploratory sequential design, explanatory sequential design, and the embedded design ( Hadi et al., 2013 , Pluye and Hong, 2014 ). Scholars believe that there are several factors to consider when selecting the typology or model of mixed methods research to use. These factors include: the order of qualitative and quantitative data collection (concurrent vs. sequential); priority of data (i.e., which type of data has priority between quantitative and qualitative data); purpose of integration of the data (e.g., triangulation); and number of data strands ( Hadi et al., 2013 , Pluye and Hong, 2014 ). In mixed methods research, integration of qualitative and quantitative findings is critical, and this research approach does not simply involve the collection of these data ( Ryan et al., 2015 ).

Summary and Take-Home Messages

• • In the era of evidence-based practice, it is not sufficient to propose new pharmacy services or roles without evidence of their benefit.
• • New pharmacy services and new roles must be proven to be feasible, acceptable, beneficial, and cost-effective.
• • Practice-based research provides such evidence and can inform policy, confirm the value of the new service, and change practice.
• • Various study designs, including, but not limited to experimental, quasi-experimental, observational, qualitative, and mixed-methods designs, have been used in pharmacy practice research.
• • Pharmacy practice researchers need to be competent in the selection, design, application, and interpretation of these methodological and analytical approaches.
• • The choice of any study design in pharmacy practice research is driven by the expertise of the investigator, type of research question or hypothesis, data availability, time orientation, ethical issues, and availability of funding.

There is a great demand for innovation and quality in pharmacy practice. These can be achieved partly through robust and well-designed pharmacy practice research. Pharmacy students, practitioners, educators, and policy-makers are exposed to a variety of research designs and methods. We need to have the best evidence (e.g., in policy, regulation, practice) for making decisions about the optimal research design that ensures delivering an ultimate pharmacy practice and a quality patient care.

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Emerging from disruption: The future of pharma operations strategy

In the past, many pharmaceutical companies (pharmacos) deprioritized operations strategy in the face of competing business pressures. This is now changing. Factors such as the COVID-19 pandemic, inflation, geopolitics, new therapeutic modalities, and new ways of working make it vital for pharmacos to carefully reconsider their long-term choices in sourcing, manufacturing, and supply chain.

Now is exactly the right time for this renewed emphasis on operations strategy, as pharmacos emerge from two years of intense firefighting. Succeeding in pharma under these new and challenging conditions will require succeeding in operations.

The focus for operational leaders may need to shift from the prevailing emphasis on continuous improvement—including cost savings, quality assurance, and constant readiness to deliver—to longer-term external challenges. These include high inflation and an increase in complexity and risk, as well as the compounding effects these forces have on each other.

Pharma operations leaders now have an opportunity to deliver even greater value to their organizations by achieving this shift in focus, but they must act quickly to keep abreast of the challenges confronting the industry. The effort will require enormous mobilization and thoughtful prioritization. This task will fall to leadership; only the CEO and head of operations are in the right positions to make it happen.

This article explores the challenges facing pharma leaders and the steps they can take to develop a more strategic, long-term, and integrated approach to operations strategy. It presents questions leaders can ask as they design the solutions needed to make sure operations can protect enterprise continuity while still delivering to patients.

A perfect storm of external challenges

The pharma industry is facing a multitude of challenging trends (Exhibit 1). Global demand is growing rapidly, and the unprecedented need for COVID-19 vaccines and therapeutics has put additional pressure on the industry. The industry’s ability to find innovative solutions to deliver COVID-19 vaccines while still meeting overall demand is a remarkable achievement, but rising global demand is still a significant challenge for the industry in the long term.

The product landscape also is changing swiftly. New modalities, such as cell and gene therapy and mRNA vaccine technology, have increased from 11 to 21 percent of the drug development pipeline—the fastest growth ever seen in the sector. This change is likely to bring more fragmentation of technology, new supply chains, and unique product life cycles.

In addition to these industry-specific trends, pharma has also been affected by broader global trends, such as supply chain pressures. While the pharma industry is considered somewhat protected by its high inventory levels and long-standing dual sourcing, over a given ten-year period, the likelihood of supply chain disruptions still represents a potential loss of 25 percent of EBITA . Inflation has risen in recent months to levels not seen for decades, leading to increasing costs for labor, raw materials, and transportation. This is over and above the persistent price pressures pharma is already facing, particularly in generics. Since pharma customers are not expected to fully absorb these cost increases, profit margins are under pressure.

Meanwhile, increased state interventions and protectionist trade policies are creating new pressures on manufacturing networks and could drive increased regionalization. This would be a capital-intensive exercise: to regionalize just 10 percent of current vaccine trade in one particular geographical region, governments would need to invest an estimated $100 million.

