Chapter 11 Mass balance

219

CHAPTER 11

MASS BALANCE

THOMAS R. BROWNE, 1 GEORGE K. SZABO ~ and ALFRED AJAMI 2 1Departments of Neurology and Pharmacology, Boston University School of Medicine, Neurology Service, Boston Department of Veterans Affairs Medical Center; 2MassTrace, Woburn, MA

1. INTRODUCTION

Regulatory agencies in many countries require a human mass balance/metabolite identification (MB/MI) study as part of the testing of a new drug. Historically, MB/MI studies have been performed by: (1) administering radioactive (14C) labeled drug; (2) measurement of radioactivity in urine and feces (to measure mass balance); (3) chromatography of urine to divide dissolved material into peaks; (4) detection and quantitation of peaks containing drug or metabolite by measurement of radioactivity; and (5) identification of structure of drug or metabolite in "hot" chromatography peaks by various mass spectrometry techniques. Preliminary data on the pharmacokinetics of the drug can be obtained from radioactivity versus time relationships in serum, blood and urine. The analytic aspects of this methodology are simple and have served pharmaceutical research well for many years. Recently, alternatives to traditional radioactive labeling techniques for performing MB/MI studies have been sought for several reasons: (1) regulations for storage and disposal of radioactive specimens have become more restrictive; (2) institutional review boards are increasingly reluctant to approve any work exposing humans to radioactivity; (3) it has been nearly impossible to perform radioactive tracer studies in children, even though drug metabolism in children often differs significantly from adults; (4) synthesis of radioactive analogues of some compounds can be difficult; and (5) the sponsor may be assuming long-term liability risk if the subjects or employees later develop cancer or other diseases. These considerations may increase the cost and delay performance of MB/MI studies. Human MB/MI data should be obtained as early in drug development as possible to determine the presence and

220 extent of potentially active or toxic metabolites and to guide future pharmacokinetic studies (e.g. collection of patient urine samples for drug analysis may, or may not, be useful, depending on results of MB/MI studies). This chapter reports on work to develop simple general methods for performing an MB/MI study on any new drug using stable isotope labeling (SIL) and detection as an alternative to radioactive labeling and detection. Two SIL techniques show promise of achieving this objective of a simple general MB/MI methodology: (1) continuous flow-isotope ratio mass spectrometry and (2) high-performance liquid chromatography combined with chemical reaction interface mass spectrometry. There are many examples of combining SIL and MS for identification of specific metabolites of specific drugs which are covered in Chapters 3-5, and 12.

2. CONTINUOUS FLOW-ISOTOPE RATIO MASS SPECTROMETRY (CF-IRMS) 2.1. Technique and History These topics are covered in Chapter 6.

2.2. Assumptions in Using CF-IRMS for MB/MI Studies Use of CF-IRMS methods for performing human MB/MI studies assumes: (1) simple, reliable CF-IRMS instruments are commercially available; and (2) the commercially-available CF-IRMS instruments possess the necessary sensitivity, precision and accuracy to determine label in urine, feces, serum and blood and to detect and quantify labeled drug and metabolites in HPLC peaks collected from urine, feces and serum.

2.2.1. Reliable instrumentation Early IRMS instrumentation was problematic because: (1) each instrument was unique and "made by hand"; (2) complete liberation of all atoms of a given molecule by oxidation was difficult; (3) transfer by hand of N2 and C02 gases from elemental analyzer to IRMS was problematic; (4) each specimen was run by hand (lack of automation); and (5) factors 1-4 made IRMS difficult and inconsistent (1, 2). Recently, refined commercially-available instruments using a helium carrier gas to carry combustion products to the IRMS have become available from tl~ree sources (Europa Scientific, Ltd., Finnegan MAT and VG Instruments). The authors purchased a Europa (Europa Scientific, Inc.,

221 Franklin, Ohio, USA) ANCL-SL (elemental analyzer) 20/20 (mass analyzer) CFIRMS and found the instrument performed up to specifications and with very high precision as delivered (see below).

