Experimental design to optimize an Haemophilus influenzae type b conjugate vaccine made with hydrazide-derivatized tetanus toxoid

Abstract

The introduction of type b Haemophilus influenzae conjugate vaccines into routine vaccination schedules has significantly reduced the burden of this disease; however, widespread use in developing countries is constrained by vaccine costs, and there is a need for a simple and high-yielding manufacturing process. The vaccine is composed of purified capsular polysaccharide conjugated to an immunogenic carrier protein. To improve the yield and rate of the reductive amination conjugation reaction used to make this vaccine, some of the carboxyl groups of the carrier protein, tetanus toxoid, were modified to hydrazides, which are more reactive than the ε -amine of lysine. Other reaction parameters, including the ratio of the reactants, the size of the polysaccharide, the temperature and the salt concentration, were also investigated. Experimental design was used to minimize the number of experiments required to optimize all these parameters to obtain conjugate in high yield with target characteristics. It was found that increasing the reactant ratio and decreasing the size of the polysaccharide increased the polysaccharide:protein mass ratio in the product. Temperature and salt concentration did not improve this ratio. These results are consistent with a diffusion controlled rate limiting step in the conjugation reaction. Excessive modification of tetanus toxoid with hydrazide was correlated with reduced yield and lower free polysaccharide. This was attributed to a greater tendency for precipitation, possibly due to changes in the isoelectric point. Experimental design and multiple regression helped identify key parameters to control and thereby optimize this conjugation reaction.

Affordable experimental design with tens of variables

“…For many experimental designs, the number of variables being investigated; however, is less than a dozen; in fact, in most cases, it is only three or four factors (Alkhatib, Muyibi, & Amode, 2011;Anotai, Thuptimdang, Su, & Lu 2012;Christin et al, 2008;Job, Sukumaran, & Jayachandran 2010;Laferriere et al, 2011;Larentis, Sampaio, Martins, Rodrigues, & Alves, 2011;Marwa et al, 2011;Rigas, Papadopoulou, Philippoussis, Papadopoulou, & Chatzipavlidis, 2009;Sudheer, Varakumar, & Reddy, 2010;Vishwantha, AppuRao, & Singh, 2010). For many cases, the intention is to predict the parameters for the characterization of the system with a second-order model (Anotai et al, 2012;Cabrera-Ríos, Mount-Campbell, & Irani, 2002;Larentis et al, 2011;Laferriere et al, 2011, Mahapatra, 2009Marwa et al, 2011;Nobuyuki, Mello, Melo Santa Anna, & Pereira, 2010;Rigas et al, 2009;Sudhankar & Nagarajan 2011;Vaithanomsat et al, 2011). The experimental design used for these systems are usually the fractional factorial design, the central composite design, or the Box-Behnken design (Anotai et al, 2012;Cabrera-Ríos et al, 2002;Job et al, 2010;Laferriere et al, 2011;Marwa et al, 2011;Nair et al, 2008).…”

“…For many cases, the intention is to predict the parameters for the characterization of the system with a second-order model (Anotai et al, 2012;Cabrera-Ríos, Mount-Campbell, & Irani, 2002;Larentis et al, 2011;Laferriere et al, 2011, Mahapatra, 2009Marwa et al, 2011;Nobuyuki, Mello, Melo Santa Anna, & Pereira, 2010;Rigas et al, 2009;Sudhankar & Nagarajan 2011;Vaithanomsat et al, 2011). The experimental design used for these systems are usually the fractional factorial design, the central composite design, or the Box-Behnken design (Anotai et al, 2012;Cabrera-Ríos et al, 2002;Job et al, 2010;Laferriere et al, 2011;Marwa et al, 2011;Nair et al, 2008).…”