Introduction
Human immunodeficiency virus (HIV) remains a significant global health challenge, and antiretroviral therapy (ART) has been pivotal in reducing HIV-associated morbidity and mortality. Lopinavir is a protease inhibitor used in combination with ritonavir to improve its pharmacokinetics. However, its clinical effectiveness is compromised by poor aqueous solubility, extensive first-pass metabolism, and low oral bioavailability (~20–25%).
Nanostructured lipid carriers (NLCs), second-generation lipid-based nanocarriers, are composed of a blend of solid and liquid lipids. They provide a less ordered lipid matrix that accommodates higher drug payloads and reduces drug expulsion during storage. NLCs offer several advantages: improved solubility, enhanced bioavailability, controlled drug release, and the ability to bypass first-pass metabolism.
The rational development of NLC formulations requires optimization of multiple formulation and process parameters. Conventional trial-and-error approaches are time-consuming and resource-intensive. In contrast, the Design of Experiments (DoE) approach allows systematic evaluation of multiple variables and their interactions using fewer experimental runs, thereby enhancing the efficiency of formulation development.
This study focuses on designing Lopinavir-loaded NLCs and optimizing their formulation parameters using a central composite DoE approach, with the goal of improving physicochemical properties and drug release behavior.
2. Materials and Methods
2.1 Materials
Lopinavir (API) was obtained as a gift sample from a pharmaceutical manufacturer. Glyceryl monostearate (solid lipid), oleic acid (liquid lipid), polysorbate 80 (surfactant), and other analytical-grade reagents were procured from standard suppliers.
2.2 Preparation of Lopinavir-Loaded NLCs
Lopinavir-loaded NLCs were prepared by the hot homogenization–ultrasonication method. Briefly, Lopinavir was dissolved in the melted lipid phase containing glyceryl monostearate and oleic acid at 75 °C. The aqueous phase containing polysorbate 80 was heated to the same temperature and added dropwise to the lipid phase under high-speed homogenization (15,000 rpm for 5 min). The resultant pre-emulsion was sonicated (probe sonicator) for varying times based on experimental design, then cooled to room temperature to form NLCs.
Figure 1 – 3D response surface plot (particle size vs lipid ratio & surfactant)
Table 1. Central Composite Design (CCD) matrix of independent variables
| Run | X1: Lipid Ratio (Solid:Liquid) | X2: Surfactant (%) | X3: Sonication Time (min) |
|---|---|---|---|
| 1 | 60:40 | 1.0 | 4 |
| 2 | 80:20 | 1.0 | 4 |
| 3 | 60:40 | 3.0 | 4 |
| 4 | 80:20 | 3.0 | 4 |
| 5 | 60:40 | 1.0 | 8 |
| 6 | 80:20 | 1.0 | 8 |
| 7 | 60:40 | 3.0 | 8 |
| 8 | 80:20 | 3.0 | 8 |
| 9 | 70:30 | 2.0 | 6 |
| 10 | 70:30 | 2.0 | 6 (center point) |
2.3 Experimental Design (DoE)
A central composite design (CCD) was used to optimize three independent variables:
- X1: lipid ratio (solid:liquid)
- X2: surfactant concentration (%)
- X3: sonication time (minutes)
Dependent responses were:
- Particle size (Y1)
- Polydispersity index (PDI) (Y2)
- Zeta potential (Y3)
- Entrapment efficiency (EE%) (Y4)
- Drug release at 12 h (Y5)
Design Expert® software was used for statistical modeling, regression analysis, and generation of response surface plots.
