Studying Epigenetic Modifications of DNA Using UV Resonance Raman Spectroscopy

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Patrick Lavery is an Editor for the MJH Life Sciences brands LCGC and Spectroscopy, and their respective websites, 

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The rising popularity of diclofenac sodium spheres as a drug delivery system is the subject of a new study in 

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

 that examines the drug loading of diclofenac sodium and release rate during the coating process (1). Not only were drug loading and release rate monitored, but both were given real-time, in-line predictions with the use of near-infrared (NIR) spectroscopy.

diclofenac word display on tablet | Image Credit: © JoyImage - stock.adobe.com

The 13-author study was a collaboration between researchers affiliated with Shandong University and Shandong SMA Pharmatech Co., both in Shandong, China. They began their report by explaining how process analytical technology (PAT) has been increasingly incorporated into manufacturing best practices in the pharmaceutical industry in recent years, with PAT being described as an indispensable and powerful tool for the in-line and real-time control and monitoring of drug production, whether with NIR, Raman spectroscopy, or optical coherence tomography (OCT) (1).

Diclofenac sodium enteric-coated tablets are commonly prescribed in the United States under the brand name Voltaren, which is also offered in gel form (2,3). It is a nonsteroidal anti-inflammatory drug (NSAID) that is used for arthritis relief, providing anti-inflammatory, analgesic, and antipyretic effects according to the U.S. Food and Drug Administration (FDA). The research team in Shandong, meanwhile, reported that diclofenac sodium spheres, as a multi-unit, film-coated drug delivery system, offer good fluidity and a stable release rate, with fluidized bed coating widely used in industrial production (1).

Fluidized bed coating involves the spraying of a functional polymer with coating dispersion, generally leaving a uniform film coating. It has the advantages of fast heat and mass transfer, a large contact area between gas and solid phases, and a small temperature gradient (1). As part of this process, the researchers said, testing and analysis of the drug loading and release rate—the critical quality attributes (CQAs) of diclofenac sodium—ensures the delivery system’s safety and efficacy, but off-line methods are time-consuming in such a way that delays analysis unnecessarily.

In this application, a real-time in-line prediction model using NIR spectroscopy allowed for strong anti-interference, in turn permitting sucrose spheres to be introduced into the experiment in varying feeding amounts (1). The researchers said such a design would prove the robustness of the model.

Near-infrared spectroscopy is used where multicomponent molecular vibrational analysis is required in the presence of interfering substances. The near-infrared spectra consist of overtones and combination bands of the fundamental molecular absorptions found in the mid-infrared region. Near-infrared spectra consist of generally overlapping vibrational bands that are non-specific and poorly resolved. The use of chemometric mathematical data processing can be used to calibrate for qualitative of quantitative analysis despite these apparent spectroscopic limitations.

A micro NIR spectrometer with a diffuse reflection module and high-temperature external probe was used in concert with the procedure of fluidized bed coating. Results of this experiment were said to be successful, with the team finding it was able to verify the analytical ability of the model. Because of this, the authors suggested further study would be done in this area, to give more scientific backing to intelligent and modern drug manufacturing processes.

(1) Sun, Z.; Zhang, K.; Lin, B.; et al. Real-Time In-Line Prediction of Drug Loading and Release Rate in the Coating Process of Diclofenac Sodium Spheres Based on Near Infrared Spectroscopy. 

(2) Voltaren® (diclofenac sodium enteric-coated tablets) – Tablets of 75 mg – Rx only – Prescribing Information. 

https://www.accessdata.fda.gov/drugsatfda_docs/label/2009/019201s038lbl.pdf

Voltaren Arthritis Pain Relief Gel & Dietary Supplements | Voltaren

Articles

The method of analysis described by these researchers had the advantages of being in-line and in real time, compared to more traditional approaches such as high performance liquid chromatography (HPLC).

Near-Infrared Spectroscopy Predicts Drug Loading and Release Rate of Coating of Diclofenac Sodium Spheres

Sample similarity assessment has broad applications ranging from model generalizability to predicting outliers (1). As a procedure, sample similarity assessment is a process that evaluates the similarities between two samples under analysis. However, despite the many advancements made in assessing sample similarity, there is currently no well-established procedure for quantifying it. In a recent study published in 

, researchers from Idaho State University, in Pocatello, Idaho, developed a novel approach for determining sample similarity (1).

