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Modeling in Pharma Development, Scale-up, & Manufacturing

In the realm of pharmaceutical CMC, modeling and digitalization can be used to catalyze rapid development, scale-up, and optimization of manufacturing processes. Typical models leveraged include advanced engineering calculations and computational techniques. The ultimate goals of these are to provide an understanding, simulate, and predict various aspects of pharmaceutical and biopharmaceutical process development, scale-up, and manufacturing.

Modeling and digitalization also encompass the creation of digital twins for virtual development and optimization of processes. These models leverage advanced tools such as artificial intelligence, machine learning, mixing models, data analytics, molecular dynamics, kinetics, engineering calculations, and process optimization modeling to describe, simulate, and optimize various aspects of pharmaceutical and biopharmaceutical manufacturing.

Real-time data from sensors and other sources can be integrated with models in Process Analytical Technology (PAT) frameworks to monitor and control the real-life process. Process data can also be incorporated into a digital twin of the physical system to optimize the manufacturing processes in a virtual environment, allowing for rapid development and optimization while minimizing the need for costly physical experimentation and trial-and-error approaches.

Here, we delve into the multifaceted applications of modeling in pharmaceutical CMC and its transformative impact on the industry.

To Whom is Modeling Important & Why?

Modeling holds immense significance for various stakeholders involved in CMC within the pharmaceutical industry:

  • For research and development teams, modeling enables understanding of complex reactions, optimization of specific unit operations, and streamlining of production workflows.

  • Manufacturers rely on modeling to enhance process efficiency, minimize variability, and meet regulatory requirements consistently.

  • For regulatory agencies, modeling plays a significant role in Quality by Design (QbD) by providing predictive assessments of product performance, facilitating the identification of Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs), and supporting risk-based decision-making. Regulators rely on modeling as part of QbD submissions to assess the robustness of pharmaceutical processes, evaluate the impact of process changes on product quality, and determine the adequacy of control strategies. By leveraging modeling, regulators can gain insights into the relationship between process parameters and product attributes, enabling them to make informed decisions regarding product approval and post-approval changes.

Modeling Techniques

There are various modeling techniques available to CMC professionals to assist them in process development, scale-up, and manufacturing. These techniques aid in optimizing processes, ensuring successful scale-up and tech transfer, predicting outcomes, and ensuring product quality. These techniques include:

Process Modeling

Process modeling in pharmaceutical manufacturing involves describing a unit operation using mathematical models to understand, optimize, and control production processes. These models predict how changes in parameters affect product quality and process efficiency. There are three main types: 1. empirical; 2. mechanistic; and 3. hybrid. Empirical models rely on experimental data; mechanistic models use fundamental principles; and hybrid models combine both approaches.

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Process Modelling

Process modeling aids in optimizing processes, scaling up production, and ensuring product quality and regulatory compliance. It helps in predicting process behavior, identifying optimal conditions, and troubleshooting issues. Ultimately, process modeling plays a crucial role in pharmaceutical manufacturing by providing insights into process dynamics and facilitating continuous improvement initiatives.

At APC, we use not only standard and novel commercial software available for process modeling, but we also we create in-house custom-made web applications. Web applications have been an excellent tool for easy model dissemination among people with different levels of expertise, standardizing the quality of the work by fixing the workflow with high standards and replacing different software with such customized web applications.

Custom-made Web Applications

Web applications, or WebApps, are software programs that run on a web server rather than being installed on a local computer, meaning no download or installation is required. WebApps have grown in popularity due to their versatility, convenience, and ease of use, offering dynamic and interactive user experiences. In the pharmaceutical industry, these digital platforms are designed to streamline research, development, and manufacturing processes, offering tailored solutions that enhance efficiency, data integrity, and decision-making.

At APC, we design bespoke WebApps, tailored to address the unique challenges and complexities of the pharma industry. Our expertise in both the digital and scientific realms ensures that each WebApp is not just a tool, but a comprehensive solution.

  • Model Dissemination: We transform complex scientific models into accessible, user-friendly tools, empowering non-technical users to leverage advanced analytics without the need for in-depth domain knowledge or coding expertise. This service not only democratizes data analysis but also enhances decision-making processes, making sophisticated modeling accessible to all.

  • Workflow Digitalization: Through our interactive WebApps, we streamline complex workflows, integrating smart tools and guides that minimize errors and boost efficiency. Our solutions are crafted to optimize your operational performance, ensuring flawless execution and enhanced productivity.

