Hi, my name is Jaya and over the past few months, I’ve been researching personalized medicine and pharmacogenomics! My last 2 articles have been about things like why it is important, and certain issues and ethical gaps surrounding pharmacogenomics. In this article, I’ve combined all my learnings and I share how we can ensure genetic privacy within pharmacogenomics.
What if I told you that every medicine you took from this day forward could be personalized to fit your genes? But what about medicine as small as the prescriptions we take every day? Yep that too, with personalized medicine anything is possible!
Personalized medicine takes into account the patient’s individual genetic makeup, medical history, lifestyle, and other factors to develop a treatment plan that is fit for the individual. The goal of personalized medicine is to provide the most effective, efficient, and safest care possible. There are many benefits of personalized medicine, including:
1. Increased Effectiveness: By taking into account a patient’s unique genetic makeup, doctors can more accurately target the root cause of a disease or condition. This can lead to more effective treatment and, ultimately, better health outcomes.
2. Fewer Side Effects: Personalized medicine can also help to minimize the risk of side effects from treatment. By targeting the root cause of a disease, personalized medicine can reduce the need for potentially harmful medications or treatments.
3. Improved Quality of Life: Personalized medicine can also improve a patient’s quality of life. By providing more effective and safer treatment, personalized medicine can help patients live longer, healthier, and happier lives.
4. Reduced Costs: In the long run, by allowing doctors to target treatments specifically to each individual patient’s needs, patients’ treatments will be more successful. This means that patients are less likely to experience side effects from treatments, and they won’t require multiple treatments/follow-up treatments.
There are currently so many applications of personalized medicine, here are some examples:
- Cancer treatment: Personalized medicine has been particularly effective in treating cancer, where doctors use genomic testing to identify genetic mutations that are driving the growth of cancer cells. This information can then be used to develop targeted therapies that are tailored to the individual patient’s specific genetic profile.
- Pharmacogenomics: This field involves the use of genetic testing to identify how an individual will respond to a particular drug. For example, some people may metabolize certain drugs more quickly than others, which can lead to adverse reactions or reduced effectiveness. By using pharmacogenomics, doctors can select drugs that are more likely to be effective and less likely to cause side effects in each patient.
- Prevention of cardiovascular disease: Personalized medicine can be used to identify individuals who are at high risk of developing cardiovascular disease. By analyzing genetic and lifestyle factors, doctors can develop personalized prevention strategies that are tailored to each patient’s unique risk profile. This may include changes to diet and exercise, as well as the use of medications to manage blood pressure and cholesterol levels.
- Rare diseases: Personalized medicine is particularly useful in the treatment of rare diseases, where there may be limited information about effective treatments. By analyzing genetic and other factors, doctors can develop targeted therapies that are tailored to the specific genetic mutations that are causing the disease.
- Mental health: Personalized medicine can also be used to develop personalized treatment plans for mental health conditions such as depression, anxiety, and bipolar disorder. By analyzing genetic and other factors, doctors can identify the most effective medications and therapy approaches for each individual patient
So now that you have an understanding of personalized medicine and its applications, how can this be applied to the field of pharmaceuticals?
Although it may seem like prescription drugs are personalized enough or that there not important enough to be personalized. This isn't true, in America alone every year 2 million develop serious side effects and over 100,000 die because of prescription drugs.
This is because prescriptions are only personalized to things such as your weight and age. While they should actually be personalized to your genes and your DNA.
The way this can be personalized is called pharmacogenomics. Pharmacogenomics is the study of your genes and their effects on medicine. By understanding your genes we can understand how you will react to drugs of different sorts.
Pharmacogenomics encompasses two main factors. It studies pharmacokinetics and pharmacodynamics, here's a description of both and what they study:
Pharmacodynamics:
Pharmadynamics studies the effects of drugs and the mechanism of their actions. Whether the target is a receptor, ion channel, enzyme, or something else. In pharmacodynamics, you study the biochemical, physiologic, and molecular effects of the drug, along with that it surveys the receptor binding, post-receptor effects, and overall if the drug is successful or not on its target.
Biochemical effects → Biochemical effects refer to the impact the drug has on the biochemical pathways. Biochemical pathways are also known as metabolic pathways, these are step-by-step series of interconnected biochemical reactions. In biochemical pathways, each step is catalyzed by an enzyme. Biochemical pathways lead to a certain product or change in the cell, and drugs can have an effect on this.
