Pharmaceutical development and manufacturing in a Quality by Design perspective: methodologies for design space description

In the last decade, the pharmaceutical industry has been experiencing a period of drastic change in the way new products and processes are being conceived, due to the introduction of the Quality by design (QbD) initiative put forth by the pharmaceutical regulatory agencies (such as the Food and Drug Adminstration (FDA) and the European Medicines Agency (EMA)). One of the most important aspects introduced in the QbD framework is that of design space (DS) of a pharmaceutical product, defined as “the multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality”. The identification of the DS represents a key advantage for pharmaceutical companies, since once the DS has been approved by the regulatory agency, movements within the DS do not constitute a manufacturing change and therefore do not require any further regulatory post-approval. This translates into an enhanced flexibility during process operation, with significant advantages in terms of productivity and process economics. Mathematical modeling, both first-principles and data-driven, has proven to be a valuable tool to assist a DS identification exercise. The development of advanced mathematical techniques for the determination and maintenance of a design space, as well as the quantification of the uncertainty associated with its identification, is a research area that has gained increasing attention during the last years. The objective of this Dissertation is to develop novel methodologies to assist the (i) determination of the design space of a new pharmaceutical product, (ii) quantify the assurance of quality for a new pharmaceutical product as advocated by the regulatory agencies, (iii) adapt and maintain a design space during plant operation, and (iv) design optimal experiments for the calibration of first-principles mathematical models to be used for design space identification. With respect to the issue of design space determination, a methodology is proposed that combines surrogate-based feasibility analysis and latent-variable modeling for the identification of the design space of a new pharmaceutical product. Projection onto latent structures (PLS) is exploited to obtain a latent representation of the space identified by the model inputs (i.e. raw material properties and process parameters) and surrogate-based feasibility is then used to reconstruct the boundary of the DS on this latent representation, with significant reduction of the overall computational burden. The final result is a compact representation of the DS that can be easily expressed in terms of the original physically-relevant input variables (process parameters and raw material properties) and can then be easily interpreted by industrial practitioners. As regards the quantification of “assurance” of quality, two novel methodologies are proposed to account for the two most common sources of model uncertainty (structural and parametric) in the model-based identification of the DS of a new pharmaceutical product. The first methodology is specifically suited for the quantification of assurance of quality when a PLS model is to be used for DS identification. Two frequentist analytical models are proposed to back-propagate the uncertainty from the quality attributes of the final product to the space identified by the set of raw material properties and process parameters of the manufacturing process. It is shown how these models can be used to identify a subset of input combinations (i.e., raw material properties and process parameters) within which the DS is expected to lie with a given degree of confidence. It is also shown how this reduced space of input combinations (called experiment space) can be used to tailor an experimental campaign for the final assessment of the DS, with a significant reduction of the experimental effort required with respect to a non-tailored experimental campaign. The validity of the proposed methodology is tested on granulation and roll compaction processes, involving both simulated and experimental data. The second methodology proposes a joint Bayesian/latent-variable approach, and the assurance of quality is quantified in terms of the probability that the final product will meet its specifications. In this context, the DS is defined in a probabilistic framework as the set of input combinations that guarantee that the probability that the product will meet its quality specifications is greater than a predefined threshold value. Bayesian multivariate linear regression is coupled with latent-variable modeling in order to obtain a computationally friendly implementation of this probabilistic DS. Specifically, PLS is exploited to reduce the computational burden for the discretization of the input domain and to give a compact representation of the DS. On the other hand, Bayesian multivariate linear regression is used to compute the probability that the product will meet the desired quality for each of the discretization points of the input domain. The ability of the methodology to give a scientifically-driven representation of the probabilistic DS is proved with three case studies involving literature experimental data of pharmaceutical unit operations. With respect to the issue of the maintenance of a design space, a methodology is proposed to adapt in real time a model-based representation of a design space during plant operation in the presence of process-model mismatch. Based on the availability of a first-principles model (FPM) or semi-empirical model for the manufacturing process, together with measurements from plant sensors, the methodology jointly exploits (i) a dynamic state estimator and (ii) feasibility analysis to perform a risk-based online maintenance of the DS. The state estimator is deployed to obtain an up-to-date FPM by adjusting in real-time a small subset of the model parameters. Feasibility analysis and surrogate-based feasibility analysis are used to update the DS in real-time by exploiting the up-to-date FPM returned by the state estimator. The effectiveness of the methodology is shown with two simulated case studies, namely the roll compaction of microcrystalline cellulose and the penicillin fermentation in a pilot scale bioreactor. As regards the design of optimal experiments for the calibration of mathematical models for DS identification, a model-based design of experiments (MBDoE) approach is presented for an industrial freeze-drying process. A preliminary analysis is performed to choose the most suitable process model between different model alternatives and to test the structural consistency of the chosen model. A new experiment is then designed based on this model using MBDoE techniques, in order to increase the precision of the estimates of the most influential model parameters. The results of the MBDoE activity are then tested both in silico and on the real equipment

