The Future of Pharmaceutical Manufacturing Sciences

From lab to industrial development of lipid nanocarriers using quality by design approach

Lipid nanocarriers have attracted a great deal of interest in the delivery of therapeutic molecules. Despite their many advantages, compliance with quality standards and reproducibility requirements still constrain their industrial production. The relatively high failure rate in lipid nanocarrier research and development can be attributed to immature bottom-up manufacturing practices, leading to suboptimal control of quality attributes. Recently, the pharmaceutical industry has moved toward quality-driven manufacturing, emphasizing the integration of product and process development through the principles of quality by design. Quality by design in the pharmaceutical industry involves a thorough understanding of the quality profile of the target product and involves an assessment of potential risks during the design and development phases of pharmaceutical dosage forms. By identifying essential quality characteristics, such as the active ingredients, excipients and manufacturing processes used during research and development, it becomes possible to effectively control these aspects throughout the life cycle of the drug. Successful commercialization of lipid nanocarriers can be achieved if large-scale challenges are addressed using the QbD approach. QbD has become an essential tool because of its advantages in improving processes and product quality. The application of the QbD approach to the development of lipid nanocarriers can provide comprehensive and remarkable knowledge enabling the manufacture of high-quality products with a high degree of regulatory flexibility. This article reviews the basic considerations of QbD and its application in the laboratory and large-scale development of lipid nanocarriers. Furthermore, it provides forward-looking guidance for the industrial production of lipid nanocarriers using the QbD approach.

Optimized detection of Salmonella typhimurium using aptamer lateral flow assay

Salmonella typhimurium, a pathogenic bacterium with significant implications in medicine and the food industry, poses a substantial threat by causing foodborne illnesses such as typhoid fever. Accurate diagnosis of S. typhimurium is challenging due to its overlap symptoms with various diseases. This underscores the need for a precise and efficient diagnostic approach. In this study, we developed a biosensor using the Taguchi optimization method based on aptamer lateral flow assay (LFA) for the detection of S. typhimurium. Therefore, signal probe and nanobioprobe were designed using anti-Salmonella aptamer, conjugated with gold nanoparticles (GNPs), and used in LFA. The strategy of this test is based on a competitive format between the bacteria immobilized on the membrane and the bacteria present in the tested sample. Moreovere, the optimization of various factors affecting the aptamer LFA, including the concentration of bacteria (immobilized and into the sample) and the concentration of nanobioprop, were performed using the Taguchi test designing method. The data showed that the optimal conditions for the LFA reaction was 108 CFU/mL of immobilized bacteria and 1.5 μg/μL of nanobioprop concentration. Then, the visual detection limit of S. typhimurium was estimated as 105 CFU/mL. The reaction results were obtained within 20 min, and there were no significant cross-reactions with other food pathogens. In conclusion, the aptamer-LFA diagnostic method, optimized using the Taguchi approach, emerges as a reliable, straightforward, and accurate tool for the detection of S. typhimurium. Overall, this method can be a portable diagnostic kit for the detection and identification of bacteria.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Soft sensor for viable cell counting by measuring dynamic oxygen uptake rate

Regulatory authorities in biopharmaceutical industry emphasize process design by process understanding but applicable tools that are easy to implement are still missing. Soft sensors are a promising tool for the implementation of the Quality by Design (QbD) approach and Process Analytical Technology (PAT). In particular, the correlation between viable cell counting and oxygen consumption was investigated, but problems remained: Either the process had to be modified for excluding CO2 in pH control, or complex kLa models had to be set up for specific processes. In this work, a non-invasive soft sensor for simplified on-line cell counting based on dynamic oxygen uptake rate was developed with no need of special equipment. The dynamic oxygen uptake rates were determined by automated and periodic interruptions of gas supply in DASGIP® bioreactor systems, realized by a programmed Visual Basic script in the DASware® control software. With off-line cell counting, the two parameters were correlated based on linear regression and led to a robust model with a correlation coefficient of 0.92. Avoidance of oxygen starvation was achieved by gas flow reactivation at a certain minimum dissolved oxygen concentration. The soft sensor model was established in the exponential growth phase of a Chinese Hamster Ovary fed-batch process. Control studies showed no impact on cell growth by the discontinuous gas supply. This soft sensor is the first to be presented that does not require any specialized additional equipment as the methodology relies solely on the direct measurement of oxygen consumed by the cells in the bioreactor.

