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Pharmacogenomics in Spaceflight

  • Michael A. SchmidtEmail author
  • Caleb M. Schmidt
  • Thomas J. Goodwin
Living reference work entry

Abstract

Pharmacogenomics is the study of how genes influence an individual’s response to medication. By extension, pharmacogenomics is the precise analysis of gene variants that influence the regulation of drug metabolism and the attendant development of therapeutic strategies. Whereas traditional pharmacokinetics and pharmacodynamics are applied to populations in order to understand the range of a drug’s effects, pharmacogenomics can be used to personalize drug therapy to an individual. In short, pharmacogenomics represents an emergent method available to tailor drug therapy to the individual astronaut, so that the drug countermeasure solution optimizes the chance for benefit (efficacy), while minimizing the chance for adverse events (safety). While the application of pharmacogenomics is progressing on Earth, there is presently almost no application of pharmacogenomics in space. This represents a substantial gap in our capability, but it also represents an opportunity to better enable humans to thrive in the space environment. The present review explores the fundamentals of pharmacogenomics, which includes examination of the genetic variants of the primary drug-metabolizing enzymes. It next briefly summarizes the limited evidence for how the space condition may influence these systems. The pharmacogenomic implications of the current ISS drug list are explored by example. A hypothetical design reference mission is proposed to illustrate how pharmacogenomics might be employed in the space environment. In general, we highlight how the application of intelligent clinical pharmacogenomics to astronauts can be used to guide the use of pharmaceuticals in space today, while the much-needed prospective pharmacogenomic research is conducted in parallel.

Keywords

Pharmacogenomics Pharmacometabolomics Personalized medicine Precision medicine Spaceflight Astronaut CYP450 Drug metabolism 

Introduction

The unique conditions of human spaceflight require substantial biological adaptation and a suite of countermeasures sufficient to meet those demands. As human exploration missions expand in difficulty, distance, and duration, these demands will only increase. Pharmacological management of basic medical conditions, as well as drug-based countermeasures forged for the unique conditions of space, will become increasingly important, as mission complexity grows. Conversely, as mission complexity grows, there will be diminishing tolerance for adverse events associated with countermeasures intended to provide benefit.

Though the space context is unique, the outcome of pharmaceutical use in astronauts is inherently similar to that of pharmaceuticals being used on Earth. These projected outcomes suppose that a single drug given to a population will yield a combination of the following outcomes, based on individual member responses. The drug may be:
  • Beneficial and nontoxic

  • Beneficial but toxic

  • Not beneficial but nontoxic

  • Not beneficial but toxic

Clearly, providing a drug solution to a mission team that is beneficial and nontoxic is the primary objective. Achieving this will require that we fully recognize the significant variability in drug metabolism that exists between individuals. The following review explores how pharmacogenomics may formally address these individual differences and support the provision of solutions that are beneficial and nontoxic for each astronaut. This discussion necessarily favors personalization. Because mission cohorts are small and operational complexity high, one could argue that astronauts are among the most compelling group to which personalization should be applied (Schmidt and Goodwin 2013). Before proceeding with this discussion, however, it is important to briefly explore the gaps in knowledge related to pharmaceuticals used in the spaceflight environment.

NASA Human Research Roadmap: Exploration Medical Capability Element

The NASA Human Research Roadmap has identified a series of knowledge gaps, surrounding the use of pharmaceuticals in space. These include, but are not limited to, the following (Antonsen et al. 2017):
  • We do not have detailed information about how medications are being used during spaceflight.

  • We do not have the capability to provide a safe and effective pharmacy for exploration missions (duration of exploration missions may exceed the expiration date of a medication).

  • Exposure of the medication in space may diminish its actives and produce novel by-products that may be toxic.

  • We do not know the extent to which spaceflight alters drug pharmacokinetics (absorption, distribution, metabolism, and excretion).

  • We do not know the extent to which spaceflight alters drug pharmacodynamics (the relationship between drug concentration at the site of action and the resulting effect).

  • We do not understand the wide variance in the individual astronaut response to drugs on Earth or in space.

Faced with the obstacle of access to in-flight medical care, limitations of vehicle space, time, and communications, NASA and other space organizations must prioritize which medical consumables are manifested for a given flight (referred to hereafter as the mission drug formulary) and which medical conditions will be addressed. In addition to advocating for expanded prospective research intended to partially address such gaps, NASA’s Human Research Program Exploration Medical Capabilities Element has recommended formalizing retrospective analyses, using the following resources (Antonsen et al. 2017):
  • Mercury, Gemini, Apollo, Space Shuttle, and ISS mission data

  • The NASA Lifetime Surveillance of Astronaut Health (LSAH)

  • NASA’s Life Sciences Data Archive (LSDA)

  • Integrated Medical Model predictive risk analysis

  • Integrated trade space analysis tools

  • International and collaborative spaceflight data

  • Occupational health tracking data

  • Dose Tracker experimental data

  • Medical Consumables Tracker experimental data

Pharmacogenomics represents a critical discipline that can also begin to partially address these gaps. First, pharmacogenomics can become a central element of the ongoing research that must be conducted in order to optimize the use of drugs in the space environment. Second, pharmacogenomics, while not yet fully developed as a clinical solution, does provide actionable insights that can be used to optimize the clinical use of selected drugs in spaceflight today. Before exploring these two foundational concepts, it is important to provide a brief overview of the ontology of pharmacogenomics, along with its central tenets.

Foundations of Pharmacogenomics

Pharmacogenomics refers to the study of drug exposure and/or response as related to variations in DNA and RNA characteristics (see the International Conference on Harmonization (ICH) E15 Definitions for Genomic Biomarkers, Pharmacogenomics, Pharmacogenetics, Genomic Data, and Sample Coding Categories). Specifically, pharmacogenomics is the study of gene variants that influence the regulation of drug metabolism. It broadly considers single-nucleotide polymorphisms, insertions, deletions, copy number variants, and other modifications to the genes involved in (1) Phase I biotransformation (CYP450), (2) drug receptors, (3) drug transporters, (4) Phase II conjugation enzymes, (5) enzymes governing the regeneration of Phase II conjugation agents (e.g., glutathione reductase, glutathione synthase, glutathione-S-transferase), and (6) coenzymes governing the conversion of xenobiotics (e.g., NAD and the enzyme NADPH-cytochrome P450 oxidoreductase).

From a practical standpoint, pharmacogenomics is a tool that enables the physician to more precisely tailor a drug to the individual’s specific genotype for the purpose of optimizing benefit and minimizing adverse events. When deriving the drug-metabolizing phenotype from the genotype for drug selection, one must consider that there are many converging variables that impact the reliability of the prediction. Thus, pharmacogenomics must be considered within the context of the greater biological complexity of individuals.

Pharmacometabolomics

Pharmacometabolomics examines the body’s complement of small molecule metabolites in an effort to more thoroughly characterize the changes in metabolic networks associated with administration of a drug. Pharmacometabolomics is considered complementary to genomic, epigenomic, transcriptomic, and proteomic (systems biology) approaches to understanding drug metabolism. It contributes to a comprehensive understanding of drug effects by taking into account both intrinsic and extrinsic contributions to interindividual variation in drug response (Beger et al. 2016).

Significant funding has been provided from the National Institute of General Medical Sciences (NIGMS) to the Pharmacogenomics Research Network (PGRN) and the Pharmacometabolomics Research Network (PMRN) in an effort to understand how genetic and metabolic data alone, or in combination, can inform about treatment outcomes and mechanisms that underlie variation in the treatment response. More than ten classes of therapies have been studied in patients to illustrate the concept and its generalizability in human studies for the purposes of refining the methodologies of precision medicine. Metabolome profiles (untargeted metabolic phenotyping) have been shown to provide insights about variation of response to antipsychotics, antidepressants, antihypertensives, antiplatelet therapies, statins, and other drugs (Kaddurah-Daouk et al. 2014). These efforts, along with other initiatives, have established the important linkage between pharmacogenomics and pharmacometabolomics. For the purpose of brevity and simplicity, the present discussion will be limited to pharmacogenomics (with the exception of the discussion of the small molecule pool involved in Phase II drug conjugation below).

Drug-Metabolizing Phenotype Based on Genotype

Historically, the interpretation of genotype/diplotype has been based on grouping genotypes into four categories. These include (1) poor metabolizer (PM), (2) intermediate metabolizer (IM), (3) extensive metabolizer (EM), and (4) ultrarapid (UM) metabolizer phenotype groups. The EM group has more recently been changed to normal metabolizer (NM).

Roughly 10 years ago, Gaedigk et al. introduced the activity score (AS), which was later adopted by the Clinical Pharmacogenetics Implementation Consortium (CPIC), as the basis for their drug-gene pair recommendations (Gaedigk et al. 2008). In this system, each allele is assigned a value of 0, 0.5, or 1. This corresponds to no function, decreased function, or normal function, respectively. With alleles with two or more gene copies, the value of the allele is multiplied by the number of gene copies. For instance, CYP450 2D6*1 × 2 gene is assigned a value of 2 to calculate the activity score. This is referred to as a binning system. Using this approach, the binning usually leads to six AS groups. These are AS = 0, 0.5, 1, 1.5, 2, and ≥ 3.

Gaedigk et al. have published a recent review (Gaedigk et al. 2018), reporting on the utility of this method over the past 10 years. They note that the concept of an “activity score” has gained broad acceptance, but they also assert that additional work is needed to further characterize the influences on interindividual variability in drug clearance and other factors that give rise to the drug response.

