Targeted therapy in cancer

Charlton, Philip; Spicer, James

doi:10.1016/j.mpmed.2015.10.012

Cited by 27 publications

(23 citation statements)

References 14 publications

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“…In order to establish a proper control signal, the inverse of the model has to be constructed. If one takes equation (2) and rearrange the terms, it leads to…”

Section: Robust Fixed Point Transformationmentioning

confidence: 99%

“…While they are proven to be effective in the majority of cases, many side effect arose during the treatment process. In the past decades a new methodology appeared called Targeted Molecular Therapies (TMT) which targets the key mechanisms of tumor growth [2]. This paper focuses of anti-angiogenic therapy which is one of the targeted methods, and it aims to eradicate the vascular network of the tumor.…”

Section: Introductionmentioning

confidence: 99%

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Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method

Czakó

Sápi

Kovács

2017

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)

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Cancerous diseases are being responsible for the death of many around the globe. Treating these illnesses pose a significant challenge to the medical professionals. While conventional methods, chemotherapy or radiotherapy for example, provide a remedy to the issue their side effects are not negligible. In the past few decades new methods have emerged, which could hinder the strength of the side effects and most remarkably, antiangiogenic therapy can make a notable difference in every day cancer treatment. While the technique has many advantages the cost of treatments are often expensive due to the non-personalized administration medical protocols. In this paper a model based solution is provided which aims to lower the medical expenses during the treatment by creating personalized administration plans with the help of control engineering.

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“…In order to establish a proper control signal, the inverse of the model has to be constructed. If one takes equation (2) and rearrange the terms, it leads to…”

Section: Robust Fixed Point Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method

Czakó

Sápi

Kovács

2017

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)

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“…While identifying general essential genes or disease specific gene dependencies provides a better understanding of potential disease specific targets, loss of function screens are not readily available for the majority of individual cancer patients. Tools that could predict cell line specific gene dependencies from more readily available data such as mutations and gene expression may offer new opportunities for affordable tailored therapies (Charlton & Spicer, 2016;Benstead-Hume et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Biological network topology features predict gene dependencies in cancer cell lines

Benstead-Hume

Wooller

Dias

et al. 2019

Preprint

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In this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene essentiality data, human protein-protein interaction (PPI) network data and individual cellline genomic alteration data we have built a range of machine learning classification models to predict cell line specific acquired essential genes. Genetic alterations found in each individual cell line were modelled by removing protein nodes to reflect loss of function mutations and changing the weights of edges in each PPI to reflect gain of function mutations and gene expression changes.We found that PPI networks can be used to successfully classify human cell line specific acquired essential genes within individual cell lines and between cell lines, even across tissue types with AUC ROC scores of between 0.75 and 0.85. Our novel perturbed PPI network models further improved prediction power compared to the base PPI model and are shown to be more sensitive to genes on which the cell becomes dependent as a result of other changes. These improvements offer opportunities for personalised therapy with each individual's cancer cell dependencies presenting a potential tailored drug target.The overriding motivation for predicting cancer cell line specific acquired essential genes is to provide a low-cost approach to identifying personalised cancer drug targets without the cost of exhaustive loss of function screening.

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“…Antibodies and other molecules capable of altering the action of ligands (growth factors), receptors and intracellular pathways, are used as targeted therapy. This therapy is directed for the purpose of inhibiting dysregulated pathways in order to block multiplication of malignant cells [11].…”

Section: Introductionmentioning

confidence: 99%

Biological Evaluation of Arylsemicarbazone Derivatives as Potential Anticancer Agents

et al. 2019

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Fourteen arylsemicarbazone derivatives were synthesized and evaluated in order to find agents with potential anticancer activity. Cytotoxic screening was performed against K562, HL-60, MOLT-4, HEp-2, NCI-H292, HT-29 and MCF-7 tumor cell lines. Compounds 3c and 4a were active against the tested cancer cell lines, being more cytotoxic for the HL-60 cell line with IC50 values of 13.08 μM and 11.38 μM, respectively. Regarding the protein kinase inhibition assay, 3c inhibited seven different kinases and 4a strongly inhibited the CK1δ/ε kinase. The studied kinases are involved in several cellular functions such as proliferation, migration, cell death and cell cycle progression. Additional analysis by flow cytometry revealed that 3c and 4a caused depolarization of the mitochondrial membrane, suggesting apoptosis mediated by the intrinsic pathway. Compound 3c induced arrest in G1 phase of the cell cycle on HL-60 cells, and in the annexin V assay approximately 50% of cells were in apoptosis at the highest concentration tested (26 μM). Compound 4a inhibited cell cycle by accumulation of abnormal postmitotic cells at G1 phase and induced DNA fragmentation at the highest concentration (22 μM).

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Targeted therapy in cancer

Cited by 27 publications

References 14 publications

Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method

Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method

Biological network topology features predict gene dependencies in cancer cell lines

Biological Evaluation of Arylsemicarbazone Derivatives as Potential Anticancer Agents

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