Process control and scale-up of pharmaceutical wet granulation processes: a review

Material Transport Characteristics in Planetary Roller Melt Granulation

Melt granulation for improving material handling by modifying particle size distribution offers significant advantages compared to the standard methods of dry and wet granulation in dust reduction, obviating a subsequent drying step. Furthermore, current research in pharmaceutical technology aims for continuous methods, as these have an enhanced potential to reduce product quality fluctuations. Concerning both aspects, the use of a planetary roller granulator is consequential. The process control with these machines benefits from the enhanced ratio of heated surface to processed volume, compared to the usually-applied twin-screw systems. This is related to the unique concept of planetary spindles flowing around a central spindle in a roller cylinder. Herein, the movement pattern defines the transport characteristics, which determine the energy input and overall processing conditions. The aim of this study is to investigate the residence time distribution in planetary roller melt granulation (PRMG) as an indicator for the material transport. By altering feed rate and rotation speed, the fill level in the granulator is adjusted, which directly affects the average transport velocity and mixing volume. The two-compartment model was utilized to reflect these coherences, as the model parameters symbolize the sub-processes of axial material transport and mixing.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R²= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

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.

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 Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

A linear scale-up approach to fluid bed granulation

Fluid bed granulation (FBG) is used extensively in the pharmaceutical industry and it is known to be a complex process, because the final product quality of the FBG process is determined by a complex interplay between the process parameters, fluid dynamics, and material properties. Due to this complexity, the FBG process is inherently nonlinear and as such difficult to scale-up. The field of chemical engineering has shown that complex nonlinear processes can be assumed to be linear under limiting conditions. We leverage this idea and present a linear scale-up approach (LiSA) to the FBG process. We derive the key LiSA equation from first principles, and then use it in combination with the similarity principle for scale-up purposes. Furthermore, we present a novel regression-based LiSA. The regression-based LiSA is founded on the hypothesis that there is a linear relationship between the moisture content and a scaling parameter called the Maus factor. This hypothesis is based on our experience and it is shown to be plausible due to high R² values ranging from 0.86 to 0.98. Moreover, we successfully demonstrate that LiSA is effective under typical industrial process settings by applying it to two different formulations during pharmaceutical drug product development.

Development and characterization of ibuprofen co-crystals granules prepared via fluidized bed granulation in a one-step process - a design of experiment approach

OBJECTIVE

The focus of this study was to investigate the possibility of producing ibuprofen-nicotinamide (IBU-NIC) and ibuprofen-isonicotinamide (IBU-INA) cocrystal-containing granules, using a one-step fluidized bed dryer granulation manufacturing process, and evaluate their mechanical properties.

SIGNIFICANCE

Pharmaceutical cocrystals represent a suitable strategy to improve properties of active pharmaceutical ingredients (APIs), such as solubility and processability. Ibuprofen (IBU) is a small molecule API which can form cocrystals with different coformers, including NIC and INA. An improvement in mechanical properties for IBU-NIC cocrystals relative to IBU was previously reported but, to date, the formulation of IBU cocrystals in a solid dosage form has not been investigated.

METHODS

In situ cocrystallization and granulation were achieved concurrently by processing in a lab-scale fluidized bed granulator following a design of experiment (DoE) approach using a two-level factorial design with both process and formulation variables. Solid-state, micrometric, dissolution, and mechanical (tabletability) characteristics of granules were assessed post-processing.

RESULTS

Granules containing cocrystals were successfully prepared for 11 of 16 DoE runs. Parameters with a significant effect on granule drug loading, flow function, porosity, and size could be identified from the DoE model. Process yield was increased by using a high inlet temperature at high solution feed rate. To avoid the formation of sticky particles, caking and over-wetting of the powder during the process, the utilization of high inlet temperature, low API + coformer:filler ratio, low API concentration in solution and low solution feed rate were suggested by the model.

CONCLUSION

The multivariable model developed enables accurate optimization of the granulation process for IBU cocrystals.

Artificial intelligence in drug discovery and development

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

Dual-process of starch modification: Combining ozone and dry heating treatments to modify cassava starch structure and functionality

This work evaluated for the first time the effect of dual modification of cassava starch by using ozone (O₃) and dry heating treatment (DHT). The dual modification was capable to promote fissures on the surface of the starch granule (DHT + O₃), affected the starch amorphous domains, presented greater degree of starch oxidation (DHT + O₃) and different profiles of starch molecular size distribution. These modifications resulted in starches with different properties. Moreover, the sequence of treatments was decisive for the hydrogel properties: while DHT + O₃ resulted in formation of stronger gels, O₃ + DHT resulted in weaker gels. In conclusion, this proposed dual modification was capable to produce specific modified starch when compared with the isolated treatments, also expanding the potential of cassava starch applications.

