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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Impact of covariates on drug pharmacokinetics and pharmacodynamics
  5. Conclusion
  6. References

Martinez, M., Modric, S. Patient variation in veterinary medicine: part I. Influence of altered physiological states. J. vet. Pharmacol. Therap.33, 213–226.

In veterinary medicine, the characterization of a drug’s pharmacokinetic (PK) properties is generally based upon data that are derived from studies that employ small groups of young healthy animals, often of a single breed. These are also the data from which population predictions are often generated to forecast drug exposure characteristics in the target population under clinical conditions of use. In veterinary medicine, it is rare to find information on the covariates that can influence drug exposure characteristics. Therefore, it is important to recognize some of the factors that can alter the outcome of PK studies and therefore potentially alter the pharmacological response. Some of these factors are easily identified, such as breed, gender, age, and body weight. Others are less obvious, such as disease, heritable traits, and environmental factors. This manuscript provides an overview of the various stressors (such as disease, inflammation, pregnancy, and lactation) that can substantially alter drug PK. Part II of this series provides an overview of the potential impact of physiological variables such as age, weight, and heritable traits, on drug PK. Ultimately, failure to identify appropriate covariates can lead to substantial error when predicting the dose–exposure relationship within a population.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Impact of covariates on drug pharmacokinetics and pharmacodynamics
  5. Conclusion
  6. References

When pharmacokinetic (PK) data are generated in small groups of normal healthy animals, it is often assumed that these data reflect the drug’s PK characteristics across the intended patient population. The population inferential value of those data is rarely considered, and Monte Carlo simulations based upon such limited datasets render population predictions that, in fact, reflect only the normal healthy animals from which the original information was obtained.

Depending upon the drug in question, population diversity in clearance, distribution, and drug–receptor interactions can lead to clinically relevant differences in dose–response relationships (Goldstein et al., 2007). This same perspective was stated in a guidance document developed by the FDA’s Center for Drug Evaluation and Research (CDER), ‘Guidance for Industry: Exposure–Response Relationships – Study Design, Data Analysis, and Regulatory Applications' (FDA’s Guidance for Industry, 2003). Therefore, in human medicine, where the advancements of personalized medicine now impact drug development, the relationship between patient physiology, genetic predisposition, and drug PK has become an important component of the drug development process. An illustration of this point was the Food and Drug Administration’s (FDA’s) first approval of a drug aimed for a specific subset of a population – BiDil (isosorbide dinitrate/hydralazine hydrochloride), approved for the treatment of heart failure in self-identified black patients (Angus et al., 2005). The race-specific approval was a result of two initial trials that failed to show effectiveness in the general population of severe heart failure patients, buts showed a 43% reduction in death and a 39% decrease in hospitalization for heart failure (as compared to placebo) in black patients.

Ethnicity-related differences in cytochrome P450 (CYP 450) activity in humans have been well-defined (Ambrosone et al., 2008). Particularly in the case of drugs with a narrow therapeutic window, such variants can lead to clinically relevant safety or efficacy issues if dosages are not adequately adjusted. Examples of the highly variant enzyme systems in humans include CYP2C9, CYP2D6, CYP3A, and CYP2C19 (Schwartz, 2003; de Leon et al., 2006; Anglicheau et al., 2007).

Despite a similar magnitude of population diversity among veterinary species (as reviewed by Fleischer et al., 2008 and Martinez et al., 2008), such information is rarely available because of the difficulty in collecting blood samples in the animal patient under clinical conditions of use. Furthermore, while human new drug applications are required to contain PK data (21 CFR 320), no corresponding regulatory requirements are associated with applications for new veterinary drug approvals. Although the very limited number of subjects in veterinary clinical trials and PK and safety studies challenge the identification of covariates or subpopulations, such factors can influence the safety and effectiveness of veterinary therapeutics.

In this first of a 2-part series, we cover the available information on how stress, infection, inflammation, renal or hepatic impairment, and pregnancy and lactation can affect the dose–exposure–response relationships. For each of these factors, we have included tables (Tables 1–6) with the results of PubMed searches conducted by including the variable of interest, the word ‘pharmacokinetics’ and major veterinary species (dog, cat, swine, horse cattle) in the search line. For each factor, we list the number of hits obtained from such searches, and the number of relevant papers contained within these hits. A relevant paper was defined as a manuscript that attempted to address how each of these variables influenced drug PK properties in that species, and we required that such papers included a comparison of an altered state to observations associated with ‘control’ conditions. The citations for each of those relevant articles are provided. Inclusion of these papers is not intended to reflect our endorsement of their conclusions or of the experimental methodologies employed, but rather reflect our attempt to provide as inclusive a list of references as possible. Relevant information derived from our search is provided in the body of this manuscript.

