article · Journal of King Saud University - Science
Standard bioassays using rodents to test the cancer-mitigating potential of chemical compounds are expensive and require animal sacrifice. To address this, a quantitative structure-activity relationship model was built to evaluate the activity and toxicity of chemicals against the leukaemia P388ADR cell line. The model utilised a dataset of 85 compounds, split into a training set of 68 compounds and a test set of 17 compounds. The resulting computational model showed good statistical predictive performance for both lethality and growth inhibition metrics. The analysis indicates that carcinogenicity is reduced by the absence of methanal fragments, a low dipole moment, and the presence of specific two-dimensional autocorrelated molecular descriptors. Additionally, molecular branching, size, and shape were identified as crucial parameters governing the carcinogenicity-mitigating characteristics of prospective drugs.
Evaluating potential cancer treatments usually relies on costly animal testing that raises ethical issues. By predicting toxicity and therapeutic potential using computational models instead, researchers can screen chemical compounds more quickly and identify safer anti-leukaemia drug candidates while reducing the need for rodent bioassays.
The work could enable early-stage drug discovery teams and pharmaceutical developers to computationally screen chemical libraries for anti-leukaemia activity prior to synthesis or testing. As a purely computational modelling effort based on 85 compounds, the technology is at an early research stage and would require further validation against larger datasets and experimental biological systems before commercial deployment.
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Cancer-causing nature is one of the toxicological endpoints bringing about the most elevated concern. Likewise, the standard bioassays in rodents used to survey the cancer-mitigating capability of chemicals and medications are expensive and require the sacrifice of animals. Thus, we have endeavored the development of a worldwide QSAR model utilizing an information set of 85 compounds, including drugs for their anti-leukemia potential. Considering expansive number of information focuses with different structural elements utilized for model development (ntraining = 68) and model validation (ntest = 17), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.833, R2pred = 0.716) for pLC50 and (leave-one-out Q2 = 0.744, R2pred = 0.614) for pGI50. Our developed model suggests that the absence of methanal fragments, low dipole moment and presence of some 2D autocorrelated molecular descriptors reduces the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-mitigating carcinogenicity.
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DOI: 10.1016/j.jksus.2018.05.023
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