Latent variable modeling approaches to assist the implementation of quality-by-design paradigms in pharmaceutical development and manufacturing

With the introduction of the Quality-by-Design (QbD) initiative, the American Food and Drug Administration and the other pharmaceutical regulatory Agencies aimed to change the traditional approaches to pharmaceutical development and manufacturing. Pharmaceutical companies have been encouraged to use systematic and science-based tools for the design and control of their processes, in order to demonstrate a full understanding of the driving forces acting on them. From an engineering perspective, this initiative can be seen as the need to apply modeling tools in pharmaceutical development and manufacturing activities. The aim of this Dissertation is to show how statistical modeling, and in particular latent variable models (LVMs), can be used to assist the practical implementation of QbD paradigms to streamline and accelerate product and process design activities in pharmaceutical industries, and to provide a better understanding and control of pharmaceutical manufacturing processes. Three main research areas are explored, wherein LVMs can be applied to support the practical implementation of the QbD paradigms: process understanding, product and process design, and process monitoring and control. General methodologies are proposed to guide the use of LVMs in different applications, and their effectiveness is demonstrated by applying them to industrial, laboratory and simulated case studies. With respect to process understanding, a general methodology for the use of LVMs is proposed to aid the development of continuous manufacturing systems. The methodology is tested on an industrial process for the continuous manufacturing of tablets. It is shown how LVMs can model jointly data referred to different raw materials and different units in the production line, allowing to understand which are the most important driving forces in each unit and which are the most critical units in the line. Results demonstrate how raw materials and process parameters impact on the intermediate and final product quality, enabling to identify paths along which the process moves depending on its settings. This provides a tool to assist quality risk assessment activities and to develop the control strategy for the process. In the area of product and process design, a general framework is proposed for the use of LVM inversion to support the development of new products and processes. The objective of model inversion is to estimate the best set of inputs (e.g., raw material properties, process parameters) that ensure a desired set of outputs (e.g., product quality attributes). Since the inversion of an LVM may have infinite solutions, generating the so-called null space, an optimization framework allowing to assign the most suitable objectives and constraints is used to select the optimal solution. The effectiveness of the framework is demonstrated in an industrial particle engineering problem to design the raw material properties that are needed to produce granules with desired characteristics from a high-shear wet granulation process. Results show how the framework can be used to design experiments for new products design. The analogy between the null space and the Agencies’ definition of design space is also demonstrated and a strategy to estimate the uncertainties in the design and in the null space determination is provided. The proposed framework for LVM inversion is also applied to assist the design of the formulation for a new product, namely the selection of the best excipient type and amount to mix with a given active pharmaceutical ingredient (API) to obtain a blend of desired properties. The optimization framework is extended to include constraints on the material selection, the API dose or the final tablet weight. A user-friendly interface is developed to aid formulators in providing the constraints and objectives of the problem. Experiments performed industrially on the formulation designed in-silico confirm that model predictions are in good agreement with the experimental values. LVM inversion is shown to be useful also to address product transfer problems, namely the problem of transferring the manufacturing of a product from a source plant, wherein most of the experimentation has been carried out, to a target plant which may differ for size, lay-out or involved units. An experimental process for pharmaceutical nanoparticles production is used as a test bed. An LVM built on different plant data is inverted to estimate the most suitable process conditions in a target plant to produce nanoparticles of desired mean size. Experiments designed on the basis of the proposed LVM inversion procedure demonstrate that the desired nanoparticles sizes are obtained, within experimental uncertainty. Furthermore, the null space concept is validated experimentally. Finally, with respect to the process monitoring and control area, the problem of transferring monitoring models between different plants is studied. The objective is to monitor a process in a target plant where the production is being started (e.g., a production plant) by exploiting the data available from a source plant (e.g., a pilot plant). A general framework is proposed to use LVMs to solve this problem. Several scenarios are identified on the basis of the available information, of the source of data and on the type of variables to include in the model. Data from the different plants are related through subsets of variables (common variables) measured in both plants, or through plant-independent variables obtained from conservation balances (e.g., dimensionless numbers). The framework is applied to define the process monitoring model for an industrial large-scale spray-drying process, using data available from a pilot-scale process. The effectiveness of the transfer is evaluated in terms of monitoring performances in the detection of a real fault occurring in the target process. The proposed methodologies are then extended to batch systems, considering a simulated penicillin fermentation process. In both cases, results demonstrate that the transfer of knowledge from the source plant enables better monitoring performances than considering only the data available from the target plant

