Assessing Viral Variant Detection Algorithms To Improve Characterization Of Gene Therapy Products

By Hiral Desai, Yanfei Zhou, Ph.D., Bradley Hasson, Alexandra Bridgeland, William Dolan, Amber Overgard

Identity testing is an evolving requirement of regulatory agencies to characterize biologics intended for viral and gene therapy products. Next generation sequencing (NGS) can be used to characterize and confirm the identity of viral vectors delivering genetic material to affected cells in a patient by creating a full genetic profile of all nucleic acids contained within the test sample.

It is important to characterize viral vectors to ensure they do not contain variants that can negatively impact patient outcomes. Downstream bioinformatics analysis must capture true positive variants and limit spurious results to establish sequence identity and purity of the expression vectors while maintaining sensitivity and specificity. In addition, all steps of a bioinformatics workflow must be carefully examined to configure necessary requirements for optimal results. Advancements in best practices and standards for viral variant detection are critical to ensure the safety of patients and meet the expectations of regulatory guidance.

Here, the analysis of over 10 variant callers and other bioinformatics tools for viral variant detection are discussed to better understand how the outcomes can be applied to improve the characterization of gene therapy products.