Elucidation of the Molecular Mechanism of Wet Granulation for Pharmaceutical Standard Formulations in a High-Speed Shear Mixer Using Near-Infrared Spectroscopy

The granulation process of pharmaceutical standard formulation in a high-speed shear wet granulation (HSWG) was measured by in-line near-infrared spectroscopy (NIRS) and agitation power consumption (APC) methods. The F-1, F-2, and F-3 formulations (500 g) contained 96% w/w α-lactose monohydrate (LA), potato starch (PS), and a LA:PS = 7:3 mixture, respectively, and all the formulations contained 4% w/w hydroxypropyl cellulose. While adding purified water at 10 mL/min, the sample powder was mixed. The calibration models to measure the amount of binding water (Wa) and APC of the HSWG formulations were established based on NIRS of the samples measured for 60 min by partial least-squares regression analysis (PLS). Molecular interaction related to APC between the particle surface and binding liquor was analyzed based on NIRS. The predicted values of Wa and APC for all formulations were superimposed with the measured values on a straight line, respectively. The regression vector (RV) of the calibration model for Wa indicated the chemical information of all the water in the samples. In contrast, the RV for APC suggested that APC changes in the processes are related to powder aggregation because of surface tension of binding water between particles.

Converting a batch based high-shear granulation process to a continuous dry granulation process; a demonstration with ketoprofen tablets

When one wishes to convert a batch based manufacturing process of an existing tablet product to a continuous process, there are several available strategies which can be adopted. Theoretically, the most straightforward way would be to proceed with the corresponding processing principles, for example to change a wet granulation (WG) batch process into its continuous WG counterpart. However, in some cases, the choice of roller compaction (RC) could be very attractive due to the notably simpler and inherently continuous nature of the RC manufacturing principle. The aim of this study was to examine a process conversion from batch based high-shear wet granulation (HSWG) to continuous RC manufacturing, without any significant formulation changes. An optimization of the formulation is often needed during the process conversion. However, our primary goal was to demonstrate the possibilities to perform this kind of process adaptation with minimal formulation changes. Furthermore, the effect of three different locations of lubrication feeding with two production rate levels was studied. An additional target was to identify possible over-lubrication with these manufacturing configurations, and to clarify which of these three possibilities steps produced a final product that conformed to the same quality requirements as HSWG tablets. Previously, the effects of lubrication only on compacted ribbons (Miguelez-Moran A.M, 2008) and final product with CDC (continuous direct compression) (Taipale-Kovalainen, et al., 2017; 2019) have been investigated. Here, the effect of lubrication on both ribbon and on final product was examined. No signs of over-lubrication were observed, but there was a clear effect of the feeding location of lubricant on the final product. On the basis of these results, it is concluded that in the future, if a good product/process understanding of the alternative manufacturing process with different techniques can be obtained, it will be possible to devise more flexible and effective ways to allow the pharmaceutical industry to switch from batch manufacturing towards CM.

Continuous Manufacturing of a Polymer Stabilized Emulsion Monitored with Process Analytical Technology

Moving from batch to continuous manufacturing (CM) requires implementation of process analytical technology (PAT), as it is crucial to monitor and control these processes. CM of semi-solids has been demonstrated but implementation of a broader range of PAT tools with in- or on-line process interfacing at the end of the CM line has not been demonstrated. The goal of this work was to continuously manufacture creams and to investigate whether in- and on-line measurement of viscosity, changes in the concentration of active pharmaceutical ingredient (API), and pH could be used to support optimization of a model cream product. Additionally, the torque of the mixers was assessed for determination of the physical properties of the cream. Two Raman probes with different probe optics were compared for characterization of the API concentration. The API concentration, amount of neutralizer, and mixing speed of the CM line were systematically varied. Both the PhAT probe with a larger sampling volume and immersion Raman probe with a smaller sampling volume could detect the step changes in the API concentration. The torque from the mixer was compared with the viscosity measurements, but the torque signal could not be correlated with the viscosity due to the dynamic nature of the polymer conformation and the time-dependency of this property. Adjustment of pH of the cream could be monitored with the current installation. The investigated PAT tools could be implemented into a continuous line and, further, be used to support the optimization of a model cream composition and related process parameters.