The Clinical Phenotype Associated with the Drug-Metabolizing Genotype

The clinical phenotype associated with drug metabolism is strongly dependent upon the number of variant alleles on one or more genes in an individual astronaut (Table 1). For example, possessing two variant alleles in a CYP450 enzyme results in loss of function. This is the poor metabolizer (PM) phenotype. Possessing multiple copies of the same DNA strand containing the wild-type CYP450 gene can result in gain of function. This is the ultrarapid metabolizer (UM) phenotype. A single-variant allele would yield an intermediate phenotype.
Table 1

Drug metabolism genotype and phenotype characteristics

Drug metabolism phenotype

Genotype and phenotype characteristics

Normal metabolizers (NM; formerly extensive metabolizers)

There are no variant CYP450 alleles on either one of the astronaut’s paired chromosomes

Have two active CYP450 enzyme gene alleles, resulting in an active enzyme molecule

Intermediate metabolizers (IM)

There is one active and one inactive CYP450 enzyme gene allele

The variant allele produces a “loss-of-function” enzyme, while the normal allele produces normal enzymes

May require lower dosage than normal, though pro-drugs may require higher dose

Poor metabolizers (PM)

Two variant alleles are present, resulting in two “loss-of-function” enzymes

Lack active CYP450 enzymes

Astronaut may suffer more adverse events at usual doses of active drugs due to reduced metabolism and increased concentrations

Astronaut may not respond to administered pro-drugs that must be converted by CYP450 enzymes into active metabolites

Ultrarapid metabolizers (UM)

Have 3 or more active CYP450 gene alleles

Rapidly metabolize the associated drug(s)

Astronaut may not reach therapeutic concentrations at usual, recommended doses of active drugs

Astronaut may suffer adverse events from pro-drugs that must be converted by CYP450 enzymes into active metabolites

May require higher doses of pro-drugs

In general, gain-of-function variants lead to increased drug clearance and lower drug concentrations. Loss-of-function variants lead to reduced drug clearance and increased drug concentrations. If the drug is a pro-drug (meaning the administered drug must be metabolically converted to its active form), the converse is true (Zanger and Schwab 2013). Codeine is an example of a pro-drug, which is activated by CYP4502D6 to morphine. The nuances of the codeine to morphine conversion are further described below.

Ultrarapid metabolizers experience rapid metabolism of an active drug, little or no drug effect, and poor clinical outcomes. Conversely, PM will experience slow (or no) drug conversion, high plasma levels of the drug, and increased adverse events. As a practical matter, the poor metabolizer (PM) and ultrarapid metabolizer (UM) phenotypes would deserve first-order attention among space medicine physicians. These are less common phenotypes, but the likelihood of poor outcomes is greater without an adjustment to the drug’s recommended usage. These are also the circumstances where the pursuit of alternate drugs addressing the same clinical endpoint can be useful.

Pharmacokinetics and Pharmacodynamics

Pharmacogenomics has a significant impact on pharmacokinetics and pharmacodynamics. However, this chapter will not attempt to address these very complex issues of drug metabolism in space. This will be left to other authors. The present focus is on the influence of individual genotype variations that govern and influence drug metabolism and how this knowledge can be used to advance pharmaceutical research in space and advance the individualized clinical care of astronauts.

Pharmacogenomics and Precision Medicine

One of the promises of pharmacogenomics applied to spaceflight is the inherent capacity to advance the practice of personalized medicine in astronauts. In this regard, it is helpful to clarify an evolving ontology. The terms personalized medicine, individualized medicine, and precision medicine are widely used to describe the use of advanced molecular profiling (and other methods of assessment) in an effort to understand the molecular landscape of an individual (though precision can also be applied to populations). These data are carefully interpreted, assigned biological meaning, and used to guide some form of tailored therapy.

According to the National Research Council, “personalized medicine” is an older term with a meaning similar to “precision medicine.” However, there was concern that the word “personalized” could be misinterpreted to imply that treatments and preventions are being developed uniquely for each individual. The NRC notes that, in precision medicine, the focus is on identifying which approaches will be effective for which patients based on genetic, environmental, and lifestyle factors. The Council therefore preferred the term “precision medicine” to “personalized medicine.”

In the spaceflight application, the goal is, indeed, personalization. However, precision is also sought. In the present review, the terms personalized medicine and precision medicine are used in a complementary fashion. This is done to recognize the fact that the approach is necessarily tailored to the person but that its precision is rooted in the use of molecular profiling to more accurately develop preventive measures, treatments, countermeasures, or training approaches tailored to the individual (Fig. 1).
Fig. 1

How pharmacogenomics may guide precision medicine in space. In the near future, drug prescribing for astronauts in space will rely upon pharmacogenomics. Each astronaut will be provided a drug-metabolizing enzyme profile, based on genotyping. The drug-metabolizing phenotype will guide the selection of drugs from each application area (analgesic, antibiotic, etc.), which will be tailored (personalized) to the astronaut. Phenotyping can guide when to use drugs as directed, when to use with caution, when to monitor closely, and when to select a drug with similar action that is metabolized via a different enzyme. Precision selection would be expected to optimize for favorable outcomes in the astronaut. This advent of personalized (precision) medicine must also consider a range of dynamics beyond genotype. But genotyping will form the foundation of future astronauts in space. (Image credit; Sovaris Aerospace)

General Principles of Pharmacogenomics

The functional ontology describing the biotransformation of xenobiotics (e.g., drugs) is generally described as taking place within three phases. These are designated as Phase I, Phase II, and Phase III (Table 2). Phase I is characterized by transformation via oxidation, reduction, or hydrolysis. Phase II is characterized by conjugation of the drug metabolite to a polar amino acid or other moiety. Phase III is characterized by transporters. The systems are enormously complex and are governed by a wide array of genetic variants.
Table 2

The primary enzymatic processes in Phase I, Phase II, and Phase III biotransformation of drugs and xenobiotics. (Adapted from Zhang et al. 2013)

Categories

Enzymes

Locations

Functions

Phase I

Aldo-keto reductase (AKR)

Carboxylesterases (CES)

Cytochrome P450 monooxygenase (CYP)

Epoxide hydrolase

Liver

Lung

GI tract kidney

Oxidize

Reduce

Hydrolyze xenobiotics and drugs

Phase II

γ-glutamylcysteine synthetase (GCL)

Glutathione peroxidase (GPX)

Glutathione S-transferase (GST)

Heme oxygenase 1 (HO-1)

Menadione reductase (NMO)

N-acetyltransferase (NAT)

NADPH quinine oxidoreductase 1 (NQO-1)

Peroxiredoxin (PRX)

Sulfiredoxin (SRXN)

Sulfotransferase (SULT)

Thioredoxin (Trx)

Thioredoxin reductase (TrxR)

UDP-glucuronosyltransferase (UGT)

Varies depending on the specific gene and its subfamily

Conjugate drug metabolites or endobiotics via:

 Acetylation

 Glucuronidation

 Glutathionylation

 Methylation

 Sulfation

Making them more hydrophilic or degrade heme or quinone

Phase III

Multidrug resistance associated protein (MRP)

Organic anion-transporting polypeptide 2 (OATP2)

P-glycoprotein (P-gp) transporters

Brain

Liver

Intestine

Kidney

Transport or excrete drug metabolites out of cells

This discussion will focus on Phase I and Phase II of drug biotransformation, though it will further limit the discussion to the related molecular processes that are more thoroughly characterized. In addition, this review will frequently refer to the International Space Station (ISS) mission formulary. This is done for the purpose of contextualizing the pharmacogenomic concepts with current human spaceflight practice. It should be noted, however, that the principles described herein can be applied to any spaceflight drug formulary, regardless of the setting.

Pharmaceuticals on the ISS and Their Pharmacogenomic Profile

Stingl et al. have conducted the most comprehensive assessment to date of the pharmacogenomic profile of drugs in the ISS formulary. At the time of their publication, the ISS contained 78 drugs, 60 of which are delivered systemically (intravenous or oral). The most common conditions on the ISS for which drugs are given include infections, pain and inflammation, nausea, sleep disturbances, and allergies. These are followed by gastrointestinal disorders, hypertension, and psychological conditions. This mirrors many of the most common conditions encountered on Earth. The main Phase I enzymes of biotransformation involved in metabolizing the current suite of drugs on the ISS are CYP2D6, CYP3A4, CYP2C19, CYP2C9, CYP1A2, CYP2E1, and UGT (Stingl et al. 2015; NASA 2016). It is expected that, in the future, mission drug repositories will contain drugs that mirror the full spectrum of drugs used on Earth.

The present discussion of drug biotransformation relevant to spaceflight begins with a review of these Phase I enzymes (with emphasis on the cytochrome P450 enzyme system), followed by a review of Phase II processes.

Phase I Drug Metabolism: Cytochrome P450

The CYP450 superfamily of enzymes is responsible for metabolizing 70–80% of all prescription drugs (Zanger and Schwab 2013). Cytochrome P450 is a heme protein. In a CYP450-substrate reaction, heme iron reacts with molecular oxygen, NADPH, and a substrate to produce a reactive intermediate. In general, the most common reaction consists of the insertion of one atom of oxygen into the aliphatic position of an organic substrate (RH), while the other oxygen atom is reduced to water (Meunier et al. 2004). The reactive intermediates produced in Phase I generally proceed through a second (conjugation) step referred to as Phase II. Some CYP450 genes are highly polymorphic, resulting in enzyme variants that may shape alterations in drug-metabolizing capacities, as established through research on Earth (1G). Little is known about their behavior in the conditions of microgravity and radiation of space. For context, it is estimated that genetics account for 20–95% of variability in drug disposition and effects (Hitchen 2006). The CYP450 family of enzymes includes, but is not limited to, the following important enzymes (and their associated genes; Wu 2011).

