Nano-drug delivery systems of endometriosis: Animal model-based implications and machine learning-guided treatment optimization

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.