Test tube row. Concept of medical or science laboratory, liquid drop droplet with dropper in blue red tone background, close up, macro photography picture, AI Generated | Image Credit: © Vladislava - stock.adobe.com

The methodology the researchers created was called physiochemical responsive integrated similarity measure (PRISM), which is designed to integrate spectral similarity information with contextual factors (1). The goal of this methodology was to assess sample similarity accurately, efficiently, and simply (1). Quantifying sample similarity is regarded as an onerous task; the goal of PRISM was to improve this process, particularly when dealing with unlabeled target samples (1).

PRISM amplifies hidden physicochemical properties embedded within spectral data, providing a more comprehensive understanding of sample similarity. To test its efficacy, PRISM was applied to four diverse near-infrared (NIR) data sets, each representing distinct application areas in analytical chemistry (1). These applications encompassed prediction reliability assessment, model updating for enhanced generalizability, outlier detection, and basic matrix matching evaluations (1).

The results of the study demonstrated how PRISM could be an improved way to quantify sample similarity. It successfully collected and integrated substantial amounts of similarity information, enabling precise quantitative evaluations of sample similarity between the target and source domains (1). Moreover, PRISM exhibited adaptability to biological samples with additional physiochemical variations, expanding its utility across various domains of analytical chemistry (1).

One of the most promising aspects of PRISM is its potential for applications beyond NIR data. Although initially tested in the context of NIR measurements, the underlying principles of PRISM could be applied to other measurement systems affected by matrix effects, which only increases the versatility of this new methodology (1).

As analytical chemists continue to look at ways to improve processes in their everyday work, determining sample similarity will be one of these key procedures that will be continued to be improved upon. The research team from Idaho State University, in the meantime, offers a new method that serves as a better alternative to traditional methods while creating a pathway for further innovation across various scientific disciplines.

(1) Spiers, R. C.; Norby, C.; Kalivas, J. H.Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra. 

Researchers from Idaho State University developed a novel approach called physicochemical responsive integrated similarity measure (PRISM) that can better determine sample similarity.

Physiochemical Responsive Integrated Similarity Measure Enhances Sample Similarity Assessment 

Finding more effective methods for classifying recycling is key for environmental protection. This ensures that all recyclable waste like plastic and glass, are properly disposed.

Researchers from Hefei University of Technology in China are searching for more effective methods of analyzing recyclable waste. The team 

using laser-induced breakdown spectroscopy (LIBS) as a tool for categorizing recyclable materials.

LIBS is an atomic emission spectroscopy technology that detects the elemental composition of the sample based on emission spectra, the researchers wrote in the study. Unlike many spectroscopic techniques, LIBS technology is not impacted by the environment or lighting, or other factors such as the sample’s shape or color.

 spoke with Lei Yang, an author of the study and a professor at Hefei University of Technology, about how LIBS technology could improve the classification of recyclable items.

Why is classifying waste a growing challenge for environmental protection?

The growth of the population and the improved living standards have led to a sharp increase in both the categories and quantities of waste. Excess waste will cause serious environmental pollution and loss of resources if it is not properly managed and disposed of. In fact, many so-called wastes also have a significant value if recycled. By using waste classification management, we can maximize the utilization of waste resources, reduce the amount of waste disposal, and improve the quality of the general living environment. Waste classification is an urgent issue that needs to be addressed for environmental protection.

A diagram of the LIBS experiment system. Courtesy of Lei Yang. 

Why is laser induced breakdown spectroscopy a good technique for identifying and classifying recyclables?

LIBS is a technology for identifying the elemental composition of the sample. The purpose of identifying and classifying waste is to reduce environmental pollution, and more importantly, to reuse recyclable waste resources. Resource reuse is generally classified based on the composition of waste. Therefore, identifying and classifying methods based on composition analysis will yield high accuracy in waste sorting. Compared to other methods, LIBS technology is not affected by the ambient environment and light, sample shape, and color, and without preprocessing of the sample, which can provide automatic real-time detection, high-precision identification, and classification for recyclable waste.

What other methods are typically used to analyze waste and why is LIBS better?

The methods based on the differences in physical properties of waste—such as electrostatic separation and eddy current separation-—image-based waste classification method, and artificial classification method, are used to analyze waste. Their classification results are inaccurate due to the influence of background, light, geometry, color of waste, and other factors. Infrared spectroscopy, hyperspectral imaging, and fluorescence spectroscopy are also used to analyze waste, but these techniques need to have clean and prepared waste samples in advance and therefore cannot realize automated real-time detection. Compared to these methods, LIBS is not affected by the ambient environment and waste samples and provides fast and accurate analysis, which gives it an advantage in waste classification over other analytical techniques.

A breakdown of how the research team classifies recyclable waste. Courtesy of Lei Yang. 

What challenges did you face during this research?

