Gene editing and the growing synthetic biology field promise to change how we conceive of medical intervention. Instead of fixing things after they have gone wrong, it is now becoming conceivable to prevent diseases before birth or before disease processes manifest.
Engineering the patient’s own cells — through adding or subtracting genes — can restore functions that native mutated cells may have lost. This methodology of using cells that are harvested, edited and then reimplanted into a patient’s own body has been the source of many recent breakthroughs.1 The clinical success of using viral vectors and next-generation gene-editing platforms for editing patient cells directly inside the patient (gene therapy) and editing extracted cells which are then replaced (cell therapy) opens the door to broader applications of cell and gene therapies. However, many challenges remain, for example, gene therapy is efficient only in certain areas and organs of the body, and can often be administered only once, due to the body’s natural immune response to the viral-vector delivery system that is used to carry the DNA to the cells.
Another challenge facing therapeutics are bacterial and fungal pathogens, which are rapidly becoming resistant to existing interventions. The World Health Organization estimates that by 2050 10 million people will die each year due to antibiotic resistance.2
However, the Identification of potential new classes of drugs is being vastly accelerated owing to recent and multiplying advances in artificial intelligence. Better datasets and better ways of mining them sets are set to improve alongside AI tools. Metagenomics is yielding new discoveries as well, not only identifying new tricks from existing genomes but also harvesting strategies from prehistoric species.
Today’s cutting-edge gene and cell therapies are united in being highly personalised, which accounts for the tremendous price tag they carry. Bringing down those costs is the only way to democratise these treatments, which will require economies of scale and generalisation.
KEY TAKEAWAYS
Genetic and synthetic-biology approaches are changing our approach to disease treatment and prevention by providing tools that can operate at both the symptom level and deeper causal level. Cell and gene therapy will provide lifelong corrections to errant biology as well as fixing defective or missing genes. Research is making progress in understanding the mechanisms behind immune-system regulation and responses, and by Hacking immunity we can not only restore and augment the innate balance of the system when it goes wrong, but also program the system to attack new, precise targets. Developing new Anti-infectives is an urgent goal, given the threat posed by antimicrobial resistance, with several promising routes under investigation, including metagenomics and combinational approaches. All these will be aided by new approaches to Drug discovery which exploit technologies such as generative AI and machine learning for protein synthesis and data mining to offer the promise of vastly accelerated and improved pharmaceutical development.
Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.
Gene-transfer technologies, including AAV vectors and lipid nanoparticles, are repurposed to deliver epigenetic effectors. First human trials begin for gene therapy that targets only affected neurons, treating treatment-resistant epilepsy.9,10Oligonucleotides are approved to target more rare diseases. Clinical trials on RNA antisense therapeutics reveal whether the therapy can help people with ALS and other neurodegenerative diseases.
Glycosylation or other metabolic markers offer more specific targets for immune-cell-based therapy.12 Pivot towards editing and delivery methods that can be batch-produced for many patients across different diseases. Therapies that use engineered immune cells such as CAR-T cells become standardised.
The next frontier is selectivity. The health risks of taking immune cells out of patients for editing can be avoided with therapies that act directly on tissues within the body. But these rely on the ability to ferry gene editors into specific tissues or cells. The most commonly used viral delivery method, adeno-associated virus (AAV), can reach accessible tissues such as the eyes and blood, but has more difficulty in penetrating muscle, specific locations in the brain and solid tumours.6 However, new site-specific gene-editing methods can infiltrate tumours and preferentially target brain areas linked to focal epilepsy.7 Engineered immune cells can now contain logic gates that selectively target leukaemia cells.8 Other disease-sensitive approaches could target metabolic signatures of diseased cells.
To affordably advance regenerative medicine, immunotherapy and cancer therapies requires moving from personalised therapies to greater generalisation.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
Synthetic-biology approaches deprogram the immune system. Microfluidic devices, models used to recapitulate tumour complexity, start to decipher the molecular mechanisms driving treatment inefficiencies and to evaluate new targeted immunotherapies. New treatments are developed for a rare disease of the white blood cells that guard against repeat infections and cancer; insights from this development inform new research into the broader immune system.21 AI starts to make links between personal history and autoimmune disorder development.
