Application of artificial neural networks for optimization of preparation of insulin nanoparticles composed of quaternized aromatic derivatives of chitosan

Development of Perphenazine-Loaded Solid Lipid Nanoparticles: Statistical Optimization and Cytotoxicity Studies

OBJECTIVE

Perphenazine (PPZ), as a typical antipsychotic medical substance, has the same effectiveness compared to atypical antipsychotic medications for the treatment of schizophrenia. Despite the lipophilic essence, PPZ encounters limited bioavailability caused by the first-pass metabolism following oral administration. In the present study, PPZ-containing solid lipid nanoparticles (PPZ-SLNs) were prepared and optimized based on different factors, including lipid and surfactant amount, to develop appropriate and safe novel oral dosage forms of PPZ.

METHODS

The solvent emulsification-evaporation method was utilized to form SLNs by using soybean lecithin, glycerol monostearate (GMS), and Tween 80. Statistical optimization was done by the Box-Behnken design method to achieve formulation with optimized particle size, entrapment efficiency, and zeta potential. Also, transmission electron microscopy, in vitro release behavior, differential scanning calorimetry (DSC), and powder X-ray diffractometry (P-XRD) studies and cytotoxicity studies were assessed.

RESULTS

Optimization exhibited the significant effect of various excipients on SLN characteristics. Our finding indicated that the mean particle size, zeta potential, and entrapment efficiency of optimized PPZ-SLN were, respectively, 104 ± 3.92 nm, -28 ± 2.28 mV, and 83% ± 1.29. Drug release of PPZ-SLN was observed to be greater than 90% for 48 h that emphasized a sustained-release pattern. The DSC and P-XRD studies revealed the amorphous state of PPZ-SLN. FTIR spectra showed no incompatibility between the drug and the lipid. Performing cytotoxicity studies indicated no significant cytotoxicity on HT-29 cell culture.

CONCLUSION

Our study suggests that PPZ-SLNs can make a promising vehicle for a suitable therapy of schizophrenia for the oral drug delivery system.

Preparation, Statistical Optimization and In-vitro Characterization of a Dry Powder Inhaler (DPI) Containing Solid Lipid Nanoparticles Encapsulating Amphotericin B: Ion Paired Complexes with Distearoyl Phosphatidylglycerol

The aim of this study was to prepare dry powder inhalers (DPIs) containing amphotericin B-loaded solid lipid nanoparticles (AMB-SLNs) as an alternative approach for prevention of pulmonary aspergillosis. For solubilizing AMB in small amounts of organic solvents ion paired complexes were firstly formed by establishing electrostatic interaction between AMB and distearoyl phosphatidylglycerol (DSPG). The SLN formulations containing AMB-DSPG complexes were prepared using glycerol monostearate (GMS) as the lipid matrix and soybean lecithin and tween 80 as the surfactants by solvent emulsification-evaporation technique. The nanoparticles were optimized through a fractional factorial design. DPIs were prepared by lyophilization technique using lactose as the inhalational carrier and then after, the formulations were evaluated in terms of aerodynamic particle size distribution using an Andersen cascade impactor. The morphology of the particles was examined using scanning electron microscopy (SEM) and in-vitro drug release profiles were evaluated. Following the statistical results, the particle size, Poly dispersity index (PdI), zeta potential, entrapment efficiency (EE%), and drug loading (DL%) of the optimized SLNs were 187.04 ± 11.97 nm, 0.188 ± 0.028, -30.16 ± 1.6 mV, 89.3 ± 3.47 % and 2.76 ± 0.32 %, respectively. Formulation containing 10% w/v of lactose with the calculated fine particle fraction value as 72.57 ± 4.33% exhibited the appropriate aerodynamic characteristics for pulmonary drug delivery. SEM images revealed de-agglomerated particles. In-vitro release studies showed sustained release of AMB from the carriers and the release kinetics were best fitted to the first order kinetic model.

Alignment-Free Method to Predict Enzyme Classes and Subclasses

The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.