<p>  </p> <p>In the pharmaceutical industry, special care must be taken by companies to guarantee high quality medications that are free from byproducts and impurities. The development process involves various considerations including solvent selection, solubility screening, unit operation selection, environmental, and health impact evaluations. Traditionally, pharmaceutical manufacturing consisted of large, centralized facilities to meet pharmaceutical demands; however, there has been a recent shift toward distributed manufacturing. With distributed manufacturing platforms, rapidly changing supply chain needs can be met regionally in addition to supplying small-volume medications and personalized medicines to hospitals and pharmacies. To produce quality pharmaceuticals, distributed manufacturing platforms should integrate digital design, novel unit operations, and process analytical technology (PAT) tools for quality monitoring and control. In this dissertation, a process design and development framework is proposed and implemented for a small-scale pharmaceutical manufacturing platform: MiniPharm.</p> <p>Various approaches to process design are detailed in this dissertation, which include heuristic-based and digital or simulation-based design. For heuristic-based design, the knowledge of the researchers was utilized to provide unit operation evaluation and screening of process alternatives. In cases when unit operations were highly complex, digital or simulation-based design was utilized to conduct sensitivity analyses and simulation-based design of experiments. With the implementation of simulation-based design, material and time needs were reduced while gaining knowledge about the system. The integration of various unit operations comes with increased understanding of start-up dynamics and operational constraints. What was found to be the most successful approach was the combination of heuristics and digital design to combine researcher knowledge and experience with the information gained from process modeling and simulation to create process alternatives that utilized system dynamics to reach desired process outcomes. </p> <p>Additionally, MiniPharm was used for process model development at the small-scale. Various software packages have been made commercially available that focus on production scale; however, models for small-scale operations are not typically implemented in these packages. Models for unit operations were fit with collected experimental data to estimate model parameters for small-scale synthesis, liquid-liquid extraction, and crystallization unit operations. The models were implemented to better capture the heat and mass transfer of the milli-fluidic scale platform, which consist of unit operations housed within microchannels. MATLAB was utilized for estimation of parameters such as kinetic rate constants and overall mass transfer coefficients. These parameters were used for design space determination and process disturbance simulation. The exploration of the impact of various process parameters on quality attributes helps researchers gain a deeper understanding about the manufacturing process and helps to demonstrate how to control the process. </p> <p>An important aspect of MiniPharm is the process development progress that has been demonstrated. With the construction of a modular and reconfigurable platform, various process alternatives can now be experimentally validated. The integration of unit operations operated at a decreased scale makes MiniPharm an example of process intensification. The implementation of integrated unit operations decreases handling time of intermediates and reduces the overall footprint for manufacturing. Designed to allow for increased flexibility of operation, perfluoroalkoxy alkane (PFA) tubing was used for synthesis and purification. With PFA tubing clean in place procedures can be implemented using continuous solvent flow or the low cost, PFA tubing can be replaced. The modular nature of the platform also allows for the investigation of individual unit operations for performance evaluation. </p> <p>Finally, a novel continuous solvent switch distillation unit operation was designed and constructed along with customized reactor and crystallizers for process alternative screening for the synthesis and purification of two compounds: Diphenhydramine hydrochloride and Lomustine. Diphenhydramine hydrochloride is a low-value, high volume allergy medication commonly found in Benadryl and Lomustine is a high-value, low volume cancer medication used to treat glioblastoma and Hodgkin Lymphoma. The production of the compounds demonstrated the flexibility of the manufacturing platform to produce both a generic and a specialty medication. A versatile platform is needed for distributed manufacturing because of quickly changing supply chain needs. Overall, this dissertation successfully demonstrates the process design, development, and simulation for small-scale manufacturing.</p>

Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.

[ES] El acceso a una atención sanitaria de alta calidad es un marcador importante del desarrollo de las sociedades humanas. Los aportes tecnológicos a la medicina han mostrado un potencial relevante para descubrir procedimientos efectivos a nivel preventivo, diagnóstico y terapéutico. En particular, los métodos computacionales permiten el procesamiento eficaz de datos médicos y, por tanto, pueden modelar sistemas biológicos complejos. Esto ha influido en el desarrollo de la Medicina Personalizada (MP) durante las últimas décadas, donde la obtención de conocimiento específico de cada caso permite realizar intervenciones a medida, todo ello a un coste de recursos accesible. La simulación de remodelación ósea es un campo prometedor en el contexto de la MP. Predecir un proceso de adaptación ósea en un caso concreto puede dar lugar a numerosas aplicaciones en el campo de las enfermedades óseas, tanto a nivel clínico como experimental. Mediante la combinación del Método de Elementos Finitos (FEM) y los algoritmos de remodelación ósea, es posible obtener modelos numéricos de un hueso específico a partir de datos médicos (por ejemplo, una tomografía computarizada). Todo ello puede dar lugar a una revolución en la medicina personalizada. / [CA] L'accés a una atenció sanitària d'alta qualitat és un marcador important del desenvolupament de les societats humanes. Les aportacions tecnològiques a la medicina han mostrat un potencial rellevant per a descobrir procediments efectius a nivell preventiu, diagnòstic i terapèutic. En particular, els mètodes computacionals permeten el processament eficaç de dades mèdiques i, per tant, poden modelar sistemes biològics complexos. Això ha influït en el desenvolupament de la Medicina Personalitzada (MP) durant les últimes dècades, on l'obtenció de coneixement específic de cada cas permet realitzar intervencions a mesura, tot això a un cost de recursos accessible. La simulació de remodelació òssia és un camp prometedor en el context de la MP. Predir un procés d'adaptació òssia en un cas concret pot donar lloc a nombroses aplicacions en el camp de les malalties òssies, tant a nivell clínic com experimental. Mitjançant la combinació del Mètode d'Elements Finits (*FEM) i els algorismes de remodelació òssia, és possible obtindre models numèrics d'un os específic a partir de dades mèdiques (per exemple, una tomografia computada). Tot això pot donar lloc a una revolució en la medicina personalitzada. / [EN] Access to high-quality healthcare is an important marker of the development of human societies. Technological contributions to medicine have shown relevant potential to discover effective procedures at a preventive, diagnostic and therapeutic level. In particular, computational methods enable efficient processing of medical data and can therefore model complex biological systems. This has influenced the development of Personalized Medicine (PM) over recent decades, where obtaining specific knowledge of each case allows for tailored interventions, all at an affordable resource cost. Simulation of bone remodeling is a promising field in the context of PM. Predicting a bone adaptation process in a specific case can lead to numerous applications in the field of bone diseases, both clinically and experimentally. By combining the Finite Element Method (FEM) and bone remodeling algorithms, it is possible to obtain numerical models of a specific bone from medical data (for example, a CT scan). All of this can lead to a revolution in personalized medicine. / Thanks to the Valencian funding programme FDGENT/2018, for providing economic resources to develop this long-term work. / Gutiérrez Gil, J. (2023). Personalization of Bone Remodelling Simulation Models for Clinical Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202059