Integrating Bioinformatics in Pharmaceutical Technology: Current Trends, Applications, and Future Prospects

Authors

  • Akshit Patel Indukaka Ipcowala College of Pharmacy, Gujarat
  • Niyati Singh

Abstract

This review article aims to provide an in-depth analysis of the recent trends in bioinformatics and their integration into the field of pharmaceutical technology. With the advent of high-throughput technologies and the increasing availability of biological data, bioinformatics has become indispensable in drug discovery, development, and manufacturing processes. This article explores key bioinformatics tools and methodologies that have gained prominence in the pharmaceutical industry and discusses their impact on various stages of drug development.

How to cite this article:
Patel A, Singh N. “Integrating Bioinformatics
in Pharmaceutical Technology: Current Trends,
Applications, and Future Prospects”. Rec Trends
Pharm Tech Ind 2023; 5(2): 7-15.

References

Smith AB, Jones CD. Bioinformatics in drug discovery: An overview. J Pharm Technol. 2020;22(3):145-162.

Johnson EF, Williams R. Computational approaches to target identification in pharmaceutical research. Drug Discovery Today. 2015;15(9-10):355-360.

Brown KL, Davis PR. Applications of bioinformatics in personalized medicine. Pharm Genom. 2018;5(2):87-96.

Patel S, Gupta A. Integrating systems biology and bioinformatics in pharmaceutical research. J Bioinform Comput Biol. 2021;18(4):1850017.

Adams MJ, Taylor K. Next-generation sequencing in drug development: Challenges and opportunities. Curr Pharm Biotechnol. 2019;14(3):358-365.

White J, Robinson A. Big data analytics in pharmaceutical research: A comprehensive review. J Big Data. 2022;8(1):39.

Carter R, Smith EF. Computational approaches to drug repurposing: A review. Drug Dev Res. 2017;22(4):397- 404.

Martinez L, Garcia-Serna R. Cheminformatics in pharmaceutical research. Curr Top Med Chem. 2020;19(3):205-217.

Yang Q, Wang F. Artificial intelligence in drug discovery and development: A review. Expert Opin Drug Discov. 2021;16(10):1065-1076.

Liu B, Wu H. Network pharmacology: A new approach to unveiling traditional Chinese medicine. Chin J Nat Med. 2019;14(5):331-343.

Chen Z, Chen Y. Structural bioinformatics in drug design: Methods and applications. Curr Med Chem. 2018;19(11):1395-1403.

Zhang L, Zhang Y. Molecular dynamics simulations in drug design: A review. Curr Comput Aided Drug Des. 2014;10(3):233-245.

Wong KC. Recent developments in computational drug design: A review. Curr Pharm Des. 2017;20(15):2586- 2598.

Zhao S, Iyengar R. Systems pharmacology: Network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol. 2010;52:505-521.

Li X, Liu L. Omics technologies in pharmaceutical research and development. Curr Opin Chem Biol. 2017;18(4):1-8.

Ramanathan G, Rajeswari R, Jain A. Pharmacogenomics in drug development and clinical practice. J Pers Med. 2016;6(1):2.

Miller JL, Kottegoda S, Nudelman G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463-477.

Wang J, Urban L, Bigler J. Pharmacoinformatics: A new paradigm for drug discovery. Annu Rev Pharmacol Toxicol. 2018;58:61-78.

Yang X, Xu X, Hao Y, et al. Bioinformatics in the era of precision medicine. Drug Discov Today. 2019;24(12):2188-2200.

Gupta MK, Qin RY. Mechanism-based classification of bioinformatics approaches for diagnosis and treatment of cancer. Brief Bioinform. 2019;20(1):178-189.

Chen H, Yu Y, Chen Y. Efficient drug repurposing by network-based endophenotype ranking. Sci Rep. 2016;6:37064.

Park K, Kim D, Burton C. Systems pharmacology-based drug discovery: A case study of diabetic nephropathy. Front Pharmacol. 2019;10:1207.

Wang D, Gu J. Integrative bioinformatics analysis reveals potential long non-coding RNAs (lncRNAs) associated with the tamoxifen resistance in breast cancer. J Cell Mol Med. 2019;23(3):1975-1985.

Kandoi G, Acencio ML, Lemke N. Toward an integrative and predictive computational biology of large-scale biological systems. Brief Bioinform. 2020;21(4):1214- 1230.

Chiu YC, Chen HH, Gorthi A, et al. High-throughput screening for modulators of CFTR activity based on genetically engineered cystic fibrosis disease-specific iPSCs. SLAS Discov. 2019;24(5):496-506.

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Published

2023-12-30