Quality by Design through multivariate latent structures

La presente tesis doctoral surge ante la necesidad creciente por parte de la mayoría de empresas, y en especial (pero no únicamente) aquellas dentro de los sectores farmacéu-tico, químico, alimentación y bioprocesos, de aumentar la flexibilidad en su rango ope-rativo para reducir los costes de fabricación, manteniendo o mejorando la calidad del producto final obtenido. Para ello, esta tesis se centra en la aplicación de los conceptos del Quality by Design para la aplicación y extensión de distintas metodologías ya exis-tentes y el desarrollo de nuevos algoritmos que permitan la implementación de herra-mientas adecuadas para el diseño de experimentos, el análisis multivariante de datos y la optimización de procesos en el ámbito del diseño de mezclas, pero sin limitarse ex-clusivamente a este tipo de problemas. Parte I - Prefacio, donde se presenta un resumen del trabajo de investigación realiza-do y los objetivos principales que pretende abordar y su justificación, así como una introducción a los conceptos más importantes relativos a los temas tratados en partes posteriores de la tesis, tales como el diseño de experimentos o diversas herramientas estadísticas de análisis multivariado. Parte II - Optimización en el diseño de mezclas, donde se lleva a cabo una recapitu-lación de las diversas herramientas existentes para el diseño de experimentos y análisis de datos por medios tradicionales relativos al diseño de mezclas, así como de algunas herramientas basadas en variables latentes, tales como la Regresión en Mínimos Cua-drados Parciales (PLS). En esta parte de la tesis también se propone una extensión del PLS basada en kernels para el análisis de datos de diseños de mezclas, y se hace una comparativa de las distintas metodologías presentadas. Finalmente, se incluye una breve presentación del programa MiDAs, desarrollado con la finalidad de ofrecer a sus usuarios la posibilidad de comparar de forma sencilla diversas metodologías para el diseño de experimentos y análisis de datos para problemas de mezclas. Parte III - Espacio de diseño y optimización a través del espacio latente, donde se aborda el problema fundamental dentro de la filosofía del Quality by Design asociado a la definición del llamado 'espacio de diseño', que comprendería todo el conjunto de posibles combinaciones de condiciones de proceso, materias primas, etc. que garanti-zan la obtención de un producto con la calidad deseada. En esta parte también se trata el problema de la definición del problema de optimización como herramienta para la mejora de la calidad, pero también para la exploración y flexibilización de los procesos productivos, con el objeto de definir un procedimiento eficiente y robusto de optimiza-ción que se adapte a los diversos problemas que exigen recurrir a dicha optimización. Parte IV - Epílogo, donde se presentan las conclusiones finales, la consecución de objetivos y posibles líneas futuras de investigación. En esta parte se incluyen además los anexos.Aquesta tesi doctoral sorgeix davant la necessitat creixent per part de la majoria d'em-preses, i especialment (però no únicament) d'aquelles dins dels sectors farmacèutic, químic, alimentari i de bioprocessos, d'augmentar la flexibilitat en el seu rang operatiu per tal de reduir els costos de fabricació, mantenint o millorant la qualitat del producte final obtingut. La tesi se centra en l'aplicació dels conceptes del Quality by Design per a l'aplicació i extensió de diferents metodologies ja existents i el desenvolupament de nous algorismes que permeten la implementació d'eines adequades per al disseny d'ex-periments, l'anàlisi multivariada de dades i l'optimització de processos en l'àmbit del disseny de mescles, però sense limitar-se exclusivament a aquest tipus de problemes. Part I- Prefaci, en què es presenta un resum del treball de recerca realitzat i els objec-tius principals que pretén abordar i la seua justificació, així com una introducció als conceptes més importants relatius als temes tractats en parts posteriors de la tesi, com ara el disseny d'experiments o diverses eines estadístiques d'anàlisi multivariada. Part II - Optimització en el disseny de mescles, on es duu a terme una recapitulació de les diverses eines existents per al disseny d'experiments i anàlisi de