Why the study?
Pulmonary hypertension is a complex disease with multiple pathogenesis, prompting interest in integrating pharmacogenetics, omics, and machine learning to improve drug and gene therapies.
Integrating multi-omics and machine learning with pharmacogenetics offers a promising pathway for personalized medicine and novel therapeutic strategies in pulmonary hypertension.
Encourages omics/ML integration for personalized PH care; leaves open gene therapy validation in rigorous trials.
It is a complex disease with multiple pathogenesis that causes pulmonary hypertension (PH). In addition to the commonly used drugs for treating PH, pharmacogenomic therapies are facilitating the innovation of personalized medicine to treat PH. Meanwhile, gene therapy has been extensively studied but is a quite challenging task. Furthermore, the gradual widespread application of omics analyses, big data and machine learning is a trend to improve the efficacy of drugs and gene therapy of PH. They are widely used in the screening of drug therapeutic targets and predicting drugs therapeutic effect. The emergence of innovative diagnosis and treatment approaches for PH opens pathways for combining personalized medicine with established pharmacological and gene therapies to achieve effective treatment strategies and further decrease the mortality rate associated with this disease.
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Li et al. (2025) studied this question.
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