The Convergence of Gene Therapy and Artificial Intelligence: Outlook for 2025

Introduction to Gene Therapy

Gene therapy represents a revolutionary approach to treating diseases by modifying a patient's genetic material ¹. This innovative therapeutic modality aims to address the underlying genetic causes of disease by either replacing defective genes, silencing harmful gene expression, or introducing new genetic material to help the body combat disease ⁴. Gene therapy offers potential solutions for a wide range of conditions, including various cancers, inherited genetic disorders, and certain infectious diseases ².

Several gene therapies have already received approval from regulatory bodies such as the U.S. Food and Drug Administration (FDA) ⁴, though the majority of potential applications remain in clinical development phases ⁴. What distinguishes gene therapy from conventional pharmaceutical approaches is its focus on addressing the root genetic causes of diseases rather than merely managing symptoms. By correcting fundamental genetic abnormalities, gene therapy offers the potential for long-term therapeutic benefits and, in some cases, possible cures for previously untreatable conditions.

Additional approaches include gene addition, where therapeutic genes are introduced without replacing existing genes, and post-transcriptional gene silencing, which targets messenger RNA to reduce expression of specific genes ³. The versatility of these mechanisms highlights gene therapy's adaptability for addressing diverse genetic conditions and diseases.

The development of precise gene editing technologies, particularly CRISPR-Cas9, has transformed the field by providing unprecedented control over genetic modifications and expanding the potential applications of gene therapy across the medical landscape.

Gene Delivery Systems

Successful gene therapy depends critically on the effective delivery of therapeutic genetic material to target cells. This delivery is typically accomplished using specialized carriers known as vectors ². These vectors can be broadly categorized as either viral or non-viral delivery systems.

Viral vectors are derived from modified viruses that have been engineered to be non-pathogenic while retaining their ability to efficiently deliver genetic material into cells ². Common viral vectors include adeno-associated viruses (AAVs), which have emerged as leading delivery vehicles due to their safety profile and ability to target specific tissues, and lentiviruses, which can integrate genetic material into the host genome for long-term expression.

Non-viral gene delivery methods include plasmid DNA (circular DNA molecules carrying therapeutic genes) and liposomes (small lipid vesicles that can encapsulate genetic material) ². These approaches generally have lower immunogenicity but may offer reduced delivery efficiency compared to viral vectors.

Gene therapy can be administered through two primary approaches: ex vivo and in vivo ³. In ex vivo therapy, cells are harvested from the patient, genetically modified in the laboratory, and then reinfused into the patient. In vivo therapy involves direct administration of the vector containing the therapeutic gene into the patient's body, targeting the affected tissues or organs ³.

The selection of an appropriate delivery system is crucial for the safety and efficacy of gene therapy interventions. While viral vectors are widely used due to their delivery efficiency, they can potentially trigger immune responses. Consequently, significant research efforts are focused on developing safer and more targeted delivery systems, including modifications to viral vectors for enhanced tissue specificity and reduced immunogenicity, as well as exploration of innovative non-viral delivery technologies.

Current Applications and Advancements in Gene Therapy

Gene therapy has advanced significantly in recent years, with multiple therapies receiving regulatory approval and transitioning from experimental treatments to standard therapeutic options. The U.S. Food and Drug Administration (FDA) and other global regulatory bodies have approved numerous gene therapies for various indications as of March 2025 ⁴.

Notable recent approvals include Casgevy™ and Lyfgenia™ for sickle cell disease, representing the first CRISPR-based gene therapies to receive FDA approval ⁴. Elevidys has been approved for certain pediatric patients with Duchenne muscular dystrophy ⁴, while KYMRIAH™ is available for specific forms of leukemia and lymphoma ⁴.

Other significant approved therapies include Luxturna™ for an inherited retinal dystrophy caused by mutations in the RPE65 gene ⁴, ROCTAVIAN™ for adults with severe hemophilia A without Factor VIII inhibitors ⁴, SKYSONA® for boys with early, active cerebral adrenoleukodystrophy (CALD) ⁴, Zolgensma® for pediatric patients with spinal muscular atrophy (SMA) ⁴, and Zynteglo® for transfusion-dependent beta thalassemia ⁴.

A recent addition to the approved therapies is Encelto (revakinagene taroretcel-lwey), which received FDA approval in March 2025 as a cell-based gene therapy for adults with idiopathic macular telangiectasia type 2 (MacTel) ⁷.

The expanding list of approved gene therapies also includes ABECMA, ADSTILADRIN, AMTAGVI, AUCATZYL, BEQVEZ, BREYANZI, CARVYKTI, and numerous other products addressing a wide range of conditions ⁷.

