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Citation: Tian, T.; Jing, J.; Li, Y.;
Wang, Y.; Deng, X.; Shan, Y. Stable
Isotope Labeling-Based Nontargeted
Strategy for Characterization of the
In Vitro Metabolic Profile of a Novel
Doping BPC-157 in Doping Control
by UHPLC-HRMS. Molecules 2023,28,
7345. https://doi.org/10.3390/
molecules28217345
Academic Editors: Angelo
Antonio D’Archivio and
Alessandra Biancolillo
Received: 28 September 2023
Revised: 25 October 2023
Accepted: 28 October 2023
Published: 30 October 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
molecules
Article
Stable Isotope Labeling-Based Nontargeted Strategy for
Characterization of the In Vitro Metabolic Profile of a Novel
Doping BPC-157 in Doping Control by UHPLC-HRMS
Tian Tian †, Jing Jing †, Yuanyuan Li, Yang Wang, Xiaojun Deng and Yuanhong Shan *
Shanghai Anti-Doping Laboratory, Shanghai University of Sport, Shanghai 200438, China;
tiantian@sus.edu.cn (T.T.); jingjing@sus.edu.cn (J.J.)
*Correspondence: shanyuanhong@sus.edu.cn
†These authors contributed equally to this work.
Abstract:
Traditional strategies for the metabolic profiling of doping are limited by the unpredictable
metabolic pathways and the numerous proportions of background and chemical noise that lead
to inadequate metabolism knowledge, thereby affecting the selection of optimal detection targets.
Thus, a stable isotope labeling-based nontargeted strategy combined with ultra-high-performance
liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) was first proposed for
the effective and rapid metabolism analysis of small-molecule doping agents and demonstrated via
its application to a novel doping BPC-157. Using
13
C/
15
N-labeled BPC-157, a complete workflow
including automatic
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
m/zpair picking based on the characteristic behaviors of
isotope pairs was constructed, and one metabolite produced by a novel metabolic pathway plus eight
metabolites produced by the conventional amide-bond breaking metabolic pathway were successfully
discovered from two incubation models. Furthermore, a specific method for the detection of BPC-
157 and the five main metabolites in human urine was developed and validated with satisfactory
detection limits (0.01~0.11 ng/mL) and excellent quantitative ability (linearity: 0.02~50 ng/mL with
R
2
> 0.999; relative error (RE)% < 10% and relative standard deviation (RSD)% < 5%; recovery > 90%).
The novel metabolic pathway and the in vitro metabolic profile could provide new insights into the
biotransformation of BPC-157 and improved targets for doping control.
Keywords: stable isotope labeling; BPC-157; metabolic profile; doping control; UHPLC-HRMS
1. Introduction
The continuing progress of modern anti-doping analytics has allowed for great achieve-
ments in the last few years, enabling the detection of infinitesimal amounts of prohibited
substances and thereby effectively reducing the occurrence of doping events [
1
]. However,
new doping agents keep being discovered and used in professional sports competitions in
an attempt to enhance performance [
1
,
2
]. For example, BPC-157, a novel emerging peptide-
derived drug (GEPPPGKPADDAGLV), has been classified as S0. Non-approved substances
was added to the 2022 Prohibited List by the World Anti-Doping Agency (WADA) due
to its potential to enhance athlete performance [
3
]. As a fragment of protein BPC (body-
protecting compound, 40 kDa), BPC-157 (also called PL 14736, PL-10, and bepecin) is
thought to be sufficient and responsible for the protective and physiological effects [
4
].
Pharmacological studies, as well as non-scientific online testimonials, both suggest that
this peptide aids in muscle, tendon, and ligament healing, promoting cytoprotection and
wound healing [
5
–
8
]. Thus, despite proper clinical trials in human subjects having not
yet been performed to substantiate the healing claims and potentially harmful effects of
BPC-157, it is being used by athletes looking to gain a competitive edge [
9
]. For this reason,
it is important to have a deep awareness of biotransformation for such new prohibited
substances and implement it into routine doping screening, considering the health risks to
Molecules 2023,28, 7345. https://doi.org/10.3390/molecules28217345 https://www.mdpi.com/journal/molecules
Molecules 2023,28, 7345 2 of 16
athletes and the fairness of competitions. To date, limited information is available on the
metabolism of this substance, prompting the need for further research and analysis.
Logically, the most straightforward and effective path for the metabolic study is the
human administration of the target. However, this is generally difficult to conduct due to
ethical constraints and safety risks, especially for new doping agents, since the majority
may not be approved for marketing [
10
,
11
]. In response to this limitation, proper
in vitro
models have been sought out for the better prediction of doping metabolites, such as
human serum/plasma, liver, or kidney microsomes and the S9 fraction, and recombinant
proteases [
1
]. Among them, studies with subcellular fractions, such as microsomes and
the S9 fraction, usually provide a high number of metabolites and are useful to quickly
generate many potential metabolites and obtain retention times (RTs) and mass spectra
to be matched with
in vivo
samples. Combined with advanced analytical techniques,
especially UHPLC-HRMS-based approaches, remarkable progress has been made in the
investigation of doping metabolism based on
in vitro
experiments [
12
,
13
]. However, some
issues remain challenging. The conventional strategy is based on the drug structure com-
bined with commercial software or researchers’ experience for metabolite prediction [
14
,
15
],
which may lead to inadequate metabolic knowledge and missing metabolite information
because the metabolic pathways cannot be fully foreseen, especially affecting the finding
of potential long-term metabolic targets [
15
,
16
]. In addition, the incubation systems are
complicated, and a large amount of mass spectral information contains numerous propor-
tions of background and chemical noise, which prevents the effective and rapid screening
of targets.