Immunotherapy (adding specifically curated receptors to the T-cells of the immune system and others to recognise cancers and other desired targets, commonly referred to as CAR-T cell therapy) has delivered on its early promise against cancer. But it remains more effective for cancers like leukaemia, lymphoma and melanoma than for inaccessible and immunosuppressive solid tumours. Even for non-solid cancers, CAR-T can fail or induce cytokine toxicity. Lentiviral vectors, derived from the HIV-1 retrovirus, are showing promise to deliver gene editors to tumours and against other immune disorders.13, 14 Trials are also now beginning for cancer vaccines in England15 and the US. These work by stimulating a patient’s immune system to recognise and destroy any remaining cancer cells — and stop their reappearance.16
Immunotherapy is not just being investigated for cancer, however. For people with gene errors predisposing them to immune dysfunction, gene editing techniques show promise to precisely correct T-cells wrongly targeting the body’s own healthy cells. CAR techniques being used against multiple sclerosis17 as well as myasthenia gravis18, stiff person syndrome19 and even intractable lupus20 are showing promising early results, along with strategies against other autoimmune disorders.
However, all of this is fighting against symptoms. A future medical approach to address the underlying cause of immune-system dysregulation will require a more precise understanding of the triggers for autoimmune disease.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
The widespread use of antibiotics, antivirals and antifungals has driven the evolution of antimicrobial resistance, set to kill 10 million people a year by 2050. Besides antimicrobial resistance, other challenges to combating infectious diseases include, for example, those of tuberculosis, which has immune tolerance that makes it extremely hard to target with a single strategy,23 and the chronic fungal infections that immunosuppressed patients often develop.24 But new drugs are hard to discover and develop, as they do not make much money competing against cheap generics, such as current antibiotics on the market.
One such recent candidate, zosurabalpin, overcomes existing antibiotic resistance mechanisms in a major hospital-acquired pathogen, carbapenem-resistant Acinetobacter baumannii (CRAB), and is now in human trials. Although AI holds the promise to speed up drug-screening processes, a major challenge in developing new antibiotic medicines remains the progression of new chemotypes that kill bacteria effectively in vitro into human medicines that carry well-balanced drug-like properties, including safety profiles.
It will be necessary to embrace entirely new ideas. First, existing antibiotics may be made more effective by using them in combination with non-antibiotic drugs. The use of bacteria-infecting viruses known as phages can treat bacterial infections and reduce resistance to antibiotics by removing physiological barriers — biofilms — which contribute to resistance. However, infrastructure is under-developed for clinical trials, and research is still needed to answer important questions about the specific actions of phages against bacteria, and how bacteria develop resistance against phages.25 Regulatory infrastructure also needs to be updated for the research into phages to advance.26 Research is also under way to develop microbiome strategies to cultivate gut flora to outcompete pathogenic strains. In addition to faecal transplants, more sophisticated RNA-based sculpting of the microbiome can selectively kill certain bacteria.27 Immunotherapy is also being investigated for deployment against bacterial infections28 and fungus.29 The more alternatives to defeating antimicrobial resistance the better, to minimise the chance of pathogens evolving mechanisms to evade the treatment — that is, resistance.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
Finding new small-molecule drugs is a multibillion-dollar, multi-year effort that often fails.30 AI-based drug development promises a faster, more effective path and has yielded novel drugs, some of which are now entering trials.31 It is poised to become an increasingly valuable tool.
Larger data sources are planned at the nation-state level. AI clinical-trial data mining links targets to diseases where no links had previously been identified. Frameworks and new chemical-synthesis systems are put in place to more quickly and robustly turn AI discoveries into compounds.
AI-mined trial data has now identified disease targets as intervenable nodes and generated proteins and small molecules to intervene. Drug-discovery time is shortened to days instead of years. Sequencing and synthesis is automated, reducing costs such that rare-disease drug treatments become viable.
First, machine learning can identify the complicated lock-and-key relationships between new compounds and the relevant disease-related proteins they may affect. AI systems like Google DeepMind’s AlphaFold, which can predict a protein's 3D structure from its sequence, are teaming up with major pharmaceutical companies to hasten the discovery of small-molecule therapeutics.32 AI also holds promise for synthesising new chemical compounds not seen in nature which bacteria therefore have not had a chance to build defences against.33
Second, AI can mine the metagenome — the diversity of all existing natural molecules34 — and beyond; one project is mining the genetic information of all available extinct organisms, from extinct human relatives such as Neanderthals to ice-age animals like the woolly mammoth and giant sloth. These have identified previously employed defence mechanisms, now extinct, that contemporary pathogens have not evolved to evade.35
Third, machine learning can be deployed to find patterns in other complex datasets, for example in the heaps of data generated during clinical trials, whether they succeed or fail.
But AI and machine learning are not magic tools and are useless without good data. Data generation requires well-curated and standardised public datasets, and functionalising AI also requires next-generation manufacturing and regulatory approaches to bring synthesis into line with discovery.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
