Dissolution apparatuses are indispensable tools in the pharmaceutical industry for evaluating drug release from dosage forms under standardized conditions. They support formulation development, quality control, and regulatory compliance. Despite their widespread use, dissolution testing often yields variable results, often due to differences in hydrodynamic conditions within the dissolution apparatus. The hydrodynamic is influenced by several factors, including apparatus geometry, formulation properties, medium composition, and operating parameters. Variability in dissolution testing can lead to serious consequences, including product recalls, costly investigations, and regulatory delays. Therefore, a mechanistic understanding of these factors is critical for developing physiologically relevant and reliable dissolution methods. Experimental techniques and computational modeling have been employed to characterize flow behavior and drug release mechanisms. However, their predictive capabilities remain constrained by simplifying assumptions and the complexity of dissolution processes. Additionally, dissolution data are used to establish in vitro-in vivo correlations (IVIVCs) and support biowaivers in bioequivalence studies. However, the development of robust IVIVCs is often limited by the availability of insufficient or poor-quality datasets. This review provides an overview of current experimental and computational approaches to understanding drug release in dissolution apparatuses, highlighting key challenges in method development and modeling that must be addressed to ensure reliable and clinically relevant outcomes.

INTRODUCTION:Proteolysis-targeting chimeras (PROTAC) are an innovative treatment approach that selectively breaks down disease-relevant proteins by utilizing the ubiquitin-proteasome system. Other than PROTAC, Molecular glue, Lysosome-Targeting Chimaera (LYTAC), GlueTAC, Autophagy-Targeting Chimaera (AUTAC), Autophagosome Tethering Compound (ATTEC), and Antibody-based PROTAC (AbTAC) are emerging targeted protein degradation (TPD) techniques, of which PROTAC offers several benefits. AREAS COVERED:This review discusses the development of proteolysis-targeting chimeras (PROTACs) for targeted protein degradation, highlighting their mechanism of action via the ubiquitin-proteasome system. It examines key physicochemical and pharmacokinetic challenges that limit clinical translation. Advanced formulation strategies, including nanoformulations and amorphous solid dispersions, prodrug improve solubility, bioavailability, and therapeutic efficacy. Additionally, characterization techniques are summarized, and the review outlines recent progress and critical considerations for the successful clinical translation of PROTAC-based therapeutics. Relevant articles from PubMed, Scopus, and Web of Science, spanning publications up to 2026, were gathered. EXPERT OPINION:PROTACs represent a transformative therapeutic modality, enabling selective protein degradation beyond conventional inhibition. Future research should focus on improving bioavailability, targeted delivery, and stability, while advancing prodrug strategies, E3 ubiquitin ligase selectivity, oral formulations, and predictive models for clinical translation. Additionally, it should emphasize scalable manufacturing, regulatory frameworks, and integration with emerging targeted protein degradation technologies.

In this study, a data-driven framework for the prediction of blend uniformity in a Y-Cone batch blending process is presented utilizing ML techniques. The aim was to develop a prediction model utilizing PCA-guided feature analysis, identifying the most influential material and process attributes contributing to blend uniformity, where the purpose of the analysis was to interpret feature contributions to principal components, ensuring that variables with meaningful structural influence were highlighted for model understanding and process control design. This approach allowed for the investigation of the factor loadings of each feature. Key features were selected using multivariate regression analysis and PCA, capturing material variability, density flow behavior, moisture sensitivity, and process dynamics. A five-year data history containing 7,000 instances was collected from Addis Pharmaceutical Factory (APF) and was preprocessed. To establish a robust predictive relationship, three machine Learning (ML) techniques, namely Feed-Forward Neural Network (FFNN), Random Forest (RF), and extreme gradient boosting (XGBoost), were developed and evaluated. With Mean Squared Error (MSE) as the loss function, the FFNN was trained and validated using the Adam optimization algorithm, incorporating dropout regularization (0.1) and early stopping to prevent overfitting. While the XGBoost model was optimized through gradient boosting with an MSE objective function and regularization terms to control model complexity, BO was employed for the RF model hyperparameter tuning, with bagging and random feature subset selection. Model’s performance was evaluated using the RMSE, MAE, and R² across the split datasets. Accordingly, FFNN attained an R² value of 0.9919, demonstrating higher predictive capability, outperforming the other two models and models from the literature, demonstrating superior capability in capturing nonlinear blending dynamics. The FFNN also exhibited the lowest Root Mean Squared Error (RMSE) (0.0018) and Mean Absolute Error (MAE) (0.0012), confirming its robustness and generalization ability. These findings demonstrate the suitability of neural network-based approaches in capturing the nonlinear relationships governing pharmaceutical blend uniformity and support their application as predictive tools for process optimization and control.

