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Advances in Nano Research Volume 19, Number 3, September 2025 , pages 237-244 DOI: https://doi.org/10.12989/anr.2025.19.3.237 |
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Nano-drug delivery systems of endometriosis: Animal model-based implications and machine learning-guided treatment optimization |
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Jing Gong, Jie Lou, Xiaojuan Tang, Li Liao, Liujing Zheng, Xiuyue Liao, Fei Xiang, Fengxia Yang and Xin Zheng
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| Abstract | ||
| Endometriosis is a gynecological condition, which is chronic in nature and is associated with ectopic expansion of endometrial tissue resulting in the onset of pelvic pain, infertility, and diminished quality of life. There has been a drawback of conventional pharmacological treatment that is characterized by bad bioavailability, systemic effects, and a lack of targeting. Nano-drug delivery systems offer the potential solution to get past those obstacles since it allows site-specific delivery, prolonged release profile, and enhanced therapeutic efficacy. Animal models of endometriosis are used in the present study to assess the pharmacokinetics, biodistribution and efficacy of nanoformulated drugs in relation to the conventional agents. Liposomes, polymeric nanoparticles and dendrimer-based systems are examined as nanocarriers to achieve drug targeting to endometriotic lesions. In addition, machine learning is incorporated to get the best treatment protocols that predict the drug release profile, lesion regression, and systemic safety depending on the multi-parameter datasets. A translational platform of personalized therapeutic approaches has been achieved by investigating the discovery of the in-silico predictions and translation of the experimental results poised in animal models. Such integrative solution points to the promise of nanotechnology and artificial intelligence to transform the way endometriosis is treated, promising more effective, safer, and patient-specific options. | ||
| Key Words | ||
| animal models; endometriosis; machine learning; nano-drug delivery; treatment optimization | ||
| Address | ||
| Jing Gong, Li Liao, Liujing Zheng, Xiuyue Liao, Fei Xiang, Fengxia Yang and Xin Zheng: Department of Pharmacy, Chongqing Changshou District Traditional Chinese Medicine Hospital Jie Lou: College of Pharmacy and Bioengineering, chongqing university of technology Xiaojuan Tang: Department of Pharmacy, Chongqing Tongliang Hospital of Traditional Chinese Medicine | ||