Combination therapy design for maximizing sensitivity and minimizing toxicity

Abstract

Background: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells. Results:The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations. Conclusions:The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.

Heterogeneity Aware Random Forest for Drug Sensitivity Prediction

“…Each set contains cell lines from two separate cancer types. The cell lines are modeled on a proliferation network structure based on target inhibition maps 28 , 29 . We have chosen to model each cell line utilizing a single block where each block contains a set of kinases, ϕ , connected in parallel.…”

Growth inhibitory role of the p53 activator SCH�529074 in non‑small cell lung cancer cells expressing mutant�p53

“…Nevertheless, combination treatment with low concentration of SCH 529074 and the autophagy inhibitor CQ led to reduced tumor cell survival rates compared with those observed following single drug treatment, indicating the efficacy of this combination therapy in NSCLC cells. It has been previously revealed that the application of combination therapy can maximize sensitivity and minimize the toxicity observed in the treatment of cancer cells (38). The precise role of SCH 529074 in autophagy requires further investigation by the analysis of additional autophagic markers.…”

Deep Functional and Molecular Characterization of a High-Risk Undifferentiated Pleomorphic Sarcoma

“…Probabilistic Target Inhibition Map (PTIM) modeling [13][14][15][16][17][18] integrates patient-specific chemical screening data with matched genomic and transcriptomic sequencing data to design personalized drug combinations. PTIM modeling identifies drug combinations where the individual agents may not slow or stop tumor growth but in combination will be synergistic and slow tumor growth.…”