Precision anesthesia and pharmacogenomics: a scoping review of personalized drug response

Background: Anesthetic agent selection and dosing have historically relied on empirical models without taking into account inter-individual variability in drug response, leading to adverse drug reactions (ADRs). Precision medicine, specifically leveraging pharmacogenomics (PGx), offers a paradigm shift toward personalized anesthesia, enhancing efficacy and safety.

Methods: This scoping review synthesized literature from 2015 to 2025, using systematic database searches and Artificial Intelligence (AI)-powered tools, to identify the most extensively studied genetic variants impacting the pharmacokinetics and pharmacodynamics of common perioperative medications.

Results: Key genetic variants in metabolic enzymes, transporters, and receptors significantly influence anesthetic outcomes. Examples include Reduced Metabolism/Prolonged Effects: Variations in CYP3A4/5 and POR alter midazolam metabolism, risking prolonged sedation. CYP2B6*6 is associated with decreased clearance of propofol and ketamine. BChE deficiency causes significantly prolonged paralysis with succinylcholine. Altered Efficacy/Increased Dose Requirements: OPRM1 118 A > G (G-allele) carriers show a blunted response to morphine, requiring higher doses. CYP2D6 ultra-rapid metabolizers (UMs) can have reduced efficacy of ondansetron and risk toxicity from pro-drugs like codeine and tramadol. Pathogenic mutations in RYR1 and CACNA1S identify patients susceptible to Malignant Hyperthermia from volatile anesthetics. Drug-Drug Interactions (DDIs): PGx overlaps with chronic medications (e.g., antidepressants, beta-blockers) that inhibit CYP2D6, creating a phenoconversion risk that functionally mimics a Poor Metabolizer (PM) phenotype, drastically altering opioid efficacy.

Conclusions: PGx holds transformative potential for the field of anesthesiology by offering actionable insights for drug selection and dose adjustment to mitigate ADRs and optimize pain control.

1 Introduction

The practice of anesthesiology is intrinsically high-stakes, relying on the predictable performance of potent pharmacological agents across a heterogeneous patient population. For decades, the dosing and selection of anesthetic medication have been guided by empirical, “one-size-fits-all” models, often resulting in significant inter-individual variability in drug response and adverse outcomes. Contemporary perioperative medicine is undergoing a paradigm shift toward personalized anesthesia, driven by the need to enhance efficacy and reduce risks (1). This approach tailors anesthetic regimens to a patient's genetic profile, comorbidities, and physiological parameters, aiming to minimize complications, optimize pain management, support Enhanced Recovery After Surgery (ERAS) protocols, ultimately improve patient safety and satisfaction (1–4). The concept of genetics influencing drug response emerged in the mid-20th century, but the practical development of precision medicine began with the Human Genome Project (HGP) in the 1990s (5). The HGP's completion in 2003 provided a framework for identifying millions of single nucleotide variants (SNVs), leading to the rise of pharmacogenomics (PGx) (5, 6). PGx studies genetic variations affecting drug pharmacokinetics and pharmacodynamics, particularly in genes encoding metabolic enzymes like the cytochrome P450 family, drug transporters, and receptors, to predict drug efficacy and toxicity (1, 7). In anesthesia, this has profound implications: adverse drug reactions (ADRs) to crucial agents like opioids and intravenous anesthetics are frequently linked to molecular variants in genes such as COMT, ABCB1, OPRM1, CYP2B6, CYP3A4, and CYP2D6 (8). These variants could affect drug metabolism, sensitivity, and duration of action, directly impacting the predictability and safety of clinical care. Currently, the U.S. Food and Drug Administration (FDA) recognizes the potential utility of pharmacogenomic information in prescribing certain medications and recommends its inclusion in drug labels (9).

