Anesthetic agents often produce varying effects across patients due to multiple factors, including genetic variations. These genetic differences influence key aspects of anesthesia, such as drug metabolism, sensitivity, duration, and side effects, potentially limiting the predictability and safety of clinical anesthesia practices. Recent advancements in pharmacogenomics have highlighted the impact of genetic variations on the pharmacokinetics and pharmacodynamics of anesthetic drugs. This review investigates the potential for utilizing genetic biomarkers to personalize anesthesia protocols, optimizing dosing based on individual genetic profiles. Key genetic factors, including polymorphisms in cytochrome P450 enzymes, butyrylcholinesterase (BCHE), and opioid receptor genes (OPRM1), are reviewed for their influence on the metabolism and efficacy of commonly used anesthetics. Furthermore, genetic profiles are explored for their impact on anesthetic drug efficacy, recovery times, and the likelihood of adverse events. A comprehensive analysis of pharmacogenetic studies outlines the potential for genetic testing to improve precision in anesthesia practice, reduce perioperative complications, and enhance patient outcomes. Establishing standardized pharmacogenomic protocols is necessary for ensuring the safe and effective implementation of personalized anesthesia in clinical settings, offering the promise of more predictable and individualized care.

The role of anesthesia providers in clinical practice has evolved significantly with advancements of anesthetic techniques and protocols. Today, they are expected to not only provide optimal intraoperative surgical care, but also pre and post operative management of coexisting conditions that may interfere with pain management [1]. Importantly, the focus of anesthesia has expanded to include post surgical outcomes, which are now central to clinical research involving new drugs and techniques [1]. This shift highlights the growing emphasis on personalized anesthesia care, tailoring interventions to each patient’s unique needs, including their comorbidities. By understanding patients at a genetic level, providers can enhance the safety and effectiveness of anesthetic care while also reducing costs.  The critical role of anesthesia in surgical procedures, coupled with anesthesiologists’ expertise in managing patients from preoperative to postoperative phases, allows them to address coexisting diseases, mitigate postoperative complications through judicious preoperative medication use, and tailor anesthetic and analgesic techniques for optimal outcomes [1]. Patient responses to anesthesia can vary significantly due to several factors including comorbid conditions, age, body mass index, and gender. However, it is important to consider how genetic differences play a role in how the patient will respond to treatment with certain drugs. When using anesthesia in the elderly, considerations must be made regarding the complex physiological response that occurs after administration that is further compounded by age-related declines in renal clearance [2]. Through integration of genetic data, providers can alter anesthetic treatment of different groups with different comorbidities more effectively and safely. Obesity is another factor that can alter the response to anesthesia as it represents a multifaceted state of physiological dysfunction driven by intricate interactions among genetic, environmental, and hormonal factors [2]. Formulating a genetic framework for how anesthetics interact with different comorbidities and patient populations will push the field of anesthesia forward, creating more favorable patient outcomes. Pharmacogenomics play a critical role in this evolution while studies have begun to isolate genes that would allow guidance when considering anesthesia response. Specifically, this includes genes involved in drug metabolism, drug targets, and neural pathways [3]. Leveraging information regarding potential involved genes allows providers to optimize treatment, improve patient safety, and enhance outcomes across diverse populations. How an individual’s genetic makeup influences their reaction to drugs and other medical therapies is the basis of pharmacogenomics. The ability to personalize medical management at the molecular level has largely included oncologic treatment and other gene therapies; however, pharmacogenomics should be considered when administering anesthesia to patient populations. A unique personalization to anesthesia can optimize drug selection and dosing, while minimizing the risk of adverse events, especially for patients with multiple comorbidities where anesthesia may pose a greater risk than harm [3]. Pharmacogenomic integration into anesthesia clinical practice allows providers the opportunity to analyze how metabolism, drug targets, and pain pathways play critical roles in the types of drugs providers choose and which drugs provide the most benefit to patients [3]. An integrated method of delivering care is powerful and should be further investigated as a way to reduce postoperative complications as well as reduce adverse anesthesia events. The integration of pharmacogenetics into anesthetic planning provides an exciting opportunity for the practice of anesthesia. Complex comorbid conditions coupled with differences in response to anesthesia prove that the effort to further investigate how technology is utilized to explore a safer and more effective delivery of anesthesia is not futile. Sensitivity to drugs and how quickly they are metabolized all rely on exploring genetic variations among patients for more personalized anesthesia protocols [3]. By embracing pharmacogenetics, anesthesia providers can pave the way for more precise, patient-centered care.