Recently, a promising approach referred to as stable isotope labeling has attracted ex-
tensive interest in the fields of metabolomics, single-cell omics, disease biomarker exploring,
and natural product screening, etc., and was proved to be efficient in the screening of endoge-
nous or exogenous metabolites [
17
–
20
]. Based on the same physicochemical properties and
characteristic mass difference of isotope pairs, combined with HRMS, it is specific and rapid
for nontargeted screening and enables the unbiased identification of numerous metabolites.
For example, upon isotope-coded derivatization with a pair of labeling reagents, HBP-d (0)
and HBP-d (5), characteristic neutral fragments of 79 Da and 84 Da were generated for the
aldehyde analytes [
19
]. In combination with the unique isotopic doubles (
∆
m/z= 5 Da) and
dual neutral loss scanning (dNLS), it allowed the nontargeted profiling and identification of
endogenous aldehydes even amidst noisy data. Accordingly, a generic screening strategy was
developed by Huang et al. [
19
], and its application to the screening of cinnamon extracts led
to the finding of 61 possible natural aldehydes and guided the discovery of 10 previously
undetected congeners in this medicinal plant. In terms of metabolomics analysis, using
newly designed stable isotope-labeled reagents ([d(0)]-/[d(3)]-/[d(6)]-DMMIC), Liu et al. [
21
]
achieved rapid nontargeted screening of amine submetabolomes in human esophageal tissues
based on the formed set of molecular ions with an increase of 3.02 m/zand characteristic
fragment ions with m/z204.1: 207.1: 210.1. Through this technique’s application in the
analysis of metabolic differences between the carcinoma and paracarcinoma tissues of
esophageal squamous cell carcinoma, 13 amine metabolites were discovered with the
highest potential differential. Furthermore, the absence and presence of isotopic labels in
reaction products can trace reaction pathways to reveal the reaction mechanisms. Based on
13
C stable isotope labeling in combination with HPLC-HRMS, Geng et al. [
22
] discovered
the binding products of quinone–quinone that are not derived from either nucleophilic
reactions or redox reactions, providing a new perspective for quinone metabolic pathway
in foods. In the field of anti-doping, stable isotope labeling-based strategies have also
been attempted for metabolite screening. Thevis et al. [
13
,
23
] developed an isotope-labeled
reporter ion screening strategy for prohibited protein and large peptide substances and
demonstrated its feasibility by applying it to the metabolite identification of HGH, IGF-
1, etc. In their study, the feature of commonly appearing immonium ions generated by
the internal dissociation of single amino acids from the peptide backbone as “flags” for
metabolites was exploited. The signals of metabolites can be obtained by extracting labeled
Molecules 2023,28, 7345 3 of 16
immonium ions from the AIF data, while the exact intact mass of the analytes may be
hard to determine. In addition, for small-molecule doping agents, characteristic diagnostic
ions similar to large peptides are not easily generated. Thus, a proper isotope labeling
strategy for the metabolism analysis of small-molecule doping agents, which are the major
components of the WADA prohibited list, needs to be developed.
In the present study, a stable isotope labeling-based nontargeted strategy combined
with UHPLC-HRMS was first proposed for metabolism analysis of small-molecule doping
agents to realize the rapid and effective screening of targets and demonstrated via its
application to the
in vitro
metabolism of a novel peptide doping BPC-157. Although
Cox et al. [
9
] reported the
in vitro
plasma metabolism of BPC-157 and found several
degradation metabolites, more in-depth studies with different
in vitro
incubation models
need to be performed to further elucidate the biotransformation in organisms due to the
diversity of administration routes. Using
13
C/
15
N-labeled BPC-157, based on the similar
chromatographic behavior and fixed mass differences of isotope pairs, a complete workflow
that includes automatic
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
m/zpair picking was constructed to mine
potential metabolites in the human liver microsomes and human skin S9 incubation systems.
As a result, extensive metabolism was observed in the two incubation systems with an
obvious difference, and one metabolite produced from a novel metabolic pathway plus
eight metabolites produced from the conventional amide-bond breaking metabolic pathway
were discovered in total, providing six more metabolites than a previous study [
9
]. The
five main metabolites were identified with the aid of synthetic reference materials (RMs).
Furthermore, a sensitive and specific method for qualitative and quantitative analysis of
BPC-157 and the five main metabolites in human urine was first developed and validated
systematically with satisfactory results. The novel metabolic pathway and the metabolic
profile in the two incubation models could provide new insights into the metabolic process
of BPC-157 in organisms and improved analytical targets for doping control.
