Testing Anti-Cancerous Drugs: A Comprehensive Approach

This assignment delves into the intricacies of testing anti-cancerous drugs, highlighting the importance of a comprehensive approach that incorporates multiple techniques. Cell culture models, genetically modified mice, and computational methods are discussed as essential tools for assessing drug efficacy and potential side effects. The document emphasizes the need for a holistic approach to cancer drug development, considering the complex interactions between tumor cells and their micro-environment.

Running head: TOXICOLOGY
Toxicology
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................2
Answer 1....................................................................................................................................2
Cell culture or tissue culture..................................................................................................2
Animal Model or Xenograft Model.......................................................................................3
Computer Stimulations Model...............................................................................................3
Answer 2....................................................................................................................................4
Answer 3....................................................................................................................................4
References..................................................................................................................................5

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Introduction
According to the latest report published by University of Warwick in Chemie
International Edition, a new drug derived from metal iridium is capable of manipulating the
signaling pathways of the body. They named this drug ZL105 (Habtemariam et al., 2016).
Primary data indicates that this novel drug is 10 times more active than other available anti-
cancer drugs which are used for treating ovarian cancer, colon cancer, breast cancer, renal
cancer and melanoma (Habtemariam et al., 2016).
Answer 1
Same drug will behave differently in relation to the surrounding host environment and
hence modeling of the anti-cancerous drug is crucial in order check the efficacy of the drugs
however, the entire process is extremely daunting and it is difficult to create host specific
environment each time (). Some of the common methods, which are used for drug modeling,
include cell or tissue culture technique, Animal or xenograft model and computer stimulation
model.
Cell culture or tissue culture
The application of tumor cell lines helps the researchers to check the efficacy of the
compounds in its highly reproducible form. A characterized cell line also ensures parallel
screening assays where the effect of drug proliferation on multiple cell-lines can be
effectively determined. For example: NCI-60 Human Tumor Cell Line is offered by the
development therapeutics program of National Cancer Institute in order to check the efficacy
of the drug (Abaan et al., 2013). However, cell line also has certain share of disadvantage like
they lack genotypic and phenotypic heterogeneity of neoplastic cells present in the tumors
and are highly selective. Moreover, the differentiation of the cancer cell line in the cell
culture medium at times results in the generation of genetic and epigenetic changes that

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hampers the overall activity and specificity of the drugs (Breslin & O’Driscoll, 2013). Present
research technique employs in-vitro three-dimensional (3D) cancer model to test the efficacy
of the anti-cancerous drugs (Imamura et al., 2015).
Animal Model or Xenograft Model
Xenograft models are generated via injecting or implanting the tumor cells of human
origin into immune-deficient mice. Common immune-deficient mice models used for this
process include severe combined immunodeficiency (SCID) mice (Prkdcscid), nude mice
(Foxnlnu), NOD-SCID mice and RAG1 and RAG2 deficient mice (Gould, Junttila & de
Sauvage, 2015). However, the success of the xenograft model is low because the nude mice
are thymus deficient and are thus devoid of T-cell lines and hence unable to tract the drug
reaction in relation to T-cell (Gould, Junttila & de Sauvage, 2015). SCID mice on the other
hand lack both T-cell and B-cell and hence have potential susceptibility towards the injury
resulting out of radiation. Moreover, only 20 to 40% of the human cancer cell-line propagates
in the nude mice. In order to beat overcome this drawbacks, genetically engineered mice
models are used which show normal molecular mechanisms, neoplastic progression along
with other metastatic changes (Gould, Junttila & de Sauvage, 2015).
Computer Stimulations Model
Computer-Aided Drug Design (CADD) is best-suited technique to increase the rate of
success of the novel drugs. This process utilize targeted search other that using combinational
chemistry. It helps to get a detailed insight about the molecular basis of the therapeutic
activity of the drugs and thereby assist in predicting the possible derivatives that have
immense potentials towards improving the overall activity of the drug (Chen, 2013). Other
advantage of CADD includes careful modeling of the drug metabolism and pharmacokinetics
and comparison of the novel drug with other available chemo types via homology modeling
(Chen, 2013).

