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This area of innovation is all about bringing technology into biotechnology. The high cost of drug prices is driven by the high cost of drug discovery. The statistics show that a drug can cost well over $1 billion to develop and 90% of the drugs that enter phase 1 fail to reach commercial. The high cost comes from the high level of failure. Bringing technology into the discovery process can help lower the cost. It can automate complex processes by replacing workers with robotics. This can save a lot on costs and remove human error. Automation in the lab is a big cost savings to many companies. The other place where technology can really help is using Artificial Intelligence and Machine Learning algorithms to improve on the rate of success and help lower costs. In traditional drug discovery, hundreds of molecules would have to be tested by hand in the lab to see which molecule would perform best. The use of AI and ML algorithms can screen thousands of molecules and model which ones have the highest rates of success. This can significantly reduce costs and improve rates of success. The use of AI can also be used to take genetic and biological data and make connections between mechanisms of biology and genetics that we may never have thought of before. This can open up new potential breakthroughs in diseases that have been hard to crack like Alzheimer's. The future of biotechnology will belong to the companies that embrace these new technologies. Technology can improve the ability of companies to make drugs cheaper and with higher rates of success. This could significantly reduce drug cost over the long term.

Artificial Intelligence

Artificial Intelligence is known as AI. These are computers that run software to mimic human thought or reasoning. This might sound like the plot of some science fiction movie, but it can have a huge impact on the discovery of new drugs. I can sit down and read all the literature on every disease and every drug. It would probably take me 100 years. The computer can take all that information and store it. It can recall it at any time and it can use it to make conclusions from that data. It can take the reasoning we do as humans and grow it to a massive scale. They can feed the AI all the genetic data on diseases from millions of patients, and the AI can take that data and make conclusions about why some patients respond better to one drug while others respond poorly. It can take all the clinical data from all the drugs ever discovered and use them to make conclusions about which drugs might work best together and for which patient based on genetic information. This concept is about empowering scientists to do what they do every day, but on a much larger scale.

Machine Learning

Machine Learning is known as ML. It uses computer code and mathematical algorithms to sort through vast amounts of data and pick the right data. The key thing about a ML algorithm is it needs a data set to teach it. You can take a ML algorithm and give it vast amounts of data from the pharmacokinetics of drugs. Pharmacokinetics, often called PK, is the process by which the body takes in, metabolizes and excretes the drug. This often determines the side effects of the drug. There are a lot of genetic variations in genes that can alter how people can process these drugs. Some people can be very strong responders while others will have dramatic side effects. We can teach an algorithm to screen thousands of potential drug candidates to pick the handful that would perform the way we want them to perform. What would have taken scientists months or years to do in a lab by trial and error can be done in days or weeks using a computer. This is another tool we can use to empower scientists to help discover drugs.

Computer Modeling

This has been around for a long time if you ever used one of those computer aided design programs to build a house on your computer. In drug discovery, this is about building models for chemistry and physics. We can model how a protein folds and moves as it performs its work. We can model how antibodies bind and react to pathogens. All of these computer modeling programs can help scientists do in a few hours or days what would have taken them weeks or months in the lab. There is even a potential to generate data from models like the physics or chemistry of drug compounds and use them to create data which can become the training set for a Machine Learning Algorithm.

Automation

There are a lot of processes in a lab that are extremely redundant. In the old days, they paid people to sit there and do these repetitive tasks. Today, we build machines that automate those redundant tasks. This lowers cost in the lab. A machine has an upfront cost which might be more, but it is a fixed cost. The companies don't have to keep around thousands of employees to do these redundant tasks. Especially when there are so many companies competing for so few qualified scientists. Using automation can allow a company to run millions of experiments per week and use cameras to record the data and feed it into a supercomputer where all the data can be used by AI and ML to help discover new drugs.

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CRISPR

CRISPR is a gene editing tool which can revolutionize the way we treat genetic diseases and completely create new ways to take on other diseases like cancer. It offers a low cost, easy to use, and very powerful set of tools to alter the DNA. Since its discovery, it has completely changed the landscape of biotechnology. This gives us the ability to rewrite human code. We can build therapies using cells by reprogramming them. We can knock out genes that cause harm. We can insert new genes and replace a lost gene. We can fix a gene that has a mutation. The biggest benefit from CRISPR will be what it can do in cell engineering. It offers the ability to engineer complex cells like stem cells or immune cells that can target and kill cancer. It can be used to create new life in plants that can resist drought or grow bigger. It can engineer microbes that can produce fertilizer right in the field for the plants. It can be used to engineer microbes that become sustainable manufacturing for ingredients. Gene editing tools can enable us to change the genetic code of people, plants, animals, and microbes. This technology could change the world. It can help us solve some of our biggest environmental issues like carbon emissions and global warming. Combined with other technologies, it can be a very powerful tool.