CYP450 2D6

CYP 2D6 is also known as debrisoquine hydroxylase, an enzyme that catalyzes the oxidation of approximately 25% of all the commonly used therapeutic drugs in clinical practice today and metabolizes a significant amount of the compounds relevant to the ISS and spaceflight environment. In contrast to its significant activity, 2D6 only comprised about 2–4% of the total hepatic CYP protein content. 2D6 is primarily expressed in the liver but also shows a strong expression in the central nervous system (CNS), where it metabolizes endogenous compounds. These include hydroxytryptamines, neurosteroids, and m- and p-tyramine, which it metabolizes into dopamine.

Known as a mixed-function oxidase enzyme, 2D6 functions in the addition or removal of certain functional groups, specifically hydroxylation, demethylation, and dealkylation. 2D6 also works to activate pro-drugs from their inactive to active forms (Wang et al. 2009). For instance, codeine is metabolized by CYP2D6 to morphine. In such cases, enhanced CYP2D6 activity (i.e., in CYP2D6 ultrarapid metabolizers) predisposes one to opioid intoxication (Gasche et al. 2004). Of all the enzymes belonging to the CYP450 superfamily, CYP2D6 has the highest levels of phenotypic variability, due to the large array of possible variant mutations, with more than 100 SNPs.

Substrates for 2D6 are characteristically basic molecules with a protonatable nitrogen atom close to the site of metabolism (Zanger and Schwab 2013). This includes many opioids, antidepressants (i.e., most SSRIs and SNRIs), beta-blockers, antiarrhythmics, and stimulants. Cytochrome P4502D6 is sensitive to strong induction by glutethimide, as well as dexamethasone, rifampicin, and haloperidol. Inhibitors of 2D6 include some SSRIs, antiarrhythmic agents, antipsychotics, and antihistamines.

Exemplary ISS Drugs Metabolized via CYP450 2D6

Metoprolol

Promethazine

Diphenhydramine

Tamsulosin

Cetirizine

Acetaminophen

Loratadine

Hydrocodone

Meclizine

Venlafaxine

Ondansetron

Aripiprazole

CYP 2C9

CYP2C9 reacts through an oxidation mechanism of xenobiotics and other endogenous compounds. The oxidation of endogenous compounds occurs primarily outside of the liver, where it has epoxygenase activity for metabolites, such as serotonin and arachidonic acid (Spector and Kim 2015). 2C9 accounts for 18% of CYP proteins in the liver and is responsible for the clearance of up to 15–20% of all drugs undergoing Phase I metabolism.

With over 50 variants in the regulatory and coding regions of the gene, CYP 2C9 is a highly polymorphic member of the CYP450 family and shows a strong selectivity for the oxidation of small, lipophilic anions as substrates (Zanger and Schwab 2013). Thus, its mechanism of action is believed to utilize a basic residue in the active site of the enzyme to bind to small anionic substrates (Zhou et al. 2010). In aggregate, the many variant forms of 2C9 account for 18% of CYP proteins in the liver and are responsible for the clearance of up to 15–20% of all drugs undergoing Phase I metabolism. Its substrates are characteristically weakly acidic molecules with a hydrogen-bond acceptor. 2C9 metabolizes over 100 drugs, such as many NSAIDS, sulfonylureas (antidiabetic drugs), and oral anticoagulants.

2C9 is sensitive to strong induction by the bactericidal rifampicin and the barbiturate secobarbital and weak induction by a larger set of molecules. The inhibition profile of 2C9 is known to be a much wider array of drugs such as antifungals, antibacterials, anticonvulsants, statins, SSRIs (serotonin reuptake inhibitors), antiarrhythmics, and chemotherapeutics.

Changes in metabolic activity of CYP2C9 against its set of known substrates play a major role in the pathogenesis of adverse drug events. Astronauts with low CYP2C9 enzymatic activity may be at risk for adverse events when using drugs with a narrow therapeutic window, especially for drugs such as warfarin (Van Booven et al. 2010).

Exemplary ISS Drugs Metabolized via CYP450 2C9

  • Ibuprofen

  • Phenytoin

  • Ketamine

  • Acetylsalicylic acid

  • Sulfamethoxazole

  • Loperamide

CYP450 2C19

CYP 2C19, also known as S-mephenytoin hydroxylase, acts on weakly or strongly basic drugs containing one hydrogen-bond donor or, if there are functional groups containing carbon or sulfur, double bonded to oxygen present in the substrate (Zanger and Schwab 2013). Present in the liver, 2C19 is thought to metabolize at least 10% of the drugs currently in clinical use. In addition to its action on xenobiotics, it also possesses epoxygenase activity against endogenous metabolites. Along with other members of the CYP450 superfamily (CYP2C8, CYP2C9, and CYP2J2), 2C19 is responsible in forming epoxyeicosatrienoic acids (EETs) from arachidonic acid. With over 30 known variants, CYP2C19 is highly polymorphic, rendering its drug-metabolizing phenotype highly variable.

CYP 2C19 is responsible for the metabolism of anticonvulsant drugs, proton-pump inhibitors, and drugs that inhibit platelet function, as well as antidepressants, antiepileptics, and warfarin. These drugs are characteristically neutral or weakly basic molecules or amides with two or three hydrogen-bond acceptors. Several drugs in use on the ISS are metabolized via 2C19. This includes medication for sleep, such as antihistaminergics, benzodiazepine receptor agonists, and benzodiazepines (Stingl et al. 2015). The antidepressant sertraline is also metabolized via CYP 2C19. In this case, those with the poor metabolizer phenotype (based on the CYP2C19 genotype) may require as little as one half the dosage in order to avoid cytotoxic effects.

2C19 is sensitive to induction by the bactericidal rifampicin, aspirin, and prednisone. The inhibition profile of 2C9 is known to be a much wider array of drugs, such as strong inhibitors chloramphenicol, fluvoxamine, moclobemide, and fluoxetine. Weak inhibitors and those of unknown strength include several anticonvulsants and proton-pump inhibitors, among others.

Exemplary ISS Drugs Metabolized via CYP450 2C19

  • Diazepam

  • Sertraline

  • Omeprazole

CYP3A4

CYP3A4 is involved in the oxidation of the largest range of substrates of all the CYPs. It is the most abundantly expressed P450 in human liver and is the most versatile CYP450 enzyme, known to metabolize more than 120 unique drug substrates. This accounts for almost 50% of all drugs metabolized through the CYP450 superfamily. 3A4 possessed activity in the synthesis and metabolism of endogenous ligands. In terms of xenobiotics, 3A4 performs an assortment of modifications on its substrates. In aggregate, these reactions include aromatic oxidation, oxidation of heteroatoms, hydroxylation, N- and O-dealkylation reactions, aldehyde oxidations, epoxidation of olefins, dehydrogenation reactions, and aromatase activity. This wide array of reactions is due to its large active site and ability to bind more than one ligand at a time (Shahrokh et al. 2012).

More than 28 variants have been reported in the 3A4 gene, with many of the gene variants conferring moderate or significant functional variation compared to wild-type 3A4 (Lee and Goldstein 2005). Substrates include immunosuppressants, chemotherapeutics, antifungals, macrolides, antidepressants and SSRIs, antipsychotics, analgesic opioids, benzodiazepines, statins, calcium-channel blockers, sex hormone agonists and antagonists, protease inhibitors, and certain glucocorticoids. These molecules are characteristically large and lipophilic molecules of very diverse structures (Zanger and Schwab 2013).

CYP3A4 also is sensitive to enzyme induction, which tends to lower plasma concentrations of CYP3A4 substrates, resulting in reduced efficacy of the substrate. Inducers include antiandrogen, glucocorticoids, anticonvulsants mood stabilizers, barbiturates, hypoglycemics, and capsaicin. Compounds that inhibit 3A4 include protease inhibitors, macrolide antibiotics, antifungals, calcium-channel blockers, and bergamottin (constituent of grapefruit juice), among others.

Exemplary ISS Drugs Metabolized via CYP450 3A4

Acetaminophen

Ethinyl estradiol

Omeprazole

Caffeine

Fexofenadine

Ondansetron

Carbamazepine

Hydrocodone

Prednisone

Clindamycin

Lidocaine

Sertraline

Codeine

Loratadine

Zaleplon

Dexamethasone

Methylprednisolone

Zolpidem

Diazepam

Modafinil

 

CYP450 1A2

CYP1A2 is a central member of the CYP450 superfamily primarily found in the liver, accounting for 5–15% of the total hepatic CYP pool. It is localized and is localized, like most CYP450 enzymes, to the endoplasmic reticulum. Mechanistically, CYP1A2 is a member of the mixed-oxidase enzyme system. However, the precise mechanism by which 1A2 metabolizes its xenobiotic substrates remains unelucidated.

Some studies indicate that interindividual variability may account for up to a 70-fold difference in the metabolic activity of 1A2. This is due to many SNPs associated with the gene (there are at least 17 known alleles), as well as exposures to other chemicals and drugs that induce or inhibit the enzyme (Desta and Flockhart 2017).

The substrates of 1A2 include caffeine, melatonin, naproxen, warfarin, acetaminophen, estradiol, and some antidepressants. These drugs are characteristically planar, aromatic, polyaromatic, and heterocyclic amides and amines (Zanger and Schwab 2013). Inducers include tobacco, foods, and herbs, such as broccoli, Brussels sprouts, cauliflower, and chargrilled meat. Among medications are insulin, the eugeroic modafinil, nafcillin, and the proton-pump inhibitor omeprazole. Inhibitors include ciprofloxacin, many fluoroquinolones, St. John’s wort, some herbs and herbal teas (e.g., peppermint, chamomile, Echinacea), amiodarone, interferon, grapefruit juice, cumin, and turmeric. CYP1A2 is involved in the metabolism of a smaller number of drugs, though some may be relevant in space.