In our work, it is a challenge to use only a LIBS system without adding other auxiliary instruments to collect spectra of plastics, glass, and other wastes. However, surprisingly, for most of the waste samples, a simple LIBS system can collect sufficient useful spectral information for sample identification and classification.

We are continuing our research in this area. Firstly, we will increase the category and quantity of test waste. Secondly, the waste subclassification will be mainly researched to meet the needs of refined classification for waste recycling and processing factories. Furthermore, we hope to deepen the understanding of transparent glass detection with LIBS.

This interview has been lightly edited for length and clarity.

Lei Yang, a professor at Hefei University of Technology, outlines how her team is using laser induced breakdown spectroscopy (LIBS) to analyze recycling. 

Seeking More Effective Methods for Recyclable Waste Classification

. Direct correspondence about this article to 

We are still learning new things about our Moon. As of September 6, it has been 54 years, 1 month, and 22 days since the historic Apollo 11 landing on the Moon, which occurred on July 20, 1969. Even so there are new discoveries to be made about the composition of the lunar surface.

An image of the Moon silhouetted by darkness | Image Credit: © Jerome Workman, Jr.

India's Chandrayaan-3 mission, which is the first successful attempt to land on the moon’s southern region, has made history by uncovering intriguing discoveries on the lunar south pole (1). Its laser-induced breakdown spectroscopy (LIBS) instrument, developed by the Laboratory for Electro-Optics Systems (LEOS)/ISRO (2) in Bengaluru, formerly known as Bangalore, India, has conducted pioneering in-situ measurements of the lunar surface (1). These measurements have led to the discovery of previously unknown elements present in the south polar region.

LIBS, a sophisticated scientific technique, employs high-energy laser pulses to analyze the composition of materials on the Moon’s surface. By focusing laser pulses onto the lunar soil, it generates a localized high-temperature plasma, and the emitted light from this plasma is optically captured and analyzed. Each element emits distinct wavelengths of light when in a plasma state, allowing scientists to identify the elemental composition of the sample material.

The preliminary analysis conducted using LIBS revealed the presence of several elements on the lunar surface, including aluminum (Al), sulfur (S), calcium (Ca), iron (Fe), chromium (Cr), and titanium (Ti). Subsequent measurements have also unveiled the presence of manganese (Mn), silicon (Si), and oxygen (O) (1). However, further investigation is still ongoing to determine the presence of hydrogen (H).

In a remarkable development, LIBS detected sulfur (S) in the lunar soil near the south pole, confirming its presence unequivocally (1). This discovery is significant as it was previously not possible to confirm the presence of this element using remote orbiter analysis techniques.

Simultaneously, the alpha particle X-ray spectrometer (APXS) onboard the Chandrayaan-3 rover Pragyan has contributed to these exciting findings. Developed by the Physical Research Laboratory (PRL) in Ahmedabad, India, with support from the Space Application Centre (SAC) Ahmedabad, and a deployment mechanism built by UR Rao Satellite Centre (URSC) in Bengaluru, India. APXS has shown to be ideal for in-situ analysis of the elemental composition of soil and rocks on celestial bodies with minimal atmospheres, such as the Moon (3).

APXS works by emitting alpha particles and X-rays onto the lunar surface, which interact with the surface soil, causing the atoms within the soil to become excited. As the excited atoms return to their ground state, they emit characteristic X-ray lines unique to each element present in the sample. By measuring the energies and intensities of these X-ray lines, researchers can identify the elements present and their concentrations. Notably, APXS observations have also confirmed the presence of sulfur, among other elements, alongside major expected elements like aluminum, silicon, calcium, and iron (3).

These pioneering discoveries by the Chandrayaan-3 mission's LIBS and APXS instruments have provided new insights into the lunar south pole's composition, advancing our understanding of Earth's nearest celestial neighbor. As researchers continue to analyze the data, we can anticipate even more revelations about the lunar surface's intriguing secrets.

(1) Indian Space Research Organisation, Department of Space, August 28, 2023, 

(2) Electro-Optics Systems (LEOS)/ISRO, Bengaluru, India, 

(3) Indian Space Research Organisation, Department of Space, August 30, 2023, 

India's Chandrayaan-3 mission is the first successful attempt to land on the moon’s southern region and has uncovered several noteworthy discoveries on the lunar south pole using laser-induced breakdown spectroscopy (LIBS) and other spectroscopic techniques.

New Discoveries from India’s Chandrayaan-3 Rover at the Lunar South Pole Using LIBS and APXS Spectroscopy

A new study conducted by researchers at Sungkyunwan University in Suwon, South Korea, and Hanyang University in Seoul, South Korea, proposes a technique for accurately predicting carbon dioxide (CO

) concentrations in flow fields with temperature gradients (1). The new approach combines machine learning with single-laser path absorption spectrum measurements using simulated data (1).

