An image classification tool was developed to classify subvisible particles, namely silicone oil (SO) and non-silicone oil (NSO; protein aggregate, rubber closure, and air bubble) particles, present in biopharmaceuticals using feature extraction in FlowCam (R) images, and the outcomes were validated with six machine learning (ML) classifiers. The image classification tool set at specific configuration: condition 1 - CDEP {(compactness, diameter, elongation, perimeter) = (8.06, 10.44, 13.30, 27.20)} identified SO, while the configuration set at condition 2 - CDEL {(compactness, diameter, elongation, length) = (3.14, 24.48, 1.97, 4.28)} detected NSO. The classification tool was particularly useful in detecting the release of SO after exposure to the stress sources. Additionally, the morphological features-based classification tool (p < 0.05) enhanced the predictive accuracy of the ML classification tools (>= 97.2 %). Specifically, CNN (100 %) outperformed na & iuml;ve Bayes (99.3 %), linear discriminant analysis (98.4 %), artificial neural network (98.1 %), support vector machine (SVM 97.2 %). Bootstrap forest was excluded because it failed to classify SO in a large dataset. The developed classification tool could be an alternative in classifying the image datasets without the burden of complex ML tools. Such image-based classification tool can be computationally economical solution in quality control of the protein formulations.

An abrupt cessation of antidepressant medication can be challenging due to the appearance of withdrawal symptoms. A slow hyperbolic tapering of an antidepressant, such as citalopram hydrobromide (CHB), can mitigate the withdrawal syndrome. However, there are no viable dosage forms on the market to implement the tapering scheme. A solution using a tunable modular design (TMD) approach to produce flexible and accurate doses of CHB is proposed. This design consists of two parts: 1) a module with a fixed amount of preloaded CHB in a freeze-dried polymer matrix, and 2) fine-tuning the CHB dose by inkjet printing. A noncontact food-grade printer, used for the first time for printing pharmaceuticals, is modified to allow for accurate printing of the highly concentrated CHB ink on the porous CHB-free or CHB-preloaded modules. The produced modules with submilligram precision are bench-marked with commercially available CHB tablets that are manually divided. The TMD covers the entire range of doses needed for the tapering (0.5-23.8 mg). The greatest variance is 13% and 88% when comparing the TMD and self-tapering, respectively. Self-tapering is proven inaccurate and showcases the need for the TMD to make available accurate and personalized doses to wean off treatment with CHB.

The integrity of the intestinal mucus barrier is crucial for human health, as it serves as the body's first line of defense against pathogens. However, postnatal development of the mucus barrier and interactions between maturity and its ability to adapt to external challenges in neonatal infants remain unclear. In this study, we unveil a distinct developmental trajectory of the mucus barrier in preterm piglets, leading to enhanced mucus microstructure and reduced mucus diffusivity compared to term piglets. Notably, we found that necrotizing enterocolitis (NEC) is associated with increased mucus diffusivity of our large pathogen model compound, establishing a direct link between the NEC condition and the mucus barrier. Furthermore, we observed that addition of sodium decanoate had varying effects on mucus diffusivity depending on maturity and health state of the piglets. These findings demonstrate that regulatory mechanisms governing the neonatal mucosal barrier are highly complex and are influenced by age, maturity, and health conditions. Therefore, our results highlight the need for specific therapeutic strategies tailored to each neonatal period to ensure optimal gut health.