Exemplary ISS Drugs Metabolized via CYP450 1A2

  • Melatonin

  • Caffeine

  • Lidocaine

CYP450 2E1

CYP2E1 is primarily found in the liver, bound to the membrane of the endoplasmic reticulum in hepatic cells. Expression levels of 2E1 have been found to be more than 50% of all CYP hepatic mRNA levels (Bieche et al. 2007) but comprise only 6–16% of all hepatic CYP protein content. There are more than 12 known genetic variations in 2E1 which accounts for more than 16 common haplotypes.

2E1 acts to detoxify xenobiotics, through electron transfer via an oxidation mechanism. Of all the CYP450 active sites, 2E1 is the smallest. Its less bulky reactive site may explain its specificity and small number of substrates. However, it still positions its heme group effectively for metabolic activity. A collection of hydrophobic residues close to the active site make a hydrophobic channel only allowing specificity to certain substrates, such as small molecules. These are characteristically small, generally neutral and hydrophilic, planar molecules, including aliphatic alcohols and halogenated alkanes (Zanger and Schwab 2013).

Substrates include anesthetics; paracetamol; ethanol, as well as industrial toxic substances; and theophylline (a metabolite of caffeine metabolism). 2E1 is particularly relevant, because the conversion of acetaminophen to NAPQI is a step mediated by the enzyme and can lead to significant hepatotoxicity. Similarly, 2E1 bioactivates a variety of common anesthetics, such as halothane, enflurane, and isoflurane, where oxidation of these molecules can also lead to toxic metabolites that damage the liver. This may become of particular concern in spaceflight where surgery is required.

Inducers of 2E1 include ethanol, glycerin, phenobarbital, St. John’s wort, rifampicin, and capsaicin, among others. 2E1 is inhibited by a variety of small molecules, including indazole, 4-methylpyrazole, diethyldithiocarbamate, and disulfiram. Of the drugs used in spaceflight conditions, acetaminophen is likely the most frequent concern regarding CYP450 2E1 genotypes.

Exemplary ISS Drugs Metabolized via CYP450 2E1

  • Acetaminophen

  • Ethanol

  • Theophylline

  • Chlorzoxazone

    Zopiclone

  • Anesthetics (halothane, enflurane, etc.)

Phase II Drug Metabolism: Conjugation

For many drugs, metabolism via CYP450 is the first phase of drug biotransformation, sometimes generating highly reactive intermediates. The intermediate products of these same drugs frequently pass through a Phase II biotransformation reaction. Phase II drug metabolism reactions are generally characterized as conjugation reactions, wherein a polar metabolite (e.g., glutathione, taurine) is bound to the drug metabolite to improve solubility for eventual excretion.

Characterization of Phase II processes generally considers at least three features of Phase II enzymes. These include:
  1. 1.

    SNP variant profiles of Phase II conjugation enzymes

     
  2. 2.

    Adequacy of micronutrient cofactors of Phase II enzymes

     
  3. 3.

    Adequacy of conjugation agents that directly bind drugs, as part of Phase II conjugation

     

Understanding genetic variants of Phase II enzymes will be helpful in designing individualized drug regimens.

Phase II Drug Metabolism: Genomics of Conjugation Enzymes

The following summarizes the primary Phase II enzymes under genetic regulatory control.

UDP-Glucuronosyltransferases (UGTs)

UDP-glucuronosyltransferases (UGTs) is an enzymatic superfamily, which is involved in conjugation of endogenous compounds (bilirubin, steroidal hormones, thyroid hormones, biliary acids, vitamins) and exogenous compounds (drugs, carcinogens, and polluting dietary elements) that are transformed in N-, O-, S-, and C-glucuronates. The variation in the UGT superfamily, with more than 20 genes, gives rise to a polymorphic phenotype for UGT Phase II metabolic activity. The clinical impact of this consideration is amplified by the understanding that UGTs metabolize more than 35% of currently prescribed drug therapies (Crettol et al. 2010).

Glutathione S-Transferases (GSTs)

Glutathione S-Transferases (GSTs) comprise an enzymatic superfamily, of which there are 13 known classes and 22 class members. Classes are defined based on their structure, as found in the cytosol and mitochondria (only kappa; Franklin 2007). In addition to the large class size, the genetic variation of these may also account for significant variations in GST metabolic function. The primary role of GSTs, which can constitute up to 10% of all cytosolic protein, is to catalyze a detoxification by the nucleophilic attack on the C, S, or N of nonpolar xenobiotic compounds (Josephy 2010). This reaction prevents damage to other cellular proteins and DNA structure. Due to the transfer of a glutathione molecule to the detoxified substrate, GSTs are dependent upon a steady supply of glutathione as a cofactor.

In most cases of glutathione conjugation, more polar glutathione conjugates are eliminated into the bile or are subsequently subjected to other metabolic steps. This eventually leads to formation of mercapturic acids, which are excreted in urine. This process depletes glutathione (GSH) stores, as GSH binds drugs through the conjugation of Phase I intermediate metabolites. This depletion can limit the amount of glutathione available for future drug metabolism reactions and, also, alter the REDOX balance of the cell.

Glutathione transferase can be used as an example of the merits of coupling pharmacogenomics and pharmacometabolomics. Gene variants of glutathione transferase may impact the conjugation of glutathione. Genetic variants in glutathione synthesis may also be present, such as cases where an astronaut may possess a deletion or SNP for glutathione synthase (GSS) or glutathione reductase (GSR), wherein the ability to synthesize or reduce GSH may be impaired.

In practice, one may analyze the genes for GST, GSS, and GSR in astronauts. This can be coupled with cellular analysis for glutathione and glutathione disulfide (GSSG; the oxidation product of GSH). Prior to embarking on missions, glutathione or glutathione precursors (N-acetylcysteine) can be provided as part of pre-mission countermeasures. These compounds can, of course, become valuable components of a mission drug formulary.

Sulfotransferases (SULTs)

Sulfotransferases (SULTs) are a superfamily of enzymes that transfer a sulfo group (\( {\mathrm{SO}}_4^{2-} \)) from a donor molecule (most commonly 3′-phosphoadenosine-5′-phosphosulfate or PAPS) to an alcohol (forming a sulfate) or to an amine (forming a sulfamate). The depletion of sulfate groups caused by the utilization of inorganic sulfate (or the secondary donor cysteine) can cause a substrate-dependent decrease in sulfation (James and Ambadapadi 2013). This depletion may become problematic in situations of high-dose drugs, such as acetaminophen, and can lead to an inability to properly remove harmful intermediates (e.g., NAPQI), leading to hepatotoxicity. Smaller sulfated metabolites are generally excreted in the urine by the kidney, while larger molecules that have undergone sulfation are excreted in the bile. Similar to other Phase II drug metabolism superfamilies, SULTs exhibit a wide array of classes, members, and variants.

N-Acetyltransferases (NATs)

N-Acetyltransferases (NATs) catalyze the conjugation of acetyl groups from acetyl CoA to xenobiotic substrates in the liver for Phase II metabolism and excretion. The most significant members of this family are the well-described NAT1 and NAT2, each of which is characterized by genetic variants and influences biotransformation (Sim et al. 2014).

Methyltransferases (e.g., COMT)

Methyltransferases (MTs) are a large family of enzymes that transfer a single methyl group from a donor molecule (such as S-adenosyl Methionine; SAM) to an acceptor molecule (such as the N-terminus of a protein substrate; Ghodke-Puranik and Lamba 2017). Types of MTs include histone methyltransferases, N-terminal methyltransferases, DNA/RNA methyltransferases (DNMTs), and natural product methyltransferases (NPMTs). With regard to drug metabolism, methylation is a common, though minor, route of xenobiotic transformation and detoxification. In contrast to other modes of Phase II metabolism, methylation generally decreases the solubility of xenobiotics. Conjugation generally occurs at an O-, N, or S- moiety and utilizes SAM as the main cofactor for methyl group donation. Genetic variation in the genes that code for these enzymes alters the metabolic activity and clinical phenotype. The methyl pool is maintained by essential nutrients, including methionine (S-adenosylmethionine; SAMe), vitamin B12, vitamin B6, betaine (trimethylglycine), choline, and folate. Each of these measures, including surrogate measures like homocysteine, can be assessed (blood, tissues, cells) to determine baseline status of the methyl pool in astronauts.

Amino Acid Conjugating Enzymes (AACEs)

Amino acid conjugating enzymes (AACEs) are enzymes that conjugate amino acids to xenobiotic substrates in a Phase II drug metabolism capacity, in order to detoxify and eliminate them. Amino acid groups that are conjugated include glycine, taurine, cysteine, glutamine, glutamic acid, ornithine, and arginine (Liska 1998). These reactions are less well characterized than UGTs and other Phase I and II reactions, which are principle due to the limited number of molecules and lack of structural diversity of xenobiotics that can undergo amino acid conjugation (Knights et al. 2007).

