Continuous Manufacturing Process Research Articles

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 1. Calibration workflow

In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system’s response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.

Motion priority optimization framework towards automated and teleoperated robot cooperation in industrial recovery scenarios

In this study, we introduce an optimization framework to enhance the efficiency of motion priority design in scenarios involving automated and teleoperated robots within an industrial recovery context. The increasing utilization of industrial robots at manufacturing sites has been instrumental in reducing human workload. Nevertheless, achieving effective human–robot collaboration/cooperation (HRC) remains a challenge, especially when human workers and robots share a workspace for collaborative tasks. For instance, when an industrial robot encounters a failure, such as dropping an assembling part, it triggers the suspension of the corresponding factory cell for safe recovery. Given the limited capacity of pre-programmed robots to rectify such failures, human intervention becomes imperative, requiring entry into the robot workspace to address the dropped object while the robot system is halted. This discontinuous manufacturing process results in productivity loss. Robotic teleoperation has emerged as a promising technology enabling human workers to undertake high-risk tasks remotely and safely. Our study advocates for the incorporation of robotic teleoperation in the recovery process during manufacturing failure scenarios, which is referred to as “Cooperative Tele-Recovery”. Our proposed approach involves formulating priority rules designed to facilitate collision avoidance between manufacturing and recovery robots. This, in turn, ensures a continuous manufacturing process with minimal production loss within a configurable risk limitation. We present a comprehensive motion priority optimization framework composed of an HRC simulator and a cooperative multi-robot controller to identify optimal parameters for the priority function. The framework dynamically adjusts the allocation of motion priorities for manufacturing and recovery robots while adhering to predefined risk limitations. Through quantitative and qualitative assessments, we validate the novelty of our concept and demonstrate its feasibility.

ROM-based stochastic optimization for a continuous manufacturing process

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.

ATO and Cs0·33WO3 loaded bilayer nanofiber membrane doped polymer dispersed liquid crystals for infrared shielding

Liquid crystals possess a wide range of applications in modulating infrared (IR) light and have drawn great interest especially in near-infrared shielding and passive radiative cooling. Here, a composite structure containing polymer dispersed liquid crystals (PDLC) with antimony doped tin oxide and cesium tungsten oxide loaded bilayer nanofiber membranes with IR shielding properties is proposed. The composite film and the blank liquid crystal cell are irradiated simultaneously under IR light, and the temperature detector shows that the average temperature drop of the PDLC composite film in the scattered state and the fully transparent state over the blank sample are 6.8 °C and 4.1 °C, respectively. More importantly, the dynamic IR shielding properties of the composite films are demonstrated by patterning the nanofiber films. In addition, this organic/inorganic hybrid PDLC system not only modulates IR light, but also exhibits excellent electro-optical properties, aging resistance, flexural stability (1000 cycles), and tensile resistance. The composite film combines ease of preparation with compatibility for industrial-scale roll-to-roll or continuous manufacturing processes, highlighting the great potential of PDLC for applications in tunable optical heat transport materials.

Blend uniformity monitoring in a continuous manufacturing mixing process for a low-dosage formulation using a stream sampler and near infrared spectroscopy

Continuous manufacturing has the potential to offer several benefits for the production of oral solid dosage forms, including reduced costs, low-scale equipment, and the application of process analytical technology (PAT) for real-time process control. This study focuses on the implementation of a stream sampler to develop a near infrared (NIR) calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5–7.5 % w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler for flowing powders. A continuous mixer shaft speed of 250 RPM was selected to operate the mixing process based on a variability analysis developed with in-line spectral data acquired using the stream sampler at 6 RPM. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39 % w/w and a bias of 0.05 % w/w. An independent experimental run conducted two days later revealed that the continuous mixing process and the NIR calibration model presented low day-to-day variation. The minimum practical error (MPE) and sill values through variographic analysis showed low variance associated with the sampling process using the stream sampler. Results demonstrated the promising capacity of the stream sampler coupled to an NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration.

HUB3D: Intelligent Manufacturing HUB System

HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.

Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process

Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process

3D-Printed Flexible Microfluidic Health Monitor for In Situ Sweat Analysis and Biomarker Detection.

Wearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals' physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.

