Prediction of Dissolution Profiles From Process Parameters, Formulation, and Spectroscopic Measurements

3D Printed Microneedles for the Transdermal Delivery of NAD+ Precursor: Toward Personalization of Skin Delivery

3D printing of microneedles (μNDs) for transdermal therapy has the potential to enable patient personalization based on the target disease, site of application, and dosage requirements. To convert this concept to reality, it is necessary that the 3D printing technology can deliver high resolution, an affordable cost, and large print volumes. With the introduction of benchtop 4K and 8K 3D printers, it is now possible to manufacture medical devices like μNDs at sufficient resolution and low cost. In this research, we systematically optimized the 3D printing design parameters such as resin viscosity, print angle, layer height, and curing time to generate customizable μNDs. We have also developed an innovative 3D coating microtank device to optimize the coating method. We have applied this to the development of novel μNDs to deliver an established NAD+ precursor molecule, nicotinamide mononucleotide (NMN). A methacrylate-based polymer photoresin (eSun resin) was diluted with methanol to adjust the resin viscosity. The 3D print layer height of 25 μm yielded a smooth surface, thus reducing edge-ridge mismatches. Printing μNDs at 90° to the print platform yielded 84.28 ± 2.158% (n = 5) of the input height thus increasing the tip sharpness (48.52 ± 10.43 μm, n = 5). The formulation containing fluorescein (model molecule), sucrose (viscosity modifier), and Tween-20 (surface tension modifier) was coated on the μNDs using the custom designed microtank setup, and the amount deposited was determined fluorescently. The dye-coated μND arrays inserted into human skin (in vitro) showed a fluorescence signal at a depth of 150 μm (n = 3) into the skin. After optimization of the 3D printing parameters and coating protocol using fluorescein, NMN was coated onto the μNDs, and its diffusion was assessed in full-thickness human skin in vitro using a Franz diffusion setup. Approximately 189 ± 34.5 μg (5× dipped coated μNDs) of NMN permeated through the skin and 41.2 ± 7.53 μg was left in the skin after 24 h. Multiphoton microscopy imaging of NMN-coated μND treated mouse ear skin ex vivo demonstrated significantly (p < 0.05) increased free-unbound NADPH and reduced fluorescence lifetime of NADPH, both of which are indicative of cellular metabolic rates. Our study demonstrates that low-cost benchtop 3D printers can be used to print high-fidelity μNDs with the ability to rapidly coat and release NMN which consequently caused changes in intracellular NAD+ levels.

Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) - microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f2 = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes.

Prediction of in-vitro dissolution and tablet hardness from optical porosity measurements

Advanced manufacturing technologies such as continuous processing require fast information on the quality of intermediates and products. Process analytical technologies (PAT) to monitor many critical quality attributes (CQAs) have been developed and successfully implemented in pharmaceutical industry. However, there are some CQAs, which still have to be measured off-line with significant effort due to the lack of suitable PAT sensors. Two prominent examples are the in-vitro dissolution and the tablet hardness. Both are obtained via destructive measurement, and the dissolution is tedious and time-consuming to determine. In this study, these two CQAs were predicted via correlation with the optical porosity of tablets. The optical porosity was measured via a novel combination of gas in scattering media absorption spectroscopy (GASMAS) and photon time of flight spectroscopy (pTOFS) with a SpectraPore instrument. The approach was tested in a continuous tableting line and showed promising results in predicting the amount of drug released after specific dissolution times as well as the tablet hardness. This indicates that the measurement of optical porosity can support control strategies within the real-time release testing (RTRT) concept.

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets

Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets.

Regulatory Experience with Continuous Manufacturing and Real Time Release Testing for Dissolution in New Drug Applications

Regulatory submissions involving the use of continuous manufacturing (CM)1 and/or real-time release testing for dissolution (RTRT-D) to the United States Food and Drug Administration (FDA) were identified spanning several years. The submissions were for orally administered IR tablets and they were examined from a biopharmaceutics perspective to highlight commonly occurring issues which the FDA's assessment teams identified with the proposed use of CM and/or RTRT-D. The objective of this study is to provide recommendations for best practices that will help advance the field by (i) generating greater opportunities for (drug) Applicants2 to benefit from the implementation of advanced manufacturing approaches, (ii) improving high quality regulatory submissions involving CM and RTRT-D, and thus (iii) lessening the regulatory review burden. This paper has identified several common deficiencies, such as inadequate strategies for stratified sampling of drug product (DP) units, inappropriate design of experiments (DoE), inability of the proposed RTRT-D model to account for dissolution variability and to predict the entire time course of dissolution, insufficient documentation, and unsuitable in vitro dissolution methods.

Impact of Raman mapping area and intra-tablet homogeneity on the accuracy of sustained-release tablet dissolution prediction

This exploratory study investigated the minimum required Raman mapping area for predicting sustained-release tablet dissolution profiles based on intra-tablet homogeneity. The aim was to minimize scanning time while achieving reliable dissolution profile predictions. To construct the sample set, we controlled the blending time to introduce variability in the homogeneity of the tablets. The dissolution prediction models were established using the partial least squares regression under different Raman mapping area. The accuracies of the prediction results were evaluated according to the difference factor f1 and Intersection-Union two one-sided t-tests (IU TOST) methods, and the implications conveyed by the results were discussed. The results showed that the homogeneity of sustained-release tablet affects the minimum required mapping area, and the tablets with higher homogeneity show higher prediction accuracy when using the same mapping area to model the dissolution profiles of tablets.