Intravenous drugs are often co-administrated in the same intravenous catheter line due to which compatibility issues, such as complex precipitation processes in the catheter line, may occur. A well-known example that led to several neonatal deaths is the precipitation due to co-administration of ceftriaxone- and calcium-containing solutions. The current study is exploring the applicability of Raman spectroscopy for testing intravenous drug compatibility in hospital settings. The precipitation of ceftriaxone calcium was used as a model system and explored in several multi-drug mixtures containing both structurally similar and clinically relevant drugs for co-infusion. Equal molar concentrations of solutions containing ceftriaxone and calcium chloride dihydrate were mixed with solutions of cefotaxime, ampicillin, paracetamol, and metoclopramide. The precipitate formed was collected as an "unknown" material, dried, and analyzed. Several solid-state analytical methods, including X-ray powder diffraction, Raman spectroscopy, and thermogravimetric analysis, were used to characterize the precipitate. Raman microscopy was used to investigate the identity of single sub-visual particles precipitated from a mixture of ceftriaxone, cefotaxime, and calcium chloride. X-ray powder diffraction suggested that the precipitate was partially crystalline; however, the identity of the solid form of the precipitate could not be confirmed with this standard method. Raman spectroscopy combined with multi-variate analyses (principal component analysis and soft independent modelling class analogy) enabled the correct detection and identification of the precipitate as ceftriaxone calcium. Raman microscopy enabled the identification of ceftriaxone calcium single particles of sub-visual size (around 25 μm), which is in the size range that may occlude capillaries. This study indicates that Raman spectroscopy is a promising approach for supporting clinical decisions and especially for compatibility assessments of drug infusions in hospital settings.

A UV imaging release-testing setup comprising an agarose gel as a model for tumorous tissue was developed. The setup was optimized with respect to agarose concentration (0.5% (w/v)), injection procedure, and temperature control. A repeatable injection protocol was established allowing injection into cavities with well-defined geometries. The effective resolution of the SDi2 UV imaging system is 30–80 µm. The linear range of the imaging system is less than that of typical spectrophotometers. Consequently, non-linear cAMP calibration curves were applied for quantification at 280 nm. The degree of deviation from Beer’s law was affected by the background absorbance of the gel matrix. MATLAB scripts provided hitherto missing flexibility with respect to definition and utilization of quantification zones, contour lines facilitating visualization, and automated, continuous data analysis. Various release patterns were observed for an aqueous solution and in situ forming Pluronic F127 hydrogel and PLGA implants containing cAMP as a model for STING ligands. The UV imaging and MATLAB data analysis setup constituted a significant technical development in terms of visualizing behavior for injectable formulations intended for intra-tumoral delivery, and, thereby, a step toward establishment of a bio-predictive in vitro release-testing method.

Data-enriched edible pharmaceuticals (DEEP) is an approach to obtain personalized medicine, in terms of flexible and precise drug doses, while at the same time containing data, embedded in quick response (QR) codes at a single dosage unit level. The aim of this study was to fabricate DEEP with a patient-tailored dose, modify drug release and design to meet patients’ preferences. It also aimed to investigate physical stability in terms of the readability of QR code patterns of DEEP during storage. Cannabinoids, namely, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), were used as the model active pharmaceutical ingredients (APIs). Three different substrates and two colorants for the ink were tested for their suitability to fabricate DEEP by desktop inkjet printing. Flexible doses and customizable designs of DEEP were obtained by manipulating the digital design of the QR code, particularly, by exploring different pattern types, embedded images and the physical size of the QR code pattern. Modification of the release of both APIs from DEEP was achieved by applying a hydroxypropyl cellulose (HPC) polymer coating. The appearance and readability of uncoated and polymer-coated DEEP did not change on storage in cold and dry conditions; however, the HPC polymer layer was insufficient in preserving the readability of the QR code pattern in the extreme storage condition (40 °C and 75% relative humidity). To sum up, the DEEP concept provides opportunities for the personalization of medicines, considering also patients’ preferences.

An image-based method for determining H2O2 vapor pressures in powder systems was developed based on cellulose pellets loaded with potassium titanium oxide oxalate (PTO Pellets) as probe particles. Solid titanyl salts change color after exposure to hydrogen peroxide vapor and the quantitative response of PTO pellets to H2O2 has been established by comparing reactions with H2O2 in liquid and solid states. Analysis of pictures of the color changes of PTO Pellets mixed into powders can be used to monitor the presence of ppm levels of H2O2 vapor inside powder systems such as bleach containing dry detergent powders.•H2O2 vapor quantification in dry systems with titanyl loaded cellulose particles.•Simple fabrication of H2O2 probe particles.•High sensitivity with LOD 0.190 ppm H2O2.

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release.