FDA-Approved Gene Therapies (as of March 2025)

This growing list of approved gene therapies demonstrates the field's rapid advancement and increasing clinical application. As these therapies move from experimental to standard treatments, they are revolutionizing care for patients with previously untreatable or difficult-to-manage conditions.

AI in Biotechnology

Artificial intelligence (AI) is rapidly transforming numerous industries, including biotechnology. With its ability to analyze vast amounts of complex data at unprecedented speeds, AI has become an invaluable tool for accelerating research and development processes, ultimately aiming to improve patient outcomes [16]. These advanced AI tools are being deployed across the entire drug development pipeline, from initial discovery and development to clinical trial optimization and pharmaceutical manufacturing enhancement [16].

What makes AI particularly well-suited for biotechnology applications is its ability to mimic human cognitive functions such as decision-making and problem-solving, which are essential for navigating the intricate processes involved in bringing new treatments to market [17]. By leveraging these capabilities, AI can help overcome many traditional bottlenecks in drug development, facilitating a more streamlined and efficient process that leads to the creation of safer and more effective therapeutic agents [17].

AI in Drug Discovery and Design

One of the most significant applications of AI in biotechnology is in drug discovery and design. Advanced AI algorithms can rapidly analyze extensive datasets of biological and chemical information to identify potential drug candidates that might be overlooked by conventional methods [16]. These sophisticated algorithms excel at identifying promising compounds, accelerating the validation of drug targets, and enhancing the structural design of pharmaceutical agents [16].

By processing massive and diverse datasets—including genetic information, patient medical histories, and chemical compound libraries—AI significantly expedites the identification of viable drug candidates [18]. Furthermore, AI's predictive capabilities prove exceptionally valuable for assessing drug efficacy and safety profiles by analyzing historical data to forecast performance in clinical trials [18]. This allows researchers to prioritize the most promising compounds and minimize the risk of trial failures, thereby streamlining the drug development process.

AI in Clinical Trials and Personalized Medicine

AI is also becoming increasingly instrumental in optimizing clinical trials and advancing the field of personalized medicine. In clinical trials, AI can enhance various aspects, including treatment regimen design, participant recruitment strategies, and data analysis methodologies [17]. For personalized medicine applications, AI can analyze individual patient data encompassing genetic profiles, lifestyle factors, and environmental influences to customize drug treatments for maximum efficacy [18].

AI algorithms can process extensive patient datasets to identify patterns and correlations, leading to the development of tailored treatment plans and more accurate predictions of individual responses to specific therapies [20]. Additionally, AI can analyze large-scale genomic datasets to identify rare genetic variants and elucidate their implications for personalized treatment strategies [20]. The integration of AI in personalized medicine not only enhances treatment outcomes but also promotes a more patient-centered approach to healthcare. AI's capacity to learn from treatment outcomes and dynamically adjust therapeutic regimens in real-time further amplifies its value in this evolving field.

AI's Specific Role in Gene Therapy Research and Development

Artificial intelligence is making significant contributions to the complex domain of gene therapy research and development. AI tools, including advanced large language models (LLMs), prove particularly valuable for navigating the vast amounts of genetic and molecular data generated through research [15]. This facilitates the identification of potential therapeutic gene targets and the elucidation of critical relationships within complex biological systems, substantially accelerating the early stages of gene therapy development [5].

Machine learning (ML) algorithms can analyze extensive genetic sequence datasets, guiding the development of more precise and effective genome editing technologies by predicting potential off-target effects [5]. This capability is crucial for enhancing both the safety and efficacy of gene therapy interventions, addressing one of the field's most significant challenges.

Vector Design and Optimization

AI is also changing how we design and improve viral vectors, which are key for delivering therapeutic genes into cells.

AI algorithms can be used to improve the outer shells (capsids) of viral vectors.

This can lead to better delivery of genes (transfection), targeting of specific tissues, and easier manufacturing [15].

Computer modeling and simulation, powered by AI, can help assess how well we can produce working viral vectors.

This increases the chances of successful and cost-effective manufacturing [15].

Also, AI-driven studies are helping to design adeno-associated virus (AAV) capsids that are better at targeting specific tissues and avoiding existing immune responses in patients.

This is a major limitation of current gene therapies [15].

Predicting Outcomes and Identifying Biomarkers

Beyond finding targets and designing vectors, AI is important for predicting how gene therapy will work and finding relevant biomarkers.

By combining different data from preclinical and clinical studies, AI can build models to predict how effective and safe gene therapy will be and to improve treatment plans [21].

AI algorithms can also analyze genetic and molecular data to find important patterns and potential biomarkers.

These biomarkers can be used to track how well gene therapy treatments are working and to personalize the treatments [21].

