Drug Metabolism and Disposition Fast Forward
First published on January 28, 2008; DOI: 10.1124/dmd.107.019315
0090-9556/08/3604-715-720$20.00
DMD 36:715-720, 2008
Exploration of the African Green Monkey as a Preclinical Pharmacokinetic Model: Intravenous Pharmacokinetic Parameters
Keith W. Ward,
David James Coon,
Daniel Magiera,
Sanjeev Bhadresa,
Ernell Nisbett, and
Matthew S. Lawrence
RxGen, Hamden, Connecticut (K.W.W., D.J.C., M.S.L.); Molecular MS Diagnostics, Cranston, Rhode Island (D.M., S.B.); and St. Kitts Biomedical Research Foundation, St. Kitts, West Indies (E.N.)
(Received October 17, 2007;
accepted January 23, 2008)
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Abstract
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The value of cynomolgus and rhesus monkeys to predict human pharmacokinetic parameters has been well established in recent years. However, practical limitations on cost and accessibility can often be a deterrent to obtain data in these valuable species, and the characterization of the predictive power of other nonhuman primates would be useful. Therefore, the present investigation was designed to evaluate the pharmacokinetics of a test set of marketed compounds in the African green monkey, to compare the pharmacokinetics of these agents between nonhuman primate species, and to validate the ability of the African green monkey to predict human pharmacokinetics. Intravenous pharmacokinetics were evaluated for 11 test compounds in this study and compared with data from rats, dogs, cynomolgus/rhesus monkeys, and humans. The results from this investigation indicate that African green monkeys deliver reasonable prediction of human clearance and mean residence time and volume of distribution, although somewhat less accurately than cynomolgus and rhesus monkeys, particularly for volume of distribution, potentially because of body size or composition or experimental design differences. Furthermore, use of an optimized clearance prediction algorithm from the literature enhanced predictivity over a simple liver blood flow-based extrapolation methodology. The data from this study show that African green monkeys have the potential to be used as a surrogate for cynomolgus or rhesus monkeys in preclinical pharmacokinetic studies, particularly for the study of clearance processes, and should be considered as an alternate nonhuman primate test species.
The vital role of nonclinical in vivo pharmacokinetic studies to assess the disposition of new chemical entities during lead optimization in drug discovery is widely accepted. Although both in vitro and in silico methodologies may be useful to help predict human pharmacokinetics, in vivo studies continue to be a well established means to accurately characterize discovery stage compounds and provide data to help determine their likely success as human therapeutics (Ward, 2005
). Given the importance and widespread use of such preclinical pharmacokinetic data, substantial research has been performed regarding the optimum species selection and scaling methodologies for accurate prediction of human pharmacokinetics. Although rats are commonly used for this purpose, and rodent data can be useful under certain circumstances to predict human disposition (Jolivette and Ward, 2005; Ward et al., 2005a), numerous studies have shown that optimum predictivity is achieved from preclinical pharmacokinetic data in the nonhuman primate (Ward and Smith, 2004a,2004b; Ward et al., 2005b; Evans et al., 2006; Tang H et al., 2007, Tang H et al., 2007). To date, most studies involving predicting human pharmacokinetics from nonhuman primate data have used either cynomolgus or rhesus monkeys because these are the most commonly used monkey species in pharmacokinetic research. However, the relatively short supply and high cost of performing studies in this species can be barriers to efficient compound profiling, and the identification of alternative suitable nonhuman primates for these studies would be highly desirable.
One potential alternative nonendangered nonhuman primate for such pharmacokinetic studies would be the vervet, or African green monkey (Chlorocebus aethiops sabaeus). African green monkeys have been used extensively in biomedical research, particularly in studying neurological (Jentsch et al., 1997; Taylor et al., 1997; Elsworth et al., 2000), cardiovascular (Cook et al., 1995), and viral diseases (Norley, 1996; Weiss et al., 2003). However, to date, only limited pharmacokinetic data have been published in this species, none which involved the characterization of a test set of compounds evaluated under standardized conditions. Therefore, the present investigation was designed to evaluate the pharmacokinetics of a test set of marketed compounds in the African green monkey, to compare the pharmacokinetics of these agents between nonhuman primate species, and to validate the ability of the African green monkey to predict human pharmacokinetics.