Food and Nutrition in Pharmacogenomics

The human diet is rich with compounds that act as substrates, inhibitors, and inducers of the major drug-metabolizing enzymes. Though an in-depth discussion of these effects is beyond the scope of this chapter, it warrants noting that a comprehensive analysis of the strong dietary modulators of drug metabolism would be a useful component of spaceflight mission planning. Many of the studies of food and nutrient effects on biotransformation rely upon cell- and animal-based studies, which are limited in their translational value. Table 3 provides a glimpse into the breadth of food substances that have potential impact on the enzymes of xenobiotic biotransformation in the clinical setting (Mallhi et al. 2015; Hodges and Minich 2015). This should be contextualized with the knowledge that numerous food constituents may also confer substantial benefit to the efficient function of drug-metabolizing processes.
Table 3

Food, beverages, and bioactive compounds with potential, clinical impact on drug biotransformation (Hodges and Minich 2015)

Candidate food and nutrient modulators of drug-metabolizing enzymes

Food or beverage

Nutrient bioactive compounds

Allium vegetables

Apiaceous vegetables

Black raspberry

Black tea

Blueberry

Chamomile tea

Chicory root

Citrus

Coffee

Cruciferous vegetables (with potential for distinct effects of different crucifers)

Dandelion tea

Garlic

Ghee

Ginger

Grapefruit

Green tea

Honeybush tea

Peppermint tea

Pomegranate

Purple sweet potato

Rooibos tea

Rosemary

Soybean/black soybean

Turmeric

Astaxanthin

Caffeic acid

Catechins (including EGCG)

Chrysin

Curcumin

Daidzein

Ellagic acid

Ferulic acid

Fish oil

Genistein

Luteolin

Lycopene

MCTs

Myricetin

N-acetyl cysteine naringenin

Quercetin

Resveratrol

Retinoic acid (vitamin A)

Exemplary Cases of Pharmacogenomic Influences

Case One: Acetaminophen Conversion to NAPQI

The analgesic acetaminophen represents an example of the convergence of Phase I and Phase II biotransformation, coupled with dependence upon the limiting elements of cofactor availability (NAD; nicotinamide) and conjugating nutrient availability (glutathione; sulfhydryl groups). Under normal conditions, acetaminophen is partially metabolized via CYP450 2E1. This process leads to the formation of the hepatotoxin N-acetyl-p-benzoquinone imine (NAPQI). The next critical step in removal of NAPQI is conjugation via glutathione to form an NAPQI-GSH conjugate (Fig. 2).
Fig. 2

Acetaminophen conversion to NAPQI and removal by glutathione

There are several genetic components that can limit this conversion. The first is SNP variants of CYP450 2E1. The second is SNP variants of glutathione-S-transferase (GST), which governs the conjugation of GSH with NAPQI. Next, there must be sufficient glutathione in order to execute the conjugation with acetaminophen. This step is limited by dietary precursors of glutathione (cysteine, glycine, glutamic acid) and by SNP variants governing the synthesis (glutathione synthase) and the reduction (glutathione reductase) of glutathione. If glutathione is in limited supply, the resultant NAPQI can exert highly toxic effects by covalent reactions with proteins, such as those found in mitochondria. This can lead to the liver damage typically associated with acetaminophen use (Jaeschke and Bajt 2006).

If glutathione depletion is identified through blood chemistry (low GSH or low GSH:GSSG ratio), pre-mission glutathione or glutathione precursors can be provided at the dosage needed to assure optimum mission status. Stable glutathione precursors, such as N-acetylcysteine, can be provided on missions.

Case Two: Codeine Conversion to Morphine

Codeine is among the opioid analgesic that is used to relieve mild to moderately severe pain. CYP4502D6 converts codeine to the active metabolite morphine, which is responsible for the analgesic effects of codeine. In those who carry two inactive copies of 2D6 alleles (poor metabolizers; ~10% conversion to morphine), there may be inadequate pain relief, due to inadequate morphine formation. Conversely, those who carry more than two functional 2D6 copies more rapidly and completely convert codeine to morphine. These are the ultrarapid metabolizers (40–50% conversion to morphine) wherein even normal doses of codeine may lead to symptoms of morphine overdose, shallow breathing, sleepiness, confusion, sweating, nausea, and others.

The FDA drug label for codeine states that even at labeled dosage regimens, individuals who are ultrarapid metabolizers may have life-threatening or fatal respiratory depression or experience signs of overdose. The Clinical Pharmacogenetics Implementation Consortium (CPIC) recommends that codeine be avoided in UM, because of toxicity. In PM, codeine should be avoided, due to lack of efficacy (Dean 2012).

Differences in Drug Metabolism Based on Ethnicity

Ethnic differences in CYP450 isoforms, such as CYP2D6, CYP2C19, and CYP3A4, have been extensively characterized and can be pronounced. For instance, most Western populations are characterized by roughly 93% normal metabolizers, 7% poor metabolizers, and 1% ultrarapid metabolizers of CYP2D6. In contrast, only 1% of Asians are considered poor metabolizers of CYP2D6. Roughly 20% of Asians are poor metabolizers via CYP2C19, while only about 4% of Caucasians are considered poor metabolizers via this isoform (Jain 2009). CYP3A5 expression also varies widely with ethnicity. For instance, more than 50% of African Americans express CYP3A5, while some 30% of Caucasians express this isoform. It is beyond the scope of this chapter to further explore the extant literature on the influence of ethnicity on drug-metabolizing enzymes. However, it is important to note that, while ethnic differences have been well established, there is a growing view that individualized pharmacogenomic profiling is sufficiently informative to the extent that ethnicity does not provide additional insight about the drug-metabolizing capacity of an individual (Shah and Gaedigk 2018).

Differences in Drug Metabolism in Males and Females

Sex differences in Phase I and Phase II metabolism are believed to be a major cause of the varied pharmacokinetics and adverse drug reactions between women and men. Zhang et al. recently conducted a genome-wide gene expression study of 112 female and 112 male livers (Zhang et al. 2011). They identified more than 1300 genes where mRNA expression was significantly affected by sex. The study revealed that 75% showed higher expression in females. Of these genes, 40 were ADME and ADME-related. Those showing higher expression and activity in females included CYP1A2, CYP3A4, and CYP7A1. Those showing higher expression and activity in males included CYP3A5, CYP27B1, and UGT2B15. Chu has reviewed the general differences in Phase I and Phase II enzyme activity in males versus females (Table 4). Some of the contradictory results reported between the single study by Zhang and the review of studies by Chu may be attributed to the influence of pregnancy, ethnicity, and other factors. This highlights the complexity of the subject matter and further illustrates the importance of individualized pharmacogenomic assessment.
Table 4

Sex-dependent activity of drug-metabolizing enzymes (Chu 2014)

Sex-dependent activity of drug-metabolizing enzymes

Phase I enzymes

Phase II enzymes

CYP 1A2

M > W

UGTs

M > W

CYP 2A6

W > M

Sulfotransferases

M > W

CYP 2B6

W > M

N-acetyltransferases

W > M

CYP 2C9

M = W

Methyltransferases

M > W

CYP 2C19

M = W

  

CYP 2D6

Mostly W > M

  

CYP 3A4

Mostly W > M

  

While the present discussion highlights drug-metabolizing enzymes among the clinical targets to personalize drug therapy, the reasons for different adverse drug events by sex are more complex. The diversity of responses is likely due to a convergence of factors, such as pharmacokinetic or pharmacodynamic factors, polypharmacy, nutritional status, availability of conjugating nutrients, differences in reporting patterns, different body composition, body size, or frequency of ingestion of drugs. Presently, little is known about sex differences in the drug response in space, and this should be considered when an individual enters the space environment.

Gut Microbial Metagenome and Drug Metabolism

While pharmacogenomics describes how an individual’s genotype influences drug metabolism, individual variation in the gut microbiota may also contribute to the metabolism of drugs. This contribution may influence drug efficacy and safety in ways important to spaceflight. Therefore, the examination of spaceflight pharmacogenomics would not be complete without at least modest attention to gut microbial metagenomics (the study of the so-called second genome).

Fundamentally, this is a nascent field. There are roughly 4,000 molecular entities that have been approved for human use by major markets worldwide, including the United States (Huang et al. 2011). Despite the extensive metabolic potential of the gut microbiota, there are currently only about 40 commercial drugs that have been studied as substrates of gut microbial metabolism (Haiser and Turnbaugh 2013). Yip and Chan have reviewed the effect of microbiota-host co-metabolism on drug metabolism, leading to a summary of 30 drugs that are co-metabolized by host and gut microbiota. The molecular mechanisms involved in gut microbial metabolism of these drugs are summarized in Table 5 and contain numerous mechanisms relevant to space.
Table 5

Molecular mechanism of gut microbial action on 30 therapeutic drugs (Yip and Chan 2015)

Molecular mechanisms of gut microbial drug metabolism

Reduction

Proteolysis

Hydrolysis

Denitration

Deconjugation of drugs excreted in bile as inactive conjugates

Amine formation and hydrolysis of amide linkage

Removal of succinate group

Thiazole ring-opening

Dehydroxylation

Isoxazole scission

Acetylation

N-Demethylation

Deacetylation

Competition of microbial metabolite for Phase II drug clearance

Cleavage of N-oxide bond

Competition of microbial metabolite for hepatic uptake of drug

Acetaminophen can be used to illustrate the potential impact of host-gut microbial co-metabolism in space. Clayton et al. administered 1 g of acetaminophen while assessing urinary p-cresol sulfate, acetaminophen sulfate, and acetaminophen glucuronide in humans (Clayton et al. 2009). Individuals that had high pre-dose urinary levels of p-cresol sulfate had a lower post-dose urinary ratio of acetaminophen sulfate to acetaminophen glucuronide.

This finding recognizes that microbially derived p-cresol from the gut requires Phase II sulfonation in the liver. This process apparently competes with available hepatic sulfur groups for the metabolism (conjugation) of other substrates, such as acetaminophen. When acetaminophen is delivered to those in whom p-cresol is being produced by gut bacteria (e.g., clostridial species), there is competition for hepatic sulfur groups between the two substrates. When this occurs, acetaminophen metabolism is partially shunted toward glucuronidation, leading to formation of higher levels of acetaminophen glucuronide (and lower levels of acetaminophen sulfate).