A semi-theoretical model for simulating the temporal evolution of moisture-temperature during industrial fluidized bed granulation

Moisture plays a major role in determining the attributes of granules prepared by fluidized bed granulation (FBG). Here, a semi-theoretical droplet-based evaporation rate model was developed and incorporated into moisture mass-enthalpy balances to simulate the temporal evolution of bed moisture-temperature. Experimental data from a GPCG30 unit were used to fit the model parameters. With only two fitting parameters, the model demonstrated excellent capability to describe the moisture-temperature evolution for a wide range of operating conditions. Then, in a global process model (GPM) approach, the evaporation parameters were fitted to multi-linear functions of inlet air temperature, binder concentration, and spray rate. The GPM was validated successfully by simulating a different data set which was not used in its calibration. As the GPM demonstrated a good predictive capability, it was further used to investigate the impacts of process parameters. Numerical simulations suggest that the proposed GPM predicts the experimentally well-established trends of moisture-temperature profiles in previously published data, proving the applicability of the GPM approach. This study has demonstrated the capabilities of simple process models as a practical approach to predict time-wise evolution of bed moisture-temperature profiles in industrial FBG modeling, while also pointing out their limitations.

Phenytoin-loaded lipid-core nanocapsules improve the technological properties and in vivo performance of fluidised bed granules

Lipid-core nanocapsules (LNCs) were recently reported by our group as a suitable binder system to produce fluidised bed granules. However, there is still a lack of knowledge about the influence of using these nanocarriers loaded with a drug on the properties of the granules and their in vivo performance. Therefore, this study was designed to produce innovative fluidised bed granules containing phenytoin-loaded LNCs (LNC_PHT) as a strategy to evaluate the influence of the presence of the drug-loaded nanocarriers on their in vitro and in vivo properties. Granules were produced using a mixture of maltodextrin and phenytoin (1:0.004 w/w) as substrate. They were prepared by fluid bed granulation using water or LNC_PHT as the liquid binder, affording good yields (73-82%) of granules with low moisture content (<5%). Granules prepared with LNC_PHT had larger mean size (122 μm) compared to maltodextrin primary particles (50 μm) due to the formation of solid bridges. Moreover, the use of LNC_PHT as the liquid binder improved their powder flow properties. The nanocarriers were recovered after aqueous dispersion (3.00 mg.mL^-1 of PHT) with a redispersibility close to 90%. After reconstitution in water, granules containing LNC_PHT showed an improved dissolution behaviour compared to those prepared without them. In addition, they showed a higher mucoadhesive effect due to a combined effect of the LNC_PHT and maltodextrin in the interactions with porcine intestinal mucosa. Regarding the in vivo studies, granules containing the combination of non-encapsulated PHT and PHT-loaded lipid-core nanocapsules increased the latency to seizures compared to placebo granules, showing effective anticonvulsant effect in mice. In conclusion, the use of drug-loaded nanocapsules as binder is an encouraging approach to produce fluidised bed mucoadhesive granules with improved technological properties and in vivo performance.

Formation of Indomethacin-Saccharin Cocrystals during Wet Granulation: Role of Polymeric Excipients

Formulation of a cocrystal into a solid pharmaceutical dosage form entails numerous processing steps during which there is risk of dissociation. In an effort to reduce the number of unit operations, we have attempted the in situ formation of an indomethacin-saccharin (INDSAC) cocrystal during high-shear wet granulation (HSWG). HSWG of IND (poorly water-soluble drug) and SAC (coformer), with polymers (granulating agents), was carried out using ethanol as the granulation liquid and yielded INDSAC cocrystal granules. Therefore, cocrystal formation and granulation were simultaneously accomplished. Our objectives were to (i) evaluate the influence of polymers on cocrystal formation kinetics during wet granulation and (ii) mechanistically understand the role of polymers in facilitating the cocrystal formation. Polyvinylpyrrolidone (PVP), hydroxypropyl cellulose (HPC), and polyethylene oxide (PEO) were chosen to investigate the influence of soluble polymers. The cocrystal formation kinetics was influenced by the polymer (PVP < HPC < PEO) and its concentration. The interaction of the polymer with cocrystal components inhibited the cocrystal formation. Complete cocrystal formation was observed in the presence of PEO, a polymer which does not interact with IND and SAC.

