NeuPD—A Neural Network-Based Approach to Predict Antineoplastic Drug Response

Author:

Shahzad Muhammad1ORCID,Tahir Muhammad Atif1ORCID,Alhussein Musaed2,Mobin Ansharah1,Shams Malick Rauf Ahmed1ORCID,Anwar Muhammad Shahid3ORCID

Affiliation:

1. FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan

2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

3. Department of AI and Software, Gachon University, Seongnam-si 13120, Republic of Korea

Abstract

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.

Funder

King Saud University, Riyadh, Saudi Arabia

Publisher

MDPI AG

Subject

Clinical Biochemistry

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