Pharmacogenomics in anaesthesia, recent advancements

As the old saying goes, “one man’s meat is another man’s poison.” A medication may be highly effective in one individual, ineffective in another, and potentially harmful in a third. Drug handling by the body varies significantly between individuals, and genetic makeup is one of the major factors contributing to this variability. Pharmacogenomics, a branch of genomic medicine, examines how genetic variation influences pharmacologic response. It is directed toward improving precision in pharmacotherapy through individualized treatment strategies.^{1, 2}

The foundations of pharmacogenomics can be traced as far back as 1902 when the concept of “chemical individuality” was introduced by Sir Archibald Garrod.³ The term pharmacogenetics was later introduced by Friedrich Vogel in the 1950s. The 1950s and 1960s also marked discoveries related to genetic variants of pseudocholinesterase and malignant hyperthermia.^{4, 6} The completion of the human genome project in 2003, drove the evolution faster and since then researchers have identified genetic basis that affect many drugs.¹³

Anesthesia is strongly influenced by pharmacogenomics, with several key genetic pathways involving commonly used anesthetic agents such as propofol, ketamine, codeine, succinylcholine, mivacurium, sevoflurane, and ondansetron. Alterations in these pathways can result in clinically significant adverse outcomes including prolonged apnea, malignant hyperthermia, respiratory depression, and inadequate anesthesia.^5,6,8,9

Table 1: Pharmacogenomic associations in some common agents used in Anesthesia.^5,6,7,8,9,11

Agent	Gene(s)	Variant/Phenotype	Effect	Clinical concern	Recommendation
Succinylcholine, Mivacurium	BCHE (Butyrylcholinesterase)	Atypical, K, silent variants	Decreased pseudocholinesterase activity	Prolonged neuromuscular blockade, apnea	Use alternative agents
Volatile agents (sevoflurane, desflurane, isoflurane), succinylcholine	– RYR1(Ryanodine receptor Type 1)- CACNA1S (Calcium Voltage-Gated Channel Subunit Alpha 1S)- STAC3 (SH3 and cysteine rich domain 3)	Malignant hyperthermia variants	Dysregulated calcium release	Malignant hyperthermia	Avoid volatiles and succinylcholine. Have dantrolene ready.
Propofol	– CYP2B6 (Cytochrome P450 Family 2 Subfamily B Member 6)- UGT1A9 (UDP glucuronosyltransferase Family 1 Member A9- CYP2C9	CYP2B6*6 allele	Decreased metabolism and clearance	Deeper sedation, delayed emergence	Caution with prolonged infusion
Ketamine	CYP2B6, CYP3A4	CYP2B6*6	Decreased clearance	Prolonged psychomimetic effects	Caution with use as repeated bolus or infusion
Midazolam	CYP3A4CYP3A5	CYP3A4*22CYP3A5*1	3A4*22 Decreased clearance3A5* Increased metabolism	Adverse drug effectReduced effect
CodeineTramadol	CYP2D6	Poor metabolizer (PM)Ultra-rapid metabolizer (UM)	PM: no conversion to morphineUM: excessive conversion	PM: no analgesiaUM: respiratory depression	Avoid in children, use morphine or hydromorphone instead
Morphine, Fentanyl, Hydromorphone	OPRM1 (Opioid Receptor Mu 1)	A118G polymorphism	Decreased opioid receptor sensitivity	Higher dose requirement
Ondansetron	CYP2D6	Ultra-rapid metabolizer (UM)	Increased drug clearance	Reduced antiemetic efficacy	Use alternatives
Metoprolol	CYP2D6	PMUM	PM: BradycardiaUM: reduced therapeutic effect

Recent pharmacogenomic advancements in anesthesia

Current pharmacogenomic advancements in anesthesia aim to improve precision anesthesia by tailoring anesthetic administration to individual patients. In principle, this can be achieved by integrating precise genetic information with other patient variables such as anthropometric values, age, and sex. A recent systematic review suggests that pharmacogenomic testing may assist in individualizing propofol dosing during total intravenous anesthesia (TIVA) based on genotypes in UGT1A9, CYP2B6, and CYP2C9.⁹ This review demonstrated that UGT1A9 polymorphisms exert a greater influence on propofol pharmacokinetics than the CYP enzymes, whose effects on clinical outcomes appear to be comparatively modest.