Table 2.   Summary of veterinary literature on the impact of hepatic impairment on drug pharmacokinetics
SpeciesNo. hitsNo. relevant hitsCitations
Dog1765Takata et al. (1998), Boothe et al. (1994, 1992), Waterman and Kalthum (1990) and Brown et al. (1975)
Cat 360 
Swine 773Mentha et al. (1992), Kostopanagiotou et al. (2006) and Kroker (1985)
Horse 220 
Cattle 780 
Table 5.   Summary of veterinary literature on the impact of pregnancy on drug pharmacokinetics
SpeciesNo. hitsNo. relevant hitsCitations
Dog1481Matar et al. (1999)
Cat 250 
Swine2123Klemcke (1995), Küng et al. (1994) and Petracca et al. (1993)
Horse 291Santschi and Papich (2000)
Cattle2306Karzis et al. (2007), Igarza et al. (2006, 2004, 2002), Bengtsson et al. (1997) and Girard and Matte (1995)
Table 6.   Summary of veterinary literature on the impact of lactation on drug pharmacokinetics
SpeciesNo. hitsNo. relevant hitsCitations
Dog  91Merino et al. (2006)
Cat  20 
Swine 402Küng et al. (1994) and Petracca et al. (1993)
Horse  61Santschi and Papich (2000)
Cattle2595Igarza et al. (2004, 2002), Bengtsson et al. (1997), Rule et al. (1996) and Nouws et al. (1983)

This manuscript, along with its counterpart, Part II, represents an effort to promote not only a better understanding of the variables influencing drug response but also the importance of not over-generalizing the characteristic effects that may occur in response to these various factors. Whenever possible, veterinary examples are provided. However, in some cases, either veterinary examples were lacking or human examples were considered to be more illustrative. The fundamental principles derived from the studies in this table, as well as references from the human literature and from rodent studies, are summarized in the body of this manuscript. Finally, while most of the discussions are addressed from the perspective of target animal safety and effectiveness, the implications of altered PK on human food safety and withdrawal times should also be considered when reviewing the information in this article. The possible human food safety implications are particularly pronounced when the covariate impact results in the prolonged residence or increased bioavailability of the drug and/or its active metabolites.

It is our hope that this work will stimulate further discussion and research to support the rapidly evolving therapeutic landscape impacting veterinary pharmacology and therapeutics.

Impact of covariates on drug pharmacokinetics and pharmacodynamics

  1. Top of page
  2. Abstract
  3. Introduction
  4. Impact of covariates on drug pharmacokinetics and pharmacodynamics
  5. Conclusion
  6. References

Disease

Drug PK and pharmacodynamics (PD) can be affected by the presence of concomitant diseases. The most profound differences in the PK and PD response are generally associated with hepatic, renal, and cardiovascular diseases, but other disease processes, such as inflammation, endotoxemia, and stress, can also significantly alter a drug response.

Renal impairment

Although the most obvious effect of renal impairment on drug therapy is a decrease in renal excretion (and to a lesser degree, renal metabolism), renal impairment has also been associated with changes in drug absorption, hepatic metabolism, plasma protein binding, and drug distribution. These PK changes can occur even if the kidney is not the primary route of elimination. For example, chronic renal failure may lead to changes in water and ionic transport (Hatch & Freel, 2008), which may be attributable to regulation by angiotension II (Jin et al., 1998).

The effect of renal impairment or disease on drug pharmacokinetics among veterinary species is summarized in Table 1. Toutain et al. (2000) showed that although renal insufficiency in dogs resulted in a 40–55% decrease in enalaprilat clearance (a renally cleared compound), it also lead to a 260% increase in the apparent clearance of benazeprilat (which is cleared both by renal and hepatic mechanisms). Furthermore, renal impairment can modify the concentration of converting enzyme, its affinity to the angiotensin converting enzyme (ACE) inhibitor, and its plasma vs. tissue location, and this effect appears to be drug specific. In the same study by Toutain et al. (2000), renal injury was found to have no significant influence on the ability of benazeprilat to inhibit ACE while the inhibitory effect of enalaprilat was significantly increased. The difference in the impact of renal impairment on the effects of enalaprilat vs. benazaprilat was attributed to the greater maximum drug binding to ACE (a drug-independent effect) in conjunction with the nonlinear binding characteristics of ACE inhibitors to ACE such that the greatest impact of renal failure is observed at drug concentrations well below the plateau of the concentration–effect curve.

Of particular clinical importance are the complications that can be associated with the use of aminoglycosides in renally impaired animals. As these compounds are eliminated primarily by the kidneys, renal impairment can lead to excessively high systemic drug concentrations which, in turn, can result in both vestibular damage and renal damage (Clark, 1977). However, even in the presence of therapeutic blood levels, prolonged exposure can in and of itself lead to renal toxicity. As renal function becomes compromised, systemic drug exposure can increase, leading to further renal damage. This nephrotoxicity appears to be correlated to the transport of the drug into the renal proximal tubular cells (accumulation) and to the intrinsic toxicity of the drug to the intracellular organelles (Brion et al., 1984). Because of the complexities associated with the administration of these compounds, dosing regimens should be individualized on the basis of patient pharmacokinetics, with particular attention given to those animals presenting with compromised renal function (Frazier et al., 1988).

