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Dear Readers, Welcome to the latest issue of Micr
Analytical testing lies at the foundation of modern science and industry. Analytical labs are the bailiffs of science, pointing to the evidence to help make critical decisions about processes and the safety of finished products through methods like the identification of known substances, determining the presence of unwanted materials,characterizing biologics, monitoring a process, or assessing the safety of a product.
Increased complexity of pharmaceutical and biotechnology products results in larger datasets, more stringent timelines, greater pressures from regulations, and more complex samples. Laboratories must do more with less by improving accuracy, reproducibility, and throughput, while also reducing the cost of testing. All these complexities support the need for advanced analytical methods.
Modern analytical laboratories now transcend traditional tests and embed high resolution instrumentation, automation, artificial intelligence, advanced spectroscopy, mass spectrometry, chromatography, imaging, sensors, digital workflows, and real time analytics to analyze a sample.
This new era of analytical technologies seeks to test rapidly, understand comprehensively, detect the previously undetectable, and make decisions with greater confidence.
Sample → Preparation → Instrument → Data → Analyst → Report
This workflow, while still fundamental, is marked for change with the introduction of various new technologies.
Sample preparation is evolving to automated systems. Instruments are increasingly capable of producing high resolution, high dimensionality data. Software is available to interpret data in real time. AI systems are aiding the interpretation of data. Cloud-based systems are offering collaboration and centralized management of data.
Sample → Automated Preparation → Intelligent Instrumentation → Real-Time Data → Advanced Analytics → Actionable Insight
This transition is especially relevant for laboratories that work in heavily regulated environments; these laboratories need to produce accurate, reliable results quickly, while maintaining the integrity of their data.
Mass spectrometry is one of the best tools that modern laboratories have at their disposal. High-resolution mass spectrometry (HRMS) has pushed the boundaries even further on how scientists can characterize complicated samples.
Even with the incredible amount of information that HRMS systems can provide, mass spectrometry’s increasing complexity is producing even greater volumes of data. In this time, advanced software and AI assistance in data analysis become critical.
As opposed to just producing spectra, next-generation systems tend to assist in data analysis by helping scientists find signal, prioritize data analysis, perform data comparison, and even interpret datasets.
Chromatography is essential for analysis in the fields of pharmaceutical, biopharmaceutical, food, environmental, and chemical industries.
However, laboratories need to rely on chromatographic techniques that can help increase throughput and level of automation, all while maintaining resolution and reliability.
The next generation of chromatography will focus more on connection.
Chromatography is an increasingly connected field within the software of its corresponding instruments. These systems are capable of monitoring system performance, and flagging anomalies within the system. Automated calculations and reporting of results within the laboratory information systems are all made possible by increasing levels of interconnectivity within chromatography.
Traceability and precision of results can be improved, while time spent “hands-on” with the system is actively reduced.
Like chromatography, spectroscopic technologies are progressing rapidly. In research, quality control, manufacturing, and process monitoring, Raman, NIR, FTIR, NMR, and UV-Vis spectroscopy are highly utilized. Of the many spectroscopic methods, one of the major advances within the field is the ability to perform analysis with minimal or no sample preparation. This is particularly useful for analysis with rapid demands.
Raman and NIR spectroscopy are excellent technologies for rapid identification and characterization of materials, and are easily adaptable for use in manufacturing.
With appropriate calibration models and chemometric tools, spectroscopic systems can provide real time information on the properties of the material, and process control information.This facilitates the shift from the traditional concept of laboratory testing toward process-focused analytical science.
One of the most important advancements in analytical science is the focus on situ testing in a manufacturing context.
Process Analytical Technology (PAT) empowers manufacturers to determine process quality and attributes in a real-time, in-process context, as opposed to a traditional end-product testing context.
Phase variation during the manufacturing process can be recognized to help improve process consistency.
For pharmaceutical manufacturing, PAT (Process Analytical Technology) helps to implement Quality by Design (QbD), which is the concept of designing and controlling a manufacturing process.
Rather than wondering if the end product conforms to manufacturing specifications, a manufacturer can better understand and control how the manufacturing process impacts the product.
Integrating artificial intelligence into analytical science is perhaps the most significant change that has occurred.
Each analytical experiment may generate thousands of data points.
Human analysts are essential, but individually evaluating the results can be an overwhelming task.
Speed is not the only advantage of AI. AI can help laboratories enhance the information contained within their data.
Anomaly detection is particularly useful and innovative.
Analytical workflows are typically designed to include criteria against which results are evaluated. If a result falls outside of a criterion, a process is initiated to investigate the discrepancy. AI can be used to determine discrepancy results that are not conventional failures, yet are also unusual.
Laboratories could then shift to anticipatory testing from reactive testing. Automated analytical laboratories rely on advanced instruments, but become less valuable with additional manual steps. Therefore, automation is a key element of next generation analytical laboratories.
When these features are available as a complete unit, laboratories can devise a fully automated workflow.
