Project Interview: Combination therapy and biomarkers for Multiple Sclerosis – CombiMS

What are general and long term goals of the project?
The main goal of the CombiMS project was to identify a new method for drug discovery based on systems biology and to validate this approach by identifying novel combination therapies for MS. We adopted a systems biology approach that integrates experimental findings in vitro, assays on cells from MS patients with computational models and the analysis of a proprietary database of protein and drug interactions. This approach enables approved drugs to be repositioned and combined with neuroprotective therapies currently under development by the company coordinating the project, Bionure Farma SL. The methodology defined and the tools developed will be applicable to other immune diseases and eventually, to other types of complex diseases (e.g., cancer), helping to improve future therapeutic options for these conditions.

Please state a few more specific objectives of the project.

  1. To improve and devise new computational pathway models suitable to capture targets for immunomodulatory and neuroprotective therapies for MS;
  2. To redefine the clinical phenotypes of MS based on molecular and dynamic signalling parameters;
  3. To predict sets of putative combinatorial therapeutic targets and to experimentally validate at least one of these sets of targets;
  4. To develop an integrated experimental/computational methodology that enables efficacious and safe combination therapies and biomarkers to be identified, and that may be of wider use in biomedical research, and to the pharmaceutical and biotech industry.

The completion of these objectives has led to a more comprehensive understanding of the biological mechanism of action of some current MS therapies and to the development of new tools to design combination therapies, targeting MS in particular, but also complex diseases in general.

Describe the methodology, approach and technologies used.
The approach that was adopted involved first understanding the biological networks at play in the disease and how current MS therapies affect these networks. The immune response is mediated by interactions between cells, many of which involve the transmission of signals. Thus, we measured biological features of the cells in the immune system that handle these signals and that are involved in the disease, comparing them to the same features in cells from healthy individuals. In this way, we could define the differences between the healthy and disease state, and attempt to identify combinations of drugs or compounds that reverse the changes in the diseased cells so that they appear more like the healthy cells. Given that intercellular communication is often mediated by cell surface receptors that transduce their signals via phosphorylation cascades, we focused our attention on this modification and on the proteins involved in such pathways. Prior to validating and adopting the combination therapies, suitable algorithms were employed to address safety issues and the possible side-effects associated with the combinations identified, as well as the potential commercial viability.

The overall strategy was as follows:

  1. The pathogenic changes and the response to selected therapies were examined in primary cells isolated from patients in order to define the molecular signature of the specific clinical phenotypes. This was achieved by performing assays of protein activation (xMAP assays) on peripheral blood mononuclear cells (PBMCs) isolated from the blood of MS patients;
  2. The phosphoproteome networks that reflect the cellular and clinical phenotypes were defined;
  3. Network models were produced by applying logic-based ODE and dynamic modelling to reflect the clinical situation. These focused on the relevant phosphoproteomic changes in the immune system and the genotypes relevant to the diseases. The models could then be used to evaluate the effects of therapeutic drugs and their capacity to revert the pathological changes towards the healthy state.
  4. Applying further computational network analyses, the potential safety issues and side-effects that the selected combination therapies might provoke was evaluated.
  5. To evaluate the effects of the combination therapies identified, in vitro assays on human cell from patients or animal models helped improve translation towards clinical application.

This drug discovery method established by applying the strategy outlined above aims to improve the efficacy in selecting suitable drug combinations for use in complex human diseases. In addition, it should enable biomarkers of disease progression and prognosis to be identified, as well as to predict side-effects.

How is the project progressing, any results you wish to highlight?
The CombiMS project terminated having achieved most of its goals. The project identified 3 potential combination therapies, as well as candidate biomarkers of disease progression, prognosis and response to therapy. The 3 potential combination therapies have initially been validated in an animal model of MS, indicating that they can progress from preclinical to clinical development.

We have established an integrated experimental/computational modelling methodology aimed at the discovery of combination therapies for complex diseases like MS. Having demonstrated the validity of this method and pipeline, it can now be applied to other complex diseases, even those dependent on modifications that can be detected through other ‘omics technologies (e.g., metabolomics, cytomics, etc…).

This project provides one of the first proofs-of-concept that systems biology can be applied to medical research, and specifically, to the drug discovery process, identifying novel combination therapies that improve efficacy, safety and ultimately, the patient’s quality of life.

The Consortium is preparing a scientific publication in which the results of the project will be presented.

Funding source and funding duration:
The European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 305397, from 01/01/2013 to 31/12/2014.

Mar Massó