Standard requirements for GCP-compliant data management in multinational clinical trials

<p>Abstract</p> <p>Background</p> <p>A recent survey has shown that data management in clinical trials performed by academic trial units still faces many difficulties (e.g. heterogeneity of software products, deficits in quality management, limited human and financial resources and the complexity of running a local computer centre). Unfortunately, no specific, practical and open standard for both GCP-compliant data management and the underlying IT-infrastructure is available to improve the situation. For that reason the "Working Group on Data Centres" of the European Clinical Research Infrastructures Network (ECRIN) has developed a standard specifying the requirements for high quality GCP-compliant data management in multinational clinical trials.</p> <p>Methods</p> <p>International, European and national regulations and guidelines relevant to GCP, data security and IT infrastructures, as well as ECRIN documents produced previously, were evaluated to provide a starting point for the development of standard requirements. The requirements were produced by expert consensus of the ECRIN Working group on Data Centres, using a structured and standardised process. The requirements were divided into two main parts: an IT part covering standards for the underlying IT infrastructure and computer systems in general, and a Data Management (DM) part covering requirements for data management applications in clinical trials.</p> <p>Results</p> <p>The standard developed includes 115 IT requirements, split into 15 separate sections, 107 DM requirements (in 12 sections) and 13 other requirements (2 sections). Sections IT01 to IT05 deal with the basic IT infrastructure while IT06 and IT07 cover validation and local software development. IT08 to IT015 concern the aspects of IT systems that directly support clinical trial management. Sections DM01 to DM03 cover the implementation of a specific clinical data management application, i.e. for a specific trial, whilst DM04 to DM12 address the data management of trials across the unit. Section IN01 is dedicated to international aspects and ST01 to the competence of a trials unit's staff.</p> <p>Conclusions</p> <p>The standard is intended to provide an open and widely used set of requirements for GCP-compliant data management, particularly in academic trial units. It is the intention that ECRIN will use these requirements as the basis for the certification of ECRIN data centres.</p

Antibiotics and antibiotic-resistant bacteria in waters associated with a hospital in Ujjain, India

<p>Abstract</p> <p>Background</p> <p>Concerns have been raised about the public health implications of the presence of antibiotic residues in the aquatic environment and their effect on the development of bacterial resistance. While there is information on antibiotic residue levels in hospital effluent from some other countries, information on antibiotic residue levels in effluent from Indian hospitals is not available. Also, concurrent studies on antibiotic prescription quantity in a hospital and antibiotic residue levels and resistant bacteria in the effluent of the same hospital are few. Therefore, we quantified antibiotic residues in waters associated with a hospital in India and assessed their association, if any, with quantities of antibiotic prescribed in the hospital and the susceptibility of <it>Escherichia coli </it>found in the hospital effluent.</p> <p>Methods</p> <p>This cross-sectional study was conducted in a teaching hospital outside the city of Ujjain in India. Seven antibiotics - amoxicillin, ceftriaxone, amikacin, ofloxacin, ciprofloxacin, norfloxacin and levofloxacin - were selected. Prescribed quantities were obtained from hospital records. The samples of the hospital associated water were analysed for the above mentioned antibiotics using well developed and validated liquid chromatography/tandem mass spectrometry technique after selectively isolating the analytes from the matrix using solid phase extraction. <it>Escherichia coli </it>isolates from these waters were tested for antibiotic susceptibility, by standard Kirby Bauer disc diffusion method using Clinical and Laboratory Standard Institute breakpoints.</p> <p>Results</p> <p>Ciprofloxacin was the highest prescribed antibiotic in the hospital and its residue levels in the hospital wastewater were also the highest. In samples of the municipal water supply and the groundwater, no antibiotics were detected. There was a positive correlation between the quantity of antibiotics prescribed in the hospital and antibiotic residue levels in the hospital wastewater. Wastewater samples collected in the afternoon contained both a higher number and higher levels of antibiotics compared to samples collected in the morning hours. No amikacin was found in the wastewater, but <it>E.coli </it>isolates from all wastewater samples were resistant to amikacin. Although ciprofloxacin was the most prevalent antibiotic detected in the wastewater, <it>E.coli </it>was not resistant to it.</p> <p>Conclusions</p> <p>Antibiotics are entering the aquatic environment of countries like India through hospital effluent. In-depth studies are needed to establish the correlation, if any, between the quantities of antibiotics prescribed in hospitals and the levels of antibiotic residues found in hospital effluent. Further, the effect of this on the development of bacterial resistance in the environment and its subsequent public health impact need thorough assessment.</p

Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products

The study of experimental design in conjunction with artificial neural networks for optimization of isocratic ultra performance liquid chromatography method for separation of mycophenolate mofetil and its degradation products has been reported. Experimental design showed to be suitable for selection of experimental scheme, while Kennard-Stone algorithm was used for selection of training data set. The input variables were column temperature and composition of mobile phase including percentage of acetonitrile, concentration of ammonium acetate in buffer, and its pH value. The retention factor of the most retentive component and selectivity factors were used as the dependent variables (outputs). In this way, artificial neural network has been applied as a predictable tool in solving a method optimization problem using small number of experiments. Network architecture and training parameters were optimized to the lowest root-mean-square error values, and the network with 5-4-4-4 topology has been selected as the most predictable one. Predicted data were in good agreement with experimental data, and regression statistics confirmed good ability of trained network to predict compounds retention. The optimal chromatographic conditions included column temperature of 40 degrees C, flow rate of 700 mu l min(-1), 26% of acetonitrile and 9 mM ammonium acetate in mobile phase, and buffer pH of 5.87. The chromatographic analysis has been achieved within 5.2 min. The validation of the proposed method was also performed considering selectivity, linearity, accuracy, precision, limit of detection, and limit of quantification, and the results indicated that the method fulfilled all required criteria. The method was successfully applied to the analysis of commercial dosage form. Copyrigh