Trend of pharmaceuticals 3D printing in the Middle East and North Africa (MENA) region: An overview, regulatory perspective and future outlook

The traditional method of producing medicine using the "one-size fits all" model is becoming a major issue for pharmaceutical manufacturers due to its inability to produce customizable medicines for individuals' needs. Three-dimensional (3D) printing is a new disruptive technology that offers many benefits to the pharmaceutical industry by revolutionizing the way pharmaceuticals are developed and manufactured. 3D printing technology enables the on-demand production of personalized medicine with tailored dosage, shape and release characteristics. Despite the lack of clear regulatory guidance, there is substantial interest in adopting 3D printing technology in the large-scale manufacturing of medicine. This review aims to evaluate the research efforts of 3D printing technology in the Middle East and North Africa (MENA) region, with a particular emphasis on pharmaceutical research and development. Our analysis indicates an upsurge in the overall research activity of 3D printing technology but there is limited progress in pharmaceuticals research and development. While the MENA region still lags, there is evidence of the regional interest in expanding the 3D printing technology applications in different sectors including pharmaceuticals. 3D printing holds great promise for pharmaceutical development within the MENA region and its advancement will require a strong collaboration between academic researchers and industry partners in parallel with drafting detailed guidelines from regulatory authorities.

Is it advantageous to use quality by design (QbD) to develop nanoparticle-based dosage forms for parenteral drug administration?

Parenteral administration is one of the most commonly used drug delivery routes for nanoparticle-based dosage forms, such as lipid-based and polymeric nanoparticles. For the treatment of various diseases, parenteral administration include intravenous, subcutaneous, and intramuscular route. In drug development phase, multiparameter strategy with a focus on drug physicochemical properties and the specificity of the administration route is required. Nanoparticle properties in terms of size and targeted delivery, among others, are able to surpass many drawbacks of conventional dosage forms, but these unique properties can be a bottleneck for approval by regulatory authorities. Quality by Design (QbD) approach has been widely utilized in development of parenteral nanoparticle-based dosage forms. It fosters knowledge of product and process quality by involving sound scientific data and risk assessment strategies. A full and comprehensive investigation into the state of implementation and applications of the QbD approach in these complex drug products can highlight the gaps and challenges. In this review, the analysis of critical attributes and Design of Experiment (DoE) approach in different nanoparticulate systems, together with the proper utilization of Process Analytical Technology (PAT) applications are described. The essential of QbD approach for the design and development of nanoparticle-based dosage forms for delivery via parenteral routes is discussed thoroughly.

Estimation of the concentration boundary layer adjacent to a flat surface using computational fluid dynamics

Dissolution-permeation (D/P) experiments are widely used during preclinical development due to producing results with better predictability than traditional monophasic experiments. However, it is difficult to compare absorption across in vitro setups given the propensity to only report apparent permeability. We therefore developed an approach to predict the concentration boundary layer for any D/P device by using computational fluid dynamics (CFD). The Navier-Stokes and continuity equation in 2D were solved numerically in MATLAB and by finite element methods in COMSOL v6.1 to predict the momentum [Formula: see text] and concentration ηg boundary layer for a flow over a flat plate, i.e. the classical Blasius boundary layer flow. A MATLAB algorithm was developed to calculate the edge of either boundary layer. The methodology to determine the concentration boundary layer based on Blasius's analysis provided an accurate estimate for both [Formula: see text] and ηg, resulting in, [Formula: see text] , at high Schmidt numbers (Sc ∼ 1000) within 14 % of the Blasius solution and 6.6 % of the accepted Schmidt number correlation ( [Formula: see text] ). The methodology based on the Blasius analysis of the concentration boundary layer using velocity and concentration profiles computed using CFD presented herein will enable characterization/analysis of complex D/P apparatuses used in preclinical development, where an analytical solution may not be available.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty

The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive - which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty.