  • Database Access and Integrity: With our intuitive interfaces, navigating vast databases becomes trivial. We uphold the highest standards of data accuracy and integrity, implementing robust validation checks to prevent erroneous entries and safeguard your data.

Crafted by scientists for scientists, our WebApps stand out for their precision, scientific accuracy, and relevance to the pharma industry's needs. From small molecule to large molecule process development, we can deliver an end-to-end digital solution, from model dissemination and workflow digitalization to data integrity, tailored specifically for the pharma sector.

Computational Fluid Dynamics (CFD)
& Discrete Element Methods (DEM)

Computational Fluid Dynamics (CFD) plays a crucial role in simulating and analyzing fluid flow, heat transfer, and mass transfer within various equipment and processes. CFD models offer a comprehensive view of the intricate fluid dynamics phenomena taking place in reactors, mixers, tubing lines, spray nozzles, and other pivotal unit operations. By mathematically solving equations governing fluid behavior, CFD simulations provide insights into fluid velocity profiles, temperature distributions, and concentration gradients within the system.

Discrete Element Methods (DEM) simulation is used to simulate particle motion, breakage, and agglomeration. The coupling of CFD and DEM can provide a powerful understanding of parameters such as residence time distribution in crystallizers or perfusion bioreactors for example. This combination can also enable the optimization of more complex systems such as crystallization, filtration, transfection, and milling.

Pharmaceutical CMC professionals harness CFD and DEM not only to optimize equipment design, improve mixing efficiency, and enhance heat and mass transfer rates but also for scale up and tech transfer. Additionally, CFD and DEM assist in troubleshooting operational challenges, identifying areas for enhancement, and assessing the impact of process modifications on product quality and performance. Through precise modeling and simulation, CFD and DEM contributes significantly to the development of robust manufacturing processes, ensuring adherence to product quality standards, efficiency, and regulatory compliance in pharmaceutical production.

Data Analytics

Data analytics incorporates a wide range of approaches utilized by pharmaceutical companies to monitor, troubleshoot, and optimize manufacturing processes. This is a vital tool used in Quality by Design (QbD) as data analytics can reveal relationships and influences between critical process parameters (CPPs) and critical quality attributes (CQAs) and as a result, be used to understand and give insights on operation and control strategies. Data analysis techniques can be used to identify trends, patterns, and outliers as well as demonstrate equivalence across equipment or manufacturing sites. Distribution and outlier analysis are pivotal tools in determining the reproducibility of a process at both laboratory and manufacturing scales while power analysis and equivalence testing are extremely useful in demonstrating comparability during the tech transfer process.

Multivariate data analysis (MVDA) plays a pivotal role in dissecting large, complex datasets obtained from manufacturing processes. MVDA involves techniques such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and exploratory analysis to identify parameters that are the primary cause of variability in a process and highlight interactions between factors which can then be controlled to maximize efficiency while ensuring CQAs are met.

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Example of Data Analytics

Process Analytical Technology (PAT) can then be employed alongside MVDA to provide in-line monitoring of these process parameters and provide a holistic understanding of the process in real-time. Batch records can be integrated with current data to create robust statistical models that can facilitate early detection of deviations and potential risks to product quality. MVDA has become an integral part of the regulatory framework due to its transparency and ease of validation. This allows pharmaceutical companies to demonstrate the predictability and reproducibility of their process to regulatory bodies. By understanding the interactions between various factors in a process, data analysis enables pharmaceutical companies to make informed decisions, streamline processes, and ensure regulatory compliance in terms of product safety and quality.

Design of Experiments (DoE)

Design of Experiments (DoE) is a systematic statistical technique widely used in pharmaceutical manufacturing to optimize processes and enhance product quality. It involves planning and executing a series of carefully designed experiments to understand the relationship between input variables (factors) and output responses (such as critical quality attributes). DoE is typically employed with one of three experimental objectives in mind: Screening, Optimization, or Robustness Testing.

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Design of Experiments

A screening DoE is used to identify the most influential factors in a process, and then to determine the ranges across which they should be investigated further. Optimization DoE’s are concerned with defining a combination of the important factors that will result in optimal operating conditions to satisfy Critical Process Parameters (CPPs) & Critical Quality Attributes (CQAs). Robustness testing can then be employed to determine how sensitive a product or process is to small changes in the factor settings to help define Proven Acceptable Ranges (PARs) for the process. DoE is an efficient and cost-effective way to understand complex manufacturing processes where multiple factors influence product performance.

