Computational Evaluation and Structure-based Design for Potentiation of Nicotine Vaccines

Existing therapeutic options for the alleviation of nicotine addiction have been largely ineffective at stemming the tide of tobacco use. Immunopharmacotherapy, or vaccination, is a promising, alternate therapy that is currently being explored. Results from previous studies indicate that nicotine vaccines (NVs) are effective in subjects that achieve high drug-specific antibody titers, though overall efficacy has not been observed. Consequently, improvement of these vaccines is necessary before they can achieve approval for human use. In this report, three separate approaches towards NV potentiation are explored.

The first approach applied physiologically-based pharmacokinetic (PBPK) modeling to better assess NV potential. Rat and human physiological and pharmacological parameters were obtained from literature and used to construct compartmentalized models for nicotine and cotinine distribution. These models were then calibrated and validated using data obtained from literature. The final models verified the therapeutic potential of the NV concept, identified four key parameters associated with vaccine success, and established correlates for success that could be used to evaluate future NVs prior to clinical trials.

In the second approach, conjugate NV scaffoldings were engineered by using wild-type (WT) and chimeric human papilloma (HPV) 16 L1 protein virus-like particles (VLPs). The chimeric protein was created by removing the last 34 C-terminal residues from the WT protein and then incorporating a multi-epitope insert that could universally target major histocompatibility complex (MHC) class II molecules. The proteins were subsequently expressed in E. coli and purified using a multi-step process. Comparisons between the separation outcomes revealed that the insert was able to modulate individual process outcomes and improve overall yield without inhibiting VLP assembly.

In the third approach, commonly used carrier proteins were computationally mined for their MHC class II epitope content using human leukocyte antigen (HLA) population frequency data and MHC epitope prediction software. The most immunogenic epitopes were concatenated with interspacing cathepsin cleavage sequences and the resulting protein was re-evaluated using the earlier methods. This work represents the first ever in silico design of chimeric antigens that could potentially target all of the major HLA DQ and HLA DR allotypes found in humans.