The importance of correlating renal function to therapeutic outcome has lead to the development of the FDA-CDER Guidance for Industry titled ‘Pharmacokinetics in Patients with Impaired Renal Function – Study Design, Data Analysis, and Impact on Dosing and Labeling.’ (FDA’s Guidance for Industry, 2006) Within this guidance, it is stated that for most drugs that are likely to be administered to human patients with renal impairment, PK should be assessed in patients with renal impairment and the necessary dose adjustments considered. The guidance also advises that a study in renally impaired patients should be considered when a drug exhibits a combination of high hepatic clearance and significant plasma protein binding, as renal impairment could significantly increase unbound concentrations.

Unlike that associated with liver disease (for which there is often no clear correlation between the stage of impairment and changes in PK parameters), the correlation between the magnitude of renal impairment and altered PK is generally apparent for renally cleared compounds. Barbhaiya et al. (1991) measured plasma cefepime concentration in human patients with varying degrees of renal function and found that the elimination half-life of cefepime was 2 h in normal volunteers, and increased to 4 and 12 h in subjects with moderate and severe renal impairment, respectively. This was attributable to altered clearance (cefepime is a renally cleared compound), and effectively no change in the volume of distribution was observed. In this case, there was a direct correlation between creatinine clearance, drug systemic clearance, and dose-corrected area under the curve.

However, it should not be automatically assumed that the presence of renal impairment will necessitate dosage adjustments, even if the drug is largely cleared via the kidneys. For example, ramiprilat, the active metabolite of ramipril, is cleared both by the liver and the kidneys. However, despite a statistically significant decrease in the free plasma concentration of ramiprilat (P < 0.01), it was concluded that no dosage adjustments are needed in dogs presenting with moderate renal impairment (Lefebvre et al., 2006). Therefore, the potential impact of renal impairment on the clinical response to a renally cleared compound, and corresponding adjustments in dosage regimen, needs to be considered from the perspective of known exposure–response characteristics of that drug.

Within the veterinary community, questions have been raised regarding the potential for increased adverse reactions that may be associated with factors such as aging because of the effect it may have on renal and hepatic function. For example, addressing concerns about fluoroquinolone-induced retinal damage in cats, Wiebe and Hamilton (2002) questioned if the dose of fluoroquinolones should be reduced in geriatric cats or those with renal or hepatic failure. They noted that the accumulation of fluoroquinolone metabolites in dogs and of the parent compound in humans with decreased renal function has been reported. Accordingly, they suggest that since therapeutic monitoring in cats may not always be feasible, renal panels with dose or frequency reduction in response to the degree of renal impairment and the site and severity of infection may help to reduce the risk of retinal toxicosis.

Renal disease can influence the clearance of drugs that are not renally eliminated. Lefebvre et al. (1997) suggested that the increased clearance of tolfenamic acid observed in renally impaired dogs was attributable to the altered hepatic metabolism and/or altered enterohepatic cycling of tolfenamic acid. These results further underscore the complex physiological interactions that can impact drug PK during disease. With these concerns in mind, Dowling (2005) recommended that for the geriatric dog or cat, which may present with renal or heptic impairment, the route of drug elimination needs to be carefully considered prior to dosing. When possible, therapeutic drug monitoring would be recommended. However, when monitoring is not feasible, the veterinarian should ascertain whether or not there are clinically proven adjusted dosage regimens for specific drugs (effect of age is reviewed in detail in Part II).

Hepatic impairment

Drugs can alter hepatic function, and hepatic function can likewise impact drug PK (as reviewed by Papich & Davis, 1985). Furthermore, drugs can alter the activity of certain hepatic enzymes, with or without the induction of hepatic failure. For example, strong CYP1A induction is observed following the administration of fenbendazole to pigs (Savlík et al., 2006a, 2006b). It has been suggested that this induction could negatively affect the efficacy of fenbendazole or other concomitantly administered drugs.

Hepatic impairment can reflect a multitude of problems involving drug PK, including dysfunction of transporters (Kullak-Ublick et al., 2004) and changes in hepatic drug metabolism, biliary secretion, hepatic extraction ratio, liver blood flow, and portal-systemic shunting (Shaffer, 2006). Hepatic metabolism in particular is greatly affected by various types of liver diseases, although not all metabolic pathways are equally influenced by a specific disease. In some cases, the drug metabolizing enzymes may be induced by the presence of liver disease (Aweeka et al., 1999; Lu & Cederbaum, 2008), whereas in other cases, they can be inhibited (Orlando et al., 2006). In addition to these more obvious effects of liver disease, hepatic dysfunction can also lead to changes in plasma protein binding (e.g., Klammt et al., 2008), intestinal absorption (Takaya et al., 1989; Khemawoot et al., 2007), and decreased renal perfusion and glomerular filtration (Gundling et al., 2008).