2.2.2. Adequate sensitivity: Theoretical computations It is possible to compute the lowest quantity of drug quantifiable with a precision (coefficient of variation, CV) of 5 percent or less for a given drug using the maximal resolution and minimal total sample size of a CF-IRMS instrument, the carbon or nitrogen content of a biological specimen, and the molecular weight and 15N or 13C content of the tracer drug. The equation for this computation is as follows: LQ = MR,- x M(c,n) x Tmw x N

(1)

where MR/is a mass spectrometer's instrument resolution taken as the mole ratio of 15N/14N focused on masses 29/28 or 13C/~2Cfocused on masses 45/44 at natural abundance which can be measured with 5 percent or better precision (data taken from manufacturer's or literature values); M(c,n)is the moles of natural abundance isotopolog(s) per unit volume (time) in the biological matrix that is to be spiked with 15N or 13C tracer (data taken from published elemental composition of various biological matrices); Tmw is the molecular weight of a tracer drug (assumed to be 200 g/mol in this paper); N is the number of labeled atoms per mole of tracer drug. The results of Eq. (1) applied to a typical CF-IRMS instrument are shown in Table 1. Note the following: (1) procedures which reduce background carbon or nitrogen (deproteinization, extraction, chromatography) increase sensitivity; (2) sensitivity for a given molecule increases directly with the number of atoms labeled with an additional neutron; and (3) the theoretical sensitivity of CF-IRMS appears sufficient to perform MB/MI studies on drugs of medium or low (but not high) potency using one 15N or two 13C labels.

2.2.3. Adequate sensitivity, precision and accuracy: Empirical studies In 1993, we presented preliminary evidence that an early commercial CFIRMS instrument (Europa Roboprep CN/Tracer Mass) may possess sufficient sensitivity, precision and accuracy to quantitate some drugs with one 13C or two 15N labels (2, 3). Stable isotope labeling in therapeutic and subtherapeutic quantities of 15N2 13C-phenobarbital were quantitated in urine and in HPLC peaks from urine. Standard curves were reproducible and linear (r2> 0.985)

222 TABLE 1. Lowest Quantity of Drug Quantifiable with a Precision (CV) of 5% or less Using CF-IRMS 1

Desired value

lSN1 label

~3C~ label

~3C2 label

A. Total label Blood (whole)

0.1 ~g/mL

42.2 i~g/mL

>42.2

>42.2

Blood (deproteinized)

0.1 ~g/mL

0.006 ~g/mL

Serum (whole) 0.1

0.1 i~g/mL

1.4 i~g/mL

Serum (deproteinized)

0.1 i~g/mL

0.006 i~g/mL

0.006 ~g/mL

0.003~g/mL

Urine (whole)

1 i~g/mL

1.0 i~g/mL

1.4 i~g/mL

0.7 ~g/mL

Feces (whole)

1 mg/24 hr

0.3 mg/24 hr

2.3 mg/24 hr

1.2 mg/24 hr

Feces (extracted)

1 mg/24 hr

0.05 mg/24 hr

0.7 mg/24 hr

0.4 mg/24 hr

0.006 i~g/mL

0.003~g/mL

0.006 ~g/mL >1.4 i~g/mL

0.003~g/mL >1.4 ~g/mL

B. Labeled drug or metabolite in an HPLC peak drug Serum or urine

0.02 ~g/mL

0.006 ~g/mL

1Europa ANCL-SL 20/20 instrument.

over the range of 3-100 i~g/ml for whole urine (15N2 or 13C labeling) and 0.1-8.0 ~g/ml for HPLC peaks derived from urine (lSN2 labeling). The lower limit of quantitation values for urine drug concentration were 0.46-2.62 ~g/ml in whole urine and 0.10-0.701~g/ml in HPLC peaks. Validation samples quantitated with these standard curves yielded close to expected values. We have been working since then on further empirical verification of CFIRMS analytic determinations of stable isotope-labeled drug concentration in biological matrices using a newer CF-IRMS instrument (Europa ANCL-SL 20/20). We calculated the new instrument should be more sensitive than the older instrument (4). Several interim reports of our (not yet completed) work to confirm these calculations are available. We first studied ~SN~ labeled drug in human urine (5, 6). Standard curves of atom percent excess of ~SN (above natural abundance) times total nitrogen values versus drug (~SN-acetaminophen) concentration were regressed over a concentration range (in whole urine) of 1.0 to 200.0 ~g/ml (Figure 1). Weighted (1/X 2) and unweighted least squares linear regression analysis techniques gave coefficient of determination (r 2) and lower limit of quantitation (LLQ)

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224 TABLE 2.