In most cases, the instrument is not the bottleneck in an analytical process. In many cases sample preparation is the major time sink in a laboratory. Weighing, dilutions, extractions, filtrations, digests, and derivative conversions are a set of transformations that can add a lot of variability to a process. Automated preparation of samples is necessary.
A robotic system can perform repetitive processes with high precision potentially reducing analysts’ exposure to hazardous materials. All the processes can substantially increase productivity in high throughput laboratories.
More complex scientific questions require more than one analytical method. Sometimes, answers may only be partial with a single instrument.
Representing an integrated analytical approach, the combination of two or more methods provides a richer characterization of a sample.
This is especially true for complex biologics and advanced therapies, impurities, degradation products, and biological systems that may require more than an answer for molecular identity.
The analytical revolution is now penetrating the biological dimension as well. Thanks to single-cell technologies, researchers are now able to describe biological systems with great detail. Instead of examining a population of cells that are perceived as a single unit, disparities among cells that are inconspicuous can now be revealed. In addition, spatial analysis provides a description of the localization of biomolecules, proteins, or cells in tissues.
The understanding of biological systems is being revolutionized by the interplay of ultra-modern and highly advanced analytical technologies and computing systems.
Highly advanced and powerful analytical technologies are resulting in laboratories producing massive amounts of data.
It has become as important to manage this information effectively as it is to generate it.
Data integrity | Metadata | Traceability | Storage | Integration | Accessibility | Security
The future analytical laboratory will not be defined by the instruments it has.
It will be defined more by the manner in which it manages and uses its data.
Traditional laboratory testing has often caused delays in the time between a measurement and the making of a decision. This has been addressed with next generation systems.
Processing data in real-time has allowed laboratories to analyze data and identify patterns in the midst of an ongoing experiment or process. This has been used in manufacturing for timely and efficient control and correction. This has been used in research to allow scientists the ability to alter a given experiment while it is still in process.
This has been used in quality control to speed up the process of evaluating a given quality. The goal of this technology is to change the function of historical analytical data from being a record of past events to a data resource that is actively utilized during the process of making a decision.
Decentralization is becoming even more apparent in the realm of analytical testing. Previously, the need for advanced analysis required the use of large, complex instrumentation only found in specialized laboratories. This has been changed due to rapid advances in miniaturization, sensors, microfluidics, portable spectroscopy, and compact mass spectrometry.
There are numerous applications for portable technologies in testing and analysis. Decentralized testing will always have a role to play in reducing the time from sample collection to the delivery of actionable insights.
There is also a need to lessen the impact that analytical laboratories have on the environment. Certain analytical methods are based on the use of a large amount of solvents, water, energy, and disposables.
The further development of Green Analytical Chemistry will also be a priority. A laboratory of the future will be able to provide faster, more accurate, and more sustainable analysis.
Speed and intelligence mean nothing if results are not reliable.As laboratories leverage AI, Automation and other Disruptive Technologies, a primary focus must remain confidence in results.
Automation should not mask variability, it should minimize it. AI should assist in answer formation, not eliminate guidance.
The future will bring a variety of changes to the way analytical laboratories operate as compared to how they function today. In the future, instruments and software will utilize more automation and AI. Robotics will be used to prepare samples. Data generated by analytics will be able to be used across networks, rather than being confined to a specific instrument.
The most significant change in the future will be the ability of analytics to be predictive, rather than simply descriptive. Rather than simply asking,
“What is in this sample?” advanced analytics will provide answers to the more complex and descriptive questions, including:
The next frontier of analytical science lies in these questions.
A strategic, rather than a reactive, approach is needed when organizations attempt to modernize their analytical capabilities.
The analytical problem, as opposed to the prevailing analytical technology, should dictate the choice of technology.
Value will only be derived from advanced analytics when systems are integrated.
Automation will bring the most benefit in terms of increased speed and consistency.
The future of analytics and AI will be based on the availability of quality data.
The future of analytical science will require a new generation of scientists that possess an amalgamation of skills in instrumentation and automation as well as data science.
Laboratory infrastructure should be able to undergo changes to support new technology.
Innovations in laboratory analytics are beginning to fundamentally shift the way organizations conduct laboratory analytics.
The lab is relocating from an area where samples undergo measurement to a zone where data is continuously generated, interpreted, integrated, and converted into scientific insights.
High-resolution instruments enhance measurement capabilities and exhibit greater depth of analyses. Automation allows for faster and more consistent analyses. Artificial Intelligence (AI) analyzes and interprets complex data sets. Digital connectivity promotes and allows collaboration. Real-time data analyses describe and define the speed of underlying processes.
These science and technology advances exemplify the beginning of a paradigm shift in analytical sciences. The future of the analytical laboratory will exemplify quicker analyses, more intelligent interpretations, increased reliabilities, and improved decisions.
Tomorrow’s advanced analytical laboratories will not be defined by a particular instrument.Rather, the laboratories will be exemplified by the amalgamation of technologies, information, and automation, and by the integration of scientific and analytical intelligence. As the complexities of science grow, the integration of technologies, information, and automation may be the most important analytical capability of all.