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Materials and Methods
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Materials. Test agents were acquired from standard commercial sources and were of the highest available purity. Compounds were formulated per the manufacturer's instructions if available, or with up to 10% dimethyl sulfoxide and 22.5% hydroxypropyl-β-cyclodextrin. High-performance liquid chromatography (HPLC)-grade acetonitrile and methanol and American Chemical Society-grade formic acid were purchased from EMD (Durham, NC). HPLC-grade water was purchased from Burdick and Jackson (Muskegon, MI). All the other reagents and materials were purchased from standard vendors and were of the highest available purity. All the compounds were administered as solutions, with dosages based on either clinical or laboratory animal experience, and all the dosages are expressed as milligram parent molecule per kilogram body weight.
Animals. For all the studies, adult male African green monkeys (4.0–6.7 kg b.wt.) were used. The animals were collected and studied under the auspices of the St. Kitts Biomedical Research Foundation (St. Kitts and Nevis, West Indies), and all the work was conducted with the prior approval of the Institutional Animal Care and Use Committee of that facility. Monkeys were individually housed in 3 x 2.6 x 2-ft squeeze cages in free-standing enclosures exposed to ambient environmental conditions, which approximated a 12:12-h light/dark cycle with temperatures between 25 and 30°C. Monkeys were fed a standard laboratory primate chow (TekLad, Madison, WI) and were fasted for 6 h before sedation with water provided ad libitum by Lixit valve and an additional water gavage dose at 8 h after compound delivery to maintain urine output. Ketamine/xylazine 5:1 was administered i.m. (0.2 ml/kg of 100 mg/ml ketamine and 20 mg/ml xylazine) for the placement of a catheter in the saphenous vein, which permitted both test compound administration and serial blood draws to the 120-min time point. Animals were maintained under continuous sedation for that interval; catheter patency was maintained with a 0.9% saline drip of approximately 60 ml/h. After 120 min, blood was collected by femoral venipuncture under repeated i.m. ketamine/xylazine sedation (0.2 ml/kg). Monkeys were monitored at all times for distress or other signs of adverse drug reactions.
Pharmacokinetic Studies. All the studies were conducted using either an i.v. bolus or rapid infusion (1.0–2.5 ml/kg dose volume, depending on the solubility of the test agent), using n = 3 animals/compound. Following test article administration, timed venous blood samples were obtained and centrifuged to obtain plasma. Samples were taken predose and at 5, 15, 30, 60, 120, 240, 360, 480, 720, and 1440 min postdose. At each phlebotomy time point 1.5 to 2.0 ml of blood was collected into a heparinized syringe, transferred to a centrifuge tube, and immediately centrifuged at 3000 rpm for 15 min at 4°C. The plasma supernatant was then flash-frozen in liquid nitrogen for transport.