Through analysis of data obtained from the planned DoE, a theoretical model is created, and further statistical methods like hypothesis testing and analysis of variance (ANOVA) allow for the quantification of factor effects and determination of the most influential parameters. Ultimately, DoE empowers pharmaceutical CMC professionals to systematically explore process variables, optimize conditions, and establish robust manufacturing processes, ensuring consistent product quality while minimizing development time and resources.

Kinetic Modeling

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Kinetic Modeling

One important Quality by Design (QbD) tool, kinetic modeling is used to understand and predict how a chemical or biochemical reaction behaves over time. This modeling approach is combined with smart experimentation to determine and perform a minimal amount of process runs to provide a robust calibration data set. It also requires a thorough analytical method development to allow the identification and quantification of products and byproducts as this information is fed into the model.

Once the model is calibrated with the initial data set, process development engineers and scientists use this model to understand reaction mechanisms and extract rate constants and activation energies. This information is then used to predict the behavior of such reaction by virtually performing in a large design space varying factors such as temperature, concentration, and dosing rates. The large number of experiments simulated save development time and cost, accelerating process development, and producing knowledge about the reaction.

This is also a powerful tool to elucidate the stability of pharmaceutical and biopharmaceutical products by predicting degradation paths and rates under various storage conditions. Manufacturing contingency plans can also be established using kinetic modeling, as well as prevention of impurity formation, and design of strategies for impurity purge.

Atomistic/Molecular Simulations

Molecular simulations are a valuable tool for providing fundamental understanding of the relationships governing pharmaceutical processes. This is invaluable in identifying optimum process conditions and a right solvent to control resulting solution and solid state properties of active ingredients and pharmaceutical excipients, such as solubility, chemical purity, crystal form and particle morphology. Traditional solvent screening methods involve time and resource-intensive trial and error experimentation. To reduce this experimental effort, a high throughput in silico solvent screening can be carried out to effectively tests all the possible solvent combinations to guide the selection of a preferred solvent, pointing towards optimum solubility and nucleation kinetics while avoiding solvents which are incompatible with the API. In addition to the solvent screen, the molecular simulations are highly efficient in pre-clinical development, such as crystal form selection, streamlining in silico polymorph, co-crystal, and salt screening. The fundamental understanding provided by molecular simulations benefits also existing manufacturing processes, efficiently guiding process troubleshooting and intensification.

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cosmo workflow

Several software packages and state of the art computational methodologies are used to find the solutions and optimize the process. The best example of such a hybrid approach that is currently used in process development is predicting the solubility and stability of a molecule with the use of exhaustive conformational search by applying molecular mechanics methods and calculating the solute-solvent interactions at electronic level using Density Functional Theory Methods. The data generated by using the above methods are post processed by applying AI and machine learning algorithms to accelerate the data processing and identify patterns, thereafter machine learning models are developed to integrate them into the process development.

Population Balance Modeling

Population Balance Modeling (PBM) is a specialized technique extensively used in pharmaceutical manufacturing to predict and control the evolution of particle size distribution during various processes such as crystallization, precipitation, and granulation. PBM accounts for the dynamic interactions between different particle populations, considering factors like nucleation, growth, agglomeration, and breakage. By describing the evolution of particle size distribution over time, PBM helps in optimizing process conditions to achieve desired product characteristics such as particle size, shape, and distribution.

Pharmaceutical CMC professionals use PBM to design and scale-up processes, predict product performance, and troubleshoot issues related to particle properties. Through simulation and analysis, PBM facilitates the development of robust manufacturing processes that consistently meet quality specifications. Additionally, PBM supports decision-making by providing insights into the impact of process parameters on product quality, enabling efficient process optimization and control.

Artificial Intelligence (AI) and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in pharmaceutical manufacturing, offering advanced analytical capabilities and predictive modeling tools. These technologies enable pharmaceutical CMC professionals to extract valuable insights from vast amounts of data generated during various stages of the manufacturing process. AI and ML algorithms can analyze complex datasets to identify patterns, trends, and correlations, helping optimize process parameters, predict product properties (e.g. solubility), and detect anomalies.