2. Results and Discussion
2.1. Stable Isotope Labeling-Based Nontargeted Strategy for the Metabolism Analysis
Traditional metabolite screening technologies are limited by unpredictable metabolic
pathways and the numerous proportions of background in mass spectral information that
lead to inadequate metabolism knowledge. Within this study, a stable isotope labeling-
based strategy combined with UHPLC-HRMS was developed for the metabolic profiling of
small-molecule doping agents for the first time to achieve the effective and rapid screening
of targets. Differentiated from the method reported by Thevis et al. [
13
], the strategy
established here was based on differences in the mass spectrometric features (fixed mass
differences in ions and similar cleavage behaviors) and the chromatographic features
(highly similar retention times and peak shapes) between the light and heavy labels for
metabolite mining, which was more suitable for small-molecule doping agents.
The labeling site and number of heavy isotopes were key considerations in developing
the strategy. For the metabolic analysis of small-molecule doping agents, the labeling sites
are preferred to groups that are not easily involved in the metabolic process, such as the
benzene ring, alicyclic ring, and heterocyclic ring, so that the metabolite ion pairs with
isotopic characteristics can be retained to the maximum extent. According to previous
studies [
24
–
26
], peptides are usually metabolized through the degradation of amino acids
at both ends to form metabolites in the body. Thus, in this study, the lysine in the middle of
the peptide sequence was selected to be labeled with heavy isotopes to avoid the loss of
metabolite information as much as possible, although this cannot be completely avoided.
The number of heavy isotopes labeled for small-molecule doping is usually no less than
three so that it can form a significant mass difference. For peptides, the labeled number
needs to be greater because such substances tend to form multi-charged ions (mainly in
double charge) due to the carboxyl and amino groups of amino acid residues in the structure.
Accordingly, the carbon and nitrogen atoms on the lysine of BPC-157 were all labeled with
13
C and
15
N, given that significant mass differences can be formed (
∆
m = 8.01420 Da)
Molecules 2023,28, 7345 4 of 16
(Figure 1). In addition,
13
C and
15
N are more stable and not as easily metabolized as
deuterium. Under the current LC-MS conditions, there are several notable characteristics in
the chromatographic and mass spectrometric behaviors between the labeled and unlabeled
BPC-157, as shown in Figure 2: (1) they have RT differences within 0.05 min; (2) they share
similar peak shapes and signal intensities at the same concentration; (3) they share the
same kinds of additive ions that are detected in the major form of +2 charge, and the m/z
values show an exact difference of 4.00710
±
0.003; and (4) they share the same cleavage
pathway in the secondary mass spectrum. Thus, it was speculated that these characteristics
will also be present in the labeled and unlabeled metabolites. Based on this, a custom
program was edited to automatically pick
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
ion pairs with m/zvalues
separated by exactly 4.00710
±
0.003 and
∆
RT within 0.05 min. Given the high resolution
of the Orbitrap mass spectrometer, the mass window for screening ion pairs could be set at
the millidalton level, greatly reducing the false positive rate. In addition, based on similar
peak shapes and signal intensities, the false positive rate was further reduced. Finally,
elemental composition analysis and targeted MS/MS were performed to further confirm
the suspected metabolites and provide structural information.
Molecules 2023, 28, x FOR PEER REVIEW 4 of 18
in the middle of the peptide sequence was selected to be labeled with heavy isotopes to
avoid the loss of metabolite information as much as possible, although this cannot be
completely avoided. The number of heavy isotopes labeled for small-molecule doping is
usually no less than three so that it can form a significant mass difference. For peptides,
the labeled number needs to be greater because such substances tend to form multi-
charged ions (mainly in double charge) due to the carboxyl and amino groups of amino
acid residues in the structure. Accordingly, the carbon and nitrogen atoms on the lysine
of BPC-157 were all labeled with
13
C and
15
N, given that significant mass differences can
be formed (Δm = 8.01420 Da) (Figure 1). In addition,
13
C and
15
N are more stable and not
as easily metabolized as deuterium. Under the current LC-MS conditions, there are several
notable characteristics in the chromatographic and mass spectrometric behaviors between
the labeled and unlabeled BPC-157, as shown in Figure 2: (1) they have RT differences
within 0.05 min; (2) they share similar peak shapes and signal intensities at the same
concentration; (3) they share the same kinds of additive ions that are detected in the major
form of +2 charge, and the m/z values show an exact difference of 4.00710 ± 0.003; and (4)
they share the same cleavage pathway in the secondary mass spectrum. Thus, it was
speculated that these characteristics will also be present in the labeled and unlabeled
metabolites. Based on this, a custom program was edited to automatically pick
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
ion pairs with m/z values separated by exactly 4.00710 ± 0.003 and ΔRT within
0.05 min. Given the high resolution of the Orbitrap mass spectrometer, the mass window
for screening ion pairs could be set at the millidalton level, greatly reducing the false
positive rate. In addition, based on similar peak shapes and signal intensities, the false
positive rate was further reduced. Finally, elemental composition analysis and targeted
MS/MS were performed to further confirm the suspected metabolites and provide
structural information.