Chronic liver disease affects drug disposition more than any other form of liver disease. For some drugs, such as cephalosporins, it has been reported that alterations in PK parameters in chronic parenchymal liver disease were highly correlated to clinical indices of hepatic impairment and the stages of liver impairment (Ko et al., 1991). More typically, however, the effects of liver diseases are unpredictable, lack sensitivity and do not correlate well with the type of liver damage, its severity, or liver laboratory test results (Davis, 2007).

In addition to the PK changes, alterations in the PD response as a result of liver disease can also be profound. Clinical effects of drugs can vary independent of drug bioavailability, especially in chronic liver disease, such as in the case of cerebral sensitivity to opioids and sedatives (Freye & Levy, 2004). Altered cerebral drug receptors are believed to be responsible for this effect.

As illustrated in Table 2, there have been very few studies among major veterinary species where the effect of hepatic impairment has been studied in both diseased and healthy animals. As expected, in most of these studies, and with a variety of drugs investigated, there were significant changes in drug disposition in the body due to the presence of liver disease, brought about by severe disruption of blood flow and biotransformation in the liver, as well as by hemodynamic changes in the animals with acute hepatic disease.

Stress

Stress is known to influence drug response. The impact of stress has mostly been studied in humans and in laboratory animals. In veterinary species, there are only a few articles that specifically address the effect of stress on drug pharmacokinetics (Marple & Cassens, 1973; Gue et al., 1989; Mistiaen et al., 2002), and of those, the latter two evaluated the effect of stress on the gastrointestinal transit time in dogs, rather than directly on the drug PK. In rats, unfavorable conditions, such as crowding, isolation, food or water restrictions, alterations of light–dark cycle, immobilization, drug administration, etc., have been shown to result in physiological changes such as the release of the adrenal corticotropic hormone, thyroid hormone, insulin, and many of the pituitary hormones. These hormonal responses can, in turn, modify the response to various toxicants (Ader & Cohen, 1993). For example, an increase in the toxicity of a variety of compounds (including metals and organophosphates) in response to such environmental stressors as temperature, nutritional state, and salinity, was observed in aquatic species (Heugens et al., 2001). Similarly, stress can affect human drug responses. For example, pyridostigmine, a carbamate acetylcholinesterase does not readily penetrate the blood–brain barrier. However, soldiers during the Persian Gulf War were found to have a threefold higher central nervous system (CNS) concentration of this compound. This increase (and the corresponding reports of CNS toxicity) is believed to be attributable to a stress-induced disruption of the blood–brain barrier (Friedman et al., 1996).

Stress can also delay gastric emptying (Watanabe et al., 2002). In dogs, acoustic stress affects gastric and intestinal postprandial motility in dogs and delays the recovery of normal fasted gastro-intestinal (GI) electrical activity after a meal. These stress-induced responses result in a transient slowing of gastric emptying, and an enhanced food-induced release of gastrin, pancreatic polypeptide, and somatostatin (Gue et al., 1989). Similar results are observed when dogs are transported to an unfamiliar environment (Mistiaen et al., 2002). In swine, some animals appear to have a lower threshold to stress, and those sensitive animals have a more rapid cortisol clearance rate as compared to ‘normal’ swine. Interestingly, it would appear that although breed is associated with inherent differences in factors such as cortisol level, average daily gain, and lymphocytic responses to antigenic challenge, breed does not influence the physiological response to chronic stress. Rather, a statistically significant difference in physiological response to stress appears to be a function of social status, as stress compromises the immune response of subordinate animals to a far greater extent than it does in dominant animals (Sutherland et al., 2006).

Stress can also alter swine reproductive functions, which can in turn affect drugs’ PK response by various drug/hormonal interactions. The nature of these changes depends upon several factors, one being whether the stress is acute or chronic. Prolonged or chronic stress usually inhibits swine reproduction, while acute stress can either simulate or impair reproduction, depending upon the stage of the reproductive cycle during which the stress occurs. The nature of the impact of acute stress on swine reproduction is also influenced by the animal’s genetic predisposition to stress and the type of stress (Einarsson et al., 2008). Kaur and Bansal (2004) evaluated the effect of oxidative stress on the spermatogenesis and lactate dehydrogenase-X (LDH-X) activity in mouse testis and showed that oxidative stress in the selenium-deficient mice negatively affects the spermatogenic process with a reduction in mature sperm and in turn the LDH-X levels. In terms of a direct effect on drug PK, Morley-Fletcher et al. (2004) examined the influence of prenatal stress on 3,4-methylenedioxymethamphetamine (MDMA) in adolescent female SD rats (30 days) and found that the metabolic rate of MDMA, as well as the MDMA-induced motor alterations, were higher in the prenatal stress group than in the control group. These findings provided evidence that prenatal stress increases sensitivity to toxins, which may also have long-term effects on the offspring.