This image shows biochemical pathways, and just how complicated they can be.
Physiologic effects → Physiologic effects refer to the effect the drug has on your body, kind of like the side effects of the drugs. The drug could affect parts of your body such as your hands, legs, and feet. It can also affect the circulatory system and other systems, overall physiologic effects are unwanted effects that the drug causes. Your physiologic effects can be determined by the structure of the drug and your body.
A diagram of the Physiological effects Adderall can cause on your body.
Molecular effect → Your body is made up of cells and these cells are made up of molecules. The molecular effect of drugs refers to the effect the drug has on your cells and molecules. That could be there is no effect or that the drug infects or kills some of your cells. For example, certain medicines target your cells, specifically the infected ones which are causing you harm.
Receptor binding process and post-receptor effects →Pharmacodynamics studies the receptor binding process effects along with the post-receptor effects. Once a drug reaches its target it binds to and activates a receptor. A receptor is a protein molecule which is either on or in a cell. Its role is to bind to specific substances and cause an effect in the cell. Once the drug binds to and activates a receptor it then causes an alteration to intercellular messengers/proteins (effectors).
The effectors’ purpose as a molecule is that once it receives this chemical signal it can go and alter cell structure and functions. This means it can alter infected cells that carry things such as the diseases and toxins which the drug is targeting. Along with that, it can trigger cells’ defence response to fight diseases. Studying the receptor binding process and the post-receptor effects and ensuring that it is successful is a very important part of pharmacodynamics.
Pharmacokinetics:
Pharmacokinetics encompasses four processes; absorption, distribution, metabolism, and excretion.
Absorption → Absorption refers to how the drug enters the bloodstream after which it enters the body. It describes the transportation process of the drug from the entry point to the circulation system. Drugs can be taken as pills, inhalants, injections, and more. A factor of absorption is bioavailability which measures how much of the drug enters circulation.
Distribution → Distribution studies where the drug travels after the absorption process, and how much of this drug actually reaches the target site. After absorption once the drug is in the bloodstream, it needs to reach the liver for the metabolism process to occur. It then continues in the bloodstream until the target site is reached. Distribution can be a fast or slow process, depending on the person a lot of the drug can reach its target site or some can be lost along the way. The distribution process is never an exact and even thing. Some determining factors include fat, muscle mass, water composition, gender, and where the target site is in your body.
Metabolism → When you eat an apple and it enters your body, metabolism is how that apple is converted into energy and how fast this is done. That’s what the metabolism process does to the drugs. Most drugs travel through the liver during the distribution process. Once in the liver, enzymes such as the P-450 enzyme convert prodrugs to active metabolites or convert active drugs to inactive form. Few drugs such as antidepressants, anticonvulsants, and anesthetics are not metabolized by the liver. Metabolism can be a slow or a fast process based on your body, genes, and how healthy your liver is. Examples of genes which contribute to metabolism are the LEPR, UCP and MC4R genes.
Excursion → Excursion describes how the drug leaves the body after its job is done whether this is through urine, exhalation, or another method.
As you can see pharmacokinetics is the study of how drugs travel throughout the body. The drug starts by entering the body and bloodstream (Absorption), then it needs to reach the site (Distribution). The drug needs to be processed and broken down(Metabolism), then finally how it leaves the body(Excursion).
There are so many aspects that go into the process of a prescription drug and how it works. This is why pharmacogenomics studies all these different aspects in order to personalize the prescription. But how can we understand this and personalize prescriptions just from reading your genes?
In order to understand that, here's how your genes work and what role they play in your body.
Your genes are long segments of DNA, which encode proteins. The process by which your body makes proteins is quite complicated. In order to make proteins DNA, mRNA, and tRNA all work together.
DNA is made up of nucleotides(A, G, C, and T are the DNA’s nucleotides). Think of DNA as the original “code” or “coding” of a protein. DNA is found in the nucleus, and inside a nuclease, there are chromosomes where long strands of DNA are found. (A32 small amount of DNA can be found in the mitochondria).
The cells nuclease — Chromosomes — DNA
Once protein synthesis begins (the making of proteins). A gene gets activated (genes are a section of DNA). Once this section of DNA is activated the DNA starts to open up or “unzip”. (When the DNA strand is open people often refer to the nucleotides as free bases)
DNA and activated (open) DNA
Then the RNA polymer attaches to the open DNA, this enzyme moves along the DNA. As it moves along the DNA a strand of messenger RNA(mRNA) is made. The role of mRNA is to pair with DNA so it can transport protein information.