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The pharma industry is also facing talent shortages linked to wider labor market trends, including the 20 percent increase in demand for STEM-related roles across the life sciences industry in the United States. The current pool of pharma digital talent is at least 14 percent lower than demand, and many companies are finding it challenging to recruit technical talent. Compounding this challenge is the rise of remote working, which has increased employee expectations for flexibility. In response, nearly all pharmacos are experimenting with hybrid working models.

A few major trends point to an industry tailwind; one of them is the advancement of digital and analytics tools. Digital tools, robots, and sensors are becoming cheaper and easier to access, and they can be used to capture all manner of raw data. In addition, edge computing and cloud analytics are providing real-time optimization and transparency. Pharmacos are working to leverage the power of data to become more agile and resilient. However, to date, no pharmaco has emerged as a true global leader in this field.

The pharma industry is facing a multitude of industry-specific and global trends. But a few major trends point to an industry tailwind; one of them is the advancement of digital and analytics tools.

Each of these global trends represents significant challenges in and of itself, and the trends may be compounded and strengthened through their interactions. This compounding effect can add to the complexity of evaluating an effective strategic response.

Major implications for pharma

These global trends have six major implications for pharmacos: rising operational complexity, increasing risk, shifting capability requirements, higher capital expenditure requirements, variable-cost increases, and opportunities for savings (Exhibit 2).

Operations leaders may need to become comfortable navigating a more complex ecosystem as they respond to increased operational complexity. Risks may increase due to rising environmental, social, and governance (ESG) expectations and skills gaps, while new modalities and digital acceleration will also likely lead to a shift in capability requirements. This could necessitate reskilling and upskilling of staff, as well as a renewed focus on recruiting from outside of the pharma industry.

From a cost perspective, the pharma industry may see significantly increased capital expenditure requirements related to the construction of new sites and new digital infrastructure. Increases are also likely in variable costs in areas such as raw materials, transportation, and employee attrition, reskilling, and salaries.

Future of pharma operations

Pharma companies are experiencing a wave of innovations – from new treatment modalities, to smart machines, advanced analytics, and digital connectivity.

Although these implications are challenging, they may represent possible opportunities for savings in several areas. For example, ESG commitments on waste reduction could reduce costs, as could successful digital implementation. However, the challenge lies in monetizing these cost savings, given that the industry has long created value largely through revenue expansion rather than through cost savings.

Rising to the challenge: Actions to deliver value

To respond to these challenges, pharmaco leaders may now need to emphasize the importance of their operations strategy. They should consider taking a longer-term view and scaling activity across four key themes: network strategy and resilience, digital, operating model, and talent.

Expand focus on longer-term, transformative solutions

Operations leaders can address these challenges through several short-term and long-term responses. For example, problems associated with a more unpredictable supply chain could be addressed with a short-term approach of increasing inventory or a long-term initiative to establish an end-to-end supply chain digital nerve center.

Short-term levers can be an important part of the total response but are insufficient to fully mitigate the challenges facing the industry. To respond effectively, companies may need to accelerate new ways of working and embrace long-term thinking. This will require concrete action with a focus on making sure that strategies are put in place to weather the long-term headwinds the industry is facing.

Accelerate and scale responses across four strategic domains

To identify the actions that pharmacos could take, it may help to group these in terms of four strategic domains: network and resilience, digital strategy, operating model and ecosystem, and talent strategy (Exhibit 3). While these themes are likely to be familiar to any business leader, they now require a substantial shift in mindset. Acting on them also calls for a large investment of resources.

• Plan for and manage future resilience and reliability needs . Recent supply chain disruptions have pushed supply chain resilience up corporate agendas. Companies have been forced into reactive modes that employ short-term levers like building inventory. However, companies could better position themselves by solving multiple variables and building resilience into their operations strategy through longer-term actions like network design and dual sourcing.
• Scale end-to-end adoption of digital and automation . Digital has proven itself highly valuable to pharma operations. However, many companies struggle to move from targeted, single use cases to a fully scaled suite of solutions. And while the adoption of full-scale digital solutions can require heavy investment—around $50 million to $100 million per year for two to three years—the rewards can include significant cost savings, improved quality, and increased resilience, as well as greater employee effectiveness. Companies that truly scale and implement digital can better protect themselves from the pressures of the forces increasing costs for the industry. More and more companies are moving toward network-wide and end-to-end digitization; to date, the World Economic Forum has recognized 103 as “lighthouses,” based on their advanced application of digital technologies . Johnson & Johnson, for example, has successfully launched multiple Industry 4.0 lighthouses, including some focused on end-to-end patient connectivity and order fulfillment.
• Expand adoption of end-to-end partner ecosystems . Companies could also consider changing their operating model from a traditional hub configuration around originators to an end-to-end ecosystem of true strategic partners. More than 50 percent of companies already expect to intensify their collaboration models with other industry players through, for example, service agreements, joint ventures, or eco­systems. Some are already in motion; examples include Pfizer and BioNTech, which have already established a strategic partnership in mRNA technology discovery, and AstraZeneca and Huma, which are collaborating to scale innovation for digital health. These partnerships are indicative of increasing collaborations throughout the industry across functions.

