Bayesian optimization has proven effective for optimizing expensive-to-evaluate functions in Chemical Engineering. However, valuable physical insights from domain experts are often overlooked. This article introduces a collaborative Bayesian optimization approach that integrates both human expertise and large language models (LLMs) into the data-driven decision-making process. By combining...
Design of experiments for process scale-up can be described as a double-edged sword for the pharmaceutical industry: intensification of experiments expands the knowledge of the process (uncertainty reduction) but increases resource expenditure. On the other hand, moving forward without enough process understanding is the first stone in a path of deviations, lack of quality, and even safety...
Capability indexes can be used to estimate how likely a given supplier of raw materials is to meet customer's requirements for these raw materials. It is therefore usually used by a customer operating a process as a criterion for selecting raw material suppliers. However, both univariate and multivariate capability indexes provided so far in the literature assume that the specifications are...
In process robustness studies, experimenters are interested in comparing the responses at different locations within the normal operating ranges of the process parameters to the response at the target operating condition. Small differences in the responses imply that the manufacturing process is not affected by the expected fluctuations in the process parameters, indicating its robustness. In...
The Quality by Design (QbD) approach has been widely adopted in the development of both novel and generic pharmaceutical formulations1. Extending these principles to the analytical domain, Analytical Quality by Design (AQbD) has emerged as a structured framework for optimizing analytical methodologies2. The aim of the present work was to outline a comprehensive framework for development of a...
Introduction: As Steve Jobs famously stated, "You can't connect the dots looking forward; you can only connect them looking backward." This reflective insight resonates perfectly in this work. Adopt a Retrospective Quality by Design (rQbD) perspective and connect the dots of past manufacturing experiences to drive continuous improvement and innovation in legacy drug products and their...
Process analytic technologies (PAT) are routinely used to rapidly assess quality properties in many industrial sectors. The performance of PAT-based models is, however, highly related to their ability to pre-process the spectra and select key wavebands. Amongst the modeling methodologies for PAT, partial least squares (PLS) (Wold, Sjöström and Eriksson, 2001) and interval partial least squares...
In the pharmaceutical industry, drug solubility is a critical quality attribute. For example, drug solubility in organic solvents mixtures is usually screened in drug development to select the best solvent system for crystallization in such a way as to design the manufacturing process. Solubility is also important in the final product because it has a direct impact on the way the drug is...
Keywords: Bioreactor modeling, Feed forward neural network, Hybrid semiparametric model, Physics-informed neural network, Fed-batch reactor
Abstract
Bioreactors are fundamental to bioprocess technology, yet the complexity of bioreactor systems continues to challenge effective digitalization and optimization. The intricate, dynamic nature of cell...
Keywords: Biopharma 4.0, Deep learning, Physics Informed Neural Networks, Bioreactors, Digital Twin
Abstract
Hybrid modeling combining First-Principles with Machine Learning (ML) is becoming a pivotal methodology for Industry 4.0 enactment. The combination of ML with prior knowledge generally improves the model predictive power and transparency while reducing the amount of data for process...
Keywords: Digital Twin, Hybrid Modeling, Machine Learning, Supercritical Carbon Dioxide Extraction, Process Optimization
Abstract
Supercritical carbon dioxide (ScCO2) extraction is a separation process that presents several advantages over traditional extraction methods of nonpolar solutes, eliminating the need for harmful organic solvents and costly post-processing steps required to...
Five samples soils from agricultural farms of Manica province (Mozambique), two of Manica and three of Sussundenga district, were collected by random sampling. The random sampling was done in a zigzag manner in a four years period, from 2021 to 2024 - a total of fifteen samples, five per farm in three campains, were collected [1,2].
Twenty-seven physical-chemical parameters were analysed for...
Hybrid modeling has emerged as a cost-effective and time-saving approach for process modeling, significantly advancing model-based process development within the biopharmaceutical industry. By integrating mechanistic and data-driven modeling techniques, hybrid models provide a comprehensive framework that enhances process efficiency and scalability. This combination leverages the strengths of...
In the Ceramic district of the cityt of Beira in Mozmabique the drinking water became from treated piped water and from traditional wells without water treatment. Nine sampling sites of water from wells use for human consumation in the Ceramic district near a cemetery were chosen to do an evaluation of the quality of the water of the wells and check the similitudes of the water of different...
Spectroscopic technologies have seen a substantial rise in applications in recent years, particularly in the monitoring of cell cultures. In addition to this established use, novel applications are emerging, such as the utilization of spectroscopic techniques in microbial fermentations. Alongside these advancements, innovative methods are being developed to create calibration models that...
In the rapidly evolving field of pharmaceutical bioprocess development, the application of transfer learning techniques presents a transformative opportunity to enhance model performance and streamline process development activities. One of the key challenges in bioprocess development is the high cost and time associated with generating new experimental data. Transfer learning offers a...
The production of solid lipid nanoparticles (SLNs) in the pharmaceutical field presents significant challenges, particularly in terms of optimizing drug loading and colloidal properties, as well as enhancing product quality and manufacturing efficiency. This study aims to address these challenges by investigating the transfer of a process guided by Quality by Design (QbD) principles....