Concept depicting the issue of carbon dioxide emissions and its impact on nature in the form of a pond in the shape of a co

 symbol located in a lush forest | Image Credit: © Gan - stock.adobe.com

 concentrations in flow fields relies on tunable diode laser absorption spectroscopy (1). This method calculates concentrations based on the ratio of two integrated absorbances from spectral lines with different temperature dependencies (1). However, this approach has limitations: because of the presence of temperature gradients, significant deviations from actual concentrations often emerge, leading to inaccuracies (1).

Because of these challenges, the research team proposed a new method that leverages the entire absorption feature of CO

 (1). The researchers decided that their model should be data-driven and predictive, with the idea that it would then be able to compensate for the impact of nonuniform temperature fields on the absorption spectrum (1).

Using simulated data helped the researchers construct and program the model, given the impracticality of collecting a comprehensive data set covering all possible temperature gradient conditions (1). To validate their model, the researchers conducted experiments using real-world data, comparing its performance to the traditional two-line method. In all test cases, the newly proposed model outperformed the conventional approach, demonstrating its superior predictive capabilities in the presence of temperature gradients (1).

Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model harnesses both peak intensities and changes in the shape of absorption lines for prediction, further highlighting the effectiveness of the method (1).

Currently, significant interest in climate science has triggered many research endeavors that all seek to combat climate change concerns. An approach for detecting CO

 concentrations accurately helps in this context. This new method proposed by the research team resolves the longstanding challenges posed by temperature gradients, paving the way for more precise and reliable environmental data, which is key to finding solutions to preserve the environment and our planet.

(1) Choi, J.; Bong, C.; Yoo, J.; Bak, M.-S. Carbon Dioxide Concentration Estimation in Nonuniform Temperature Fields Based on Single-Pass Tunable Diode Laser Absorption Spectroscopy. 

A research team from Sungkyunkwan University and Hanyang University proposed a new method that can detect carbon dioxide concentration in flow fields with temperature gradients based on single laser path absorption spectrum measurements and machine learning.

Predicting Carbon Dioxide Concentrations in Temperature Flow Fields Using Machine Learning

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 

a group of researchers from the Institute of Atomic and Molecular Physics in China studied excited state intramolecular proton transfer (ESIPT) for the design of hydrophobic florescent probes (1).

ESIPT is a process where a molecule, when excited by light energy, undergoes a proton transfer within itself. In its ground state, the molecule has a stable proton position (donor site). When excited, the proton moves to another location within the molecule (acceptor site), leading to dual emission with different wavelengths upon return to the ground state. ESIPT has broad applications in chemistry and biology, and it is used for designing novel fluorescence-based probes.

Florescent probes are widely used in life sciences research due to their large Stokes shift, along with their high analytical sensitivity and selectivity, the researchers wrote. In this study, the scientists examined 3-hydroxy-2-(6-Methoxynaphthalen-2-yl)-4H-chromen-4-one (MNC) in water and 1,4-dioxane.

The scientists dissolved the MNC in water and 1,4 dioxane before examining the properties of the mixture in a PerkinElmer Lambda 950 and RF5301 fluorescence spectrophotometer.

The scientists found that in an excited S1 state, there is a strengthening of hydrogen bonds, indicating a trend towards excited proton transfer. Raman spectra and potential energy surface data confirm that MNC undergoes a process known as ESIPT in both solutions, with some modifications in the water solution.

The fluorescence quenching observed is not due to the closure of the ESIPT channel but rather the stacking of π-π bonds.

“The results demonstrate that MNC can undergo ESIPT process in water. The proton transfer mechanism of MNC in water is modified,” the researchers wrote. “Moreover, the fluorescence extinction of MNC in water is associated with molecular stacking in the aggregated state. It facilitates that the design of innovative hydrophobic fluorescent probes and organic fluorescent molecules.”

Wan, Y.; Li, Q.; Zhu, L.; Wan, Y.; Yan, L.; Guo, M.; Yin, H.; Shi, Y. Reconsideration of the ESIPT OFF Mechanism for Fluorescent Probe MNC in Aqueous Solution. 

Researchers from the Institute of Atomic and Molecular Physics in China looked at MNC dissolved in water and 1,4-dioxane.  