Role of Genetics is Anesthetic Responses

Pharmacogenetics has long been recognized for its potential to improve clinical medicine, though more clinical evidence is needed before widespread implementation. By considering an individual’s genetic makeup alongside other patient-specific factors, pharmacogenetics aims to tailor treatments to achieve the most effective outcomes [4]. Genetic variations in drug-metabolizing enzymes can significantly influence how a person responds to medications. These enzymes either activate or break down drugs in the body. For example, prodrugs require conversion to their active form by specific enzymes, but a genetic deficiency in these enzymes can prevent this conversion, rendering the drug ineffective. Conversely, drugs that are administered in their active form don’t need conversion, but if an enzyme responsible for breaking them down is deficient, the drug may accumulate in the body, potentially leading to toxicity and severe side effects [4]. Therefore, understanding these genetic variations is crucial for optimizing drug therapy, minimizing adverse effects, and advancing personalized treatment in clinical practice. Cytochrome P450 enzymes are present in many cells throughout the body, with the highest concentration in liver cells. These enzymes chemically catalyze the oxidation and metabolism of a broad range of external and internal substances. As a result, CYP450 enzymes play a crucial role in defending the body against foreign compounds and are responsible for converting drugs and toxins into more reactive intermediates [5]. The cytochrome P4503A subfamily (CYP3A), has the highest content and the broadest range of substrates. This subfamily includes four members: CYP3A4, CYP3A5, CYP3A7, and CYP3A43. CYP3A5 is only expressed in about 20% of the population affected by the gene phenotype [5]. CYP3A5*1 alleles are associated with higher levels of CYP3A5 expression, with the highest occurrence in African ethnic groups. In contrast, CYP3A5 has little effect on drug metabolism in Caucasians. In individuals expressing CYP3A5, it constitutes more than half of the total CYP3A subfamily in the liver, playing a key role in drug metabolism. Genetic variations in CYP3A5 activity across different ethnic groups are likely a major factor contributing to individual and ethnic differences in CYP3A-mediated drug metabolism [5]. Therefore, understanding the genetic variations in CYP3A5 expression is key for tailoring drug therapies to individual and ethnic differences, ultimately improving treatment outcomes and minimizing adverse effects. Exploring additional key enzymes can further illuminate the genetic factors influencing anesthesia outcomes. Butyrylcholinesterase (BChE) is an enzyme that breaks down esters, particularly acetylcholine. A study evaluating changes in butyrylcholinesterase activity during surgery after midazolam administration initially observed an early decrease in cholinesterase activity and a slight reduction in BChE mRNA expression [6]. However, at later stages, both AChE and BChE activity, along with their mRNA expression levels, increased. This suggests that BChE expression increases over time. Researchers also found that midazolam altered the di-methylation of H3K4 and its interaction with BCHE, leading them to hypothesize that midazolam may affect the cell’s epigenome by influencing histone-modifying enzymes [6]. Changes in these enzymes could significantly impact the metabolism of anesthesia by altering the expression of genes involved in drug metabolism. For instance, histone modifications that affect the expression of CYP450 enzymes may alter the speed at which anesthesia is metabolized, influencing both drug efficacy and safety. Epigenetic modifications could also impact genes related to drug receptors and transporters, potentially making patients sensitive to anesthetic agents by changing how drugs interact with their targets or are distributed in the body. Building on this, investigating opioid receptors for genetic variations can provide deeper insights into how these modifications may alter pain perception and responsiveness to anesthetic agents. Modulation of pain receptors has garnered significant interest, particularly concerning pain perception. Genetic variants associated with serotonin (5-HT), catecholamine, and opioid signaling are of particular relevance in this context. Opioid signaling is regulated by the μ-opioid receptor, a protein encoded by the OPRM1 gene [7]. A specific genetic variation (SNP) known as rs1799971 involves a change from an A to a G in the DNA sequence, altering the amino acid at position 40 from asparagine to aspartic acid. This modification removes a glycosylation site, which may affect the receptor’s functionality and its interaction with opioids. Research has demonstrated that individuals with the G allele in this genetic variation (OPRM1 A118G) tend to have higher pressure pain thresholds, indicating reduced sensitivity to pain [7]. Given that opioid signaling is crucial in how patients perceive pain and respond to opioids, commonly used in anesthesia, understanding whether a patient carries the G allele can significantly influence anesthetic drug selection and dosing. A genome-wide associated study was performed by a group to analyze three candidate SNPs: rs13093031, rs12633508, and rs6961071, for differences in severity of pain before and after analgesic administration. Individuals with the G/G genotype for the rs13093031 and rs6961071 SNPs exhibited reduced sensitivity to the analgesic effects of fentanyl [8]. The results of this study suggest that analgesic sensitivity may be predicted by identifying genotypes associated with genetic polymorphisms.  Pharmacogenetics holds great promise for enhancing personalized medicine, particularly in anesthesia and pain management. Understanding genetic variations in drug-metabolizing enzymes, such as those within the cytochrome P450 family, butyrylcholinesterase, and opioid receptors, provides crucial insights into how individuals respond to anesthetic agents. As more genetic data is collected and analyzed, it becomes increasingly possible to tailor anesthetic and analgesic treatments to an individual’s unique genetic makeup. 

