Since artificial intelligence (AI) is still a relatively new field for biologists, let's start by addressing a basic question: What is the history of AI and what has caused its exponential growth in a variety of applications? 

Science fiction tales about artificial humans from the arts in 1923 set the groundwork for artificial intelligence.  In the early 1950s, visionaries Alan Turing and John Von Neumann's computational prowess gave upward thrust to artificial intelligence (AI).  They opened the door to human-device connection via laying the basis for cybernetics.  Then, in 1956, the field of artificial intelligence (AI) commenced to take shape as researchers attempted to create computer applications that would perform complicated cognitive functions like remembering, reasoning, and gaining knowledge of. This led to the formation of the word AI.

In the present day, artificial intelligence (AI) has made it possible for people to solve problems, increase productivity, and discover new regions.  The opportunities are boundless, ranging from digital assistants to self-using automobiles and beyond.

So, how can we use AI's potential to safely enhance our world?  

Government, public, and commercial entities are increasingly working together to take advantage of AI's potential while minimizing its risks. A number of recent efforts have been made, such as the President's Council of Science and Technology Forum and President Biden's historic Executive Order, which will create new guidelines for secure and safe artificial intelligence.

Using AI to Advance Gene Therapy Development

The pipeline for gene therapies has grown dramatically in the last ten years. It is anticipated that 17 gene therapy approval decisions will be made in 2023, with several of those decisions focusing on uncommon disorders.

Numerous areas can be improved, as previously said. First, patients may find it difficult to get FDA-approved gene treatments due to their high cost. Second, although the preferred vector for gene delivery, adeno-associated virus (AAV) vector-based gene therapies, are generally regarded as safe, there are still issues with controlling side effects and averting death.

AI-Guided Gene Editing Delivers Superior Clinical Results

The majority of biotech businesses transfect cells with plasmids to create AAVs. The genes encoded by these plasmids create functional AAVs. The "payload," which is often a therapeutic human gene intended to treat illness, is encoded by one plasmid and is then coiled and bundled into the capsid. These plasmids also contain sequences that can be changed to change the payload or the shell of an AAV.

Genetic engineering of this kind is widely used in biopharmaceutical research and development. AI is currently being used to better comprehend and enhance viral vector engineering, among other things.

What Are The Present Difficulties in Developing AI for Gene Therapy?

Although AI has great promise for gene therapy research, it is critical to recognize the substantial obstacles that still need to be addressed. The quality of data used is a major source of many AI-related issues. We can make sure that the existing collaboration between AI and gene therapy development propels long-lasting change by tackling these obstacles. By means of persistent progress, investigation, and cooperation, we can fully unleash the capabilities of artificial intelligence to transform gene therapy and enhance patient results.

FAQs

In what Way Does AI Help Gene Therapy?

AI is applied to gene therapy in a number of ways to improve treatment development, optimization, and delivery. Target validation and identification, drug development and discovery, biomarker prediction, viral vector and biomanufacturing optimization, and personalized medicine are a few examples of applications.

What is the application of AI to CRISPR?

AI is being utilized to improve the precision and efficiency of gene editing in conjunction with CRISPR-based gene treatments. AI models are being utilized to optimize target selection and delivery for more accurate and efficient genome changes, as well as to forecast off-target effects.