Towards Point-of-Care Manufacturing and Analysis of Immediate-Release 3D Printed Hydrocortisone Tablets for The Treatment of Congenital Adrenal Hyperplasia

Hydrocortisone (HC) is the preferred drug in children with congenital adrenal hyperplasia due to its lower potency as well as fewer reports of side effects. Fused deposition modelling (FDM) 3D printing holds the potential to produce low-cost personalised doses for children at the point of care. However, the compatibility of the thermal process to produce immediate-release bespoke tablets for this thermally labile active is yet to be established. This work aims to develop immediate-release HC tablets using FDM 3D printing and assess drug contents as a critical quality attribute (CQA) using a compact, low-cost near-infrared (NIR) spectroscopy as a process analytical technology (PAT). The FDM 3D printing temperature (140 °C) and drug concentration in the filament (10%-15% w/w) were critical parameters to meet the compendial criteria for drug contents and impurities. Using a compact low-cost NIR spectral device over a wavelength of 900-1700 nm, the drug contents of 3D printed tablets were assessed. Partial least squares (PLS) regression was used to develop individual calibration models to detect HC content in 3D printed tablets of lower drug contents, small caplet design, and relatively complex formula. The models demonstrated the ability to predict HC concentrations over a wide concentration range (0-15% w/w), which was confirmed by HPLC as a reference method. Ultimately, the capability of the NIR model had preceding dose verification performance on HC tablets, with linearity (R² = 0.981) and accuracy (RMSECV = 0.46%). In the future, the integration of 3DP technology with non-destructive PAT techniques will accelerate the adoption of on-demand, individualised dosing in a clinical setting.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Classification and Visualization of Physical and Chemical Properties of Falsified Medicines with Handheld Raman Spectroscopy and X-Ray Computed Tomography

Analytical methods for the detection of substandard and falsified medical products (SFs) are important for public health and patient safety. Research to understand how the physical and chemical properties of SFs can be most effectively applied to distinguish the SFs from authentic products has not yet been investigated enough. Here, we investigated the usefulness of two analytical methods, handheld Raman spectroscopy (handheld Raman) and X-ray computed tomography (X-ray CT), for detecting SFs among oral solid antihypertensive pharmaceutical products containing candesartan cilexetil as an active pharmaceutical ingredient (API). X-ray CT visualized at least two different types of falsified tablets, one containing many cracks and voids and the other containing aggregates with high electron density, such as from the presence of the heavy elements. Generic products that purported to contain equivalent amounts of API to the authentic products were discriminated from the authentic products by the handheld Raman and the different physical structure on X-ray CT. Approach to investigate both the chemical and physical properties with handheld Raman and X-ray CT, respectively, promise the accurate discrimination of the SFs, even if their visual appearance is similar with authentic products. We present a decision tree for investigating the authenticity of samples purporting to be authentic commercial tablets. Our results indicate that the combination approach of visual observation, handheld Raman and X-ray CT is a powerful strategy for nondestructive discrimination of suspect samples.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Calibration set selection method based on the “M + N” theory: application to non-invasive measurement by dynamic spectrum

An appropriate method for calibration set selection is very important for a robust quantitative model, especially for the non-invasive measurement of blood components.

Acquisition of reproducible transmission near-infrared (NIR) spectra of solid samples with inconsistent shapes by irradiation with isotropically diffused radiation using polytetrafluoroethylene (PTFE) beads