Pharmaceutical product and process development is transitioning from traditional heuristics-based approaches to a Quality-by-Design (QbD) methodology, emphasising systematic process design and understanding of critical parameters. While Design of Experiments (DoE) is key for identifying critical process parameters, it has limitations in scalability and potential over-fitting. Detailed mechanistic or first-principles modelling, using distributed or discrete approaches, offers a promising tool for understanding complex, heterogeneous systems. This paper reviews the roles, opportunities, and challenges of detailed mechanistic modelling in pharmaceutical product and process development. The role of mechanistic models is first discussed from strategic, business, and regulatory perspectives. The workflow of mechanistic modelling is then described, consisting of model selection, calibration, validation, and maintenance. Case studies of key unit operation developments, such as wet granulation and fluidised bed system, are reviewed, highlighting process characteristics, model requirements, and application challenges. Proper model development and experimental design are essential to avoid pitfalls, such as limited applicability or excessive data requirements. Despite rising interest in machine-learning approaches, mechanistic modelling aligns well with data-driven methods, offering high-resolution process understanding and enabling optimal development with fewer experiments. This approach surpasses conventional trial-and-error methods, providing deeper insights and innovative solutions for pharmaceutical processes.

Background/Objectives: Among different milling techniques, spiral air jet milling can produce finer particles without the use of solvents or additives, thereby improving the bioavailability and content uniformity of the final dosage form. However, milling can complicate downstream processability of active pharmaceutical ingredients (APIs) due to reduced bulk powder flowability and post-milling lump formation. Process settings are often optimized only for particle size reduction, without sufficient consideration of manufacturability, largely because of limited API availability and a lack of knowledge about influential material properties. This study aimed to investigate the impact of material properties and process settings on milling performance and downstream manufacturability. Methods: Four APIs, examined in a total of eight grades, were characterized for their bulk mechanical properties and compression energy parameters using a compaction simulator. These grades were subjected to milling experiments within a design-of-experiments framework. Statistical analyses were performed, and population balance models (PBMs) were developed and calibrated for each experiment to link material properties and process settings to milling outcomes. Results: A higher gas flow rate was identified as the most significant contributor to particle size reduction. The influence of mechanical properties, particularly Young’s modulus and Poisson’s ratio, was evident and correlated with unmilled particle sizes. PBM analyses showed that a higher gas feed rate decreased the critical particle size for breakage, while intrinsic mechanical properties affected the breakage rate function. Conclusions: By integrating material properties and process settings into PBM analyses, specific breakage mechanisms could be identified. These findings provide a framework for optimizing jet milling not only for particle size reduction but also for downstream processability of APIs.