Pharmacokinetics and Pharmacodynamics of Anesthetics in Different Genetic Profiles

Genetic differences can significantly affect the absorption, distribution, metabolism, and excretion (ADME) of anesthetic agents, altering how the body processes these drugs. Variations in cytochrome P450 enzymes, such as CYP2D6, CYP3A4, and CYP2C9, are especially important in determining how anesthetics are metabolized. For example, patients who are poor metabolizers of CYP2D6 may experience prolonged effects or insufficient pain relief with medications like codeine, which depends on CYP2D6 for conversion to morphine. On the other hand, individuals who are rapid metabolizers may experience higher toxicity due to excessive active metabolite formation. Additionally, genetic differences in enzymes and transporters involved in drug metabolism can affect the clearance rates of anesthetic agents, impacting their duration and potency. Recognizing these factors allows clinicians to adjust anesthetic regimens to match a patient’s genetic profile, ensuring drugs are administered safely and effectively [9]. Understanding how genetic factors influence these processes is crucial for optimizing anesthetic care and minimizing risks associated with drug administration.

Genetic factors play a key role in determining the dosing, onset time, and duration of anesthetic agents. Variations in the activity of butyrylcholinesterase, for example, can significantly impact the use of neuromuscular blockers like succinylcholine. In patients with reduced enzyme activity, succinylcholine is metabolized more slowly, leading to prolonged paralysis, which can complicate surgery and recovery. Similarly, local anesthetics such as lidocaine and bupivacaine exhibit variability in how quickly anesthesia takes effect and how long it lasts, depending on genetic differences in ion channel function. These variations highlight the need for personalized dosing strategies based on genetic factors to ensure optimal therapeutic outcomes and minimize complications. Tailoring anesthetic dosing to an individual’s genetic makeup is essential for providing the safest and most effective anesthesia experience [10]. Adjusting anesthesia protocols to account for genetic variability enhances patient safety and ensures that anesthetics are administered with the right balance of efficacy and duration. Anesthetic agents exert their effects by interacting with specific molecular targets, and genetic variations in these targets can influence how patients respond to anesthesia. For example, mutations in the GABAAreceptor, which mediates sedation and amnesia, can alter the body’s response to anesthetics such as propofol and etomidate. Some genetic mutations in the receptor subunits can reduce the immobilizing effects of these drugs while leaving their hypnotic properties intact. Understanding how these genetic variations impact the action of anesthetics allows anesthesiologists to select the most appropriate drugs and dosages for each patient, ensuring both safety and efficacy in anesthesia management [11]. Additionally, variations in ion channels such as TASK-1 and TREK affect how volatile anesthetics like isoflurane produce their hypnotic and immobilizing effects. Recognition of these variations enables the development of more precise anesthetic strategies, optimizing care by tailoring drug selection and dosing to the patient’s genetic profile. Genetic differences also contribute to a wide range of clinical responses to anesthetic agents. For instance, polymorphisms in genes such as OPRM1, can affect how patients respond to opioid analgesics. Some individuals may require higher doses of opioids for adequate pain relief, while others may experience heightened sensitivity and increased risk of side effects, such as respiratory depression. Similarly, genetic variations that affect the metabolism of sedatives like benzodiazepines can result in prolonged sedation or reduced drug effectiveness. Patients with neuromuscular disorders, particularly those with mutations in the ryanodine receptor gene (RYR1), face