Two-dimensional printing of nanoparticles as a promising therapeutic method for personalized drug administration

The necessity for personalized patient treatment has drastically increased since the contribution of genes to the differences in physiological and metabolic state of individuals have been exposed. Different approaches have been considered so far in order to satisfy all of the diversities in patient needs, yet none of them have been fully implemented thus far. In this framework, various types of 2D printing technologies have been identified to offer some potential solutions for personalized medication, which development is increasing rapidly. Accurate drug-on-demand deposition, the possibility of consuming multiple drug substances in one product and adjusting individual drug concentration are just some of the few benefits over existing bulk pharmaceuticals manufacture, which printing technologies brings. With inclusion of nanotechnology by printing nanoparticles from its dispersions some further opportunities such as controlled and stimuli-responsive drug release or targeted and dose depending on drug delivery were highlighted. Yet, there are still some challenges to be solved before such products can reach the pharmaceutical market. In those terms mostly chemical, physical as well as microbiological stability concerns should be answered, with which 2D printing technology could meet the treatment needs of every individual and fulfill some existing drawbacks of large-scale batch production of pharmaceuticals we possess today.

Impact of GAMP 5, data integrity and QbD on quality assurance in the pharmaceutical industry: How obvious is it?

In the pharmaceutical industry, it is essential to ensure the safety and efficacy of medicinal products. Therefore a robust quality assurance framework is needed. This manuscript examines the impact of GAMP 5 and data integrity (DI) on quality assurance, while also highlighting the role of quality by design (QbD) principles. GAMP 5 is a widely used framework for validating automated systems that establishes quality assurance practices. DI guarantees the reliability of data collected throughout various stages of drug development. The integration of QbD principles promotes a systematic approach to development that emphasizes a deep understanding of critical quality attributes, risk management, and continuous improvement. With their implementation, organizations are able to meet regulatory requirements and provide safe medications to patients worldwide.

Can Continuous Manufacturing of Topical Semisolids by Hot Melt Extrusion Soon Be a Reality?

For more than five decades, pharmaceutical manufacturers have been relying heavily on batch manufacturing that is a sequential, multistep, laborious, and time-consuming process. However, late advances in manufacturing technologies have prompted manufacturers to consider continuous manufacturing (CM) is a feasible manufacturing process that encompasses fewer steps and is less tedious and quick. Global regulatory agencies are taking a proactive role to facilitate pharmaceutical industries to adopt CM that assures product quality by employing robust manufacturing technologies encountering fewer interruptions, thereby substantially reducing product failures and recalls. However, adopting innovative CM is known to pose technical and regulatory challenges. Hot melt extrusion (HME) is one such state-of-the-art enabling technology that facilitates CM of diverse pharmaceutical dosage forms, including topical semisolids. Efforts have been made to continuously manufacture semisolids by HME integrating the principles of Quality by Design (QbD) and Quality Risk Management (QRM) and deploying Process Analytical Technologies (PAT) tools. Attempts have been made to systematically elucidate the effect of critical material attributes (CMA) and critical process parameters (CPP) on product critical quality attributes (CQA) and Quality Target Product Profiles (QTPP) deploying PAT tools. The article critically reviews the feasibility of one of the enabling technologies such as HME in CM of topical semisolids. The review highlights the benefits of the CM process and challenges ahead to implement the technology to topical semisolids. Once the CM of semisolids adopting melt extrusion integrated with PAT tools becomes a reality, the process can be extended to manufacture sterile semisolids that usually involve more critical processing steps.

Evaluation of the influence of material properties and process parameters on granule porosity in twin-screw wet granulation

In recent years, continuous twin-screw wet granulation (TSWG) is gaining increasing interest from the pharmaceutical industry. Despite the many publications on TSWG, only a limited number of studies focused on granule porosity, which was found to be an important granule property affecting the final tablet quality attributes, e.g. dissolution. In current study, the granule porosity along the length of the twin-screw granulator (TSG) barrel was evaluated. An experimental set-up was used allowing the collection of granules at the different TSG compartments. The effect of active pharmaceutical ingredient (API) properties on granule porosity was evaluated by using six formulations with a fixed composition but containing APIs with different physical-chemical properties. Furthermore, the importance of TSWG process parameters liquid-to-solid (L/S) ratio, mass feed rate and screw speed for the granule porosity was evaluated. Several water-related properties as well as particle size, density and flow properties of the API were found to have an important effect on granule porosity. While the L/S ratio was confirmed to be the dictating TSWG process parameter, granulator screw speed was also found to be an important process variable affecting granule porosity. This study obtained crucial information on the effect of material properties and process parameters on granule porosity (and granule formation) which can be used to accelerate TSWG process and formulation development.