Figure 1. Primary structure of BPC-157 (isotope-labeled atoms were shown in red).
Figure 1. Primary structure of BPC-157 (isotope-labeled atoms were shown in red).
Molecules 2023, 28, x FOR PEER REVIEW 5 of 18
Figure 2. Chromatographic and mass spectrometry behavior of BPC-157 and labeled BPC-157.
The data processing workflow for the nontargeted screening of potential metabolites
is illustrated in Figure 3. Data acquisition of the
13
C
0
,
15
N
0
/
13
C
6
,
15
N
2
-BPC-157-incubated
samples was performed with full scan mode, and the raw data were ion-extracted with
the following parameters using Compound Discoverer software 3.1: (1) ion peak signal
intensity ≥50,000; and (2) ions detected as [M+H]
+
, [M+2H]
2+
, and [M+3H]
3+
. Although BPC-
157 and labeled BPC-157 were mainly detected in the form of +2 charge, +1 and +3 charges
were also set up to extract metabolite signals that may exist primarily in these forms. The
resulting features were exported to an Excel file and combined for subsequent isotope ion
pair picking using the custom program. Two important criteria were set in the automatic
ion pair picking for the suspected metabolites. The first criterion was that the ion pairs
with m/z values should be separated by exactly 4.00710 ± 0.003 or 8.01420 ± 0.003 or 2.67140
± 0.003. Second, the ion pairs should have an RT difference of less than or equal to (≤) 0.05
min. Further analysis was performed for the candidate ion pairs as follows: 1) eliminating
repeat features and ion pairs detected in the blank control samples; and 2) ensuring that
the ion pairs have similar peak shapes and intensities. To confirm the remaining suspected
metabolites, targeted LC-MS/MS acquisition was performed to ensure that there were
metabolites produced in the same metabolic pathway in the
13
C
0
,
15
N
0
/
13
C
6
,
15
N
2
-BPC-157-
incubated samples and verify the structural information. Finally, based on the exact m/z
values provided by HRMS and the product ion information, the elemental composition of
the metabolites was determined. The entire screening process is based on a nontargeted
analysis method, including nontargeted data acquisition with full scan mode, nontargeted
ion extraction using molecular features, and automatic isotope ion pair picking, which can
minimize the discriminatory treatment of metabolite information and enable
comprehensive data mining.
Figure 2. Chromatographic and mass spectrometry behavior of BPC-157 and labeled BPC-157.
Molecules 2023,28, 7345 5 of 16
The data processing workflow for the nontargeted screening of potential metabolites
is illustrated in Figure 3. Data acquisition of the
13
C
0
,
15
N
0
/
13
C
6
,
15
N
2
-BPC-157-incubated
samples was performed with full scan mode, and the raw data were ion-extracted with
the following parameters using Compound Discoverer software 3.1: (1) ion peak signal
intensity
≥
50,000; and (2) ions detected as [M+H]
+
, [M+2H]
2+
, and [M+3H]
3+
. Although
BPC-157 and labeled BPC-157 were mainly detected in the form of +2 charge, +1 and +3
charges were also set up to extract metabolite signals that may exist primarily in these
forms. The resulting features were exported to an Excel file and combined for subsequent
isotope ion pair picking using the custom program. Two important criteria were set in the
automatic ion pair picking for the suspected metabolites. The first criterion was that the ion
pairs with m/zvalues should be separated by exactly 4.00710
±
0.003 or
8.01420 ±0.003
or 2.67140
±
0.003. Second, the ion pairs should have an RT difference of less than or
equal to (
≤
) 0.05 min. Further analysis was performed for the candidate ion pairs as
follows: (1) eliminating repeat features and ion pairs detected in the blank control samples;
and (2) ensuring that the ion pairs have similar peak shapes and intensities. To confirm
the remaining suspected metabolites, targeted LC-MS/MS acquisition was performed
to ensure that there were metabolites produced in the same metabolic pathway in the
13
C
0
,
15
N
0
/
13
C
6
,
15
N
2
-BPC-157-incubated samples and verify the structural information.
Finally, based on the exact m/zvalues provided by HRMS and the product ion information,
the elemental composition of the metabolites was determined. The entire screening process
is based on a nontargeted analysis method, including nontargeted data acquisition with full
scan mode, nontargeted ion extraction using molecular features, and automatic isotope ion
pair picking, which can minimize the discriminatory treatment of metabolite information
and enable comprehensive data mining.
Molecules 2023, 28, x FOR PEER REVIEW 6 of 18
Figure 3. Data analysis workflow for nontargeted screening of all potential metabolites based on
stable isotope labeling.