The variability in individual drug response is further amplified by drug-drug interactions (DDIs). Anesthesiologists routinely administer multiple pharmacological agents simultaneously, often alongside a patient's chronic non-anesthetic medications. These interactions may inhibit or induce shared metabolic enzymes, altering a patient's metabolizer phenotype (10). Integrating PGx data is thus critical, as a known genetic poor metabolizer (PM) status may be drastically magnified or, conversely, offset by the patient's concomitant medication profile. Precision medicine requires accounting for both genetic predispositions and environmental (drug) influence, along with the integration of PGx results with other clinical factors such as age, existing comorbidities, and current medications to avoid suboptimal patient outcomes (1). This scoping review aims to highlight the most extensively studied genes influencing the metabolism and effects of common anesthetic drugs, evaluate their impact on clinical practice, and pinpoint the primary barriers to effectively integrating pharmacogenomics into contemporary anesthesia care.

2 Methods

This scoping review was prepared by conducting a comprehensive literature review across PubMed and Google Scholar to identify widely studied genes associated with common drugs used in perioperative care. Systematic searches were performed for articles published between 2015 and 2025. The search strategy included combinations of keywords such as “personalized anesthesia”, “pharmacogenomics”, and common anesthetic medications including “propofol”, “opioids”, “midazolam”, “rocuronium”, “local anesthetics”, “dexmedetomidine”, and “ketamine”. Artificial Intelligence (AI)-powered tools, such as Google Gemini and Research Rabbit, were employed to uncover interconnected and relevant publications. The search was restricted to human studies. Inclusion criteria prioritized randomized controlled trials, genome sequencing studies, meta-analyses, and systematic reviews investigating specific genetic variants affecting the pharmacokinetics and pharmacodynamics of anesthetic medications, with emphasis on actionable evidence supported by robust, replicated findings. The initial search was supplemented by citation tracking and snowballing techniques to uncover additional relevant studies, including those performed prior to 2015, ensuring a thorough analysis of the available research. Exclusion criteria included case reports, conference abstracts, and animal studies. Screening and data extraction were performed to identify the key genes and genetic variants, focusing on summarizing the mechanisms by which these variants affect drug response, including their impact on drug metabolism, receptor binding, and clinical outcomes in the perioperative setting. The flow diagram depicted in Figure 1 summarizes the study selection process.

Figure 1

Figure 1. Flow diagram of the methodology. Created with Google Gemini.

3 Results

The literature search identified a comprehensive body of evidence detailing the impact of pharmacogenomic variants on perioperative drug response. The most extensively studied genes associated with common anesthetics, regional anesthetics, and analgesics are summarized and categorized in Table 1. These variants primarily affect drug pharmacokinetics (metabolism and transport) and pharmacodynamics (receptor affinity), leading to significant inter-individual variability in clinical outcomes. Specific genetic variants, such as the CYP2B6*6 allele, the OPRM1 118 A > G SNV, and the butyrylcholinesterase (BChE) A variant, were consistently identified, underscoring their critical influence on drug action, which ranges from altered clearance and prolonged effects to increased dosing requirements and a heightened risk of adverse drug reactions (ADRs) such as prolonged paralysis, oversedation, or inadequate analgesia.

Table 1

Table 1. Pharmacogenomic targets of common perioperative anesthetic agents.

3.1 Pre-operative medication and pharmacogenomics

Pre-operative medication planning represents a critical window for pharmacogenomic intervention. Pre-operative pharmacogenomics focuses on optimizing sedation and anxiolysis, primarily through benzodiazepine. Variants in CYP3A4 and CYP3A5, such as CYP3A4*22 and CYP3A5*3 significantly alter midazolam metabolism, leading to prolonged sedation in patients with reduced enzyme activity. For example, patients with the POR*28 variant exhibit a 45% reduction in midazolam metabolism among CYP3A5 expressors, necessitating dose adjustments to prevent oversedation (1, 11, 12). Recent studies emphasize the role of CYP2C19 in diazepam metabolism, where PM may require dose reductions to avoid excessive sedation, requiring preemptive dose reductions (13).

