The manufacture and characterisation of hot-melt extruded enteric tablets

The aim of this highly novel study was to use hot-melt extrusion technology as an alternative process to enteric coating. In so doing, oral dosage forms displaying enteric properties may be produced in a continuous, rapid process, providing significant advantages over traditional pharmaceutical coating technology. Eudragit (R) L100-55, an enteric polymer, was pre-plasticized with triethyl citrate (TEC) and citric acid and subsequently dry-mixed with 5-aminosalicylic acid, a model active pharmaceutical ingredient (API), and an optional gelling agent (PVP (R) K30 or Carbopol (R) 971P). Powder blends were hot-melt extruded as cylinders, cut into tablets and characterised using powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC) and dissolution testing conducted in both pH 1.2 and pH 6.8 buffers. Increasing the concentration of TEC significantly lowered the glass transition temperature (T,) of Eudragit (R) L100-55 and reduced temperatures necessary for extrusion as well as the die pressure. Moreover, citric acid (17% w/w) was shown to act as a solid-state plasticizer. HME tablets showed excellent gastro-resistance, whereas milled extrudates compressed into tablets released more than 10% w/w of the API in acidic media. Drug release from HME tablets was dependent upon the concentration of TEC, the presence of citric acid, PVP K30, and Carbopol (R) 971P in the matrix, and pH of the dissolution media. The inclusion of an optional gelling agent significantly reduced the erosion of the matrix and drug release rate at pH 6.8; however, the enteric properties of the matrix were lost due to the formation of channels within the tablet. Consequently this work is both timely and highly innovative and identifies for the first time a method of producing an enteric matrix tablet using a continuous hot-melt extrusion process.

Safety and Pharmacokinetics of Dapivirine Delivery From Matrix and Reservoir Intravaginal Rings to HIV-Negative Women

Vaginal microbicides for the prevention of HIV transmission may be an important option for protecting women from infection. Incorporation of dapivirine, a lead candidate nonnucleoside reverse transcriptase inhibitor, into intravaginal rings (IVRs) for sustained mucosal delivery may increase microbicide product adherence and efficacy compared with conventional vaginal formulations. Twentyfour
healthy HIV-negative women 18–35 years of age were randomly assigned (1:1:1) to dapivirine matrix IVR, dapivirine reservoir IVR, or placebo IVR. Dapivirine concentrations were measured in plasma
and vaginal fluid samples collected at sequential time points over the 33-day study period (28 days of IVR use, 5 days of follow-up). Safety was assessed by pelvic/colposcopic examinations, clinical laboratory tests, and adverse events. Both IVR types were safe and well tolerated with similar adverse events observed in the placebo and dapivirine groups. Dapivirine from both IVR types was successfully distributed throughout the lower genital tract at concentrations over 4 logs greater than the EC50 against wild-type HIV-1 (LAI) in MT4 cells. Maximum concentration (Cmax) and area under the concentration–time curve (AUC) values were significantly higher with the matrix than reservoir IVR. Mean plasma concentrations of dapivirine were ,2 ng/mL. These findings suggest that IVR delivery of microbicides is a viable option meriting further study.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Comment on "Electron collisional excitation of argon-like Ni XI using the Breit-Pauli R-matrix method" [Eur. Phys. J. D 42, 235-241 (2007)] - Electron impact excitation of Ni XI

In a recent paper, Verma et al. [Eur. Phys. J. D 42, 235 (2007)] have reported results for energy levels, radiative rates, collision strengths, and effective collision strengths for transitions among the lowest 17 levels of the (1s(2)2s(2)2p(6))3s(2)3p(6), 3s(2)3p(5)3d and 3s3p(6)3d configurations of Ni XI. They adopted the CIV3 and R-matrix codes for the generation of wavefunctions and the scattering process, respectively. In this paper, through two independent calculations performed with the fully relativistic DARC (along with GRASP) and FAC codes, we demonstrate that their results are unreliable. New data are presented and their accuracy is assessed.