Analytical Procedures. Quantitative analysis was performed on plasma samples for each test compound using an HPLC/tandem mass spectrometric detection method optimized for each individual analyte in the appropriate biological matrix. The HPLC system used Shimadzu (Kyoto, Japan) LC-l0ADvp binary HPLC pumps, a Shimadzu SCL-10ADvp system controller, and a Leap Technologies HTC Pal (Carborro, NC) autosampler equipped with a chiller. The API 3000 mass spectrometer controlled by Analyst Software was from Applied Biosystems/MDS Sciex (Toronto, ON, Canada). Analyst version 1.4.2 was used as the data acquisition software. The analytical column used was a 4.60 x 50-mm Gemini C-18, 5 µm, from Phenomenex (Torrance, CA). Standards and quality control samples were prepared from two separate stock solutions in parallel. For all the samples a fully automated sample extraction procedure was performed using protein precipitation with a CaptiVac (Varian Inc., Palo Alto, CA) 96-well plate vacuum manifold and Captiva filtration kit. Extraction was achieved with a 2:1 volume of either methanol or acetonitrile containing the internal standard (carbamazepine). Ten to 40 µl of the filtered samples was injected on-column for analysis. A generic gradient HPLC method with a flow rate of 0.5 ml/min was used for separation, with mobile phase A 98% and mobile phase B 2% held for 30 s after injection, then a linear ramp to 90% mobile phase B over 1.0 min, 100% mobile phase B held for 30 s, then an immediate ramp to initial conditions for 60 s for re-equilibration. Mobile phase A consisted of 0.1% formic acid in water (v/v), and mobile phase B consisted of 0.1% formic acid in neat acetonitrile (v/v). The eluent was subjected to turbo ion spray positive-mode ionization multiple-reaction monitoring, and each analyte was characterized by an appropriate mass spectral transition of the parent precursor ion to a product ion, generated at an optimized collision energy. Data were reported as quantitative drug concentrations as determined by standard calibration curve analysis using linear fitting of a 1/x-weighted plot of the analyte/internal standard peak area ratio versus analyte concentration. Using these optimized conditions, lower limits of quantitation generally ranging from 1 to 10 ng/ml were achieved.
Data Analysis. Plasma concentration versus time profiles were generated for each animal. Standard noncompartmental pharmacokinetic analysis was performed using WinNonlin Professional version 5.0 (Pharsight Corporation, Mountain View, CA). For prediction of human pharmacokinetic parameters based on these data, the liver blood flow scaling technique of Ward and Smith (2004a) was used. For the purpose of this investigation, it was assumed that African green monkey liver blood flow was consistent on a weight-normalized basis to that in other old world monkeys (
45 ml/min/kg), with any potential effects of the sedation regimen not taken into account. Additionally, the optimized "monkey method" of Tang H et al. (2007), Tang H et al. (2007) was also evaluated as a predictive tool for clearance using the Tang coefficient value of 0.407 for the monkey. Steady-state volume of distribution was compared on a direct body weight-normalized basis across species, and mean residence time was compared as described by Ward and Smith (2004b). For quantitative comparisons of predictive quality of each method, the projected human parameter from the preclinical data in either African green monkeys or other nonhuman primates and this prediction was compared directly with experimentally determined human pharmacokinetic parameters. The difference (error), absolute difference (absolute error), or ratio (-fold error) between predicted and observed values was tabulated, as was the total number of compounds with projected human values within 2-fold of the experimental value.
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Results
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Intravenous pharmacokinetic data for 11 compounds were generated in this investigation. The test compounds showed reasonable molecular diversity, with molecular weights ranging from 234 to 734 and calculated logarithm of the octanol-water partition coefficient values ranging from –0.72 to 4.73. A full list of calculated molecular properties is provided in Table 1, including molecular weight, calculated logarithm of the octanol-water partition coefficient, calculated molar refractivity, number of hydrogen bond donors and acceptors, polar surface area, and the number of rotatable bonds. At the dosages administered, all the compounds were well tolerated in the monkey, with no obvious adverse effects noted. For all the compounds, a full i.v. concentration versus time profile was obtained, allowing noncompartmental data analysis to yield the key pharmacokinetic parameters (Table 2). As with the molecular properties, substantial pharmacokinetic diversity was included in this test set of compounds, with clearances ranging from 2 to 41 ml/min/kg, steady-state volumes of distribution that ranged from extracellular space to 27 times total body water, and half-lives ranging from 1.5 to 11.5 h.
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TABLE 1 Calculated two-dimensional molecular properties of compounds used in the present investigation
All the properties are those reported in Jolivette and Ward (2005), except those for verapamil, which were obtained from Evans et al. (2006).
