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.