Powder caking can dramatically affect powder handling and downstream production processes. Understanding the key factors that contribute to bulk powder caking is crucial. This article introduces the Hirschberg caking device (HCD), which is a 3D-printed device allowing for parallel testing of powder caking in a cylindrical geometry. In the HCD setup, the powder sample is stored in controlled conditions in the sample holder. On removal of the sample holder, the caked powder will remain in the shape determined by the sample geometry while the remaining powder will fall down. Caking indices can be calculated based on image analysis and weight measurement. The results obtained for the caking of lactose monohydrate with the HCD were in good agreement with the results obtained by a ring shear tester. In addition, a strain tester was used to measure the strength of the formed cakes. Using this approach, critical storage conditions and the required concentration of a given anticaking agent (talc) for lactose monohydrate could be identified. This work demonstrates the potential of rapid prototyping in powder characterization by introducing a fast and affordable approach for exploring and trouble-shooting powder caking.

An image may be defined as a two-dimensional function f(x,y) where x and y represent the spatial coordinates and the function (f) represents the amplitude at any given pair of coordinates (x,y). The amplitude (f) is also often referred to as the gray level or the intensity of that point in the image. A digital image is thus composed of a finite number of these x, y elements where all of them have an exact location and value. These picture elements are often referred to as pixels. 3D images may subsequently be defined by a three-dimensional function (x,y,z) and the individual elements are often referred to as voxels. The image quality is an important parameter in the field of radiology and the term spatial resolution refers to the capability of differentiating two neighboring objects in the image. Another important resolution property is the temporal resolution, which refers to the quality of the imaging with respect to time.

PURPOSE:Asthma is a prevalent lung disorder that cause heavy burdens globally. Inhalation medicaments can relieve symptoms, improve lung function and, thus, the quality of life. However, it is well-documented that patients often do not get the prescribed dose out of an inhaler and the deposition of drug is suboptimal, due to incorrect handling of the device and wrong inhalation technique. This study aims to design and fabricate an acoustic dry powder inhaler (ADPI) for monitoring inhalation flow and related drug administration in order to evaluate whether the patient receives the complete dose out of the inhaler.METHODS:The devices were fabricated using 3D printing and the impact of the acoustic element geometry and printing resolution on the acoustic signal was investigated. Commercial Foradil (formoterol fumarate) capsules were used to validate the availability of the ADPI for medication dose tracking. The acoustic signal was analysed with Partial-Least-Squares (PLS) regression.RESULTS:Indicate that specific acoustic signals could be generated at different air flow rates using a passive acoustic element with specific design features. This acoustic signal could be correlated with the PLS model to the air flow rate. A more distinct sound spectra could be acquired at higher printing resolution. The sound spectra from the ADPI with no capsule, a full capsule and an empty capsule are different which could be used for medication tracking.CONCLUSIONS:This study shows that it is possible to evaluate the medication quality of inhaled medicaments by monitoring the acoustic signal generated during the inhalation process.

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

Medical cannabis has shown to be effective in various diseases that have not successfully been treated with other marketed drug products. However, the dose of cannabis is highly individual and additionally, medical cannabis is prone to misuse. To combat these challenges, the concept of data-enriched edible pharmaceuticals (DEEP) is introduced. Quick Response (QR) code patterns containing lipophilic cannabinoids, i.e., cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), were printed using a desktop inkjet printer. This allows for simultaneously printing an individual dose and encapsulating information relevant to the end-users and other stakeholders in a single dosage unit, which is readable by a standard smartphone. Different doses of CBD and THC were incorporated in the DEEP by printing various (1-10) layers of the cannabinoid-containing ink on porous substrates, i.e., solid foams, prepared by solvent casting and subsequent freeze-drying. The printed DEEP were still readable after 8 weeks of storage in dry and cold conditions. This approach of 'in-drug labeling' instead of 'drug package labeling' provides a new possibility for developing a more efficient supply chain of pharmaceuticals and safer medication schemes by increasing the traceability of drug products at a single dosage unit level.

The potential of fused filament fabrication (FFF) for the administration of active pharmaceutical compounds is a recent approach to develop complex and custom-made drug delivery systems (DDSs). However, the FFF technology is characterized by certain limitations, which are associated with the nature of the process, i.e., the required mechanical properties of the feedstock, as well as the thermal stability of the incorporated polymers, excipients and active compounds. Thus, hybrid DDSs have been recently introduced, to overcome these boundaries. The concept of these systems is defined by the effective coupling of FFF with conventional manufacturing technologies, as a novel pathway to expand the available pool of raw materials and pharmaceutical applications of FFF.

