Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network

BACKGROUND

Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a progressive neurodegenerative condition that predominantly impacts the elderly population, although it can also manifest in younger people through the impairment of cognitive functions, such as memory, cognition, and behaviour. Donepezil HCl and Memantine HCl are encapsulated in Nanostructured Lipid Carriers (NLCs) to prolong systemic circulation and minimize the systemic side effects.

OBJECTIVE

This work explores the use of data mining tools to optimize the formulation of NLCs comprising of Donepezil HCl and Memantine HCl for transdermal drug delivery. Neuroprotective drugs and excipients are utilized for protecting the nervous system against damage or degeneration.

METHOD

The NLCs were formulated using a high-speed homogenization technique followed by ultrasonication. NLCs were optimized using Box Behnken Design (BBD) in Design Expert Software and artificial neural network (ANN) in IBM SPSS statistics. The independent variables included the ratio of solid lipid to liquid lipid, the percentage of surfactant, and the revolutions per minute (RPM) of the high-speed homogenizer.

RESULTS

The NLCs that were formulated had a mean particle size ranging from 67.0±0.45 to 142.4±0.52nm. Both drugs have a %EE range over 75%, and Zeta potential was determined to be - 26±0.36mV. CryoSEM was used to do the structural study. The permeation study showed the prolonged release of the formulation.

CONCLUSION

The results indicate that NLCs have the potential to be a carrier for transporting medications to deeper layers of the skin and reaching systemic circulation, making them a suitable formulation for the management of Dementia. Both ANN and BBD techniques are effective tools for systematically developing and optimizing NLC formulation.