The RNA polymerase binding and creating the mRNA transcript
Once the mRNA strand is made it leaves the nucleus and travels to the cytoplasm. In the cytoplasm, the ribosome translates the mRNA’s code so it can produce amino acid chains.
In the ribosome is tRNA, which links the mRNA to the amino so the amino acid chain can be produced. The chain of amino acids then becomes a protein
Your genes which may just seem like simple DNA, holds the information about how your entire body function. This is why when prescribing medicine it's so important to read and understand the genes because they play such a big role when it comes to the effectiveness and efficiency of the drug.
Some of your genes are good and make the medicine work faster, but other genes aren't good when it comes to medicine and make your body more resistant to the medicine.
Now, we are going to look at a real-life example of a gene that pharmacogenomics would study, This can show you and help you better understand what pharmacogenomics does.
The ABCA3 protein falls under the ABC transporter category. And is a member of the superfamily of ATP-binding cassette transporters:
Amino Acids Found In This Sample of mRNA:
- Gly/Glycine- Glycine contributes to cellular growth and health
- Lys/Lysine — Lysine is a building block for making proteins and helps make collagen
- Pro/Proline — Proline is a building block of proteins, helps metabolism and neutrinos, aids in the healing of wounds
- Asp/Aspartate — Aparate is used in the biosynthesis of proteins
It is given that the genes or strands of DNA that encode proteins are much longer than this. So consider it as a sample size, to understand what specific protein this could be for you only look at the mRNA because that’s what the ribosome reads. The underlined part is only the DNA. From this sample, you can see different colours these colours are the different amino acids.
This protein (ABCA3) the gene for it is found in chromosome 16. Each cell has 23 chromosomes and chromosome likely contains 800 to 900 genes that provide instructions for making proteins.
Now that you know all this, why is it important that pharmacogenomics studies these genes, or any genes for that reason?
As I said the ABCA3 protein causes great multidrug resistance. An example of this is chemotherapy. In cancer tumours, this protein is found and has been seen to make the treatment process longer. In one study the median expression of the ABCA3 was three times higher in patients who had failed to achieve remission.
This proves the power of how resistant the ABCA3 protein is. In fact, ABCA3 is the most likely transporter to cause drug resistance. This protein is only one of the examples of why doctors need to use pharmacogenomics. If someone’s DNA looks similar to the ones I’ve written above they would recognize the ABCA3 protein and adapt the treatment plan or prescription accordingly. But without pharmacogenomics, they wouldn’t be able to adapt the medicine, and if a problem is detected they won’t know the real reason behind it.
But what does this mean, let’s say someone comes in with a cold and gets prescribed cold medicine. Now the dosage of this cold medicine would be given based on a patient’s age and weight. The problem is that the doctor doesn’t understand the patient’s genes and how much medicine their body actually needs. More specifically, they don’t know how much of the drug will actually fight the cold. Because of the fact that they haven’t studied the patient’s genes, it’s possible that the patient could have high levels of ABCA3 which would require higher dosages.
A patient who has high ABCA3 will demonstrate higher drug resistance. This means that lower amounts of medicine can enter the circulatory system. The liver will metabolize drugs differently, and when done lower amounts of the medicine will reach the target site. Once it reaches the target site the transporter and bonding process will also be affected.
Another reason why pharmacogenomics testing is important is that it can lead to early discovery.
Although the point of pharmacogenomics is to personalize and improve medical treatments or prescriptions. It can also save lives through testing. For example in this case it’s been found that patients with very low amounts of ABCA3 are at risk for lung cancer. So while a gene test is done even if it won’t change a prescription amount it could lead to early detection of things like cancer.
Now that you've understood pharmacogenomics and its potential or importance why isn't it in our everyday life, why hasn't it been implemented yet? Well, the reason for this is called GINA.
GINA is the Genetic Information Nondiscrimination Act. GINA is a federal law in the United States that prohibits discrimination in health insurance and employment based on genetic information. GINA was signed into law in 2008 and it is enforced by the Equal Employment Opportunity Commission (EEOC) and the Department of Health and Human Services (HHS). Under GINA, employers and health insurance companies are prohibited from requesting or using genetic information in decisions related to hiring, firing, promotions, or any other terms of employment. Additionally, health insurance companies are prohibited from using genetic information to determine eligibility, premiums, or coverage for health insurance. Genetic information includes information about an individual’s genetic tests, genetic tests of an individual’s family members, and information about an individual’s family medical history. The law also provides protection against retaliation for individuals who file complaints or participate in investigations related to genetic discrimination.
