Optimizing Monoclonal Antibody Scaling

In the ever-evolving biotech landscape, the translation of monoclonal antibodies (mAb) from preclinical to clinical settings has emerged as a sophisticated scientific endeavor, hinging on the intricacies of pharmacokinetic target-mediated drug disposition (PK-TMDD) models. This process requires a keen understanding of allometric scaling principles and their underlying assumptions regarding TMDD parameters.

The translation of mAb therapy from animal models, such as monkeys, to human subjects is often facilitated by allometric scaling with fixed exponents. This scaling, when effectively executed, can provide valuable insights into the PK parameters of humans based on the data derived from non-human subjects. However, it’s not without its complexities and challenges, as it’s steeped in assumptions on TMDD parameters that can be difficult to accurately predict and validate.

While full TMDD models are less frequently employed due to their complexity, approximations like Quasi-Equilibrium (QE) and Michaelis-Menten (MM) models have become the industry staples. These models are valuable tools for predicting human PK parameters and exposure metrics, helping to map out the potential human response to a given mAb therapy.

However, these models come with their unique set of obstacles, particularly when dealing with low mAb concentrations. At these concentrations, the predictions can become less reliable due to the limitations of the models, necessitating further research and refinement to improve their predictive accuracy.

The development of bispecific mAbs, such as MCLA-128, introduces another layer of complexity to this process. Without available human data for evaluation, predictions for these novel therapies are often clouded in uncertainty. Researchers are now tasked with refining these models to better predict the behavior of these multipurpose antibodies in the human body.

The challenges of scaling monoclonal antibodies for clinical use serve to highlight the dynamic and complex nature of biotechnology. They underscore the continuous need for innovation and refinement in our predictive models and translation methods.

In an industry characterized by continuous evolution, the quest for better, more reliable translation models is a testament to the relentless pursuit of precision and efficacy in biotech. With every refinement and advancement, we move one step closer to turning promising preclinical findings into tangible clinical solutions, ultimately advancing the frontier of personalized medicine.

Contact: Eva Germovsek, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim, Germany.

Note: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: monoclonal antibody, allometric scaling, allometry, interspecies scaling, translation, extrapolation, pre-clinical, pediatric.