Integrasi Bioinformatika dan Farmakogenomik untuk Merancang Terapi Individualisasi pada Pasien dengan Resistensi Obat Tuberkulosis

Dharmika Pranidhi; Dhanan Abimanto

doi:10.59031/jnts.v1i4.768

Authors

Dharmika Pranidhi Institut Nalanda
Dhanan Abimanto Universitas Maritim AMNI

DOI:

https://doi.org/10.59031/jnts.v1i4.768

Keywords:

Bioinformatics, Drug Resistance, Pharmacogenomics, Precision Medicine, Tuberculosis

Abstract

Drug-resistant tuberculosis (TB) is an escalating global health issue, particularly with the rise of multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB), which complicate treatment and control efforts. Resistance to both first-line and second-line drugs weakens the effectiveness of standard WHO-recommended therapies, while alternative drugs can cause severe side effects and reduce patient adherence. This study aims to explore the integration of bioinformatics and pharmacogenomics in supporting personalized TB treatment to improve therapeutic success and reduce the risk of further resistance. The research employed a laboratory-based experimental design with a bioinformatics approach, involving TB patients with clinical evidence of drug resistance. Clinical samples were analyzed through whole genome sequencing to identify gene mutations associated with resistance, followed by pharmacogenomic mapping to predict pharmacological responses based on patients’ genetic variations. The results revealed several specific gene mutations consistently linked to resistance and produced individualized therapeutic recommendations that were more targeted than standard protocols. Effectiveness evaluation demonstrated that genome-based personalized therapy yielded higher treatment success rates, faster recovery times, and lower rates of subsequent resistance. These findings highlight the significant potential of precision medicine in TB management, particularly for resistant cases that are difficult to treat with conventional approaches. In conclusion, the integration of bioinformatics and pharmacogenomics plays an essential role in strengthening TB treatment strategies through a personalized, adaptive, effective, and sustainable approach. Nevertheless, its implementation still faces challenges such as high costs, limited infrastructure, and the need for clear regulations regarding the use of patients’ genomic data.

References

Baylan, O. (2012). Treatment of drug-resistant tuberculosis: Review. Turkiye Klinikleri Journal of Medical Sciences, 32(3), 788–804. https://doi.org/10.5336/medsci.2011-24602

Bhunia, S. K., Sarkar, M., Banerjee, A., & Giri, B. (2015). An update on pathogenesis and management of tuberculosis with special reference to drug resistance. Asian Pacific Journal of Tropical Disease, 5(9), 673–686. https://doi.org/10.1016/S2222-1808(15)60912-4

Bobba, S., & Khader, S. A. (2023). Rifampicin drug resistance and host immunity in tuberculosis: More than meets the eye. Trends in Immunology, 44(9), 712–723. https://doi.org/10.1016/j.it.2023.07.003

Cao, S., Wu, H.-Z., Zhou, G., Zhang, W., & Zhou, H.-H. (2011). Progress in the pharmacogenomics of infectious diseases. Chinese Journal of New Drugs, 20(13), 1202-1221.

Cava, C., Gallivanone, F., Salvatore, C., Rosa, P. A. D., & Castiglioni, I. (2014b). Bioinformatics clouds for high-throughput technologies. In Handbook of Research on Cloud Infrastructures for Big Data Analytics (pp. 489–507). IGI Global. https://doi.org/10.4018/978-1-4666-5864-6.ch020

Dey, S., Joshi, K., & Mazumder, B. (2018). Pharmacogenomics: Setting newer paradigms of genetics in therapy and medicine. In Genomics-Driven Healthcare: Trends in Disease Prevention and Treatment (pp. 37–58). Springer. https://doi.org/10.1007/978-981-10-7506-3_3

Gothi, D., & Joshi, J. M. (2011). Resistant TB: Newer drugs and community approach. Recent Patents on Anti-Infective Drug Discovery, 6(1), 27–37. https://doi.org/10.2174/157489111794407859

Hauer, B., Castell, S., & Loddenkemper, R. (2011). Drug-resistant tuberculosis: Growing problems and solutions. Pneumologe, 8(1), 25–31. https://doi.org/10.1007/s10405-010-0401-6

Kang, Y. A. (2014). Diagnosis and treatment of multidrug-resistant tuberculosis. Journal of the Korean Medical Association, 57(1), 27–33. https://doi.org/10.5124/jkma.2014.57.1.27

Kaul, G., Kapoor, E., Dasgupta, A., & Chopra, S. (2019). Management of multidrug-resistant tuberculosis in the 21st century. Drugs of Today, 55(3), 215–224. https://doi.org/10.1358/dot.2019.55.3.2927587

Koumakis, L. (2020). Deep learning models in genomics; are we there yet? Computational and Structural Biotechnology Journal, 18, 1466–1473. https://doi.org/10.1016/j.csbj.2020.06.017

