Vegetable-derived magnesium stearate functionality evaluation by DM3 approach

Assessing the Impact of Punch Geometries on Tablet Capping Using a Newly Proposed Capping Index

PURPOSE

Increased tablet anisotropy could lead to increased tablet capping propensity. Tooling design variables such as cup depth could serve as a key player for inducing tablet anisotropy.

METHODS

A new capping index (CI) consisting of the ratio of compact anisotropic index (CAI) and material anisotropic index (MAI) is proposed to evaluate tablet capping propensity as a function of punch cup depth. CAI is the ratio of axial to radial breaking force. MAI is the ratio of axial to radial Young's modulus. The impact of various punch cup depths [flat face, flat face beveled edge, flat face radius edge, standard concave, shallow concave, compound concave, deep concave, and extra deep concave] on the capping propensity of model acetaminophen tablets was studied. Tablets were manufactured at 50, 100, 200, 250, and 300 MPa compression pressure at 20 RPM on different cup depth tools using Natoli NP-RD30 tablet press. A partial least squares model (PLS) was computed to model the impact of the cup depth and compression parameters on the CI.

RESULTS

The PLS model exhibited a positive correlation of increased cup depth to the capping index. The finite elemental analysis confirmed that a high capping tendency with increased cup depth is a direct result of non-uniform stress distribution across powder bed.

CONCLUSIONS

Certainly, a proposed new capping index with multivariate statistical analysis gives guidance in selecting tool design and compression parameters for robust tablets.

Optimized L-SNEDDS and spray-dried S-SNEDDS using a linked QbD-DM3 rational design for model compound ketoprofen

A QbD-DM³ strategy was used to design ketoprofen (KTF) optimized liquid (L-SNEDDS) and solid self-nanoemulsifying drug delivery systems (S-SNEDDS). Principal component analysis was used to identify the optimized L-SNEDDS containing Capmul® MCM NF, 10 % w/w; Kolliphor® ELP, 60 % w/w; and propylene glycol, 30 % w/w. The S-SNEDDS was manufactured by spray-drying a feed dispersion prepared by dissolving the optimized KTF-loaded L-SNEDDS in an ethanol-Aerosil® 200 dispersion. A Box Behnken design was employed to evaluate the effect of drug concentration (DC), Aerosil® 200 concentration (AC) and feed rate (FR) on maximizing percent yield (PY) and loading efficiency (LE). The optimal levels of DC, AC, and FR were 19.9 % w/w, 30.0 % w/w, and 15.0 %, respectively. The optimized S-SNEDDS was amorphous, and its dissolution showed a 2.37-fold increase in drug release compared to KTF in 0.1 HCl. An optimized independent spray-dried S-SNEDDS verification batch showed that the predicted and observed PY and LE were 70.49 % and 92.49 %, and 70.02 % and 91.27 %, respectively. The optimized L-SNEDDS and S-SNEDDS also met their quality target product profile criteria for globule size <100 nm, polydispersity index < 0.400, emulsification time < 30 s, and KTF L-SNEDDS solubility 100-fold greater than its water solubility.

Predictive Performance Comparison of Computed Linear and Quadratic Multivariate Models for In-Situ UV Fiber Optics Tablet Dissolution Testing

A present investigation aimed for multivariate modeling as a solution to resolve inaccuracy in dissolution testing experienced in the use of in-situ UV fiber optics dissolution systems (FODS) due to signal saturation problems. This problem is specifically encountered with high absorbance of moderate to high dose formulations. A high absorbance not only impede a real-time assessment but can also result in inaccurate dissolution profiles. Full spectra (F) and low absorbance regions (L) were employed to develop linear and quadratic (Q) partial least squares (PLS) and principal component regression (PCR) models. The conventional dissolution of atenolol, ibuprofen, and metformin HCl immediate-release (IR) tablets followed by HPLC analysis was used as a reference method to gauge multivariate models' performance in the 'built-in' Opt-Diss model. The linear multivariate modeling outputs resulted in accurate dissolution profiles, despite the potentially high UV signal saturation at later time points. Conversely, the 'built-in' Opt-Diss model and multivariate quadratic models failed to predict dissolution profiles accurately. The current studies show a good agreement in the predictions across both low absorbance region and full spectra, demonstrating the multivariate models' robust predictability. Overall, linear PLS and PCR models showed statistically similar results, which demonstrated their applicative flexibility for using FODS despite signal saturation and provides a unique alternative to traditional and labor-intensive UV or HPLC dissolution testing.

