American Scientist

One-size-fits-all clothes have always been a source of contention in fashion. Although this approach might work for most people, many individuals will still be excluded from wearing such a garment. Likewise, medical professionals have long known that individuals vary in how they respond to standard treatments. So, why are we still treating our medicines this way?

The goal of drug development is to bring a successful candidate molecule to the market, and on average, this process costs $1.5 billion (data from 2009–2018). Due to the extensive clinical tests and lengthy approval process required by the U.S. Food and Drug Administration (FDA), it can take 10 or more years for a new drug to become available to patients. Although this process keeps our medicines safe and effective, it forces companies to put their time and effort into one candidate molecule, because the cost of development is so high. Therefore, diversity in clinical trials has been lacking, as pharmaceutical companies continue to use white men as the default standard. (See “Eliminating Sex Bias in Biomedical and Clinical Trials,” November 13, 2018.) In fact, on average 77 percent of participants in a clinical trial are white, and women of childbearing potential were only required to be in clinical trials as of 1993, reversing a prior 1977 guideline that excluded them. African American participants account for only around 5 percent of participants in clinical trials.

But designing medicines for the average patient excludes many individuals. The “average person” in the world is actually a 28-year-old Chinese man, so the patient demographic in clinical trials does not even capture the most average person on Earth. This bias leaves significant disparities for women, different races and ethnicities, and people with comorbidities. Researchers are currently trying to reverse this trend and include more diversity in clinical trials. While the current demographic is excellent news for Caucasian men, it is not acceptable for the rest of the world.

A huge factor in the approval of medications is the drug’s metabolic profile. Drug metabolism refers to the way in which a drug is broken down and cleared from the body. During this process, biochemical changes are made to the initial drug that allow the body to process it and eventually excrete the drug. Some of these changes can result in toxic metabolites which cause adverse reactions in humans. Enzymes in the liver carry out many of these reactions. There are many known variants of these enzymes, and many are associated with your genetics, which can cause you to react differently to some medicines.

A family of enzymes known as cytochrome P450 (CYP) that contribute to the diversity in genetics are mainly expressed in the liver. This family of enzymes accounted for the main route of metabolism in 52 percent of all pharmaceuticals approved from 2006–2015. There are many genetic variants of CYP enzymes, and some are highly pharmacogenetic, such as CYP2C19, CYP2C9, and CYP2D6. Essentially, when you have a certain variant, you might have different (more or less) enzyme activity. If a drug is metabolized through that enzyme, then you will have a different response as compared to someone with a “normal” variant. A facet of personalized medicine involves tailoring the dose of the medication to an individual's enzyme activity. Additionally, some of these variants result in nonfunctional enzymes, where a person will not be able to process a medicine that uses that enzyme in its metabolism, so the patient is at a higher risk for an adverse event. For example, up to 14.5 percent of African American individuals have a nonfunctional CYP2D6 enzyme, and therefore will not achieve adequate pain relief from the analgesic codeine, which is metabolized through this enzyme. Additionally, factors outside of your genetics, such as age, sex, race, health status (smoking, cancer, or having COVID-19), and body size, can all affect your body’s ability to metabolize a drug.

Health care and drug development are both moving away from the “one-size-fits-all” model—but there is still a long road ahead. Clinics in some hospitals are beginning to adapt personalized medicine to their medical practices, and this area of drug development is also being studied rigorously in academia. (See “Big Data and the Individualized Patient,” May 29, 2015.) This approach considers all factors of an individual when making medication and dosing decisions: giving the right patient, the right dose, at the right time.

Analyzing the genetics of a patient at key places in the genome is currently at the heart of personalized medicine. We can genotype with blood or saliva and perform next generation sequencing (NGS), which will provide the sequence of amino acids in your personal DNA. When clinicians know a patient’s genetics, they can make more informed decisions about what medicines might make successful treatments, versus medicines that might cause adverse reactions.

Nongenetic factors can also play a role in individual variation in drug metabolism. Clinicians can perform phenotyping tests, which assess a patient’s ability to metabolize drugs by dosing a “probe substrate.” When the enzyme that metabolizes a drug is known, the patient’s drug metabolism can be gauged based on how they metabolize a probe. The probe substrate typically has only a mild pharmaceutical effect and is well tolerated by the patient but is metabolized by the same enzyme. These tests can be critical for medicines with a narrow therapeutic index, meaning a very small range of doses can provide a therapeutic effect to the patient. There is high variability between patients in the way they metabolize drugs, which is a key reason why “one-size-fits-all medicine” will exclude individuals.

Personalized medicine will help people live longer, healthier lives. However, we are still lacking inclusion in clinical trials that will make this technology available for all. Change is on the horizon. In April 2022, the FDA released a guidance document outlining their plans to improve the diversity of clinical trials by increasing enrollment of historically excluded groups. Additionally, the U.S. House of Representatives recently introduced an act which would require increased diversity in clinical trials, as well as actions for retaining patients and training for the investigators leading the trials. We have the technology to implement personalized medicine widely but need patient data from historically excluded groups in medicine. Researchers and clinicians need to work toward capturing the diverse patient population globally to make personalized medicine a reality, and equitable for all.