The GINA law and its principles are the reason why experts are raising ethical concerns about pharmacogenomics. All though you may not hear about genetic discrimination happening every day, it does occur.
So now, what is the solution, How can we bridge this gap, and overcome ethical concerns making pharmacogenomics mainstream while still ensuring genetic privacy?
The solution is AI, Before I get into how AI can solve this problem. I’m going to explain what AI is, how it works and what it’s used for.
Artificial Intelligence (AI) is a transformative field of study and research that focuses on the development of intelligent machines capable of performing tasks that typically require human intelligence. It encompasses a broad range of technologies, algorithms, and methodologies, enabling machines to perceive, reason, learn, and interact with the world in a way that mimics human cognition. AI has witnessed remarkable advancements over the years, revolutionizing industries, driving innovation, and shaping the future of society. This essay explores the fundamental concepts, applications, and implications of artificial intelligence, highlighting its significance in various fields. At its core, artificial intelligence aims to create intelligent systems that exhibit cognitive abilities such as problem-solving, pattern recognition, decision-making, and language understanding. AI systems rely on vast amounts of data, algorithms, and computational power to process information and generate insights. Artificial intelligence finds applications across diverse domains, impacting numerous sectors. In healthcare, AI aids in diagnosing diseases, designing treatment plans, and analyzing medical imagery, leading to improved accuracy and efficiency in patient care. AI-powered virtual assistants, such as Siri and Alexa, have transformed the way we interact with technology, offering personalized experiences and streamlining everyday tasks. In transportation, self-driving cars leverage AI algorithms and sensors to navigate roads autonomously, enhancing road safety and reducing congestion. As artificial intelligence becomes more pervasive, it brings along ethical considerations and challenges. Privacy concerns arise as AI systems gather and process vast amounts of personal data, necessitating the establishment of robust regulations and frameworks to protect individuals’ rights. Bias and fairness also pose significant challenges, as AI algorithms may unintentionally reflect societal prejudices, leading to discriminatory outcomes. Ensuring transparency, accountability, and the ethical use of AI technologies is crucial to build trust and mitigate potential risks. The future of artificial intelligence holds immense promise. AI-driven advancements are likely to reshape industries, create new job opportunities, and enhance our quality of life. As technology progresses, AI systems will become increasingly sophisticated, and capable of autonomous decision-making and reasoning. Moreover, the fusion of AI with other emerging technologies like the Internet of Things (IoT) and robotics will unlock novel applications and possibilities. However, it is crucial to navigate the path ahead mindfully. Striking a balance between automation and human involvement will be essential to ensure that AI augments human capabilities rather than replacing them. Investing in AI education and upskilling will prepare the workforce for the changing job landscape, emphasizing human skills that complement the strengths of AI systems
Machine learning is considered a subset of artificial intelligence. It’s a branch of artificial intelligence that involves developing algorithms and statistical models that enable computers to learn from data, without being explicitly programmed. In other words, machine learning algorithms enable computers to automatically improve their performance on a specific task through experience or training data. The process of machine learning involves feeding large amounts of data to a computer algorithm, which then analyzes and identifies patterns and relationships within the data. These patterns and relationships are then used to make predictions or decisions about new data. There are three types, unsupervised, supervised, and reinforcement.
1. Supervised Learning.
Where the algorithm is trained on labelled data to make predictions or classify new data. In supervised learning, the machine is provided with a dataset that has both input and output data, and the algorithm learns to recognize patterns and make predictions based on the input data. For example, a supervised learning algorithm can be trained to recognize images of cats and dogs by learning to differentiate between labelled images of cats and dogs.
2. Unsupervised Learning
Where the algorithm aims to identify patterns in unlabeled data without any predefined outcomes. In unsupervised learning, the machine is not provided with labelled data but is instead left to identify patterns and relationships in the data on its own. In doing so, it can automatically identify clusters of similar cases within a data set. Once different clusters are identified they can be visualized or further analyzed. This type of learning is often used for clustering data or identifying outliers. For example, an unsupervised learning algorithm can be used to group customers based on their shopping habits without any prior knowledge of their preferences.