Purpose: The aim of this work is to demonstrate that convolutional neural networks (CNN) can be applied to extremely sparse image libraries by subdivision of the original image datasets. Methods: Image datasets from a conventional digital camera was created and scanning electron microscopy (SEM) measurements were obtained from the literature. The image datasets were subdivided and CNN models were trained on parts of the subdivided datasets. Results: The CNN models were capable of analyzing extremely sparse image datasets by utilizing the proposed method of image subdivision. It was furthermore possible to provide a direct assessment of the various regions where a given API or appearance was predominant.

This chapter is focused on the latest advances within additive manufacturing of oral solid dosage forms. Oral dosage forms are the most convenient drug delivery systems (DDS), because they have a compact design and relatively long storage stability, and patients have better compliance and adherence with orally administered medicines than with, e.g., injections or suppository.

Co-amorphous systems comprising low-molecular weight drugs and co-formers constitute an interesting approach to optimize pharmaceutical performance of drugs with low aqueous solubility. Within the different types of co-amorphous systems, the combination of a drug with its own salt may be an attractive formulation option due the absence of any inactive co-formers. The aim of this study was to investigate the possibility of forming a co-amorphous system from naproxen (NAP) and its sodium salt (NAP(Na)). Ball milling of NAP and NAP(Na) at equal molar ratio resulted in the formation of a co-amorphous system whilst NAP and NAP(Na) alone were crystalline following both, ball milling and melt quenching. Infrared spectroscopy and physical stability testing revealed that intermolecular interactions were able to maintain the ball milled NAP-NAP(Na) system amorphous for 2 months at 40 °C. Surprisingly, the dissolution rate of co-amorphous NAP-NAP(Na) was only intermediate between those of crystalline NAP and crystalline NAP(Na). In situ Raman spectroscopic measurements indicated an initial phase separation of the co-amorphous form to NAP and NAP(Na) followed by dissociation of sodium from NAP(Na) and crystallization to NAP. These findings contribute to the design of co-amorphous formulations with the combination of a drug and its own salt.

The estimation of inhalation flow rate (IFR) using acoustic devices has recently received attention. While existing work often assumes that the microphone is placed at a fixed distance from the acoustic device, this assumption does not hold in real settings. This leads to poor estimation of the IFR since the received acoustic energy varies significantly with the distance. Despite the fact that the acoustic source is passive and only one microphone is used, we show in this paper that the distance can be estimated by exploiting the inhaler actuation sound, generated when releasing the medication. Indeed, this sound is used as a reference acoustic signal which is leveraged to estimate the distance in real settings. The resulting IFR estimation is shown to be highly accurate (R2 = 80.3%).

Small interfering RNA (siRNA) is regarded as one of the most powerful tools for the treatment of various diseases by downregulating the expression of aberrant proteins. Delivery vehicle is often necessary for getting siRNA into the cells. Nanocomplex using polyamidoamine (PAMAM) is regarded a promising approach for the delivery of siRNA. The size of siRNA nanocomplexes is a critical attribute in order to achieve high gene silencing efficiency in vivo. Microfluidics provides advantages in the preparation of siRNA nanocomplexes due to better reproducibility and a potential for more robust process control. The mixing efficiency of siRNA and PAMAM is different in microfluidics systems with different geometries, therefore, resulting in nanocomplexes with varying size attributes. In this study, hydrodynamic flow focusing microfluidic chips with different channel designs, i.e. diameters/widths, channel shapes (cylindrical/rectangular) and inter-channel spacings were optimized in silico and rapidly prototyped using 3D printing and finally, used for production of siRNA nanocomplexes. The fluid mixing inside the microfluidic chips was simulated using the finite element method (FEM) with the single-phase laminar flow interface in connection with the transport of diluted species interface. The digital design and optimization of microfluidic chips showed consistency with experimental results. It was concluded that the size of siRNA nanocomplexes can be controlled by adjusting the channel geometry of the microfluidic chips and the simulation with FEM could be used to facilitate the design and optimization of microfluidic chips in order to produce nanocomplexes with desirable attributes.