Vector Design

What’s Next for Viral Vectors Within Gene Therapy Clinical Trials

In this article, we take a look at the role of viral vectors in gene therapy clinical trials, including current challenges and leveraging artificial intelligence to advance the field.

Jill Roughan, PhD

Jill Roughan, PhD

February 23, 2023

What’s Next for Viral Vectors Within Gene Therapy Clinical Trials

Gene therapies offer massive therapeutic potential and the possibility of making clinicians' dreams of treating diseases a reality: the ability to cure genetic conditions with a one-time treatment.

In general, gene therapies introduce genetic material into a patient’s cells to correct or compensate for a genetic defect. For instance, hereditary blindness can be caused by biallelic mutations in the gene RPE65, which reduce or eliminate RPE65 activity, leading to loss of vision in childhood or adolescence. The first FDA-approved gene therapy, Luxturna (voretigene neparvovec-rzyl), delivers a normal version of the RPE65 gene directly into retinal cells, producing wild-type levels of RPE65 functioning and restoring vision loss.1 

This success in 2017 opened the floodgates to gene therapy development. There is great potential to treat various inherited or acquired genetic disorders, including cancer. The activity over the past few years has led to the recent landmark approval of the first CRISPR-based gene therapy for sickle cell disease.2

While extremely promising, one of the most challenging aspects of developing a gene therapy is designing and producing a viral vector that contains the complete transgene for introduction into a cell’s nucleus. Below, we give an overview of which viral vectors are used in gene therapy development, the advantages and drawbacks of each, and how artificial intelligence is used to overcome obstacles in viral vector production.

What are Viral Vectors?

A viral vector is a virus genetically modified to deliver a specific transgene or piece of genetic material into a host cell. They are commonly used in gene therapy and vaccine development, as they can effectively transfer genetic material into cells in a controlled manner. Several types of viral vectors can be chosen depending on desired expression level, size of the transgene, the cell type that needs to be targeted, and other delivery parameters.

Common Types of Viruses Employed as Vectors to Deliver Gene Therapies

Viral vectors successfully used in the preclinical and clinical development of gene therapies include adenoviruses, adeno-associated viruses (AAVs), lentiviruses, and retroviruses.3

Adenovirus (ADV)

ADV is a non-enveloped virus that commonly causes upper respiratory tract infections.3 There are hundreds of human ADV genotypes, and the infection cycle has been heavily studied and is known to transiently transduce nearly any mammalian cell type. The virus rarely causes severe infection in most healthy children and adults. Those infected carry lifelong immunity and express ADV-specific neutralizing antibodies.3

Adeno-associated viruses (AAV)

AAVs are small, non-enveloped viruses that carry single-stranded DNA. It doesn’t cause any known human disease, yet it has been a popular laboratory tool as it has a relatively simple genome and can be easily manipulated. Thousands of AAV variants have been classified into a few serotypes, with differential affinities to a range of cell surface receptors.3 

Lentivirus (LV) vectors

LVs are complex retroviruses that are spherical, enveloped, and contain a single-stranded RNA genome. The viral particle fuses with the cell membrane upon binding to cell surface receptors, and the genome is released into the cytoplasm. Following reverse transcription, the genome is integrated into the host genome in a non-random way, favoring transcriptionally-active sites.3

The Use of Viral Vectors in Gene Therapy Clinical Trials

ADV vectors in clinical trials

Adenoviral vectors are widely used in current gene therapy clinical trials and have the longest history in gene therapy, dating back to the infamous case of Jesse Gelsinger.4 This tragedy slowed the development of gene therapies for years and raised awareness about the severe innate immune response that can be generated by ADVs (specifically the capsid protein). 

Nevertheless, for therapies where an immunological response is beneficial, ADV vectors showed promise: In 2003, Gendicine (an ADV vector expressing the p53 gene) was approved in China, becoming the first-ever gene therapy for cancer.3 

Currently, they are being investigated clinically for novel vaccine development against infectious diseases and as cancer therapies. ADV-based vaccines against Ebola, influenza, HIV-1, and SARS-CoV-2 are all in ongoing clinical trials but haven’t navigated regulatory approval.3 ADV-based anti-cancer strategies, including delivery of suicide or regulatory genes, are also under clinical investigation. 