Elucidation of Granulation Mechanisms along the Length of the Barrel in Continuous Twin-Screw Melt Granulation

In the pharmaceutical industry, innovative continuous manufacturing technologies such as twin-screw melt granulation (TSMG) are gaining more and more interest to process challenging formulations. To enable the implementation of TSMG, more elucidation of the process is required and this study provides a better understanding of the granule formation along the length of the barrel. By sampling at four different zones, the influence of screw configuration, process parameters and formulation is investigated for the granule properties next to the residence time distribution. It showed that conveying elements initiate the granulation by providing a limited heat transfer into the powder bed. In the kneading zones, the consolidation stage takes place, shear elongation combined with breakage and layering is occurring for the reversed configurations and densification with breakage and layering for the forward and neutral configurations. Due to the material build-up in the reversed configurations, these granules are larger, stronger, more elongated and less porous due to the higher degree of shear and densification. This configuration also shows a significantly longer residence time compared to the forward configuration. Hence, the higher level of shear and the longer period of time enables more melting of the binder resulting in successful granulation.

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.

Comparing Integrated Continuous Process "LaVortex®" and Conventional Batch Processes for the Pharmaceutical Manufacturing of Acetaminophen Oral Dosage Formulations: Challenges and Pharmaceutical Properties of the Granular and Tableted Products

LaVortex® was developed as a novel free-flow continuous granulation/drying (CGD) system. In this study, we compared the advantages and disadvantages of granules prepared by continuous and batchwise manufacturing systems. Granules containing 30 % acetaminophen were manufactured under various operating conditions using CGD system, with comparison granules manufactured using conventional batch systems that involve a combination of fluid bed granulation (FG), agitation granulation (AG), continuous drying, fluid bed drying, and/or shelf drying, after which the pharmaceutical properties of each type of manufactured granule were evaluated. Cumulative particle-size distributions were determined by sieving, powder flowabilities were determined by angle of repose measurements, and scanning electron microscopy was employed to examine granule morphologies. The CGD system produced fine-to-large spherical or ellipsoidal granules that exhibited excellent powder fluidities and tabletabilities that are almost identical to those of AG granules. Moreover, the CGD granules exhibited better powder flowability than the FG granules. The addition of water promoted CGD-granule growth and improved significantly powder flowability, and did a little in tabletability. Small spherical granules with good fluidity suitable for fine-particle-coating core materials, or large granules with excellent fluidity and tabletability, were prepared by adjusting the values of the elemental parameters of the CGD process.

Current and future prospective of pharmaceutical manufacturing in Saudi Arabia

This observational descriptive study that was carried out with the objective of exploring the contribution of the local pharmaceutical industry to the Saudi drug security. Using a drug formulary provided from the Saudi Food and Drug Authority, containing all registered pharmaceutical products available in Saudi Arabia, we extracted information about drug class, drug type, country and place of manufacturing, shelf-life and price. Results showed that the majority of drugs in the market are manufactured in Europe (43.86%), followed by Saudi Arabia (22.55%), China and India (20.47%), Americas (10.24%), and other nations (2.61%). Most of the manufactured drugs were prescription drugs (90.62%). In this work, the local pharmaceutical industry with the highest percentage of contribution to local drug security was Pharmaceutical Solution Industries (PSI), representing the 5% of the items available in the Saudi market. The second highest percentage was 4% by TABUK Pharmaceutical Manufacturing CO., followed by SPIMACO (3%), JAMJOOM pharmaceutical company (2%), Riyadh pharma (2%), and Jazeera pharmaceutical industries (2%). In addition, results from this study provide information about the most essential pharmaceutical products that needs to be nationally manufactured or increased in production in order to rise the contribution of local pharmaceutical industries in Saudi drug security. Unfortunately, the small contribution of the Saudi pharmaceutical industry in local drug security increases the burden on the Kingdom's annual budget due to the over-reliance on international pharmaceuticals.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Heat Transfer Model and Soft Sensing for Segmented Fluidized Bed Dryer

The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequently, a soft sensor is established by combining data sources, a real-time data communication method, and the developed drying model, and it shows the capability of predicting real-time LOD, where the error of end-point LOD is within 0.5 wt.%. The work provides detailed steps and applicable tools for developing a soft sensor, and the online deployment of the soft sensor could support continuous production in the Seg-FBD by enabling visualization of process status and determination of process end point.

