(2cn) Systems Engineering for Manufacturing of Advanced Biotherapeutics

My research interests lie in the development of mathematical tools and applications to support the design, optimization, and control of manufacturing systems for advanced biotherapeutics, such as cell therapies and genetic medicines.

Vision and Objective

Rapid advancements in the fields of cell and gene therapies and of genetic medicines have led to the recent approval of treatments for diseases that were previously incurable, such as certain types of cancer, blindness, and muscular dystrophy­. However, most advanced biotherapeutics are produced through manufacturing processes that present low efficiency and high costs. With over 1000 ongoing clinical trials for cell and gene therapies (clinicaltrials.gov), the current manufacturing yields will soon be insufficient to provide approved treatments to all patients (Elverum and Whitman, 2020). At the same time, the low manufacturing efficiency contributes to the high price of advanced therapeutics (exceeding $1 million per dose for certain gene therapies). To deliver cutting-edge biotherapies to patients, it is necessary to dramatically increase the efficiency of current biomanufacturing systems.

Process systems engineering approaches have successfully been used to optimize the design and the operation of several manufacturing sectors, including, in recent years, pharmaceutical manufacturing of small molecules, proteins, and monoclonal antibodies (e.g., see my PhD work: Destro et al., 2022; Destro and Barolo, 2022). I am interested in developing mathematical and computational tools for improving the manufacturing of advanced biotherapeutics, and in demonstrating their application in key areas of the current landscape of innovative therapies: chimeric antigen receptor (CAR) T-cells manufacturing (for cell therapy) and lentiviral vector manufacturing (for cell and gene therapy).

Future research directions will encompass applying the same paradigm and the developed computational tools to optimize the manufacturing processes of next-generation cell therapies, genetic medicines, and vaccines.

Development of Novel Mathematical and Computational Tools

Compared to other manufacturing systems, ­biomanufacturing processes pose unique challenges, such as high intrinsic stochasticity, limited data availability, typically high measurement error, and complex system dynamics. Consequently, the design, optimization, and control of biomanufacturing systems require innovative approaches. Drawing from my expertise in mechanistic, data-driven and hybrid modeling (Destro et al., 2020; 2021), I plan to develop theory and algorithms for addressing these challenges.

My ultimate goal is to deliver computational tools for the automation of model development and deployment in biomanufacturing. Within this context, a core objective will involve the development of novel techniques, specifically customized for biomanufacturing systems, within the fields of (i) model-based design of experiments, (ii) model selection, and (iii) design space determination. The algorithms will be rooted in advanced Bayesian approaches, to determine the optimal tradeoff between exploration and exploitation when developing and using a model for the design and the optimization of a biomanufacturing process. Algorithms will be developed (i) to optimize the acquisition of information from available experimental data and (ii) to design new experiments. The algorithms will serve two key purposes: (i) to obtain the best model of a process for a given purpose, in terms of accuracy, efficacy, and parsimony, and (ii) to design experiments enabling a synergistic advancement in both model development and process optimization, at the same time.

Applications: CAR-T Cells and Lentiviral Vectors Manufacturing

Novel cell therapies and genetic medicines have recently received approval by regulators for, among other applications, immunotherapies and treatment of genetic disorders. Cell therapies involve the use of living cells as therapeutic agents, while genetic medicines harness the potential of nucleic acids to prevent, diagnose or treat diseases. The most prominent categories of genetic medicines are gene therapy, gene editing, and mRNA therapeutics. Several scale-up and manufacturing challenges have to be solved for making these advanced therapeutics available to all patients in need.

Certain cell therapies and genetic medicines have reached an advanced stage of development, enabling process engineering studies that can enhance manufacturability and optimize scale-up and cost-effectiveness. In my postdoctoral work (Destro et al., 2023; Destro and Braatz, 2023), I have used model-based techniques for improving the efficiency of a manufacturing process for recombinant adeno-associated virus (rAAV), a popular vector for in vivo gene therapy. Building on this expertise, my lab’s research will optimize the biomanufacturing process of (i) CAR T-cells and (ii) lentiviral vectors. CAR T-cells are the therapeutic agents in the first FDA-approved gene-modified cell therapy, an immunotherapy for relapsing B-cell acute lymphoblastic leukemia. Lentiviral vectors are the commonly used vectors in gene therapy [1], and play a significant role in CAR T-cell manufacturing, contributing towards their high cost.

