Prediction of size distribution of lipid-peptide-DNA vector particles using Monte Carlo simulation techniques

• Published in: Biotechnology and applied biochemistry 2003-08, Vol.38 (1), p.95-102
• Main Authors: Sarkar, Supti, Zhang, Hu, Levy, Susana M., Hart, Stephen L., Hailes, Helen C., Tabor, Alethea B., Shamlou, Parviz Ayazi
• Format: Article
• Language: English
• Subjects: A, Hamaker constant (J); a1, a2, radii of primary particles 1 and 2 (m); absolute temperature (K); adjustable constant related to zeta potential; aggregation dynamics; aggregation kernel for particle i and j (m3/s); average mass size after k aggregation events (kg/m3); average mass size at t=0 (kg/m3); Biological and medical sciences; Boltzmann constant (1.381×10−23 J/K); c, ionic concentration (M); C0 initial total particle number concentration/m3; characteristic coagulation time (s); charge on electron (1.6×10−19 C); coagulation events; coagulation probability; constant number of particles for Monto Carlo simulation; Debye-Hückel parameter; DH, particle hydrodynamic diameter (m); dimensionless (=a1/a2); dimensionless [(H/a1+a2)]; dimensionless emsemble average kernel; dimensionless functions of zeta potential; dimensionless maximum coagulation kernel; DNA - analysis; dynamic viscosity of liquid (kg/ms); e, charge on electron (1.6×10−19 C); electrical repulsion and total interaction) (J; formulation; Fundamental and applied biological sciences. Psychology; Gene Targeting; Genetic Vectors; H, separation distance between two primary particles (m); Hamaker constant (J); Hydrogen-Ion Concentration; Integrins - genetics; interaction energies (van der Waals' attraction; ionic concentration (M); k, coagulation events; kB, Boltzmann constant (1.381×10−23 J/K); Kc intrinsic aggregation rate (m3/s); kij; kij dimensionless aggregation kernel for particle i and j; Kij, aggregation kernel for particle i and j (m3/s); kij〉, dimensionless emsemble average kernel; kmax; kmax, dimensionless maximum coagulation kernel; lipid-peptide-DNA vector particles; Lipids - analysis; mass of particle i (kg/m3); Mav; Mav, average mass size at t=0 (kg/m3); Mi, mass of particle i (kg/m3); Mk, average mass size after k aggregation events (kg/m3); Monte Carlo Method; Monte Carlo simulation; N, constant number of particles for Monto Carlo simulation; particle hydrodynamic diameter (m); Particle Size; Peptides - analysis; permittivity (dimensionless); pij; pij, coagulation probability; Plasmids; R, random number between 0 and 1; radii of primary particles 1 and 2 (m); random number between 0 and 1; real time corresponding to k coagulation event (s); separation distance between two primary particles (m); T, absolute temperature (K); time interval for k coagulation event (s); tk, real time corresponding to k coagulation event (s); VA, VR and VT, interaction energies (van der Waals' attraction, electrical repulsion and total interaction) (J); valence (charge number) of ionic species; VR and VT; x, dimensionless [(H/a1+a2)]; y, dimensionless (=a1/a2); z, valence (charge number) of ionic species; zeta potential; zeta potentials of particles 1 and 2; ɛ, permittivity (dimensionless); α, adjustable constant related to zeta potential; γ1, γ2, dimensionless functions of zeta potential; Δtk; Δtk, time interval for k coagulation event (s); ζ1, ζ2, zeta potentials of particles 1 and 2; κ, Debye–Hückel parameter; μ(η); μ(η), dynamic viscosity of liquid (kg/ms); τc, characteristic coagulation time (s)
• ISSN: 0885-4513; 1470-8744
• DOI: 10.1042/BA20030073

Concerns with insertional mutagenesis for retrovirus and immunogenicity for adenovirus have motivated research into development of non‐viral vectors that can safely deliver desired gene constructs to target cells in tissues and organs. Many non‐viral vectors suffer from unacceptably poor in vivo cell transfection and low transgene expression. Evidence suggests that cell transfection is linked to particle size – vector particles below about 200 nm are considered desirable. Experimental measurements indicate, however, that vector particles are susceptible to significant aggregation under most conditions of pH and ionic strength, including physiological conditions, although there are currently no means of predicting the kinetics of aggregation. The present paper addresses this challenge by presenting a mathematical framework based on the Monte Carlo simulation techniques for modelling the dynamics of aggregation. The approach is used to simulate the evolution of particle‐size distribution for an integrin‐targeting lipid–peptide–DNA vector system in buffers of different pH and ionic strength. The simulations required two input parameters, including the initial‐size distribution of the particles and a fitting parameter (α). Comparison of simulations with experimental data showed that α was closely related to the zeta potential of the particles in the buffer medium, making simulations fully predictive. The modelling approach may be used in other vector systems.