additional risks with neuromuscular blocking agents like succinylcholine. These variations in clinical response highlight the need for personalized approaches to anesthesia, ensuring that the drugs chosen and the doses administered are appropriate for each patient’s genetic makeup [9]. By incorporating pharmacogenetic insights, anesthesia management can be tailored to individual responses, enhancing the safety and effectiveness of anesthesia across diverse patient populations. Certain genetic conditions can predispose patients to adverse reactions during anesthesia, which makes early identification and preparation essential. Down syndrome is often associated with physical characteristics like macroglossia and cervical spine instability, which complicate airway management and increase the likelihood of respiratory complications during anesthesia. Patients with muscular dystrophy or other neuromuscular disorders may experience severe reactions to neuromuscular blocking agents like succinylcholine, which can cause prolonged paralysis, hyperkalemia, and arrhythmias. Craniofacial anomalies in syndromes like Treacher Collins syndrome can lead to airway obstructions that make intubation difficult and increase the risk of respiratory failure. Furthermore, mitochondrial diseases can impair the body’s ability to metabolize anesthetic drugs, requiring careful selection of drugs and adjusted dosages to avoid complications. Identifying these risks early in the preoperative process allows clinicians to adjust anesthetic plans, minimize complications, and ensure the patient’s safety during the procedure [12]. Proactive screening and individualized anesthetic management strategies can greatly reduce the risk of adverse events in these patient populations. A variety of anesthetic drugs present particular risks for patients with genetic disorders. Succinylcholine, for example, is known to cause complications such as hyperkalemia, malignant hyperthermia, and prolonged muscle paralysis, especially in patients with muscular dystrophy or malignant hyperthermia susceptibility. Patients with genetic disorders are at a higher risk for severe muscle breakdown, respiratory failure, and arrhythmias, necessitating avoidance or strict monitoring of succinylcholine. Similarly, patients with liver disease, which may be present in some genetic disorders, may have a reduced capacity to metabolize anesthetic agents. As the liver is responsible for the biotransformation of many drugs, patients with liver dysfunction may need lower doses of anesthetics to avoid prolonged sedation or drug buildup. Additionally, medications prescribed for genetic disorders, such as β-blockers, procainamide, and lithium, can interact with anesthetics, enhancing the effects of neuromuscular blocking agents or altering cardiovascular responses. Recognizing these interactions enables more informed decision-making and ensures that patients with genetic conditions receive the safest and most effective anesthesia care [12]. A comprehensive understanding of genetic drug interactions allows for better preparation and a more tailored approach to anesthesia management, optimizing patient outcomes and minimizing risks. Overall, genetic variations play a fundamental role in how anesthetic agents are processed and how they affect the body, influencing everything from drug metabolism to clinical responses and susceptibility to adverse events. Tailoring anesthetic management based on pharmacogenetic insights allows for safer, more effective care. By understanding the genetic factors that influence drug metabolism, response to anesthetics, and the risk of complications, clinicians can improve patient outcomes and minimize adverse reactions. Evolving knowledge in pharmacogenetics helps to further refine anesthesia practice, making personalized anesthesia care a reality that enhances patient safety and therapeutic efficacy. As genetic research continues to provide deeper insights into anesthetic responses, it is likely to lead to even more targeted and effective anesthesia practices in the future.

