End-to-end continuous manufacturing of conventional compressed tablets: From flow synthesis to tableting through integrated crystallization and filtration

Process analytical technology in Downstream-Processing of Drug Substances- A review

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

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

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

Current Trends in API Co-Processing: Spherical Crystallization and Co-Precipitation Techniques

Active Pharmaceutical Ingredients (APIs) do not always exhibit processable physical properties, which makes their processing in an industrial setup very demanding. These issues often lead to poor robustness and higher cost of the drug product. The issue can be mitigated by co-processing the APIs using suitable solvent media-based techniques to streamline pharmaceutical manufacturing operations. Some of the co-processing methods are the amalgamation of API purification and granulation steps. These techniques also exhibit adequate robustness for successful adoption by the pharmaceutical industry to manufacture high quality drug products. Spherical crystallization and co-precipitation are solvent media-based co-processing approaches that enhances the micromeritic and dissolution characteristics of problematic APIs. These methods not only improve API characteristics but also enable direct compression into tablets. These methods are economical and time-saving as they have the potential for effectively circumventing the granulation step, which can be a major source of variability in the product. This review highlights the recent advancements pertaining to these techniques to aid researchers in adopting the right co-processing method. Similarly, the possibility of scaling up the production of co-processed APIs by these techniques is discussed. The continuous manufacturability by co-processing is outlined with a short note on Process Analytical Technology (PAT) applicability in monitoring and improving the process.

To infinity and beyond: Strategies for fabricating medicines in outer space

Recent advancements in next generation spacecrafts have reignited public excitement over life beyond Earth. However, to safeguard the health and safety of humans in the hostile environment of space, innovation in pharmaceutical manufacturing and drug delivery deserves urgent attention. In this review/commentary, the current state of medicines provision in space is explored, accompanied by a forward look on the future of pharmaceutical manufacturing in outer space. The hazards associated with spaceflight, and their corresponding medical problems, are first briefly discussed. Subsequently, the infeasibility of present-day medicines provision systems for supporting deep space exploration is examined. The existing knowledge gaps on the altered clinical effects of medicines in space are evaluated, and suggestions are provided on how clinical trials in space might be conducted. An envisioned model of on-site production and delivery of medicines in space is proposed, referencing emerging technologies (e.g. Chemputing, synthetic biology, and 3D printing) being developed on Earth that may be adapted for extra-terrestrial use. This review concludes with a critical analysis on the regulatory considerations necessary to facilitate the adoption of these technologies and proposes a framework by which these may be enforced. In doing so, this commentary aims to instigate discussions on the pharmaceutical needs of deep space exploration, and strategies on how these may be met.

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Space is a hostile environment that threatens human health and drug stability.

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Data on the behaviour of medicines in space is critical but lacking.

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Novel drug manufacturing and delivery strategies are needed to safeguard crewmembers’ safety.

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Chemputing, synthetic biology, and 3D printing are examples of such emerging technologies.

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A regulatory framework for space medicines must be implemented to assure quality.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Control of Drug-Excipient Particle Attributes with Droplet Microfluidic-based Extractive Solidification Enables Improved Powder Rheology

PURPOSE

Industrial implementation of continuous oral solid dosage form manufacturing has been impeded by the poor powder flow properties of many active pharmaceutical ingredients (APIs). Microfluidic droplet-based particle synthesis is an emerging particle engineering technique that enables the production of neat or composite microparticles with precise control over key attributes that affect powder flowability, such as particle size distribution, particle morphology, composition, and the API's polymorphic form. However, the powder properties of these microparticles have not been well-studied due to the limited mass throughputs of available platforms. In this work, we produce spherical API and API-composite microparticles at high mass throughputs, enabling characterization and comparison of the bulk powder flow properties of these materials and greater understanding of how particle-scale attributes correlate with powder rheology.

METHODS

A multi-channel emulsification device and an extractive droplet-based method are harnessed to synthesize spherical API and API-excipient particles of artemether. As-received API and API crystallized in the absence of droplet confinement are used as control cases. Particle attributes are characterized for each material and correlated with a comprehensive series of powder rheology tests.

RESULTS

The droplet-based processed artemether particles are observed to be more flowable, less cohesive, and less compressible than conventionally synthesized artemether powder. Co-processing the API with polycaprolactone to produce composite microparticles reduces the friction of the powder on stainless steel, a common equipment material.