2.2. Metabolic Profile of BPC-157 in Two Kinds of In Vitro Incubation Models
As described in the literature, BPC-157 acts systemically in the body, which means
that it can render a certain amount of benefit in whichever part of the body needs healing
via subcutaneous/intramuscular injection or oral administration. Accordingly, the human
skin S9 fraction and human liver microsomes were selected as in vitro incubation models
in this study to simulate different drug delivery pathways of BPC-157 in humans for the
first time. After incubation at 37 °C for 2 h, following a simple sample preparation of
protein precipitation with acetonitrile, the metabolite information was retained to a large
extent for nontargeted screening. Analyzed with the presented strategy (Figure 3),
extensive metabolism was observed in the two incubation systems, but with an obvious
difference. The different protein concentrations in the two systems may account for the
different metabolic activity. As shown in Figure 4, five candidate metabolites were
detected in both incubation systems with a different content, and four candidate
metabolites were present in only one of them. For example, BPC-157 (2–14) free acid (M5)
and BPC-157 (6–15) free acid (M7) were only detected in the human skin S9 incubation
system, while m/z 575.77238 (M3), which may be BPC-157 (2–13) free acid or BPC-157 (1–
12) free acid, m/z 755.36389 (M9, [M+2H]
2+
), and m/z 763.87708 (M9, [M+H+NH
4
]
2+
) were
only detected in the human liver microsome incubation system. Similarly, for the labeled
BPC-157, metabolites produced by the same metabolic pathways were also detected with
an increase of 4.00710 ± 0.003 in the m/z (+2 charge state). Since it cannot be completely
ruled out that the metabolism will not occur on the labeled lysine, metabolites associated
with lysine degradation may be lost, although a previous study suggested that the lysine
is not involved in the metabolic processes of BPC-157 [9]. Nevertheless, no possible
metabolite signals formed by lysine cleavage were found via manual extraction in both
incubation systems.
Figure 3.
Data analysis workflow for nontargeted screening of all potential metabolites based on
stable isotope labeling.
2.2. Metabolic Profile of BPC-157 in Two Kinds of In Vitro Incubation Models
As described in the literature, BPC-157 acts systemically in the body, which means that
it can render a certain amount of benefit in whichever part of the body needs healing via
subcutaneous/intramuscular injection or oral administration. Accordingly, the human skin
S9 fraction and human liver microsomes were selected as
in vitro
incubation models in
this study to simulate different drug delivery pathways of BPC-157 in humans for the first
time. After incubation at 37
◦
C for 2 h, following a simple sample preparation of protein
Molecules 2023,28, 7345 6 of 16
precipitation with acetonitrile, the metabolite information was retained to a large extent
for nontargeted screening. Analyzed with the presented strategy (Figure 3), extensive
metabolism was observed in the two incubation systems, but with an obvious difference.
The different protein concentrations in the two systems may account for the different
metabolic activity. As shown in Figure 4, five candidate metabolites were detected in both
incubation systems with a different content, and four candidate metabolites were present in
only one of them. For example, BPC-157 (2–14) free acid (M5) and BPC-157 (6–15) free acid
(M7) were only detected in the human skin S9 incubation system, while m/z575.77238 (M3),
which may be BPC-157 (2–13) free acid or BPC-157 (1–12) free acid, m/z755.36389 (M9,
[M+2H]
2+
), and m/z763.87708 (M9, [M+H+NH
4
]
2+
) were only detected in the human liver
microsome incubation system. Similarly, for the labeled BPC-157, metabolites produced
by the same metabolic pathways were also detected with an increase of 4.00710
±
0.003
in the m/z(+2 charge state). Since it cannot be completely ruled out that the metabolism
will not occur on the labeled lysine, metabolites associated with lysine degradation may be
lost, although a previous study suggested that the lysine is not involved in the metabolic
processes of BPC-157 [
9
]. Nevertheless, no possible metabolite signals formed by lysine
cleavage were found via manual extraction in both incubation systems.
Molecules 2023, 28, x FOR PEER REVIEW 7 of 18
Figure 4. Candidate metabolites of BPC-157 detected in two in vitro incubation systems. (a) Human
liver microsome incubation system; (b) human skin S9 incubation system.
It is worth noting that in addition to the traditional amide-bond-breaking metabolic
pathway that leads to M1-M8, a temporarily unknown and novel metabolic pathway for
BPC-157 was discovered through the proposed strategy. The hereby produced metabolite
M9 has a larger molecular weight than the parent, which is unlikely to be formed by the
degradation of the peptide chain alone. The metabolite existed primarily as an adduct ion
peak of [M+H+NH
4
]
2+
(763.87708) (Figure 4), probably because it is a peptide derivative.
Another possibility has also been considered—that is, m/z 755.36389 was a deamination
peak formed by the in-source fragmentation of m/z 763.87708. However, no fragment with
m/z 755.36389 was observed in the MS/MS spectrum of m/z 763.87708, meaning that no
convincing evidence could be found for this possibility. According to the MS/MS
spectrum (Figure 5), characteristic fragments of m/z 753.35353, m/z 1253.57689, and m/z
627.29106 (+2 charge state of ion m/z 1253.57485) were generated for M9 under proper
collision energy (CE). Similarly, fragments with an increase of 8.01420 ± 0.003 or 4.00710 ±
0.003 in m/z were detected in the MS/MS spectrum of labeled M9, such as m/z 761.36955,
m/z 1261.59113, and m/z 631.29936. Then, based on the exact m/z provided by Orbitrap,
attempts were made to speculate on the elemental composition of the metabolite.