Propofol is primarily metabolized in the liver, with approximately 70% conjugated to propofol glucuronide by UGT1A9, and 29% hydroxylated to 2,6-diisopropyl-1,4-quinol and 4-hydroxypropofol by CYP2B6 and CYP2C9.^5,9 The underlying hypothesis of this research is that combining genetic variation with existing target-controlled infusion (TCI) algorithms may enhance the precision of propofol dosing during TIVA. Two genetic polymorphisms were identified as influencing propofol metabolism. CT heterozygotes of UGT1A9 exhibited lower propofol clearance, lower dose requirements, and prolonged emergence times, whereas CC homozygotes demonstrated increased clearance and faster emergence.⁹

The clinical relevance of these findings lies in their potential application toward refining infusion pump algorithms and the future development of point-of-care genetic testing devices. Propofol-TIVA TCI models such as the Marsh and Eleveld models are based on pharmacokinetic modeling that adjusts drug delivery according to predicted distribution and clearance to achieve desired plasma and effect-site concentrations. Enhancing these algorithms by incorporating patient-specific genetic data may allow infusion techniques to become not only age- and weight-adjusted but genetically individualized.⁹

Several limitations affect the practical application of pharmacogenomic-guided anesthesia. For instance, metabolism of agents like propofol is influenced by hepatic blood flow, concurrent medications, renal clearance, and cardiac output, in addition to genetic variation.⁹

Another significant limitation is the lack of readily available point-of-care genetic testing devices. Genetic testing becomes especially relevant when malignant hyperthermia (MH) is suspected. Current diagnostic approaches may involve DNA screening combined with confirmatory in-vitro contracture testing (IVCT), which is invasive and technically laborious. Furthermore, DNA-based testing frequently identifies variants of uncertain clinical significance, creating challenges regarding which variants to test, how to interpret results, and how to integrate this information into clinical decision-making.^12,13

Future directions

Despite these limitations, the future of pharmacogenomics in anesthesia remains promising. The emergence of point-of-care (POC) pharmacogenomic testing, exemplified by the Genedrive CYP2C19 ID Kit for clopidogrel pharmacotherapy with an approximate turnaround time of one hour, provides a strong foundation for optimism regarding the development of POC devices for anesthetic pharmacogenes.^14,15 In parallel, the growing field of personalized anesthesia aims to integrate genetic information, biomarkers, physiologic parameters, medical history, and artificial-intelligence-based analytics to construct truly individualized anesthetic plans.^10,11 These developments collectively represent a paradigm shift toward precision anesthesia and patient-specific pharmacologic optimization. 

