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    • The mixture of pure nanoparticles of the

    2018-11-02

    The mixture of pure nanoparticles of the influenza virus (strain A1-H1N1) and albumin proteins (as an example of impurity) can be considered as a model of dispersions in the technological process of vaccine production. The influenza virus particle was approximated as a homogeneous sphere with the mean diameter d=100nm. To determine the virus concentration, the bilayer sphere approximation [1] was used. In order to design the optimal scheme for dispersions’ on-line control, the 3D-vector (based on light order EPZ5676 parameters [21]) was suggested for differentiation of influenza virus dispersion, albumin dispersion and their mixture (Fig. 1). In the vaccine production process, it is important to know the degree of virus dispersion purification from protein impurities. It can be concluded from Fig. 1 that the positions of 3D-vectors P for the constituent dispersions of the mixture (points 1 and 2) are suitable for differentiation of these dispersions not only by value but also by sign. The preparation of vaccine will be better if the position of 3D-vector for mixture (Fig. 1, point 3) will be closer to the 3D-vector position of the influenza virus (Fig. 1, point 1). It is also possible to suppose from the mixture 3D-vector position (Fig. 1, point 3) apart of the line-connected constituent dispersions vectors, that there are interactions between virus particles and protein molecules in mixtures. The mixture of biological coli bacillus micro-particles with mineral bimodal kaolin dispersions (consisting of nano- and micro-particles) could be considered as the natural water model. Mineral bimodal kaolin dispersions were characterized by different methods [21]. The justifications of mainly different forms of particles in different modes of size distribution for kaolin 3DDS were obtained at polarization measurements at angles θ about 90 degrees (Fig. 2). The shifts of the positions of the S11(θ) minimum in Fig. 2a and of the maximum of scattered light polarization (–S12/S11) in Fig. 2b for fraction of “coarse” particles (curves 2) to θ>90 are the evidence that there are aspheric particles [3,5,6] in kaolin dispersions. The kaolin nanoparticles (the first mode [21]) can be approximated as homogeneous spheres and the kaolin micro-particles (the second mode and “tail” of particle mass distributions [21]) as homogeneous oblate (based on electronic microscopy data) ellipsoids of rotation [11]. Coli bacillus bacterial cells (Escherichia coli, E. coli, coli bacillus rods) were approximated as homogeneous prolate ellipsoids of rotation and as volume-equivalent spheres with the mean diameter of cells d=1.0 µm for strain K-802 and d=1.3µm for strain AB 1157 (Figs. 3–6). According to Ref. [18], both situations can be observed for the mixtures of coli bacillus and mineral bimodal kaolin dispersions with nano- and micro-particles (Fig. 3, based on the integral and differential static light scattering parameters [21]), i.e., the supposed interaction between the constituent dispersions for the mixture with n (500)=1.2 and the absence of that for the mixture with n (500)=1.6. The detailed analysis of the data for the mixture of kaolin with E.coli strain AB 1157 (Fig. 4 and 5) showed that there is interaction between the particles in this dispersion: the form of mixture distributions is evidence in favor of kaolin and bacteria heteroaggregation. In Fig. 6, the 16D-vectors (based on the light scattering matrix parameters [22]) of kaolin dispersion, E. coli K-802 dispersion and of their mixture are presented. It can be seen that the differentiation of dispersion vector positions in 16D parameter space is about several orders and that the “non-supposed” (according to 4D-vector approach in Fig. 3) interaction between bacterial and kaolin particles can also occur. The data of polarization measurements for kaolin 3DDS (Fig. 2) allows to predict that the (prolate bacterial – kaolin “small” spherical) and the (prolate bacterial – “coarse” oblate) particle interactions can be different. In addition to the discussion in Ref. [21] about natural 3DDS polymodality, taking into account the shape of the particles makes the model for solving inverse problem of mixtures more complex.