The ability to predict outcomes and find key indicators of treatment response is very valuable for moving gene therapies through clinical development and ensuring better results for patients.

Challenges and Opportunities

Despite the positive outlook, the biotech industry in 2025 will face some challenges.

These include possible economic uncertainties, the ongoing effects of the post-COVID economic slowdown, and debates about drug prices and access [8].

Lower productivity in research and development, along with problems in global supply chains, will also be hurdles [24].

The rules for gene therapies and AI-driven products are expected to be looked at more closely [24].

For AI, problems with the availability and quality of data, as well as cybersecurity risks, will need to be addressed [19].

However, these challenges come with significant opportunities.

The industry is likely to see more mergers and acquisitions, along with better access to funding, which will encourage more new ideas [8].

Advances in gene therapy and AI will continue to open new ways to treat rare diseases and different types of cancer [13].

The development of new biologic drugs and more collaboration between biotech companies, universities, and other groups will further drive new ideas in the sector [13].

Key Trends Shaping the Industry

Several trends will likely shape the biotech industry in 2025.

Using AI in drug discovery is expected to speed up finding new drug candidates, make clinical trials better, and improve overall productivity in biotech [8].

Spending on AI in drug discovery by the pharmaceutical industry could reach about USD 3 billion by 2025 [8].

There will be a continued and growing focus on developing advanced therapeutic medicinal products (ATMPs) like gene therapies, cell therapies, antibody-drug conjugates (ADCs), and tissue-engineered products.

These are expected to have a big impact on both biotech and healthcare [8].

Precision medicine will also advance further, with treatments becoming more tailored to individual genetic makeups to improve how well they work and reduce side effects [8].

The market for antibody-drug conjugates (ADCs) is expected to grow a lot, reaching a global value of USD 19.80 billion by 2028 [8].

Also, the approval and wider use of GLP-1 receptor agonists like Semaglutide (Ozempic, Wegovy) and Tirzepatide (Zepbound) will keep driving growth in biotech, especially for managing type 2 diabetes and obesity [8].

Finally, sustainability will become a very important factor in biotech.

Growing environmental concerns and rules will push manufacturers to use more sustainable practices [8].

Challenges and Opportunities

Despite the positive outlook, the biotech industry in 2025 will face some challenges.

These include possible economic uncertainties, the ongoing effects of the post-COVID economic slowdown, and debates about drug prices and access [8].

Lower productivity in research and development, along with problems in global supply chains, will also be hurdles [24].

The rules for gene therapies and AI-driven products are expected to be looked at more closely [24].

For AI, problems with the availability and quality of data, as well as cybersecurity risks, will need to be addressed [19].

However, these challenges come with significant opportunities.

The industry is likely to see more mergers and acquisitions, along with better access to funding, which will encourage more new ideas [8].

Advances in gene therapy and AI will continue to open new ways to treat rare diseases and different types of cancer [13].

The development of new biologic drugs and more collaboration between biotech companies, universities, and other groups will further drive new ideas in the sector [13].

Key Players in Gene Therapy to Watch

The gene therapy field has both large, established pharmaceutical companies and specialized biotech firms making important contributions to the advancement of genetic medicine. The following are some of the key players to watch in 2025:

• Amgen - A leading biotech company that has expanded its work in inflammatory diseases and is actively implementing AI-driven approaches in drug discovery relevant to gene therapies [27].
• BioMarin Pharmaceutical - A global biotech company focused on developing transformative medicines for rare and genetic disorders, leveraging its extensive expertise in genomics [27].
• Bluebird bio - Has successfully launched three gene therapies for genetic, hematologic, and neurological disorders and maintains its focus on severe genetic diseases, despite recent acquisition agreements [27].
• CRISPR Therapeutics - A pioneer in gene editing technology that achieved a significant milestone with FDA approval of Casgevy™, the first CRISPR-based gene therapy [27].
• CSL Behring - A biopharmaceutical company specializing in therapies for rare and serious diseases, combining plasma-derived and recombinant biotechnology products [27].
• Novartis - A major healthcare company with strategic focus on gene therapy, particularly for genetic neurological diseases, strengthening its position through strategic acquisitions and partnerships [27].
• Sibiono GenTech - A Chinese company notable for developing Gendicine, the world's first commercially available gene therapy [27].

Other significant companies in the gene therapy landscape include Sarepta Therapeutics (with FDA-approved ELEVIDYS for Duchenne muscular dystrophy), Cellectis, Bayer, MeiraGTx, Rocket Pharmaceuticals, 4D Molecular Therapeutics, Roche, Ultragenyx, Beam Therapeutics, uniQure, and Intellia Therapeutics [28].