Infection and inflammation

Inflammation can be acute or chronic. The two types of inflammation are associated not only with differences in inflammatory mediators but also in the physiological response to the inflammatory process. For example, acute inflammation tends to be associated with active infections or tissue injury. It lasts for only a few days and results in resolution, abscess formulation, or a development of a chronic inflammatory process. The latter is characterized by its chronic state and may be the result of the presence of persistent foreign bodies or autoimmune responses. This form of inflammation can last for decades and ultimately leads to tissue damage and/or the formation of fibrotic tissue (Serhan & Savill, 2005; Minnesota Wellness Publications, Inc., 2006).

Inflammation is caused by activation of the innate immune system in response to tissue damage or infectious agents. In turn, tissue insult leads to the release of inflammation mediators such as the cytokines, vasoactive amines (such as histamine, peptides such as bradykinin) and lipid mediators such as prostaglandins. The liver responds to these substances by releasing a battery of acute-phase mediators of inflammation, which in turn lead to a cascade of events that can influence drug PK (Morgan, 2009). Drug metabolism and protein binding are greatly affected by the presence of inflammation and infection due largely to the activation of acute phase-response proteins in the liver (Petrovic et al., 2007).

Discussion of the impact of infection and inflammation on drug PK can be a subject of entire books, and there are no cookbook predictions of the manner in which PK will change in response to inflammatory processes. An overview of the veterinary literature evaluating the impact of inflammation on drug pharmacokinetics is provided in Table 3. In some cases, as shown by Monshouwer et al. (1996), the effect of inflammation on drug PK changes is related to inflammation-induced changes in the activity of metabolizing enzymes. In this regard, it is now well recognized that significant changes in the hepatic cytochrome P450 (P450) system (Morgan, 2009) and drug transporter activity (Petrovic et al., 2007) can occur in response to inflammation and infection. The P450 enzymes are regulated by a multitude of cytokines, and subsets of the P450 system are regulated by different cytokines. This suggests not only that the specific hepatic enzyme being modified can vary according to the etiology of the inflammation/infection but also that the nature of the modification (increase or decrease in activity) can differ depending upon the reason for the inflammatory response (Aitken & Morgan, 2007). The validity of this suggestion was confirmed by the observation that enzymatic activity of the various P450 enzymes (Richardson et al., 2006) and the hepatic flavin-containing monooxygenases (Zhang et al., 2009) of rats show differing responses when the source of the inflammation is Citrobacter rodentium infection, E. coli lipopolysaccharide (LPS), or colitis induced via repeated oral administration of dextran sulfate sodium (Zhang et al., 2009).

Similar results were observed in swine, where inflammation led to marked changes in hepatic metabolism. Monshouwer et al. (1996) showed that intravenous (i.v.) injection of an Escherichia coli LPS resulted in a mild acute phase response and increased cytokine (TNF-alpha and IL-6) within 1–2 h after the first LPS injection. The acute phase response was accompanied by a decrease both in the total P450 content and in microsomal P450-dependent activities within 24 h after LPS administration. The decrease in P450 activities was accompanied by losses of cytochrome P4501A and P4503A apoproteins. Therefore, it is not suprising that there was a pronounced decrease in antipyrine plasma clearance (control 8.5 ± 0.8 vs. LPS 2.2 ± 0.7 mL·min/kg). These investigators further showed that their Actinobacillus pleuropneumoniae model had a remarkably similar effect on hepatic drug metabolism as compared to that observed with the LPS injection. In contrast, Post et al. (2003) showed that LPS can negatively affect the metabolism of enrofloxacin, and questioned whether or not the influence of LPS adequately reflects the PK changes that occur when swine are artificially infected with A. pleuropneumoniae.

Infection and inflammatory processes can lead to pronounced increases in the plasma concentrations and toxicity of various drugs (Mills et al., 1995; Schmith & Foss, 2008). For example, asthmatic patients with influenza often experience severe toxicity associated with their standard theophylline therapy, an effect that has been linked to a decreased theophylline clearance via the CYP 450 system (Renton, 2005). Importantly, the nature of the change may vary across animal species, but as already discussed for many other factors, there are not many veterinary examples on the effect of infection on drug PK (summarized in Table 4). Species differences are illustrated by the example of oxprenolol, propranolol, and verapamil, which are all associated with stereospecific metabolism. Although inflammation-induced changes in drug metabolism and protein binding lack a stereospecific component in dogs and rabbits, stereospecificity in inflammation-induced changes in drug metabolism was observed in rats (Laethem et al., 1995).

In some cases, changes in drug toxicity have been attributed to the altered expression of several important drug transporters, such as the ATP-binding-cassette (ABC) transporters including P-glycoprotein (Mealey, 2004; Martinez et al., 2008), and the solute carrier family of transporters, which includes the organic ion transporter proteins (OATP) (Petrovic et al., 2007). The inhibition of these transporter proteins is due to an involvement of pro-inflammatory cytokines such as interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)α. Nuclear hormone receptors are also believed to participate.

