Periodic Reporting for period 1 - DrugSynergy (DrugSynergy: A data-driven systems biology approach to optimize drug combination strategies)
Période du rapport: 2016-09-01 au 2018-02-28
DrugSynergy is a systems biology and systems pharmacology platform aiming at optimizing drug combination strategies using sophisticated computational pipelines. As the number of drugs for a given disease condition increase, it becomes more and more critical to develop strategies guiding their combination. Currently, most drug combinations are either empirical or based on the scientific rationale coming from heterogeneous experimental systems with limited resolution in characterizing the interactions between the two drug. We have previously shown that two stimuli can interact according 10 interaction modes, which go way beyond the classical view restricted to synergistic and antagonistic interactions. We also showed that a single cell type can integrate combinations of stimuli according to multiple of these modes. DrugSynergy is exploiting those concepts for the in-depth characterization of combinations of drugs on various target cells.
In the frame of this project we have accomplished several important steps in order to bring our platform closer to the needs of the pharmaceutical development. We have optimized our computational processes using machine learning algorithms in order to increase robustness and reliability, and provide an error model. We have also automated several computational steps towards generating interaction profiles of drug combinations, in order to facilitate and accelerate the analysis of complex transcriptomics datasets in response to drug combinations. We have established a resource database of several published transcriptomics combinatorial datasets in response to combinations of drugs in different human cellular systems, in particular cancer cell lines. Last, we have generated original data sets using stem and non-stem breast cancer cells stimulated with combinations of signaling pathway inhibitors. These datasets are in the process of being analyzed in order to identify the most interesting and impactful combination to be targeted in breast cancer. Parallel to these scientific achievements, which bring us closer to pharmaceutical and biomedical applications, we have developed an industrial partnership in order to analyze the effects of combined immune checkpoint targeting on human T cells. This partnership is a direct output of the ERC POC project, and is helping us to tailor our DrugSynergy platform to the expectations of a pharmaceutical company.
Although we still have important steps to accomplish in order to further optimize the computation and biological interpretation of our platform, we have managed to translate our basic research expertise into applications in systems pharmacology and drug development. We are currently seeking for additional industrial partners in order to move beyond the proof of concept, and to apply our DrugSynergy platform to a broader diversity of drug combinations.
In the frame of this project we have accomplished several important steps in order to bring our platform closer to the needs of the pharmaceutical development. We have optimized our computational processes using machine learning algorithms in order to increase robustness and reliability, and provide an error model. We have also automated several computational steps towards generating interaction profiles of drug combinations, in order to facilitate and accelerate the analysis of complex transcriptomics datasets in response to drug combinations. We have established a resource database of several published transcriptomics combinatorial datasets in response to combinations of drugs in different human cellular systems, in particular cancer cell lines. Last, we have generated original data sets using stem and non-stem breast cancer cells stimulated with combinations of signaling pathway inhibitors. These datasets are in the process of being analyzed in order to identify the most interesting and impactful combination to be targeted in breast cancer. Parallel to these scientific achievements, which bring us closer to pharmaceutical and biomedical applications, we have developed an industrial partnership in order to analyze the effects of combined immune checkpoint targeting on human T cells. This partnership is a direct output of the ERC POC project, and is helping us to tailor our DrugSynergy platform to the expectations of a pharmaceutical company.
Although we still have important steps to accomplish in order to further optimize the computation and biological interpretation of our platform, we have managed to translate our basic research expertise into applications in systems pharmacology and drug development. We are currently seeking for additional industrial partners in order to move beyond the proof of concept, and to apply our DrugSynergy platform to a broader diversity of drug combinations.