Recent Activities and Strategies

The gene therapy landscape is characterized by significant developments across multiple companies, highlighting the dynamic nature of this rapidly evolving field:

CRISPR Therapeutics has achieved a historic milestone with the FDA approval of Casgevy™, the first CRISPR-based gene therapy. The company is actively implementing a global launch strategy while simultaneously conducting Phase 3 studies to expand access to younger patient populations. CRISPR Therapeutics is also advancing next-generation therapeutic approaches, including targeted conditioning and in vivo editing technologies, alongside multiple clinical trials for CAR T-cell products targeting various cancers and autoimmune conditions. Updates on these programs are anticipated throughout 2025 [27].

Bluebird bio continues to commercialize its approved gene therapy portfolio, including ZYNTEGLO™, LYFGENIA™, and SKYSONA™, while navigating financial challenges that led to its recent acquisition agreement with Carlyle and SK Capital. This strategic business decision, expected to close in the first half of 2025, reflects the significant capital requirements for gene therapy commercialization. The company has continued to publish long-term data demonstrating the durable clinical benefits of its gene therapies [27].

Novartis has reported promising results from a Phase III study for its intrathecally administered gene therapy for spinal muscular atrophy, reinforcing its commitment to core therapeutic areas including gene and cell therapy. The company has strategically strengthened its position in the gene therapy market through targeted acquisitions and continues to demonstrate strong financial performance [27].

Sarepta Therapeutics secured FDA approval for ELEVIDYS, its gene therapy for Duchenne muscular dystrophy, and has established manufacturing partnerships to support the commercial launch of this groundbreaking treatment [12].

These activities collectively demonstrate the maturation of the gene therapy market, with companies progressing through various stages of the development and commercialization continuum—from regulatory approvals and product launches to strategic business decisions that will shape the future landscape of genetic medicine.

Key Players Leveraging AI in Biotechnology to Watch

The artificial intelligence revolution in biotechnology is being led by both specialized AI startups and established pharmaceutical companies. The following key players are at the forefront of this transformation:

• Owkin - Leverages AI and machine learning to decode complex biological systems, particularly in the fields of cancer and immunology [46].
• Insilico Medicine - Implements AI throughout the drug discovery and development process, with a focus on areas including fibrosis and cancer; recently achieved important milestones in preclinical drug discovery and clinical trials [46].
• Recursion - Combines AI with automated experimental biology to rapidly generate biological and chemical datasets for accelerated drug discovery; recently merged with Exscientia to create a powerhouse in AI-driven pharmaceutical research [46].
• BenevolentAI - Utilizes AI to enhance drug discovery through analysis of vast biomedical datasets, identifying novel connections and potential therapeutic targets [47].
• Generate Biomedicines - Specializes in creating novel protein therapeutics using generative AI models trained on protein structure and function [49].
• Deep Genomics - Focuses on using AI to decode the functional effects of genetic variation and identify therapeutic targets for genetic diseases [19].
• Atomwise - Pioneers the application of convolutional neural networks to molecular structure prediction for drug discovery [19].

Additional notable companies in this space include Ginkgo Bioworks, Schrödinger, PathAI, Healx, XtalPi, and Tempus. These companies focus on various applications of AI in biotechnology, from target identification and drug design to clinical trial optimization and personalized medicine [19].

Recent Activities and Strategies

The AI-driven biotechnology field is evolving rapidly, with several companies achieving significant milestones that showcase the transformative potential of these technologies:

Insilico Medicine has demonstrated remarkable progress in preclinical drug discovery, identifying numerous drug candidates and advancing several programs into clinical trials. The company recently reported positive results from a Phase IIa study for its IPF drug candidate, validating its AI-driven approach. Additionally, Insilico has secured substantial funding rounds and established multiple licensing and collaboration agreements with pharmaceutical partners, highlighting the industry's recognition of its platform's value. The company continues to enhance its AI capabilities and has pioneered the integration of a humanoid AI scientist in its robotic laboratory environment [8].

Exscientia has strategically merged with Recursion, creating a formidable entity in the AI-powered drug discovery landscape. This consolidation combines Exscientia's AI-driven small molecule design expertise with Recursion's industrialized experimental biology platform. The company has reached important milestones in its collaboration with Sanofi and expects initial Phase 1 data for its CDK7 inhibitor in 2025, along with the initiation of Phase 1 trials for additional promising candidates [13].

These developments illustrate the accelerating pace of innovation in AI-driven biotechnology, with companies progressing from theoretical applications to clinical validation of their platforms. The field is characterized by strategic partnerships, consolidation of complementary technologies, and increasing evidence of AI's ability to identify novel therapeutic candidates more efficiently than traditional approaches.