Co-rotating twin screw process for continuous manufacturing of solid crystal suspension: A promising strategy to enhance the solubility, permeation and oral bioavailability of Carvedilol

Background In the current work, co-rotating twin-screw processor (TSP) was utilized to formulate solid crystal suspension (SCS) of carvedilol (CAR) for enhancing its solubility, dissolution rate, permeation and bioavailability using mannitol as a hydrophilic carrier. Methods In-silico molecular dynamics (MD) studies were done to simulate the interaction of CAR with mannitol at different kneading zone temperatures (KZT). Based on these studies, the optimal CAR: mannitol ratios and the kneading zone temperatures for CAR solubility enhancement were assessed. The CAR-SCS was optimized utilizing Design-of-Experiments (DoE) methodology using the Box-Behnken design. Saturation solubility studies and in vitro dissolution studies were performed for all the formulations. Physicochemical characterization was performed using differential scanning calorimetry , Fourier transform infrared spectroscopy, X-ray diffraction studies, and Raman spectroscopy analysis. Ex vivo permeation studies and in vivo pharmacokinetic studies for the CAR-SCS were performed. Stability studies were performed for the DoE-optimized CAR-SCS at accelerated stability conditions at 40 ºC/ 75% RH for three months. Results Experimentally, the formulation with CAR: mannitol ratio of 20:80, prepared using a KZT of 120 ºC at 100 rpm screw speed showed the highest solubility enhancement accounting for 50-fold compared to the plain CAR. Physicochemical characterization confirmed the crystalline state of DoE-optimized CAR-SCS. In-vitro dissolution studies indicated a 6.03-fold and 3.40-fold enhancement in the dissolution rate of optimized CAR-SCS in pH 1.2 HCl solution and phosphate buffer pH 6.8, respectively, as compared to the pure CAR. The enhanced efficacy of the optimized CAR-SCS was indicated in the ex vivo and in vivo pharmacokinetic studies wherein the apparent permeability was enhanced 1.84-fold and bioavailability enhanced 1.50-folds compared to the plain CAR. The stability studies showed good stability concerning the drug content. Conclusions TSP technology could be utilized to enhance the solubility, bioavailability and permeation of poor soluble CAR by preparing the SCS.

Manufacturing process transfer to a 30 kg/h continuous direct compression line with real-time composition monitoring

Transferring an existing marketed pharmaceutical product from batch to continuous manufacturing (CM) without changes in regulatory registration is a challenging task in the pharmaceutical industry. Continuous manufacturing can provide an increased production rate and better equipment utilisation while retaining key quality attributes of the final product. Continuous manufacturing necessitates the monitoring of critical quality attributes in real time by appropriate process analytical tools such as near infra-red (NIR) probes. The present work reports a successful transfer of an existing drug product from batch to continuous manufacturing process without changing the formulation. A key step was continuous powder blending, whose design and operating parameters including weir type, agitation rate, dynamic hold-up and residence time were systematically investigated with respect to process repeatability. A NIR-based multivariate data model for in-line composition monitoring has been developed and validated against an existing quality control method for measuring tablet content uniformity. A continuous manufacturing long-run with a throughput of 30 kg/h (approx. 128,000 tablets per hour), uninterrupted for 320 min, has been performed to test and validate the multivariate data model as well as the batch to continuous process transfer. The final disintegration and dissolution properties of tablets manufactured by the continuous process were found to be equivalent to those manufactured by the original batch process.

Development of a Continuous Process for the Large-Scale Asymmetric Manufacture of (R)-3-Methoxy-2-(4-methylpiperazin-1-yl)propanoic Acid

Development of a Continuous Process for the Large-Scale Asymmetric Manufacture of (<i>R</i>)-3-Methoxy-2-(4-methylpiperazin-1-yl)propanoic Acid

GMP implementation of a hybrid continuous manufacturing process for a recombinant non-mAb protein-A case study.

