Literature DB >> 29686021

Combination Therapy and the Evolution of Resistance: The Theoretical Merits of Synergism and Antagonism in Cancer.

Elysia C Saputra1,2, Lu Huang3,4, Yihui Chen1,5, Lisa Tucker-Kellogg6,2,3,7.   

Abstract

The search for effective combination therapies for cancer has focused heavily on synergistic combinations because they exhibit enhanced therapeutic efficacy at lower doses. Although synergism is intuitively attractive, therapeutic success often depends on whether drug resistance develops. The impact of synergistic combinations (vs. antagonistic or additive combinations) on the process of drug-resistance evolution has not been investigated. In this study, we use a simplified computational model of cancer cell numbers in a population of drug-sensitive, singly-resistant, and fully-resistant cells to simulate the dynamics of resistance evolution in the presence of two-drug combinations. When we compared combination therapies administered at the same combination of effective doses, simulations showed synergistic combinations most effective at delaying onset of resistance. Paradoxically, when the therapies were compared using dose combinations with equal initial efficacy, antagonistic combinations were most successful at suppressing expansion of resistant subclones. These findings suggest that, although synergistic combinations could suppress resistance through early decimation of cell numbers (making them "proefficacy" strategies), they are inherently fragile toward the development of single resistance. In contrast, antagonistic combinations suppressed the clonal expansion of singly-resistant cells, making them "antiresistance" strategies. The distinction between synergism and antagonism was intrinsically connected to the distinction between offensive and defensive strategies, where offensive strategies inflicted early casualties and defensive strategies established protection against anticipated future threats. Our findings question the exclusive focus on synergistic combinations and motivate further consideration of nonsynergistic combinations for cancer therapy.Significance: Computational simulations show that if different combination therapies have similar initial efficacy in cancers, then nonsynergistic drug combinations are more likely than synergistic drug combinations to provide a long-term defense against the evolution of therapeutic resistance. Cancer Res; 78(9); 2419-31. ©2018 AACR. ©2018 American Association for Cancer Research.

Entities:  

Mesh:

Substances:

Year:  2018        PMID: 29686021     DOI: 10.1158/0008-5472.CAN-17-1201

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  17 in total

1.  Engineered Fn3 protein has targeted therapeutic effect on mesothelin-expressing cancer cells and increases tumor cell sensitivity to chemotherapy.

Authors:  Allison R Sirois; Daniela A Deny; Yanxuan Li; Yacine D Fall; Sarah J Moore
Journal:  Biotechnol Bioeng       Date:  2019-11-12       Impact factor: 4.530

2.  Natural Baicalein-Rich Fraction as Radiosensitizer in Combination with Bismuth Oxide Nanoparticles and Cisplatin for Clinical Radiotherapy.

Authors:  Noor Nabilah Talik Sisin; Nor Fazila Che Mat; Raizulnasuha Ab Rashid; Norhayati Dollah; Khairunisak Abdul Razak; Moshi Geso; Merfat Algethami; Wan Nordiana Rahman
Journal:  Int J Nanomedicine       Date:  2022-09-02

Review 3.  Emerging concepts in designing next-generation multifunctional nanomedicine for cancer treatment.

Authors:  Kasturee Chakraborty; Archana Tripathi; Sukumar Mishra; Argha Mario Mallick; Rituparna Sinha Roy
Journal:  Biosci Rep       Date:  2022-07-29       Impact factor: 3.976

4.  Affibody-Mediated PNA-Based Pretargeted Cotreatment Improves Survival of Trastuzumab-Treated Mice Bearing HER2-Expressing Xenografts.

Authors:  Maryam Oroujeni; Hanna Tano; Anzhelika Vorobyeva; Yongsheng Liu; Olga Vorontsova; Tianqi Xu; Kristina Westerlund; Anna Orlova; Vladimir Tolmachev; Amelie Eriksson Karlström
Journal:  J Nucl Med       Date:  2021-10-28       Impact factor: 11.082

Review 5.  Machine learning approaches for drug combination therapies.

Authors:  Betül Güvenç Paltun; Samuel Kaski; Hiroshi Mamitsuka
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

6.  ZnAs@SiO2 nanoparticles as a potential anti-tumor drug for targeting stemness and epithelial-mesenchymal transition in hepatocellular carcinoma via SHP-1/JAK2/STAT3 signaling.

Authors:  Yongquan Huang; Bin Zhou; Hui Luo; Junjie Mao; Yin Huang; Ke Zhang; Chaoming Mei; Yan Yan; Hongjun Jin; Jinhao Gao; Zhongzhen Su; Pengfei Pang; Dan Li; Hong Shan
Journal:  Theranostics       Date:  2019-06-09       Impact factor: 11.556

7.  Novel pyrrolizines bearing 3,4,5-trimethoxyphenyl moiety: design, synthesis, molecular docking, and biological evaluation as potential multi-target cytotoxic agents.

Authors:  Ahmed M Shawky; Nashwa A Ibrahim; Ashraf N Abdalla; Mohammed A S Abourehab; Ahmed M Gouda
Journal:  J Enzyme Inhib Med Chem       Date:  2021-12       Impact factor: 5.051

Review 8.  Iron oxide nanoparticles for immune cell labeling and cancer immunotherapy.

Authors:  Seokhwan Chung; Richard A Revia; Miqin Zhang
Journal:  Nanoscale Horiz       Date:  2021-07-20       Impact factor: 11.684

9.  Network-principled deep generative models for designing drug combinations as graph sets.

Authors:  Mostafa Karimi; Arman Hasanzadeh; Yang Shen
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

Review 10.  Passive immunotherapies targeting Aβ and tau in Alzheimer's disease.

Authors:  Steven S Plotkin; Neil R Cashman
Journal:  Neurobiol Dis       Date:  2020-07-16       Impact factor: 7.046

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.