Product Development, Manufacturing, and Packaging of Solid Dosage Forms Under QbD and PAT Paradigm: DOE Case Studies for Industrial Applications

An integrated approach based on QbD and PAT provides a systematic and innovative framework for product development, manufacturing, and quality risk management. In this context, the significance of the outcome of design of experiments (DOEs) to the selection of the product design, robust commercial manufacturing process, design space, and overall control strategy remains vital for the success of a drug product throughout its life cycle. This paper aims at discussing selected recent DOE case studies conducted during QbD-based and integrated QbD/PAT-based development of solid oral formulations and process improvement studies. The main focus of this paper is to highlight the rationales and importance of design selection during development and applications of mathematical models and statistical tools in analyzing DOE and PAT data for developing a design space, control strategy, and improved process monitoring. A total of 25 case studies (includes 9 PAT application studies) have been discussed in this paper which cover 11 manufacturing processes commonly utilized for solid dosage forms. Two case studies relevant to selection of packaging design for solid dosage forms are also briefly discussed to complete the scope. Overall, for a successful modern QbD approach, it is highly important that DOEs are conducted and analyzed in a logical sequence which involves designs that are phase-appropriate and quality-driven and facilitate both statistical and chemometric thinking at each development stage. This approach can result into higher regulatory flexibility along with lower economic burden during life cycle of a product, irrespective of regulatory path used (NDA or ANDA).

Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry

Process simulation using mathematical modeling tools is becoming more common in the pharmaceutical industry. A mechanistic model is a mathematical modeling tool that can enhance process understanding, reduce experimentation cost and improve product quality. A commonly used mechanistic modeling approach for powder is the discrete element method (DEM). Most pharmaceutical materials have powder or granular material. Therefore, DEM might be widely applied in the pharmaceutical industry. This review focused on the basic elements of DEM and its implementations in pharmaceutical manufacturing simulation. Contact models and input parameters are essential elements in DEM simulation. Contact models computed contact forces acting on the particle-particle and particle-geometry interactions. Input parameters were divided into two types-material properties and interaction parameters. Various calibration methods were presented to define the interaction parameters of pharmaceutical materials. Several applications of DEM simulation in pharmaceutical manufacturing processes, such as milling, blending, granulation and coating, were categorized and summarized. Based on this review, DEM simulation might provide a systematic process understanding and process control to ensure the quality of a drug product.

Tablet Scoring: Current Practice, Fundamentals, and Knowledge Gaps

Oral solid dosage formulations and/or tablets have remained the preferred route of administration by both patients and health care practitioners. Oral tablets are easy to administer, they are non-invasive and cause less risk adversity. Because of the lack of commercially available tablet dose options, tablets are being split or partitioned by users. Tablet scoring refers to the breakage of a tablet to attain a desired efficacy dose and is an emerging concept in the pharmaceutical industry. The primary reason for the tablet scoring practice is to adjust the dose: dose tapering or dose titrating. Other reasons for tablet partitioning are to facilitate dose administration, particularly among the pediatric and the geriatric patient population, and to mitigating the high cost of prescription drugs. The scope of this review is to: (1) evaluate the advantages and inconveniences associated with tablet scoring/portioning, and (2) identify factors in the formulation and the manufacturing of tablets that influence tablet splitting. Whereas tablet partitioning has been a common practice, there is a lack of understanding regarding the fundamentals underpinning the performance of tablets with respect to splitting. Several factors can influence tablet partitioning: tablet size, shape, and thickness. A requirement has recently been set by the European Pharmacopoeia and the U.S. Food and Drug Administration for the uniformity of mass of subdivided tablets. For breaking ease, an in-vivo reference test and a routinely applicable in-vitro test need to be established.

Implementation of real-time and in-line feedback control for a fluid bed granulation process

The application of process analytical technologies (PAT) to monitor critical quality attributes (CQAs) provides an important approach to enhance process understanding and improve the reliability of pharmaceutical production processes. The present study focuses on the first PAT based feedback control system for a fluid bed granulation batch process. Real-time particle size measurement by in-line spatial filtering technique (SFT) using a modified time-based buffer system was applied to define a target particle size after spraying a specific amount of binder solution. After identifying an appropriate control variable, a suitable strategy for feedback control was established, followed by tuning of the control loop to obtain best performance of the integrated system. By adapting the final target particle size within a specified range good functionality of the system could be demonstrated. Investigations of the robustness further showed that the implemented system enables the production of a predefined target particle size also by varying process and formulation parameters. The effect of increasing spray rates and binder concentrations on the particle size could be compensated in a given range by feedback control ensuring a predefined product quality. The study provides an advanced approach for quality assurance of fluid bed granulation.