A bead-incorporated transmission scheme (BITS) has been demonstrated for collecting reproducible transmission near-infrared (NIR) spectra of samples with inconsistent shapes. Isotropically diffused NIR radiation was applied around a sample and the surrounding radiation was allowed to interact homogeneously with the sample for transmission measurement. Samples were packed in 1.40 mm polytetrafluoroethylene (PTFE) beads, ideal diffusers without NIR absorption, and then transmission spectra were collected by illuminating the sample-containing beads using NIR radiation. When collimated radiation was directly applied, a small portion of the non-fully diffused radiation (NFDR) propagated through the void space of the packing and eventually degraded the reproducibility. Pre-diffused radiation was introduced by placing an additional PTFE disk in front of the packing to diminish NFDR, which produced more reproducible spectral features. The proposed scheme was evaluated by analyzing two different solid samples: density determination for individual polyethylene (PE) pellets and identification of mining locality for tourmalines. Because spectral collection was reproducible, the use of the spectrum acquired from one PE pellet was sufficient to accurately determine the density of nine other pellets with different shapes. The differentiation of tourmalines, which are even more dissimilar in appearance, according to their mining locality was also feasible with the help of the scheme.

Variation in the transmission near-infrared signal with depth in turbid media

Transmission near-infrared (NIR) measurements of a 1 mm thick aspirin disk were made at different positions as it was moved through a stack of eight 0.5 mm thick disks of microcrystalline cellulose (Avicel). The magnitude of the first derivative of absorbance for the aspirin interlayer at 8934 cm(-1) was lower when the disk was placed at the top or bottom of the stack of Avicel disks, with the largest signal observed when the aspirin was positioned at the central positions. The variation in signal with depth is consistent with that observed previously for transmission Raman spectrometry. In both cases, the trend observed can be attributed to lower photon density at the air-sample interface, relative to the center of the sample, owing to loss of photons to the air. This results in a reduction in the number of photons absorbed or Raman photons generated and subsequently detected when the interlayer occupies a near-surface position.

A novel algorithm for linear multivariate calibration based on the mixed model of samples

We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.

A study on the use of near-infrared spectroscopy for the rapid quantification of major compounds in Tanreqing injection

In this paper we describe the strategy used in the development and validation of a near infrared spectroscopy method for the rapid determination of baicalin, chlorogenic acid, ursodeoxycholic acid (UDCA), chenodeoxycholic acid (CDCA), and the total solid contents (TSCs) in the Tanreqing injection. To increase the representativeness of calibration sample set, a concentrating-diluting method was adopted to artificially prepare samples. Partial least square regression (PLSR) was used to establish calibration models, with which the five quality indicators can be determined with satisfied accuracy and repeatability. In addition, the slope/bias (S/B) method was used for the models transfer between two different types of NIR instruments from the same manufacturer, which is contributing to enlarge the application range of the established models. With the presented method, a great deal of time, effort and money can be saved when large amounts of Tanreqing injection samples need to be analyzed in a relatively short period of time, which is of great significance to the traditional Chinese medicine (TCM) industries.

A novel sample selection strategy by near-infrared spectroscopy-based high throughput tablet tester for content uniformity in early-phase pharmaceutical product development

This article proposes a new sample selection strategy to simplify the traditional content uniformity (CU) test in early research and development (R&D) with improved statistical confidence. This strategy originated from the prescreening of a large amount of tablets by a near-infrared spectroscopy (NIRS)-based high-volume tablet tester to the selection of extreme tablets with highest, medium, and lowest content of active pharmaceutical ingredient (API) for further high-performance liquid chromatography (HPLC) test. The NIRS-based high-volume tablet tester was equipped with an internally developed and integrated automated bagging and labeling system, allowing the traceability of every individual tablet by its measured physical and chemical signatures. A qualitative NIR model was used to translate spectral information to a concentration-related metric, that is scores, which allowed the selection of those extreme tablets. This sample selection strategy of extreme tablets was shown to provide equivalent representation of CU in the process compared with the traditional CU test using a large number of random samples. Because it only requires reference tests on three extreme samples per stratified location, the time- and labor-saving nature of this strategy is advantageous for CU test in early R&D. The extreme sampling approach is also shown to outperform random sampling with respect to statistical confidence for representing the process variation. In addition, a chemometric approach, which utilizes only pure component raw materials to develop an NIRS model sensitive to API concentration, is discussed with the advantage that it does not require tablets at multiple API levels. Prospective applications of this sample selection strategy are also addressed.