Hybridized nanoamorphous micellar dispersion using a QbD-DM3 linked rational product design strategy for ritonavir: A BCS IV drug

A QbD-DM³ linked rational product design strategy was adopted to create a hybridized ritonavir (RTV, BCS Class IV) nanoamorphous micellar dispersion (RTV-NAD). A DM³ research strategy was employed in conjunction with the quality-by-design spaces, and quality target product profile to link the critical material attributes and critical process parameters to the quality target product profile's critical product attributes QbD elements. A Box-Behnken design and multivariate analysis using multiple linear regression and partial least squares provided data analysis. The hybridized strategy leveraged three different mechanisms to increase RTV's solubility and four mechanisms to increase its dissolution rate. Statistically significant models were generated for critical product attributes: particle size (p = 0.0000, R² adjusted = 0.9513), polydispersity index (p = 0.0002, R² adjusted = 0.6398), zeta potential (p = 0.0000, R² adjusted = 0.9744), and drug loading on a dry basis (p = 0.0000, R² adjusted = 0.9951). The impact of drug concentration, Soluplus® concentration, and solvent:antisolvent ratio, their interactions and square effects on the critical product attributes were assessed by multivariate analysis. The QbD optimal formulation was determined for RTV-NAD. Multiple linear regression and partial least squares computational predictability was evaluated using three verification batches. The prediction error for critical product attributes was <5%. RTV-NAD and ritonavir microsuspension were characterized by x-ray diffraction and in-vitro dissolution studies. X-ray diffraction confirmed the amorphous nature of the RTV-NAD. RTV-NAD exhibited a 'spring-hover' dissolution profile at pH 4.5. At pH 6.8, a classic 'spring-parachute' dissolution behavior was observed.

Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies

The effect of storage condition (% RH) on flufenamic acid:nicotinamide (FFA:NIC) cocrystal compressibility, compactibility, and tabletability profiles was not observed after visual evaluation or linear regression analysis. However, multivariate statistical analysis showed that storage condition had a significant effect on each compressional profile. Shapiro and Heckel equations were used to determine the compression parameters: porosity, Shapiro's compression parameter (f), densification factor (Da), plastic yield pressure (YPpl), and elastic yield pressure (YPel). Latent variable models such as exploratory factor analysis, principal component analysis, and principal component regression were employed to decode complex hidden main, interaction, and quadratic effects of % RH and the compression parameters on FFA:NIC tablet mechanical strength (TMS). Statistically significant correlations between f and Da, f and YPpl, and Da and YPel supported the idea that both rearrangement and fragmentation, and plastic deformation are important to FFA:NIC TMS. To the authors knowledge, this is the first time that simultaneously operating dual mechanisms of fragmentation and plastic deformation in low and midrange compression, and midrange plastic deformation have been identified and reported. A quantitative PCR model showed that f, Da, and YPel had statistically significant main effects along with a significant antagonist storage condition-porosity "conditional interaction effect". f exhibited a 2.35 times greater impact on TMS compared to Da. The model root-mean-square error at calibration and prediction stages were 0.04 MPa and 0.08 MPa, respectively. The R² values at the calibration stage and at the prediction stage were 0.9005 and 0.7539, respectively. This research demonstrated the need for caution when interpreting the results of bivariate compression data because complex latent inter-relationships may be hidden from visual assessment and linear regression analysis, and result in false data interpretation as illustrated in this report.

