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

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.