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Combination treatment optimization using a pan-cancer pathway model
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 12, p e1009689 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science, 2021.
-
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 exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level 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 in sequential treatment 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.<br />Author summary Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.
- Subjects :
- Optimization problem
Skin Neoplasms
Computer science
Cancer Treatment
Neoplasms
Breast Tumors
Basic Cancer Research
Drug Discovery
Antineoplastic Combined Chemotherapy Protocols
Medicine and Health Sciences
Available drugs
Biology (General)
Skin Tumors
Decision Making, Computer-Assisted
media_common
Cancer Drug Discovery
Pan cancer
Ecology
Pharmaceutics
Computational Theory and Mathematics
Oncology
Modeling and Simulation
Physical Sciences
Evolution strategy
Algorithms
Research Article
Drug
Optimization
Drug Administration
Drug Research and Development
Dose
QH301-705.5
media_common.quotation_subject
Antineoplastic Agents
Computational biology
Dermatology
Models, Biological
Cellular and Molecular Neuroscience
Combined treatment
Drug Therapy
In vivo
Breast Cancer
Genetics
Humans
CMA-ES
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Cell Proliferation
Pharmacology
Cancers and Neoplasms
Computational Biology
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 17
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....60dbab5fdb9dae135af5e9bd9dafb135