Excited State Intramolecular Proton Transfer of MNC for Florescent Probes

As the demand grows for lithium-ion (Li-ion) batteries, their performance requirements and environmental impact increase. Battery performance strongly depends on the composition of the cathode materials, requiring precise elemental ratios. Meanwhile, disposing spent batteries can have a negative environmental impact, which can be greatly reduced through recycling. Inductively coupled plasma–optical emission spectroscopy (ICP-OES) provides solutions in both of these areas. By using high-precision ICP-OES, precise measurements can be made to accurately determine compositions of a variety of different cathode materials. In battery recycling, ICP-OES meets the requirements of being a multielement technique with a wide dynamic range and the ability to handle complex matrices. Therefore, it can measure both high-concentration and impurity elements resulting from the incineration of spent batteries, providing recycling facilities information about the elements present and their levels so that recoveries can be optimized.

The performance of lithium-ion (Li-ion) batteries is strongly influenced by the composition of the cathode materials: different materials have different performance characteristics. Examples of cathodes include LiCoO

, among others, with new ones continuously being developed. However, for a given material, the exact composition impacts battery performance. For example, in LiNixMnyCozO

 (NMC), the molar ratios of Ni:Mn:Co affect battery performance, and they can be adjusted depending on the battery’s intended use (1). Therefore, it is critical to determine the molar ratios of cathode materials elements accurately, precisely, and consistently.

One of the issues facing the Li-ion battery industry is finding enough lithium and other cathode elements to meet the demand (2). Larger quantities can be extracted from the earth, but this process comes with a negative environmental impact (3). To address these challenges, battery recycling is becoming more prominent so that important elements can be reused. Two common methods of battery recycling are pyrometallurgy and hydrometallurgy, both of which produce a complex mixture of carbon, ash, graphite, residual electrolyte, cathode elements, and impurity elements known as “black mass.” To determine the quantities of usable elements that can be extracted, the black mass must be analyzed for cathode material elements and elemental impurities.

Inductively coupled plasma–optical emission spectroscopy (ICP-OES) can help in both of these scenarios. With its multi-element capability, ability to handle complex matrices, and wide dynamic range, ICP-OES is uniquely suited to address the analytical requirements of battery production and recycling. Because of its robust plasma and ability to read multiple wavelengths simultaneously, ICP-OES provides the consistent, high-precision elemental ratios required to characterize cathode materials during battery production. The wide dynamic range allows both cathode and impurity elements to be measured during battery recycling, providing important information so that recoveries can be optimized.

This work shows how ICP-OES can assist in both cathode production and battery recycling.

A 0.1 g quantity of nickel manganese cobalt (NMC) or LiFePO was combined with 3 mL hydrochloric acid (HCl; concentrated) in sample digestion tubes, which were then loosely capped and heated at 120 °C for 30 min in a sample preparation block. After the solutions cooled to room temperature, they were diluted to 50 mL with deionized water and then filtered to remove any graphite. For analysis, the filtered samples were diluted another 10 times with deionized water.

Because of the presence of carbon, ash, and graphite in the black mass sample, it is extremely difficult to digest. Instead, metals were leached from black mass by placing 50 mg of sample in a sample digestion tube, followed by 5 mL of aqua regia. The tubes were loosely capped and heated at 120 °C for 30 min. After the tubes cooled to room temperature, they were diluted to 50 mL with deionized water for analysis.

All analyses were done on a PerkinElmer Avio 550 Max fully simultaneous ICP-OES instrument using the conditions and wavelengths listed in Tables I and II. For the cathode materials, a technique known as high-precision ICP-OES was used (4), which involves using manual integration to measure the internal standard and analyte at exactly the same time by setting their read and integration times to be equal. In this manner, the internal standard compensates in real time for any variation in analyte signal, which can be because of plasma flicker or pulsations from the peristaltic pump. The result is excellent precision, with relative standard deviation (RSD) values of 0.2% or less.

The black mass sample was first analyzed with SmartQuant, a semi-quantitative technique within Syngistix for ICP software, which gives a quick overview of the elemental composition of a sample. Once the major and impurity components present were determined, a comprehensive quantitative analysis was performed on those elements to attain more accurate results.

An important difference between the analyses was the sample 

introduction systems. A standard nebulizer and spray chamber were used for the cathode analysis, but because black mass can contain hydrofluoric acid (HF), an HF-re- sistant sample introduction system was used.

Because the composition of cathode materials is critical to the performance of Li-ion batteries, the elemental ratios of cathode materials must be determined with high precision, both among replicates within a sample and over time as multiple samples are analyzed. To demonstrate high precision between replicates, an NMC sample was measured consecutively three times. As shown in Table III, both the weight percent and precision (RSD) for all elements were <0.1%, demonstrating the precision attainable.