Figure 2 – 3D response surface plot (entrapment efficiency vs lipid ratio & sonication time)
Table 2. Experimental responses for each trial
| Run | Particle Size (nm) | PDI | Zeta Potential (mV) | EE (%) | Drug Release at 12h (%) |
|---|---|---|---|---|---|
| 1 | 210.4 | 0.31 | -22.1 | 76.3 | 52.8 |
| 2 | 185.9 | 0.28 | -24.5 | 81.4 | 55.2 |
| 3 | 176.2 | 0.25 | -26.2 | 82.9 | 58.1 |
| 4 | 162.3 | 0.23 | -27.6 | 85.7 | 61.4 |
| 5 | 190.7 | 0.29 | -23.8 | 79.2 | 53.7 |
| 6 | 170.1 | 0.27 | -25.7 | 83.5 | 59.3 |
| 7 | 155.4 | 0.22 | -28.1 | 87.1 | 63.8 |
| 8 | 148.5 | 0.21 | -28.7 | 88.3 | 66.7 |
| 9 | 142.3 | 0.21 | -28.4 | 89.2 | 68.5 |
| 10 | 143.1 | 0.20 | -28.5 | 89.0 | 68.1 |
2.4 Characterization of NLCs
- Particle size, PDI, and zeta potential were measured using dynamic light scattering (Malvern Zetasizer).
- Entrapment efficiency (EE%) was determined by ultracentrifugation followed by HPLC analysis of the supernatant.
- Morphology was evaluated using transmission electron microscopy (TEM).
- Thermal analysis was performed using differential scanning calorimetry (DSC).
- In vitro release was assessed using the dialysis bag method in phosphate-buffered saline (pH 7.4) at 37 °C under stirring.
Figure 3 – In vitro drug release profile of optimized NLCs
Table 3. ANOVA summary for key responses
| Response | Model F-value | p-value | R² | Significant Factors |
|---|---|---|---|---|
| Particle Size | 46.7 | < 0.0001 | 0.97 | Lipid ratio (X1), Surfactant (X2), Sonication (X3) |
| EE (%) | 38.2 | < 0.0001 | 0.96 | Lipid ratio (X1), Sonication (X3) |
| Drug Release (12h) | 29.5 | < 0.001 | 0.94 | Surfactant (X2), Sonication (X3) |
3. Results
3.1 Effect of Formulation Variables
DoE analysis revealed that increasing the lipid ratio decreased particle size and increased entrapment efficiency, while excessive liquid lipid led to larger particles. Higher surfactant concentration reduced particle size and PDI by lowering interfacial tension. Sonication time significantly influenced particle size and homogeneity.
3.2 Optimization Outcomes
The optimized formulation (predicted by the model and validated experimentally) comprised a solid-to-liquid lipid ratio of 70:30, 2% surfactant concentration, and 6 minutes sonication. This yielded:
- Particle size: 142.3 ± 4.2 nm
- PDI: 0.21 ± 0.03
- Zeta potential: −28.4 ± 1.8 mV
- EE%: 89.2 ± 2.6%
- Drug release (12h): 68.5 ± 3.1%
3.3 Morphology and Thermal Analysis
TEM images showed spherical NLCs with smooth surfaces. DSC thermograms confirmed partial amorphization of the lipid matrix and uniform drug distribution within the carrier.
3.4 In Vitro Drug Release
The optimized NLCs demonstrated an initial burst release (~20% in 2 h) followed by sustained release up to 24 h, fitting the Korsmeyer–Peppas model (R² = 0.95).
4. Discussion
The present study successfully applied a DoE-based strategy to develop and optimize Lopinavir-loaded NLCs with desirable physicochemical and release characteristics. The use of a lipid blend provided a less ordered crystalline structure, allowing high drug entrapment and controlled release. The optimized NLCs were in the ideal nano-size range (<200 nm), which can enhance intestinal absorption and lymphatic uptake.
DoE facilitated the identification of critical formulation parameters and their interactions, reducing experimental workload and development costs. The sustained release pattern may help reduce dosing frequency and improve patient adherence in HIV therapy. The findings suggest that NLCs can serve as an efficient oral delivery system for Lopinavir.
5. Conclusion
Lopinavir-loaded NLCs were successfully formulated and optimized using a DoE-guided approach. The optimized formulation exhibited small particle size, high drug entrapment, good stability, and sustained release behavior. This nanocarrier system holds promise for enhancing the oral bioavailability and therapeutic efficacy of Lopinavir in antiretroviral therapy.
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