Sesamol is a sesame seed constituent with reported activity against many types of cancer. In this work, two types of nanocarriers, solid lipid nanoparticles (SLNs) and polymeric nanoparticles (PNs), were exploited to improve sesamol efficiency against the glioma cancer cell line. The ability of the proposed systems for efficient brain targeting intranasally was also inspected. By the aid of two docking programs, the virtual loading pattern inside these nanocarriers was matched to the real experimental results. Interactions involved in sesamol–carrier binding were also assessed, followed by a discussion of how different scoring functions account for these interactions. The study is an extension of the computer-assisted drug formulation design series, which represents a promising initiative for an upcoming industrial innovation. The results proved the power of combined in silico tools in predicting members with the highest sesamol payload suitable for delivering a sufficient dose to the brain. Among nine carriers, glyceryl monostearate (GMS) and polycaprolactone (PCL) scored the highest sesamol payload practically and computationally. The EE % was 66.09 ± 0.92 and 61.73 ± 0.47 corresponding to a ΔG (binding energy) of −8.85 ± 0.16 and −5.04 ± 0.11, respectively. Dynamic light scattering evidenced the formation of 215.1 ± 7.2 nm and 414.25 ± 1.6 nm nanoparticles, respectively. Both formulations demonstrated an efficient cytotoxic effect and brain-targeting ability compared to the sesamol solution. This was evidenced by low IC50 (38.50 ± 10.37 μM and 27.81 ± 2.76 μM) and high drug targeting efficiency (7.64 ± 1.89-fold and 13.72 ± 4.1-fold) and direct transport percentages (86.12 ± 3.89 and 92.198 ± 2.09) for GMS-SLNs and PCL-PNs, respectively. The results also showed how different formulations, having different compositions and characteristics, could affect the cytotoxic and targeting ability.

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.