Automation, centralization, and new job requirements may affect nearly 90 percent of today’s workforce, and to deal with this challenge, companies could adopt effective long-term strategies. Retaining talent is challenging in the present environment, with the share of workers planning to leave their jobs in the next three to six months standing at 40 percent since 2021 . 1 Aaron De Smet, Bonnie Dowling, Bryan Hancock, and Bill Schaninger, “ The Great Attrition is making hiring harder. Are you searching the right talent pool? ” July 13, 2022. Strategies for talent retention should therefore be broad and focus on more than just salary.

A viable long-term solution to talent shortages may need to involve more than increasing wages to attract people. To solve structural talent gaps, companies could ensure long-term reskilling and upskilling of the existing workforce. For example, Roche runs an operations rotational program to attract top talent with bachelor’s and master’s degrees, and early in the COVID-19 pandemic, Novartis launched a “choice with responsibility” policy to improve overall employee experience.

Successfully developing a robust operations strategy is complex and requires dedicated resources with the ability to focus on the medium to long term. This means the C-suite will need to prioritize efforts and provide adequate resourcing. Only the CEO and head of operations can set the appropriate direction for their organization, steer their company’s effort, gather the right skills and teams, and manage complex interdependencies and resource-intensive interventions.

Are companies doing enough?

As COOs look to emerge from the disruption of the past two years, reflecting on several questions could help them evaluate their organizations’ level of preparedness to respond to the trends affecting the industry. The process could provide foundational answers to inform a renewed operations strategy.

• Have you projected the impact of today’s current trends on your business?
• Do you have a focused, skilled, and scaled operations strategy team that identifies, prioritizes, and deploys initiatives across different horizons?
• Are your resilience measures proactive and dynamic, and are they being built on talent and digital capabilities to achieve greater agility and reliability?
• Have you experienced greater access to innovation and flexibility as a result of expanding your services and strategic partnerships?
• Has your digital strategy created benefits across your network and transformed your operation from digitally enabled to digitally driven?
• Have you achieved ESG improvements, and do you have a broad, long-term road map for ESG commitments (beyond net zero)?
• Has your operating model been agile enough to adapt to rapidly changing operations requirements, such as new modalities and potential disruptions?
• Have you successfully transformed your operations workforce and comprehensively improved the employee experience?
• Do you have an established governance process that incorporates past lessons into future strategy?

Although the pharma industry has performed a remarkable feat in delivering COVID-19 vaccines while also meeting growing demand, current trends create a challenging environment for pharma­ceutical companies. Companies face greater costs, complexity, and risk.

Now is the time to rethink operational strategy to respond to these trends and remain competitive. Such change may have associated challenges and will require bold and innovative leadership. But if companies successfully implement new strategies, they could position themselves to take advantage of the industry’s remarkable growth.

Hillary Dukart is an associate partner in McKinsey’s Denver office, Laurie Lanoue is a partner in the Montreal office, Mariel Rezende is a consultant in the Miami office, and Paul Rutten is a partner in the Amsterdam office.

The authors wish to thank Joe Hughes and Jean-Baptiste Pelletier for their contributions to this article.

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Research & Reviews: Journal of Pharmaceutical Analysis

e-ISSN: 2320-0812

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• Journal impact factor : 8.59
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Welcome to the Journal

Index Copernicus value (ICV) : 73.15

Research & Reviews: Journal of Pharmaceutical Analysis (e-ISSN: 2320-0812) an International journal publishing quarterly (Electronic) on the aspect of Pharmaceutical Analysis for an effective scientific reading and public view with an aim to reach the world wide researchers.

The manuscript would be considered under the specific branch of Pharmaceutical Analysis.

