Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance

Abstract

Carvedilol, an anti-hypertensive medication commonly prescribed by healthcare providers, falls under the BCS class II category due to its low-solubility and high-permeability characteristics, resulting in limited dissolution and low absorption when taken orally. Herein, carvedilol was entrapped into bovine serum albumin (BSA)-based nanoparticles using the desolvation method to obtain a controlled release profile. Carvedilol-BSA nanoparticles were prepared and optimized using 32 factorial design. The nanoparticles were characterized for their particle size (Y1), entrapment efficiency (Y2), and time to release 50% of carvedilol (Y3). The optimized formulation was assessed for its in vitro and in vivo performance by solid-state, microscopical, and pharmacokinetic evaluations. The factorial design showed that an increment of BSA concentration demonstrated a significant positive effect on Y1 and Y2 responses with a negative effect on Y3 response. Meanwhile, the carvedilol percentage in BSA nanoparticles represented its obvious positive impact on both Y1 and Y3 responses, along with a negative impact on Y2 response. The optimized nanoformulation entailed BSA at a concentration of 0.5%, whereas the carvedilol percentage was 6%. The DSC thermograms indicated the amorphization of carvedilol inside the nanoparticles, which confirmed its entrapment into the BSA structure. The plasma concentrations of carvedilol released were observable from optimized nanoparticles up to 72 h subsequent to their injection into rats, revealing their longer in vivo circulation time compared to pure carvedilol suspension. This study offers new insight into the significance of BSA-based nanoparticles in sustaining the release of carvedilol and presents a potential value-added in the remediation of hypertension.

The Exploitation of pH-Responsive Eudragit-Coated Mesoporous Silica Nanostructures in the Repurposing of Terbinafine Hydrochloride for Targeted Colon Cancer Inhibition: Design Optimization, In Vitro Characterization, and Cytotoxicity Assessment

“…In regards to the statistical analysis of EE ( Table 3 ), the quadratic model was identified as a significant model because of its significant p -value (0.0001). This model was determined to have a high R 2 (0.9924), indicating that it can identify around 99% of the deviations [ 21 ]. In this model, the appropriate precision value was (37.76), suggesting that the model could negotiate the design space.…”

“…According to the Hixson–Crowell model, the drug release is influenced mostly by the change in particle surface area, where the rate of drug release decreases over time with decreasing the drug surface area in response to dissolution or erosion [ 43 ]. In addition, according to the n values for each formula, the Korsemeyer–Peppas model could be employed to distinguish the following competing mechanisms: Fickian (diffusion-controlled), non-Fickian (anomalous), and case II transport (relaxation-controlled) [ 21 ]. The data in Table 5 Indicate that”the ’elease mechanisms of F1 and F10 follow a non-Fickian (anomalous) pattern.…”

“…Following that, the responses of the optimized formulation were re-examined, and the comparison took place between the experimental values and predicted values suggested by the D-optimal design. Subsequently, the percentage of prediction error was calculated according to the following equation [ 21 ]: …”