To assess stability, the NMC sample was measured 10 times over 20 min to determine the stability of the measurements. As shown in Figure 1, both the RSDs of the concentrations in solution and the molar ratio are very consistent, with concentration RSDs less than 0.5% and molar ratios less than 0.3%. Taken together, these results demonstrate the ability of ICP-OES to provide the high precision and consistency required for lithium-ion battery production.

FIGURE 1: (a) Concentration and (b) molar ratio stability of Ni, Mn, Co, and Li in an NMC sample from 10 measurements over 20 min.

, the concentration of each component in the solid is important. Similar to NMC analysis, a LiFePO

 sample was analyzed nine times over 15 min. As shown in Figure 2, the RSDs over the nine measurements were less than 0.5%, demonstrating the stability of the methodology for LiFePO

FIGURE 2: Concentration stability of Li, Fe, and P in an LiFePO

 sample from nine measurements over 15 min.

The black mass sample resulted from the incineration of multiple lithium-ion batteries of various types, so it is a true unknown. However, the sample is expected to contain high concentrations of common cathode elements, but there could be various cathodes used in these batteries. Additionally, a variety of impurities could also be present.

To determine what elements are present, a quick semi-quantitative screening was performed, which showed high levels of expected elements typically present in cathodes (Co, Cu, Fe, Li, Mn, Ni, and P), along with the following impurities: Al, B, S, Ti, W, and Zr. Therefore, quantitative analyses were performed on these 13 elements. The sample was analyzed two times, with the results appearing in Table IV, along with the relative percent differences between the analyses. As expected, the results show percent levels of common cathode elements (Co, Cu, Li, Mn, Ni, and P), along with lower concentrations of several impurity elements (Al, S, Ti, W, and Zr). The relative percent differences between the analyses are less than 3%, demonstrating the consistency of the methodology.

It is surprising that Al was present at percent levels as an impurity, but its presence at this concentration was confirmed through multiple sample preparations and analyses. In addition, sample prep blanks were prepared and analyzed and showed that Al was not being introduced through the sample preparation process.

This work has shown how ICP-OES can be used in the lithium-ion battery industry to help with the development of new battery materials and in recycling spent batteries, because of its versatility. The ability of ICP-OES to provide repeatable high-precision results allows for the accurate characterization of cathode materials, while the wide dynamic range and ability to handle challenging matrices allows it to be used in battery recycling to determine elements important for cathodes and impurity elements in combusted batteries, allowing recovery procedures to be optimized. With this versatility, ICP-OES is 

an important tool for the Li-ion battery industry.

(1) Das, D.; Manna, S.; Purvankara, S. Electrolytes, Additives and Binders for NMC Cathodes in Li-Ion Batteries—A Review. 

(2) Azevedo, M.; Baczynska, M.; Hoffman, K.; Krauze, A. 

Lithium Mining: How New Production Technologies Could Fuel the Global EV Revolution

; Technical Report for McKinsey & Company: London, England, April 2022.

(3) Buchert, M.; Degrief, S.; Dolega, P. 

Ensuring a Sustainable Supply of Raw Materials for Electric Vehicles: A Synthesis Paper on Raw Material Needs for Batteries and Fuel Cells

; Technical Report for Oko-Institut: Darmstadt, Germany, 2018.

(4) PerkinElmer, HP-ICP-OES: Using the Avio 550/560 Max to Achieve the Highest Possible Precisions; Technical Note from PerkinElmer U.S. LLC: Shelton, Connecticut, 2020. 

https://www.perkinelmer.com/libraries/TCH_ Avio-500-ICP-OES-CRTSIS_38131

 is with PerkinElmer LLC, in Shelton, CT. Direct correspondence to: 

ICP-OES can accurately measure the elemental ratios necessary to optimize battery performance and improve recycling efforts so that battery recoveries are optimized. The authors explore how ICP-OES can help manufacturers meet the increasing performance demands required for lithium-ion batteries.

Using ICP-OES to Improve Lithium-Ion Battery Performance and Reduce Waste

A new revision to the U.S. Food and Drug Administration (FDA)’s 

Compliance Program Guide for Pre-Approval Inspections 

(PAIs) has added a fourth objective: Commitment to Quality in Pharmaceutical Development. PAIs are going to be very interesting now.....

R.C. McDowall is the principle of McDowall Consulting and director of R.D. McDowall Limited, and "Questions of Quality" column editor for LCGC Europe, Spectroscopy's sister magazine.

Compliance Program Guide for Pre-Approval Inspections (PAIs)

 has added a fourth objective: Commitment to Quality in Pharmaceutical Development. PAIs are going to be very interesting now.....