Continuous manufacturing (CM) of solid dosage forms in the pharmaceutical industry offers several advantages over batch processing. The most straightforward CM pathway within the pharmaceutical industry is continuous direct compression (CDC), which consists of three main consecutive steps: loss-in-weight feeding, continuous blending and tableting (die-filling and compaction). However, as the majority of the newly developed APIs are cohesive materials with a mean particle size of < 100 μm, a wide particle size distribution (PSD) and a high tendency to agglomerate, they are difficult to handle on CM lines. In this research paper, the impact of a diverse selection of glidants on the continuous blending unit was assessed. Two cohesive APIs (acetaminophen micronized and metoprolol tartrate) and three different glidants (Aerosil\protect \relax \special {t4ht=®} 200, Aerosil\protect \relax \special {t4ht=®} R972 and Syloid\protect \relax \special {t4ht=®} 244 FP) were included. Via multivariate data analysis, quantitative relationships were established between glidant concentration, blending responses (hold-up mass (HM), bulk residence time (BRT), blender fill fraction (BFF %) and relative standard deviation of the blend uniformity (RSDBU)), blend properties and process settings. The dry-coating of APIs with small quantities of glidants efficiently improved the flowability of cohesive powders, thereby optimizing the gravimetric feeding performance. Dry powder coating of the API altered its bulk properties which affected the bulk properties of the final blend as well as the blending responses (HM, BRT, BFF %). This was mainly attributed to the changes in basic flow energy (BFE), conditioned bulk density (CBD), flowability rate index (FRI) and flow function coefficient (ffc), which are all correlated to HM, BRT and BFF %. It was also observed that glidants did not improve RSDBU during continuous blending within the investigated experimental space. Moreover, adding higher concentrations of glidants can even increase RSDBU due to fluidization segregation and less paddle interactions. However, the overal RSDBU values obtained with the continuous blender were relatively low.

In the past few decades, the global pharmaceutical sector has experienced increasing pressure to enhance efficiency, as production costs have risen faster than the rate of new drug development. However, the pharmaceutical manufacturing framework primarily remains unchanged and still relies on batch manufacturing. In contrast, the current regulatory landscape promotes advancements in manufacturing, potentially replacing some traditional manufacturing methods with cleaner, more adaptable, and more efficient continuous manufacturing (CM) techniques. CM provides numerous benefits over conventional batch processing, including faster production, cost savings, greater flexibility, and significantly enhanced quality assurance. The integration of Process Analytical Technology (PAT) in CM facilitates real-time evaluation and process regulation, maintaining consistent product quality. PAT operates on the principle that product quality should be ensured throughout the manufacturing process rather than verified only through post-production testing. Raman and NIR spectroscopy are of great importance in CM because they provide rapid, non-destructive, and often inline evaluation by eliminating the necessity for sample preparation. These spectroscopic techniques enhance process efficiency and quality control by providing real-time chemical and physical insights. Their application in CM has grown significantly, contributing to a deeper understanding of process dynamics and improving manufacturing outcomes. As a result, numerous studies have been conducted in various areas of pharmaceutical technology, emphasizing the advancement in the CM of drug substances and products. Real-time assessment using NIR and Raman has been crucial in optimizing these processes, enhancing efficiency, and ensuring product quality. This review presents a comprehensive summary of the implementation of Raman and NIR spectroscopy in CM, covering primary, secondary, and end-to-end operations.

Over the past years, process analytical technology (PAT) tools have been increasingly adopted into pharmaceutical manufacturing to enable real-time process monitoring and product quality control. However, the integration of these tools into the process stream remains a significant challenge, primarily relying on empirical trial-and-error approaches. In view of this, this study demonstrates the application of Quality-by-Digital-Design (QbDD) principles for the in-process integration of Raman spectroscopy as a PAT tool in a continuous manufacturing system for pharmaceutical liquids and semisolids through a custom-built interfacing device. By applying a systematic and model-based approach, this study aimed to evaluate the interface performance by locating hydrodynamic anomalies, such as fluid circulation and dead zones within the integrated system. The PAT sensor immersion depth, volumetric flow rate, and dynamic viscosity were identified as high-risk factors. Their impact on the interface performance was investigated using a full-factorial Design of Experiments (DoE). Residence Time Distribution (RTD) analysis was performed using computational fluid dynamics (CFD) simulations to estimate fluid circulation and dead volume fraction. The CFD-RTD simulations were validated using experimentally measured RTD. A tank-in-series model with plug flow and a dead volume fraction model best described the fluid behavior within the PAT interface. CFD simulations revealed the presence of dead zones, which were located at the edges of the interface. The CFD-RTD model predictions indicated that increasing the sensor immersion depth or the dynamic viscosity of the fluid results led to an increase in the dead volume fraction within the system. Moreover, the DoE results showed that the volumetric flow rate is the most important factor affecting fluid circulation, while dynamic viscosity is the most important factor affecting the dead volume fraction.

