The optimal carrier dose is dependent on receptor expression with higher trastuzumab doses needed for higher receptor expression. a linker connecting the two. These agents must be optimized to specifically deliver the payload to cancer cells while minimizing healthy tissue uptake as they traverse multiple drug delivery barriers. After intravenous administration, ADCs flow through the blood to the tumor, extravasate from blood vessel, diffuse through the interstitial tumor tissue, ML335 bind antigens, are internalized by cancer cells, and, upon linker cleavage or digestion, release the cytotoxic payload that diffuses across membranes to their site of action (often DNA or microtubules) inside the cells. Tumors may vary in blood vessel Rabbit Polyclonal to TSPO density, receptor ML335 expression, and receptor internalization, all affecting ADC delivery. ADC distribution in solid tumors is typically heterogeneous, which can impact efficacy (2). Because of the large size of ADCs, uptake into the tumor is limited by extravasation from the blood to the tumor tissue (i.e., they are permeability-limited) (3), resulting in reduced amounts of drug in the tissue. Once in the tissue, tumor penetration is usually slow as a result of elevated interstitial pressure, making them reliant on diffusion through the interstitial space. ADCs rapidly bind to antigens surrounding tumor blood vessels and are typically internalized before they can dissociate and diffuse deeper into the tissue. For example, at clinical doses of 3.6 mg/kg, T-DM1 localizes perivascularly, as seen in multiple mouse models (4), due to its large size and high affinity to HER2 receptors. We as well as others have exhibited that some ADC regimens can improve ADC distribution, efficacy, and tolerability. As previously established in our lab, the co-administration of T-DM1 with its unconjugated antibody, trastuzumab, improves drug penetration and efficacy (5). Other work from Hinrichs et ML335 al. and Jumbe et al. show that fractionating a single dose into 3 weekly doses can lead to similar efficacy, but better tolerability (6,7). Despite these findings, the design principles underlying the best choice of regimen and drug combinations are not well-understood. For example, it is not clear if/when a carrier dose will increase drug penetration and improve efficacy. The impact of HER2 expression level and payload potency on the increase or reduction in efficacy from a carrier dose are also not well defined. Finally, the interplay of dose fractionation with carrier doses on overall efficacy is currently unknown. Testing all combinations of receptor expression, payload potencies, carrier doses, dose fractionation regimes, etc.in vivowould be a daunting and expensive process. For this reason, computational models that guide experiments and predict best drug regimens are becoming more widely used. Computational models to capture the pharmacokinetics of ADCs are already established in the field (8) (9) (10) (11), but to our knowledge, there is not a pharmacokinetic/pharmacodynamic (PKPD) model of ADC distribution that captures the heterogeneous distribution of ADCs on individual cells to connect experimental single cell PK data to overall efficacy. Here we took a systems pharmacology approach to study and predict the best ADC regimens. We developed a hybrid agent-based model (ABM) to capture ADC and/or antibody delivery and predict individual cell killing and tumor growth kinetics. This multiscale model enables detailed depictions of heterogeneous ADC delivery, cancer cell death, and tumor growth. Partial and ordinary differential equation models of ADC extravasation from multiple vessels, diffusion, binding, and processing are overlaid on a grid of individual malignancy cells (brokers). These cells undergo growth (cell division) and respond to the drug by cell death as a probability function of their intracellular payload concentration. Tumor growth rate is usually a function of the total number of cancer cells at a given time. The model was validated by comparison to experimental measurements in a HER2 positive NCI-N87 mouse xenograft model (5).With this model, the role of the carrier dose was analyzed, and predictions for the best regimen for different types of tumors overexpressing HER2 are presented. == MATERIALS AND METHODS == We built a computational model to predict the efficacy of particular ADC regimens, accounting for ADC distribution in a heterogeneous tumor microenvironment. Our model is usually a hybrid ABM comprised of cancer cells and blood vessels that compose the tumor microenvironment and behave based on predefined rules and changes in their local microenvironment. The multi-scale model has portions that describe the tumor environment and PKPD: plasma dynamics, drug dynamics for T-DM1 and trastuzumab, and individual malignancy cell dynamics (e.g., cell division, death) (Physique 1). == Fig. 1. == Model schematic. a Plasma dynamics describe the intact ADC concentration in the blood as a result of local and systemic clearance, including deconjugation. b ADCs in the blood extravasate from the blood vessel, diffuse through the interstitial tissue, bind to HER2 antigens, and are internalized. After ADC degradation in lysosomes,.