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References

1. https://www.genome.gov/genetics-glossary/Pharmacogenomics . Retrieved on December 07, 2025
2. https://www.cdc.gov/genomics-and-health/pharmacogenomics/index.html . Retrieved on December 07, 2025
3. Burgio, G. R. (1986). Inborn errors of metabolism? and ?chemical individuality? two ideas of Sir Archibald Garrod briefly revisited 50 years after his death. European Journal of Pediatrics, 145(1–2), 2–5. https://doi.org/10.1007/BF00441842
4. Somogy, A. (2008). Evolution of pharmacogenomics. Proceedings of the Western Pharmacology Society, 51, 1–4. https://doi.org/info:doi/
5. Mikstacki, A., Zakerska-Banaszak, O., Skrzypczak-Zielinska, M., Tamowicz, B., Prendecki, M., Dorszewska, J., Molinska-Glura, M., Waszak, M., & Slomski, R. (2017). The effect of UGT1A9, CYP2B6 and CYP2C9 genes polymorphism on individual differences in propofol pharmacokinetics among Polish patients undergoing general anaesthesia. Journal of Applied Genetics, 58(2), 213–220. https://doi.org/10.1007/s13353-016-0373-2
6. Jhun, E. H., Apfelbaum, J. L., Dickerson, D. M., Shahul, S., Knoebel, R., Danahey, K., Ratain, M. J., & O’Donnell, P. H. (2019). Pharmacogenomic considerations for medications in the perioperative setting. Pharmacogenomics, 20(11), 813–827. https://doi.org/10.2217/pgs-2019-0040
7. Cornelius, B. W., & Jacobs, T. M. (2020). Pseudocholinesterase Deficiency Considerations: A Case Study. Anesthesia Progress, 67(3), 177–184. https://doi.org/10.2344/anpr-67-03-16
8. Beebe, D., Puram, V., Gajic, S., Thyagarajan, B., & Belani, K. (2020). Genetics of malignant hyperthermia: A brief update. Journal of Anaesthesiology, Clinical Pharmacology, 36(4), 552–555. https://doi.org/10.4103/joacp.JOACP_360_19
9. Gerstman, M. D., Zhang, R. K., Ho, K. M., Kirkpatrick, C. M. J., Riedel, B., & Somogyi, A. A. (2025). Impact of genetic variations on the pharmacokinetics, dose requirements, and clinical effects of propofol: a systematic review. British Journal of Anaesthesia : BJA, 135(3), 594–607. https://doi.org/10.1016/j.bja.2025.05.036
10. Belani K. G. (2025). Precision medicine and the expanding perioperative role by Anaesthesiologists. Indian journal of anaesthesia, 69(8), 745–747. https://doi.org/10.4103/ija.ija_581_25
11. Zeng, S., Qing, Q., Xu, W., Yu, S., Zheng, M., Tan, H., Peng, J., & Huang, J. (2024). Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Frontiers in Medicine, 11, Article 1365524. https://doi.org/10.3389/fmed.2024.1365524
12. Miyoshi, H., Mukaida, K., Otsuki, S., Kido, K., Sumii, A., Ikeda, T., Xia, G., Noda, Y., Ishii, T., Kamiya, S., Narasaki, S., Niinai, H., & Tsutsumi, Y. M. (2025). Genetic Panel Testing for Malignant Hyperthermia in Japan: Discovery of Novel Variants and Clinical Implications. Genes, 16(8), Article 944. https://doi.org/10.3390/genes16080944
13. Saha, A. K., & Pinyavat, T. (2025). [Review of Genetic Testing for Malignant Hyperthermia Susceptibility—Threading the Needle in the Haystack]. Genes, 16(11), 1281. https://doi.org/10.3390/genes16111281
14. Burke, K. A., O’Sullivan, J., Godfrey, N., Sharma, V., Hilton, S., Wright, S. J., Greaves, N. S., Newman, W. G., & McDermott, J. H. (2025). Development and Validation of a Rapid Point-of-Care CYP2C19 Genotyping Platform. The Journal of molecular diagnostics : JMD, 27(3), 209–215. https://doi.org/10.1016/j.jmoldx.2024.12.001
15. Wu, A. H. B., Orahoske, C. M., Chen, G., Estabil, J., & Yeo, K. T. J. (2025). Pharmacogenomic Testing for CYP2C19 Variants among Stroke Patients Treated with Clopidogrel: Opportunity for the Clinical Laboratory? The Journal of Applied Laboratory Medicine, 10(4), 976–982. https://doi.org/10.1093/jalm/jfaf041

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About the author

Dr. Ifeanyi Ezeliorah earned a Bachelor of Dental Surgery (BDS) degree from the University of Nigeria and a Doctor of Dental Surgery (DDS) degree from the University of Toronto. He is currently a Postgraduate Year 1 (PGY-1) resident in Dental Anaesthesia at the University of Toronto Faculty of Dentistry. Dr. Ezeliorah is committed to continuous learning and to delivering high-quality patient care with a calm, empathetic, and patient-centered approach.