Flavonoids are a group of polyphenolic plant secondary metabolites important for plant biology and human nutrition. In particular flavonols are potent antioxidants, and their dietary intake is correlated with a reduced risk of cardiovascular diseases. Tomato fruit contain only in their peel small amounts of flavonoids, mainly naringenin chalcone and the flavonol rutin, a quercetin glycoside. To increase flavonoid levels in tomato, we expressed the maize transcription factor genes LC and C1 in the fruit of genetically modified tomato plants. Expression of both genes was required and sufficient to upregulate the flavonoid pathway in tomato fruit flesh, a tissue that normally does not produce any flavonoids. These fruit accumulated high levels of the flavonol kaempferol and, to a lesser extent, the flavanone naringenin in their flesh. All flavonoids detected were present as glycosides. Anthocyanins, previously reported to accumulate upon LC expression in several plant species, were present in LC / C1 tomato leaves but could not be detected in ripe LC / C1 fruit. RNA expression analysis of ripening fruit revealed that, with the exception of chalcone isomerase, all of the structural genes required for the production of kaempferol-type flavonols and pelargonidin-type anthocyanins were induced strongly by the LC/C1 transcription factors. Expression of the genes encoding flavanone-3 -hydroxylase and flavanone-3 5 -hydroxylase, which are required for the modification of B-ring hydroxylation patterns, was not affected by LC/C1. Comparison of flavonoid profiles and gene expression data between tomato leaves and fruit indicates that the absence of anthocyanins in LC / C1 fruit is attributable primarily to an insufficient expression of the gene encoding flavanone-3 5 -hydroxylase, in combination with a strong preference of the tomato dihydroflavonol reductase enzyme to use the flavanone-3 5 -hydroxylase reaction product dihydromyricetin as a substrate.

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The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2% w/w) prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.

The objectives of this study were to assess the utility of near-infrared reflectance spectroscopy (NIRS) in differentiating crystalline forms of pharmaceutical materials and determine the accuracy of this technique in quantifying crystalline forms of solids in binary mixtures. Various crystalline forms of sulfamethoxazole, sulfathiazole, lactose, and ampicillin, independently characterized with other methods, were analyzed qualitatively and quantitatively. The observed differences in near-infrared (NIR) spectra of crystalline form pairs were interpretable on the basis of the features of their crystalline and molecular structures and mid-infrared spectra. NIR spectra of binary physical mixtures of crystalline form pairs were obtained directly through glass vials over the wavelength range of 1100-2500 nm. The calibration lines were constructed using an inverted least-squares regression method. The ratio of the response of the second derivative of the reflectance spectra at two wavelengths was plotted versus crystal form composition. The correlation coefficients for plots of predicted versus theoretical composition were generally greater than 0.99 and standard errors were all low. Parallel studies comparing the NIRS method to a quantitative x-ray powder diffraction method using sulfamethoxazole and sulfathiazole confirmed the accuracy of the results. Additional NIRS studies were conducted in the 0-10% composition range with ampicillin and sulfamethoxazole. These results indicated that prediction down to the 1% level was possible. This study demonstrates that NIRS can be used as a quantitative physical characterization method, is comparable in accuracy to other techniques, and is capable of detecting low levels of one crystal form in the presence of another.