Research

Our mission is to develop mathematical, statistical and informatics tools to tackle biomedical questions that may potentially lead to breakthroughs in drug discovery. We are focusing on network pharmacology modeling, aiming at a systems-level understanding of how cancer signaling pathways can be inhibited by synergistic drug combinations through multi–target perturbations. These methods offer an improved efficiency to identify more effective cancer treatments for personalized medicine.

Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinics but so far showed limited efficacy, and we have limited understanding on why certain patients are non-responding. Even when there is an initial treatment response, cancer cells with high mutational potential and functional redundancy can easily develop drug resistance by activation of compensating pathways. To reach effective and sustained clinical responses, we critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance.

The Individualized Systems Medicine (ISM) platform at FIMM aims to identify novel therapeutic options that are most likely to be translated into clinics. Cancer patient samples are collected from clinics and cultured for drug sensitivity testing and molecular profiling. Since exhaustive experimentation of all the possible drug combinations for each specific cancer type or patient is not possible, computational methods offer the improved efficiency to predict the most potential drug combinations.

To facilitate the rational design of drug combinations toward a future of truly personalized cancer medicine, we will develop model-based clustering methods for the identification of patient subgroups that require specific treatment (“the right drug to the right patient”). For patients resistant to chemotherapy, we will develop network modelling approaches to predict the most potential drug combinations. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. We will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be modelled in cancer signaling networks to infer their mechanisms of action. Drug combinations with selective efficacy in individual patient samples or sample subgroups will be further translated into in treatment options. The proposed drug combination prediction, modelling and testing pipeline has the potential to lead to novel, more effective and safe treatments compared to the current cytotoxic and monotherapies.

  • Patient stratification using drug sensitivity data from cancer patient samples:We will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to targeted therapies.
  • Data integration on drug target interaction profiles:We will leverage the compound-protein bioactivity data for understanding compounds’ therapeutic efficacy and side effects..
  • Quantitative pharmacology modeling for drug combinations:We will utilize network pharmacology modeling approaches to integrate pharmacological and molecular biology data to understand the mechanisms of action of drug combinations.
  • Statistical methods for evaluating drug combination dose-response data:We will develop robust statistical methods for the initial validation of the most promising combination therapies.