Deep learning in drug discovery: an integrative review and future challenges

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Itraconazole Amorphous Solid Dispersion Tablets: Formulation and Compaction Process Optimization Using Quality by Design Principles and Tools

BCS Class II drugs, such as itraconazole (ITZ), exhibit poor solubility (1–4 ng/mL) and so require solubility enhancement. Therefore, ITZ and Kollidon^® VA64 (KOL) amorphous solid dispersions (ASDs) were produced using hot-melt extrusion (HME) to improve ITZ’s poor solubility. A novel strategy for tablet formulations using five inorganic salts was investigated (KCl, NaCl, KBr, KHCO₃ and KH₂PO₄). These kosmotopric salts are thought to compete for water hydration near the polymer chain, hence, preventing polymer gelation and, therefore, facilitating disintegration and dissolution. Out of all the formulations, the KCl containing one demonstrated acceptable tensile strength (above 1.7 MPa), whilst providing a quick disintegration time (less than 15 min) and so was selected for further formulation development through a design of the experiment approach. Seven ITZ-KOL-ASD formulations with KCl were compacted using round and oblong punches. Round tablets were found to disintegrate under 20 min, whereas oblong tablets disintegrated within 10 min. The round tablets achieved over 80% ITZ release within 15 min, with six out of seven formulations achieving 100% ITZ release by 30 min. It was found that tablets comprising high levels of Avicel^® pH 102 (30%) and low levels of KCl (5%) tend to fail the disintegration target due to the strong bonding capacity of Avicel^® pH 102. The disintegration time and tensile strength responses were modeled to obtain design spaces (DSs) relevant to both round and oblong tablets. Within the DS, several formulations can be chosen, which meet the Quality Target Product Profile (QTPP) requirements for immediate-release round and oblong tablets and allow for flexibility to compact in different tablet shape to accommodate patients’ needs. It was concluded that the use of inorganic salts, such as KCl, is the key to producing tablets of ITZ ASDs with fast disintegration and enhanced dissolution. Overall, ITZ-KOL-ASD tablet formulations, which meet the QTPP, were achieved in this study with the aid of Quality by Design (QbD) principles for formulation and compaction process development and optimization.

Identification of continuous twin-screw melt granulation mechanisms for different screw configurations, process settings and formulation

Twin-screw melt granulation (TSMG) is a promising continuous manufacturing technology for the processing of high drug load formulations and to formulate heat- and moisture-sensitive active pharmaceutical ingredients (APIs). This study evaluates the influence of process parameters for TSMG, mainly focusing on the effect of the screw configuration combined with screw speed, throughput and barrel temperature, to elucidate the melt granulation mechanisms. For the kneading zone, the stagger angle was varied between 30°, 60° and 90°, and investigated for both the forward and the reversed direction. In addition to the process parameters, the influence of the formulation differing in their API-binder miscibility was evaluated. As responses, the granule (size, friability and porosity) and process properties such as torque were evaluated, indicating that the screw configuration is the most influential factor. Nucleation, consolidation and breakage are the granulation mechanisms for the forward and the neutral configuration, while consolidation and densification with shear elongation are identified for the reversed configuration. The formulations differ mainly in the forward and neutral configuration since the immiscible formulation shows a bimodal granule size distribution with a larger fraction of fines and weaker granules is obtained. For the reversed configuration, similar granulation mechanisms are seen for both formulations.

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing

A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term variabilities and to enhance underlying long-term trends. Two approaches are presented for data analysis: first, anomaly detection using independent component analysis (ICA), where clusters of outliers are identified. The frequency and timing of such outliers yield important insights regarding maintenance schedules and actions. The second approach enables condition monitoring using principal component analysis (PCA). Long-term operational baselines are identified and shifts therein are linked with different process and equipment faults. This approach highlights the impact of equipment deterioration on shifting operational data baselines and shows the potential for the combined application of ICA and PCA for equipment condition monitoring. It can be applied within predictive maintenance applications where the installation of new specialized sensors is difficult, like in the pharmaceutical industry.

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Effect of shape on the physical properties of pharmaceutical tablets

Despite a well-established process understanding, quality issues for compressed oral solid dosage forms are frequently encountered during various drug product development and production stages. In the current work, a non-destructive contact ultrasonic experimental rig integrated with a collaborative robot arm and an advanced vision system is presented and employed to quantify the effect of the shape of a compressed tablet on its mechanical properties. It is observed that these properties are affected by the tablet geometric shapes and found to be linearly sensitive to the compaction pressures. It is demonstrated that the presented approach significantly improves the repeatability of the experimental waveform acquisition. In addition, with the increased confidence levels in waveform acquisition accuracy and corresponding pressure and shear wave speeds due to improved measurement repeatability, we conclude that pharmaceutical compact materials can indeed have a negative Poisson's ratio, therefore can be auxetic. The presented technique and instrument could find critical applications in continuous tablet manufacturing, and its real-time quality monitoring as measurement repeatability has been significantly improved, minimizing product quality variations.