Advances in manufacturing technology coupled with the increased potency of new biotherapeutic modalities have created an external environment where continuous manufacturing (CM) can address a growing need. Amgen has successfully implemented a hybrid CM process for a commercial lifecycle program. In this process, the bioreactor, harvest, capture column, and viral inactivation/depth filtration unit operations were integrated together in an automated, continuous module, while the remaining downstream unit operations took place in stand-alone batch mode. CM operations are particularly suited for so-called "high mix, low volume" manufacturing plants, where a variety of molecules are manufactured in relatively low volumes. The selected molecule fit this mold and was manufactured in a low-capital micro-footprint suite attached to an existing therapeutic production facility. Use of a hybrid process within an already operating facility required less capital and minimized complexity. To enable this hybrid CM process, an established fed-batch process was converted to a perfusion process with continuous harvest. Development efforts included both process changes and the generation of a novel cell line adapted to long-term perfusion. Chromatography resins were updated, and purification processes adapted to handle variable inputs due to the fluctuations in harvest titer from the lengthy production process. A novel automated single-use (SU) viral inactivation (VI) skid was introduced, which entailed the development of a robust pH verification and alarm system, along with procedures for product isolation to allow discard of specific cycles. The CM process demonstrated consistent performance, meaning it met predefined performance criteria (including product quality attributes, or PQAs) when operated within established process parameters and manufactured according to applicable procedures. Using a 75% reduction in scale, it resulted in a five-fold reduction in process media and buffer usage, a fifteen-fold increase in mass per thaw, and an overall process productivity increase of 45-fold (as measured by grams drug substance per liter per day.) The hybrid CM process also enabled increased material demand to be met with no change in cost of goods manufactured or plant capacity, due to the repurposing of existing facility space and the flexible duration of the hybrid CM harvest. Overall, the success of the hybrid CM platform represents an exciting opportunity to reduce costs and increase process efficiency in industry.

1,2,3-Triazole Synthesis: Development of Safe and Effective Batch and Continuous Manufacturing Processes

1,2,3-Triazole Synthesis: Development of Safe and Effective Batch and Continuous Manufacturing Processes

Slug-flow synthesis of NCMA: Effect of substitution of cobalt with aluminum on the electrochemical performance of Ni-rich cathode for lithium-ion battery

Nickle-rich Li[Ni1-x-yCoxMny]O2 (x, y ≤ 0.1) (NCM) layered materials are known as promising cathode materials for next-generation lithium-ion batteries and electric vehicles owing to their high-reversible capacity and operating voltage of up to 3.6 vs Li/Li+. However, issues, such as irreversible phase transition, cation mixing, microcrack formation, thermal and structural stability of the material prevent its widespread adoption. Although, cation doping is a well-known technique to enhance the electrochemical performance of the NCM-based cathode material, the performance of the material is very sensitive to the doping amount. In this study, three Al-doped quaternary Ni-rich cathode materials Li[Ni0.85Co(0.1-x)Mn0.05Alx]O2 (where, x = 0–0.04) (NCMA) are synthesized through three-phase slug-flow based continuous manufacturing process followed by high temperature calcination to study the effect of Al-doping on the performance of the cathode material while reducing Co. The slug flow-based production platform has several advantages, like particle size uniformity, high production rate, and homogeneity in elemental distribution. It is found that with an increase in Al content, the specific capacity decreases but the cyclic stability and rate capability increases. Optimum Al-doping not only compensates for the adverse effect of low Co by decreasing the extent of cation mixing, but it also minimizes the electrode polarization and cracking of the particles.

Co-rotating twin screw process for continuous manufacturing of solid crystal suspension: A promising strategy to enhance the solubility, permeation and oral bioavailability of Carvedilol

Background In the current work, co-rotating twin-screw processor (TSP) was utilized to formulate solid crystal suspension (SCS) of carvedilol (CAR) for enhancing its solubility, dissolution rate, permeation and bioavailability using mannitol as a hydrophilic carrier. Methods In-silico molecular dynamics (MD) studies were done to simulate the interaction of CAR with mannitol at different kneading zone temperatures (KZT). Based on these studies, the optimal CAR: mannitol ratios and the kneading zone temperatures for CAR solubility enhancement were assessed. The CAR-SCS was optimized utilizing Design-of-Experiments (DoE) methodology using the Box-Behnken design. Saturation solubility studies and in vitro dissolution studies were performed for all the formulations. Physicochemical characterization was performed using differential scanning calorimetry , Fourier transform infrared spectroscopy, X-ray diffraction studies, and Raman spectroscopy analysis. Ex vivo permeation studies and in vivo pharmacokinetic studies for the CAR-SCS were performed. Stability studies were performed for the DoE-optimized CAR-SCS at accelerated stability conditions at 40 ºC/ 75% RH for three months. Results Experimentally, the formulation with CAR: mannitol ratio of 20:80, prepared using a KZT of 120 ºC at 100 rpm screw speed showed the highest solubility enhancement accounting for 50-fold compared to the plain CAR. Physicochemical characterization confirmed the crystalline state of DoE-optimized CAR-SCS. In-vitro dissolution studies indicated a 6.03-fold and 3.40-fold enhancement in the dissolution rate of optimized CAR-SCS in pH 1.2 HCl solution and phosphate buffer pH 6.8, respectively, as compared to the pure CAR. The enhanced efficacy of the optimized CAR-SCS was indicated in the ex vivo and in vivo pharmacokinetic studies wherein the apparent permeability was enhanced 1.84-fold and bioavailability enhanced 1.50-folds compared to the plain CAR. The stability studies showed good stability concerning the drug content. Conclusions TSP technology could be utilized to enhance the solubility, bioavailability and permeation of poor soluble CAR by preparing the SCS.