Pharmacogenomics is also essential in regional anesthesia, as genetic variations modulate both the effectiveness and the risk side effects, including Local Anesthetic Systemic Toxicity (LAST). The most critical safety determinant is the SCN5A gene, which encodes the cardiac Nav1.5 sodium channel, the primary off-target site for local anesthetics. Pathogenic variants in SCN5A, also associated with Brugada syndrome, can impair cardiac conduction and are associated with a heightened susceptibility to LAST due to exaggerated cardiac effects (14). The duration and systemic toxicity of local anesthetics are further divided by their chemical class and underlying metabolic pathways. Amide-type local anesthetics (e.g., lidocaine, bupivacaine) rely on hepatic metabolism by CYP1A2 and CYP3A4. PM patients have reduced clearance of medication, which increases the risk systemic toxicity, particularly during continuous regional infusions (15). Furthermore, variants in the efflux transporter ABCB1 can alter the transport of local anesthetics across the blood-brain barrier, influencing the severity of central nervous system (CNS) neurotoxicity by modulating the drug's access to the central nervous system (16).

3.2 Intra-operative medication and pharmacogenomics

Intra-operative pharmacogenomics influences the choice and dosing of induction agents, neuromuscular blockers, and maintenance anesthetics. Propofol, a cornerstone of induction, exhibits variable metabolism due to CYP2B6 (e.g., rs3745274, rs2279343, rs3211371) and UGT1A9 polymorphisms, affecting induction doses and recovery times (17, 18). Similarly, ketamine, used for induction and maintenance, shows reduced clearance with the CYP2B6*6 allele, prolonging its effects, particularly in chronic pain patients (19). For etomidate, mutations in GABAA receptor subunits, such as GABRB2 rs121909230 (c.794A > G), can modify efficacy, requiring vigilant monitoring (20). Neuromuscular blockers (NMBs) like succinylcholine and rocuronium are highly sensitive to genetic variations. BChE variants (e.g., Atypical rs1799807) cause pseudocholinesterase deficiency, leading to prolonged paralysis with succinylcholine, while SLCO1A2 variants (e.g., rs2306283) increase rocuronium dose requirements (21–23). Other known variants related to rocuronium are ABCB1 and SLCO1B1, which have been shown to prolong the clinical duration of the drug (24).

Unlike many intravenous agents, inhalational anesthetics are almost exclusively eliminated by the lungs, meaning their effects generally do not depend on common polymorphisms in genes encoding metabolic enzymes or drug transporter proteins; however, they are still influenced by ion channel variants (KCNK9/TASK-1, KCNK2/TREK), which can alter their hypnotic effects (18, 25, 26). Genotyping surgical candidates can identify patients at risk for malignant hyperthermia (MH) due to RYR1 or CACNA1S variants, allowing providers to avoid triggering agents like volatile anesthetics or succinylcholine (27). While preemptive RYR1 screening is not currently endorsed by the Malignant Hyperthermia Association of the U.S. (MHAUS) for the general population, it is strongly advised if there is a pre-test probability for MH-susceptibility (28).

Dexmedetomidine, a widely used α2-adrenergic agonist for perioperative sedation and analgesia, exhibits both pharmacokinetic and pharmacodynamic variability driven by genetic factors. Its metabolism, handled primarily by CYP2A6 and UGT enzymes, is impacted by polymorphisms such as the CYP2A6 rs28399433 variant, which results in lower metabolic efficiency (29, 30). Furthermore, genetic influence extends to its cardiovascular effects: the GABRA2 rs279847 polymorphism has been significantly associated with the pronounced degree of heart rate decrease observed in some patients (29, 31).

3.3 Post-operative medication and chronic pain management

Inadequate Postoperative Pain (POP) management and postoperative nausea and vomiting (PONV) can lead to increased healthcare costs, prolonged length of stay, higher morbidity, and the development of chronic pain Syndromes (32, 33). PGx holds the promise of enhancing pain management by preemptively predicting an individual's reaction to a particular analgesic before treatment begins (32). Post-operative pain management relies heavily on opioids, where pharmacodynamic genes, particularly OPRM1 polymorphisms, play a critical role. The μ-opioid receptor, encoded by OPRM1, is highly studied, and the OPRM1 rs1799971 (c.118A > G; p.N40D) variant has been shown to result in a blunted response to morphine, consequently requiring higher doses to reach analgesia (32, 34). Notably, this variation is also linked to a lower incidence of nausea (33, 35). Conversely, OPRM1 304G variant has been found to enhance intrathecal fentanyl analgesia in women (1). Catechol-O-methyltransferase (COMT) gene modulates central pain signals; patients homozygous for the Val/Val genotype exhibit higher pain sensitivity whereas Met/Met patients are more opioid-sensitive.