The tablet diversion strategy, based on in-line near-infrared (NIR) tablet press feed frame measurements, can be a key component of both batch and continuous oral solid dose manufacturing processes. It enables real-time, high-frequency monitoring and control, enhancing process understanding and compliance compared to conventional interval-based sampling methods. Central to this strategy are NIR spectrometers, which serve as PAT systems for in-line blend uniformity monitoring in the feed of the tablet press. These systems, when linked with the content uniformity of the corresponding tablets, are crucial for the tablet diversion strategy. In-line NIR blend uniformity measurements in the feed frame and off-line Raman tablet content uniformity measurements were performed during step change experiments for varying process settings. The Residence Time Distribution (RTD) from Tank-in-series (TIS) models for both spectroscopic measurement locations were used to derive the RTD from the NIR measurement location in the feed frame to the tablet diverter. This approach allowed prediction of API content in tablets based on in-line NIR measurements at the feed frame, avoiding yield loss by not diverting tablets that meet specifications. The impact of spectral loss during blend uniformity monitoring due to PAT sensor auto-correction was evaluated for all potential deviations during this 20-second period. Combinations of blends with an API content distinct from the target content, along with their durations observed by the NIR probe, that would exceed the upper limit were identified. However, materials that are not within specifications would not be diverted. It was concluded that diversions failing to trigger the diversion would only occur under very extreme and unlikely conditions.

Continuous manufacturing offers advantages over traditional batch methods, including agility, efficiency, and sustainability. However, transitioning to continuous manufacturing in process development is challenging due to the need for early adoption of industrial-scale equipment. Conversely, batch processes require extensive scale-up studies before commercialization, which continuous processes can avoid. A more efficient approach is to use batch trials in early development to design formulations and processes for continuous manufacturing, requiring assessment of their transferability. This study compares the dissolution behavior of immediate-release tablets manufactured via batch and Continuous Direct Compression (CDC), using ibuprofen, a BCS Class II drug. A Design of Experiments (DoE) approach varied formulation properties and tensile strength, with three methods: i) similarity factor ([f2]), ii) Weibull model fitting and Partial Least Squares (PLS) regression, and iii) Gaussian Process Regression (GPR) to assess the transferability of batch trial data and dissolution models for CDC formulation and process design. Dissolution profiles were identical between batch and CDC trials when formulations and tensile strength matched, with differences observed only due to deviations in actual tensile strength. The PLS model indicated minimal impact of operational modes on dissolution behavior. The GPR model based only on batch trial data predicted CDC dissolution profiles with a mean R2 of 0.910 and RMSE of 4.88%. Overall, the transferability analysis confirmed the predictive capacity of the developed model using batch trial data on the dissolution behavior of tablets manufactured via a CDC line.

Tablet film coating is governed by three interrelated phenomena, namely, tablet mixing, coating-liquid spraying, and liquid evaporation, which dominate the critical quality attributes (CQAs) of the final product. This review examines how differences in coater design, key process parameters, and quality control strategies impact these phenomena and ultimately affect inter-tablet and intra-tablet coating variability. Two complementary approaches for understanding and optimizing the process are evaluated. The experimental approach, involving Design of Experiments (DoE), retrospective data analysis, and advanced Process Analytical Technology (PAT), provides empirical insight into factor–response relationships and enables real-time quality assurance. Simultaneously, model-based approaches, including thermodynamic, spray-dynamics, and particle-dynamics modelling, offer mechanistic understanding of heat and mass transfer, droplet deposition patterns, and tablet motion. Although these sub-models have advanced considerably over the years, a predictive model that treats the coating process in its entirety is still missing. Overall, this review underscores that future advancements will require integrating experimental and model-based methodologies to achieve robust, quality-driven, and predictive control of tablet film coating processes.