Another point of attention is the impact of critical illnesses, such as sepsis, on drug PK. In his review of literature on the impact of sepsis on antimicrobial PK, Fry (1996) identified that half-life of many antimicrobials is reduced in the hyperdynamic states of stress and sepsis and that the Vd is expanded, suggesting that dosing regimens of antibiotics in critically ill patients may be inadequate, accounting for treatment failures and having a possible significance in the emergence of bacterial resistance. Similar results were reported in foals, where plasma amikacin levels were found to be significantly higher in healthy foals than hospitalized ones (Bucki et al., 2004). In contrast, drug clearance is often decreased as a function of sepsis, as shown in foals by Green et al. (1992) and Wichtel et al. (1992), as sepsis is often associated with decreased organ blood flow and decreased hepatic function (van Haren et al., 2007; Klijn et al., 2008). A review of the PK and PD changes that can occur with sepsis and septic shock in human patients is reviewed elsewhere (De Paepe et al., 2002). Although this manuscript is directed toward the human patient, the findings are based both upon human and animal model studies.

Inflammation and infection can lead to enhanced tissue drug concentrations. For example, inflammation can increase capillary permeability and therefore alter drug partitioning into tissues, as was the case for flunixin in rabbits (Elmas et al., 2006). Changes in tissue composition (e.g., protein concentration, cellular constituents, or pH) can likewise affect local drug concentrations. Gips and Soback (1999) found norfloxacin PK changed significantly in the presence of subclinical (Staphylococcus aureus) and chronic (E. coli) bovine mastitis. Both infectious processes resulted in a statistically significant increase in systemic clearance, decrease in volume of distribution, and decrease in milk norfloxacin concentrations. These effects were attributed to changes in the pH of the infected udder and therefore a change in the degree of norfloxacin ionization. In horses, inflamed tissues resulted in markedly higher concentrations of several nonsteroidal anti-inflammatory drugs (NSAIDs) as compared to healthy tissues. This was seen regardless of whether the inflammation was associated with a tissue cage model (Landoni & Lees, 1995a,b; Landoni et al., 1995a,b), soft tissue inflammation (Mills et al., 1996), or a synovitis model (Firth et al., 1988; Owens et al., 1995). The partitioning of ceftiofur (Clarke et al., 1996) and erythromycin (Clarke et al., 1993) into bovine tissue chambers was likewise found to increase when the chambers were infected with Pasteurella haemolytica. Alternatively, immune cells may be responsible for drug transport to the site of infection. Boothe et al. (2009) showed that canine white blood cells are responsible for the transport and release of (14)C-enrofloxacin at sites of inflammation. This can lead to drug accumulation at the site of infection. A similar concentrating of enrofloxacin in infected canine skin tissues was seen in naturally occurring cases of pyoderma (DeManuelle et al., 1998). Whether or not altered partitioning into the site of infection will influence drug PK will depend upon the effect of that infection on the inflammatory cytokines and the volume of that compartment relative to the rest of the body.

Although many investigations suggest that infection induces a decrease in drug clearance, just the opposite outcome has also been observed. Inflammation decreased the plasma drug concentrations of erythromycin in cattle (Burrows, 1985). In another example, pigs that were artificially infected with porcine reproductive and respiratory syndrome virus (Tantituvanont et al., 2009a,b) exhibited an increase in the clearance and the volume of distribution of intramuscular ceftiofur. It was suggested that such changes could adversely influence the therapeutic dose–response relationship. These findings underscore the challenge in predicting how infection and inflammation will influence drug PK and reasons why studies are needed in both healthy and diseased animals.

Infection can alter intestinal absorption characteristics. Klein et al. (2008) showed that acute Cryptosporidium parvum infection in neonatal calves leads to severe mucosal damage (reduction in villi and microvilli). This mucosal damage was associated with higher paracellular (small molecule) permeability (i.e., a ‘leaky gut,’ where the integrity of the tight junction is compromised, thereby allowing for the absorption of molecules that otherwise would have been retained in the gut), but a reduction in the surface area for absorption. The resulting decrease in absorptive capacity leads to a marked reduction in the absorption of actively transported molecules (such as the marker d-xylose) and those that are absorbed via transcellular pathways (such as the lipophilic substance, retinyl-palmitate). At the same time, more than a 100% elevation of intestinal permeability was observed in these infected calves.

The effects of an experimental Theileria annulata infection on the PK of oxytetracycline (OTC) were investigated in crossbred calves (Kumar & Malik, 1999a,b). The hemoglobin, packed cell volume, white blood cell counts, and serum Cu, Fe, and Zn concentrations decreased after onset of the infection. Following infection, the distribution and elimination half-lives (t1/2 alpha and t1/2 beta), volume of the central compartment (Vc), AUC, and mean residence time were significantly reduced, and total body clearance was significantly increased. Although no change in the apparent and steady-state volumes of distribution were observed, there was an overall decrease in drug concentrations. The above changes may necessitate the adjustments in the OTC dosage regimen when used to treat Theileria infections in cattle under field conditions (Kumar & Malik, 1999a,b).