Recently, the FDA approved the first gene therapy based on an adenoviral vector: In December 2022, Adstiladrin was approved for the treatment of high-risk non-muscle invasive bladder cancer resistant to Bacillus Calmette-Guérin.5

AAV vectors in clinical trials

AAV-based gene therapies are being investigated in several rare diseases and there have been over 200 clinical trials worldwide using AAVs.2 Due to small patient populations and  limited enrollment, clinical study of AAV-based therapies is slow, limited to diseases with no other treatment options. Clinical trials are ongoing for age-related and diabetic macular degeneration (AMD), muscular dystrophies, hemophilia, and X-Linked myotubular myopathy (XLMTM).

While some trials have produced promising results, there have also been several noteworthy trials that have been halted due to safety concerns: A high-dose AAV vector for treating XLMTM was recently linked to the deaths of three patients.6 These challenges will need to be addressed should AAV therapies continue to be commercialized.

The FDA has approved three AAV-based therapies (as of January 2023): Luxturna for retinal dystrophy, Zolgensma for spinal muscular atrophy, and recently, Hemgenix for the treatment of adults with Hemophilia B (congenital Factor IX deficiency).1,7,8 

LV vectors in clinical trials

LV vectors have been primarily used for engineering ex vivo gene therapies, such as generating chimeric antigen receptor T (CAR-T) cells.3 The first FDA-approved ex vivo gene therapy, Kymriah, transduced autologous T cells with an LV vector carrying a CAR transgene that binds CD19 and was used to treat pediatric B-cell relapsed and refractory acute lymphoblastic leukemia.9 Other ex vivo therapies that target different cancers are being investigated, and as proof of the safety of LV vectors becomes available, the number of trials will continue to increase.3

Opportunities and Challenges of the Different Viral Vectors for Gene Therapy Development Programs

Using ADV, AAV, and LV vectors in gene therapy development has distinct advantages and drawbacks. Choosing the right vector type depends on the transgene, desired expression level, target cell type, and therapeutic application.3

Opportunities and Challenges of ADV Vectors

ADV vectors are often the preferred choice for gene therapies due to their high transduction efficiency, stability in host cells, capacity (up to 4 kb for first generation, up to 10kb for second generation and up to 36 kb for third-generation vectors), and ability to target various tissues2

However, ADVs also have some significant drawbacks. Many people have been previously infected with ADVs and therefore have pre-existing immunity that can lead to strong immune responses to the viral vector or transgene products.3 These safety concerns limit the use of ADVs to diseases that are not impacted by immune responses or even rely on the target cells being killed (e.g., in cancer treatment).

Opportunities and Challenges of AAV Vectors

AAVs are versatile and stable, with a lifespan of over 10 years in differentiated cell populations.10 However, AAVs have a limited capacity of around 5 kb, which has led to developing strategies such as split vector approaches to address this limitation.

Like ADVs, AAVs can also cause immune responses that must be considered during discovery and development. The immunogenicity of AAV-based gene therapies can increase due to production impurities, which include empty capsids and encapsidated, non-therapeutic DNA.11 These issues have led to stalled clinical development of several AAV-based gene therapies. Additionally, converting the single-stranded DNA genome into double-strand DNA, which is required for transduction in host cells, can be a rate-limiting step. 

AAVs can also be difficult for tissue-specific transgene expression, and their production can be challenging to scale up economically. 

Opportunities and Challenges of LV Vectors

LVs are often used to deliver transgenes due to their ability to integrate into the host genome effectively, express multiple transgenes (up to 9 kb) for long periods from a single vector, and infect non-dividing cells.12 They also tend to have a low immunogenicity profile. 

However, there is a risk of generating a replication-competent virus, particularly with LVs derived from HIV-1. To address this risk, third-generation vectors have been developed that do not have the genes required for genome packaging. Still, LVs have high mutation and recombination rates, which can pose a small but significant risk. One primary concern is the accidental generation of an oncogene, which is why LV vectors are only used in ex vivo applications, such as CAR-T therapy, and not in vivo therapies.

Solving the AAV Viral Vector Biomanufacturing Problem with Artificial Intelligence

The clinical successes and regulatory approvals of gene therapies set up a bright future for continued research, development, and improvement of how clinicians treat genetic disorders. However, many viral vector candidates in clinical trials face complications, particularly in biomanufacturing, where viral construct design problems delay the journey to commercialization.

Applying advances in artificial intelligence (AI) to the viral vector construct design issue and coupling it with experimental validation can unlock transformational breakthroughs in biomanufacturing efficiency and success.13 Here's how.

DNA and Protein Feature Prediction

Recently, large language models (LLMs) have been applied to DNA sequence analysis, enabling accurate functional prediction (e.g., promotors, enhancers, etc.). This allows viral construct designers to foresee gene expression complications using sequence analysis alone14. It also enables the prediction of secondary structures, tertiary structures, and CpG islands, all of which can cause complications with viral vector packaging or safety concerns.  As reviewed in our previous blog, LLMs can be further applied to protein analysis, giving cell and gene therapy developers the ability to accurately predict development and biomanufacturing outcomes15.