However, a number of molecular formulas were obtained because of the large molecular
weight, but simple types of elements, and the final molecular formula has not been
determined. The five possible molecular formulas are listed in Table 1, based on a
comprehensive analysis of the number of elements, degree of unsaturation, and mass
errors. The obtained m/z information can provide a reference for further research in
organisms. No signal of this metabolite was detected in the human skin S9 incubated
samples, suggesting that there may be some metabolic differences between the oral and
injection administration of BPC-157. To the best of our knowledge, the generation of small
peptide metabolites has been considered to originate from the cleavage of amide-bond [1],
and the metabolite M9 undoubtedly provides new insights into the biotransformation of
not only BPC-157 but also other small peptides.
Figure 4.
Candidate metabolites of BPC-157 detected in two
in vitro
incubation systems. (
a
) Human
liver microsome incubation system; (b) human skin S9 incubation system.
It is worth noting that in addition to the traditional amide-bond-breaking metabolic
pathway that leads to M1-M8, a temporarily unknown and novel metabolic pathway for
BPC-157 was discovered through the proposed strategy. The hereby produced metabolite
M9 has a larger molecular weight than the parent, which is unlikely to be formed by the
degradation of the peptide chain alone. The metabolite existed primarily as an adduct ion
peak of [M+H+NH
4
]
2+
(763.87708) (Figure 4), probably because it is a peptide derivative.
Another possibility has also been considered—that is, m/z755.36389 was a deamination
peak formed by the in-source fragmentation of m/z763.87708. However, no fragment with
m/z755.36389 was observed in the MS/MS spectrum of m/z763.87708, meaning that no
Molecules 2023,28, 7345 7 of 16
convincing evidence could be found for this possibility. According to the MS/MS spectrum
(Figure 5), characteristic fragments of m/z753.35353, m/z1253.57689, and m/z627.29106
(+2 charge state of ion m/z1253.57485) were generated for M9 under proper collision
energy (CE). Similarly, fragments with an increase of 8.01420
±
0.003 or
4.00710 ±0.003
in m/zwere detected in the MS/MS spectrum of labeled M9, such as m/z761.36955,
m/z1261.59113, and m/z631.29936. Then, based on the exact m/zprovided by Orbitrap,
attempts were made to speculate on the elemental composition of the metabolite. However,
a number of molecular formulas were obtained because of the large molecular weight,
but simple types of elements, and the final molecular formula has not been determined.
The five possible molecular formulas are listed in Table 1, based on a comprehensive
analysis of the number of elements, degree of unsaturation, and mass errors. The obtained
m/zinformation can provide a reference for further research in organisms. No signal of
this metabolite was detected in the human skin S9 incubated samples, suggesting that
there may be some metabolic differences between the oral and injection administration of
BPC-157. To the best of our knowledge, the generation of small peptide metabolites has
been considered to originate from the cleavage of amide-bond [
1
], and the metabolite M9
undoubtedly provides new insights into the biotransformation of not only BPC-157 but
also other small peptides.
Molecules 2023, 28, x FOR PEER REVIEW 8 of 18
Figure 5. MS/MS spectrum of (a) m/z 763.87708 (M9, [M+H+NH
4
]
2+
]) and (b) m/z 767.88435 (labeled
M9, [M+H+NH
4
]
2+
]).
Table 1. The five possible molecular formulas of M9.
No. Molecular Formula Molecular Weight Delta ppm
1 C
67
H
104
O
27
N
12
1508.71339 −0.015
2 C
64
H
96
O
21
N
22
1508.71204 0.787
3 C
68
H
100
O
23
N
16
1508.71472 −0.990
4 C
63
H
100
O
25
N
18
1508.71070 1.673
5 C
69
H
96
O
19
N
20
1508.71606 1.876
Subsequently, based on results obtained from the two incubation models, RMs were
synthesized for the confirmation of the major candidate metabolites. According to the
detection results, four candidate metabolites were consistent with the RMs in RT,
precursor ion, and MS/MS fragmentation, confirming our hypothesis (Table 2 (M1, M2,
M4, M8)). For m/z 575.77238 (M3), the RMs of BPC-157 (2–13) free acid and BPC-157 (1–
12) free acid were both synthesized and detected by LC-HRMS. Obvious differences were
observed in the RT and MS/MS spectrum between the two, while the former matched the
metabolite M3 (Figure 6). According to Cox’s report [9], m/z 575.77238 was detected as the
co-elution of metabolites BPC-157 (2–13) free acid and BPC-157 (1–12) free acid. However,
the RM detection results here show that the two substances can be separated under
appropriate chromatographic conditions, and BPC-157 tends to cleave the first amino acid
at the C-terminus and the last two amino acids at the N-terminus, resulting in BPC-157
(2–13) free acid (Figure 6). Altogether, nine different metabolic products were identified
by employing the combination of human skin S9 and human liver microsomes-based in
vitro metabolism and evaluation of the HRMS data with the presented strategy (Table 2,
Figure 7), providing six more metabolites than a previous in vitro plasma metabolism
study [9], potentially representing new target metabolites for the doping detection of BPC-
157 administration. Among them, BPC-157 (2–15) free acid (M8) was the most abundant
metabolite in both incubation systems, which is consistent with the previous report [9],
Figure 5.