Through it all, one of the important lessons is the potential influence that infection and inflammation can have on drug response. Accordingly, dose–concentration–effect studies based upon in vitro data or PK data derived from healthy animals may not accurately reflect relationships that will exist under actual conditions of use. As stated in the review by Kulmatycki and Jamali (2005), at least in human patients, the expression of both pro- and anti-inflammatory mediators can be influenced by various factors such as rheumatic diseases, myocardial infarction, angina, aging, obesity, and pharmacotherapy (note that this review contains an extensive table which explores altered drug response across a wide range of disease conditions). In response, certain physiological systems (e.g., various cardiovascular receptors) are down-regulated in the presence of pro-inflammatory mediators. Consequently, in people, certain chronic inflammatory conditions such as rheumatoid arthritis, aging and obesity, can lead to a reduction in drug response, (e.g., verapamil), despite increased drug concentrations. This can lead to tremendous deviations in predicted vs. observed responses, or in the variability associated with the concentration–effect curves observed under actual conditions of use for many drugs (Schmith & Foss, 2008). Although not unexpected, it would be interesting to see when similar changes in exposure–effect relationships occur across the many veterinary species.

Pregnancy and lactation

Pharmacokinetic can be affected by the physiologic changes associated with the gestational and lactation periods. During pregnancy, changes are known to occur in the levels of several hormones, plasma volume, and body composition (greater body fat). Pregnancy can also be accompanied by delayed gastric emptying; prolonged gastrointestinal transit time; increases in cardiac output, stroke volume, and heart rate; decreased albumin concentration (reduced protein binding); increased blood flow to various organs; increased glomerular filtration rate; and altered hepatic enzyme activity (Cono et al., 2006). Although the extent of physiological changes during pregnancy and lactation is extensive, the evidence-based guidelines for how drug dosing should be altered during pregnancy remain to be developed, even within human medicine.

In human medicine, pregnancy-related PK changes can necessitate dosage adjustments for numerous antimicrobial compounds, including the penicillins, fluoroquinolones, and aminoglycosides (Nahum et al., 2006). Similar results have also been reported in veterinary species, particularly for drugs that are distributed to extracellular water and have a relatively small volume of distribution (see Table 5 for all relevant veterinary literature on the effect of pregnancy on drug pharmacokinetics). Significant changes in drug clearance and volume of distribution have been reported for benzylpenicillin when administered to ewes (Oukessou et al., 1990; Oukessou & Toutain, 1992), beta-lactams in ewes and cows (Bengtsson et al.,1997), ketoprofen in cows (Igarza et al., 2004), marbofloxacin in sows (Petracca et al., 1993), and eprinomectin in dairy goats (Dupuy et al., 2001). However, because of the continual changes in physiologic parameters that occur, drug PK characteristics will vary as a function of the stages of pregnancy and lactation. Oukessou and Toutain (1992) studied the influence of the stage of pregnancy on the PK disposition of gentamicin in the ewe and showed that the steady-state volume of distribution was significantly increased from mid to late pregnancy (from 0.09 to 0.194 l/kg). Similarly, plasma clearance was increased by about 150% at the end of pregnancy. The authors concluded that these modifications should be taken into account, and that the corresponding dosage regimen and withdrawal time should be adapted accordingly during pregnancy.

The PK changes that occur in pregnancy are oftentimes similar to changes occurring during lactation. This was illustrated in a comparative gentamicin PK study in pregnant and lactating mares, where no differences in drug exposure, distribution and clearance were detected between mares in late pregnancy when compared to the ones in early lactation (Santschi & Papich, 2000). The authors concluded that, based on the results of this study, unlike the gentamicin study in ewes (Oukessou & Toutain, 1992), there was no need to adjust the dose of gentamicin in pregnant mares. However, because the gentamicin levels were compared only between pregnant and lactating mares, this still does not conclusively prove that there are no differences between pregnant and non-pregnant non-lactating mares. Furthermore, as different stages of pregnancy and lactation may have differing effects on drug PK, the results of this study might have been compounded by a relatively wide enrollment window (a period of 1–4 weeks prior to parturition vs. 1–4 weeks after parturition).

Pregnancy has been shown to affect the enzyme activity in the human liver, with the activity of certain liver cytochromes (e.g., CYP3A4, CYP2D6) increasing considerably. In contrast, the activity of CYP1A2, the major human xenobiotic-metabolizing enzyme, is decreased in pregnancy, possibly resulting in increased sensitivity to therapeutics (Cono et al., 2006). Furthermore, increases in estrogen and progesterone during pregnancy also alter hepatic enzyme activity (Uhl, 2004). Borlakoglu et al. (1993a) showed that pregnancy and lactation in rats are directly linked to depressed phase 1 and phase 2 hepatic drug metabolism. In addition to changes in some phase 1 enzymes, the authors showed that the rate of N-demethylation of aminopyrine, aldrin epoxidation, and N-demethylation of demethylnitrosamine were reduced by 53%, 74%, and 21%, respectively. At the same time, the 4-hydroxylation of aniline was increased by 71% and 31%, respectively in lactating rats, while the activities of UDP-glucuronyltransferase and glutathione S-transferase were increased by 21% and 27%, respectively, confirming the opposing effect of lactation on various liver enzymes. However, the expression of CYP2C6 and CYP3Al did not differ in pregnant and lactating rats.

