Selection of an analytical method for evaluating bovine serum albumin concentrations in pharmaceutical polymeric formulations

Bovine serum albumin (BSA) is a commonly used model protein in the development of pharmaceutical formulations. In order to assay its release from various dosage forms, either the bicinchoninic acid (BCA) assay or a more specific size-exclusion high performance liquid chromatography (SE-HPLC) method are commonly employed. However, these can give erroneous results in the presence of some commonly-used pharmaceutical excipients. We therefore investigated the ability of these methods to accurately determine BSA concentrations in pharmaceutical formulations that also contained various polymers and compared them with a new and compared with a new reverse-phase (RP)–HPLC technique. We found that the RP-HPLC technique was the most suitable method. It gave a linear response in the range of 0.5 -100 µg/ml with a correlation coefficient of 0.9999, a limit of detection of 0.11 µg/ml and quantification of 0.33 µg/ml. The performed ‘t’ test for the estimated and theoretical concentration indicated no significant difference between them providing the accuracy. Low % relative standard deviation values (0.8-1.39%) indicate the precision of the method. Furthermore, the method was used to quantify in vitro BSA release from polymeric freeze-dried formulations.

3,5-dimethyl-4-amino-1,2,4-triazole (Dat) as a tridentate ligand in the polymeric cations of [Ag-3(Dat)(2)](NO3)(3)

Colourless single crystals of [Ag-3(Dat)(2)](NO3)(3) were obtained from a reaction of silver(l) nitrate and 3,5-dimethyl-4-amino-1,2,4-triazole (Dat). In the crystal structure (orthorhombic, Fdd2, Z = 8, a = 1100.1(2), b = 3500.3(2), c = 1015.4(3) pm, R, = 0.0434) there are two crystallographically non-equivalent silver sites in a one (Ag1) to two ratio (Ag2). Both resemble linear N-Ag-N coordination although angles are 163 degrees and 144 degrees, respectively Each Dat ligand coordinates with the two ring nitrogen atoms at 216 to 219 pm and with one amino-nitrogen atom at 229 pro. According to the composition [Ag-3(Dat)(2)](3+) = [(Dat)Ag-3/2](3+), a polymeric structure is built with all Ag+ ions bridging.

A Phase I Clinical Study of Cisplatin-Incorporated Polymeric Micelles (NC-6004) in Patients with Solid Tumors

BACKGROUND: On the basis of preclinical studies of NC-6004, a cisplatin-incorporated micellar formulation, we hypothesised that NC-6004 could show lower toxicity than cisplatin and show greater anti-tumour activity in phase I study. METHODS: A total of 17 patients were recruited in a range of advanced solid tumour types. NC-6004 was administered intravenously (i.v.) every 3 weeks. The dose escalation started at 10?mg?m(-2) and was increased up to 120?mg?m(-2) according to the accelerated titration method and modified Fibonacci method. RESULTS: One dose-limiting toxicity (DLT) occurred in a patient who was given 90?mg?m(-2) of NC-6004, otherwise any significant cisplatin-related toxicity was not observed or generally mild toxicity was observed. Despite the implementation of post-hydration and pre-medication regimen, renal impairment and hypersensitivity reactions still developed at 120?mg?m(-2), which led to the conclusion that the maximum tolerated dose was 120?mg?m(-2), and the recommended dose was 90?mg?m(-2), although DLT was not defined as per protocol. Stable disease was observed in seven patients. The maximum concentration and area under the concentration-time curve of ultrafilterable platinum at 120?mg?m(-2) NC-6004 were 34-fold smaller and 8.5-fold larger, respectively, than those for cisplatin. CONCLUSION: The delayed and sustained release of cisplatin after i.v. administration contributes to the low toxicity of NC-6004.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.