MS/MS spectrum of (
a
)m/z 763.87708 (M9, [M+H+NH
4
]
2+
]) and (
b
)m/z 767.88435 (labeled
M9, [M+H+NH4]2+]).
Table 1. The five possible molecular formulas of M9.
No. Molecular Formula Molecular Weight Delta ppm
1 C67H104 O27N12 1508.71339 −0.015
2 C64H96 O21N22 1508.71204 0.787
3 C68H100 O23N16 1508.71472 −0.990
4 C63H100 O25N18 1508.71070 1.673
5 C69H96 O19N20 1508.71606 1.876
Molecules 2023,28, 7345 8 of 16
Subsequently, based on results obtained from the two incubation models, RMs were
synthesized for the confirmation of the major candidate metabolites. According to the
detection results, four candidate metabolites were consistent with the RMs in RT, precursor
ion, and MS/MS fragmentation, confirming our hypothesis (Table 2(M1, M2, M4, M8)).
For m/z575.77238 (M3), the RMs of BPC-157 (2–13) free acid and BPC-157 (1–12) free acid
were both synthesized and detected by LC-HRMS. Obvious differences were observed in
the RT and MS/MS spectrum between the two, while the former matched the metabolite
M3 (Figure 6). According to Cox’s report [
9
], m/z575.77238 was detected as the co-elution
of metabolites BPC-157 (2–13) free acid and BPC-157 (1–12) free acid. However, the RM
detection results here show that the two substances can be separated under appropriate
chromatographic conditions, and BPC-157 tends to cleave the first amino acid at the C-
terminus and the last two amino acids at the N-terminus, resulting in BPC-157 (2–13) free
acid (Figure 6). Altogether, nine different metabolic products were identified by employing
the combination of human skin S9 and human liver microsomes-based
in vitro
metabolism
and evaluation of the HRMS data with the presented strategy (Table 2, Figure 7), providing
six more metabolites than a previous
in vitro
plasma metabolism study [
9
], potentially
representing new target metabolites for the doping detection of BPC-157 administration.
Among them, BPC-157 (2–15) free acid (M8) was the most abundant metabolite in both
incubation systems, which is consistent with the previous report [
9
], while the relative
response of other metabolites showed different ratios in the range of 0.5% ~ 77% (Table 2).
Hence, differences in the proportion of metabolites excreted in different body fluids, such
as the urine and blood, should be noted. In terms of the metabolic pathway of amide-bond
breakage, BPC-157 is more likely to be degraded from the C-terminus to form a series of
truncated peptide metabolites (Figure 7). The polarity of these truncated peptide metabo-
lites gradually increased with the degradation of amino acids at the
C-terminus—that is
,
the RT gradually moved forward (Figure 4), which may be attributed to the fact that amino
acids at the N-terminus are mostly hydrophilic amino acids (e.g., lysine, glutamic acid,
and proline), and amino acids at the C-terminus are mostly hydrophobic amino acids (e.g.,
valine, leucine, and alanine). As a previous study reported [
1
], the size of small peptides
(<15–20 amino acids residues) is far below the threshold for glomerular filtration, so they
generally undergo fast renal elimination followed by excretion in urine [
27
], making this
the preferred matrix for doping testing. Thus, these metabolites are likely to be detected
in urine due to their small size and high polarity. In addition, the metabolic pathway
of degrading the first amino acid at the N-terminus was fully validated in the
in vitro
metabolic process of BPC-157, as M8 and BPC-157, M1 and M2, M3, and M4, and M5 and
M6 all differ by one glycine (the first amino acid at the N-terminus) (Figure 7). In summary,
the
in vitro
metabolic profile of human skin S9 and human liver microsomes provided a
further reference for the in vivo biotransformation of BPC-157.
Interestingly, we also observed the signal of another suspected metabolite (BPC-157
(3–15) free acid, m/z617.32733) in both incubation systems with high abundance (Figure 8,
the human liver microsomes incubation system was taken as an example), which has been
reported as a metabolite of BPC-157 in Cox’s study [
9
]. However, some issues were noted.
The metabolite shared the same RT and peak shape with the parent and also existed in the
enzyme blank sample with higher intensity (Figure 8). In addition, a major product ion (y13)
produced by the MS/MS fragmentation of the parent showed the same m/z(617.32733) and
charge (+2) with the suspected metabolite (Figure 8e). Thus, it was considered that BPC-157
was prone to in-source fragmentation, and the suspected metabolite was speculated to be
a fragment (y13) of BPC-157. As the amount of the parent is reduced due to metabolic
reactions, the amount of this fragment is also reduced, accounting for the lower signal of
this suspected metabolite in the incubation system than that in the enzyme blank sample.