Quantitation Characteristics of lSN-acetaminophen Urine 2

Urine (diluted)

Urine (urease treated)

LLQ1 ~g/ml

Run A Run B

1.6 1.0

0.9 0.7

1.5 2.2

WT 1 LLQ ~g/ml

Run A Run B

0.3 0.1

0.2 0.3

0.9 0.3

0.3680

0.3687

0.3776

0.3%

0.3%

0.1%

Mean atom% (Blank 2) C.V. n = 6 Mean total Nitrogen (w/g) C.V. n = 32

260.6 3.8%

214.4 2.9%

39.9 2.9%

1LLQ based on weighted (1/X2) least square linear regression. =Blank urines at natural abundance. From Szabo et al. (5) with permission.

values of 0.9998 to 1.000 and 0.24 to 2.2 i~g/ml, respectively. Coefficients of variation for atom percent at natural abundance ranged from 0.1 to 0.3 percent for 15N and 0.8 to 3.8 percent for total nitrogen. Spiked validation urine samples containing drug concentration values determined using standard curves showed close agreement of observed and expected concentrations for values greater than 1.51~g/ml. Similar determinations were performed in whole urine and urine diluted with water or treated with urease to reduce background nitrogen. One milliliter whole urine samples were treated with either 100 I~L of urease (10 mg/mL, 6200 units of activity/mL) or diluted with an equivalent 100 i~L of distilled water. The best results were obtained with diluted whole urine (Tables 2 and 3). Urease treatment may introduce errors because of isotope effects. When whole urine, which contains high concentrations of urea, is treated with urease two products are formed: (1) ammonia (NH3) which is volatile and can be removed under vacuum; and (2) amm o n i u m carbonate ((NH4) CO3) which crystalizes out of solution in the neutral to slightly acidic conditions optimal for urease activity. Urease treated blank urines (isotopically unenriched), measured higher atoms percent of ~5N than equivalent untreated urines. In the urease treated samples 15N at natural

225 TABLE 3. Accuracy Validation of 15N-acetaminophen (observed concentration ixg/ml) Expected concentration (l~g/ml)

Urine

Urine (diluted)

Urine (urease treated)

0.75 3 30 90

0.3 1.9 28.7 87.2

1.1 3.0 30.2 90.4

2.0 3.9 30.8 87.6

1See Table 2. From Szabo et al. (5) with permission.

abundance could be concentrating in the ammonium carbonate product, while 14N more readily forms the volatile ammonia product. The isotope effect could be due to 15N's preferential ammonium carbonate crystal formation. This explanation needs to be confirmed. As shown in Table 2, urease effectively reduces a sample's total background nitrogen load, however, the increased ~5N background levels (due to the isotope effect) limits the assay sensitivity. In a second set of experiments (7), SIL drug (~5N ~3C2 acetaminophen) was measured in urine by the dual selective detection of ~SN label in N2 gas and subsequent 13C label in CO2 gas. Twenty-five microliters of urine was added directly into tin combustion capsules and evaporated and analyzed directly. Drug spiked standards and validation control points in whole urine were assayed. Dual measurements of atoms percent of ~SN and total nitrogen as well as atoms percent ~3C and total carbon were obtained simultaneously from a single combusted sample. Standard curves of atom percent excess (APE) of ~5N (above natural abundance) times total nitrogen (APEXTN) and ~3C times total carbon (APEXTC) values, versus drug concentration were regressed over a concentration range of 0.5 to 200 i~g/ml. Our results were are follows (Tables 4 and 5). Weighted (1/X 2) and unweighted least square linear regression analysis techniques gave coefficients of determination [r 2] values of 0.9773 to 0.9995. Weighted regressions gave greater confidence at concentration values at low (near natural abundance) atom percent measures. Coefficients of variation for atom percent at natural abundance ranged from 0.01 to 0.02 percent for ~5N and ~3C with CVs of 4.8 to 5.0 percent for total nitrogen and total carbon. Observed and expected values for spiked urine samples showed close agreement for concentration >1 ~g/ml. APEXTN

226 TABLE 4. Quantitation Characteristics of ~SN, ~3C2-acetaminophen Urine whole matrix ~3C derived values

Urine whole matrix ~5N derived values r2 Weighted 1 r~

0.9994 0.9986

0.9995 0.9773

Mean atom% (Blank 2)

0.3667

1.1116

C.V. n = 4

0.01%

0.02%

Mean total Nitrogen (l~g) or carbon (~g) (Blank 2) C.V. n = 4

313.8

233.6

4.8%

5.0%

l r2 based on weighted (1/X 2) least square linear regression. 2Blank urines at natural abundance.