First Gen CRISPR

The very first CRISPR gene editors were discovered while studying bacteria. Scientists found a series of genes in the bacterial genome that were repeated. They named these segments Clustered Regularly Interspaced Palindromic Repeats or CRISPR. Bacteria have an immune system to help protect them from viruses called bacteriophages. These viruses would bind to and inject their DNA into the bacteria and use it to make more viruses. The bacteria have a series of enzymes that can chop up a virus's DNA and insert pieces of that DNA into the bacteria's genome in this CRISPR region. Then another set of enzymes would turn those segments into small fragments of RNA which would be loaded into a CRISPR associated enzyme or CAS enzyme that would use that RNA as a guide to seek out the bacteria DNA and chop it up. Scientists realized the ability of these CAS enzymes could be harnessed to make a simple and efficient editing tool for changing DNA. The first generation of CAS enzymes were taken right from the bacteria in the form of CAS9 and CAS12 and adapted to human gene editing. They have the major risk of cutting both strands of the DNA which can cause mutations. This has spurred innovation into discovery of newer generations of CAS enzymes.

Base Editing

A group of scientists came up with this process to help gene editing work without cutting both strands of DNA which comes with so much risk. They took the original CAS enzyme and added to it a deaminase enzyme. This system still uses an RNA guide to find the right place in the DNA. They modified the CAS9 enzyme so that it could only cut 1 strand. When this CAS enzyme reaches the right place in the DNA, the deaminase will remove an amino group (NH²). This facilitates a transition from one type of base to another. This can change an Adenine to a Guanine or a Cytosine to a Thymine. Then the CAS enzyme cuts the opposite strand of DNA to allow the repair machinery of the cell to repair the damage using the new changed base as a guide. This is much safer than using double stranded breaks, but it comes with limitations. Only a double strand break can allow information to be inserted into the DNA. That means Base Editing lacks the ability to insert genetic information.

Prime Editing

This new system tries to solve the limitations of both the previous systems. It is still early and it hasn't been validated as much as other systems. It takes the CAS9 enzyme and puts a Reverse Transcriptase enzyme on it. This enzyme can take small segments of RNA and write them into the DNA. This uses a modified CAS enzyme that cuts a single strand. It adds to its RNA a segment that includes the RNA template used by the Reverse Transcriptase. That allows the system to copy short segments of new information into the DNA. Then the repair machinery of the cell will fix the DNA to include the new inserted information. I have one serious concern with this method. The Reverse Transcriptase makes mistakes when it is copying the RNA template into the DNA. There is no information to prove this is safe. I have a serious concern they could insert harmful mutations into the DNA with this system.

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Cell Engineering

The DNA is the instructions for any cell. You can think of it as the programming for that cell. It contains all the instructions to develop the entire organism. If you change the DNA, you can change the behavior of that cell. Combine this with gene editing tools like CRISPR and you have the magic to rewrite life. We can now alter the behavior of cells by changing their DNA and rewriting the code of life. This has been used for years in some limitations like Genetically Modified Organisms to help improve crops or raise bigger Salmon. Today, we have the tools to go much further and completely reimagine life. This is being used in two categories with Synthetic Biology and Induced Pluripotent Stem Cells (iPSC).

Synthetic Biology

Synthetic Biology is about taking the tool that is CRISPR gene editing and combining it with the technology of cell engineering to create new life. This can be engineering plants for crops, animals for food and microbes to produce new resources. Scientists can take microbes and rewrite their DNA to create new organisms that have beneficial impacts. We can make microbes that produce sustainable sources of key proteins or chemical ingredients. We can make organisms that produce fertilizer right in the soil. We can use it to build plants that are better at surviving and produce higher yields. We can make microbes to detect or even clean pollution and other toxins in the environment. We can use it to produce cells in humans that work as therapies. Combining cell engineering, chemistry and gene editing could allow us to rewrite life on Earth. It could eventually allow us to colonize and survive on other planets.