In continuous powder handling processes, precise and consistent feeding is crucial for ensuring the quality of the final product. The intermixing effect caused by agitators, which alters the powder’s bulk density, flow rate, and flow patterns, plays a significant role in this process, yet it is often overlooked. This study combines discrete element method (DEM) modeling and experiments using a commercial-scale feeder to propose a Digital Twin (DT) framework. The DEM model accurately captures key flow features, such as bypass trajectories, stagnant zones, and preferential flow patterns, while providing quantitative predictions for the feed factor and zones prone to material accumulation. Scenario analysis is performed to identify the most favorable operating ranges of the screw-agitator ratio and screw speed, considering the cohesive properties of the powder. The study demonstrates that powders with poor flow characteristics require tighter operational constraints, as the screw-agitator ratio is susceptible to variations in mass feed rate. This contribution highlights the importance of selecting an appropriate screw-agitator ratio instead of maintaining a fixed value. Properly choosing this ratio helps determine an optimal operation window, which aims to achieve a minimum agitation level needed to induce unhindered flow and reduce variability in the mass flow rate.

Real-time release testing (RTRt) of tablet dissolution can significantly improve manufacturing efficiency along with the adoption of continuous manufacturing in the pharmaceutical industry. To assure product quality without destructive testing, models for RTRt should be sufficiently reliable and robust. Whereas mechanistic models have merits of broader applicability and interpretability, data-driven models have been common approaches due to computational speed. This paper discusses challenges and opportunities in the application of mechanistic models for dissolution testing to enable RTRt of solid dosage. After a comprehensive literature review on mechanistic dissolution models and RTRt, the potential benefits and challenges of mechanistic models are presented. Compared to data-driven models, mechanistic models require less experimental data that can reduce time and cost for RTRt development. However, to enable the implementation of mechanistic models in RTRt, computational time should be short either by using a simple mechanistic model or by applying surrogate models.

Cold plasma coating technology for surface functionalization of pharmaceutical powder particles is a promising approach to introduce new characteristics such as controlled release layers, improved powder flow properties, stability coatings, and binding of active components to the surface. This is typically achieved in a fluidized bed reactor, where a jet containing the chemical precursor and the plasma afterglow is introduced through a nozzle while extra fluidization gas is injected from the bottom plate. However, the process requires proper mixing of the particles and precursor inside the plasma active zone to ensure a homogeneous coating of all particles. Therefore, such coating processes are challenging to optimize, given the complex phenomena involved in fluidization, plasma species reactions, and surface reactions. In this study, we use the CFD-DEM approach as implemented in the CFDEM®coupling package to model the process. The functionalization rate is modeled as mass transfer from the surrounding gas onto the particles, using a plasma coating zone where this transfer may happen. Mass transfer is switched off outside this zone. The DEM contact parameters and drag force are calibrated to our cellulose beads model powder using experimental tests composed by the FT4 rheometer and spouting tests. We show that while the chemistry can make or break the process, the equipment design and process conditions have a non-negligible effect on the coating metrics and thus must be considered. Cases where the fluidization flow is not high enough to produce good mixing have a high coefficient of variation of the coating mass, and therefore, they must be avoided. In addition, we also proposed an extrapolation procedure to provide results at longer coating times, showing that it is possible to predict coating performance even when simulations of the process for more than a minute are not computationally efficient.

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 under-standing of the granule formation along the length of the barrel. By sampling at four different zones, the in-fluence 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.

