A comprehensive mathematical analysis of a novel multistage population balance model for cell proliferation

Dynamic optimization of an integrated cultivation-aggregation model for mAb production

Due to their proteinaceous structure, monoclonal antibodies (mAbs) are susceptible to irreversible aggregation, with harmful consequences on drug efficacy and patient safety. To mitigate this risk in modern biopharmaceutical processes, it is critical to comply with current good manufacturing practices (cGMP) and pursue operating strategies minimizing irreversible aggregation whilst also maximizing mAb throughput. These conflicting objectives are targeted in this study by formulating and analyzing an integrated dynamic model accounting for both cultivation and aggregation of mAbs from a Chinese Hamster Ovary (CHO) cell line. Two manipulated dynamic variables are considered here in simulation studies: firstly temperature manipulation within a batch reactor, and secondly feed flow manipulation within a series of isothermal fed-batch reactors. Following this, dynamic optimization investigations have been conducted, firstly with the single objective of maximizing mAb throughput and secondly with multiple (two) objectives of maximizing mAb throughput while also minimizing irreversible aggregate content, simultaneously. The study provides key insight into tradeoffs of how simultaneous temperature and feed flowrate manipulation affects mAb throughput and aggregation inside bioreactors.

The COVID-19 pandemic as inspiration to reconsider epidemic models: A novel approach to spatially homogeneous epidemic spread modeling

Epidemic spread models are useful tools to study the spread and the effectiveness of the interventions at a population level, to an epidemic. The workhorse of spatially homogeneous class models is the SIR-type ones comprising ordinary differential equations for the unknown state variables. The transition between different states is expressed through rate functions. Inspired by -but not restricted to- features of the COVID-19 pandemic, a new framework for modeling a disease spread is proposed. The main concept refers to the assignment of properties to each individual person as regards his response to the disease. A multidimensional distribution of these properties represents the whole population. The temporal evolution of this distribution is the only dependent variable of the problem. All other variables can be extracted by post-processing of this distribution. It is noteworthy that the new concept allows an improved consideration of vaccination modeling because it recognizes vaccination as a modifier of individuals response to the disease and not as a means for individuals to totally defeat the disease. At the heart of the new approach is an infection age model engaging a sharp cut-off. This model is analyzed in detail, and it is shown to admit self-similar solutions. A hierarchy of models based on the new approach, from a generalized one to a specific one with three dominant properties, is derived. The latter is implemented as an example and indicative results are presented and discussed. It appears that the new framework is general and versatile enough to simulate disease spread processes and to predict the evolution of several variables of the population during this spread.