In drug development, AI and ML facilitate the design of novel molecules, predict drug-target interactions, and accelerate the discovery of new therapies. Moreover, AI-powered process control systems can adapt in real-time to changing conditions, ensuring consistent product quality and regulatory compliance. By harnessing the power of AI and ML, pharmaceutical companies can improve efficiency, reduce costs, and accelerate innovation in drug development and manufacturing. These technologies are poised to revolutionize the pharmaceutical industry, driving advancements in personalized medicine, precision manufacturing, and therapeutic efficacy.

Model Predictive Control (MPC)

Model Predictive Control (MPC) is a sophisticated control strategy widely employed in pharmaceutical manufacturing to optimize process performance and ensure product quality. MPC utilizes mathematical models of the process dynamics to predict future behavior and compute optimal control actions. By continuously updating the model predictions based on real-time process data, MPC can anticipate disturbances and adjust control inputs to maintain desired process conditions and meet quality specifications.

This proactive approach improves process stability, reduces variability, and minimizes the risk of off-specification products. MPC is particularly beneficial for complex, multivariable processes where traditional control strategies may be inadequate. Pharmaceutical CMC professionals rely on MPC to enhance process control, maximize throughput, and minimize energy consumption while adhering to regulatory requirements. The implementation of MPC systems enables adaptive and agile manufacturing, facilitating the production of high-quality pharmaceutical products efficiently and cost-effectively.

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Model Predictive Control

What are the Typical Challenges Faced in Model Development and Use?

Building and utilizing models in pharmaceutical manufacturing pose several challenges, stemming from the complexity of processes, the availability and quality of data, and the dynamic nature of the industry. Some of the key challenges include:

  • Process Complexity: Pharmaceutical manufacturing processes are inherently complex, involving multiple unit operations, intricate chemical reactions, and varying physical properties of materials. Modeling such complexity requires sophisticated mathematical formulations and computational techniques, often necessitating substantial computational resources and expertise.

  • Data Availability and Quality: Models rely heavily on data for parameter estimation, validation, and calibration. However, acquiring comprehensive and high-quality data, especially in early-stage development or for novel processes, can be challenging. Incomplete or inaccurate data may lead to biased model predictions and hinder the effectiveness of the modeling approach.

  • Model Validation and Verification: Ensuring the accuracy and reliability of models is crucial for their successful application in pharmaceutical manufacturing. However, validating and verifying models against experimental data can be complex and time-consuming, particularly for dynamic processes or when experimental data are limited. Rigorous validation processes are necessary to build confidence in model predictions and ensure their suitability for decision-making.

  • Model Uncertainty and Sensitivity: Models inherently involve uncertainties due to simplifications, assumptions, and variability in process parameters. Understanding and quantifying these uncertainties are essential for assessing the reliability of model predictions and making informed decisions. Sensitivity analysis techniques are employed to identify influential factors and assess the robustness of model predictions under varying conditions.

  • Regulatory Compliance: Models used in pharmaceutical manufacturing must adhere to regulatory guidelines and standards to ensure product quality, safety, and efficacy. Regulatory authorities require transparent documentation of model development, validation, and verification processes, as well as justification of model assumptions and limitations. Meeting regulatory requirements adds complexity to the modeling process and necessitates close collaboration between modelers and regulatory agencies.

  • Integration with Process Control: Integrating models with real-time process control systems is essential for implementing advanced control strategies and optimizing manufacturing processes. However, achieving seamless integration poses technical challenges related to data communication, model updating, and control algorithm implementation.

Despite these challenges, effective modeling and simulation play a vital role in pharmaceutical manufacturing, enabling process optimization, product quality assurance, and regulatory compliance. Addressing these challenges requires interdisciplinary collaboration, advanced computational tools, and continuous improvement in modeling techniques.

Conclusion

In conclusion, modeling stands as an indispensable pillar within the realm of pharmaceutical CMC, providing a robust framework for innovation, efficiency, and regulatory compliance. From the inception of drug development to the optimization of manufacturing processes, modeling revolutionizes how pharmaceutical products are designed, manufactured, and regulated. By leveraging advanced computational techniques, modeling empowers researchers, manufacturers, and regulatory agencies to navigate the complexities of pharmaceutical CMC with precision and foresight. Through predictive assessments, optimization algorithms, and risk-based approaches, modeling enhances process understanding, accelerates development timelines, and ensures the quality and safety of pharmaceutical products.