Computational and biological modeling of IGF1R inhibition for multifocal medulloblastoma

Alyssa G Almer; Samuel V Rasmussen; Dina Kats; Matthew N Svalina; Bonnie L Cole; Mohammadreza Khani; Sonja Chen; Samuel H Cheshier; Bryn A Martin; Noah E Berlow; Charles Keller

doi:10.1038/s43856-025-00925-4

Computational and biological modeling of IGF1R inhibition for multifocal medulloblastoma

Commun Med (Lond). 2025 May 28;5(1):206. doi: 10.1038/s43856-025-00925-4.

Authors

Affiliations

¹ Children's Cancer Therapy Development Institute, Hillsboro, OR, USA.
² Children's Cancer Therapy Development Institute, Hillsboro, OR, USA. samuel@cc-tdi.org.
³ Department of Pathology, Seattle Children's Hospital, Seattle, WA, USA.
⁴ Department of Biological Engineering, University of Idaho, Moscow, ID, USA.
⁵ Alcyone Therapeutics, Inc., Lowell, MA, USA.
⁶ Department of Pathology, Nationwide Children's Hospital, Columbus, OH, USA.
⁷ Division of Pediatric Neurosurgery, Department of Neurosurgery, Huntsman Cancer Institute, Intermountain Primary Children's Hospital, University of Utah, Salt Lake City, UT, USA.
⁸ Children's Cancer Therapy Development Institute, Hillsboro, OR, USA. charles@cc-tdi.org.

Abstract

Background: Leptomeningeal metastasis in medulloblastoma poses challenges for effective treatments due to the blood-brain barrier (BBB), which may be addressed through intrathecal or intraventricular drug delivery. However, the lack of pharmacokinetic modeling for pathological cerebrospinal fluid (CSF) geometries has limited the ability to predict effective intrathecal and intraventricular drug exposure.

Methods: A patient-specific computational fluid dynamics "in silico" trial was conducted to simulate CSF movement to examine the tumor microenvironment in terms of drug-target exposure over time following intraventricular delivery via Omaya Reservoir. Simultaneously, we conducted cellular adhesion experiments to test the therapeutic potential of IGF1R inhibition on metastasis under patient-specific flow conditions generated by computational analysis.

Results: A 3-dimensional computational fluid dynamics (CFD) model based on patient-specific conditions was obtained to predict an efficacious drug concentration, providing guidance for therapeutic drug exposure at targeted sites. Microfluidic experiments for IGF1R inhibition of cellular adhesion showed the potential for reduced attachment of medulloblastoma to leptomeningeal cells to prevent metastasis.

Conclusions: This study offers insights from patient-specific in silico trials for the precision delivery of small-molecule drugs for the treatment of central nervous system (CNS) malignancies.

Plain language summary

Medulloblastoma is a fast-growing tumor in the brain that can be treated through several strategies. Tumor that has spread (metastatic) can be difficult to treat because many drugs cannot reach the tumor location, or the tumors obstruct drug exposure in the spinal fluid. We conducted proof-of-concept 3D dynamic fluid modeling using computers to determine how drugs would spread and be delivered. We then checked these findings with studies that mimicked a patient’s brain and tested the delivery of a specific drug. We found the drug could reach the target tumor in the simulation and help stop new metastasis formation, but did not impact existing metastases.