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.

Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis

Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Application of chemometrics using direct spectroscopic methods as a QC tool in pharmaceutical industry and their validation

This present review described the application of chemometrics using direct spectroscopic methods at the quality control (QC) laboratory of Pharmaceutical Industries. Using chemometrics methods, all QC assessments during the fabrication processes of the drug preparations can be well performed. Chemometrics methods have some advantages compared to the conventional methods, i.e., non-destructive, can be performed directly to intake samples without any extractions, unnecessary performing stability studies, and cost-effective. To achieve reliable results of analyses, all methods must be validated first prior to routine applications. According to the current Pharmacopeia, the validation parameters are specificity/selectivity, accuracy, repeatability, intermediate precision, range, detection limit, quantification limit and robustness. These validation data must meet the acceptance criteria, that have been described by the analytical target profile (ATP) of the drug preparations.

Harnessing artificial intelligence for the next generation of 3D printed medicines

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

Quality by Design (QbD) and chemometric models are different sides of the same coin. While QbD models utilize experimentally designed settings for optimization of some quality attributes, these settings can also be utilized for chemometric prediction of the same attributes. We aimed to synchronize optimization of comparative dissolution results of carvedilol immediate release tablets with chemometric prediction of dissolution profile and content uniformity of the product. As an industrial application, selection of variables for optimization was done by performing risk assessment utilizing the archived product records at the pharmaceutical site. Experimental tablets were produced with 20 different settings with the variables being contents of sucrose, sodium starch glycolate, lactose monohydrate, and avicel Ph 101. Contents of the excipients were modelled with F1 dissimilarity factor and F2 similarity factor in HCL, acetate, and USP dissolution media to determine the design space. We initiatively utilized Partial Least Square based Structural Equation Modelling (PLS-SEM) to explore how the excipients and their NIR records explained dissolution of the product. Finally, the optimized formula was utilized with varied content of carvedilol for chemometric prediction of the content uniformity.

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design

Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low efficiency. Current research aims to integrate various computational tools, including machine learning (ML), molecular dynamic (MD) simulation and physiologically based pharmacokinetic (PBPK) modeling, to accelerate the development of SD formulations. Firstly, based on a dataset consisting of 674 dissolution profiles of SD, the random forest algorithm was used to construct a classification model to distinguish two types of dissolution profiles: "spring-and-parachute" and "maintain supersaturation", and a regression model to predict the time-dependent dissolution profiles. Both of the two prediction models showed good prediction performance. Moreover, feature importance was performed to help understand the key information that contributes to the model. After that, the vemurafenib (VEM) SD formulation in previous report was used as an example to validate the models. MD simulation was used to investigate the dissolution behavior of two SD formulations with two polymers (HPMCAS and Eudragit) at the molecular level. The results showed that the HPMCAS-based formulation resulted in faster dissolution than the Eudragit formulation, which agreed with the reported experimental results. Finally, a PBPK model was constructed to accurately predict the human pharmacokinetic profile of the VEM-HPMCAS SD formulation. In conclusion, combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations. The integrated computational methodology will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Tackling quantitative polymorphic analysis through fixed-dose combination tablets production. Pyrazinamide polymorphic assessment

Pyrazinamide (PZA), Rifampicin (RIF), Isoniazid (ISH) and Ethambutol (ETB) form the core for the treatment of Tuberculosis, today a devastating disease in low-income populations around the world. These drugs are usually administrated by fixed-dose combination (FDC) products, to favour the patient compliance and prevent bacterial resistance. PZA exists in four enantiotropically-related polymorphs (Forms α, δ, β and γ), but only Form α is considered suitable for pharmaceutical products due to its stability and bioavailability properties. The classical approaches to address solid-state (microscopy, X-ray diffraction and calorimetry) shows limitations for quantification of polymorphs in the presence of excipients and other active components, as in the case of FDC tablets. In this work, an overall strategy was developed using near infrared spectroscopy (NIR) coupled to partial least squares regression (PLS) to quantify Form α of PZA in drug substance (raw material) and PZA/RIF/ISH-FDC tablets. For this purpose, two PLS models were constructed, one for drug substance preparing training (n = 30) and validation (n = 18) samples with a ternary composition (Form α/Form δ/Form γ), and other for FDC drug products, also including the appropriate amount of RIF, ISH and the matrix of excipients in order to simulate the environment of PZA/RIF/ISH association. The NIR-PLS models were optimized using a novel smart approach based on radial optimization (full range, 3 L V and MSC-D' and SNV-D' as pre-treatment, for raw material and FDC tablets, respectively). During the validation step, both methods showed no bias or systematic errors and yielded satisfactory recoveries (102.5 ± 3.1 % for drug substance and 98.7 ± 1.5 % for FDC tablets). When commercial drug substance was tested, NIR-PLS was able to predict the content of Form α (0.98 ± 0.01 w/w). The model for FDC tablets allowed estimating polymorphic purity in intact (0.984 ± 0.003 w/w), sectioned (0.986 ± 0.002 w/w), and powered (0.985 ± 0.004 w/w) tablets, showing the methodology could be applied to a different stage of the process (i.e premixed-powders or granulates). The suitability of the method was also verified when Form α was satisfactorily analysed in FDC fortified with Form δ and Form γ to reach 0.78, 0.88 and 0.98 w/w, Form α. This strategy results in an excellent alternative to ensure the polymorphic purity of PZA throughout the overall pharmaceutical manufacturing process.