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Mohammadi G.,Kermanshah University of Medical Sciences | Nokhodchi A.,Universities of Kent and Greenwich | Barzegar-Jalali M.,Tabriz University of Medical Sciences | Lotfipour F.,Tabriz University of Medical Sciences | And 3 more authors.
Colloids and Surfaces B: Biointerfaces | Year: 2011

The objective of the present study was to prepare clarithromycin (CLR) loaded biodegradable nanoparticles (NPS), with a view to investigate its physicochemical properties and anti-bacterial activity. PLGA was used as a biodegradable polymer and the particles were prepared by nano-precipitation method in 3 different drugs to polymer ratios. Evaluation of the physicochemical properties of the prepared nanoparticles was performed using encapsulation efficiency, nanoparticle production yield, dissolution studies, particle size analysis, zeta potential determination, differential scanning calorimetry, Fourier-transform infrared spectroscopy and X-ray powder diffractometry. The antimicrobial activity against Staphylococcus aureus was determined using serial dilution technique to achieve the minimum inhibitory concentration (MIC) of NPs. The particles were between 189 and 280. nm in size with narrow size distribution, spherical shape and 57.4-80.2% entrapment efficiency. Zeta potential of the NPs was fairly negative. The DSC thermograms and X-ray diffraction patterns revealed reduced drug crystallinity in the NPs. FT-IR spectroscopy demonstrated possible noncovalent interactions between the drug and polymer. In vitro release study showed an initial burst followed by a plateau during a period of 24. h. The NPs were more effective than intact CLR against S. aureus so that the former showed equal antibacterial effect at 1/8 concentration of the intact drug. In conclusion, the prepared CLR nanoparticles are more potent against S. aureus with improved MICs and appropriate physicochemical properties that may be useful for other susceptible microorganisms and could be an appropriate candidate for intravenous, ocular and oral and topical preparations. © 2011 Elsevier B.V.

Loo R.L.,Universities of Kent and Greenwich | Loo R.L.,Imperial College London | Chan Q.,Imperial College London | Brown I.J.,Imperial College London | And 5 more authors.
American Journal of Epidemiology | Year: 2012

Information on dietary supplements, medications, and other xenobiotics in epidemiologic surveys is usually obtained from questionnaires and is subject to recall and reporting biases. The authors used metabolite data obtained from hydrogen-1 (or proton) nuclear magnetic resonance ( 1H NMR) analysis of human urine specimens from the International Study of Macro-/Micro-Nutrients and Blood Pressure (INTERMAP Study) to validate self-reported analgesic use. Metabolic profiling of two 24-hour urine specimens per individual was carried out for 4,630 participants aged 40-59 years from 17 population samples in Japan, China, the United Kingdom, and the United States (data collection, 1996-1999). 1H NMR-detected acetaminophen and ibuprofen use was low (∼4%) among East Asian population samples and higher (>16%) in Western population samples. In a comparison of self-reported acetaminophen and ibuprofen use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 496 participants from Chicago, Illinois, and Belfast, Northern Ireland, the overall rate of concordance was 81%-84%; the rate of underreporting was 15%-17%; and the rate of underdetection was approximately 1%. Comparison of self-reported unspecified analgesic use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 2,660 Western INTERMAP participants revealed similar levels of concordance and underreporting. Screening for urinary metabolites of acetaminophen and ibuprofen improved the accuracy of exposure information. This approach has the potential to reduce recall bias and other biases in epidemiologic studies for a range of substances, including pharmaceuticals, dietary supplements, and foods. © 2012 The Author.

Nokhodchi A.,Universities of Kent and Greenwich | Hentzschel C.M.,TU Hamburg - Harburg | Leopold C.S.,TU Hamburg - Harburg
Expert Opinion on Drug Delivery | Year: 2011