Spectroscopists usually consider chromatography as sample cleanup for their analysis. However, all spectroscopists working in regulated laboratories are living with the fallout of the 2005 fraud perpetrated by the chromatographers at Able Laboratories. The problem was that the Food and Drug Administration (FDA) had carried out seven pre-approval inspections (PAIs) of Able Laboratories, and they had not found any compliance problems until a whistleblower informed them of the compliance issues.

After a two-month inspection by four inspectors, a 483 was issued (1), and the FDA withdrew all product licenses that triggered a recall of over 3100 batches, resulting in Able’s bankruptcy.

The problem for the FDA was that inspectors focused only on paper printouts—not electronic records during the seven PAIs. Reading the 483, you’ll see how the chromatography data system audit trail aided the inspectors (1). The FDA’s response was to issue a revision of 

 to become effective in 2012 after inspector training. This had three objectives:

Although data integrity runs through all three, it is the third objective where
inspectors should look to see if there was a laboratory data integrity problem.

(CPG) was updated (3), with more emphasis in Objective 3 on laboratory data integrity violations that had been identified since 2010, and this was discussed in a previous “Focus on Quality” column (4). During a PAI, there will be an inspection focus on research and development (R&D) with the documentation and data used to support the product license application inspected under Objectives 2 and 3. Technology transfer to Quality Control is also within scope, as well as any analysis performed for registration purposes.

Living in London, we have an expression—”Wait all day for a bus, and three turn up at once.” Here’s the FDA equivalent: in October 2022, the FDA issued another update to 

(5). Connoisseurs of FDA know that the time between the issue of a draft and final (or more likely another draft) guidance document can be glacial, but three final versions of 

 in 12 years qualifies for the bus comment.

New Objective 4: Quality in Pharmaceutical Development

As mentioned in the abstract, the main change in the latest version is the addition of a fourth objective to 

: Commitment to Quality in Pharmaceutical Development. “Pharm Development” 

and “Quality” mentioned in the same sentence? Is this a contradiction?

Assess the pharmaceutical development program by evaluating the extent to which it is supported, defined, managed and continually assessed for its effectiveness as well as its use in supporting continual improvement of the Pharmaceutical Quality System (PQS) (5).

What does this mean in practice? The PQS, pharmaceutical development, and continual improvement are mentioned. However, this is within the context of the overall CPG, where one overarching aim is to ensure that data in a regulatory submission is accurate, complete, and consistent, and not been cherry picked. To put this in context, we need to consider the Data Integrity Model (6,7), shown in Figure 1.

Figure 1: A laboratory portion of a data integrity model.

An earlier “Focus on Quality” column, and related publication, discussed a data integrity model within a Pharmaceutical Quality System (PQS) that consisted of a foundation and three levels involving qualification and validation of instruments and systems, validation of methods and application of all to the analysis of samples (6,7). In addition, there must be QA oversight of all work (Figure 1). Objective 4 can be mapped to the foundation level of the model that is essentially data governance:

Data integrity procedures with effective training

The goal is to find the right people and approach to do the job. If an organization can’t get these fundamentals right, we end up with another Able Laboratories.

The CPG breaks down Objective 4 down into four areas that are indicative of an organization’s commitment to quality in pharmaceutical development and shown in Table I. The four areas are:

Multidisciplinary Integrated Development Team

Objective 4 reflects the integration of the principles of 

(9) into FDA PAIs. This is a good example why any regulated organization must keep current.

The aim of pharmaceutical development is to design a quality product and its manufacturing process to consistently deliver the intended performance of the product [8].

This means development of a manufacturing process, the product formulation, and all associated analytical procedures covering raw materials, in-process samples, and finished product analysis.

We’ll discuss two aspects of Objective 4 here:

Technology transfer of analytical procedure from Analytical Development to Quality Control.

Management leadership in the foundation layer of the Data integrity Model in Figure 1 is the key to data integrity and quality success. What is expected of senior management? From a data integrity perspective, the 2018 FDA guidance is very explicit:

Meaningful and effective strategies should consider the design, operation, and monitoring of systems and controls based on risk to patient, process, and product. Management’s involvement in and influence on these strategies is essential in preventing and correcting conditions that can lead to data integrity problems.

It is the role of management with executive responsibility to create a quality culture where employees understand that data integrity is an organizational core value and employees are encouraged to identify and promptly report data integrity issues. In the absence of management support of a quality culture, quality systems can break down and lead to CGMP non-compliance (10).

This data integrity expectation of FDA is then rolled into the scope and intent of Objective 4 in the 

Assess the establishment’s ability to develop and manufacture drugs of consistent quality. This includes determining whether an establishment has implemented and follows a development program that applies sound science and principles of material science, engineering, knowledge management, and quality risk management in a holistic manner (5).