iPSC

Induced Pluripotent Stem cells is an older technology that was discovered about 10 years ago. It was about taking differentiated cells like skin cells, and engineering them back into stem cells where they can be developed into any cell of the body. Stem Cells are immortal and they create all the cells of the body. To induce a differentiated cell into a stem cell, they use a series of transcription factors which remodel the DNA of the cells and turn them back into the stem cells state. For a long time this technology really had no purpose. Then along came CRISPR gene editing and everything changed. The ability to take these stem cells produced by iPSC and edit them using CRISPR opened up endless possibilities. We can now engineer stem cells that can advance down any cell lineage to create all kinds of therapies. One such example is creating new islet cells for Diabetes. This science is now in its infancy and the possibilities are endless. Someday, we will be able to take sample cells from a patient and create whole organs from iPSC cells by guiding them down the path to differentiation. Today, the focus is on using iPSC cells to create low cost manufacturing for immune cells in cell therapies against cancer. They can edit a stem cell with all the desired edits and use it to create a master bank of engineered stem cells. Then they can walk them down the path of growth into T cells or NK cells for fighting cancer. This offers a low cost way to make thousands of doses at an extremely low cost. The power of this technology isn't in a single therapy, but in how the technology will change the way we manufacture and develop new cell therapies.

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Personalized Medicine

Have you ever wondered why one person can take a medication and do very well on it while another person can take the same drug and have no response at all? The answer to this question is genetics. There is a lot of genetic variation across the human genome from person to person. One person can have a gene that allows them to make enzymes that do very well on a drug. The next person might make an enzyme that doesn't process the medication very well and they don't respond to the drug. The next person might make an enzyme that handles the medication incorrectly causing a toxic side effect. This is the concept of pharmacogenetics.

Sequencing

The first area of Personalized Medicine is Sequencing the DNA so that we know exactly what information that patient's DNA contains. We have machines called sequencers that take the DNA and read it. They print out a sequence of the DNA so that it can be read by the computers. Doctors and scientists can use that information to treat patients or design new drugs for a specific disease. There are a lot of places within the sequencing space from the companies that build the actual machines, to the companies that make the kits to collect the sample to the companies that collect the samples and record the results. All of them play a role in testing patients for genetics. The major driver of this space is cancer genetic testing. This is a rapidly growing field of sequencing, and it dwarfs all the other uses for sequencing. One of the most popular tests is the Minimal Residual Disease test which tests if any cancer cells are still detectable after treatment. When it comes to cancer, it is treat, test and treat again until there is no detectable cancer left.

Kinase Inhibitors

The one place where genetics really matters in medicine is cancer. Cancer is a genetic mutation that drives uncontrolled cell growth. Some of this genetic mutation can be inherited while some can be acquired from environmental factors like chemical exposure. To make cancer even worse, each cell in each tumor in each person can have vastly different genetic mutations. Targeted therapies like Kinase inhibitors target the specific mutations in a cell that drive the uncontrolled growth. Many of these mutations occur in the many signaling pathways inside a cell that regulate cell behavior. When one of them gets mutated the balance between grow and don't grow signals gets thrown out of balance. This drives cancer growth. Inside these cellular pathways are things called a kinase. They are proteins that act like an on switch. When it is activated, it will perform some kind of signaling action. When the DNA that encodes these kinase proteins mutates, they can become hyperactive and drive cancer growth. A kinase inhibitor goes into the cell and blocks the hyperactive kinase to prevent it from driving cancer growth.

Targeted Protein Degraders

Inside every cell is a tiny machine called a Proteasome. The cell constantly makes new proteins to perform work. When those proteins are no longer required, they get tagged for recycling. This is done by adding a protein structure to them called ubiquitin. That ubiquitin facilitates the loading of that protein into the proteasome. The proteasome will chop up the proteins into tiny fragments. This system can be harnessed to create a new class of drugs called Targeted Protein Degraders (TPD). This uses a molecule which goes in and binds to the ligase enzyme that is responsible for adding ubiquitin groups to the proteins for destruction. It retargets that ligase toward the desired protein the scientists want to destroy. Then that protein gets loaded with ubiquitin and sent to the proteasome for recycling. This can be used to target key mutated proteins in disease including cancer. Any of the kinase proteins can be targeted with TPD. This is a new class of drugs, but early data shows it has robust activity with less potential side effects from Kinase Inhibitors. The data is still really early so it can still go either way.

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