The same research group also showed that administration of polychlorinated biphenyls (PCBs) to lactating rats resulted in enhanced formation of all PCB-metabolites. These results were interpreted to suggest that lactation may protect, at least in part, against the inductive effect of PCBs, possibly by enhanced clearance of these chemicals via lactation (Borlakoglu et al., 1993b). Lactating rats were found to exhibit enhanced p-nitrophenol UDP-glucuronosyltransferase activity, as well as an increase in the hepatic content of UDP-glucuronic acid (Luquita et al., 1994). These findings, and the fact that lactation increased the liver-to-body-weight ratio, emphasize the changing role of the liver in the metabolism of xenobiotics during lactation.

During the course of pregnancy, plasma volume increases by up to 50%, resulting in a higher volume of distribution for most drugs. At the same time, plasma albumin concentrations decrease, resulting in higher amounts of free, unbound drug (Beierle et al., 1999). The potential impact of these changes in protein binding can be considered from the perspective of the drug’s therapeutic index, its hepatic extraction efficiency, the volume of distribution, and its route of administration (Benet & Hoener, 2002a,b; Toutain & Bousquet-Melou, 2002).

Drug PK during the postpartum and lactating periods may also be influenced by the hormonal-induced changes in body fat proportion, body weight, and muscle mass. When compared to the effects of pregnancy, lactation can often have a more dramatic effect on drug PK, with animals typically showing increased clearance and volume of distribution, and shorter terminal half-life (Oukessou et al., 1990; Petracca et al., 1993; Soback et al., 1994; Shem-Tov et al., 1998; Marín et al., 2007). Table 6 lists all relevant veterinary articles that evaluated the effect of pregnancy on drug PK; again, the number of relevant hits emphasizes a relative lack of in-depth knowledge on the various effects of lactation among our veterinary patients. For those drugs excreted in milk, lactation can markedly increase drug clearance. The amount of drug secreted in the milk is influenced by such factors as the drug’s degree of ionization and lipophilicity, its molecular weight, and its protein binding characteristics (Atkinson & Begg, 1990). Transfer is greatest in the presence of low plasma protein binding and high lipid solubility. In addition, milk is slightly more acidic than plasma (pH of milk is approximately 7.2 and plasma is 7.4), allowing weakly basic drugs to transfer more readily into breast milk and become trapped secondary to ionization. A good example of ion trapping is the PK of orbifloxacin, which, like other fluoroquinolones, is amphoteric because of the presence of a carboxylic acid and one or more basic amine functional groups. Orbifloxacin exhibits extensive penetration from blood into goat’s milk based on the ion trap mechanism (Marín et al., 2007). As discussed previously, changes in milk pH during infection and inflammation can alter the degree of ionization of some compounds (such as the amphoteric flurouquinolones) and therefore alter drug concentrations in plasma and milk.

Similar to the pregnancy effect, the influence of lactation on drug PK will vary with the stage of lactation. However, in lactation this effect is largely due to its impact on milk fat and protein content. For example, Bengtsson et al. (1997) showed that serum concentrations of beta-lactam antibiotics in ewes and cows were markedly lower in early than in late lactation, with significantly higher weight-corrected values of clearance and volume of distribution, and markedly shorter mean residence time and half-life. Similarly, Soback et al. (1994) compared norfloxacin levels in ewes during nursing, 1 day after weaning and 1 month after weaning, and found significantly lower drug exposure during nursing than at various times after weaning, and concluded that it might be appropriate to adjust fluoroquinilone dosage regimens based not only on the presence of lactation but also on the stage of lactation.

Finally, it should be noted that although it is typically assumed that the effect of lactation will be seen as either lower drug exposure and/or enhanced clearance, this is not always the case. For example, plasma concentrations of ceftazidime were significantly higher in lactating than in non-lactating cows after both i.v. and intramuscular administration of the drug (Rule et al., 1996). Although the difference was not of a magnitude that would require dose adjustment, it confirms that the effects of lactation are not fully predictable.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Impact of covariates on drug pharmacokinetics and pharmacodynamics
  5. Conclusion
  6. References

The above examples underscore the potential changes in blood level profiles that can occur across a population of animals under clinical use conditions. With these illustrations in mind, it is evident how understanding the effects of altered physiology on drug PK can assist practitioners in making rational dosage selection.

The population factors that can affect drug response continue in Part II of this series, where we discuss the potential influence of physiological variables on drug PK.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Impact of covariates on drug pharmacokinetics and pharmacodynamics
  5. Conclusion
  6. References
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