• Analytical Research and Development
• Pharmaceutical Additives Analysis
• Pharmaceutical Polymer Analysis
• Pharmaceutical Cosmetic Analysis
• Counterfeit Medicine Analysis
• Elemental Analysis and Trace Metals
• Nanoparticles in Pharmaceutical Products Analysis
• Organic Volatile Impurity Analysis
• Chemical Imaging
• Analysis of Crude Drugs
• Environmental and soil Analysis for Pharmaceutical Contamination
• Pharmaceutical Powder and Particle Morphology
• Solid State Characterization
• Stability and Pharmaceutical Testing
• Analysis of Traditional Indian System of Medicine (AYUSH)
• Pharmaceutical Analysis of Complex System
• Quality Control and Methods of Biotech Drug
• Cleaning Validations
• Quantitative and Qualitative Analysis In The Drug Screening Process
• Tracer Analysis in Molecular Pharmacology
• Quantitative Analysis in Biopharmaceutics
• Biological and Radio-immune Assays
• Thermo-Analytical Methods of Analysis
• Sterility Testing Methods
• Validation: Method, Cleaning and Personal Validations

For manuscript submission go through the below link

You may submit manuscripts as an email attachment to [email protected] or you may submit manuscripts online at https://www.scholarscentral.org/submissions/research-reviews-pharmaceutical-analysis.html

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Acceptance of manuscripts is driven entirely by handling editorial team considerations and independent peer-review, ensuring the highest standards are maintained no matter the route to regular peer-reviewed publication or a fast editorial review process. The handling editor and the article contributor are responsible for adhering to scientific standards. The article FEE-Review process of $99 will not be refunded even if the article is rejected or withdrawn for publication.

The corresponding author or institution/organization is responsible for making the manuscript FEE-Review Process payment. The additional FEE-Review Process payment covers the fast review processing and quick editorial decisions, and regular article publication covers the preparation in various formats for online publication, securing full-text inclusion in a number of permanent archives like HTML, XML, and PDF, and feeding to different indexing agencies.

Pharmaceutical Analysis Research

Pharmaceutical Analysis is an Analytical Method used to determination the quality and quantity of the pharmaceutical products. It also gives the information about the purity and safety of the products. Briefly it can be described as it identifies, determines, quantifies, purifies and separates the active compound from the mixture.

Related Journal of Pharmaceutical Analysis

Pharmaceutical Analytical Chemistry: Open Access, Pharmaceutica Analytica Acta, Current Pharmaceutical Analysis, Journal of Pharmaceutical Analysis.

Chromatography Method

Chromatography is used to separate the compounds from their mixture. They contain stationary phase and a mobile phase. The mobile phase passes through the stationary phase, the components which have affinity towards mobile phase elude faster and the components affinity towards stationary phase eludes later. No two components have same affinity.

Related Journal of Chromatography

Journal of Liquid Chromatography & Related Technologies, Journal of Chromatography A, The Journal of Chromatographic Science (JCS), Journal of Chromatographic Sciences, International Journal of Chromatographic Science, Journal of Liquid Chromatography & Related Technologies, Chinese Journal of Chromatography

Analytical Method

An analytical method is a technique that is used for quantitative and qualitative analysis of a chemical compounds. Lots of techniques used, from simple gravimetric analysis, titrimetric to very advanced techniques like HPLC, Gas Chromatography, and UV –visible spectroscopy etc…

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Analytical Methods, Journal of Analytical Methods in Chemistry, Analytical Chemistry, Brazilian Journal of Analytical Chemistry, Comprehensive Analytical Chemistry, Current Analytical Chemistry, Indian Journal of Chemistry - Section A Inorganic, Physical, Theoretical and Analytical Chemistry, International Journal of Analytical Chemistry

High-Performance Liquid Chromatography

High-Performance Liquid Chromatography is an analytical technique used to separate the compounds in a mixture, to identify each component, to quantify and purify the component. High performance liquid chromatography is an improved technique form of column chromatography. Absorption is the principle involved in this technique.

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Chromatography & Separation Techniques, Chinese Journal of Chromatography, Chinese Journal of Chromatography, Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences.

Liquid Chromatography Method

Liquid Chromatography is a technique used to separation of a liquid sample into its individual parts. Based on the mobile phase and stationary phase the separation of takes place. Liquid-solid column chromatography is the common chromatography in this liquid mobile phase filters through the solid stationary phase, causing separation of compounds in that.