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

Digital Twin Fundamentals of mRNA In Vitro Transcription in Variable Scale Toward Autonomous Operation.

The COVID-19 pandemic caused the rapid development of mRNA (messenger ribonucleic acid) vaccines and new RNA-based therapeutic methods. However, the approval rate for candidates has the potential to be increased, with a significant number failing so far due to efficacy, safety, and manufacturing deficiencies, hindering equitable vaccine distribution during pandemics. This study focuses on optimizing the production of mRNA, a critical component of mRNA-based vaccines, using a scalable machine by investigating the key mechanisms of mRNA in vitro transcription. First, kinetic parameters for the mRNA production process were determined. The validity of the determination and the robustness of the model are demonstrated by predicting different reactions with and without substrate limitations as well as different transcripts. The optimized reaction conditions, including temperature, urea concentration, and concentration of reaction-enhancing additives, resulted in a 55% increase in mRNA yield with a 33% reduction in truncated mRNA. Additionally, the feasibility of a segmented flow approach allowed for high-throughput screening (HTS), enabling the production of 20 vaccine candidates within a short time frame, representing a 10-fold increase in productivity, compared to nonsegmented reactions limited by the residence time in the plug flow reactor. The findings presented for the first time here contribute to the development of a fully continuous and efficient manufacturing process for mRNA and other cell and gene therapy drugs/vaccine candidates as presented in our previous work, which discussed the integration of process analytical technologies and predictive process models in a Biopharma 4.0 facility to enable the production of clinical and large-scale doses, ensuring a rapid and resilient supply of critical therapeutics. The results in this study especially highlight that the same machine and equipment can be used for screening and manufacturing different drug candidates in continuous operation. By streamlining production and adhering to quality standards, this approach enhances the industry's ability to respond swiftly to pandemics and public health emergencies, addressing the urgent need for accessible and effective vaccines.

Proceedings of the 2023 Viral Clearance Symposium, Session 1: Regulatory Updates.

At the time of the 2023 Viral Clearance Symposium in Vienna, the ongoing revision of ICH Guideline Q5A(R1) Viral Safety Evaluation of Biotechnology Products Derived from Cell Lines of Human or Animal Origin clearly was the dominant regulatory topic. At the symposium, the changes expected for Q5A(R2) to mirror advances of scientific knowledge, for example, the inclusion of new products, including viral-vector-derived ones, that can be subject to virus clearance, deliberations around continuous manufacturing processes, the use of prior knowledge to supplement or in part replace virus validation studies, and new molecular methods for detection of adventitious viruses, were discussed by a European and a US regulator as well as representatives from industry associations that had been involved with the drafting process.

Innovative measurement system for saber curvature observation in straightening processes

High-strength wire materials are usually available as strip material and are subjected to a forming process such as punch-bending to produce parts for the electronics industry, for example. During the manufacturing process of the semi-finished product, residual stresses and plastic deformations are introduced into the wire by rolling and drawing processes. Straightening machines are used in the production lines to compensate for these. To increase the sustainability of these production lines, the straightening process is an essential step. Before the continuous manufacturing process starts, the straightening process must be set up and the optimal roller positions must be found. Once the process is set up, the roller position settings are usually not changed. Due to missing measurement systems for the straightening quality, it is not possible to react to fluctuations in the material properties. This leads to deviations in the dimensional accuracy of the components to be produced and thus to an increase in the rejection rate in the manufacturing processes.This paper presents two innovative measuring systems that can detect changes in saber curvature in flat wire during the straightening process. With the help of this information, the quality of the straightening process can be recorded during the running process. This enables the chance to build a control structure for the straightening quality and thus to implement demand-oriented straightening of semi-finished products. On the one hand, this means that the flat wire no longer must be overbent, which means that the strength of the material is not reduced anymore. On the other hand, the sensor information collected can be passed on to downstream processes. This creates the possibility of networking for the individual processes in a production line. This makes an important contribution to the development of intelligent manufacturing processes in line with the guiding principle of Industry 4.0.