TABLE 5. Accuracy Validation of lSN, 13C2-acetaminophen (observed concentration i~g/ml) Expected concentration (~g/ml)

Urine whole matrix ~5N derived values

Urine whole matrix ~3C derived values

Unweighted

Weighted 1

Unweighted

Weighted

0.75 3.0 30.0 90.0

0.75 2.3 28.4 92.6

1.0 2.6 28.6 92.7

2.0 3.4 27.2 92.4

1.4 2.9 28.5 98.4

1Values based on weighted (1/X 2) least square linear regression.

values versus APEXTC values were also regressed and gave a correlation coefficient [r] of 1.0000 and an r 2 of 0.9999 (Figure 2). In a third set of experiments (8), SIL drug (~3C6 levodopa) was measured in serum. Twelve and a half microliters of whole matrix serum, or twentyfive microliters of diluted serum (1:1 with H20) was added directly into tin combustion capsules and evaporated and analyzed directly. Drug spiked

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standards and validation control points in whole matrix serum and diluted serum (to reduce carbon load) were assayed. Measurements of atoms percent of ~3C and total carbon were obtained. Standard curves of atom percent excess (APE) of ~3C (above natural abundance) times total carbon values versus drug concentration were regressed over a concentration range of 0.01

228 TABLE 6. Quantitation Characteristics of 13Cs-acetaminophen

Serum whole matrix

Serum whole matrix diluted

0.9998 0.9926

0.9999 0.9977

Mean atom% (Blank2)

1.1030

1.1145

C.V. n = 4

0.05%

0.02%

r2

Weighted 1

r 2

Mean total Nitrogen (l~g) of carbon (i~g) 535.1 (Blank2) C.V. n = 4

0.6%

520.4 2.0%

lr2 based on weighted (1/X 2) least square linear regression. 2Blank serums at natural abundance.

to 100 ~g/ml. Our results were as follows (Tables 6 and 7). Weighted (1/X 2) and unweighted least square linear regression analysis techniques gave coefficients of determination [r 2] values of 0.9926 and 0.9998, respectively, for undiluted whole matrix serum samples from a range of 2.5 to 100 ~g/ml. Diluted serum samples were linear from 0.25 to 100 i~g/ml with respective r = values of 0.9977 and 0.9999. Coefficents of variation for atom percent at natural abundance ranged from 0.02 to 0.5 percent for 13C and 0.6 to 2.0 percent for total carbon. Observed and expected values for spiked serum standard curve samples showed close agreement for concentrations > 1.0 i~g/ml. The above results are in agreement with predictions of Table 1 and suggest current CF-IRMS instruments possess sufficient sensitivity, precision and accuracy to perform MB/MI studies of many drugs. More confirmatory work is necessary.

2.3. Advantages of the CF-IRMS Method The CF-IRMS method eliminates the problem of special facilities for radioactive specimen storage and disposal, radioactive drug synthesis, special human review procedures, and sponsor liability for exposure of subjects and

229

TABLE 7. Accuracy Validation of 13Cs-levodopa (observed concentration i~g/ml) Expected concentration (l~g/m)l

Serum whole matrix

Serum whole matrix diluted

Unweighted

Weighted 1

Unweighted

Weighted

0.01 0.25 0.5 1.0 2.5 5.0 10.0 20.0 40.0 70.0 100.0

OR2 OR2 OR2 OR2 3.3 5.1 9.3 19.7 39.9 NC3 100.1

OR2 OR2 OR2 OR2 2.6 4.6 9.0 20.0 41.5 NC3 105.4

OR2 0.38 0.58 1.2 2.6 5.0 9.7 19.6 40.0 70.5 99.8

OR2 0.26 0.46 1.1 2.5 5.0 9.7 19.7 40.3 71.2 100.9

1Values based on weighted (1/X 2) least square linear regression. 2OR = Out of range of standard curve. 3NC = Not included in assay runs.

research personnel to radioactivity. This should speed up the performance of MB/MI studies and make MB/MI data available earlier in drug development. The relative cost of an MB/MI study done with stable isotopes versus one done with radioactive methods will vary depending on costs for subjects, special facilities, drug synthesis, and analytic work. In general, the cost for an MB/MI study done with CF-IRMS methods should be comparable to, or lower than, the cost for an MB/MI study done with radioactive methods. An added advantage of the CF-IRMS method is that the specimens can be stored indefinitely without loss of label or special precautions and later analyzed for drug serum concentration versus time relationships using any convenient method. Thus, the subject observations and plasma and specimens obtained in the MB/MI study can also be used to generate a single dose volunteer safety/pharmacokinetic study. This reduces subjects and the time necessary for the single dose volunteer studies required for FDA Phase 1. An economic analysis of SIL methods is contained in Chapter 24. Counting of radioactive label in biological matrices has many biomedical applications in addition to MB/MI studies. It can be predicted that quantitation

230 of stable label in biological matrices by CF-IRMS also will find many biomedical applications.