Real-Time Monitoring of Critical Quality Attributes during High-Shear Wet Granulation Process by Near-Infrared Spectroscopy Effect of Water Addition and Stirring Speed on Pharmaceutical Properties of the Granules

To produce high-quality pharmaceuticals, a real-time monitoring method for the high-shear wet granulation process (HSWG) was developed based on near-infrared spectroscopy (NIRS). Samples consisting of lactose, potato starch, and hydroxypropyl cellulose were prepared using HSWG with varying amounts of purified water (80, 90, and 100 mL) and impeller speed (200, 400, and 600 rpm), which produces granules of different characteristics. Twelve batches of samples were used for the calibration and nine batches were used for validation. After drying, the median particle size (D50), tapped density (TD), and Hauser ratio (HR) were measured. The best calibration models to predict moisture content (MC), D50, TD, and HR were determined based on pretreated NIR spectra using partial least squares regression analysis (PLSR). The temporal changes in the pharmaceutical properties under different amounts of water added and stirring speed were monitored in real time using NIRS/PLSR. Because the most important critical quality attribute (CQA) in the process was MC, granule characteristics such as D50, TD, and HR were analyzed with respect to MC. They might be used as robust and simple monitoring methods based on MC to evaluate the pharmaceutical properties of HSWG granules.

A Multivariate Formulation and Process Development Platform for Direct Compression

The efficient development of robust tableting processes is challenging due to the lack of mechanistic understanding on the impact of raw material properties and process parameters on tablet quality. The experimental determination of the effect of process and formulation parameters on tablet properties and subsequent optimization is labor-intensive, expensive and time-consuming. The combined use of an extensive raw material property database, process simulation tools and multivariate modeling allows more efficient and more optimized development of the direct compression (DC) process. In this study, key material attributes and in-process mechanical properties with a potential effect on tablet processability and tablet properties were identified. In a first step, an extensive characterization of 55 raw materials (over 100 material descriptors) (Van Snick et al., 2018) and 26 formulation blends (31 material descriptors) (Dhondt et al., 2022) was performed. These blends were subsequently compacted on a compaction simulator under multiple process conditions through a design of experiments (DoE) approach. A T-shaped partial least squares (T-PLS) model was established which correlates tablet quality attributes with process settings, raw material properties and blend ratios. During future development of the DC formulation and process for a new active pharmaceutical ingredient (API), this model can then be used to provide a preliminary formulation and compaction process settings as starting point to be further optimized during development trials based on well-defined raw material characteristics and compaction tests. This study hence contributes to a better understanding on the impact of raw material properties and process settings on a DC process and final properties of the produced tablets; and provides a platform allowing a more efficient and more optimized development of a robust tableting process.

Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.

Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning

This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control.

Mechanical Characterization of Pharmaceutical Powders by Nanoindentation and Correlation with Their Behavior during Grinding

Controlling the size of powder particles is pivotal in the design of many pharmaceutical forms and the related manufacturing processes and plants. One of the most common techniques for particle size reduction in the process industry is powder milling, whose efficiency relates to the mechanical properties of the powder particles themselves. In this work, we first characterize the elastic and plastic responses of different pharmaceutical powders by measuring their Young modulus, the hardness, and the brittleness index via nano-indentation. Subsequently, we analyze the behavior of those powder samples during comminution via jet mill in different process conditions. Finally, the correlation between the single particle mechanical properties and the milling process results is illustrated; the possibility to build a predictive model for powder grindability, based on nano-indentation data, is critically discussed.

Stabilizing vaccines via drying: Quality by design considerations

Pandemics and epidemics are continually challenging human beings' health and imposing major stresses on the societies particularly over the last few decades, when their frequency has increased significantly. Protecting humans from multiple diseases is best achieved through vaccination. However, vaccines thermal instability has always been a hurdle in their widespread application, especially in less developed countries. Furthermore, insufficient vaccine processing capacity is also a major challenge for global vaccination programs. Continuous drying of vaccine formulations is one of the potential solutions to these challenges. This review highlights the challenges on implementing the continuous drying techniques for drying vaccines. The conventional drying methods, emerging technologies and their adaptation by biopharmaceutical industry are investigated considering the patented technologies for drying of vaccines. Moreover, the current progress in applying Quality by Design (QbD) in each of the drying techniques considering the critical quality attributes (CQAs), critical process parameters (CPPs) are comprehensively reviewed. An expert advice is presented on the required actions to be taken within the biopharmaceutical industry to move towards continuous stabilization of vaccines in the realm of QbD.