In this context, it is important to revisit the previous example of the metabolism of acetaminophen in the absence of adequate sulfur groups (e.g., GSH), wherein the toxic intermediate NAPQI is produced (Fig. 2). Reduction of the hepatic sulfur pool by gut microbial metabolites that must be metabolized through Phase II conjugation in the liver may also limit the sulfur pool available for conjugation of a range of drug substrates beyond acetaminophen, where sulfonation is a crucial metabolic pathway.

The Effect of Spaceflight on Drug-Metabolizing Enzymes in Animals

There is a paucity of data surrounding the response of drug-metabolizing enzymes in spaceflight or spaceflight analog conditions on Earth. The following will briefly explore the effects of spaceflight on drug-metabolizing enzymes in animals, the effect of spaceflight on drug-metabolizing enzymes in humans, and the effect of radiation (space analog) on drug-metabolizing enzymes on Earth.

Moskaleva et al. have used mass spectrometry to evaluate the hepatic gene expression profile of numerous CYP450 isoforms in mice after 30 days of spaceflight (Moskaleva et al. 2015). Significant changes in the expression of CYP2C29 (1.4 times), CYP2E1 (1.8 times), and CYP1A2 (1.9 times) genes were seen in mice of the flight group in comparison with the ground control group. Seven days after landing, the expression of CYP2C29 and CYP1A2 returned to control levels, though the CYP2E1 level remained upregulated.

Of additional note is the high interindividual variability found during 30 days in space. The highest interindividual variability of tested CYP450 content in the mouse livers of the ground control experiment did not exceed 32% (though CYP4502D9 was at 46%). In contrast, the in-flight variability was 60% for 3A11, 3A16, 3A41, followed by 78% for 2C39. This may be interpreted to mean that the increased content of three CYP450 isozymes, as well as an increase in the interindividual variability, might be associated with the influence of space flight conditions.

Other studies conducted by Baba et al. examined CYP450 genes and their protein expression (Baba et al. 2008). The purpose of their study was to measure the expression of 11 CYP genes and protein distribution in the liver of rats after spaceflight, using real-time polymerase chain reaction and immunohistochemistry. Two stress-related oxidative damage genes, cold-inducible RNA-binding protein and CYP4A1, were significantly elevated, while heat-shock protein HSP90 and the p53 tumor suppressor were decreased in the spaceflight group.

In a study by Merrill et al., liver samples derived from rats that had flown aboard Cosmos 1887 were analyzed for protein, glycogen, and lipids, along with the activities of a number of key enzymes involved in metabolism of these compounds and xenobiotics (Merrill et al. 1990). A significant decrease in the amount of microsomal cytochrome P-450 was detected, coupled with a decline in the activities of aniline hydroxylase and ethylmorphine N-demethylase, cytochrome P-450-dependent enzymes. Rabot et al. studied the effect of short-duration US space shuttle flights on hepatic CYP450 enzymes in Sprague-Dawley rats (Rabot et al. 2000). The missions included Spacelab Life Sciences missions, SLS-1 (a 9-day space flight) and SLS-2 (a 14-day space flight). In SLS-1, there was an induction of intestinal glutathione-S-transferase. Additional analyses in SLS-2 showed a decrease of hepatic CYP450. After a postflight recovery period equal to the mission length, modifications of the hepatic and intestinal xenobiotic-metabolizing enzymes persisted.

Bion-M1 was a Russian Federation space capsule launched in 2013 with a series of animal experiments on board. In one study, mice were flown for 30 days and compared to control mice on Earth (Anselm et al. 2017). Anselm et al. performed proteomic profiling, using the method of shotgun mass spectrometry and label-free quantification. These analyses produced data showing 1086 identified proteins and 12,206 unique peptides. Data revealed 218 upregulated and 224 downregulated proteins in the postflight compared to the other groups.

Investigators observed upregulation of CYP4502D subfamily members (CYP2D9, CYP2D10, and CYP2D26), accompanied by increased levels of CYP4V3, CYP1A2, CYP2F2, and CYP4A12A. Members of the CYP4503A subfamily (CYP3A11 and CYP3A13) were downregulated. CYP4A12A demonstrated a fivefold increase in the flight group compared to the postflight group. The subfamily CYP2D was elevated 6.1-fold in the flight group compared with the postflight group (2.1-fold). Importantly, the finding that no significant difference in CYP450 profile between control and postflight suggests that re-adaption to the usual drug-metabolizing CYP450 levels occurs within 7 days after landing.

Of great interest, the most pronounced increases (>twofold change) were in the flight group CYP4A12A, an enzyme involved in fatty acid ω-oxidation. In contrast, medium-chain specific acyl-CoA dehydrogenase, long-chain-specific acyl-CoA dehydrogenase, and peroxisomal bifunctional enzyme (necessary for β-oxidation of fatty acids) were downregulated. These data suggest an impairment where mitochondrial β-oxidation of fatty acids is shunted toward extra-mitochondrial ω-oxidation, a condition of impaired mitochondrial function. This may have further adverse implications in the liver spaceflight. These findings should be taken with a note of caution. The study consisted of a relatively small sample size, due to the fact that only 16 out of 45 mice (36%) survived the flight to space and back. Yet it hints at patterns of variance that should be at the center of future missions examining drug-metabolizing enzymes in space.

The preceding studies are insufficient to draw robust conclusions on the direction of the effect of spaceflight on CYP450 enzymes. However, the small pool of animal studies does reveal an effect and suggests that we must consider the influence of space on these drug-metabolizing enzyme systems, as the pharmacogenomics capability is developed for human spaceflight.

The Effects of Spaceflight on Drug-Metabolizing Enzymes in Humans: The NASA Twins Study

The only human experimentation specifically targeting CYP450 isozymes or drug-metabolizing genes in spaceflight is derived from the NASA Twins Study. At the time of this writing, the Twins Study findings have not been published, though publication is expected in 2019. However, a preliminary review of the data suggests that 1 year in space has an impact on CYP450 enzymes in a human, though the effects are complex and vary between cell types (Chris Mason, personal communication, April 30, 2018).

The Effect of Spaceflight Analog Conditions (Radiation) on Drug-Metabolizing Enzymes in Animals

A small number of investigators have attempted to characterize the response of CYP450 enzymes to radiation, using Earth-based space analog conditions. Rendic et al. have provided the most comprehensive review to date on the effect of ionizing and non-ionizing radiation of CYP450 and related drug-metabolizing enzymes (Rendic and Guengerich 2012). For reasons of brevity, the reader is referred to this work.

To determine these consequences expression of cytochrome P450s was assessed in the livers of 60Co gamma-irradiated rats after receiving three gray (Gy) of gamma-irradiation. Chung et al. found CYP2E1 induction with a 3.6-fold increase in the mRNA at 24 h, whereas the expression of other liver genes CYP1A2 and CYP3A was not changed (Chung et al. 2001). Concordantly, chlorzoxazone (a specific substrate of CYP2E1) was surveyed, and the amount of 6-hydroxychlorzoxazone excreted in 8 h urine was significantly greater than that of control rats. Gamma radiation of 0.5–1.0 Gy did not induce hepatic CYP2E1, whereas rats irradiated at 6–9 Gy displayed smaller increases in mRNA. This was likely due to liver damage compared to those irradiated at a single 3 Gy dose of gamma-rays. Elevated exposure levels of gamma irradiation reduced by 30–90% the activity of hepatic aconitase, a key enzyme in energy metabolism in mitochondria. Mitochondrial DNA per gram of wet liver was decreased by 50% in rats exposed to 3 Gy of gamma-rays, demonstrating gamma-ray irradiation at these exposure levels induced organelle dysfunction and elevation of CYP2E1 in the liver associated with mitochondrial damage.

The Effect of Spaceflight on Cofactors of Drug-Metabolizing Enzymes: PARP AND NAD

While space radiation can have direct effects on CYP450 and other drug-metabolizing enzymes, attention must also be given to the cofactors required for the efficient execution of these processes. As noted previously, CYP450 biotransformation utilizes NAD (nicotinamide adenine dinucleotide) as a cofactor in the reduction of xenobiotics (as NADPH-cytochrome P450 oxidoreductase). NADPH, thus, can become a limiting component for efficient drug metabolism by intact CYP450 enzymes. NADPH status, by extension, is also strongly influenced by the availability of NADPH precursors (e.g., nicotinamide, nicotinic acid) through the diet.

The impact of space radiation on NADPH is rooted in the initiating step in DNA repair associated with radiation (Poly-ADP-ribose polymerase-1 [PARP-1] activation). The mechanisms responsible for activation of PARP-1 include single-strand DNA breaks produced by radiation, reactive oxygen species, and reactive nitrogen species (Kim et al. 2004). Poly-ADP-ribose polymerase-1 (PARP-1) facilitates DNA base excision repair after DNA damage by initiating poly(ADP-ribosyl)-ation of histones, topoisomerases, and DNA polymerases, recruiting them to DNA break sites and altering DNA structure to make it more accessible to the repair proteins. This helps maintain genomic integrity and stability (Sissi and Palumbo 2009; Kirkland 2012).

Remarkably, following PARP-1 activation, more than 200 molecules of NAD+ may be consumed, as the ADP-ribose sub-units are robbed from NAD and gathered one by one for assembly into a single large PARP polymer (which uses the 200 NAD to construct a single PARP molecule). The construction of many such PARP-1 proteins triggered by radiation may lead to depletion or exhaustion of NAD stores, inhibition of electron transport, energy impairment, possible cell death via NAD+ depletion, and possible impairment of CYP450 (Kanai et al. 2003). To our knowledge, the extent of the impact of radiation on PARP-1 activation, NADPH depletion, and eventual impairment of CYP450 activity has not been widely studied in space. But the mechanisms have been explored on Earth, which has led to an entire class of drugs based on PARP inhibition. Alternately, NAD modulators, such as nicotinamide riboside, have emerged as viable precursors used to elevate blood and tissue levels of NAD in humans (Martens et al. 2018).

