Multi-scale modeling to optimize the antitumor effect of antibody-dependent cell-mediated cytotoxicity (ADCC) enhanced therapies

Antibody-dependent cell-mediated cytotoxicity (ADCC) is thought to augment the efficacy of IgG1 mAb therapeutics (eg. cetuximab) via the recruitment and activation of CD16+ cells, which in turn will mediate tumor cell killing. This observation has led to drug design efforts to intentionally exploit ADCC for enhanced anti-tumor efficacy. For example, GA201 (Roche pRED Oncology) is a humanized and glycoengineered IgG2 anti-EGFR monoclonal antibody (mAb) with enhanced ADCC currently undergoing investigation in phase 2 trials.
The dual mechanisms of action of ADCC-enhanced mAbs such as GA201 result in new and challenging drug development questions, for example:
1. Can we quantify the relative contributions of ADCC and target inhibition toward tumor shrinkage?2. In light of (1), what markers (eg. KRAS mutation status, NK cell function) are predicted to confer sensitivity or resistance to the mAb?
3. Given observed kinetics of CD16+ cell depletion and recovery following administration of the mAb, what is the optimal schedule to optimize ADCC?
4. In the case of combination therapy with an immune-enhancing treatment, what dose and schedule would provide optimal synergy with the mAb's ADCC effect?
We propose a mathematical modeling framework for ADCC with the intention of addressing the questions posed above across several ADCC-relevant therapies. The proposed model would integrate clinically obtainable data sources, for example: FACS counts of (CD16+/56+) cells, NK cell function (CD107a, K562), tumor size (CT scan), mAb concentration in blood, and downstream target inhibition (eg pERK).