A Kinase Inhibition Map Approach for Tumor Sensitivity Prediction and Combination Therapy Design for Targeted Drugs

Abstract

Drugs targeting specific kinases are becoming common in cancer research and are a basis for personalized cancer therapy. Some of these drugs have the capacity to target multiple kinases. Promiscuous kinase inhibitors can be effective but the "off-target" effects can bring in toxicity for the patient. Thus the success of targeted cancer therapies with nominal harmful side effects is dependent on administering a single or multiple combinations of kinase inhibitors that targets the minimum number of kinases required to inhibit the tumor pathways. This requires a framework to predict the tumor sensitivities of a drug or drug combination based on the knowledge of the kinase inhibitors of a drug. In this article, we present a novel approach to predict the tumor sensitivities of a drug based on the generation of deterministic and stochastic Kinase Inhibition Maps. We build sensitivity maps or truth tables for a cell line from experimentally generated tumor sensitivities to kinase inhibitor drugs and use them to predict the sensitivity of a new drug or drug combinations based on known kinase inhibitor targets. We test our algorithms on a dataset of a dog osteosarcoma cell line with 317 possible kinase inhibitor targets after application of 36 targeted drugs. Our proposed algorithms are able to predict the sensitivities with high accuracy based on the given kinase inhibitor targets.

In-Silico Methodologies for Cancer Multidrug Optimization

“…Pal and Berlow [44] used tumor drug sensitivities and kinase inhibitor profiles to predict sensitivity to drug combinations for four canine osteosarcoma cell lines. The authors assumed that inhibiting a super set of a set of effective kinases, will also be effective.…”

“…Their method is based on the work of Pal and Berlow [44]. In TIMMA, a drug-target inhibition profile was built for each drug.…”

“…Their goal is to maximize the efficacy on heterogeneous tumor cells while minimizing the toxicity over normal cells. They created a probabilistic target inhibition map (PTIM) [43][44][45][46] that model the tumor proliferation. They generated a set of PTIM models for different breast-cancer and B-cell lymphoma cancer cell lines from the GDSC database [47].…”

Combination therapy design for maximizing sensitivity and minimizing toxicity

“…Based on these two biological constraints and limited drug perturbation experiments, we can arrive at an inferred PTIM model that can provide an estimate of sensitivity for all possible target inhibitions. The details of the model are available at [4][5][6] along with biological validation at [17]. Note that a PTIM can also be approximately represented as a tumor proliferation circuit as shown in Fig.…”

“…The genetic characterization based methodologies have severe limitations when the cancer type shows numerous aberrations among the samples and consequently predicting sensitivity based on similar steady state genetic characterizations provide limited accuracy. We have recently considered the modeling of tumor sensitivity using functional drug response data [4,5], along *Correspondence: ranadip.pal@ttu.edu 1 Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, 79409 Lubbock, TX, USA Full list of author information is available at the end of the article with a functional and genetic characterization based integrated modeling [6]. Models were designed based on the in vitro tumor response to a set of drugs with known targets.…”