QbD Implementation in Biotechnological Product Development Studies

Biotechnological drug development is an extensive area still growing and coming into prominence day by day. Since biotechnological product manufacturing is irreversible, highly expensive, and contains so many critical parameters throughout the process, quality control tests applied to the finished product become inefficacious; therefore, maintaining predefined quality is crucial. Quality by Design (QbD), a systematic approach, is designing and optimizing of formulation and production processes in order to provide a predefined product quality by following a risk and scientific-based path. Determining the critical variables for biotechnological products and their manufacturing via risk assessment is the first and most vital stage of QbD approach, before exploring the multivariate relations among the independent and dependent critical variables by mathematical modeling with the assistive technologies. Response Surface Method (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) are some of the assistive technologies used to perform mathematical modeling. After modeling, additional knowledge is vested and this provides the chance to find a range in which the product quality is always ensured, called as “Design space”. So, product quality is procured all along the process by keeping the critical variables under control with less effort, money, and mistakes

KINETIC MODELING AND ITS APPLICATION IN THE BIOPHARMACEUTICAL INDUSTRY

It has been well recognized that biologics are efficient for cancer and immune disease treatment. Kinetic modeling is to mathematically model or to quantitatively illustrate how reactions occur in a biological or chemical process. A systematic study and understanding of kinetic model and its application in the biopharmaceutical industry are important for both scientific research and industrial technology development. This work consists of six chapters. First, a review of kinetic modeling and its application for cell culture was introduced. Second, the current status of biologics development and screening strategies of biomarkers and indications were discussed. Third, one experimental and kinetic modeling study for the temperature effects and temperature shift strategy development was presented. Forth, novel kinetic models were built up and applied to elucidate lactate dehydrogenase catalyzed reactions, which is a crucial metabolic process within tumor cells and Chinese hamster ovary cells. In the fifth chapter, strategies of biologics quality control via process development were briefly summarized. And finally, summary and outlook were made based on the above five chapters. Though kinetic modeling is not a FDA request tool, kinetic data are required for regulation approval of new drug discovery and process development. These data are applicable for a rapidly screening of the best biologics and the optimized manufacturing process with little extra cost via kinetic modeling. This work is potentially beneficial for speeding up and better understanding of the current biologics development in biopharmaceutical industry

A Systematic Framework to Optimize and Control Monoclonal Antibody Manufacturing Process

Since the approval of the first therapeutic monoclonal antibody in 1986, monoclonal antibody has become an important class of drugs within the biopharmaceutical industry, with indications and superior efficacy across multiple therapeutic areas, such as oncology and immunology. Although there has been great advance in this field, there are still challenges that hinder or delay the development and approval of new antibodies. For example, we have seen issues in manufacturing, such as quality, process inconsistency and large manufacturing cost, which can be attributed to production failure, delay in approval and drug shortage. Recently, the development of new technologies, such as Process Analytical Tools (PCT), and the use of statistical tools, such as quality by design (QbD), Design of Experiment (DoE) and Statistical Process Control (SPC), has enabled us to identify critical process parameters and attributes, and monitor manufacturing performance. However, these methods might not be reliable or comprehensive enough to accurately describe the relationship between critical process parameters and attributes, or still lack the ability to forecast manufacturing performance. In this work, by utilizing multiple modeling approaches, we have developed a systematic framework to optimize and control monoclonal antibody manufacturing process. In our first study, we leverage DoE-PCA approach to unambiguously identify critical process parameters to improve process yield and cost of goods, followed by the use of Monte Carlo simulation to validate the impact of parameters on these attributes. In our second study, we use a Bayesian approach to predict product quality for future manufacturing batches, and hence mitigation strategies can be put in place if the data suggest a potential deviation. Finally, we use neural network model to accurately characterize the impurity reduction of each purification step, and ultimately use this model to develop acceptance criteria for the feed based on the predetermined process specifications. Overall, the work in this thesis demonstrates that the framework is powerful and more reliable for process optimization, monitoring and control

Quality-by-Design (QbD) process evaluation for phytopharmaceuticals on the example of 10-deacetylbaccatin III from yew

