Capsule-Based Dropwise Additive Manufacturing with Pharmaceutical Suspensions

Current manufacturing of pharmaceutical products focuses on creating a standard dosage of the active pharmaceutical ingredient (API); however, dosages often need to be altered or customized to account for a patient’s age, weight, comorbidity, and other genetic factors. A potential method for dispensing precise dosages of API suspensions through dropwise addition is detailed in the following paper. By using a drop-on-demand printing rig, a series of suspensions comprised of varying volume fractions of a micron-scale API in a carrier fluid were printed, and individual drop volumes were analyzed using high-resolution imaging. From this, capsules with 1 mg dosages and 100 mg dosages were manufactured. Completed trials yielded respective means of 1.043 mg and 99.946 mg of API being deposited across varying suspension compositions. The relative standard deviations of the 1 mg capsules averaged to be 1.51% and 0.30% for the 100 mg capsules. Further combinations of APIs and carrier fluids are continuing to be tested. The relative standard deviations of both dosage sizes are well under the 6% maximum variability imposed by the US Food and Drug Administration to regulate dosages of API, which provides evidence for the feasibility of printing pharmaceutical suspensions to create customized dosages for patient consumption

Resilience and risk analysis of fault-tolerant control design in continuous pharmaceutical manufacturing

PresentationThe effects of the paradigm shift from batch to continuous manufacturing on pharmaceutical industry, in terms of process safety and product quality, e.g., danger of dust explosions and risk of off-spec products, are of major concerns in the recent research progress in control system design. Specifically, a fault-tolerant control of critical process parameters (CPPs) and critical quality attributes (CQAs) is of paramount importance for the continuous operation with built-in safety and quality. In this study, a systematic framework for fault-tolerant control design, analysis, and evaluation for continuous pharmaceutical solid-dosage manufacturing is proposed, consisting of system identification, control design and analysis (controllability, stability, resilience, etc.), hierarchical three-layer control structures (model predictive control, state estimation, data reconciliation, etc.), risk mapping, assessment and planning (Risk MAP) strategies, and control performance evaluation. The key idea of the proposed framework is to identify the potential risks in the control design, material variance, and process uncertainties, under which the control strategies are evaluated. The framework is applied to a continuous direct compaction process, specifically the feeding-blending system wherein the major source of variance in the process operation and product quality arises. It can be demonstrated that the process operation failures and product quality variances in the feeding-blending system can be mitigated and managed through the proposed systematic fault-tolerant control system design and risk analysis framework

The Use of Near Infrared and Microwave Sensing for On-line Real Time Monitoring of Moisture Content and Composition of Powder Blend

Online process analytics has been a topic of interest by pharmaceutical companies as a method of determining how operating parameters affect the final quality of their products. One form of online process analytics that has been found to be effective is microwave sensing. While it has been found that microwave resonance sensing can be used to measure parameters such as moisture content and density, it has yet to be discovered if such sensors have the ability to measure changes in content uniformity of raw materials pharmaceutical companies use. Data was collected using a spin riffler fitted with a microwave sensor and a near infrared probe (NIR) (more commonly used method of monitoring) that is to be tested against. Various known compositions mixtures of acetaminophen (APAP) and microcrystalline cellulose (MCC) powders were created and tested for content uniformity and moisture content by passing it over the sensor. The raw data was passed through MATLAB’s neural networks software and a calibration model was created for content uniformity that can be used to predict values. Upon analyzing the data, it was found that an accurate reading of composition uniformity could be determined using a microwave sensor. The model created aided in determining the composition of unknown blends of powder and proved to be accurate. This calibration model will serve as a contribution to the ongoing research being performed in online process analytics. By utilizing these techniques, pharmaceutical companies have the ability to more efficiently analyze their products in an online real-time process

A framework for the practical development of condition monitoring systems with application to the roller compactor

Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems. Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects

Valuation of project portfolios: An endogenously discounted method

Real options analysis (ROA) has been developed to value assets in which managerial flexibilities create significant value. The methodology is ideal for the valuation of projects in which frequent adjustments (e.g. investment deferral, project scope changes, etc) are necessary in response to the realization of market and technological uncertainties. However, ROA has no practical application when valuing portfolios of multiple concurrent projects sharing resources, as the size of the problem grows exponentially with the number of projects and the length of the time horizon. In this paper an extension of ROA suitable for the valuation of project portfolios with substantial technological uncertainty (e.g. R&D portfolios) is proposed. The method exploits the distributed decision making strategy encountered in most organizations to decompose the portfolio valuation problem into a decision-making sub-problem and a set of single project valuation sub-problems that can be sequentially solved. Discrete event simulation is used for the first sub-problem, while a tailored ROA based strategy is used for the set of valuation sub-problems. A case study from the pharmaceutical industry is used to compare the decision tree analysis (DTA) method and the proposed method.Real options analysis Portfolio Projects Simulation Decision analysis Finance