End-to-End (E2E) models and Digital Twins in the pharmaceutical industry enhance efficiency, improve decision-making, allow for real-time monitoring, optimization, predictive analytics, and ultimately strengthen quality control and reduce costs. A key component of E2E models is the use of Monte Carlo simulations to capture uncertainties and variability within complex processes.
Typically,...
Despite advancements in Systems Biology, developing purely ODE-based mechanistic models remains challenging due to incomplete knowledge of parameters or computational inefficiencies. In such cases, hybrid and data-driven approaches provide viable alternatives. To facilitate seamless simulation and analysis alongside classical ODE-based models, it is advantageous to encode data-driven models in...
Introduction: The blood-brain barrier (BBB) severely restricts the passage of drugs into the brain, posing a significant challenge in treating central nervous system disorders such as glioblastoma (GBM). Therefore, there is an urgent need for advanced in vitro models that accurately characterize both BBB permeability and GBM behavior [1]. The evolution from two-dimensional (2D) to...
Quality by Design (QbD) is a systematic approach for building quality into a product. The Design Space Profiler in JMP helps solve the fundamental QbD problem of determining an optimal operating region that assures quality as defined by specifications associated with Critical Quality Attributes (CQAs) while still maintaining flexibility in production.
We will discuss a simulation approach to...
Establishing multivariate specification regions for selecting raw material lots entering a customer’s plant is crucial for ensuring smooth operations and consistently achieving final product quality targets. Moreover, these regions guide the selection of suppliers. By meeting these specifications, suppliers contribute to customer satisfaction, which can, in turn, enhance market share. Latent...
Inception for Petroleum Analysis (IPA) [1] is a deep convolutional network inspired from state-of-the-art computer vision architectures. IPA showed improved performance, compared to PLS, without depending on complex pre-processing operations thanks to its several computational blocks. The network begins with three stacked convolutions, followed by a multi-branch module consisting of four...
In artificial intelligence (AI), the complexity of many models and processes often surpasses human interpretability, making it challenging to understand why a specific prediction is made. This lack of transparency is particularly problematic in critical fields like healthcare, where trust in a model's predictions is paramount. As a result, the explainability of machine learning (ML) and other...
Convolutional Neural Networks (CNNs) have been increasingly used to build NIR based chemometric models with applications ranging from chemical sample analysis to food quality control. In the latter, NIR spectroscopy combined with CNNs enable rapid, non-destructive SOTA predictions of important quality parameters such as dry matter content in fruit [1].
The lack of a standard CNN architecture...
Design of experiments is one of the main Quality by Design (QbD) tools within the process industry.
However, "classic" DoE is increasingly challenged by modern techniques such as Bayesian Optimization and Active Learning.
These innovative methods are promoted as faster and more intuitive, offering greater flexibility in experimentation.
In this talk, I will provide a direct comparison of...
Bayesian Optimization (BO) has been recently shown as an efficient method for data-driven optimization of expensive and unknown functions. BO relies on a probabilistic surrogate model, commonly a Gaussian Process (GP), and an auxiliary acquisition function that balances exploration and exploitation for a goal-oriented experimental design, with the aim of finding the global optimum under a...
Carefully designing experiments is crucial for gaining a deeper understanding of process behaviour. Design of Experiments (DOE) is a well-established active learning methodology with an extensive track record of solid contributions to research and industry in various areas, including screening, modelling, optimisation, specification matching, and robust design. Based on a reduced set of...
Waste lubricant oil (WLO) is a hazardous residue that requires proper management. Among the options available, regeneration is the preferred approach to promote a sustainable circular economy. However, WLO regeneration is only viable if the WLO does not coagulate during processing as it can cause operational problems and possibly lead to a premature shutdown of the process for cleaning and...
Real-time monitoring of chemical processes is key for optimizing yield, preventing out of specification product, and improving overall process efficiency. Process analytical technology (PAT) tools, such as near-infrared and infrared (IR) spectroscopies, provide a real-time window into chemical processes, enabling non-destructive monitoring of analyte concentrations, and reducing dependance on...
Monitoring formulation quality during Continuous Direct Compression (CDC) and therefore remaining within product specifications is complex and cannot easily be inferred from process measurements. Process Analytical Technology (PAT) sensors allow in-line process monitoring and control of Critical Quality Attributes (CQAs), reducing the time and effort required for both sampling and off-line...
Online analysis has been widely developed to monitor the chemistry or the physics on batch or continuous processes. One of the major issues concerns the sampling part to integrate the analytical solution into the process. Optical spectroscopy is one of the most used technologies as it can be implemented directly inline and does not necessary required a sampling loop to adapt the process to the...
In the Chemical Manufacturing Industry, a diverse array of sensor technologies and data collection methods provide valuable insights into monitoring physical and chemical phenomena, equipment status, process conditions, raw material attributes, product quality, emissions, and logistics. Despite the extensive use of sensors, critical process information such as leaks, corrosion, and insulation...
Background
Monoclonal antibodies (mAbs) are highly specific proteins used in personalized therapeutics, with applications ranging from cancer treatment to autoimmune disease management. In this study, we focus on the production of 86 monoclonal antibody (mAb) molecules, each potentially having unique production characteristics.
Due to the confidential nature of proprietary data in the...