Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques

Advancing modified biochar for sustainable agriculture: a comprehensive review on characterization, analysis, and soil performance

Biochar is a carbon-rich material produced through the pyrolysis of various feedstocks. It can be further modified to enhance its properties and is referred to as modified biochar (MB). The research interest in MB application in soil has been on the surge over the past decade. However, the potential benefits of MB are considerable, and its efficiency can be subject to various influencing factors. For instance, unknown physicochemical characteristics, outdated analytical techniques, and a limited understanding of soil factors that could impact its effectiveness after application. This paper reviewed the recent literature pertaining to MB and its evolved physicochemical characteristics to provide a comprehensive understanding beyond synthesis techniques. These include surface area, porosity, alkalinity, pH, elemental composition, and functional groups. Furthermore, it explored innovative analytical methods for characterizing these properties and evaluating their effectiveness in soil applications. In addition to exploring the potential benefits and limitations of utilizing MB as a soil amendment, this article delved into the soil factors that influence its efficacy, along with the latest research findings and advancements in MB technology. Overall, this study will facilitate the synthesis of current knowledge and the identification of gaps in our understanding of MB.

Graphical Abstract

Development of a cost-effective confocal Raman microscopy with high sensitivity

Confocal Raman microscopy is a powerful technique for identifying materials and molecular species; however, the signal from Raman scattering is extremely weak. Typically, handheld Raman instruments are cost-effective but less sensitive, while high-end scientific-grade Raman instruments are highly sensitive but extremely expensive. This limits the widespread use of Raman technique in our daily life. To bridge this gap, we explored and developed a cost-effective yet highly sensitive confocal Raman microscopy system. The key components of the system include an excitation laser based on readily available laser diode, a lens-grating-lens type spectrometer with high throughput and image quality, and a sensitive detector based on a linear charge-coupled device (CCD) that can be cooled down to -30 °C. The developed compact Raman instrument can provide high-quality Raman spectra with good spectral resolution. The 3rd order 1450 cm-1 peak of Si (111) wafer shows a signal-to-noise ratio (SNR) better than 10:1, demonstrating high sensitivity comparable to high-end scientific-grade Raman instruments. We also tested a wide range of different samples (organic molecules, minerals and polymers) to demonstrate its universal application capability.

Raman spectroscopy to investigate early biochemical alterations in human tears caused by contact lenses

CLINICAL RELEVANCE

The interaction between contact lenses and tear film plays a critical role in lens biocompatibility and can influence discomfort during wear. Early detection of biochemical changes is important for improving ocular surface health, preventing complications, and developing advanced lens materials to enhance long-term comfort.

BACKGROUND

The pursuit of biocompatible lens materials requires a thorough understanding of their interaction with tear environment. The aim of this study is to evaluate different approaches to investigate early alterations in tear biomolecular profiles induced by lens wear.

METHODS

Two participants wore different CLs and underwent ocular analysis before CL insertion (T0), after 2-hour wear (T2), and two hours after removing the CL (T4), evaluating non-invasive tear break-up time (NIBUT), tear meniscus height (TMH), and osmolarity. Forty CLs were used. At the same time points, tears were collected and analysed by Raman spectroscopy.

RESULTS

Tear film stability, as measured by NIBUT, decreased post-lens wear (T0 vs T2, p = 0.034 and 0.025 for S1 and S2, respectively) but showed recovery within 2 hours (T0 vs T4, p > 0.05 for both S1 and S2), while TMH and osmolarity remained unchanged (T0 vs T2 vs T4, p > 0.05 for both S1 and S2). Raman spectroscopy highlighted notable changes in the biomolecular profile of tears after lens wear, particularly in the 2700-3200 cm-1 spectral window related to C-H stretching, which is sensitive to CH, CH2 and CH3 groups, their functional chemistry group, and the surrounding microenvironment.

CONCLUSIONS

Contact lens wear induces subtle biochemical, subclinical alterations in tears that can be detected early by Raman spectroscopy. The method is promising for analysing the subject-specific responses to lenses, also with possible individual variability in the timing of these changes. In addition, this approach can provide insights for designing biocompatible materials and for the early detection of subclinical biomarkers.