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Chromatography & Separation Techniques, Journal of Liquid Chromatography and Related Technologies, Current Pharmaceutical Analysis

Gas Chromatography

Gas Chromatography is a chromatography used in analytical chemistry for separating and analyzing chemicals which have volatile nature i.e. they can be vaporized without decomposition. GC mainly used for testing the purity or separating the different compounds in a mixture. The mobile phase is a carrier gas, usually an inert gas (helium). The stationary phase is a liquid or polymer on an inert solid etc…

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Journal of Liquid Chromatography & Related Technologies, The Journal of Chromatographic Science (JCS), Journal of Chromatographic Sciences, International Journal of Chromatographic Science, Chinese Journal of Chromatography

Thin-Layer Chromatography

Thin-Layer Chromatography is a chromatography technique used to separate non-volatile mixtures. Thin-layer chromatography the stationary phase may be a sheet of glass, plastic, or aluminum foil, which is coated with a thin layer of adsorbent material, usually silica gel, aluminum oxide, or cellulose. It is advanced technique of paper chromatography.

Related Journal of Thin-Layer chromatography

The Journal of Chromatographic Science (JCS), Journal of Chromatographic Sciences, International Journal of Chromatographic Science, Chinese Journal of Chromatography

Paper chromatography

Paper chromatography is an analytical method used to separation of pigments i.e. color substances or chemicals. This can use in ink experiment for detecting primary or secondary colors. Advanced technique thin layer chromatography takes the place of this technique but still it is used.

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Bio Analysis

Bio Analysis is a type of analytical method generally takes place in biological system. They analyses the small drug particles and its metabolites content in our body. Bioanalysis used for drugs used in illegal purposes, forensic science, toxicology, anti-doping testing in sports.

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Bioanalysis, Journal of Bioanalysis and Biomedicine

Chemical Analysis

Chemical Analysis deals with the information about the chemical compounds. In chemical analysis it identifies, separates and structure elucidates the atoms or group of atoms present in a molecules. The chemical analysis may be quantitatively or qualitatively done.

Related Journals of Chemical Analysis

Analytical Chemistry, Journal of Analytical Chemistry, International Journal of Chemical and Pharmaceutical Analysis, American Journal of Analytical Chemistry, Trends in Analytical Chemistry, Asian Journal of Pharmaceutical Analysis and Medicinal Chemistry

Gravimetry is a quantitative measurement of a pure analyte, usually involving the precipitation, filtration, drying, and weighing of the precipitate. For example analysis of solids present in a water sample. Water is filtered and the remaining solid precipitate is collected and weighed.

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Chinese Journal of Inorganic Chemistry, Comments on Inorganic Chemistry, European Journal of Inorganic Chemistry, European Journal of Inorganic Chemistry, Reviews in Inorganic Chemistry

Titration is a technique used to determine the concentration of unknown solution by using known solution concentration. The known solution i.e. titrant is taken in burette and the unknown solution titrate is taken in beaker. The titrant is added from a burette to titrate until desired end point (as a color change) is reached.

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Advances in Inorganic Chemistry, Analytical Chemistry, Analytical and Bioanalytical Chemistry, Chinese Journal of Inorganic Chemistry, Comments on Inorganic Chemistry, European Journal of Inorganic Chemistry, European Journal of Inorganic Chemistry, Reviews in Inorganic Chemistry

Polarimetry

Polarimetry in analytic chemistry is a sensitive technique for measuring the optical activity of inorganic and organic compounds. A compound is said to be optically active if polarized light is rotated when passing through it.

Related Journal of Polarimetry

Analytical & Bioanalytical Techniques, Journal of Inorganic Biochemistry

Gel Permeability

The another word of Gel Permeability is size exclusive chromatography which is used to separate the compounds based on molecular sizes. Gels are used as stationary phase and mobile phase should be a good solvent. The smaller molecules which are trapped in the gels elude later and the larger molecules eludes first.

Fluorescence

If any atom or ion travels from exited state to ground state it emits radiation called fluorescence. When an atom travels from high energy to low energy ground state because of stability (ground state is more stable than exited state) it releases some energy to come to ground state that energy is called fluorescence.

Biotechnology Product

The Biotechnology products are obtained by using of living organisms or systems to develop the medicinal products for improving the health quality. Red biotechnology is a branch involves medical processes i.e. production of new drugs from organisms, regeneration of damaged tissues or re-grows the entire organs by using stem cells.

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This first-in-human study aims to assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of NBI-1117567 in healthy adult participants.

The compound, a muscarinic M1 preferring (M1/M4) selective agonist, holds promise for addressing cognitive symptoms in patients with neurological and neuropsychiatric conditions.

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There's no single cause of mild cognitive impairment (MCI), although MCI may be due to early Alzheimer's disease. There's no single outcome for the disorder. Symptoms of MCI may remain stable for years. Or MCI may progress to Alzheimer's disease dementia or another type of dementia. In some cases, MCI may improve over time.

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