Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data
Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. disease...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2009
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| Online Access: | http://hdl.handle.net/1721.1/49845 |
| _version_ | 1826193951127764992 |
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| author | Mitsos, Alexander Melas, Ioannis N. Siminelakis, Paraskeuas Chairakaki, Aikaterini D. Saez-Rodriguez, Julio Alexopoulos, Leonidas G. |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Mitsos, Alexander Melas, Ioannis N. Siminelakis, Paraskeuas Chairakaki, Aikaterini D. Saez-Rodriguez, Julio Alexopoulos, Leonidas G. |
| author_sort | Mitsos, Alexander |
| collection | MIT |
| description | Understanding the mechanisms of cell function and drug action is a major endeavor in
the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the
drug (i.e., selectivity and potency) and the specific signaling transduction network of the
host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased
approach to identify drug effects by monitoring drug-induced topology alterations.
With the proposed method, drug effects are investigated under several conditions on a
cell-type specific signaling network. First, starting with a generic pathway made of
logical gates, we build a cell-type specific map by constraining it to fit 13 key
phopshoprotein signals under 55 experimental cases. Fitting is performed via a
formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a
procedure that drastically outperforms previous fitting schemes. Then, knowing the cell
topology, we monitor the same key phopshoprotein signals under the presence of drug
and cytokines and we re-optimize the specific map to reveal the drug-induced topology
alterations. To prove our case, we make a pathway map for the hepatocytic cell line
HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal
Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily
predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway)
but we also uncover unanticipated effects due to either drug promiscuity or the cell’s
specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is
able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect
that cannot be extracted from binding affinity based approaches. Our method represents
an unbiased approach to identify drug effects on a small to medium size pathways and
is scalable to larger topologies with any type of signaling perturbations (small molecules,
3
RNAi etc). The method is a step towards a better picture of drug effects in pathways,
the cornerstone in identifying the mechanisms of drug efficacy and toxicity. |
| first_indexed | 2024-09-23T09:47:31Z |
| format | Article |
| id | mit-1721.1/49845 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:47:31Z |
| publishDate | 2009 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/498452022-09-30T16:52:54Z Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data Mitsos, Alexander Melas, Ioannis N. Siminelakis, Paraskeuas Chairakaki, Aikaterini D. Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Mechanical Engineering Mitsos, Alexander Saez-Rodriguez, Julio Mitsos, Alexander Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased approach to identify drug effects by monitoring drug-induced topology alterations. With the proposed method, drug effects are investigated under several conditions on a cell-type specific signaling network. First, starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental cases. Fitting is performed via a formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell topology, we monitor the same key phopshoprotein signals under the presence of drug and cytokines and we re-optimize the specific map to reveal the drug-induced topology alterations. To prove our case, we make a pathway map for the hepatocytic cell line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell’s specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect that cannot be extracted from binding affinity based approaches. Our method represents an unbiased approach to identify drug effects on a small to medium size pathways and is scalable to larger topologies with any type of signaling perturbations (small molecules, 3 RNAi etc). The method is a step towards a better picture of drug effects in pathways, the cornerstone in identifying the mechanisms of drug efficacy and toxicity. 2009-11-24T19:16:57Z 2009-11-24T19:16:57Z 2009-10 Article http://purl.org/eprint/type/SubmittedJournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/49845 Mitsos, Alexander, et al. "Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data." PLoS Computational Biology 5(12): e1000591. en_US http://dx.doi.org/10.1371/journal.pcbi.1000591 PLoS Computational Biology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Public Library of Science Alexander Mitsos |
| spellingShingle | Mitsos, Alexander Melas, Ioannis N. Siminelakis, Paraskeuas Chairakaki, Aikaterini D. Saez-Rodriguez, Julio Alexopoulos, Leonidas G. Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title | Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title_full | Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title_fullStr | Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title_full_unstemmed | Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title_short | Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data |
| title_sort | identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data |
| url | http://hdl.handle.net/1721.1/49845 |
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