A Novel Label-Free Method for Efficient Detection of Bisphosphonate Drug Dissolution Rates Using Surface-Enhanced Raman Spectroscopy

We developed a novel method utilizing surface-enhanced Raman spectroscopy (SERS) with calcium ion-modified silver nanoparticles as the enhancing substrate. This approach enables label-free detection of the dissolution rate of bisphosphonates. This technique is fast, sensitive, and highly reproducible, significantly advancing drug quality control. Experimental results demonstrated detection limits for etidronate, alendronate, and risedronate as low as 1 ng/mL. Notably, this method accurately identifies characteristic drug peaks without interference from excipients, allowing for precise quantitative detection. In summary, our SERS-based method offers a simple, efficient, and reliable approach for the qualitative and quantitative quality control of bisphosphonate drugs.

Baseline Raman Spectral Fingerprints of Zebrafish Embryos and Larvae

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids.

The Role of Hydrogen Bonding in the Raman Spectral Signals of Caffeine in Aqueous Solution

The identification and quantification of caffeine is a common need in the food and pharmaceutical industries and lately also in the field of environmental science. For that purpose, Raman spectroscopy has been used as an analytical technique, but the interpretation of the spectra requires reliable and accurate computational protocols, especially as regards the Resonance Raman (RR) variant. Herein, caffeine solutions are sampled using Molecular Dynamics simulations. Upon quantification of the strength of the non-covalent intermolecular interactions such as hydrogen bonding between caffeine and water, UV-Vis, Raman, and RR spectra are computed. The results provide general insights into the hydrogen bonding role in mediating the Raman spectral signals of caffeine in aqueous solution. Also, by analyzing the dependence of RR enhancement on the absorption spectrum of caffeine, it is proposed that the sensitivity of the RR technique could be exploited at excitation wavelengths moderately far from 266 nm, yet achieving very low detection limits in the quantification caffeine content.

Photon counting Raman spectroscopy: a benchmarking study vs surface plasmon enhancement

We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples' transient responses and enables the isolation of Raman scattering from fluorescence signals.

Drug content determination of low-dosed hot-melt extruded filaments using Raman spectroscopy

The aim of this study was to evaluate the suitability of a non-disruptive Raman spectroscopic method to quantify drug concentrations below 5 w% within a polymer matrix produced by hot-melt extrusion (HME). For calibration, praziquantel (PZQ)-polyvinylpyrrolidone-vinylacetat-copolymer (PVP-VA) mixtures were extruded. By focusing the laser light of the Raman probe to a diameter of 1 mm and implementing a self-constructed filament holder, the signal-to-noise (S/N) ratio could be reduced considerably. The obtained Raman spectra show quite high fluorescence, which is likely to be caused by dissolved pharmaceutical active ingredient (API) in the polymer matrix. For content determination, HPLC analysis was conducted as a reference method using the same filament segments. A partial least squares (PLS) model, regressing the PZQ concentrations from HPLC method analysis versus the off-line collected Raman spectra, was developed. The linear correlation for a suitable extrusion run for the production of low-dosed filaments (extrusion 1, two kneading zones) is acceptable (R2 = 0.9915) while the correlation for a extrusion set-up with low miscibility (extrusion 2; without kneading zone) is unacceptable (R2 = 0.5349). The predictive performance of the calibration model from extrusion 1 is rated by the root mean square error of estimation (RMSEE), which was 0.08%. This calibration can now be used to validate the content of low-dosed filaments during HME.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

Naloxone-AuNPs@ZIF-8-Based Impedimetric Sensor Platform for Ultrasensitive Detection of Fentanyl and Fabrication of Fen-Track Prototype for Real-Field Analysis

Opioids are considered to be a global threat, and we are facing the worst opioid crisis of the decade. Synthetic opioids like fentanyl are highly potent and deadly toward human body, and hence its detection is an inevitable requirement globally. Naloxone is known for its antagonist property toward fentanyl, and we performed computational simulations to find their interactions and use this principle to build the first of a kind impedimetric sensor device, transduced by 3D-ZIF-8 with in situ encapsulated naloxone-gold nanoparticles. The probe is synthesized using a unique encapsulation strategy, thoroughly characterized by various physicochemical and microscopic tools. The sensor is highly selective toward fentanyl and can detect fentanyl up to 100 ppm in a synthetic sample. A prototype device is also built based on the synthetic calibration and applied to the spiked urine sample, and the performance is evaluated using statistical and machine learning tools.

Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection

An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L-1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L-1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.

Exploring the complexities of drug formulation selection, storage, and shelf-life for exploration spaceflight

Medications have been a part of space travel dating back to the Apollo missions. Currently, medical kits aboard the International Space Station (ISS) contain medications and supplies to treat a variety of possible medical events. As we prepare for more distant exploration missions to Mars and beyond, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Emerging innovation and technologies in pharmaceutical development, delivery, quality maintenance, and validation offer promise for addressing these challenges. The present editorial will summarize the current state of knowledge regarding innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

Viewing life without labels under optical microscopes

Optical microscopes today have pushed the limits of speed, quality, and observable space in biological specimens revolutionizing how we view life today. Further, specific labeling of samples for imaging has provided insight into how life functions. This enabled label-based microscopy to percolate and integrate into mainstream life science research. However, the use of labelfree microscopy has been mostly limited, resulting in testing for bio-application but not bio-integration. To enable bio-integration, such microscopes need to be evaluated for their timeliness to answer biological questions uniquely and establish a long-term growth prospect. The article presents key label-free optical microscopes and discusses their integrative potential in life science research for the unperturbed analysis of biological samples.

Advancing non-destructive analysis of 3D printed medicines

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

Risk control drives risk assessment and risk review: A cause and effect model of pharmaceutical drug recall on patient safety

Background

Pharmaceutical drug recall is a relentless issue that is composed of multidimensional criteria. The distinct criteria that contributed to drug recalls have been identified in previous literature; however, there is limited information regarding the causal relationships between each criterion. Highlighting key influential aspects and criteria of pharmaceutical drug recall is critical in addressing this ongoing issue and promoting patient safety.

Objective

The objective of this study is as follows: (1) identify critical criteria of pharmaceutical drug recalls for improvements, (2) determine the interrelationships among the criteria, and (3) define the causal relationships of pharmaceutical drug recall and provide theoretical insights and practice recommendations to minimize risks associated with pharmaceutical recalls and maximize patient safety.

Design

This study proposes five aspects and 42 criteria to identify the impact of pharmaceutical drug recalls on patient safety by evaluating the interrelationships between the criteria by employing the fuzzy decision-making trial and evaluation laboratory method.

Methods

A group of 11 professionals across the pharmaceutical industry, hospitals, ambulatory care, regulatory authority, and community care settings were selected for interviews.

Results

Risk control is the influencing aspect of pharmaceutical drug recalls that has the most substantial impact on risk assessment and risk review; it generates medium effects on risk communication and technology. Risk assessment, risk communication, and risk review demonstrated comparative weak interrelationships, while risk communication exhibits a weak unidirectional effect on risk review. Finally, risk assessment exerts a weak influence on technology application and development. Product contamination, product subpotent or superpotent, injury to patients, product not sterile or impure, and system detectability of hazards have the strongest influence in the causal group of pharmaceutical drug recalls.

Conclusion

The study shows that risk control drives risk assessment and risk review in the pharmaceutical industry manufacturing process. To achieve patient safety, this study suggests focusing on risk control strategies, as this aspect displays the most substantial effect on influencing other critical risk management aspects such as risk assessment and risk review.

Emerging analytical techniques for pharmaceutical quality control: Where are we in 2022?

Quality control is a fundamental and critical activity in the pharmaceutical industry that guarantees the quality of medicines. QC analyses are currently performed using several well-known techniques, mainly liquid and gas chromatography. However, current trends are focused on the development of new techniques to reduce analysis time and cost, to improve the performances and decrease ecological footprint. In this context, analytical scientists developed and studied emerging technologies based on spectroscopy and chromatography. The present review aims to give an overview of the recent development of vibrational spectroscopy, supercritical fluid chromatography and multi-dimensional chromatography. Selected emerging techniques are discussed using SWOT analysis and published pharmaceutical QC applications are discussed.