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Answer 2
Efficacy is defined as the maximum response that is available from a given dose
(Guney et al., 2016). Toxicity is defines as a process of determining the possible side effects
of the drugs. Lethality is used to measure the fetal threat of the drug towards human beings
(Holohan et al., 2013).
All these above-mentioned tests are crucial for a comprehensive assessment of the
drugs. Efficacy is important in order to determine the dosage administration. Toxicity is turn
is dependent on the efficacy and is used to determine the minimum dose of the drug that is
capable of generating toxic results (Holohan et al., 2013). Lethality is the most crucial factor
behind the clinical approval of the drug even 1 percent of lethality may ban the use of the
drug. This is because lethal threat is against the ethical principle of justice and beneficence
and non-malfeasance (Mailankody & Prasad, 2015).
Answer 3
There is no single cell culture or in-vivo model that is comprehensive for testing anti-
cancerous drug. All these above discussed techniques are equally important for testing anti-
cancerous drug. Cell-culture model assists in testing the efficacy of the novel drugs over the
primary tumor however, it is unable to mimic the complexity of reciprocal interaction
between proliferating tumor and surrounding micro-environment. Genetically modified mice
model helps in recapitulation of the pathogenesis of the human cancer in association with
germ line syngeneic models (Gould, Junttila & de Sauvage, 2015). Computational methods
promote homology modeling while predicting the possible side effect of the drugs (Chen,
2013). The same methods can be employed in order to test the other drugs, cosmetics and
herbal medicines. However, herbal medicines have negligible side effects and hence lethality
test might not be mandatory (Bone & Mills, 2013).

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References
Abaan, O. D., Polley, E. C., Davis, S. R., Zhu, Y. J., Bilke, S., Walker, R. L., ... & Holbeck,
S. L. (2013). The exomes of the NCI-60 panel: a genomic resource for cancer biology
and systems pharmacology. Cancer research, 73(14), 4372-4382.
Bone, K., & Mills, S. (2013). Principles and Practice of Phytotherapy-E-Book: Modern
Herbal Medicine. Elsevier Health Sciences.
Breslin, S., & O’Driscoll, L. (2013). Three-dimensional cell culture: the missing link in drug
discovery. Drug discovery today, 18(5-6), 240-249.
Chen, C. Y. C. (2013). A novel integrated framework and improved methodology of
computer-aided drug design. Current Topics in Medicinal Chemistry, 13(9), 965-988.
Gould, S. E., Junttila, M. R., & de Sauvage, F. J. (2015). Translational value of mouse
models in oncology drug development. Nature medicine, 21(5), 431.
Guney, E., Menche, J., Vidal, M., & Barábasi, A. L. (2016). Network-based in silico drug
efficacy screening. Nature communications, 7, 10331.
Habtemariam, A., Liu, Z., Soldevila, J. J., Pizarro, A. M., & Sadler, P. J. (2016). U.S. Patent
No. 9,408,853. Washington, DC: U.S. Patent and Trademark Office.
Holohan, C., Van Schaeybroeck, S., Longley, D. B., & Johnston, P. G. (2013). Cancer drug
resistance: an evolving paradigm. Nature Reviews Cancer, 13(10), 714.
Imamura, Y., Mukohara, T., Shimono, Y., Funakoshi, Y., Chayahara, N., Toyoda, M., ... &
Minami, H. (2015). Comparison of 2D-and 3D-culture models as drug-testing
platforms in breast cancer. Oncology reports, 33(4), 1837-1843.

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Mailankody, S., & Prasad, V. (2015). Five years of cancer drug approvals: innovation,
efficacy, and costs. JAMA oncology, 1(4), 539-540.