Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology

Abstract

Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab-DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC-target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.

Figure 1

Model development workflow. First, the ADC model was developed by integrating three submodels: (1) a minimal PBPK submodel to capture ADC PK, (2) a Krogh cylinder submodel to capture ADC distribution within the tumor, and (3) a tumor growth inhibition submodel to capture ADC PD. The integrated ADC model was next verified with T‐DM1 clinical PK/PD in metastatic breast cancer as a test case. The verified model was then applied to examine the impact of dosing on PK/PD of a hypothetical ADC. ADC, antibody‐drug conjugate; PBPK, physiologically‐based pharmacokinetic; PD, pharmacodynamic; PK, pharmacokinetic; T‐DM1, Ado‐trastuzumab emtansine.

Figure 2

Schematic of the mechanistic ADC model. (a) A minimal PBPK model captures ADC PK and disposition to plasma, lymph, tight, leaky, and tumor vasculature compartments. Tight and leaky tissue compartments represent lumped organs as described in ref. 13 ADC penetration from the tumor vasculature into and across the tumor is modeled by (b) the Krogh cylinder. The Krogh radius (R_Krogh) defines the maximal distance the ADC diffuses into the tumor (i.e., R_Krogh is half the intercapillary distance within the tumor). ADC diffusion (D) across the tumor is modeled at discrete spatial points at distances dR apart. ADC penetration across the capillary wall is driven by the capillary radius (R_cap), capillary permeability (C_p), and the ADC concentration in the tumor vasculature (C_P), and at the spatial point next to the capillary wall in the tumor (C_S1). The ADC concentration within the tumor is modified by the tumor void volume (ɛ). (c) At each spatial point, the ADC binds to the antigen present on the cell surface and is internalized, where the toxin is released via antibody degradation and/or linker cleavage. The released toxin causes cell death which is captured by the Simeoni tumor growth inhibition model. ²⁰ The tumor growth inhibition is modeled by four transit compartments, labeled here as different cell populations for conceptual ease: proliferating, damaged, dying, or nearly dead cells. ADC, antibody‐drug conjugate; CL, clearance; PBPK, physiologically‐based pharmacokinetic; PD, pharmacodynamics.

Figure 3

ADC model verification with T‐DM1 clinical pharmacokinetics (PKs)/pharmacodynamics as a test case. (a) Observed (n = 15) vs. predicted plasma PK for total trastuzumab (T‐DM1 conjugated and unconjugated antibody) and T‐DM1 (conjugated antibody only) following a single T‐DM1 clinical dose at 3.6 mg/kg. ²¹ (b) The observed clinical objective response rates (ORRs) for monotherapy T‐DM1 dosed at 3.6 mg/kg is compared to model predicted. ²² , ²³ All observed data are given as median ± 95% confidence interval, and simulated data is represented as the median ± 95% confidence interval represented by the a shaded region or b error bars. Predicted confidence intervals were obtained by simulating 100 virtual patients across 10 groups. AUC, area under the curve; C_max, maximum concentration.

Figure 4

ADC model sensitivity analysis. Sensitivity analyses were conducted on two key parameters tunable by the ADC properties (a) dissociation constant (K _D) between the ADC and antigen and (b) toxin potency (IC₅₀), and two parameters based on the tumor type (c) antigen expression and (d) tumor doubling time defined by the linear tumor growth parameter. (Top row) The total TVR after 6 months of treatment was determined by altering each parameter across a range of three orders of magnitude lower and three orders of magnitude higher than the model baseline value. (Bottom row) Spatial TVR, the reduction in tumor volume at each spatial point across the tumor, was determined after 6 months of treatment for each parameter at baseline and 1 order of magnitude higher and lower than baseline. For each sensitivity analysis, the ADC was fractionated to QW, Q2W, Q3W, and Q4W dosing. ADC, antibody‐drug conjugate; IC₅₀, half maximal inhibitory concentration; K _D, dissociation constant; TVR, tumor volume reduction.

Figure 5

Impact of fractionated ADC dosing on receptor occupancy. (a, b) Simulated percent receptor occupancy across time and a theoretical tumor over 1 month is given for ADC dosing fractionated at (a) 10 mg QW or (b) 40 mg Q4W. RO radially across the tumor at day 1 (solid line) and day 21 (dashed line) are given for the (c) QW and (d) Q4W dose schedules.

Figure 6

Impact of payload potency and target expression on percent tumor kill. A hypothetical ADC with either high (IC₅₀ = 10 pM) or low (IC₅₀ = 0.10 µM) potency targeted a generalized tumor with either high (10⁷ receptors/cell) or low (10⁵ receptors/cell) antigen expression. Spatial TVR, the reduction in tumor volume at each spatial point across the tumor, is given as a function of time in each scenario. ADC, antibody‐drug conjugate; IC₅₀, half maximal inhibitory concentration; RO, receptor occupancy; TVR, tumor volume reduction.

Figure 7

Relationship between dose fractionation and parameters on % ORR. The % ORR after 6 months of treatment was determined for patients receiving doses fractionated to QW, Q2W, Q3W, or Q4W using baseline parameter values (Table S1 ). The effects of four key parameters (a) K _D, (b) IC₅₀, (c) antigen expression, and (d) tumor doubling time for each dose schedule were determined by altering the parameter 10‐fold higher and lower than the baseline value. The % ORR given parameter alteration and dose schedule are represented as the difference from the % ORR given by Q4W dose scheduling with baseline parameters. A positive % ORR indicates higher efficacy, and a negative % ORR indicates lower efficacy, relative to Q4W dose scheduling with baseline parameters. Virtual patients were defined by calibrating parameter variability to the clinical response rate observed for T‐DM1. ²² Variability was estimated by simulating 100 random patients 10 times for each treatment condition, and error bars represent the 95% confidence interval. ORR, Overall response rate; IC₅₀, half maximal inhibitory concentration; K _D, dissociation constant.