ABSTRACT
Transthyretin amyloidosis is a rare yet lethal disease caused by an increase in the destabilization of transthyretin tetramer into monomers, leading to amyloid fibril aggregates in tissues. Multiple therapeutics have been developed to limit or halt disease progression by altering tetramer kinetics. Small molecules, like Tafamidis and AG10, stabilize the tetrameric structure while genetic therapies, like Patisiran and NTLA-002, limit the production of TTR protein by silencing genetic expression. Both of these interventions slow the accumulation of fibrils by intervening at different points in the tetramer-monomer-fibril pathway. We developed a mathematical model to compare the pharmacological efficacies of these modalities by comparing each drug's ability to reduce the rate of tetramer to monomer formation, or "tetrameric flux." The model was trained on in vitro tetramer data as well as clinical measurements of tetramer concentration in humans. Overall, genetic silencers reduced tetrameric flux more than small molecule stabilizers. Properties that led to an improvement in small molecule stabilizer function and potential benefit of gene therapy - small molecule combination were explored. This study exemplifies how modeling can be used to compare modalities with differing mechanisms of action.
ABSTRACT
A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.