The focus of pharmaceutical product development lies on assuring excellent product quality at the end of a cost-efficient process. The Quality-by-Design (QbD) concept shifts the focus from quality assurance through testing to quality control by process understanding, resulting in very robust processes with high quality product. QbD was originally intended by authorities for biologics, where product quality proven completely by analytics is not desired. Product quality has to be controlled by means of appropriate processes and operations as well. These demands were developed in order to improve patients' safety by optimal drug quality at more efficient manufacturing processes reducing costs for healthcare systems. Furthermore, production of biologics includes feedstock variability and complex multi-step manufacturing processes in batch operation with variable lots – condition, which apply to botanicals as well. The use of rigorous (physico-chemical) process modeling in combination with QbD results in a high degree of process understanding. This offers, contrary to popular prejudices, great benefit for manufacturers with little extra effort during development. The methodical QbD-based approach is pursued to develop a process for extraction and purification of 10-deacetylbaccatin III from yew needles. A short history and key elements of the QbD-based application are introduced. The line of argument for basic process conception is described and initial risk assessment is shown. Typical raw material variation and vaporization are identified as causes of process variability, therefore, the implications to subsequent process steps are pointed out. Finally, influences of load and flow rate on the chromatographic separation of 10-deacetylbaccatin III are shown to exemplify sensitivity of purification design

Quality-by-Design of nano-pharmaceuticals - a state of the art

International audiencePharmaceutical Quality-by-Design is a risk-based approach of drug development relying on the understanding of both the product and the process. This state of the art analyzes 24 studies published during the last ten years. A risk modeling of the nanomaterial formulation and manufacturing is firstly presented. After a brief history of the QbD approach, its basic components are recalled in a second part. The most critical material attributes, process parameters, quality variables and measurement technologies are reviewed. Specific deficiencies are also emphasized such as the absence of prior risk assessment, production scale-up, process analytical technology and control strategy. Finally, perspectives and development priorities are drawn to improve the implementation of this integrative approach of quality and safety in nanomedicine

Informatics: the fuel for pharmacometric analysis

The current informal practice of pharmacometrics as a combination art and science makes it hard to appreciate the role that informatics can and should play in the future of the discipline and to comprehend the gaps that exist because of its absence. The development of pharmacometric informatics has important implications for expediting decision making and for improving the reliability of decisions made in model-based development. We argue that well-defined informatics for pharmacometrics can lead to much needed improvements in the efficiency, effectiveness, and reliability of the pharmacometrics process. The purpose of this paper is to provide a description of the pervasive yet often poorly appreciated role of informatics in improving the process of data assembly, a critical task in the delivery of pharmacometric analysis results. First, we provide a brief description of the pharmacometric analysis process. Second, we describe the business processes required to create analysis-ready data sets for the pharmacometrician. Third, we describe selected informatic elements required to support the pharmacometrics and data assembly processes. Finally, we offer specific suggestions for performing a systematic analysis of existing challenges as an approach to defi ning the next generation of pharmacometric informatics

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Emerging technologies for the non-invasive characterization of physical-mechanical properties of tablets

The density, porosity, breaking force, viscoelastic properties, and the presence or absence of any structural defects or irregularities are important physical-mechanical quality attributes of popular solid dosage forms like tablets. The irregularities associated with these attributes may influence the drug product functionality. Thus, an accurate and efficient characterization of these properties is critical for successful development and manufacturing of a robust tablets. These properties are mainly analyzed and monitored with traditional pharmacopeial and non-pharmacopeial methods. Such methods are associated with several challenges such as lack of spatial resolution, efficiency, or sample-sparing attributes. Recent advances in technology, design, instrumentation, and software have led to the emergence of newer techniques for non-invasive characterization of physical-mechanical properties of tablets. These techniques include near infrared spectroscopy, Raman spectroscopy, X-ray microtomography, nuclear magnetic resonance (NMR) imaging, terahertz pulsed imaging, laser-induced breakdown spectroscopy, and various acoustic- and thermal-based techniques. Such state-of-the-art techniques are currently applied at various stages of development and manufacturing of tablets at industrial scale. Each technique has specific advantages or challenges with respect to operational efficiency and cost, compared to traditional analytical methods. Currently, most of these techniques are used as secondary analytical tools to support the traditional methods in characterizing or monitoring tablet quality attributes. Therefore, further development in the instrumentation and software, and studies on the applications are necessary for their adoption in routine analysis and monitoring of tablet physical-mechanical properties

Nanogels for pharmaceutical and biomedical applications and their fabrication using 3D printing technologies

Nanogels are hydrogels formed by connecting nanoscopic micelles dispersed in an aqueous medium, which give an opportunity for incorporating hydrophilic payloads to the exterior of the micellar networks and hydrophobic payloads in the core of the micelles. Biomedical and pharmaceutical applications of nanogels have been explored for tissue regeneration, wound healing, surgical device, implantation, and peroral, rectal, vaginal, ocular, and transdermal drug delivery. Although it is still in the early stages of development, due to the increasing demands of precise nanogel production to be utilized for personalized medicine, biomedical applications, and specialized drug delivery, 3D printing has been explored in the past few years and is believed to be one of the most precise, efficient, inexpensive, customizable, and convenient manufacturing techniques for nanogel production