# Multiscale Modeling to Optimize Inhibition of Oncogenic ERK Pathway Signaling

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $674,422

## Abstract

PROJECT SUMMARY/ABSTRACT
Small molecule inhibitors targeting the RAF/MEK/ERK pathway have become potent tools in precision medicine,
but their clinical efficacy is highly variable across the diversity of RAS- and BRAF-mutated cancers. Even in
susceptible cancers, these inhibitors rarely give durable responses. Studying the causes of resistance, which
include ‘paradoxical’ ERK pathway activation by RAF inhibitors, has revealed complex molecular adaptations in
the complicated networks comprised of RAF and ERK pathway kinases. These complexities limit our ability to
understand and predict effectiveness of targeted therapies, especially in combination – despite decades of
intense study, including mathematical modeling. Accurate predictions require understanding not only of the
molecular complexities of protein kinase regulation and the intricate systems-level behavior of the networks that
kinase constitute, but also of how these two levels of control are coupled. The challenge of accurately predicting
effectiveness of targeted therapies and their combinations therefore demands an amalgamation of molecular
and systems biology approaches. The systems biology project proposed here aims to identify optimal
combinations of kinase inhibitors through mechanistic models that integrate understanding of both:
1) Conformation selectivity of kinase inhibitors – affecting structural, thermodynamic and kinetic properties of the
targeted kinase(s); and 2) Systems-level network properties, including feedback loops, mutations and
kinase/scaffold abundances, which can modify feedback loops and allow normally inconsequential kinase
isoforms to compensate for isoform-specific kinase inhibition. Combining these features necessitates novel
approaches to modeling cell signaling that directly link molecular/structural and network facets to predict which
inhibitors and their combinations can efficiently suppress oncogenic signaling while disabling or delaying signal
recovery, growth, and drug resistance. We propose to develop such next-generation multiscale models of
oncogenic ERK signaling and drug responses, and to establish a new conceptual foundation for discovering
effective drug combinations by integrating structural, thermodynamic and kinetic information – and combining
short time-scale molecular dynamics (MD) with long time-scale modeling of systems-level dynamics. We will test
our model predictions rigorously by integrating and iterating modeling and experimental studies. Experimental
studies will begin in paired isogenic cancer cell lines with defined mutational differences. Once model predictions
are suitably robust, we will progress to panels of cancer cell lines, then to cell line-derived xenografts in vivo,
and then to patient-derived xenografts and genetically engineered mouse models (GEMMs) of melanoma – as
a presage to clinically integrated predictions. We will determine if the strategy of hitting a kinase by two (or more)
inhibitors with distinct conformati...

## Key facts

- **NIH application ID:** 10337242
- **Project number:** 5R01CA244660-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** William S Hlavacek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $674,422
- **Award type:** 5
- **Project period:** 2020-02-07 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10337242

## Citation

> US National Institutes of Health, RePORTER application 10337242, Multiscale Modeling to Optimize Inhibition of Oncogenic ERK Pathway Signaling (5R01CA244660-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10337242. Licensed CC0.

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