In this podcast, we talk with Dr. Raymond Peterson of DNA Analytics who is cracking the gene algorithm one sequence at a time!
What is a genetic probe and why is it important?
A genetic probe in biotechnology is a short, single strand of DNA or RNA that is used to query or match a specific gene sequence in our genome. Our genome has 2,000 unique based pairs, 4,000 if you paired them up.
It’s not so easy to get a gene probe that is unique to a gene. The reason you’d want this is because in therapy, you’d want to target a very specific gene and none others in the genome.
There are only four bases of chemical letters in the nucleic acids: A, C, G, and T.
A pairs with T
C pairs with G
Because you have such a limited alphabet there, you have two billion opportunities just from random chance, there’s maybe going to be matches elsewhere in the genome.
DNA Analytics makes algorithms that scan the genome and look at the biophysical properties of those genes and identify the best location in that gene to put that gene probe, so that a diagnostic will have very clear signal for that gene. For example, say cystic fibrosis, you might want to know whether you have that gene or not. But you don’t want it be confused with another gene. It’s the same thing with gene editing or gene therapy. If there is a defective gene, and you want to change that and repair it, well you only want to make that one repair in that one location. You don’t want change other areas of the genome because that could lead to problems.
Why are the risks and possible side effects of gene probing?
There are a number of ways in which things can go wrong.
If a gene probe matches with a gene and makes and edit in another gene, that would presumably destroy the function of that information in that other gene.
Some of these gene therapies have been potentially causing an immunological response in patients. We have to be able to put guide strands into the cells. One delivery mechanism is to put these guide strands inside the shell of a virus and infect the person with that dormant virus. But that virus does attach to cells and then gets that genetic material, the guide strand, inside the cells and then it does its business inside the cells. This may cause our immune system to recognize it as a foreign entity.
There is evidence that putting this material inside the cell sometimes causes the cellular machinery to act in such a way that the cell becomes somewhat cancerous and divides uncontrollably.
The best gene probe would mean using a lower amount of drug in the body which translate into less opportunity for side effects.
Where do algorithms come into play?
When two nucleic acid strands come together - A matches with T, for example - there’s a strength of hybridization. The strength of it depends on the order of the letters. That is what their algorithms measure. They have done experiments, generating the data for all possible matches, over a short distance and with that they are able to train their algorithms.
This is called sequence dependence.
You have an experiment design, a number of candidates, you test and optimize those in the laboratory. That process can take anymore from six to nine months to two years because the number of candidates can be 2,000 candidates.
What’s the point?
This is used in hybridization studies in which they gain visibility as to how a putative drug is going to bind to its candidate gene and also how it looks like it is going to bind elsewhere in the genome.
This gives them a more complete picture vs traditional drug discovery where you can show a drug being effective with a particular cell, but not necessarily the side effects.
The algorithms DNA Analytics is designing use advanced technologies and the result is that they can better predict side effects of drugs – the main reason for pharmaceutical failure – and so they can design around them to prevent them from happening.
We hope you’ll tune in for the rest of the podcast for the rest of the story!
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The information contained in this website and podcast are purely informational and not considered investment recommendations. Tim Dougherty’s participation in Biotech Insights is separate and apart from his role as an investment advisor representative. Nothing contained herein may be construed as a recommendation or endorsement of any of the companies discussed. Tim Dougherty has no financial affiliation with any of the companies mentioned in this communication. Tim Dougherty makes no representation that the information conveyed in this material is accurate and is under no obligation to update this information as changes occur.