Using polygenic risk scores to identify people who respond better to drugs
George Busby, Allelica CSO & Co-Founder
We have previously reported how polygenic risk scores (PRS) can be used to identify people at high genetic risk of disease, paving the way for their use as tools for the prevention of common, chronic disease.
But PRSs can also be effective in other situations. For example, pharmaceutical companies can benefit from a range of PRS applications; from super powering clinical trials by identifying people at higher risk of disease to identifying people who might be responders to specific drugs.
In this article we’ll discuss how PRSs have been used by pharmaceutical companies in their clinical trials to identify groups of people in whom drugs work better. Given the potential of this information to save failed trials, which both accelerates the identification of valid drugs and reduces costs, we argue that it is now time for PRSs to be routinely used across the pharmaceutical industry to build better drugs.
Linking genetic variation with drug response
People respond differently to therapeutics and we now know that genetic variation plays a role. This is the concept behind the field of pharmacogenomics, often abbreviated as “PGx,” which is the study of how the genome influences an individual’s response to drugs. It combines pharmacology and genomics to analyze how our genetic makeup affects how we respond to drugs.
Pharmacogenomics aims to optimize drug therapy by considering patients’ genotypes to achieve maximum efficiency with minimal adverse effects. This field correlates DNA mutations with pharmacokinetic, pharmacodynamic, and immunogenic endpoints to tailor drug treatments based on genetic variations. By utilizing pharmacogenomics, healthcare providers can move away from the traditional one-size-fits-all approach to drug therapy towards precision and personalized medicine, enhancing treatment outcomes and reducing adverse drug reactions.
Examples of pharmacogenomic effects
Pharmacogenomics plays a crucial role in tailoring drug treatments based on individual genetic variations, leading to more personalized and effective healthcare interventions. Examples of key PGx interactions include:
- Alirocumab and Coronary Artery Disease: Alirocumab is an antibody that blocks PCSK9, a protein that lowers the liver’s ability to remove LDL-cholesterol from the bloodstream. Individuals with a high PRS for CAD who take alirocumab benefit from a larger reduction in risk of major adverse cardiovascular events compared to those without a high CAD PRS. This association positions CAD PRS as an independent tool for risk stratification and precision medicine.
- Antiviral Drug Abacavir (Ziagen) and HIV Patients: Pharmacogenomics is used to test HIV-infected patients for a genetic variant before prescribing the antiviral drug abacavir. This genetic testing helps identify individuals who are more likely to have adverse reactions to the drug, allowing for personalized treatment approaches.
- Antidepressant Drug Citalopram (Celexa) and Depression: Genetic variations have been identified that influence the response of depressed individuals to citalopram, a selective serotonin reuptake inhibitor (SSRI). Clinical trials are underway to determine if genetic tests predicting SSRI response can improve patient outcomes, showcasing the potential of pharmacogenomics in mental health treatment.
- Breast Cancer Drug Trastuzumab (Herceptin): This therapy is effective for women with breast tumors exhibiting a specific genetic profile that leads to overproduction of a protein. Pharmacogenomic testing helps identify patients who will benefit from trastuzumab, demonstrating the importance of genetic information in optimizing cancer treatment.
Polygenic risk scores and pharmacogenomics
What about polygenic variation and pharmacogenomics? A 2022 study described the development of a PGx PRS for drug response prediction using a novel PRS-PGx method. This research showed significant improvements in prediction accuracy and the ability to capture drug response variations, emphasizing the utility of PRS in predicting drug outcomes.
A recent systematic review delved into the use of polygenic scores in pharmacogenomics research, shedding light on the construction and performance of polygenic models for predicting drug outcomes. Incorporating pharmacogenetic variants into PRSs lead to significant associations with drug outcomes, with many studies demonstrating improved predictions beyond clinical models alone.
Additional studies have explored the interplay between polygenic risk and drug response. These include the identification of an association between a PRS and resistance to first-line treatment (typically clozapine) in people with schizophrenia; the demonstration, now almost 10 years ago, that individuals with a high PRS for coronary artery disease showed the greatest benefit from statin use in terms of overall cholesterol reduction; and a growing understanding that PRSs can be used to predict antidepressant treatment response for people with major depressive disorder.
These studies collectively provide compelling evidence that polygenic risk scores can be effectively employed to identify responders to specific drugs. By leveraging genetic information through PRS, researchers and pharmaceutical companies can enhance drug response predictions, tailor treatments to individual genetic profiles, and advance precision medicine practices for more personalized and effective healthcare interventions.
Rescuing clinical trials by identifying sub-groups of individuals
These results point towards a significant relationship between genetic variation and drug response. So while PRSs have the potential to be of immense utility to the pharmaceutical industry, the truth is that PRSs are rarely employed.
There are at least two clear ways in which PRSs generated from the genotypes of clinical trials participants can be used to potential save clinical trials:
- Using PRS as an exploratory biomarker: Biomarkers are increasingly being used in early-phase clinical trials. Exploratory biomarkers can be used to identify subgroups of individuals in a trial with differential treatment response, which can then be used to adjust placebo and treatment arms and potentially increase the statistical power to identify an effect between the two arms. PRSs are a perfect tool for exploratory biomarker analysis. For example a PRS for the disease for which a drug is being trialled can be used to segment the population and adjust for genetic predisposition of disease. In addition, given the ease at which a range of PRSs can be generated from the underlying genetic data, it is possible to search through a wide selection of PRSs to find one that accurately stratifies trial participants and can rescue the trial.
- Developing a new PRS for trial drug response: A second approach is to develop a PRS for drug response using trial data matched to genetic data from participants from a failed clinical trial. The PRS can then be used as a biomarker to identify subgroups of individuals where the drug will work better in future trials. For example, by quantifying treatment success (survival) in the trial cohort and then developing a PRS-based clinical biomarker to predict response in this cohort. The PRS can then be used as a biomarker in a second clinical trial that would account for underlying genetic differences in drug response across the cohort.
In both cases, the FDA has pathways for incorporating this new information into the clinical trial regulatory process, thereby ensuring that all of the necessary steps to integrate the PRS into clinical trials are taken, for example through the design of confirmatory trials and bridging studies.
