Applying pharmacometabolomic data for personalized medicine

April 03, 2024 - As healthcare evolves and science advances, the industry has increasingly focused on personalized medicine as an alternative to blanket, one-size-fits-all treatments. This approach is an attempt to address ongoing concerns around treatment resistance, adverse drug reactions, and dosing. Pharmacometabolomics is critical in precision medicine, presenting an additional tool for understanding treatment response.

Pharmacometabolomics contributes to multiple vital aspects of personalized medicine, including biomarker discovery, precision dosing, understanding mechanisms of action, drug reaction predictions, and treatment monitoring.

Comparing Pharmacogenomics and Pharmacometabolomics

Pharmacogenomics has been a critical tool for personalized medicine as it analyzes a patient’s genetic profile to assess how they may react to a specific pharmaceutical product or treatment; however, researchers in Pharmaceuticals point out that pharmacogenomics fails to acknowledge lifestyle factors and how they may impact treatment response.

“PGx alone seldom predicts or explains a substantial or predominant part of intra-patient and inter-patient pharmacokinetic and pharmacodynamic variability affecting the response to a drug treatment,” noted researchers in a Metabolites article.

The article states that only 20–40% of differences in drug response or metabolism are attributed to genetic factors, implying that 60–80% are due to other factors such as environment and lifestyle components.

Understanding pharmacogenomic limitations prompted the emergence of a new field: pharmacometabolomics. Pharmacometabolomics is defined as “an enhanced understanding of mechanisms for drug or xenobiotic effect and increased ability to predict individual variation in drug response phenotypes, based on using both baseline metabolic profiles prior to treatment and also effects of drug treatment over time.”

This field looks toward metabolic signatures, including metabolite levels such as amino acids, fatty acids, organic acids, and carbohydrates, to understand a patient’s response to a particular drug.

According to a chapter of Metabolomics - Methodology and Applications in Medical Sciences and Life Sciences, pharmacometabolomics approaches can encompass multiple kinds of omics data, including metabolomics, genomics, transcriptomics, and proteomics; however, its traditional definition focuses on metabolomics to understand a patient’s response to medications.

Despite the industry standard of developing drugs as a general treatment for all patients impacted by a particular disease, the healthcare sector has noticed how many factors can influence treatment response, including general health status, genetics, gender, dietary habits, smoking and alcohol consumption, exercise habits, and more.

“Among all ‘-omics’ approaches, metabolomics is the most effective of qualifying and quantifying the perturbation of metabolite concentrations under external and internal factors. Thus, joining metabolomics with other ‘-omic’ sciences (e.g., genomics) is essential for a comprehensive understanding of disease onset and pathogenesis and provides a better diagnosis and treatment,” noted the book.

There are two main categories of metabolomics. Untargeted metabolomics looks at all detectable metabolites in a biological sample and compares different groupings of samples (i.e., healthy vs symptomatic patient samples). In contrast, targeted metabolomics focuses on a specific metabolite. Generally, targeted metabolomics is more applicable in drug development processes.

Although there isn’t one singular technique or platform for quantifying all metabolite levels, researchers may use a combination of approaches in pharmacometabolomics, including Fourier-transform infrared spectroscopy, nuclear magnetic resonance spectroscopy, liquid chromatography–mass spectrometry, and gas chromatography–mass spectrometry.

Applications of Pharmacometabolomics in Personalized Medicine

Pharmacometabolomics has many applications in personalized medicine and can help providers and researchers choose the appropriate treatments for their patients. Using pharmacometabolomics early on can help increase the probability of finding an effective first-line treatment that works for each patient.

One example of pharmacometabolomic treatment in personalized medicine is its application in urologic treatments. According to a 2022 article in Pharmaceuticals, there are many available treatment options for urologic cancers, including chemotherapy, hormone therapy, targeted therapy, and immunotherapy; however, treatment resistance following long-term exposure threatens patient outcomes. Researchers note that using pharmacogenomic data is one way to identify the optimal treatment option for patients with urologic cancer. Still, this method fails to account for other factors where pharmacometabolomics can help determine the ideal treatment for patients.

Multiple aspects of personalized medicine can leverage pharmacometabolomics, including biomarker discovery, precision dosing, mechanism of action understanding, adverse drug reaction predictions, and treatment monitoring.