Computational Predictability of Microsponge Properties Using Different Multivariate Models

The capabilities of principal component regression (PCR) and multiple linear regression (MLR) were evaluated to decipher and predict the impact of formulation and process parameters on the modeled metronidazole benzoate (MB)-ethyl cellulose (EC) microsponge (MBECM) properties. MBECM were prepared by a quasi-emulsion solvent diffusion method. A minimum experimentation was designed using Box-Behnken approach with one center point after initial screening experiments. Data was modeled by principal component analysis (PCA), PCR, and MLR. Two distinct groupings of developed MBECM was observed in initial qualitative PCA as a function of their respective formulation and processing parameters. Group A formulations with low dichloromethane, high PVA, and low stirring speed exhibited larger particle size, lower entrapment efficiency (EE), and lower actual drug content (ADC) than Group B formulations. Optimized quantitative PCR and MLR models demonstrated a linear dependence of particle size and quadratic dependence of EE and ADC on the studied formulation and process parameters. Interestingly, MLR models showed relatively better predictability of the selected MBECM formulation properties when compared with PCR. MBECM were amorphous in nature and spherical shaped. Carbopol® 940 NF based hydrogel of selected MBECM formulation exhibited a prolonged MB release than the commercial MB gel (Metrogyl®), showing no signs of necrosis in the goat mucosa. Thus, a properly designed minimum experimentation coupled with multivariate modeling generated a knowledge-rich target space, which enabled to understand and predict the performance of developed MBECM within a prescribed design space.

Deciphering magnesium stearate thermotropic behavior

Magnesium stearate (MgSt) is the most commonly used excipient for oral solid dosage forms, yet there is significant commercial physicochemical variability that can lead to variable performance of critical product attributes. Differential scanning calorimetry (DSC) is often used as a quality control tool to characterize MgSt, but little data is available regarding the physicochemical relevance for the DSC thermograms. The main aim of this study was to decipher MgSt's complex thermotropic behavior using DSC, thermogravimetric analysis, capillary melting point, polarized hot-stage microscopy, and temperature dependent small-angle X-ray scattering (SAXS) and assign physicochemical relevance to the DSC thermograms. Several DSC thermal transitions are irreversible after the first heating cycle of a heat-cool-heat-cool-heat cycle. Interestingly, after the first heat cycle, the complex cool-heat-cool-heat DSC thermograms were highly reproducible and exhibited 6 reversible exothermic-endothermic conjugate pairs. SAXS identified 5 distinct mesophases at different temperatures with Phase C' persisting to 250 °C. MgSt maintained molecular ordering beyond 276 °C and did not undergo a simple melting phenomena reported elsewhere. This research serves as a starting point to design heat-treatment strategies to create more uniform MgSt starting material.

Dipalmitoylphosphatidylcholine (DPPC): Annealing Strategy to Mitigate Variability in Thermotropic and Moisture Sorption Behavior

Dipalmitoylphosphatidylcholine (DPPC) demonstrated complex differential scanning calorimetry (DSC) thermal behavior. Transitions below 100°C showed variability in their thermotropic reversibility. An experimental design employing a DSC heat-cool-heat-cool-heat cycle and modulated DSC were used to gain insight into the DPPC's complex thermal nature. An annealing strategy was developed to reduce DPPC's thermotropic variability, moisture uptake rate, and rate variability. Samples annealed at 110°C for 5 min provided a reproducible, thermally reversible material. The annealed material also exhibited an 8-fold decrease in moisture sorption rate and a statistically significant (p = 0.0233) 100-fold decrease in water sorption rate variability compared to DPPC "as is." An optimized validated stability-indicating high performance liquid chromatography with evaporative light scattering detection method was developed and showed no change in DPPC chemical stability under the annealing treatment conditions.