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TABLE 2 Noncompartmental pharmacokinetic parameters of 11 test compounds in the African green monkey
All the values represent the mean ± S.D. of each parameter calculated from n = 3 monkeys unless otherwise noted.
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The pharmacokinetic data generated in this study were combined with previous pharmacokinetic data for these test compounds in other species obtained under similar (although not identical) experimental conditions to provide a comprehensive evaluation of the relative ability of each species to successfully extrapolate to human pharmacokinetics. Using the data analysis procedures described above, the data from each species were used to predict human clearance, steady-state volume of distribution, and mean residence time; mean overall results from this analysis are presented in Table 3, with individual -fold error data for each compound displayed in Fig. 1. With respect to clearance, for these test compounds the best predictor of human clearance was the cynomolgus or rhesus monkey, followed by the African green monkey, then dog, with the rat providing the least accurate clearance prediction when the liver blood flow extrapolation was used, as determined by mean absolute error (Table 3). This trend was similar for average -fold error, although the dog provided a slightly better average -fold error than the African green monkey using the liver blood flow calculation technique. Interestingly, mean error of the clearance prediction (a measure of bias) was lowest for the African green monkey; alternatively, the number of compounds with predictions greater than 2-fold in error was greatest for the African green monkey (although on average the prediction was within 2.5-fold of the observed value). For volume of distribution, whereas the other preclinical species provided generally similarly accurate predictions, the African green monkey showed a substantially larger prediction error than the other species, with the largest average error, absolute error, -fold error, and number of compounds with more than a 2-fold prediction error. Despite this species difference in volume of distribution, prediction of mean residence time (which is a function of both clearance and volume of distribution) was not substantially worse with the African green monkey than other species. For mean residence time, the African green monkey produced the lowest mean error (and was the only species with positive prediction bias for human mean residence time) and using the liver blood flow technique showed lower mean absolute error than the rat or dog at predicting human mean residence time. However, the African green monkey showed the largest average -fold error for mean residence time and had the largest number of compounds with greater than 2-fold error in the human mean residence time prediction (although on average the prediction was still within 2.5-fold of the true value).
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TABLE 3 Accuracy of various methods for predicting human pharmacokinetic parameters
LBF represents the clearance prediction method of Ward and Smith (2004a), and Tang represents the clearance prediction method of, Tang H et al. (2007), Tang H et al. (2007).
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FIG. 1. Prediction error (represented as -fold difference of predicted value from the measured human value) for clearance (A), steady-state volume of distribution (B), and mean residence time (C) for each test compound in this investigation.
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Another analysis performed in this study was to compare the liver blood flow technique of Ward and Smith (2004a) with the "optimized" method of Tang H et al. (2007), Tang H et al. (2007). Of course, the Tang methodology was optimized for cynomolgus/rhesus monkey, and liver blood flow is not yet known in the African green monkey; this is an important area for future research. Despite these caveats, application of the Tang optimization improved predictive accuracy for human clearance for cynomolgus and rhesus monkeys, as well as the African green monkey (Table 3), with a significant difference in -fold error (p < 0.001, two-tailed Wilcoxon matched-pairs test). When the two different clearance predictions were used to project mean residence time, however, the prediction was slightly (but not significantly) less accurate for the (unoptimized) Tang methodology for the African green monkey than using the liver blood flow technique.
Finally, as a direct primate-to-primate comparison, the absolute prediction error of each compound in this test set from both African green monkeys and the literature data on cynomolgus and rhesus monkeys was compared (Fig. 2). For clearance, the African green monkey was a better predictor of human clearance for 2 of the 11 test compounds (prednisone and lidocaine), whereas for volume of distribution the African green monkey was a better predictor for three compounds (quinidine, propranolol, and ciprofloxacin), and mean residence time was best predicted by the African green monkey for a different three compounds (phenytoin, quinidine, and verapamil). These individual data support the average data from Table 3; although cynomolgus/rhesus monkeys were overall more predictive, reasonable predictive accuracy was achieved with the African green monkey.
