Lipid Nanoparticles for Brain Tumor Theranostics: Challenges and Status.

Lipid nanoparticles have been recognized as a powerful weapon for delivering various imaging and therapeutic agents to the localized solid tumors, especially brain tumors individually or in combination. Promisingly, lipid-based nanosystems have been considered as safe delivery systems which are even approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). One recent spotlight of lipid nanoparticles as COVID-19 mRNA vaccines where lipid nanoparticles play an important role in effectively protecting and delivering mRNA to the desired cells. As of now, successive progress in lipid-based nanocarriers, viz., nanoliposomes, solid lipid nanoparticles, ionizable lipid nanostructures, etc., with better biochemical and biophysical stabilities, has been noticed and reported. Moreover, lipid nanostructures have been considered as versatile therapeutics platforms for a variety of diseases due to their biocompatibility, ability to protect and deliver therapeutics to the localized site, and better reproducibility and reliability. However, lipid nanoparticles still face morphological and biochemical changes upon their in vivo administration. These changes alter the specific biological and pathological response of lipid nanoparticles during their personalized brain tumor theranostics. Second, lipid nanomedicine still faces major challenges of zero premature leakage of loaded cargo, long-term colloidal stability, and off targeting. Herein, various lipid-based nanomedicines for brain tumor imaging and therapeutics "theranostics" have been reviewed and summarized considering major aspects of preclinical and clinical studies. On the other hand, engineering and biological challenges of lipid theranostics systems with relevant advantages and guidelines for clinical practice for different brain tumors have also been discussed. This review provides in-depth knowledge of lipid nanoparticle-based theranostics agents for brain tumor imaging and therapeutics.

Designing of xanthine-based DPP-4 inhibitors: a structure-guided alignment dependent Multifacet 3D-QSAR modeling, and molecular dynamics simulation study.

The DPP-4 enzyme degrades incretin hormones GLP-1 and GIP. DPP-4 inhibitors are found effective in the prevention of the degradation of incretins. Xanthine scaffold-bearing molecules are reported as potential DPP-4 inhibitors for treating type 2 diabetes mellitus, e.g. the marketed drug linagliptin. In this work, structure-guided alignment-dependent atom- and Gaussian field-based 3D-QSAR have been performed on a dataset of 75 molecules. The robustness and predictive ability of the developed multifacet 3D-QSAR models were validated on different statistical parameters and found to be statistically fit. The favorable and unfavorable pharmacophoric features were mapped for each multifacet 3D-QSAR model based on three alignment sets (1-3). A five-point common pharmacophore hypothesis was generated separately for each set of alignments. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (Compounds 12, 40 and 57) compared to reference standard linagliptin to study the binding energy and stability of target-ligand complexes. The MM-PBSA calculations revealed that the binding free energy and stability of compounds 12 (-40.324 ± 17.876 kJ/mol), 40 (-80.543 ± 21.782 kJ/mol) and 57 (-50.202 ± 16.055 kJ/mol) were better than the reference drug linagliptin (-20.390 ± 63.200 kJ/mol). The generated contour maps from structure-guided alignment-dependent multifacet 3D-QSAR models offer information about the structure-activity relationship (SAR) and ligand-target binding energy and stability data from MD simulation may be utilized to design and develop target selective xanthine-based novel DPP-4 inhibitors.Communicated by Ramaswamy H. Sarma.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications.

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes' binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. -87.437 ± 19.728 and -73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure-activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity.Communicated by Ramaswamy H. Sarma.