Senior management does not escape the responsibility for the PQS under E

Senior management has the ultimate responsibility to ensure an effective Pharmaceutical Quality System is in place, adequately resourced, and that roles, responsibilities, and authorities are defined, communicated and implemented throughout the organization. Senior management’s leadership and active participation in the Pharmaceutical Quality System is essential. This leadership should ensure the support and commitment of staff at all levels and sites within the organization to the Pharmaceutical Quality System (11).

Thus, senior management can make or break integrity and quality.

At Able Laboratories (1), senior management had an expectation that all product batches would meet specifications—or you’re fired. How’s that for an incentive to bend the rules? Recently, intense management and investor pressure on research staff at vaccine startup Laronde led to data falsification and the inability to replicate experiments, resulting in cancellation of two late-stage research projects (12). An R&D version of Able Laboratories
without the heavy plod of FDA footsteps!

Technology Transfer of Analytical Procedures

For the second area of Objective 4, we need to look at technology transfer. Stage left we have Analytical Development, who are responsible for developing and validating analytical procedures. Stage right we have Quality Control (QC), who have the dubious task of establishing a validated procedure and using it for routine analysis of production batches. Center stage, there is space to build a large wall between the two departments. The building of the wall is dependent on how the two departments communicate for technology transfer. No wall equals much communication; high wall…...

Is QC consulted on the development of the procedure?

How is the analytical procedure communicated? Throw the report over the wall and let QC get on with it? Are there discussions with QC and their involvement in the development and validation?

Are equivalent instruments available in both departments to ease the transfer?

Are Analytical Development’s spectrometers qualified to the same parameters used in QC? Indeed, are they qualified at all?

Are the same data systems used in both Departments?

Why are these questions important? Let us return to the trigger for this column, 

…. comprehensive process and product understanding to the extent possible (5).

If there is no collaboration between the two departments, how can this information and knowledge be transferred to QC? Where is the commitment to quality required by Objective 4?

Table II compares the functions of analytical development and quality Control; the key to our discussion is the bottom row with the relationships for transfer and establishment of an analytical procedure in QC.

In an ideal world, technology transfer would be facilitated by using the same spectrometers and software so that the instrument methods can be transferred electronically with spectra. This would give the receiving laboratory more detail than is generally available in a written document. Rarely is it an ideal world, as R&D and Production have different budgets. There are two options for optimal transfer of analytical procedures:

Share the overall validation between the two departments. The main work is conducted by Analytical Development, but intermediate precision involves QC staff working with their instruments in their laboratories. Both departments are co-signatories of the final validation report.

A member of QC could work in the originating laboratory to learn and understand the procedure and speed establishment in their own laboratory, simplifying the transfer protocol.

Regardless of the transfer mechanism, how long should Analytical Development support QC after the procedure has been transferred? QC analysts may need access to development records, in case of situations where a procedure may need to be modified in the future. Ongoing data sharing and collaboration is critical for success.

, the frequency of inspecting Objective 4 is noted (5):

Periodically in subsequent PAIs, with the frequency based on risk

In addition, when there have been major changes to the quality system, management team or corporate structure (at the rate of change and merger in some organizations this can mean every PAI).

The depth of coverage during an inspection will vary based on the risk and application specific issues identified. To summarize, if you have been good boys and girls in a stable environment, then you’ll see less of an inspector for this objective. Otherwise, you could be on first name terms….

The publication of a new FDA compliance policy for PAIs brings a new area for inspection: quality in pharmaceutical development. Senior management are responsible for ensuring the culture and ethos in R&D, and should not apply pressure on staff or data integrity suffers. Communication and involvement in analytical procedure validation and transfer between Analytical Development and QC is critical for success.

(1) Able Laboratories Form 483 Observations. 2005; Available from: 

 Compliance Program Guide CPG 7346.832 Pre-Approval Inspections

. 2010, Food and Drug Adminsitration: Silver Spring, Maryland.

 Compliance Program Guide CPG 7346.832 Pre-Approval Inspections.

 2019, Food and Drug Administration: Silver Spring, Maryland.

(4) McDowall, R. D. Do You Understand the FDA’s Updated Approach to Pre-Approval Inspections? 

 2022, Food and Drug Administration: Silver Spring. Maryland.

(6) McDowall, R. D. Understanding the Layers of a Laboratory Data Integrity Model. 

Data Integrity and DataGovernance: Practical Implementation in Regulated Laboratories; R

The publication of a new FDA compliance policy for pre-approval inspections (PAIs) introduces a new objective to inspect research and development in 
pharmaceutical development. We explore how this could influence the PAI process now.

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