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FIG. 2. Absolute error of prediction for clearance (A), steady-state volume of distribution (B), and mean residence time (C) for African green monkeys and cynomolgus/rhesus monkeys for each test compound in this investigation.
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Discussion
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The present work presents the first known validation of the African green monkey as a species for extrapolative pharmacokinetic studies. The extant literature on pharmacokinetics in the African green monkey, particularly i.v. studies with molecules for which relevant human data are available, is quite limited. In one study, the elimination rate of alprazolam and lorazepam appeared higher in African green monkeys than in humans; however, this study involved p.o. administration, and absolute bioavailability was not determined (Friedman et al., 1991). Likewise, elimination of chlorzoxazone appears to be more rapid in African green monkeys than in humans, albeit again from p.o. administration (Lee et al., 2006). Burri et al. (1994) found both i.v. clearance and volume of distribution of the trypanocidal drug melarsoprol to be greater in African green monkeys than in humans, although data from other nonhuman primates are not available for this agent. A useful interspecies comparative study of the pharmacokinetics ics of ochratoxin A has been published, in which clearance and volume of distribution in rhesus and African green monkeys was nearly identical; however, the pharmacokinetics of this toxin cannot ethically be studied in humans (Stander et al., 2001). Finally, Tang C et al. (2007), Tang C et al. (2007) have shown in an in vitro setting using liver microsomes and recombinant enzymes that African green monkeys appear to show somewhat different quantitative metabolism of diclofenac than rhesus or cynomolgus monkeys or humans. Altogether, these previously published studies were fairly inconclusive regarding the possible use of African green monkeys in pharmacokinetic studies designed to predict human pharmacokinetics.
Overall, the data from this investigation support the use of African green monkeys as a suitable alternative to rhesus and cynomolgus monkeys for preclinical pharmacokinetic studies. With respect to clearance, the quantitative error analyses indicate that although not quite as accurate as other nonhuman primates, African green monkeys provided superior predictivity versus rats and dogs. Interestingly, the bias in human clearance prediction produced by the African green monkey was lower than that achieved with other nonhuman primates for this test set (absolute error of 0.7 versus 2.4 ml/min/kg, respectively). Also, it should be noted that even though the optimized monkey method of Tang H et al. (2007), Tang H et al. (2007) was originally developed for cynomolgus and rhesus monkey data, the improvement in predictivity also held in African green monkeys, with this method giving a slightly better absolute error than the liver blood flow method (6.3 versus 6.6 ml/min/kg). One major caveat to these extrapolations is that they were performed assuming that African green monkey liver blood flow is similar to that in other nonhuman primates, and not substantially altered by the sedation regimen used; this is an area of further needed research that may further optimize this prediction scheme. The wild-captured African green monkeys in this study were sedated for the purpose of blood collection, whereas it is common for captive-bred cynomolgus and rhesus monkeys to undergo unsedated venipuncture or carry an indwelling venous catheter for blood collection. The effects of anesthetics on hepatosplanchnic blood flow are well known (Gumbleton et al., 1990), and it is possible that this experimental procedure could affect the results of this extrapolation exercise. Finally, it should be noted that most of the compounds in this test set are low or moderate clearance in humans, with only propranolol and metoprolol being high clearance compounds in humans (both of which were somewhat underpredicted by African green monkey data). It is unclear how well the African green monkey would perform overall as a species to identify these high clearance molecules; further work in this area is required.
