Sequential and Simultaneous Optimization Strategies for Increased Production of Monoclonal Antibodies

摘要

Monoclonal antibodies (mAbs) represent a significant class of biopharmaceutics with a wide range of diagnostic and therapeutic applications.Typically, mAbs are produced by cultivated mammalian cells, to meet the high quality product specifications.For new products and cell lines, an adaptation of cultivation conditions is required.This is usually performed experimentally.Model-based approaches can be a powerful tool to reduce experimental efforts and accelerate process development.We present optimizations using the process model in Kappatou et al.(2018), which is based on Quiroga et al.(2016).In particular, we perform fed-batch optimizations following a sequential and a simultaneous approach.In the sequential approach, we first find optimal initial conditions for the batch process using different objectives, and then we optimize the feeding for constant initial conditions.In the simultaneous approach, we directly optimize for initial conditions and appropriate feed rates.The optimizations lead to significant improvements compared to the base case presented in Quiroga et al.(2016).The results indicate that the sequential approach is sometimes able to outperform the simultaneous one by overcoming limitations of the local optimization used.This may be due to the flexibility of the sequential approach to use different objectives for the two steps (batch and fed-batch).Therefore, the results further highlight the importance of utilizing good initialization procedures in local optimization.