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Somatic Gene Therapy: On the Cusp of Major Innovation

What would a world without disease look like?

Like we learned from one of the best motion pictures featuring dinosaurs, DNA holds the building blocks for life. Within that genetic code are clues to how the human body responds to the presence of disease and if that individual was born with an increased risk for certain conditions. What that popular movie didn’t teach us is that a genetic code can be changed or mutated after birth.

Novel approaches in somatic gene therapy (SGT), or the transfer of gene(s) into body cells other than germ (ova or sperm), show promising results for successfully editing the genetic code that cause diseases, such as cancer. However, like all life sciences breakthroughs, safety is paramount. When modifying the genetic code, there is a risk for the modification to inadvertently be transmitted to the individual’s offspring (germline transmission).

Charles River employs assays for sequences of nucleic acids, which enable pathologists to evaluate the pharmacokinetics (PK) and biodistribution of the SGT as well as determine the potential risk of off-target effects and germline transmission. Ultimately, these assays are critical for the evaluation of the efficacy of novel gene therapies. Surely, John Hammond would be intrigued.

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3d illustration of glass model of DNA molecule.

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Delivering the gene

In order to deliver new genetic code to a diseased cell, Charles River developed deep learning, artificial intelligence (AI)-based algorithms that automatically assay digital transmission electron microscopy (TEM) images for the quality of genetic material packaged within viral capsids. Capsids are molecular structures that not only serve as a protective coating for the new code, but also help to facilitate its entry into a diseased cell.

The figure below demonstrates classification of viral capsids as empty (red), full (green), and partially full (blue) capsids, which are used to automatically provide a ratio of empty:full capsids.
Fig. 1 Classification of viral capsids: empty (red); full (green); partially full (blue). 

Not every capsid produced will contain the optimal gene construct, and the SGT will be compromised if too many empty capsids are present. Based upon the electron density of negatively stained preparations, our trained AI determines whether a capsid contains the new genetic code (full capsid) or does not have the full code (partial or empty) (Fig. 1).

We also perform good laboratory practice (GLP)-compliant imaging, algorithm qualification, and verification for determination of the empty/partial to full capsid ratio using the validated image analysis platform (Visiopharm). This software enables our cross-functional team of scientists to replace a manual and laborious counting process with an automated quantitative method for the capsid gene therapy delivery system.

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Accepting the code change

Even with successful gene delivery, there are two primary concerns for the success of SGT: the potential for germline transmission and the risk of an immune response triggered by the therapeutic. The latter can compromise the efficacy and safety of the therapy candidate.

Our immunohistochemistry (IHC) and specialty pathology teams recently carried out several animal model studies on behalf of clients pursuing SGT treatments for conditions like heart disease and hereditary angioedema, a rare disease characterized by recurrent attacks of severe swelling of the skin and mucous membranes.

Original imaging data and representative image showing pixel classification for chromogenic staining.
Fig. 2 Original imaging data and representative image showing pixel classification for chromogenic staining. 

To detect the gene therapies delivered by their respective adeno-associated virus (AAV) vectors, our team used in situ hybridization (ISH) or fluorescence ISH (FISH) to visualize the gene therapy agent and to evaluate potential germline transmission into the testes (sperm) and ovary (ovum). Associated immune infiltrate in the diseased tissue is visualized using IHC markers for macrophages, neutrophils, and lymphocytes. The team developed deep learning, AI-based algorithms and collaborated with board certified veterinary pathologists, to analyze the stained slides.

 

Ready to take the next step?

Because of the ethical sensitivities surrounding SGT, a comprehensive efficacy and safety approach is imperative in order to convey the full potential and risks for a SGT therapy candidate. We understand the gravitas of this moment. With a portfolio spanning from standard toxicologic pathology to AI-enabled segmentation of AAV vector positive cells, our pathology services stand alone.

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