dades per mit-jans tradicionals relatius al disseny de mescles, així com d'algunes eines basades en variables latents, tals com la Regressió en Mínims Quadrats Parcials (PLS). En aquesta part de la tesi també es proposa una extensió del PLS basada en kernels per a l'anàlisi de dades de dissenys de mescles, i es fa una comparativa de les diferents metodologies presentades. Finalment, s'inclou una breu presentació del programari MiDAs, que ofe-reix la possibilitat als usuaris de comparar de forma senzilla diverses metodologies per al disseny d'experiments i l'anàlisi de dades per a problemes de mescles. Part III- Espai de disseny i optimització a través de l'espai latent, on s'aborda el problema fonamental dins de la filosofia del Quality by Design associat a la definició de l'anomenat 'espai de disseny', que comprendria tot el conjunt de possibles combina-cions de condicions de procés, matèries primeres, etc. que garanteixen l'obtenció d'un producte amb la qualitat desitjada. En aquesta part també es tracta el problema de la definició del problema d'optimització com a eina per a la millora de la qualitat, però també per a l'exploració i flexibilització dels processos productius, amb l'objecte de definir un procediment eficient i robust d'optimització que s'adapti als diversos pro-blemes que exigeixen recórrer a aquesta optimització. Part IV- Epíleg, on es presenten les conclusions finals i la consecució d'objectius i es plantegen possibles línies futures de recerca arran dels resultats de la tesi. En aquesta part s'inclouen a més els annexos.The present Ph.D. thesis is motivated by the growing need in most companies, and specially (but not solely) those in the pharmaceutical, chemical, food and bioprocess fields, to increase the flexibility in their operating conditions in order to reduce production costs while maintaining or even improving the quality of their products. To this end, this thesis focuses on the application of the concepts of the Quality by Design for the exploitation and development of already existing methodologies, and the development of new algorithms aimed at the proper implementation of tools for the design of experiments, multivariate data analysis and process optimization, specially (but not only) in the context of mixture design. Part I - Preface, where a summary of the research work done, the main goals it aimed at and their justification, are presented. Some of the most relevant concepts related to the developed work in subsequent chapters are also introduced, such as those regarding design of experiments or latent variable-based multivariate data analysis techniques. Part II - Mixture design optimization, in which a review of existing mixture design tools for the design of experiments and data analysis via traditional approaches, as well as some latent variable-based techniques, such as Partial Least Squares (PLS), is provided. A kernel-based extension of PLS for mixture design data analysis is also proposed, and the different available methods are compared to each other. Finally, a brief presentation of the software MiDAs is done. MiDAs has been developed in order to provide users with a tool to easily approach mixture design problems for the construction of Designs of Experiments and data analysis with different methods and compare them. Part III - Design Space and optimization through the latent space, where one of the fundamental issues within the Quality by Design philosophy, the definition of the so-called 'design space' (i.e. the subspace comprised by all possible combinations of process operating conditions, raw materials, etc. that guarantee obtaining a product meeting a required quality standard), is addressed. The problem of properly defining the optimization problem is also tackled, not only as a tool for quality improvement but also when it is to be used for exploration of process flexibilisation purposes, in order to establish an efficient and robust optimization method in accordance with the nature of the different problems that may require such optimization to be resorted to. Part IV - Epilogue, where final conclusions are drawn, future perspectives suggested, and annexes are included.Palací López, DG. (2018). Quality by Design through multivariate latent structures [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115489TESI

