Mariana Landin

Artificial neural networks as an alternative to the traditional statistical methodology in plant research.

In this work, we compared the unique artificial neural networks (ANNs) technology with the usual statistical analysis to establish its utility as an alternative methodology in plant research. For this purpose, we selected a simple in vitro proliferation experiment with the aim of evaluating the effects of light intensity and sucrose concentration on the success of the explant proliferation and finally, of optimizing the process taking into account any influencing factors. After data analysis, the traditional statistical procedure and ANNs technology both indicated that low light treatments and high sucrose concentrations are required for the highest kiwifruit microshoot proliferation under experimental conditions. However, this particular ANNs software is able to model and optimize the process to estimate the best conditions and does not need an extremely specialized background. The potential of the ANNs approach for analyzing plant biology processes, in this case, plant tissue culture data, is discussed.

Konjac glucomannan/xanthan gum enzyme sensitive binary mixtures for colonic drug delivery

The polysaccharide konjac glucomannan (KGM) is degraded in the colon but not the small intestine, which makes it potentially useful as an excipient for colonic drug delivery. With xanthan gum (XG) KGM forms thermoreversible gels with hitherto unexplored biodegradation properties. In this work, rheological measurements of KGM and KGM/XG systems incubated with and without Aspergillus niger beta-mannanase (used to mimic colonic enzymes) showed that KGM was degraded by the enzyme even when interacting with XG. Tablets with KGM/XG/sucrose matrices that varied in accordance with a simplex design and bore diltiazem as a typical highly soluble drug load were prepared by wet granulation, and in most cases were found to possess satisfactory mechanical strength and exhibit slow, nearly zero-order drug release. Drug release from these tablets remained zero-order, but was accelerated (presumably due to degradation of KGM), in the presence of A. niger beta-mannanase at concentrations equivalent to human colonic conditions. However, marked differences between Japanese and American varieties of KGM as regards degree of acetylation and particle size led to significant differences in swelling rate and drug release between formulations prepared with one and the other KGM: whereas a formulation with Japanese KGM released its entire drug load within 24h in the presence of beta-mannanase, only 60% release was achieved under the same conditions by the corresponding formulation with American KGM, suggesting that with this KGM it will be necessary to optimize technological variables such as compression pressure in order to achieve suitable porosity, swelling rate, and drug release. To sum up, the results of this study suggest that sustained release of water-soluble drugs in the colon from orally administered tablets may be achieved using simple, inexpensive formulations based on combinations of KGM and XG that take the variability of KGM characteristics into account.

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.

Characterization of β-Lapachone and Methylated β-Cyclodextrin Solid-state Systems

The purpose of this research was to explore the utility of β cyclodextrin (βCD) and β cyclodextrin derivatives (hydroxypropyl-β-cyclodextrin [HPβCD], sulfobutylether-β-CD [SB\CD], and a randomly methylated-β-CD [RMβCD]) to form inclusion complexes with the antitumoral drug, β-lapachone (βLAP), in order to overcome the problem of its poor water solubility. RMβCD presented the highest efficiency for βLAP solubilization and was selected to develop solid-state binary systems. Differential scanning calorimetry (DSC), X-ray powder diffractometry (XRPD), Fourier transform infrared (FTIR) and optical and scanning electron microscopy results suggest the formation of inclusion complexes by both freeze-drying and kneading techniques with a dramatic improvement in drug dissolution efficiency at 20-minute dissolution efficiency (DE20-minute 67.15% and 88.22%, respectively) against the drug (DE20-minute 27.11%) or the βCD/drug physical mixture (DE20-minute 27.22%). However, the kneading method gives a highly crystalline material that together with the adequate drug dissolution profile make it the best procedure in obtaining inclusion complexes of RMβCD/βLAP convenient for different applications of βLAP.

Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases.

Plant tissue growth can be regulated and controlled via culture media composition. A number of different laborious and time-consuming approaches have been used to attempt development of optimized media for a wide range of species and applications. However, plant tissue culture is a very complex task, and the identification of the influences of process factors such as mineral nutrients or plant growth regulators on a wide spectrum of growth responses cannot always well comprehended. This study employs a new approach, data mining, to uncover and integrate knowledge hidden in multiple data from plant tissue culture media formulations using apricot micropropagation databases as an example. Neurofuzzy logic technology made it possible to identify relationships among several factors (cultivars, mineral nutrients and plant growth regulators) and growth parameters (shoots number, shoots length and productivity), extracting biologically useful information from each database and combining them to create a model. The IF-THEN rule sets generated by neurofuzzy logic were completely in agreement with previous findings based on statistical analysis, but advantageously generated understandable and reusable knowledge that can be applied in future plant tissue culture media optimization.