Patients with the COMT rs4680 (p.Val158Met) are less opioid-responsive, consequently require higher opioid doses and are thus paradoxically at a higher risk of toxicity when escalated doses are used (10, 36). Pharmacokinetic variability mediated by CYP2D6 variants further complicate opioid therapy. This gene is highly polymorphic, translating to enzymatic activities ranging from poor PMs to ultra-rapid metabolizers (UMs) (37, 38). This greatly affects pro-drugs like codeine and tramadol, with UMs at risk of fatal toxicity and PMs at risk of inadequate pain relief (10, 32). The Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines recommend avoiding codeine in CYP2D6 UMs to prevent overdose (38). Since analgesic effect is a result of the cumulative impact of multiple genes, a multifactorial model integrating genetic data with biological, physical, and social factors is necessary. Given the costs, initially targeting high-risk patients with poor pain control or those at risk of chronicity represents the best practice for minimizing costs and complications (32, 39–41). For instance, for chronic pain, CYP2D6-guided therapy has shown improved pain scores in intermediate metabolizers and PM (39, 40). This also highlights the importance of employing multimodal pain management strategies to achieve effective pain control while minimizing the risks associated with opioids.

Additionally, ondansetron, used for antiemetic prophylaxis, shows reduced efficacy in CYP2D6 UM, increasing the risk of PONV. Genetic testing for CYP2D6 can guide the selection of alternative antiemetics, such as tropisetron, for these patients (42).

3.4 Drug-drug interactions

DDIs in anesthesia are exacerbated by genetic variations in shared metabolic pathways and thus, are a potential risk in the perioperative setting (Table 2). Propofol, for example, demonstrates a concentration-dependent inhibition of CYP2B1 and CYP1A1 enzymes, posing risks when co-administered with certain cardiac drugs that are also metabolized by these CYP450 enzymes. Examples of affected medications are quinidine, amiodarone, and nifedipine, potentially leading to prolonged effects or cardiovascular instability in polymedicated patients (43). Similarly, beta-blockers, which CYP2D6 metabolizes, can cause hypotension and bradycardia in presence of propofol (44). Carbamazepine, a common anticonvulsant, upregulates SLCO1A2, reducing rocuronium's duration of action (45). DDIs can also alter a patient's metabolizer phenotype, a phenomenon known as phenoconversion (46). For instance, CYP2D6-mediated interactions are crucial for drugs like codeine and tramadol, which are converted into active metabolites. When co-administred with CYP2D6 inhibitors such as fluoxetine and paroxetine, patients can mimic PM phenotypes, reducing efficacy (47). Furthermore, genetic factors can intersect with non-genetic ones: individuals with lower levels of HDL, often associated with Apolipoprotein-A1 (APO-A1) deficiency, also have a higher risk of having low levels of BChE, significantly increasing their risk of prolonged paralysis from NMBs (48–50). The integration of pharmacogenomics with other clinical factors, such as age, existing comorbidities, and current medications, is imperative to provide optimal patient outcomes. Since DDI can functionally alter a patient's metabolizer phenotype, accounting for both inherited genetic variability and drug-induced enzymatic change is essential for precision medicine and minimizing potential complications (46).

Table 2

Table 2. Pharmacogenomic overlap and drug-drug interactions (DDIs) between common chronic medications and anesthetic agents, highlighting the importance of preemptive dose adjustment in the polymedicated surgical patient.