Unlike clearance, however, volume of distribution tended to be somewhat greater in African green monkeys than in humans, and they gave a less accurate prediction of human distributional volume than other nonhuman primates (absolute error of 4.4 versus 1.7 l/kg). Consequently, because clearance was similar, the larger distributional volume drove a general longer half-life for the African green monkey. The mechanism(s) responsible for this phenomenon are not clear. Among the factors that can influence volume of distribution, obvious considerations include sequestration into tissues, variations in plasma protein binding or lipoprotein binding, phlebotomy volume, or hydration status, which was elevated in this study cohort to facilitate urine collection. However, for these compounds, there was no clear relationship between any of the physicochemical parameters shown in Table 1 and interspecies difference in either absolute values for pharmacokinetic parameters or prediction accuracy (data not shown), suggesting that more complex factors may be involved with these apparent differences. One such factor may be body composition; the present investigation used relatively lean wild-captured animals as opposed to captive-bred animals, which may be more sedentary and whose body composition profile may be different. Also, the animals in the present study were kept exposed to ambient natural environmental conditions rather than a controlled indoor environment. The role that these and other factors may play in this phenomenon should be further explored to better understand the biological significance of these observed interspecies differences.
Another interesting observation from the present study involves the in vivo-in vitro correlation for one of the test compounds in this set, diclofenac. Previously, Tang C et al. (2007), Tang C et al. (2007) have shown in mixed liver microsomes that monkeys differed substantially in their catalytic efficiency and regioselectivity of diclofenac 4'- and 5-hydroxylation. Likewise, these two oxidation pathways were also significantly different between humans and monkeys in stably expressed CYP2C and CYP3A enzymes, and in each case, preparations from African green monkeys were substantially different from either rhesus or cynomolgus preparations. Therefore, it would be reasonable to expect that in vivo human diclofenac clearance might not be predicted well from monkeys in general, and that African green monkeys might be less predictive in vivo than data from cynomolgus/rhesus monkeys. However, this was not observed in the present study. In fact, in both cynomolgus and African green monkeys, diclofenac showed the most accurate prediction (predictive error of –0.14 and –0.56 ml/min/kg, respectively) of all the compounds tested. This observation highlights the complexity of in vivo-in vitro correlation and further supports the use of in vivo preclinical pharmacokinetic studies in lead optimization wherever feasible.
In summary, the present data suggest that African green monkeys can be used as a surrogate for cynomolgus or rhesus monkeys in preclinical pharmacokinetic studies, particularly for the study of clearance processes. These animals often provide greater availability at lower cost than cynomolgus and rhesus monkeys and are more accessible than other primates such as baboons or chimpanzees. There are an estimated 25,000 wild African green monkeys on the island of St. Kitts (and a similar number on the nearby island of Nevis; McGuire, 1974). These animals are widely considered a nuisance by the populace and the government, and population reduction activities are welcomed. Because of trapping and testing in-country, utilization of outdoor enclosures, and the availability of skilled workers in a suboptimal local job market, substantial cost advantages can ensue. Moreover, being smaller in size, African green monkeys require less test compound, which can be an important advantage during early drug discovery and development. One of the major caveats from this work is the relatively small sample size for this validation set compared with the 110-compound validation set available for rats, dogs, and cynomolgus/rhesus monkeys (Ward and Smith, 2004a; Evans et al., 2006); additional compounds may be required for a more thorough analysis. Additionally, further work will focus on better understanding the volume of distribution data from this study (including protein binding determination), as well as characterizing the metabolism and renal excretion profile of various agents and exploring the performance of the African green monkey in predicting human oral exposure of test molecules.
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Acknowledgments
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We thank Junior Swanston, Mike Struharik, and Samuel Phipps of the St. Kitts Biomedical Research Foundation (St. Kitts, West Indies) for expert technical assistance.
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Footnotes
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Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.
doi:10.1124/dmd.107.019315.
ABBREVIATION: HPLC, high-performance liquid chromatography.
Address correspondence to: Keith W. Ward, Global Preclinical Development, Bausch & Lomb, 1400 N. Goodman Street, Rochester, NY 14603. E-mail: keith.w.ward{at}bausch.com
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Abstract
Materials and Methods
