Development of and insights from systems pharmacology models of antibody‐drug conjugates

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Streptavidin-drug conjugates streamline optimization of antibody-based conditioning for hematopoietic stem cell transplantation

Hematopoietic stem cell transplantation (HSCT) conditioning using antibody-drug conjugates (ADC) is a promising alternative to conventional chemotherapy- and irradiation-based conditioning regimens. The drug payload bound to an ADC is a key contributor to its efficacy and potential toxicities; however, a comparison of HSCT conditioning ADCs produced with different toxic payloads has not been performed. Indeed, ADC optimization studies in general are hampered by the inability to produce and screen multiple combinations of antibody and drug payload in a rapid, cost-effective manner. Herein, we used Click chemistry to covalently conjugate four different small molecule payloads to streptavidin; these streptavidin-drug conjugates can then be joined to any biotinylated antibody to produce stable, indirectly conjugated ADCs. Evaluating CD45-targeted ADCs produced with this system, we found the pyrrolobenzodiazepine (PBD) dimer SGD-1882 was the most effective payload for targeting mouse and human hematopoietic stem cells (HSCs) and acute myeloid leukemia cells. In murine syngeneic HSCT studies, a single dose of CD45-PBD enabled near-complete conversion to donor hematopoiesis. Finally, human CD45-PBD provided significant antitumor benefit in a patient-derived xenograft model of acute myeloid leukemia. As our streptavidin-drug conjugates were generated in-house with readily accessible equipment, reagents, and routine molecular biology techniques, we anticipate this flexible platform will facilitate the evaluation and optimization of ADCs for myriad targeting applications.

Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs)

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.