Research Projects

Conventional drug delivery systems frequently lead to erratic plasma drug concentrations, marked by transient peaks that cause systemic toxicity and troughs that reduce therapeutic efficacy. These pharmacokinetic inconsistencies not only exacerbate adverse side effects but also compromise patient adherence due to the requirement for frequent dosing. To overcome these clinical limitations, our study employs a synergistic framework of statistical modelling and mathematical analysis to develop a controlled drug delivery platform. We center our approach on layered double hydroxides (LDHs), synthesized via co-precipitation, which provide adjustable interlayer spacing conducive to effective drug encapsulation and pH sensitive release profiles. Quantum mechanical simulations based on density functional theory (DFT), coupled with machine learning analyses, reveal thermodynamically favorable interactions between doxorubicin (DOX) along with other bioactive species and the LDH layers. These computational insights align with experimentally observed release kinetics modelled across physiological pH gradients, confirming the potential of LDHs as intelligent drug carriers. Building upon this foundation, we further engineer polymer-grafted LDH nanocomposites to enhance both the mechanical robustness and precision of the delivery system. These hybrid constructs are designed to respond adaptively to the tumor microenvironment while maintaining structural stability. Statistical models will be employed to analyze drug release patterns and cytotoxic response variability, ensuring reproducibility and dosage control. Simultaneously, DFT will continue to inform and refine molecular-level interactions, guiding optimization of composite architecture for improved therapeutic payload performance. The efficacy of these nanocomposites will be validated through comprehensive in vitro assays and in vivo studies using bioluminescent imaging in melanoma-bearing mice. These experiments will evaluate targeted biodistribution, cellular uptake, and therapeutic outcomes, establishing a direct correlation between in silico predictions and biological response. By integrating nanotechnology, quantum chemical modelling, statistical pharmacokinetics, and advanced data analytics, our strategy delivers a next-generation, biocompatible platform tailored for cell-specific cancer therapy. This multifaceted design paradigm promises not only to elevate therapeutic precision but also to reshape the clinical landscape of drug delivery in oncology.

LDHs, Machine Learning, Drug Delivery, Cancer