Kumar, K., & Kon, O. M. (2021). Personalised medicine for tuberculosis and non-tuberculous mycobacterial pulmonary disease. Microorganisms, 9(11), 2220. https://doi.org/10.3390/microorganisms9112220

Li, H., Xue, Y., & Zeng, X. (2021). Investigation of data mining technique and artificial intelligence algorithm in microflora bioinformatics. E3S Web of Conferences, 267, 01040. https://doi.org/10.1051/e3sconf/202126701040

Liu, F., Ji, Z., Xu, X., & Wang, L. (2017). A review of RNA-Seq reads mapping algorithm. In Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS 2017) (pp. 907–911). IEEE. https://doi.org/10.1109/ICSESS.2017.8343057

Matsumoto, T., Ohno, M., & Azuma, J. (2014). Future of pharmacogenetics-based therapy for tuberculosis. Pharmacogenomics, 15(5), 601–607. https://doi.org/10.2217/pgs.14.38

Munir, A., Kumar, N., Ramalingam, S. B., Tamilzhalagan, S., Shanmugam, S. K., Palaniappan, A. N., Nair, D., Priyadarshini, P., Natarajan, M., Tripathy, S., Ranganathan, U. D., Peacock, S. J., Parkhill, J., Blundell, T. L., & Malhotra, S. (2019). Identification and characterization of genetic determinants of isoniazid and rifampicin resistance in Mycobacterium tuberculosis in southern India. Scientific Reports, 9(1), 10283. https://doi.org/10.1038/s41598-019-46756-x

Ngo, T.-M., & Teo, Y.-Y. (2019). Genomic prediction of tuberculosis drug-resistance: Benchmarking existing databases and prediction algorithms. BMC Bioinformatics, 20(1), 68. https://doi.org/10.1186/s12859-019-2658-z

Padmanabhan, S. (2014). Handbook of pharmacogenomics and stratified medicine. Elsevier. https://doi.org/10.1016/C2010-0-67325-1

Patrinos, G. P., Andritsou, A., Chalikiopoulou, K., Mendrinou, E., & Tsermpini, E.-E. (2019). Pharmacogenomics in clinical care: Implications for public health. In Applied Genomics and Public Health (pp. 111–130). Elsevier. https://doi.org/10.1016/B978-0-12-813695-9.00006-6

Prasanthi, B., Santosh Kumar, T., & Vijaya Ratna, J. (2013). The probable factors that lead to multi-drug resistant tuberculosis and its control: A critical review. International Journal of Pharmaceutical Sciences Review and Research, 23(2), 108–116.

Shankarkumar, U., Shankarkumar, A., & Ghosh, K. (2011). Human immunodeficiency virus therapeutics and pharmacogenomics. Indian Journal of Human Genetics, 17(Suppl 1), S22–S26. https://doi.org/10.4103/0971-6866.80354

Shoaib, M., Singh, A., Gulati, S., & Kukreti, S. (2021). Mapping genomes by using bioinformatics data and tools. In Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences (pp. 245–278). Elsevier. https://doi.org/10.1016/B978-0-12-821748-1.00002-6

Subbarayudu, Y., Rakesh, M., Lingappa, E., & Umar, S. (2017). Overview of the new bioinformatics virus goes from the front of next generation sequencing in genomics based on datamining. In Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS 2017) (pp. 1261–1264). IEEE. https://doi.org/10.1109/ICCONS.2017.8250670

Suman, S. K., Chandrasekaran, N., & Doss, C. G. P. (2023). Micro-nanoemulsion and nanoparticle-assisted drug delivery against drug-resistant tuberculosis: Recent developments. Clinical Microbiology Reviews, 36(4), e00088-23. https://doi.org/10.1128/cmr.00088-23

Swaminathan, S., Sundaramurthi, J. C., Palaniappan, A. N., & Narayanan, S. (2016). Recent developments in genomics, bioinformatics and drug discovery to combat emerging drug-resistant tuberculosis. Tuberculosis, 101, 31–40. https://doi.org/10.1016/j.tube.2016.08.002

Traoré, A. N., Rikhotso, M. C., Mphaphuli, M. A., Patel, S. M., Mahamud, H. A., Kachienga, L. O., Kabue, J.-P., & Potgieter, N. (2023). Isoniazid and rifampicin resistance-conferring mutations in Mycobacterium tuberculosis isolates from South Africa. Pathogens, 12(8), 1015. https://doi.org/10.3390/pathogens12081015

Vemula, D., Singothu, S., & Bhandari, V. (2023). Concepts in pharmacogenomics: Tools and applications. In Recent Advances in Pharmaceutical Innovation and Research (pp. 41–76). Springer. https://doi.org/10.1007/978-981-99-2302-1_2

Zhang, G., Zhang, Y., Ling, Y., & Jia, J. (2015). Web resources for pharmacogenomics. Genomics, Proteomics & Bioinformatics, 13(1), 51–54. https://doi.org/10.1016/j.gpb.2015.01.002