CONCLUSIONS

Droplet-based extractive solidification is an attractive particle engineering technique for improving powder processing and may aid in the implementation of continuous solid dosage form manufacturing.

Continuous Isolation of Particles with Varying Aspect Ratios up to Thin Needles Achieving Free-Flowing Products

The continuous vacuum screw filter (CVSF) for small-scale continuous product isolation of suspensions was operated for the first time with cuboid-shaped and needle-shaped particles. These high aspect ratio particles are very common in pharmaceutical manufacturing processes and provide challenges in filtration, washing, and drying processes. Moreover, the flowability decreases and undesired secondary processes of attrition, breakage, and agglomeration may occur intensively. Nevertheless, in this study, it is shown that even cuboid and needle-shaped particles (l-alanine) can be processed within the CVSF preserving the product quality in terms of particle size distribution (PSD) and preventing breakage or attrition effects. A dynamic image analysis-based approach combining axis length distributions (ALDs) with a kernel-density estimator was used for evaluation. This approach was extended with a quantification of the center of mass of the density-weighted ALDs, providing a measure to analyze the preservation of the inlet PSD statistically. Moreover, a targeted residual moisture below 1% could be achieved by adding a drying module (Tdry = 60 °C) to the modular setup of the CVSF.

Enablers of continuous processing of biotherapeutic products

The benefits of continuous processing over batch manufacturing are widely acknowledged across the biopharmaceutical industry, primary of which are higher productivity and greater consistency in product quality. Furthermore, the reduced equipment and facility footprint lead to significantly lower capital costs. Technology enablers have a major role in this migration from batch to continuous processing. In this review, we highlight the various enablers that are facilitating adoption of continuous upstream and downstream bioprocessing. This includes new bioreactors and cell retention devices for upstream operations, and on-column and continuous flow refolding, novel continuous chromatography, and single-pass filtration systems for downstream processes. We also elucidate the significant roles of process integration and control as well as of data analytics in these processes.

Tracking raw material flow through a continuous direct compression line Part I of II: Residence time distribution modeling and sensitivity analysis enabling increased process yield

Continuous manufacturing (CM) offers advantages in quality and space-time yield compared to common batch manufacturing. However, higher yield losses due to the start-up procedure make a broad application uneconomical. This work discusses the possibility of reducing yield losses by adjusting the degree of back-mixing. Back-mixing of nonconforming material from disturbances or start-up will result in the contamination of subsequent material. Therefore, higher degrees of back-mixing cause the discharge of additional material. Choosing an advantageous setting of operational parameters may be a simple way to change the degree of back-mixing. Based on direct compression, this work demonstrates the identification of promising parameters. Therefore, step-change experiments using color-marked material in the feeder, blender, and tablet press quantify the impact of three operational parameters per device. Models for the devices and the entire process result from those measurements. Subsequently, a global variance-based sensitivity analysis identifies the most influential parameters. As a result, adjusting the minimal filling level of the feeder and the rotational feed frame speed of the tablet press reduces back-mixing by more than 30%. At high costs of the raw materials, the resulting savings can significantly improve the economic performance of CM compared to batch manufacturing.

A systematic techno-economic approach to decide between continuous and batch operation modes for injectable manufacturing

A comprehensive approach is proposed to systematically determine the optimal mode of operation between continuous and batch injectable manufacturing considering product and market conditions. At the core of this approach are two integrated complete mathematical modules for discrete and continuous injectable manufacturing, which are supplemented with an economic evaluation module that can then be used to explore the impact of all relevant process parameters (e.g., lot-size, number of operators, solubility, product demand, raw material costs). When the developed approach was applied to two case studies, it was found that batch production was preferred at low to moderate solution (raw material) costs. In contrast, at higher solution costs, the preference for batch and continuous production processes changed back and forth as the annual product demand changed. The study also found that continuous production processes became increasingly preferred at medium to large final dosage volumes and a competitive alternative even at moderate solution costs. From a decision-making point of view, batch injectable manufacturing will be preferred over the novel continuous manufacturing technology unless there is a significant economic incentive to overcome the perceived technology risk. The proposed approach is intended as a decision-support tool for pharmaceutical process engineers.