A two-step approach for fluidized bed granulation in pharmaceutical processing: Assessing different models for design and control

Various modeling techniques were used to understand fluidized bed granulation using a two-step approach. First, Plackett-Burman design (PBD) was used to identify the high-risk factors. Then, Box-Behnken design (BBD) was used to analyze and optimize those high-risk factors. The relationship between the high-risk input variables (inlet air temperature X1, binder solution rate X3, and binder-to-powder ratio X5) and quality attributes (flowability Y1, temperature Y2, moisture content Y3, aggregation index Y4, and compactability Y5) of the process was investigated using response surface model (RSM), partial least squares method (PLS) and artificial neural network of multilayer perceptron (MLP). The morphological study of the granules was also investigated using a scanning electron microscope. The results showed that X1, X3, and X5 significantly affected the properties of granule. The RSM, PLS and MLP models were found to be useful statistical analysis tools for a better mechanistic understanding of granulation. The statistical analysis results showed that the RSM model had a better ability to fit the quality attributes of granules compared to the PLS and MLP models. Understanding the effect of process parameters on granule properties provides the basis for modulating the granulation parameters and optimizing the product performance at the early development stage of pharmaceutical products.

Characterization of Synthesized and Commercial Forms of Magnesium Stearate Using Differential Scanning Calorimetry, Thermogravimetric Analysis, Powder X-Ray Diffraction, and Solid-State NMR Spectroscopy

Magnesium stearate is the salt of a complex mixture of fatty acids, with the majority being stearate and palmitate. It has multiple crystalline forms and, potentially, an amorphous form. Magnesium stearate is used in the pharmaceutical manufacturing industry as a powder lubricant, and typically is added at low levels (∼1%) during the manufacturing process and blended for a relatively short time (∼5 min). Proper levels and mixing times are needed, as too short a mixing time or too small a quantity will result in improper lubrication, and too much can negatively impact dissolution rates. The complex mixture of multiple fatty acids and crystalline forms in magnesium stearate leads to variability between commercial sources, and switching between sources can impact both the amount of lubricant and mixing time needed for proper lubrication. In order to better understand the complex nature of magnesium stearate, a variety of analytical techniques were used to characterize both synthesized and commercial magnesium stearate samples. The results show that correlation among differential scanning calorimetry, thermogravimetric analysis, solid-state NMR spectroscopy, and other techniques provides a unique insight into the forms of magnesium stearate. Finally, the ability to monitor form changes of magnesium stearate in an intact tablet using solid-state NMR spectroscopy is shown.

Statistical Optimization of Evaporative Light Scattering Detection for Molten Sucrose Octaacetate and Comparison With Ultraviolet Diode Array Detection Validation Parameters Using Tandem HPLC Ultraviolet Diode Array Detection/Evaporative Light Scattering Detection-Specific Stability-Indicating Method

A sucrose octaacetate (SOA) gradient HPLC evaporative light scattering detection (ELSD) and low-wavelength UV-diode array detection (UV-DAD)-specific stability-indicating method development and validation comparison is reported. A central composite response surface design and multicriteria optimization was used to maximize molten SOA area-under-the-curve response and signal-to-noise ratio. The ELSD data were also analyzed using multivariate principal component analysis, analysis of variance, and standard least squares effects modeling. The method suitability and validation parameters of both methods were compared. To the authors' knowledge, this is the first report that validates an ELSD method using a molten analyte. SOA exhibited a low molar absorptivity of 439 absorption units/cm/M in water at 210 nm requiring low-wavelength UV-DAD detection. The low-wavelength UV-DAD method provided substantially better intraday and interday precision, intraday and interday goodness-of-fit, detection limit, and quantitation limit than ELSD. ELSD exhibited a 60-fold greater area-under-the-curve response, better resolution, and 58% more theoretical plates. On balance, the UV-DAD method was chosen for SOA chemical kinetic studies. This study illustrates that ELSD may not always be the best alternative to gradient HPLC low-wavelength UV detection.