3.5 Clinical practice, anesthesiologist perspective, and barriers to implementation of pharmacogenomics

Even though the benefits of PGX and precision medicine are clear, their integration into clinical anesthesia faces several clinical and logistical barriers, including the limited availability of rapid, high-quality genotyping tests, high costs, and a lack of reimbursement. In the U.S., clinical laboratories are required to be accredited, and concerns about false positives/negatives persist due to test design limitations (57). Anesthesiologists often cite an insufficient understanding of PGx, a lack of integration into electronic medical records (EMR), and inadequate clinical decision support as major hurdles (9). Additionally, providers have reported that the most common reason for not considering PGx results, even when available, was forgetting to access the information, indicating that the information was not yet part of their usual clinical workflow (2). Other barriers cited by providers include: “testing is not worth the financial costs” (17%), “my awareness about the existence of pharmacogenomics information is lacking” (13%), and “there is insufficient pharmacogenomics information for most drugs” (13%) (9).

Another hurdle in the implementation of PGx is that certain ethnic and racial groups are underrepresented in genetic studies, which can limit the generalizability of current PGx findings and lead to disparities in care (10). To overcome these barriers and successfully implement PGx into routine clinical and perioperative practice, a robust clinical decision support (CDS) infrastructure is necessary (58). This system must act as a bridge, translating accurate and comprehensive raw genetic data into meaningful clinical actions. It must seamlessly integrate standardized resources, such as the CPIC guidelines, to provide anesthesiologists with immediate, actionable recommendations for drug selection and dosing (41, 58). Initiatives like the “All of Us Research Program” are vital for providing the large-scale, diverse genetic datasets to standardize protocols and address disparities in genetic data representation (59). Lastly, training healthcare professionals to recognize DDI and inter-individual variability, while simultaneously developing AI-driven predictive models, remains critical for the successful and widespread implementation of personalized medicine (57, 59).

3.6 Limitations

While this review provides a comprehensive synthesis of pharmacogenomic targets in anesthesia, several limitations inherent to the current manuscript and the nature of a scoping review must be acknowledged. First, because a scoping review methodology was employed rather than a formal systematic review or meta-analysis, this paper does not provide a quantitative assessment of the effect size or the level of evidence for each genetic association described. Furthermore, the reliance on existing literature introduces a significant geographic and ancestral bias; the majority of the data synthesized herein originates from Caucasian populations, which limits the applicability of our conclusions to more diverse global populations who may harbor unique, uncharacterized alleles in genes.

The manuscript is also constrained by the lack of robust, large-scale prospective clinical trials specifically investigating the impact of preemptive genotyping on hard surgical outcomes, meaning that many of the clinical recommendations discussed are extrapolated from pharmacokinetic models or smaller observational cohorts. Additionally, this review focuses primarily on single-gene-drug interactions, potentially oversimplifying the highly polygenic nature of anesthetic response, which is governed by complex gene-gene and gene-environment interactions that are not yet fully elucidated in the literature.

Finally, while we address the critical role of phenoconversion and drug-drug interactions, the current manuscript cannot provide a dynamic tool for real-time clinical application, as static genetic data often fails to capture the fluctuating physiological and pharmacological state of the perioperative patient.

4 Conclusion

Pharmacogenomics holds transformative potential for the field of anesthesiology by enabling tailored anesthetic regimens that enhance safety and efficacy while reducing ADRs and costs. This scoping review highlights key genetic variants, such as those in CYP2B6, CYP3A4, OPRM1, and BChE, that could influence the metabolism and action of common anesthetic drugs, offering actionable insights for personalized care. However, barriers such as limited access to rapid genotyping, high costs, and insufficient provider training hinder robust implementation. By addressing these challenges through standardized protocols, EMR integration, and AI-driven decision support, the implementation of pharmacogenomics can potentially revolutionize anesthesia practice and ensure precision medicine benefits all patients.

Author contributions

OE-L: Conceptualization, Investigation, Supervision, Visualization, Writing – original draft, Writing – review & editing. SN: Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing. RW: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. Figure 1 was created with Google Gemini.

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