Biomedical subjects

B Rambali

Publications and source records attributed to B Rambali.

6 recordsLinked to original sources

PubMed2003

Using deepest regression method for optimization of fluidized bed granulation on semi-full scale.

This study applied the deepest regression method to estimate the granule size of unsuccessful fluidized bed granulation runs. This study uses data from a previous study [Int. J. Pharm. 220 (2001) 149] on optimization of fluidized granulation process, wherein 8 of the 30 runs did not succeeded due to overwetting of the powder bed. The "complete data" (the observed and the estimated granule size by the depth regression method) were used to develop two regression models for the granule size: an empirical model based on the process variables (inlet air temperature, inlet airflow rate, spray rate, and inlet air humidity) and a fundamental model based on the powder bed moisture content and the relative droplet size. The regression models based on the incomplete data from the previous study and the regression models of the "complete data" were comparable in the sense that the contour plots based on the respective models and the predicted granule size were comparable.

Linear Models↗

PubMed2003

Scaling up of the fluidized bed granulation process.

The scaling up of a fluidized bed granulation process from small scale to production scale is often done empirically in the pharmaceutical industry. In this study, a more practical and systematic method was developed in order to achieve a similar granule size in the scaled up fluid bed. The scaling up is based on the relative droplet size, and the powder bed moisture content at the end of the spraying cycle. The present study describes the scaling up of the fluidized granulation process from small (5 kg scale) to medium (30 kg scale) and to production fluid bed scale (120 kg scale). The granulation process is scaled up with as target a geometric mean granule size of 400 microm. First, the effect of the relative droplet size on the granule size was investigated in the different fluid beds. The effect of the change in relative droplet size on the granule size was different for each fluid bed. Second, experimental design is applied on the small and the medium fluid scale, and regression models for the granule size are proposed in order to scale up the granulation process on the small to medium scale. The granulation process was also successful by scaling-up to the large fluid bed, considering only the relative droplet size.

Powders↗

PubMed2001

Using experimental design to optimize the process parameters in fluidized bed granulation on a semi-full scale.

A face-centered central composite design was applied in order to optimize the granulation process on a semi-full scale (30-kg batch) for the geometric mean granule size. The granulation process variables investigated were: inlet air temperature, inlet airflow rate, spray rate and inlet air humidity. Based on the process variables, the theoretical powder bed moisture content after the spraying process and a measure for the droplet size were determined. Multiple regression modeling was used to develop two models for the granule size: an empirical model, based on the four process parameters, and a fundamental model, based on the balance between the granule growth affected by the theoretical powder bed moisture content and the droplet size and the breakage effect of the airflow rate. These regression models were used to optimize the granulation process to obtain a granule size between 300 and 500 microm. Additional experiments confirmed that these models were valid. Other granule properties, namely the geometric standard deviation, the Hausner index, the angle of repose and the moisture content, were evaluated at the optimal operation conditions.

Research Design↗

PubMed2001

Influence of the roll compactor parameter settings and the compression pressure on the buccal bio-adhesive tablet properties.

Miconazole buccal tablets were prepared via a dry granulation process. By applying a factorial design (2(4)), the roll compactor parameters (compaction force, gap between the rolls, type of the rolls (smooth, ribbed) and the sieve aperture) were optimised for the tablet strength. The compaction force and the roll type significantly affected the tablet strength. Afterwards, a quarter fractional factorial design (2(5-2)) was applied, consisting of the four compactor parameters and additionally the compression pressure, in order to optimise these parameters for the dissolution profile and the buccal bio-adhesion characteristics (bio-adhesive force and energy). In order to evaluate the dissolution profiles properly, the similarity factor between sample and a zero-order release reference profile was used. The compression pressure and the roll type significantly affected the dissolution profile. The sieve aperture had a significant effect on the buccal adhesion properties and the compaction force had a significant effect on the dissolution profile and the bio-adhesive energy. The gap between the rolls affected the bio-adhesive force significantly.

Antifungal Agents↗

PubMed2001

Using experimental design to optimize the process parameters in fluidized bed granulation.

In this study many parameters were screened for a small-scale granulation process for their effect on the yield of granules between 75 and 500 microns and the geometrical granule mean size (d50). First a Plackett-Burman design was applied to screen the inlet air temperature, the inlet flow rate, the spray rate, the nozzle air pressure, the nozzle spray diameter, and the nozzle position. The Plackett-Burman design showed that the key process parameters were the inlet flow rate and the spray rate and probably also the inlet air temperature. Afterward a fractional factorial design (2(5-2)) was applied to screen the remaining parameters plus the nozzle aircap position and the spraying time interval. The fractional factorial design showed that the nozzle air pressure was also important. As the target values for the granule yield (between 75 and 500 microns) and the geometric mean granule size (between 300 and 500 microns) were reached during the screening experiments, further optimization was not considered necessary.

Air Movements↗

PubMed2001

Susceptibility testing of pathogenic fungi with itraconazole: a process analysis of test variables.

A 2(10-5) fractional factorial model was used to investigate the influence of 10 process variables in broth microdilution susceptibility tests with itraconazole against eight isolates of Candida species and six isolates of filamentous fungi in two growth media. An analysis of variance (ANOVA) indicated that glucose concentration and incubation time both significantly influenced control turbidity optical density (OD) values for most of the Candida spp. isolates, while incubation in >10% CO(2) versus ambient air, incubation temperature and inoculum size significantly influenced these OD values for about half of the yeast isolates. Control OD values for the mould isolates were most influenced by incubation time and temperature, and by occlusion of the wells with an adhesive sticker. Three statistical approaches, ANOVA, rank transformation and Mann-Whitney U-test, were used to assess the influence of the variable combinations on MIC, determined with a 50% growth reduction end-point. Incubation temperature and time, glucose concentration and inoculum size were the variables that most often affected susceptibility results to the level of statistical significance; however, the supplier of RPMI 1640 medium, the use of adhesive stickers and the atmosphere of incubation significantly influenced the MIC for some isolates. The medium used to prepare the test inoculum, the solvent used to prepare the stock solution and the shape of the microdilution plate wells significantly affected outcome, but only sporadically. A principal component analysis of the data matrix confirmed this order of relative influence of the test variables on the MIC. Since each fungal isolate responded differently to combinations of process variables in the test, we conclude that any unified method for antifungal susceptibility determination represents a compromise, rather than an idealized system.

Antifungal Agents↗