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

The recent and significant technological advances applied to biology places the researchers in front of an unprecedented new influx of large data set from different levels as genomics, transcriptomics, proteomics, metabolomics and ionomics (Hirai et al., 2004; Belostotsky & Rose, 2005; Schauer & Fernie, 2006; Kliebenstein, 2010). Thousands of data sets including millions of measurements have been generated, and moreover, most are freely available for plant researchers worldwide from plant specific databases, as for example the whole sequencing of different plant genomes like rice, Arabidopsis, poplar, papaya, grapevine and others... (Jaillon et al., 2007; Ming et al., 2008; Brady & Provart, 2009). There is a wide concern of integrating molecular, cellular, histological, biochemical, genetic and physiological information in plant biology (Katagiri, 2003; Thum et al., 2003; Trewavas 2006; Boone et al., 2007; Alvarez-Buylla et al., 2007) and also in other related fields such as crop improvement (Hammer et al., 2002), ecology (Hilbert & Ostendorf, 2001; Jimenez et al., 2008) and biological engineering (Huang, 2009). Biological processes are both time variant and nonlinear in nature, and their complexity can be understood as the composition of many different and interacting elements governed by non-deterministic rules and influenced by external factors (Coruzzi et al., 2009, Gago et al., 2009). Commonly, most of biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data set are complex, noisy, vague, uncompleted or formed by different kind of data (Prasad & Dutta Gupta, 2008; Gago et al., 2010a). It is important to point out that many times the behaviour of a biological system over a time period is difficult to understand and interpret and additionally, genetic and environmental factors show a very high degree of intraand inter-individual variability, yielding a wide spectrum of biological responses (Karim et al., 1997; Guegan et al., 1998). The Scientific community agrees with the idea that plant biology requires more efforts in developing platforms to integrate multidimensional data and to derive models for describing biological interactions in plants (Kitano, 2002; Hammer et al., 2004; Struik et al., 2005; Tardieu, 2003; Yuan et al., 2008; Brady & Provart, 2009). In this sense, more efforts are recommended to shift our view from a reductionist way to a systems-level view. This concept can be illustrated by the Coruzzi & co-workers (2009) example of the painting “La Grande Jatte” by the

Strengths of artificial neural networks in modeling complex plant processes.

Commonly, simple mathematical models can not be used to describe exactly the biological processes due to their higher complexity. In fact, most biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data are complex or noisy. ANNs allows an accurate description of those kind of biological processes in plant science, offering new advantages over traditional treatments as the possibility of a model, prediction and optimize results. Different kind of data can be analyzed using a unique and “easy to use” technology. Researchers with a high specialized mathematical background are not required and ANNs offer the possibility of achieving the whole view of the experimental study with a limited number of experiments and costs. Additionally, it is possible to add new inputs and outputs to the database to reach a new understanding.

Dissolution rate enhancement of the novel antitumoral β-lapachone by solvent change precipitation of microparticles

beta-Lapachone [betaLAP] is a novel antitumor drug, which was recently on clinical trials with promising preliminary results. Problems derived from its low water solubility, its instability in solution and its high therapeutic dose constitute some challenges for pharmaceutical researchers. The purpose of the present work is to enhance the limited dissolution rate of betaLAP by the design of particles using a solvent change precipitation process. The procedure induces the spontaneous crystalline growth of the betaLAP in the presence of a stabilizing polymer (Hydroxypropylmethylcellulose) that limits the size of the particles generated. Physicochemical characterization of microparticles and the betaLAP dissolution rate was carried out. The utility of the betaLAP microcrystals in the development of tablets with adequate dissolution properties was also stated. The procedure was optimized in order to obtain stable and homogeneous particles with a small mean particle size (approximately 3 microm) and a narrow particle size distribution. There were no differences between the drying methods evaluated (in an oven and freeze-drying) with regard to particle morphology or dissolution behaviour, which is almost instantaneous. Tablets having suitable mechanical properties were produced by dry granulation prior to compression. The compression process did not compromise betaLAP dissolution characteristics.

Artificial neural networks modeling the in vitro rhizogenesis and acclimatization of Vitis vinifera L.

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting the in vitro rhizogenesis and acclimatization of two cultivars of Vitis vinifera L. Albariño and Mencía. The effects of three factors (inputs), the type of cultivar, concentration and exposure time to indolebutyric acid (IBA), on the success of in vitro rhizogenesis and acclimatization were evaluated. The developed model, using ANNs software, was assessed using a separate set of validation data and was in good agreement with the observed results. Exposure time to IBA was found to have the dominant role in influencing the height of acclimatized plantlets. ANNs can be a useful tool for modeling different complex processes and data sets, in plant tissue cultures or, more generally, in plant biology.

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.