AI promises to accelerate drug discovery.³⁹ Synthetic organisms and AI will help advance genome editing for human applications in several crucial ways, including improved ways to deliver the editing payload to the cell and experimental organisms that provide a better proxy for human testing.⁴⁰

Future Horizons:

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5-yearhorizon

Synthetic biology circuits go in vivo

Extremely rapid progress in machine learning and AI solves many obstacles to engineering proteins and enzymes. AI helps create de novo gene editors. Genome reading and writing allows us to build large genetic circuits composed of many repeated guide RNA sequences that enable us to simultaneously target multiple genes. AI leads to engineered proteins and enzymes.

10-yearhorizon

Chimeras, synthetic viruses and other models become mainstream

Synthetic biology circuits, now in mammalian cell cultures, find applications in vivo and for enhanced control of genome editors for gene therapies. Chimeras generated by injecting human stem cells into animal embryos grow organs for xenotransplantation or grow human-like brain structures to study the effects of gene edits. Improved synthetic viruses and genome editors knock out genes in animal organs to supply the increasing need for organ donation without the risk of rejection. Engineered cells and tissues serve as novel delivery systems to easily grafted tissues such as bone and skin.

25-yearhorizon

Universal editors emerge

Engineered cells and tissues are grafted into complex tissues like brain or the endocrine system. With prime and base editing, plus tissues grown outside the body and reimplanted, modification becomes easier and cheaper, rivalling in vivo. Genetically modified viruses, synthetic viruses and large genetic circuits are widely deployed for pre-emptive “gene surgery” on otherwise healthy people, directly linking genetic circuits to genome editors. We see the first demonstration in humans of universal cells carrying gene circuitry.

AI will help to quickly scan whole genomes and then design vectors that can be used more universally. Work is also under way in AI design of entirely new proteins and editors.⁴¹ However, human screeners are still needed to identify the small percentage that will work as they are meant to. This situation may improve with access to more training data. Generally, advances here will require more collaboration between mathematicians and biologists.

Furthermore, machine-learning algorithms may help identify the relationships among genes, gene networks and other variables (such as epigenetic factors) involved in disease, as well as the potential consequences of edits to these.⁴² AI-enabled searches through microbial data obtained from uncultivated samples may reveal more suitable enzymes — helicases, nucleases, transposases or recombinases — that solve the problems of currently available editors.

Recent rapid advances in stem-cell engineering, stem-cell-derived embryo models, organoids (artificial and simplified versions of an organ) and tissue engineering are helping research move towards providing experimental organisms based on human physiology that will help predict the functionalities of genome editors outside the human body and before clinical applications.⁴³ ⁴⁴

Engineered organisms and AI-based tools - Anticipation Scores

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:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

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.