Epigenetic and Variant Effect Prediction

Deep learning architectures – trained on epigenetic and transcriptional datasets – have been utilized to yield precise gene expression prediction16. The ML algorithms use long-range interactions, as far as 100 kb away, for accurately forecasting the impacts of mutations and methylation. Again, these methods can be directly applied to construct optimization for viral vectors and comparative analysis of candidate vector sequences.

Truncation Propensity Prediction

A major issue in viral vector biomanufacturing, specifically with adeno-associated viruses (AAVs), is the incomplete packaging of the viral genome into a capsid. These “truncated” AAVs reduce the potency and purity of AAV preparations. AI can help in modeling and predicting truncation by identifying DNA sequences associated with this phenomena. It can also help characterize the quality of AAV preparations from sequencing data, making analysis easier. This provides developers with insights that enable the optimization of constructs with a reduced propensity for truncation, improving the manufacturability of AAV-based gene therapies. 

The development of a neural network model has been able to, with high accuracy, predict where truncations during viral packaging can occur, further validating the utility of machine learning in gene therapy development17. In doing so, machine learning has been useful in solving a multi-billion dollar industry problem that plagues nearly all AAV gene therapy developers.


Viral vectors for gene therapy play a crucial role in the development of these therapeutics and different types of viral vectors can be used depending on the specific requirements of the therapy. ADVs, LVs and AAVs have all been used successfully in preclinical and clinical gene therapy trials. 

While there are potential challenges and risks associated with the use of viral vectors, such as the innate immune response, the potential benefits of gene therapies make them a worthwhile area of continued research and development. With the implementation and acceptance of artificial intelligence into the gene therapy development workflow, meeting the requirements and safety standards of regulatory agencies and commercialization will continue to become more streamlined and cost-effective, making powerful therapeutics more readily available to the patients who need them18,19

Learn how Form Bio’s artificial intelligence strategies will accelerate your gene therapy development program.

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  2. The First CRISPR Drug: Vertex Pharmaceuticals’ Casgevy Wins U.K. Approval for Sickle Cell Disease. Genetic Engineering and Biotechnology News website. Accessed December 4, 2023. Published November 16, 2023
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  4. Raper SE, Chirmule N, Lee FS, et al. Fatal systemic inflammatory response syndrome in a ornithine transcarbamylase deficient patient following adenoviral gene transfer. Mol Genet Metab. 80(1-2):148-158 (2003)
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  7. FDA approves innovative gene therapy to treat pediatric patients with spinal muscular atrophy, a rare disease and leading genetic cause of infant mortality. FDA website. Accessed January 10, 2023. Published May 24, 2019.
  8. FDA Approves First Gene Therapy to Treat Adults with Hemophilia B. FDA website. Accessed January 10, 2023. Published November 22, 2022.
  9. FDA approval brings first gene therapy to the United States. FDA website. Accessed February 11, 2023. Published August 30, 2017
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  11. Distinguishing AAV Empty and Fragmented Capsids: Closing the Gene Therapy Accessibility Gap with Improved Manufacturability. Form Bio website. Accessed December 4, 2023. Published August 23, 2023
  12. Sanyal S. The Pros and Cons of Lentiviral and Adeno-Associated Viral Vectors. The MedicineMaker. Published October 7, 2022. Accessed January 7, 2023.
  13. Validating Computational Models with Real World Biological Experiments. Form Bio website. Accessed December 4, 2023. Published August 9, 2023
  14. The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics | bioRxiv. Accessed February 8, 2023.
  15. New Study Finds Large Language Models Applied to DNA Sequences Enabled Accurate Molecular Phenotype Prediction. Published and Accessed February 2022.
  16. Avsec Ž, Agarwal V, Visentin D, et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods. 2021;18(10):1196-1203.
  17. Developing Machine Learning Powered Solutions for Cell and Gene Therapy Development. Form Bio Resource Center. Published Dec 2022. Accessed Jan 17 2023.
  18. Understanding How Best to Allocate Resources When Training Large Language Models in Gene Therapy Development Programs. Form Bio Resource Center. Published January 2023. Accessed February 2023.
  19. Solving Gene Therapy Product Development Challenges Through Analytical Standardization. Form Bio Resource Center. Published October 17, 2023. Accessed December 8, 2023. 

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