2.4. Disadvantages of the CF-IRMS Methods CF-IRMS methods have five disadvantages. First, the lower limit of quantitation of CF-IRMS methods (Table 1) may not be adequate to quantitate potent drugs whose concentrations in biologic matrices are in the nanogram/ml range (although use of multiple labels on a molecule improves sensitivity). Second, special synthesis of drug with 13C or lSN label(s) is required. Third, CF-IRMS instruments are relatively scarce at the present time. However, CF-IRMS are available from three commercial suppliers, and CF-IRMS services are available through contract laboratories. Fourth, each HPLC fraction analyzed requires special handling and input to the CF-IRMS. This makes analysis of multiple HPLC fractions labor intensive and time consuming. HPLC-CRIMS may obviate this problem (see below). Fifth, it has not yet been proven that the CF-IRMS method will provide data on new drugs of a quality acceptable to regulatory agencies.

3. CHEMICAL REACTION INTERFACE MASS SPECTROMETRY (CRIMS)

3.1. Technique and History These topics are covered in Chapter 6.

3.2. Assumptions The use of HPLC-CRIMS depends upon several assumptions: (1) reliable and proven equipment is available; (2) the HPLC solvent(s) must carry all of the drug products in a sample (i.e. no drug product is lost in the solvent front or remains behind on the column); (3) the drug product is completely broken down by the microwave-induced plasma; and (4) all of the atoms being monitored react with the reactant gas and are transported to the MS. Note also that all solvents and buffers as well as reactant gas must be volatile. There is preliminary evidence from the owners of the CRIMS technology that these assumptions are true based upon studies using known drugs and metabolites and using solvent systems designed for them (9-12). Extensive and independent validation of HPLC-CRIMS on older drugs has not been

231 performed, and study of new drugs with unknown metabolites by HPLCCRIMS has yet to be reported. 3.3. HPLC-CRIMS Aplications to Mass Balance Studies

By summing the label counted in each HPLC peak, it is possible to estimate the total label present in a sample of urine or other biological material. This technique has been successfully demonstrated in vivo for cortisol (9) and acetaminophen (10). 3.4. HPLC-CRIMS Applications to Metabolite Identification Studies

Rapid, on line, continuous measurement of stable isotope label in HPLC peaks makes CRIMS and extremely powerful technique for detecting labeled peaks in HPLC effluents derived from samples containing unknown metabolites. This technique has been successfully employed to detect the urinary metabolites of cortisol (9) and acetaminophen (10). 3.5. HPLC-CRIMS: Advantages

For both mass balance and metabolite identification studies, HPLC-CRIMS has the following advantages: (1) absence of radiation and associated problems (see above); (2) absence of effects of analyte structure on methodology; (3) no specimen preparation; (4) combination of mass balance and metabolite identification data from one analytic specimen; and (5) quantitative results (912). For metabolite identification, HPLC-CRIMS offers the following additional advantages: (1) rapid and simple detection of all metabolites in a specimen; and (2) preliminary identification data can be obtained using enzymes (to cleave conjugates) and atom specific monitoring to detect rare atoms derived principally from drug (e.g. S, CI, 14C) (9-12). 3.6. Disadvantages of HPLC-CRIMS

Disadvantages of HPLC-CRIMS for MB/MI studies include: (1) difficulty proving assumption of complete recovery listed above; (2) requirement of mass spectrometer, interface, CRIMS and appropriate peripherals; and (3) absence of proof to date that method will provide data on new drugs of a quality acceptable to regulatory agencies.

232

4. STATE OF THE ART Both CF-IRMS and HPLC-CRIMS have shown promise that they can be combined with stable isotope labeling to produce a simple, rapid, general method for performing MB/MI studies. Neither method has been fully validated, applied to study of a new drug, or received official recognition by a regulatory agency. Perhaps the optimal strategy is to employ both CF-IRMS and HPLC-CRIMS for human SIL tracer MB/ML studies. CF-IRMS would be used to count the total label in a specimen for mass balance and as a check for the completeness of collection and quantitation of the specimen when analyzed by HPLC-CRIMS. This strategy takes advantage of the simplicity and accuracy of CF-IRMS for total label counts and the simplicity and accuracy of HPLC-CRIMS for multiple metabolite detection. Furthermore, the most problematic aspect of HPLCCRIMS, verification of complete detection, is obviated.

ACKNOWLEDGEMENT Supported by the United States Department of Veterans Affairs.

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