Personalized Medicine in Anesthesia

Personalized medicine is a healthcare approach that tailors treatments to a patient’s unique characteristics. Instead of relying on universal treatment plans, this method incorporates genetics, lifestyle choices, medical history, and environmental influences to each patient. By analyzing a patient’s genetic makeup, anesthesiologists can gain insights into how each patient may respond to specific anesthetic agents, enabling them to choose the most effective treatment options while minimizing potential side effects. Personalized medicine can optimize efficacy and minimize adverse effects by tailoring dosing to individual needs. In doing so, routine genetic testing before procedures and treatment could identify genetic variations that could influence drug metabolism thus, enhancing patient outcomes. Most drugs undergo multiple phases of metabolism, with the CYP450 enzyme commonly assisting in phase 1 of this process [13]. Higher levels of CYP450 increase drug metabolism, which could be complicated by reduced drug efficacy, increased dosing requirement, and potential treatment failure. In contrast, lower levels of CYP450 could lead to slower rates of drug metabolism because medications are slowly eliminated from the body, with advantages to achieving therapeutic effects. Pain medications, for example, require sustained therapeutic effects for those with decreased tolerance to pain. Most pain medications like hydrocodone, codeine, oxycodone, methadone, tramadol, and fentanyl are metabolized by the different forms of the CYP450 family, while morphine, hydromorphone, and oxymorphone are not subject to the first phase of metabolism [13]. As such, patients will benefit from care tailored to their genetic variations that increase or decrease the availability of metabolic enzymes. While patient safety is at the core of medicine, accounting for improved outcomes are added benefits. Advanced monitoring devices should be utilized to manage medications based on physiologic responses. By monitoring patients’ reactions, anesthesiologists can identify early signs of complications, allowing for immediate interventions and reducing risks of side effects and recovery time. Drug-to-drug interactions should be accounted for since anesthesiologists use a variety of pharmacological agents concurrently to stabilize patients. For example, propofol is a widely used anesthetic agent; however, it also demonstrates a concentration-dependent inhibition of CYP2B1 and 1A1 enzymes [14]. This inhibition can pose risks when co-administering drugs metabolized by CYP450 enzymes, such as cardiac medications like quinidine, amiodarone, and nifedipine, which are frequently used to manage blood pressure during sedation [14]. Furthermore, genetic factors can further complicate metabolism. Individuals with lower levels of HDL, especially those with Apolipoprotein-A1 (APO-A1) Deficiency, also have a higher risk of having low levels of BChE [15]. BchE are beneficial enzymes that metabolize anesthetics, including neuromuscular blocking agents, succinylcholine and mivacurium, as well as ester local agents, procaine and tetracaine. Improved monitoring data with a compilation of diverse population groups will be useful in tailoring treatment outcomes to each patient while decreasing adverse effects. While increased extubation success rates, early mobilization, decreased complications, and effective pain management are strong predictors of the length of hospital stays, addressing psychological responses to health burdens could improve overall health outcomes in anesthesia. Although anesthesiologists are well equipped to identify and influence these positive outcomes, it is essential to acknowledge that achieving positive outcomes can be more challenging in certain patients. Individuals with specific genetic markers associated with serotonin or dopamine regulation may be more susceptible to developing anxiety and or depression [16]. Health-related stress combined with an individual’s genetic predisposition can lead to varying psychological responses. Understanding these genetic influences allows for integrating care among anesthesiologists and other healthcare providers to implement targeted psychological support strategies. 