Radiation Effects on the Gut Microbiome in Space

The importance of the gut microbiota in regulating drug metabolism in space must also be examined in relation to space radiation. Most microbes are unstable when exposed to radiation, based on higher-dose Earth-based studies (Packey and Ciorba 2010; Lam et al. 2012). Galactic cosmic rays (GCR) might further damage astronaut gut microbiota in ways that influence drug metabolism. For context, the chronic radiation exposure from GCR, when outside the protective environment of the Earth’s magnetosphere, occurs at a dose rate of roughly 1.3 mGy/day. Total doses of a transit to and from Mars can add up to 0.5 Gy (Zeitlin 2012; Hu et al. 2009). Recent data from the RAD (Radiation Assessment Detector) experiment on the Mars Science Laboratory (Zeitlin et al. 2013) reveal that during a 360-day Mars transit mission, an astronaut would receive a dose of about 662 millisieverts (mSv).

Casero et al. examined fecal samples from mice after 10 and 30 days of exposure to 16O (600 MeV/n) at 0.1, 0.25, and 1 Gy and compared them with sham treatment (non-irradiated mice). They observed three primary outcomes. First, there were robust changes in ecological communities of the gut microbiota as a consequence of high LET exposures. Second, there was a shift in the population equilibrium toward an increase in opportunistic pathogens, coupled with a decrease in normal microbiota. Third, the changes were associated with functional shifts in the fecal metabolome. Of primary significance, metabolic network modeling demonstrated that specific changes in the metabolome are connected to irradiation-induced changes in the abundance of specific taxa (Casero et al. 2017).

Much additional work will be required to understand how the gut microbiota influence drug metabolism in space. This work should be conducted in parallel with the general investigations being done on the dynamics of the gut microbiome in space.

Drug-Drug Interactions in Space

While this chapter is focused on pharmacogenomics, the phenomenon of drug-drug interaction must also be explored, as this can strongly impact the very systems described above. Many drugs on a spaceflight mission drug formulary will act either as inhibitors or inducers of CYP450 isozymes. For example, drug A may inhibit or induce a CYP450 enzyme needed for the metabolism of drug B that is being used by the astronaut.

For example, CYP450 3A4 is required for the metabolism of zolpidem (Ambien). In this case, zolpidem is the substrate. If circumstances also require the administration of clarithromycin (Biaxin; an inhibitor of CYP450), the co-administration of clarithromycin will slow down zolpidem metabolism and increase its effect. This may result in prolonging the effect of zolpidem with potential concomitant adverse effects on mission execution or safety. It is important to note that this drug-drug phenomenon occurs, regardless of the drug-metabolizing phenotype of the individual. It can be easily seen, however, that a genetic variant in CYP450 3A4 might further impair or alter the drug disposal capacity of the individual astronaut faced with this scenario.

These drug-drug dynamics warrant formal consideration in mission planning, along with attention to the individual astronaut genotype and drug-metabolizing phenotype. Fortunately, the substrate, inhibitor, and inducer profile for prescription and non-prescription drugs is generally well-described (Table 6). For a detailed review of drug-drug interactions (DDIs) involving CYP enzymes, the reader is referred to Bahar et al. (Bahar et al. 2017).
Table 6

Substrates, inhibitors, and inducers of human CYP450s A partial list of CYP450 inhibitors, substrates, and inducers. For a complete list, see Flockhart (2007)

 

CYP1A2

CYP2B6

CYP2C9

CYP2C19

CYP2D6

CYP2E1

CYP3A4

Substrates

Caffeine

Imipramine

Tacrine

Theophylline

R-warfarin

Bupropion

Midazolam

Tamoxifen

Verapamil

Testosterone

Diclofenac

Losartan

Phenytoin

Tolbutamide

S-warfarin

Omeprazole

Phenytoin

Indomethacin

R-warfarin

Bufuralol

Codeine

Desipramine

Lidocaine

Acetaminophen

Ethanol

Chlorzoxazone

Sevoflurane

Nifedipine

Erythromycin

Midazolam

Testosterone

Inhibitors

Ciprofloxacin

Furafylline

Mibefradil

Ticlopidine

Ketoconazole

Tranylcypromine

Troglitazone

Orphenadrine

Fluconazole

Sulfaphenazole

Paroxetine

Cimetidine

Ketoconazole

Paroxetine

Ticlopidine

Quinidine

Methadone

Cimetidine

Fluoxetine

Disulfiram

Ketoconazole

Erythromycin

Grapefruit juice

Ritonavir

Inducers

Insulin

Omeprazole (cruciferous vegetables)

(char-grilled meat)

(tobacco)

Dexamethasone

Phenobarbital

Rifampin

Sodium valproate

Rifampin

Secobarbital

Prednisone

Rifampin

None identified

Ethanol

(starvation)

Carbamazepine

Phenobarbital

Phenytoin

Rifampin

Development of Standards for Spaceflight Pharmacogenomics

Presently, there are no standards for the application of pharmacogenomics in spaceflight. There exist a number of organizations working to develop standards for the practice of medicine on Earth. The Clinical Pharmacogenetics Implementation Consortium (CPIC), the Dutch Pharmacogenetics Working Group (DPWG), and the European Pharmacogenetics Implementation Consortium (Eu-PIC) are working on guidelines for applying or interpreting pharmacogenetic information in clinical practice. In addition, the Institute of Medicine Roundtable on Translating Genomic-Based Research for Health and the CPIC have collaborated in an effort to standardize the terminology of pharmacogenomic testing.

From a regulatory vantage point, the Genomics and Targeted Therapy Group is located within US FDA’s Office of Clinical Pharmacology, where it works to apply pharmacogenomics and other biomarkers in drug development and clinical practice. Moreover, FDA scientists work in multiple ways to ensure that pharmacogenomic strategies are applied appropriately in all phases of drug development. Core functions include regulatory review, research, policy development, education, and outreach. The spaceflight medicine community would be expected to benefit from collaboration with such organizations while bringing the unique knowledge garnered from years of studying humans in space.

Short of having these standards, there is value in considering an exercise in the development of a hypothetical design reference mission (DRM), based on pharmacogenomics. A DRM is a plan developed to a sufficient level of detail to elucidate the content, risks, difficulties, technologies, scale, and cost of a possible future space mission. This can be envisioned using pharmacogenomic methods employed in clinical medicine today. Such an exercise may also provide insight into how we might apply pharmacogenomics to the current astronaut population in advance of the years of research that may be required to satisfactorily reveal the complexities of PK and PD in space.

Mission Planning Considerations

Hypothetical DRM (Design Reference Mission): Personalization Based on Drug-Metabolizing Enzyme Profile

Here, one can envision a hypothetical DRM that governs the use of pharmacogenomics on a Deep Space Gateway mission, Lunar mission, or a mission to Mars. The following DRM is composed of three key elements:
  1. 1.

    Workflow diagram for individualized pharmacogenomic testing (Fig. 3)

     
  2. 2.

    Sample pharmacogenomic report for a single astronaut that governs selected drugs commonly used in the current ISS spaceflight environment (Tables 7 and 8)

     
  3. 3.

    Team-based pharmacogenomic report, consisting of the genotype for a team of six astronauts (Table 9)

     
Fig. 3

Spaceflight pharmacogenomics workflow. The figure shows a proposed workflow that includes (1) sample acquisition, (2) genotyping, (3) classification of drug-metabolizing phenotype based on genotype, (4) correlation of phenotype with the mission drug list, (5) classification of significance of predicted drug-gene interaction, (6) generation of usage recommendations for individual astronauts, and (7) generation of a team pharmacogenomic report. (Image credit; Sovaris Aerospace)

Table 7

Analgesic pharmacogenomic summary: astronaut A. A personalized pharmacogenomic profile is provided specifically to guide analgesic selection for Astronaut A. This hypothetical scenario uses a classification, based on no, moderate, and significant gene-drug interaction. This would be used in conjunction with other clinical and pharmacological information in order to select drugs optimized to the astronaut, the mission, and to the clinical endpoint

 

No significant gene-drug interaction

DRUGa

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

Hydromorphone (Dilaudid)

      
 

Moderate gene-drug interaction

 

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

Ketorolac (Toradol)

      
 

Significant gene-drug interaction

 

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

Acetaminophen

      

aExemplary drugs are based on the current NASA ISS drug formulary

bCells would be completed with designation of the following for each drug-metabolizing enzyme: NM normal metabolizer, IM intermediate metabolizer, PM poor metabolizer, UM ultrarapid metabolizer

Table 8

Space motion sickness pharmacogenomic summary: astronaut A. A personalized pharmacogenomic profile is provided specifically to guide selection of space motion sickness medication for Astronaut A. This hypothetical scenario uses a classification, based on no, moderate, and significant gene-drug interaction. This would be used in conjunction with other clinical and pharmacological information in order to select drugs optimized to the astronaut, the mission, and to the clinical endpoint

 

No significant gene-drug interaction

DRUGa

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

Promethazine (Phenergan)

      
 

Moderate gene-drug interaction

 

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

Ondansetron (Zofran ODT)

      
 

Significant gene-drug interaction

 

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

aExemplary drugs are based on the current NASA ISS drug formulary

bCells would be completed with designation of the following for each drug-metabolizing enzyme: NM normal metabolizer, IM intermediate metabolizer, PM poor metabolizer, UM ultrarapid metabolizer

Table 9

Team-based pharmacogenomic profile. A hypothetical team pharmacogenomic profile can be developed to classify each of six astronauts on a mission to Mars, according to CYP450 (and Phase II) genotype. This can be used to further inform mission formulary composition

 

CYP450 and UGT isozyme

Astronaut

1A2

2D6

2C9

2C19

3A4/3A5

UGT2B7/1A3

A

      

B

      

C

      

D

      

E

      

F

      

aCells would be completed with designation of the following for each drug-metabolizing enzyme: NM normal metabolizer, IM intermediate metabolizer, PM poor metabolizer, UM ultrarapid metabolizer

Under the hypothetical pharmacogenomic DRM (Tables 6 and 7), one would select hydromorphone for pain over ketorolac, due to the lack of significant gene-drug interaction. One would also proceed cautiously with acetaminophen, due to the significant gene-drug interaction profile. Similarly, promethazine (Table 8) would be favored for motion sickness, because of no significant gene-drug interaction (in contrast to ondansetron with moderate gene-drug interaction).