Comprehensive Study of Intermediate and Critical Quality Attributes for Process Control of High-Shear Wet Granulation Using Multivariate Analysis and The Quality by Design Approach

A robust manufacturing process and the relationship between intermediate quality attributes (IQAs), critical quality attributes (CQAs), and critical process parameters (CPPs) for high-shear wet granulation was determined in this study. Based on quality by the design (QbD) approach, IQAs, CQAs, and CPPs of a telmisartan tablet prepared by high-shear wet granulation were determined and then analyzed with multivariate analysis (MVA) to evaluate mutual interactions between IQAs, CQAs, and CPPs. The effects of the CPPs on the IQAs and CQAs were quantitatively predicted with empirical models of best fit. The models were used to define operating space, and an evaluation of the risk of uncertainty in model prediction was performed using Monte Carlo simulation. MVA showed that granule size and granule hardness were significantly related to % dissolution. In addition, granule FE (Flow Energy) and Carr's index had effects on tablet tensile strength. Using the manufacture of a clinical batch and robustness testing, a scale-up from lab to pilot scale was performed using geometric similarity, agitator torque profile, and agitator tip speed. The absolute biases and relative bias percentages of the IQAs and CQAs generated by the lab and pilot scale process exhibited small differences. Therefore, the results suggest that a risk reduction in the manufacturing process can be obtained with integrated process parameters as a result of the QbD approach, and the relationship between IQAs, CQAs, and CPPs can be used to predict CQAs for a control strategy and SUPAC (Scale-Up and Post-Approval Guidance).

Scale-up of pharmaceutical spray drying using scale-up rules: A review

Spray drying is one of the widely used manufacturing processes in pharmaceutical industry. While there are voluminous experimental studies pertaining to the impact of various process-formulation parameters on the quality attributes of spray dried powders such as particle size, morphology, density, and crystallinity, there is scant information available in the literature regarding process scale-up. Here, we first analyze salient features of scale-up attempts in literature. Then, spray drying process is analyzed considering the fundamental physical transformations involved, i.e., atomization, drying, and gas-solid separation. Each transformation is scrutinized from a scale-up perspective with non-dimensional parameters & multi-scale analysis, and comprehensively discussed in engineering context. Successful scale-up entails similar key response variables from each transformation across various scales. These variables are identified as droplet size distribution, outlet temperature, relative humidity, separator pressure loss coefficient, and collection efficiency. Instead of trial-and-error-based approaches, this review paper advocates the use of mechanistic models and scale-up rules for establishing design spaces for the process variables involved in each transformation of spray drying. While presenting a roadmap for process development and scale-up, the paper demonstrates how to bridge the current gap in spray drying scale-up via a rational understanding of the fundamental transformations.

An Industrial Perspective on Scale-Down Challenges Using Miniaturized Bioreactors

Miniaturized stirred bioreactors (MSBRs) are gaining popularity as a cost-effective approach to scale-down experimentation. However, realizing conditions that reflect the large-scale process accurately can be challenging. This article highlights common challenges of using MSBRs for scale-down. The fundamental difference between oxygen mass transfer coefficient (k_La) and oxygen transfer rate scaling is addressed and the difficulty of achieving turbulent flow and industrially relevant tip speeds is described. More practical challenges of using MSBR systems for scale-down are also discussed, including the risk of vortex formation, changed volume dynamics, and wall growth. By highlighting these challenges, the article aims to create more awareness of these difficulties and to contribute to improved design of scale-down experiments.