Applying Mechanistic Understanding of Optical Absorption and Scattering Phenomena to Enhance the Spectroscopic Analyses of Pharmaceutical Materials

The dissertation uses spatially-resolved spectroscopy to separate absorption and scattering behaviors when NIR light interacts with pharmaceutical materials. The separated absorption and scattering were utilized to enhance mechanistic understanding of NIR diffuse reflectance spectroscopy and improve practical spectroscopic analysis in pharmaceutical applications. Near-Infrared (NIR) chemical imaging based spatially-resolved spectroscopy was used to measure radially-diffused reflectance on pharmaceutical materials. A Monte Carlo simulation based partial least square (PLS) model was constructed to determine the absorption and reduced scattering coefficients in pharmaceutical samples from the measured radially-diffused reflectance. The separated absorption and reduced scattering coefficients were combined with Monte Carlo simulation to provide understanding of the effects of physical properties (e.g., particle size and tablet density) on NIR spectral responses, including absorption and depth of penetration profiles. It was discovered that absorption and reduced scattering coefficients are the dominant factors in determining NIR absorbance and depth of penetration profiles, respectively. Both empirical measurements and Monte Carlo simulation were used to explore the photon radial movements in a chemical imaging setting. It is well understood that radial photon movements among adjacent pixels leaded to blurred 2-D chemical images. A Monte Carlo simulation based deconvolution filter was developed to sharpen a blurred feature in a 2-D image while maintaining the original chemical content of that feature. A new scattering correction method via the reduced scattering coefficient was proposed to specifically reduce physical interference with predictions of chemical properties. The wavelength- and absorption- dependent properties of the reduced scattering coefficient were found to allow specific suppression of physical interference and maintain the original chemical information. Combing the separated optical coefficients with contemporary efficient calibration approaches was found to simplify multivariate model calibration using a reduced calibration dataset, allowing parsimonious multivariate models, and reaching the same or even lower prediction error. To the author\u27s best knowledge, this work is the first example of the application of spatially-resolved spectroscopy to the pharmaceutical field. The enhanced understanding and improved spectroscopic analysis demonstrated in this dissertation is expected to provide groundwork for a wide variety of applications of spatially-resolved spectroscopy in pharmaceutical analyses

Solid-State Physical Form Detection and Quantitation of Pharmaceuticals in Formulations

The majority of pharmaceutical dosage forms are marketed as solids, and the active pharmaceutical ingredient (API) can exist in various physical forms. These physical forms can be either crystalline or amorphous, and will have different physical properties. The physical form of the API is selected to provide the appropriate solubility, stability, and bioavailability for the formulation. However, processing steps involved in the production of the formulation can induce changes in the physical form of the API and thus impact the performance of the formulation. Therefore, it is critical to characterize the physical form of the API in the formulation, and to monitor it for any changes in the physical form during storage. Multiple solid-state characterization techniques are typically employed in order to identify and quantitate physical forms of APIs. However, most of these techniques suffer from significant issues related to the interference of excipient signals with API signals when analyzing formulations. Additionally, in order to perform quantitative measurements pure standards are needed and calibration curves must be generated for most of these characterization techniques. This dissertation demonstrates the superior ability of solid-state nuclear magnetic resonance (SSNMR) spectroscopy to both detect and quantitate physical forms of APIs within formulations, relative to other solid-state characterization techniques. Specifically, SSNMR has been used to understand the dehydration of levofloxacin hemihydrate, including the discovery of a previously unreported anhydrous polymorph. Three formulations for pulmonary delivery have been characterized by both differential scanning calorimetry (DSC) and SSNMR. DSC could not clearly identify the physical form of the APIs in the formulations, but SSNMR was able to unambiguously determine the physical forms of the APIs in all cases. The work presented in the dissertation also demonstrates that SSNMR can be used to quantitate the relative amounts of physical forms of APIs within formulations without pure standards or calibration curves. Finally, the relative ability of SSNMR and other solid-state characterization techniques were compared and clearly illustrate that SSNMR is considerably more powerful in the relative quantitation of API physical form in both pure materials and formulations

Estimation of chemical information in scattering media using radiative transfer theory to remove multiple scattering effects

Two approaches for removing multiple light scattering effects using the radiative transfer theory in order to improve the performance of multivariate calibration models have been proposed namely: partial correction of multiple scattering effects and full correction of multiple scattering effects. The first approach is applicable for predicting the concentration of a scattering-absorbing (particulate) component as well as the concentration of an absorbing only species. The second approach is applicable only for estimation of the concentration of an absorbing only species. Application of the first approach to a simulated four component system showed that it will lead to calibration models which perform appreciably better than when empirically scatter corrected measurements of total transmittance or total reflectance are used. The validity of the method was tested experimentally using a two-component (polystyrene-water) and a fourcomponent (polystyrene - ethanol - water - deuterated water) system. The proposed methodology of partial correction showed significantly better performance than the empirically pre-processed direct measurements (total transmittance, total reflectance and collimated transmittance) in all experiments. The results of applying the full correction approach showed that despite all errors the performance of PLS calibration model built on the corrected bulk absorption coefficient was marginally better than the performance of PLS model built on uncorrected bulk absorption coefficient. Finally, the benchmarking analysis revealed that there is still a significant potential for an improvement in the prediction performance in the quantitative analysis of turbid samples.EThOS - Electronic Theses Online ServiceMarie Curie FP6 (project INTROSPECT)GBUnited Kingdo

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system