Combination treatment optimization using a pan-cancer pathway model
Abstract
The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustivein vivoor evenin vitroinvestigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at amolecularlevel have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used insequentialtreatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 28, 2021
- Source ID
- 10.1371/journal.pcbi.1009689
Entities
People
- Ali Sinan Saglam
- Fabian Fröhlich
- Gabriele Farina
- James Faeder
- Robin Schmucker
- Tuomas Sandholm
Organizations
- Division of Computing and Communication Foundations
- Division of Information and Intelligent Systems
- Meta
- National Institutes of Health
- United States Army Research Laboratory