Novel, lean and environment-friendly granulation method: Green fluidized bed granulation (GFBG)

The Green fluidized bed granulation (GFBG) technology is based on the moisture activated dry granulation (MADG) technique and consists only of a mixing and a spraying process using a fluidized bed granulator, requiring no heating process. This provides a less energy-consuming and environment-friendly granulation method compared to current fluidized bed granulation (FBG) and high-shear granulation (HSG) methods. The aim of this study is to compare and evaluate the manufacturability, and granule and tablet properties among GFBG, MADG, FBG and HSG. The GFBG process time took less than 20 min for producing final blends at a 700 g scale, which was comparable to MADG. This process time was significantly shorter than that of FBG and HSG. GFBG not only had the shortest process time but also reduced the number of manufacturing machines compared to FBG and HSG. The Hausner ratio (HR) of granules from GFBG (1.30) indicated a good flowability, and no problems were observed in the tablet mass variability during compression. Tablets produced using GFBG achieved sufficient tensile strength (>1.5 MPa) even at a low compression force and demonstrated the fastest disintegration time compared to the other manufacturing methods. Tablet disintegration is related to wettability and porosity, therefore the tablet wettability (initial and capillary wetting) and tablet porosity were investigated. As a result, the capillary wetting of the tablets produced using GFBG was 3.6 times higher than the tablets produced using FBG, which might have affected the fast disintegration of the tablets produced using GFBG.

Simplified end-to-end continuous manufacturing by feeding API suspensions in twin-screw wet granulation

This study focussed on investigating the coupling of continuous manufacturing of drug substance and continuous manufacture of drug product. An important step in such an integrated end-to-end continuous manufacturing was envisioned by dosing the API as suspension into a twin-screw wet granulation process. To achieve this goal, a model drug substance (ibuprofen) was fed as a concentrated aqueous suspension (50% w/w) into a twin-screw granulator and compared against traditional solid feeding of the model drug substance to meet a target ibuprofen load of 60% w/w in the formulation. Granulation and compaction behaviour were evaluated to determine the impact of feeding API as suspension in twin-screw wet granulation on the critical quality attributes of the drug product. It was demonstrated that the ibuprofen suspension feed is comparable with the ibuprofen dry blend feed in twin-screw wet granulation. Next to enabling end-to-end continuous manufacturing, API suspension feed in twin-screw wet granulation could afford a number of additional advantages including manufacturing efficiency by removing the drying step for API, or overcoming processing issues linked to the bulk properties of the API powder (e.g. flowability).

Programming of Multicomponent Temporal Release Profiles in 3D Printed Polypills via Core-Shell, Multilayer, and Gradient Concentration Profiles

Additive manufacturing (AM) appears poised to provide novel pharmaceutical technology and controlled release systems, yet understanding the effects of processing and post-processing operations on pill design, quality, and performance remains a significant barrier. This paper reports a study of the relationship between programmed concentration profile and resultant temporal release profile using a 3D printed polypill system consisting of a Food and Drug Administration (FDA) approved excipient (Pluronic F-127) and therapeutically relevant dosages of three commonly used oral agents for treatment of type 2 diabetes (300-500 mg per pill). A dual-extrusion hydrogel microextrusion process enables the programming of three unique concentration profiles, including core-shell, multilayer, and gradient structures. Experimental and computational studies of diffusive mass transfer processes reveal that programmed concentration profiles are dynamic throughout both pill 3D printing and solidification. Spectrophotometric assays show that the temporal release profiles could be selectively programmed to exhibit delayed, pulsed, or constant profiles over a 5 h release period by utilizing the core-shell, multilayer, and gradient distributions, respectively. Ultimately, this work provides new insights into the mass transfer processes that affect design, quality, and performance of spatially graded controlled release systems, as well as demonstrating the potential to create disease-specific polypill technology with programmable temporal release profiles.

Application of near-infrared spectroscopy to optimize dissolution profiles of tablets according to the granulation mechanism

We developed a method for the optimization of dissolution properties of solid oral dosage forms manufacturing using high shear wet granulation (HSWG) by using near-infrared spectroscopy (NIRS) with chemometrics in small-scale experiments. The changes in rheology and NIR spectra of the granules were monitored to verify the granulation mechanism and determine the suitable water amount for model formulation during the HSWG. Tablets were manufactured by altering the added water amount to investigate the impact of the granulation mechanism on drug product qualities. Model formulation granules were prepared with 10-20% w/w water in a funicular state, corresponding to the plateau region in score plots obtained by principal component analysis (PCA). The dissolution rate of model formulation tablets manufactured with more than 20% w/w of water was significantly delayed while tablets manufactured with 15% w/w water showed 100% dissolution at 15 min. NIRS and PCA are applicable to the optimization of dissolution properties via the process understanding of HSWG at the early formulation development stage and could facilitate drug development.