R.C. Rowe

Characterisation of the surface energetics of milled dl-propranolol hydrochloride using inverse gas chromatography and molecular modelling

Inverse gas chromatography (IGC) has been successfully used to characterise the nature of the surface of samples of dl-propranolol hydrochloride which have been produced under conditions of increasing milling intensity. It has been shown that the surface becomes increasingly more energetic as indicated by an increase in the dispersive component of the surface free energy and more electron donating as indicated by the adsorption of tetrahydrofuran and dichloromethane. Both effects increase until at a critical particle size a plateau is reached with no further change with a reduction in particle size. The critical particle size coincides with the brittle ductile transition determined previously by mechanical measurement. Molecular modelling was used to predict which surfaces would predominate by making use of calculations of attachment energies. The face which had the lowest attachment was postulated to be the plane which predominately fractures during high intensity milling. Visualisation showed that the π-electron rich naphthalene moiety of dl-propranolol hydrochloride dominated this surface supporting the data from IGC.

Scale-up of a pharmaceutical granulation in fixed bowl mixer-granulators

Scale-up in fixed bowl mixer-granulators has been studied by applying the classical dimensionless numbers of Power, Reynolds and Froude to end-point prediction in a range of geometrically similar machines. The simple relationship of Power number/Reynolds number, as suggested by previous workers, has been found to be inappropriate for large-scale production machines where corrections have to be made for gross vortexing and powder bed height variation by the incorporation of the Froude number and a scaling factor. When corrections are made, data from 25-, 100- and 600-1 machines all fall on the same curve allowing predictions of optimum granulation end-point conditions to be made for production-scale equipment from measurements on laboratory-scale equipment and vice-versa.

The effect of experimental design on the modeling of a tablet coating formulation using artificial neural networks.

The aim of this study was to investigate the effect of experimental design strategy on the modeling of a film coating formulation by artificial neural networks (ANNs). Box-Behnken, central composite and pseudo-random designs of 102, 90 and 100 simulated records, respectively were used to train a multilayer perceptron (MLP) ANN comprising six input and two output nodes separated by a single hidden layer of five nodes. Network over-training was limited by using a test set of 40 pseudo-randomly distributed records. The models were validated using a set of 60 pseudo-randomly distributed records. Crack velocity was highly curved with respect to pigment particle size and size distribution. Similarly, film opacity was highly curved in response to pigment concentration and film thickness. The Box-Behnken and central composite designs generated models that were unable to predict crack velocity and showed extensive bias in prediction of film opacity. The pseudo-random design was unable to predict crack velocity of the test data set but yielded acceptable predictions for the validation set. Film opacity was well predicted by the pseudo-random design model. The poor predictive ability of the Box-Behnken and central composite models was attributed to poor interpolation of the high curvature of the response surfaces. In contrast, the pseudo-random design mapped the interior of the design space allowing improved interpolation and predictive ability. It is concluded that Box-Behnken and central composite experimental designs are inappropriate for ANN modeling of highly curved responses and that extensive internal mapping of the design space is essential to generate predictive ANN models.

Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations.

This study has investigated the utility and potential advantages of an artificial intelligence technology - neurofuzzy logic - as a modeling tool to study direct compression formulations. The modeling performance was compare with traditional statistical analysis. From results it can be stated that the normalized error obtained from neurofuzzy logic was lower. Compared to the multiple regression analysis neurofuzzy logic showed higher accuracy in prediction for the five outputs studied. Rule sets generated by neurofuzzy logic are completely in agreement with the findings based on statistical analysis and advantageously generate understandable and reusable knowledge. Neurofuzzy logic is easy and rapid to apply and outcomes provided knowledge not revealed via statistical analysis.

The changes in surface energetics with relative humidity of carbamazepine and paracetamol as measured by inverse gas chromatography

The surface energetic parameters of carbamazepine and paracetamol have been studied using inverse gas chromatography modified to produce dry and ambient conditions within the column. The values of the dispersive component of the surface free energy (gamma(S)D) do not change significantly at the increased relative humidity. In contrast the specific component of the free energy of adsorption (-DeltaG(A)SP) as measured by polar probes, can either remain constant or decrease by up to 10%, depending on the material and the probe. This indicates that an increase in the relative humidity causes a decrease in the surface energetics of the powder surface. It is proposed that where the water molecules are adsorbing to the same sites as the polar probes, the interaction of these probes with the surface is decreased. To identify these sites, the preferential interaction of each probe, including water, with the drug molecule has been modelled.

The effect of batch size on scale-up of a pharmaceutical granulation in a fixed bowl mixer granulator

The effect of two batch sizes of a pharmaceutical granulation processed in a fixed bowl mixer granulator has been evaluated using the classical dimensionless numbers, Power, Reynolds and Froude recently shown to apply to this type of mixer. Despite a correction for the differences in the heights of the powder bed the relationships were significantly different. This is thought to be due to the differences in the downward force acting on the impeller as a result of the changes in flow pattern as the powder bed contacts the sloping sides at the top of the mixer.

Interpretation of the differences in the surface energetics of two optical forms of mannitol by inverse gas chromatography and molecular modelling.

Inverse gas chromatography (IGC) has been successfully used to characterise the nature of the surface of two optical forms of mannitol, DL and betaD. This has shown that the surface energetics of the two forms are significantly different with the DL form having higher values for the interactions with the dispersive and basic probes. Molecular modelling was used to predict the slip planes by utilising attachment energy calculations and so the dominant faces exposed upon milling could be predicted. Imaging these faces showed that the orientation of the molecules at these surfaces differed between the two forms. A visual comparison of the faces indicated that the DL form had a higher density of acidic and dispersive sites exposed at the surfaces than the betaD form. The results from the modelling agreed with the trends seen in the changes in surface energetics as measured by IGC. This suggests that the components of the surface energetic terms reflect the density of exposed groups at the particle surfaces.

Structural changes during the dehydration of dicalcium phosphate dihydrate

Abstract The crystal structure and molecular packing of dicalcium phosphate dihydrate (DCPD) and dicalcium phosphate anhydrous (DCPA) have been investigated and compared. Major differences have been found between these materials. Water is bound to the calcium ions in the DCPD crystal lattice which collapses when water is removed to form DCPA. X-ray powder diffraction including variable temperature studies, and Fourier Transform Raman spectroscopy have been used and provide insight into the mechanism of dehydration of DCPD.

Modelling formulations using gene expression programming – A comparative analysis with artificial neural networks

This study has investigated the utility and potential advantages of gene expression programming (GEP)--a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system--to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R(2) these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data.

Particle size effects on the dehydration of dicalcium phosphate dihydrate powders

Abstract The particle size of dicalcium phosphate dihydrate (DCPD) has a strong influence on its dehydration behaviour, specifically the weight loss in the first stage of dehydration. This weight loss has been found to be inversely proportional to the mean particle size of the samples. Mean particle size may be a useful parameter in predicting the dehydration behaviour of DCPD and is clearly a contributing factor in explaining batch and source variation.