Combination therapy design for maximizing sensitivity and minimizing toxicity

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.

[1]  J. Lehár,et al.  Synergistic drug combinations improve therapeutic selectivity , 2009, Nature Biotechnology.

[2]  Marc S. Lavine,et al.  The Right Combination , 2006, Science.

[3]  Byung-Jun Yoon,et al.  Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics , 2011, BMC Bioinformatics.

[4]  Ranadip Pal,et al.  Inference of tumor inhibition pathways from drug perturbation data , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[5]  Michael Costanzo,et al.  Systematic exploration of synergistic drug pairs , 2011, Molecular systems biology.

[6]  Jacob D. Feala,et al.  Search Algorithms as a Framework for the Optimization of Drug Combinations , 2008, PLoS Comput. Biol..

[7]  Ranadip Pal,et al.  Erratum: Functionally defined therapeutic targets in diffuse intrinsic pontine glioma , 2015, Nature Medicine.

[8]  J. Lehár,et al.  Systematic discovery of multicomponent therapeutics , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Ranadip Pal,et al.  A Kinase Inhibition Map Approach for Tumor Sensitivity Prediction and Combination Therapy Design for Targeted Drugs , 2011, Pacific Symposium on Biocomputing.

[10]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[11]  J. Lehár,et al.  Multi-target therapeutics: when the whole is greater than the sum of the parts. , 2007, Drug discovery today.

[12]  M. Wadman Medical research: Them and us no longer , 2006, Nature.

[13]  Nci Dream Community A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .

[14]  R. Pal,et al.  An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge , 2014, PloS one.

[15]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[16]  Ranadip Pal,et al.  A Diverse Stochastic Search Algorithm for Combination Therapeutics , 2014, BioMed research international.

[17]  Ranadip Pal,et al.  A new approach for prediction of tumor sensitivity to targeted drugs based on functional data , 2013, BMC Bioinformatics.

[18]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[19]  Qian Wan,et al.  An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  Li Mao,et al.  Algorithmic Guided Screening of Drug Combinations of Arbitrary Size for Activity against Cancer Cells , 2022 .

[21]  R. Sun,et al.  Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm , 2008, Proceedings of the National Academy of Sciences.

[22]  Nicholas J. Wang,et al.  Functionally-defined Therapeutic Targets in Diffuse Intrinsic Pontine Glioma , 2015, Nature Medicine.

[23]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..