Big data collection in pharmaceutical manufacturing and its use forproduct quality predictions

Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual production batches of selected medicine. This dataset includes incoming raw material quality results, compression process time series and final product quality results for the selected product. The data is highly valuable for it provides an insight into every 10 seconds of the process trajectory for 1005 actual production batches along with product quality collected over several years. It therefore offers an opportunity to develop advanced analysis models and procedures which would lead to the omission of current conventional and time consuming laboratory testing. Benefits for both the industry and patient are obvious: reducing product lead times and costs of manufacture.

Measurement(s)	Incoming raw material quality (particle size distribution, water content, impurities level, residual solvents, pH) • In process control measurements of tablet core and film coated tablets (weight, thickness, diameter, hardness, yield of a process) • Final medicine quality on a representative sample of film coated tablets (drug release in defined time, active ingredient content, impurities level, residual solven content) • Process time series of selected tablet compression parameters (every 10 s of the compression process)
Technology Type(s)	Laboratory based analysis (particle sizer using laser diffraction, loss on drying method, HPLC method, GC method) • Automatic IPC check machine (combining balance, hardness, thickness and diameter measurements) • HPLC (High performance liquid chromatography), GC (gas chromatography) • Tablet compression machine calibrated sensors for the following main parameters: main and pre-compression force, fill depth, cylindrycal height, ejection force, number of wasted tablets.
Factor Type(s)	batch • code • strength • size • start • api_code • api_batch • smcc_batch • lactose_batch • starch_batch • api_water • api_total_impurities • api_l_impurity • api_content • api_ps01 • api_ps05 • api_ps09 • lactose_water • lactose_sieve0045 • lactose_sieve015 • lactose_sieve025 • smcc_water • smcc_td • smcc_bd • smcc_ps01 • smcc_ps05 • smcc_ps09 • starch_ph • starch_water • tbl_min_hardness • tbl_max_hardness • tbl_av_hardness • tbl_min_thickness • tbl_max_thickness • fct_min_thickness • fct_max_thickness • tbl_min_weight • tbl_max_weight • tbl_rsd_weight • fct_rsd_weight • fct_min_hardness • fct_max_hardness • fct_av_hardness • tbl_tensile • fct_tensile • tbl_yield • batch_yield • time series: tbl_speed • time series: fom • time series: main_comp • time series: tbl_fill • time series: SREL • time series: pre_comp • time series: produced • time series: waste • time series: cyl_main • time series: cyl_pre • time series: stiffness • time series: ejection
Sample Characteristic - Organism	Selected medicine
Sample Characteristic - Environment	manufacturing process
Sample Characteristic - Location	Pharmaceutical industry

Therapeutic RNA-silencing oligonucleotides in metabolic diseases

Recent years have seen unprecedented activity in the development of RNA-silencing oligonucleotide therapeutics for metabolic diseases. Improved oligonucleotide design and optimization of synthetic nucleic acid chemistry, in combination with the development of highly selective and efficient conjugate delivery technology platforms, have established and validated oligonucleotides as a new class of drugs. To date, there are five marketed oligonucleotide therapies, with many more in clinical studies, for both rare and common liver-driven metabolic diseases. Here, we provide an overview of recent developments in the field of oligonucleotide therapeutics in metabolism, review past and current clinical trials, and discuss ongoing challenges and possible future developments.

Direct Compaction Drug Product Process Modeling

Most challenges during the development of solid dosage forms are related to the impact of any variations in raw material properties, batch size, or equipment scales on the product quality and the control of the manufacturing process. With the ever pertinent restrictions on time and resource availability versus heightened expectations to develop, optimize, and troubleshoot manufacturing processes, targeted and robust science-based process modeling platforms are essential. This review focuses on the modeling of unit operations and practices involved in batch manufacturing of solid dosage forms by direct compaction. An effort is made to highlight the key advances in the past five years, and to propose potentially beneficial future study directions.