3. Reinforcement Learning
Where the algorithm learns to take actions in an environment to maximize a reward or minimize a penalty. In reinforcement learning, the machine learns through trial and error by taking actions and receiving feedback based on the outcome of those actions. The goal is to maximize a reward or minimize a penalty over time. For example, a reinforcement learning algorithm can be used to teach a robot how to navigate a maze by learning which actions lead to rewards and which lead to penalties.
Neural networks:
Artificial neural networks are inspired by the connectivity of neurons and structures found within the human brain. Theres input layers, multiple hidden layers, and an output layer. Each layer has multiple artificial neurons (all connected) these are typically connected to the neurons in the next layer (feed-forward network). The number of neurons in the input layer is typically equal to the number of input features. The number of neutrons in the output layer is typically equal to the number of classes in case of a classification problem. So by increasing the number of hidden layers and neurons in each layer, the network has an increasing ability to solve more complex non-linear problems. However, as this happens it becomes more difficult to optimize and train the model. Artificial neural networks with many hidden layers are often called deep neural networks.
Deep neural networks
Deep neural networks(DNNs) are a subset of machine learning. One of its main purposes in precision medicine is image analysis.
These deep learning techniques are used for medical technology, and it currently holds great promise in medical image classification, image quality improvement, and segmentation. Deep neural networks are particularly good for image analysis.
Artificial Intelligence And Precision Medicine
One of the current problems in the field of precision medicine is accessibility. The number of patients who receive the most appropriate care/treatment is very low. Instead of developing treatments for populations and making the same medical decisions based on a few similar physical characteristics among patients, medicine has shifted towards precision medicine. And evidentiary AI has been the key to this shift. As the national institutes of health described it, there is no precision medicine without AI.
Using AI To Bridge The Gap
Like I mentioned earlier the problem with pharmacogenomics is the ethics behind it and the concern about genetic privacy. And as you know artificial intelligence has so much potential. So I looked into how you could create a system that uses AI and patient information to help doctors personalize prescriptions.
How It Would Work
A pharmacogenetic test uses a sample of saliva or blood. The sample goes to a lab, which runs tests on the genes that determine how the body will handle some medicines. This is where AI comes in. Instead of having a doctor read the genes and determine whether or not the patient may be resistant to drugs, have a high metabolism, or anything of that sort, the computer will help them do that. So instead of allowing the doctors to read the genes only the computer will understand the DNA, its condons, what proteins they have and how that affects the medicine they take.
And once it reads the DNA and proteins, it will give the doctor that information, once again, not allowing the doctors to see the patient's genes. Here’s a small sample of what the code currently looks like, it can currently only translate it into amino acids. However, I’m working on a version that adds the next step and translates it into proteins. And after that, the computer can make recommendations all by itself.
There are many companies and research teams that are working on amazing things in the field of pharmacogenomics:
- Myriad Genetics, Inc. is a molecular diagnostics company that specializes in genetic testing and personalized medicine. It was founded in 1991 and is based in Salt Lake City. They specialize in genetic testing and personalized medicine, including pharmacogenomics. They offer tests to identify how a patient’s genetic profile may influence their response to specific drugs.
- Illumina, Inc. is a leading biotechnology company based in San Diego, California, USA. They are known for their work in genetic sequencing and genomics. They don’t offer direct consumer services, they provide the tools and technology used by many other companies and research institutions in the field of pharmacogenomics.
- Thermo Fisher offers a range of genetic analysis and sequencing technologies, which are widely used in pharmacogenomic research and clinical testing.
- Qiagen is a biotechnology company that specializes in providing sample and assay technologies for molecular diagnostics, applied testing, academic and pharmaceutical research, and various other life science applications. The company was founded in Germany in 1984 and has its headquarters in Venlo, Netherlands, with offices and facilities worldwide. They provide molecular diagnostics and sample preparation technologies that are used in pharmacogenomic research and clinical testing.
- PerkinElmer, Inc. is a global corporation that provides a wide range of products and services in the fields of life sciences, diagnostics, and applied services. Their diagnostics and applied services are related to pharmacogenomics and personalized medicine.
- Invitae offers genetic testing services, including pharmacogenomic testing, to help guide personalized treatment decisions.
- Mayo Clinic is one of the world's most renowned medical institutions. They offer pharmacogenomic testing services to healthcare providers and patients. This vastly improves the quality of the medicine they are providing to everyday patients.
Thanks for reading, and if you're interested in personalized medicine and want to connect feel free to reach out to me my email is: srajaya02@gmail.com
