Presentation
iSTaRT - in Silico Targeted Radionuclide Therapy: Designing Inhibitor-Chelator Conjugates
DescriptioniSTaRT - in Silico Targeted Radionuclide Therapy: Designing Inhibitor-Chelator Conjugates
Targeted radiopharmaceutical therapy (TRT) offers a precise and potent cancer treatment modality by delivering radioactive payloads directly to tumor cells. However, designing effective TRT agents - those that are cell-permeable, stable, and tumor-specific - remains a multifactorial challenge involving molecular, cellular, and tissue-level considerations. To address this, we developed iSTaRT (in Silico Targeted Radionuclide Therapy): an HPC-enabled framework that unites generative AI, multiscale simulation, and multicellular agent-based modeling to accelerate the design and optimization of TRT candidates.
Our pipeline begins with GEMMINI, a GenAI platform that generates linker molecules optimized for key physicochemical and ADMET properties and customized property predictors. Generated molecules are filtered using toxicity screens and permeability heuristics based on target membrane permeabilities ranges to ensure selectivity between oncogenic and normal cells.
We then use LipidLure, an HPC-based multiscale molecular dynamics pipeline, to compute the membrane permeability of selected complex TRT constructs across asymmetric bilayers reflective of normal versus oncogenic cells. These simulations of over ~15 µs of sampling on Frontier leverage the Inhomogeneous Solubility-Diffusion (ISD) model to compute permeability coefficients and membrane transport energetics.
For radionuclide-chelator stability, iSTART incorporates membrane-aware QM/MM simulations to evaluate actinium-DOTA complex binding across distinct bilayer environments. This approach captures relativistic and electronic effects unique to Ac3+ that surrogates like La3+ fail to model and quantifies environmental free energy penalties (ΔΔG) that affect stability in low-dielectric regions such as lipid cores.
Crucially, our framework links molecular and physical modeling with multicellular agent-based modeling (ABM) to simulate therapeutic impact within a digital twin of the tumor microenvironment. ABM enables spatial modeling of TRT diffusion, cellular uptake, and radiation-induced damage across heterogeneous cancer cell populations. This allows us to assess compound efficacy, selectivity, and synergy with radiosensitizers under realistic biological scenarios.
Our proof-of-principle centers on Ac-225–labeled sotorasib analogs targeting oncogenic protein KRAS G12C. By combining GenAI molecule generation with HPC-scale physical modeling and multicellular simulation, we can downselect high-performing candidates in days - substantially reducing the timeline for early-stage radiopharmaceutical design. These prioritized constructs are now advancing to experimental validation.
iSTART exemplifies how HPC can ignite innovation in cancer care by bridging molecular design, physical modeling, and systems-level prediction into a cohesive framework. The approach is modular and extensible, enabling application to other cancer targets, payloads, and patient-specific digital twins, with a long-term vision of guiding individualized therapy design through simulation.
Targeted radiopharmaceutical therapy (TRT) offers a precise and potent cancer treatment modality by delivering radioactive payloads directly to tumor cells. However, designing effective TRT agents - those that are cell-permeable, stable, and tumor-specific - remains a multifactorial challenge involving molecular, cellular, and tissue-level considerations. To address this, we developed iSTaRT (in Silico Targeted Radionuclide Therapy): an HPC-enabled framework that unites generative AI, multiscale simulation, and multicellular agent-based modeling to accelerate the design and optimization of TRT candidates.
Our pipeline begins with GEMMINI, a GenAI platform that generates linker molecules optimized for key physicochemical and ADMET properties and customized property predictors. Generated molecules are filtered using toxicity screens and permeability heuristics based on target membrane permeabilities ranges to ensure selectivity between oncogenic and normal cells.
We then use LipidLure, an HPC-based multiscale molecular dynamics pipeline, to compute the membrane permeability of selected complex TRT constructs across asymmetric bilayers reflective of normal versus oncogenic cells. These simulations of over ~15 µs of sampling on Frontier leverage the Inhomogeneous Solubility-Diffusion (ISD) model to compute permeability coefficients and membrane transport energetics.
For radionuclide-chelator stability, iSTART incorporates membrane-aware QM/MM simulations to evaluate actinium-DOTA complex binding across distinct bilayer environments. This approach captures relativistic and electronic effects unique to Ac3+ that surrogates like La3+ fail to model and quantifies environmental free energy penalties (ΔΔG) that affect stability in low-dielectric regions such as lipid cores.
Crucially, our framework links molecular and physical modeling with multicellular agent-based modeling (ABM) to simulate therapeutic impact within a digital twin of the tumor microenvironment. ABM enables spatial modeling of TRT diffusion, cellular uptake, and radiation-induced damage across heterogeneous cancer cell populations. This allows us to assess compound efficacy, selectivity, and synergy with radiosensitizers under realistic biological scenarios.
Our proof-of-principle centers on Ac-225–labeled sotorasib analogs targeting oncogenic protein KRAS G12C. By combining GenAI molecule generation with HPC-scale physical modeling and multicellular simulation, we can downselect high-performing candidates in days - substantially reducing the timeline for early-stage radiopharmaceutical design. These prioritized constructs are now advancing to experimental validation.
iSTART exemplifies how HPC can ignite innovation in cancer care by bridging molecular design, physical modeling, and systems-level prediction into a cohesive framework. The approach is modular and extensible, enabling application to other cancer targets, payloads, and patient-specific digital twins, with a long-term vision of guiding individualized therapy design through simulation.
Event Type
Workshop
TimeMonday, 17 November 202510:30am - 10:50am CST
Location241