Analyzing pathway design from drug perturbation experiments

Drugs that target specific kinases are becoming common in cancer research. In this article, we analyze the design of a modeling approach for drug sensitivity prediction and combination targeted therapy design based on drug perturbation experiments. We consider a target inhibition map model that predicts the tumor sensitivities for all possible combination of target inhibitions. The estimation of the model is based on experimental sensitivity data for multiple target inhibitory drugs. The target inhibition map model provides a steady-state snapshot of the underlying dynamical model. To analyze the robustness of the combination therapy design approach, we consider the inverse problem of possible dynamic models that can generate the target inhibition map model and their transient and steady state response to drugs. We showed that the knowledge of the steady state target inhibition map can be used to estimate the directional pathway using a small number of steady state target expression measurements.

1. INTRODUCTION

Personalized medicine for cancer is one of the primary objectives for systems medicine researchers. Use of a generic pathway for an individual cancer patient limits the success of targeted therapies since there are huge variations in the regulatory pathways of distinct cancer patients [1], [2]. However, generating a detailed model of the specific regulatory pathway of the individual patient is extremely difficult due to the enormous experimental data requirements on model parameter estimation. Often, only a specific aspect of the regulatory system is considered based on the final objective of modeling. For instance, the goal of individual tumor sensitivity to targeted drugs is frequently predicted based on genetic mutations [3] in the tumor samples and/or gene/protein expression measurements [4]. A great deal of research is ongoing in tumor sensitivity prediction based on genetic mutations and/or gene/protein expression measurements; some initial success has been achieved but multiple limitations of those approaches have been revealed. The approach of using genetic mutations for predicting the sensitivity is restricted by the presence of non-functional mutations and other latent variables. Application of gene/protein expression to predict the tumor sensitivity requires multiple expression measurements of tumor cells and solving the problem of functional importance of specific gene/protein expression. We have considered a functional approach based on the tumor sensitivity to multiple target inhibitor drugs [5]. A target inhibitor drug screen allows faster and cheaper data collection. The initial result of applying this approach was quite promising with a leave one out error for tumor sensitivity prediction of 7% for a canine cell culture [5]. However, the model developed from this approach is just able to predict the steady state behavior of target inhibitor combinations and does not provide us with the dynamics of the model. In this article, we analyze the generation of possible dynamic models satisfying the steady state model representation and utilizing them for the selection of a minimum number of target expression measurements for inferring the actual dynamic model.