Introduction: Today, the properties of many new chemical entities have shifted towards higher molecular weights and this in turn increases the lipophilicity hence decreasing aqueous solubility. The low solubility of drugs usually has in vivo consequences such as low bioavailability, increased chance of food effect and incomplete release from the dosage form. Areas covered: The present review discusses the advantages of the liquisolid technology in formulation design of poorly water soluble drugs for dissolution enhancement and highly water soluble drugs for slow release pattern. Expert opinion: With the advent of high throughput screening and combinatorial chemistry, it has been shown that most of the new chemical entities have a high lipophilicity and poor aqueous solubility, hence poor bioavailability. In order to improve the bioavailability, the release rate of these drugs should be enhanced. Although there are multiple technologies to tackle this issue, they are not cost effective due to the involvement of sophisticated machinery, advanced preparation techniques and complicated technology. As the liquisolid technology uses a similar production process as the conventional tablets, this technology to improve the release rate of poorly water soluble drugs will be cost effective. This technology also has the capability to slow down drug release and allows preparing sustained release tablets with zero order drug release pattern. The excipients required for this technology are conventional and commonly available in the market. The technology is in the early stages of its development with extensive research currently focused on. It is envisaged that the liquisolid compacts could play a major role in the next generation of tablets. © 2011 Informa UK, Ltd.

Newby D.,Universities of Kent and Greenwich | Freitas A.A.,University of Kent | Ghafourian T.,Universities of Kent and Greenwich | Ghafourian T.,Tabriz University of Medical Sciences
Journal of Chemical Information and Modeling | Year: 2013

There are currently thousands of molecular descriptors that can be calculated to represent a chemical compound. Utilizing all molecular descriptors in Quantitative Structure-Activity Relationships (QSAR) modeling can result in overfitting, decreased interpretability, and thus reduced model performance. Feature selection methods can overcome some of these problems by drastically reducing the number of molecular descriptors and selecting the molecular descriptors relevant to the property being predicted. In particular, decision trees such as C&RT, although they have an embedded feature selection algorithm, can be inadequate since further down the tree there are fewer compounds available for descriptor selection, and therefore descriptors may be selected which are not optimal. In this work we compare two broad approaches for feature selection: (1) a "two-stage" feature selection procedure, where a pre-processing feature selection method selects a subset of descriptors, and then classification and regression trees (C&RT) selects descriptors from this subset to build a decision tree; (2) a "one-stage" approach where C&RT is used as the only feature selection technique. These methods were applied in order to improve prediction accuracy of QSAR models for oral absorption. Additionally, this work utilizes misclassification costs in model building to overcome the problem of the biased oral absorption data sets with more highly absorbed than poorly absorbed compounds. In most cases the two-stage feature selection with pre-processing approach had higher model accuracy compared with the one-stage approach. Using the top 20 molecular descriptors from the random forest predictor importance method gave the most accurate C&RT classification model. The molecular descriptors selected by the five filter feature selection methods have been compared in relation to oral absorption. In conclusion, the use of filter pre-processing feature selection methods and misclassification costs produce models with better interpretability and predictability for the prediction of oral absorption. © 2013 American Chemical Society.

Newby D.,Universities of Kent and Greenwich | Freitas A.A.,University of Kent | Ghafourian T.,Universities of Kent and Greenwich | Ghafourian T.,Tabriz University of Medical Sciences
Journal of Chemical Information and Modeling | Year: 2013

Class imbalance occurs frequently in drug discovery data sets. In oral absorption data sets, in the literature, there are considerably more highly absorbed compounds compared to poorly absorbed compounds. This produces models that are biased toward highly absorbed compounds which lack generalization to industry settings where more early stage drug candidates are poorly absorbed. This paper presents two strategies to cope with unbalanced class data sets: undersampling the majority high absorption class and misclassification costs using classification decision trees. The published data set by Hou et al. [J. Chem. Inf. Model.2007, 47, 208-218], which contained percentage human intestinal absorption of 645 drug and drug-like compounds, was used for the development and validation of classification trees using classification and regression tree (C&RT) analysis. The results indicate that undersampling the majority class, highly absorbed compounds, leads to a balanced distribution (50:50) training set which can achieve better accuracies for poorly absorbed compounds, whereas the biased training set achieved higher accuracies for highly absorbed compounds. The use of misclassification costs resulted in improved class predictions, when applied to reduce false positives or false negatives. Moreover, it was shown that the classical overall accuracy measure used in many publications is particularly misleading in the case of unbalanced data sets and more appropriate measures presented here may be used for a more realistic assessment of the classification models' performance. Thus, these strategies offer improvements to cope with unbalanced class data sets to obtain classification models applicable in industry. © 2013 American Chemical Society.

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