Towards Continuous Primary Manufacturing Processes—Particle Design through Combined Crystallization and Particle Isolation

Integrated continuous manufacturing processes of active pharmaceutical ingredients (API) provide key benefits concerning product quality control, scale-up capability, and a reduced time-to-market. Thereby, the crystallization step, which is used in approximately 90% of API productions, mainly defines the final API properties. This study focuses on the design and operation of an integrated small-scale process combining a continuous slug flow crystallizer (SFC) with continuous particle isolation using the modular continuous vacuum screw filter (CVSF). By selective adjustment of supersaturation and undersaturation, the otherwise usual blocking could be successfully avoided in both apparatuses. It was shown that, during crystallization in an SFC, a significant crystal growth of particles (Δd50,3≈ 220 µm) is achieved, and that, during product isolation in the CVSF, the overall particle size distribution (PSD) is maintained. The residual moistures for the integrated process ranged around 2% during all experiments performed, ensuring free-flowing particles at the CVSF outlet. In summary, the integrated setup offers unique features, such as its enhanced product quality control and fast start-up behavior, providing a promising concept for integrated continuous primary manufacturing processes of APIs.

Real-time concentration monitoring using a compact composite sensor array for in situ quality control of aqueous formulations

Recent advancements have demonstrated the feasibility of refrigerator-sized pharmaceutical manufacturing platforms (PMPs) for integrated end-to-end manufacturing of active pharmaceutical ingredients (APIs) into formulated drug products. Unlike typical laboratory- or industrial-scale setups, PMPs present unique requirements for process analytical technology (PAT) with respect to versatility, flexibility, and physical size to fit into the PMP space constraints. In this proof of principle study, a novel compact composite sensor array (CCSA) combining ultraviolet (UV) and near infrared (NIR) features at four different wavelengths (280, 340, 600, 860 nm) with temperature measuring capability in a 380 × 30 mm housing (length x diameter, 7 mm diameter at the probe head), were evaluated. The results indicate that the CCSA prototype is capable of measuring the solution and suspension concentrations in aqueous formulations of four model APIs (warfarin sodium isopropanol solvate, lidocaine hydrochloride monohydrate, 6-mercaptopurine monohydrate, acetaminophen) in situ and in real-time with similar accuracy as an established Raman spectrometer commonly applied for method development.

Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing

This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.

Digital twin of low dosage continuous powder blending - Artificial neural networks and residence time distribution models

In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.

Fast Dissolution Electrospun Medicated Nanofibers for Effective Delivery of Poorly Water-Soluble Drugs

BACKGROUND

Electrospinning is developing rapidly from an earlier laboratory method into an industrial process. The clinical applications are approached in various ways through electrospun medicated nanofibers. The fast-dissolving oral drug delivery system (DDS) among them is one of the most promising routes in the near future for commercial applications.

METHODS

Related papers are investigated, including the latest research results, on electrospun nanofiber-based fast-dissolution DDSs.

RESULTS

Several relative topics have been concluded: 1) the development of electrospinning, ranging from 1-fluid blending to multi-fluid process and potential applications in the formation of medicated nanofibers involving poorly water-soluble drugs; 2) Selection of appropriate polymer matrices and drug carriers for filament formation; 3) Types of poorly water-soluble drugs ideal for fast oral delivery; 4) The methods for evaluating fast-dissolving nanofibers; 5) The mechanisms that promote the fast dissolution of poorly water-soluble drugs by electrospun nanofibers; 6) the important issues for further development of electrospun medicated nanofibers as oral fast-dissolving drug delivery systems. Conclusions & Perspectives: The unique properties of electrospun-medicated nanofibers can be used as oral fast dissolving DDSs of poorly water-soluble drugs. However, some significant issues need to be investigated, such as scalable productions and solid dosage form conversions.

Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy

Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding. The RTD was determined using the active pharmaceutical ingredient (API) as a tracer with multiple designs of experiment (DoE) studies to determine the effect of critical process parameters (CPPs) on the process dynamics. To achieve quality control through material diversion from feeding data, soft sensor-based process control tools were designed using the RTD model. The operation block model of the system was designed to select feasible experimental setups using the RTD model, and feeder characterizations as digital twins, therefore visualizing the output of theoretical setups. The concept significantly reduces the material and instrumental costs of process design and implementation.

Evolution of flow-oriented design strategies in the continuous preparation of pharmaceuticals

This review focuses on the flow-oriented design (FOD) in the multi-step continuous-flow synthesis of active pharmaceutical ingredients.