Stability of dobutamine 500 mg in 50 ml syringes prepared using a Central Intravenous Additive Service

Objectives A pharmacy Central Intravenous Additives Service (CIVAS) provides ready to use injectable medicines. However, manipulation of a licensed injectable medicine may significantly alter the stability of drug(s) in the final product. The aim of this study was to develop a stability indicating assay for CIVAS produced dobutamine 500 mg in 50 ml dextrose 1% (w/v) prefilled syringes, and to allocate a suitable shelf life. Methods A stability indicating high performance liquid chromatography (HPLC) assay was established for dobutamine. The stability of dobutamine prefilled syringes was evaluated under storage conditions of 4°C (protected from light), room temperature (protected from light), room temperature (exposed to light) and 40°C (protected from light) at various time points (up to 42 days). Results An HPLC method employing a Hypersil column, mobile phase (pH=4.0) consisting of 82:12:6 (v/v/v) 0.05 M KH2PO4:acetonitrile:methanol plus 0.3% (v/v) triethylamine with UV detection at λ=280 nm was specific for dobutamine. Under different storage conditions only samples stored at 40°C showed greater than 5% degradation (5.08%) at 42 days and had the shortest T95% based on this criterion (44.6 days compared with 111.4 days for 4°C). Exposure to light also reduced dobutamine stability. Discolouration on storage was the limiting factor in shelf life allocation, even when dobutamine remained within 5% of the initial concentration. Conclusions A stability indicating HPLC assay for dobutamine was developed. The shelf life recommended for the CIVAS product was 42 days at 4°C and 35 days at room temperature when protected from light.

Power allocation strategies for distributed space-time codes in two-way relay networks

We study a two-way relay network (TWRN), where distributed space-time codes are constructed across multiple relay terminals in an amplify-and-forward mode. Each relay transmits a scaled linear combination of its received symbols and their conjugates,with the scaling factor chosen based on automatic gain control. We consider equal power allocation (EPA) across the relays, as well as the optimal power allocation (OPA) strategy given access to instantaneous channel state information (CSI). For EPA, we derive an upper bound on the pairwise-error-probability (PEP), from which we prove that full diversity is achieved in TWRNs. This result is in contrast to one-way relay networks, in which case a maximum diversity order of only unity can be obtained. When instantaneous CSI is available at the relays, we show that the OPA which minimizes the conditional PEP of the worse link can be cast as a generalized linear fractional program, which can be solved efficiently using the Dinkelback-type procedure.We also prove that, if the sum-power of the relay terminals is constrained, then the OPA will activate at most two relays.

Stabilizing swarm intelligence search via positive feedback resource allocation

A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.

Resource allocation analysis of the stochastic diffusion search

The Stochastic Diffusion Search (SDS) was developed as a solution to the best-fit search problem. Thus, as a special case it is capable of solving the transform invariant pattern recognition problem. SDS is efficient and, although inherently probabilistic, produces very reliable solutions in widely ranging search conditions. However, to date a systematic formal investigation of its properties has not been carried out. This thesis addresses this problem. The thesis reports results pertaining to the global convergence of SDS as well as characterising its time complexity. However, the main emphasis of the work, reports on the resource allocation aspect of the Stochastic Diffusion Search operations. The thesis introduces a novel model of the algorithm, generalising an Ehrenfest Urn Model from statistical physics. This approach makes it possible to obtain a thorough characterisation of the response of the algorithm in terms of the parameters describing the search conditions in case of a unique best-fit pattern in the search space. This model is further generalised in order to account for different search conditions: two solutions in the search space and search for a unique solution in a noisy search space. Also an approximate solution in the case of two alternative solutions is proposed and compared with predictions of the extended Ehrenfest Urn model. The analysis performed enabled a quantitative characterisation of the Stochastic Diffusion Search in terms of exploration and exploitation of the search space. It appeared that SDS is biased towards the latter mode of operation. This novel perspective on the Stochastic Diffusion Search lead to an investigation of extensions of the standard SDS, which would strike a different balance between these two modes of search space processing. Thus, two novel algorithms were derived from the standard Stochastic Diffusion Search, ‘context-free’ and ‘context-sensitive’ SDS, and their properties were analysed with respect to resource allocation. It appeared that they shared some of the desired features of their predecessor but also possessed some properties not present in the classic SDS. The theory developed in the thesis was illustrated throughout with carefully chosen simulations of a best-fit search for a string pattern, a simple but representative domain, enabling careful control of search conditions.