My lab will develop mathematical models to drive the design, optimization, and control of the key unit operations for CAR T-cells and lentivirus manufacturing. Multiscale models, based on mechanistic, machine learning, and hybrid approaches, will connect the performance of unit operations with the dynamics of small-scale phenomena, such as the intracellular reaction-transport pathways. The models will play a crucial role in optimizing the process by suggesting improved designs and operating conditions, as well as molecular biology enhancements, for the biomanufacturing process. Larger scale models will be utilized to address supply chain and operational challenges in CAR T-cells and lentivirus manufacturing. The novel computational tools developed within the research group, together with well-established techniques from the process systems engineering expertise, will provide a powerful framework for addressing the current and future challenges in advanced biotherapeutics manufacturing.

Teaching Interests

A growing number of digital tools, including high-quality open-source options, have recently emerged to support professionals in the field of chemical engineering. Undergraduate and graduate programs should equip students with the essential skills to effectively leverage these modeling and computational instruments in their future careers. With this in mind, I am interested in teaching and developing model-oriented teaching material around two key areas: (i) foundational chemical engineering courses, such as transport phenomena, and (ii) courses that apply models to solve engineering tasks, such as process control.

In the foundational courses, students will develop a solid understanding of the underlying principles that govern physical and chemical processes, and how to translate such principles into mathematical models. Although commercial and open-source libraries of models are available, it is crucial for students to learn how to develop simple mechanistic models, to understand the underlying mechanisms that run below models available in pre-existing software.

In the application-oriented courses, such as process control, I aim to emphasize the application of modeling for solving engineering problems. Students often lack the skill of developing and deploying models at the rapid pace dictated by an industry environment. However, simple models can be highly effective for a wide range of tasks in process design, automation, and monitoring. Furthermore, open-source software that enables rapid model development and deployment is now available, often at open-source. To bridge this gap, I will create teaching resources to aid students in acquiring proficiency in model-based computing, enabling them to exploit the full potential of digital tools in their future careers.

In addition, I intend to design a process systems engineering course. This application-oriented course will provide several examples highlighting how models can be swiftly developed and utilized across various applications. The course will also cover data-driven and hybrid modeling, which are often given much less importance than mechanistic modeling in traditional chemical engineering programs. I also plan to present case studies from the (bio)pharmaceutical field, showing how modeling is an invaluable instrument for reducing development time and manufacturing cost in this industry.

Selected publications

Destro, F., Joseph, J., Srinivasan, P., Kanter, J.M., Neufeld, C., Wolfrum, J.M., Barone, P.W., Springs, S.L., Sinskey, A.J., Cecchini, S., Kotin, R.M. and Braatz, R.D (2023). Mechanistic modeling explains the production dynamics of recombinant adeno-associated virus with the baculovirus expression vector system. Mol. Ther. Methods Clin. Dev. 30, 122-146.

Destro, F. and Braatz, R. D. Braatz (2023). Population balance modeling of transduction systems for synthetic biology. In preparation.

Destro, F. and Barolo, M. (2022). A review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling. Int. J. Pharm. 620, 121715.

Destro, F., Nagy, Z.K. and Barolo, M. (2022). A benchmark simulator for quality-by-design and quality-by-control studies in continuous pharmaceutical manufacturing ‒ Intensified filtration-drying of crystallization slurries. Comput. Chem. Eng. 163, 107809.

Destro, F., Hur, I., Wang, V., Abdi, M., Feng, X., Wood, E., Coleman, S., Firth, P., Barton, A., Barolo, M. and Nagy, Z.K. (2021). Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing. Chem. Eng. Sci. 244, 116803.

Destro, F., Facco, P., Munoz, S.G., Bezzo, F. and Barolo, M. (2020). A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation. J. Process Control 92, 333-351.

Elverum, K. and Whitman, M. (2020). Delivering cellular and gene therapies to patients: solutions for realizing the potential of the next generation of medicine. Gene Ther. 27, 537–544..

[1] alongside rAAVs and adenoviruses