Use of near-infrared spectroscopy and multipoint measurements for quality control of pharmaceutical drug products

Quantification of API content in pharmaceutical tablets within milliseconds by time-stretch near-infrared transmission spectroscopy

We explored the feasibility of high-speed and high-accuracy quantification of active pharmaceutical ingredient (API) content in tablet products by near-infrared (NIR) spectroscopy to improve the reliability of pharmaceuticals. For this purpose, we employed a high-power NIR time-stretch transmission spectrometer recently developed by us. By using this transmission spectrometer with a multivariate calibration model, we demonstrated the ability to quantify API content with a short measurement time of 3.9 ms per tablet for model pharmaceuticals. For the model tablet, the quantification ability of our spectrometer was comparable to that achieved by a commonly used Fourier-transform NIR (FT-NIR) spectrometer with a measurement time of several seconds. We also confirmed that the effect of irradiating tablets with the NIR pulses used in our spectrometer was negligible.

Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISμ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISμ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISμ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

Discriminative Dissolution Method Development Through an aQbD Approach

Using a one-factor-at-a-time approach for dissolution method and discrimination analysis can be time-consuming and may not yield the optimal and discriminative method. To address this, we have developed a two-stage workflow for the dissolution method development followed by demonstration of discrimination power through an analytical Quality by Design (aQbD) approach. In the first stage, an optimal dissolution method was achieved by determining the method operable design region (MODR) through a design of experiment study of the high-risk method-related parameters. In the second stage, we established a Formulation-Discrimination Correlation Diagram strategy to examine the method discrimination capability, through which one can determine the method discriminative design region (MDDR) and visualize the impact of each formulation parameter and their interactions on dissolution. The application of aQbD principles into a workflow provides a scientific-driven guidance for robust method development and demonstrating discrimination power for dissolution methods.

Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network

Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.

Towards real-time release of pharmaceutical tablets: 100% in-line control via near-infrared spatially resolved spectroscopy and 3D microwave resonance technology

In the scope of 100% in-line quality control and real-time release of pharmaceutical tablets, the authors present a flexible inspection module for in-line tablet analysis with integrated multipoint near-infrared (NIR) spectroscopy and 3D microwave resonance technology (3D MRT). Via an industrial case study on Diclofenac Sodium tablets, the abilities of this versatile process analytical technology (PAT) tool are presented. It is demonstrated that the combination of Diclofenac concentration prediction via NIR spectroscopy and mass prediction via 3D MRT allow to estimate the dosage of each individual tablet. Single sample repetition tests were performed on 5 tablets, measured 10 times on three different days. A high accuracy and precision of prediction was shown, with an average standard deviation below 0.5 mg. The inspection run demonstrated the added value of such inspection and sorting strategies based on the calculated dosage of individual tablets.

Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network

Quality control of heat sealed bottles is very important to minimize waste and in some cases protect people’s health. The present paper describes a case study where an automated non invasive and non destructive quality control system was designed to assess the quality of the seals of bottles containing pesticide. In this case study, the integrity of the seals is evaluated using an artificial neural network based on images of the seals processed with computer vision techniques. Because the seals are not directly visible from the bottle exterior, the images are infrared pictures obtained using a thermal camera. The method is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. The results show that the inspection process is effective in identifying defective seals with a precision of 98.6% and a recall of 100% and because it is automated it can be scaled up to large bottle processing plants.

Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process

Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.

Recent progress in continuous manufacturing of oral solid dosage forms

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

Inline Spectroscopy: From Concept to Function

The field of applied spectroscopy is strongly dominated by publications presenting proof-of-concepts, lab set-ups, and demonstrations. In contrast, the corresponding number of commercial successes of inline spectroscopy is surprisingly lower. This article discusses inline spectroscopy from an instrumentation perspective. It is the authors' firm belief that the success of inline spectroscopy lies in the understanding of how the design and implementation of the optical instrumentation affects the data quality, and how this in turn will limit or enhance the performance of the prediction model. This article emphasizes the need for a strong, multidisciplinary design team, whose design process is rooted in first principles, to bridge the technology "valley of death" and convert research in applied spectroscopy into commercially successful solutions.

Spatially Resolved Spectral Powder Analysis: Experiments and Modeling

Understanding the behavior of light in granular media is necessary for determining the sample size, shape, and weight when probing using fiber optic setups. This is required for a correct estimate of the active pharmaceutical ingredient content in a pharmaceutical blend via near-infrared spectroscopy. Several strategies to describe the behavior of light in granular and turbid media exist. A common approach is the Monte-Carlo simulation of individual photons and their description using mean free path lengths for scattering and absorption. In this work, we chose a complementary method by approximating these parameters via real physical counterparts, i.e., the particle size, shape, and density and the resulting chord lengths. Additionally, the wavelength dependence of refractive indices is incorporated. The obtained results were compared with those obtained in an experimental setup that included the SAM-Spec Felin probe head by Indatech for detecting spatially resolved spectra of samples. Our method facilitates the interpretation of the acquired experimental results by contrasting the optical response, the physical particle attributes, and the simulation results.

Pharmaceutical cocrystals, salts and polymorphs: Advanced characterization techniques

The main goal of a novel drug development is to obtain it with optimal physiochemical, pharmaceutical and biological properties. Pharmaceutical companies and scientists modify active pharmaceutical ingredients (APIs), which often are cocrystals, salts or carefully selected polymorphs, to improve the properties of a parent drug. To find the best form of a drug, various advanced characterization methods should be used. In this review, we have described such analytical methods, dedicated to solid drug forms. Thus, diffraction, spectroscopic, thermal and also pharmaceutical characterization methods are discussed. They all are necessary to study a solid API in its intrinsic complexity from bulk down to the molecular level, gain information on its structure, properties, purity and possible transformations, and make the characterization efficient, comprehensive and complete. Furthermore, these methods can be used to monitor and investigate physical processes, involved in the drug development, in situ and in real time. The main aim of this paper is to gather information on the current advancements in the analytical methods and highlight their pharmaceutical relevance.