A comprehensive understanding of genetic variations is vital to optimizing anesthetic responses, as these variations play a pivotal role in determining how patients metabolize and react to anesthetic agents. Polymorphisms in key enzymes, such as cytochrome P450 isoforms, directly impact the metabolism of various anesthetics, while variations in butyrylcholinesterase and opioid receptor genes can alter drug sensitivity, efficacy, and recovery times. Genetic factors contribute to significant interindividual differences in anesthetic outcomes, influencing both the onset of anesthesia and the risk of adverse effects. Despite the promising potential of pharmacogenomic applications in this field, more extensive research is required to fully delineate the complex genetic mechanisms underlying these responses. Large-scale, well-designed studies will be crucial in identifying clinically relevant genetic markers and refining their use in personalized anesthesia protocols. Furthermore, the development of standardized pharmacogenomic guidelines is essential to ensure safe and effective implementation across diverse patient populations. As research continues to evolve, the integration of genetic testing into anesthetic practice holds the promise of transforming anesthesia from a one-size-fits-all approach to a tailored, precision-based model, ultimately enhancing patient safety, reducing perioperative risks, and improving overall clinical outcomes.

Genomic and bioinformatic advancements have transformed the ability to predict individual patients’ response to anesthesia. Genetic variants, such as CYP2D6, which influence the  metabolism of drugs like codeine and tramadol, can now be identified rapidly using next-generation sequencing [17]. Using bioinformatics tools like the Pharmacogenomics Knowledgebase (PharmGKB), anesthesiologists can make genetically driven decisions to increase the predictability and safety of anesthesia [18]. With this knowledge, anaesthesiologists can optimize anesthesia by choosing drugs and adjusting dosages tailored to the genetic constitution of each patient. Machine learning models and, specifically, computational models are required to convert genetic data and clinical information into patient-specific anesthesia. These models can integrate genetic information, such as variations in BChE that affect muscle relaxant metabolism, with clinical data to predict drug responses. For instance, predictive models assessing the impact of BChE variants can help anesthesiologists anticipate prolonged neuromuscular blockade in patients with atypical expressions, allowing for necessary adjustments to improve patient safety [19]. Incorporating predictive models into clinical practice enhances the precision of anesthesia care, reducing risks and improving outcomes. Pharmacogenomics must work alongside other personalized medicine to optimize anesthesia care. Factors such as age, body mass index, and co-morbidities should be combined with genetic information such as variants in OPRM1, which predict opioid sensitivity and influence postoperative pain management [20]. By integrating pharmacogenomic, physiological, and clinical biomarkers, anaesthesiologists can deliver holistic, personalized care with optimal efficacy and safety in a broad range of patient populations.

Equality plays an essential role in driving personalized anesthesia care. Underrepresentation of certain groups in genetic studies can limit the generalizability of pharmacogenomic findings, potentially leading to disparities in care [21]. Initiatives like the “All of Us Research Program” address this issue by building an all inclusive database of the population’s genetic variation. This ensures pharmacogenomic advancements in anesthesia benefit the entire population [22]. Expanding access to these technologies will enable customized anesthesia protocols that reduce risk and maximize outcomes for everyone. By integrating genomic insights, predictive models, and equitable access to pharmacogenomic advancements, the field of anesthesiology is assured to deliver more effective and truly personalized care for diverse patient populations.