Pharmacogenomics in Relation to Drug Stability in Space

NASA has temporally assessed the chemical and physical differences of 35 essential formulations contained in identical pharmaceutical kits stowed on the International Space Station (ISS) and compared them with controls on Earth. After 28 months on ISS and upon return to Earth, activity assessment of the active pharmaceutical ingredient (API) of flight and ground samples were tested using ultra- and high-performance liquid chromatography. After 596 days in flight, fewer than half of the solid dosage forms in the flight payload kit met content (chemical composition) acceptance criteria. After 880 days of storage in flight, only 27% of solid formulations met the acceptance criteria for content (Du et al. 2011). By example, promethazine (Phenergan) is one of the frequently used medications for motion sickness in space. It is a light-sensitive compound that appears to be more susceptible to spaceflight conditions, having failed the potency requirement after storage for 353 days in space (earlier than the expiration date). In addition to concerns about radiation effects and stability of the API, it is also important to better understand the degradation products that accompany loss of potency. These degradants may pose toxicity risks and may require that clinicians modify the interpretation of the pharmacogenomic profile, as degradation products may be metabolized via enzymes other than that predicted for the primary API.

Spaceflight Transport Volume and the Mission Formulary

Payload managers for spaceflight missions have an overriding concern regarding the management of mass and volume on spacecraft while maintaining and optimizing critical functions. It is important that mission planners accurately size the spaceflight mission formulary (i.e., total volume) in order to accommodate the crew compliment and to assure the reliability and performance of pharmaceuticals over prolonged periods. The changes in potency and integrity of the pharmaceuticals described above will inevitably affect the way they are stored, especially for explorations missions. In addition, there may be ways in which personalization of the mission drug repository can further benefit the mission by positive influence on transport volume. This will have to be determined by further research.

Commercial Spaceflight Applications of Pharmacogenomics

Throughout this review, the term astronaut has been used to describe a professional space traveler who is part of a government space program (e.g., NASA, ESA, JAXA, etc.). The commercial spaceflight industry will soon embark upon suborbital flights, followed by orbital flights, orbital habitation, and missions beyond low Earth orbit. While commercial spaceflight pilots and scientists will likely have training somewhat analogous to that of professional astronauts, those embarking on recreational or exploration excursions may be of a different demographic entirely. It is anticipated that there will be individuals who are deconditioned, have medical conditions uncommon in astronauts, be consuming multiple medications, and present with more complex clinical cases than has been the case in professional astronauts to date. It is expected that the need for pharmacogenomics in the commercial spaceflight participant will be at least equal to that of professional astronauts and, perhaps, greater in some cases. This need may be low on a single flight of short duration but will undoubtedly grow with increasing mission duration, distance, and complexity.

Proposed Benefits and Challenges of Advancing Pharmacogenomics in Spaceflight

While not an exhaustive review, this chapter has summarized some of the fundamental technical and conceptual elements related to the application of pharmacogenomics in spaceflight. It is expected that pharmacogenomics will take its place in the continuum of research and clinical practice for application to humans in space. As the space medicine community strives to incorporate this discipline into its clinical and research methodologies, it is useful to reflect on the potential benefits, as well as the challenges that lie ahead. These are briefly summarized below (adapted from Alessandrini et al. 2016).

Proposed Benefits of Employing Pharmacogenomics in Spaceflight

  • Improved drug safety

  • Improved drug efficacy

  • Improved adherence to drug recommendations

  • Improved ability to individualize care (countermeasures)

  • Improved mission performance and productivity

  • Improved selection and dosing of individual drugs

  • Improved selection and dosing of drug combinations (cocktails)

  • Development of novel dosing algorithms (and continuous improvement of existing ones)

  • Improved spaceflight medicine guidelines and policies

  • Establishment of well-curated and accessible data resources for spaceflight medicine professionals

  • Establishment of a structured research pathway for spaceflight medicine

Proposed Challenges of Employing Pharmacogenomics in Spaceflight

  • Development of best evidence methodologies for spaceflight pharmacogenomics

  • Interpretation of pharmacogenomic and pharmacometabolomic data

  • Balancing the immediate clinical needs of astronauts for optimized (and personalized) drug therapeutics in space with the needs for further research

  • Acceptance by clinicians of the potential value of precision medicine testing and integration into the spaceflight care continuum

  • Training of aerospace medicine physicians

  • Integration of precision medicine testing into routine aerospace medicine practice

  • Integration of precision medicine testing into routine aerospace medicine research

  • Design of clinical trials with small spaceflight cohorts that properly assess astronaut health and performance in relation to drug-metabolizing genotypes

  • Protection of personal genetic information

  • Development of prediction tools, based on in vivo, in vitro, ex vivo, and in silico models

Conclusion

While the science of pharmacogenomics on Earth has been rapidly advancing, its adoption in the clinical setting has proceeded more slowly. This appears to be due more to legal, ethical, insurance, medical, and organizational issues, rather than scientific concerns (Zanger and Schwab 2013). The application of pharmacogenomics in spaceflight research is well behind pharmacogenomics research on Earth, while clinical pharmacogenomics in spaceflight also lags behind its medical applications on Earth. Both circumstances, however, represent substantial opportunities to formalize the pharmacogenomics approach to human spaceflight research and the clinical support of humans in space.

Advancing the research in pharmacogenomics applied to human spaceflight is rather straightforward, though its complexity must be acknowledged. It will require that investigator teams utilize the tools of integrated omics, along with specific attention to the major drug-metabolizing enzymes. Given that genes are components of a system with downstream and feedback regulatory components, this naturally incorporates genomics, epigenomics, transcriptomics, proteomics, metabolomics, gut (fecal) metagenomics, and gut (fecal) metabolomics.

Spaceflight omics studies inherently involve small subject numbers and large variable (analyte) numbers. This produces high-dimensional data sets that are prone to noise amplification and overfitting. These issues can be addressed, in part, with careful experimental design (Schmidt et al. 2016). Precision can be further enhanced by the use of longitudinal (serial) measures, which are necessities in omics studies using small subject numbers. Attention to specimen integrity and pre-analytical issues will be crucial (Kirwan et al. 2018), since specimen stability strongly influences experimental variance (and ultimately data integrity). These issues can be amplified in space, because of challenges with specimen storage and transport.

Advancing the clinical application of pharmacogenomics in human spaceflight requires a somewhat different approach. It is generally agreed that genomic markers have a confirmed impact on several CYPs, roughly in the following order: CYP2D6 > CYP2C19 ~ CYP2A6 > CYP2B6 > CYP2C9 > CYP3A4/5 (Zanger and Schwab 2013). Several organizations cited in this review have applied a “levels-of-evidence” approach to develop voluntary guidance on how to apply pharmacogenomics in the clinic (e.g., CPIC). The US FDA presently has identified roughly 100 drugs in which pharmacogenomic recommendations or considerations apply.

In addition, clinical laboratories have expanded their pharmacogenomic test offerings. Some of these are tailored to specialties like oncology and psychiatry, while others are offered more broadly. The spectrum of clinical laboratory offerings encompasses from 30 to 50 drug-metabolizing genes, covering some 200 or more gene variants. Such variants are deemed actionable in relation to more than 300 drugs. In contrast to omics studies that utilize untargeted analysis, clinical pharmacogenomics uses targeted analysis, which does not suffer from the challenges of scaling and overfitting.

Fundamentally, unpredictable drug metabolism impacts all aspects of human health, safety, and performance in the spaceflight environment. Pharmacogenomics, coupled with pharmacometabolomics and related disciplines, exist as essential tools presently available that can advance the pursuit of safety and performance for humans in space. This advancement, however, will require strong advocates, along with formalized and organizational efforts across the aerospace medicine landscape. Fortunately, the aerospace medicine community can build upon the already exceptional work developed for medicine on Earth.

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Resources

  1. Clinical Pharmacogenetics Implementation Consortium (CPIC) (https://cpicpgx.org)
  2. Human Cytochrome P450 (CYP) Allele Nomenclature Database (www.cypalleles.ki.se) Pharmacogene Variation Consortium (PharmVar; www.PharmVar.org)
  3. Pharmacogenomics Knowledgebase (PharmGKB) website (https://www.pharmgkb.org)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael A. Schmidt
    • 1
    • 2
    Email author
  • Caleb M. Schmidt
    • 2
  • Thomas J. Goodwin
    • 1
    • 2
  1. 1.Advanced Pattern Analysis & Countermeasures GroupBoulderUSA
  2. 2.Sovaris Aerospace, LLCBoulderUSA

Section editors and affiliations

  • Marlise Araújo dos Santos
    • 1
  1. 1.Porto AlegreBrazil

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