In the pharmaceutical industry, twin-screw wet granulation has become a realistic option for the continuous manufacturing of solid drug products. Towards the efficient design, population balance models (PBMs) have been recognized as a tool to compute granule size distribution and understand physical phenomena. However, the missing link between material properties and the model parameters limits the swift applicability and generalization of new active pharmaceutical ingredients (APIs). This paper proposes partial least squares (PLS) regression models to assess the impact of material properties on PBM parameters. The parameters of the compartmental one-dimensional PBMs were derived for ten formulations with varying liquid-to-solid ratios and connected with material properties and liquid-to-solid ratios by PLS models. As a result, key material properties were identified in order to calculate it with the necessary accuracy. Size-and moisture-related properties were influential in the wetting zone whereas density-related properties were more dominant in the kneading zones.

Population balance models (PBM) have been widely used to model the twin-screw wet granulation (TSWG) in the continuous manufacturing of solid dosage forms. However, some knowledge gaps, such as material properties and process parameters, remain, which hampers the applicability of new drug developments. This work presents a hybrid model of a compartmental one-dimensional PBM and partial least squares (PLS) towards a generic model. The PBM that considers aggregation and breakage was calibrated with experimental data of ten different formulations with different liquid-to-solid ratios. The PLS models were built to predict the PBM parameters based on the material properties and liquid-to-solid ratio. The proposed model was validated by testing it with four formulations containing new active pharmaceutical ingredients (APIs) using maximum mean discrepancy. The model captured the peak of each granule size distribution for all formulations. This approach has the advantage that the hybrid model of the PBM and PLS can compute the resulting granule size distribution without granulation experiments. This work enhanced the applicability of PBM, which can reduce experimental efforts in new drug development.

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.

The aim of this study is to increase process understanding of the granulation mechanism in twin-screw melt granulation by evaluating the influence of different screw configurations on granule formation and granule temperature via thermal imaging. The study used a Design of Experiments (DoE) to process a miscible and immiscible formulation (85% API/binder w/w) using a twin-screw extruder with varying screw configurations. The barrel temperature (°C), screw speed (rpm), throughput (kg/h), and kneading zone (direction and stagger angle) were varied. Granule and process properties were evaluated for samples collected at four different locations along the length of the granulation barrel to visualize the granule formation, and granule temperature was monitored by an infrared camera to measure heat transfer on the granules. The resulting temperature was linked to the granule properties and the granule formation along the length of the barrel. The most influencing factors on the granule temperature are the direction of the kneading zone and the set barrel temperature. It was observed that granule formation mainly occurred in the zones that apply more kneading on the granules. The highest temperature increase was observed when the smallest stagger angle in reverse configuration was used, and could be linked to better granule quality attributes.

Wet granulation is a key step in the pharmaceutical manufacturing of solid-dosage forms. Wet granulation is used in pharmaceutical production to enhance formulation qualities such as flowability, compressibility, etc. Different methods are used for wet granulation, including fluidised bed, twin-screw extrusion, and high-shear method, leading to different mechanisms and granule properties. Modelling wet granulation is of great importance for the pharmaceutical industry, which is aspiring to shift from batch mode to a continuous one. Moreover, model-based understanding is critical for implementing the Quality-by-Design (QbD) approach in the pharmaceutical industry. The current critical review represents an overview of current approaches in modelling wet granulation. Three major models, including the population balance model (PBM), discrete element method (DEM), and data-driven models, are evaluated, and the advantages of each approach are outlined. The PBM is the primary approach for modelling granulation in which granule properties are tracked in the process. On the other hand, DEM provides a better understanding of granulation at the particle level. Recent DEM modelling studies have significantly advanced the knowledge for design and scale-up of DEM models, such as capturing the suitable wetting physics and particle scaling and thus mitigating the traditionally known challenges in the application of DEM for wet granulation processes. However, further research is required to include particle level effects, like breakage, attrition, into the DEM model through either built-in approaches or hybrid approaches. Data-driven models are fast and accurate but do not investigate the mechanisms involved in granulation. Data-driven models based on neural networks have been indicated to be adopted for application in continuous granulation with great accuracy compared to other approaches, which can also be implemented for process control. A great joint prospect for first-principles and data-driven modelling approaches is anticipated, which can play an important role in future process design, optimisation, and control of pharmaceutical wet granulation processes.