In-line particle size measurement based on image analysis in a fully continuous granule manufacturing line for rapid process understanding and development

The present paper serves as a demonstration how an in-line PAT tool can be used for rapid and efficient process development in a fully continuous powder to granule line consisting of an interconnected twin-screw wet granulator, vibrational fluid bed dryer, and a regranulating mill. A new method was investigated for the periodic in-line particle size measurement of high mass flow materials to obtain real-time particle size data of the regranulated product. The system utilises a vibratory feeder with periodically altered feeding intensity in order to temporarily reduce the mass flow of the material passing in front of the camera. This results in the drastic reduction of particle overlapping in the images, making image analysis a viable tool for the in-line particle size measurement of high mass-flow materials. To evaluate the performance of the imaging system, the effect of several milling settings and the liquid-to-solid ratio was investigated on the product's particle size in the span of a few hours. The particle sizes measured with the in-line system were in accordance with the expected trends as well as with the results of the off-line reference particle size measurements. Based on the results, the in-line imaging system can serve as a PAT tool to obtain valuable real-time information for rapid process development or quality assurance.

The Effect of Binder Types on the Breakage and Drying Behavior of Granules in a Semi-Continuous Fluid Bed Dryer after Twin Screw Wet Granulation

Current study investigated the effect of different binder types on the granule drying process and the granule breakage behavior in a semi-continuous fluid bed dryer integrated in the C25 ConsiGma-system. The studied binders (i.e. hydroxypropyl pea starch, hydroxypropyl methylcellulose E15, polyvinylpyrrolidone K12, and starch octenyl succinate CO 01) required different liquid amounts to produce similar granule quality. These different liquid requirements were translated into different drying conditions for each binder to result in sufficiently dry granules at the end of a drying cycle. By comparing the size distribution of the granules before entering and after exiting the fluid bed dryer, granule breakage could be evaluated. No effect of the binder type on the granule breakage during drying was observed. However, differences in granule breakage were observed for the binders when processed with the horizontal set-up of the C25 system, as granule breakage during pneumatic transport depended on the binder type. Only one binder (hydroxypropyl pea starch) allowed to avoid granule breakage during the entire process. Furthermore, this research showed that the drying process was mainly steered by the liquid requirements for granulation, and that these liquid requirements depended on the binder used.

Development of Immediate Release Tablets Containing Calcium Lactate Synthetized from Black Sea Mussel Shells

Nowadays, the use of marine by-products as precursor materials has gained great interest in the extraction and production of chemical compounds with suitable properties and possible pharmaceutical applications. The present paper presents the development of a new immediate release tablet containing calcium lactate obtained from Black Sea mussel shells. Compared with other calcium salts, calcium lactate has good solubility and bioavailability. In the pharmaceutical preparations, calcium lactate was extensively utilized as a calcium source for preventing and treating calcium deficiencies. The physical and chemical characteristics of synthesized calcium lactate were evaluated using Fourier Transform Infrared Spectroscopy, X-ray diffraction analysis and thermal analysis. Further, the various pharmacotechnical properties of the calcium lactate obtained from mussel shells were determined in comparison with an industrial used direct compressible Calcium lactate DC (PURACAL^®). The obtained results suggest that mussel shell by-products are suitable for the development of chemical compounds with potential applications in the pharmaceutical domain.

Recent Trends in Assessment of Cellulose Derivatives in Designing Novel and Nanoparticulate-Based Drug Delivery Systems for Improvement of Oral Health

Natural polymers are revolutionizing current pharmaceutical dosage forms design as excipient and gained huge importance because of significant influence in formulation development and drug delivery. Oral health refers to the health of the teeth, gums, and the entire oral-facial system that allows us to smile, speak, and chew. Since years, biopolymers stand out due to their biocompatibility, biodegradability, low toxicity, and stability. Polysaccharides such as cellulose and their derivatives possess properties like novel mechanical robustness and hydrophilicity that can be easily fabricated into controlled-release dosage forms. Cellulose attracts the dosage design attention because of constant drug release rate from the precursor nanoparticles. This review discusses the origin, extraction, preparation of cellulose derivatives and their use in formulation development of nanoparticles having multidisciplinary applications as pharmaceutical excipient and in drug delivery, as bacterial and plant cellulose have great potential for application in the biomedical area, including dentistry, protein and peptide delivery, colorectal cancer treatment, and in 3D printable dosage forms.