Subsequent detection results of RM BPC-157 (3–15) free acid confirmed this conjecture. The
suspected metabolite has the same MS/MS fragment as the RM BPC-157 (3–15) free acid
(Figure 8f,g), which means that the two have the same amino acid sequence, but the RT
proved that they are not the same substance. The RTs for the suspected metabolite and
Molecules 2023,28, 7345 9 of 16
RM BPC-157 (3–15) free acid were 7.45 min and 7.57 min, respectively (Figure 8b,d), which
exceeded the RT-based qualitative criteria (0.1 min) for small molecules. In fact, in addition
to the major fragment, several other minor in-source fragments of BPC-157 that may be
mistaken for degradation metabolites were also found. Therefore, MS/MS experiments, as
well as enzyme blank sample analysis, should be carefully performed to eliminate false
positive results, given the risk of misidentification due to in-source fragmentation.
Table 2. Nine different metabolic products of BPC-157 in two in vitro incubation models.
No. Compound Sequence Molecular Weight RT
(min) m/z(+2) Peak Height
(Human Skin S9)
Peak Height
(Human Liver
Microsomes)
1 BPC-157 (parent) GEPPPGKPADDAGLV 1419.53 7.45 710.35936 4.4 ×1084.5 ×108
2 BPC-157 (2–8) free acid (M1) EPPPGKP 720.81 5.01 361.19760 9.5 ×1058.3 ×105
3 BPC-157 (1–8) free acid (M2) GEPPPGKP 777.86 5.14 389.70833 9.7 ×1057.2 ×106
4 BPC-157 (2–13) free acid (M3) EPPPGKPADDAG 1150.19 5.16 575.77238 4.4 ×105ND
5 BPC-157 (1–13) free acid (M4) GEPPPGKPADDAG 1207.24 5.23 604.28312 5.4 ×1065.9 ×104
6 BPC-157 (2–14) free acid (M5) EPPPGKPADDAGL 1263.35 6.90 632.31442 ND 5.2 ×104
7 BPC-157 (1–14) free acid (M6) GEPPPGKPADDAGL 1320.40 6.91 660.82515 1.8 ×1052.0 ×106
8 BPC-157 (6–15) free acid (M7) GKPADDAGLV 942.02 7.15 471.74818 ND 8.9 ×105
9 BPC-157 (2–15) free acid (M8) EPPPGKPADDAGLV 1362.48 7.46 681.84862 3.7 ×1079.4 ×106
10 M9 unknown see Table 18.03
755.36389 ([M+2H]2+);
763.87708
([[M+H+NH4]2+])
1.0 ×106;
5.2 ×106ND
Molecules 2023, 28, x FOR PEER REVIEW 9 of 18
while the relative response of other metabolites showed different ratios in the range of
0.5% ~ 77% (Table 2). Hence, differences in the proportion of metabolites excreted in
different body fluids, such as the urine and blood, should be noted. In terms of the
metabolic pathway of amide-bond breakage, BPC-157 is more likely to be degraded from
the C-terminus to form a series of truncated peptide metabolites (Figure 7). The polarity
of these truncated peptide metabolites gradually increased with the degradation of amino
acids at the C-terminus—that is, the RT gradually moved forward (Figure 4), which may
be attributed to the fact that amino acids at the N-terminus are mostly hydrophilic amino
acids (e.g., lysine, glutamic acid, and proline), and amino acids at the C-terminus are
mostly hydrophobic amino acids (e.g., valine, leucine, and alanine). As a previous study
reported [1], the size of small peptides (<15–20 amino acids residues) is far below the
threshold for glomerular filtration, so they generally undergo fast renal elimination
followed by excretion in urine [27], making this the preferred matrix for doping testing.
Thus, these metabolites are likely to be detected in urine due to their small size and high
polarity. In addition, the metabolic pathway of degrading the first amino acid at the N-
terminus was fully validated in the in vitro metabolic process of BPC-157, as M8 and BPC-
157, M1 and M2, M3, and M4, and M5 and M6 all differ by one glycine (the first amino
acid at the N-terminus) (Figure 7). In summary, the in vitro metabolic profile of human
skin S9 and human liver microsomes provided a further reference for the in vivo
biotransformation of BPC-157.
Figure 6. Confirmation of M3 based on synthetic RMs. (a) Extracted ion chromatogram (EIC) of m/z
575.77238 (M3) in human liver microsomes incubated sample; (b) EIC of the RM BPC-157 (2–13) free
acid; (c) EIC of the RM BPC-157 (1–12) free acid; (d) MS/MS spectrum of m/z 575.77238 (M3); (e)
MS/MS spectrum of the RM BPC-157 (1–12) free acid; (f) MS/MS spectrum of the RM BPC-157 (2–
13) free acid.
Table 2. Nine different metabolic products of BPC-157 in two in vitro incubation models.
No. Compound Sequence Molecular
Weight
RT
(min) m/z (+2) Peak Height
(Human Skin S9)
Peak Height
(Human
Liver
Microsomes)
1 BPC-157 (parent) GEPPPGKPADDAG
LV 1419.53 7.45 710.35936 4.4 × 10
8
4.5 × 10
8
2 BPC-157 (2–8) free acid
(M1) EPPPGKP 720.81 5.01 361.19760 9.5 × 10
5
8.3 × 10
5
3 BPC-157 (1–8) free acid
(M2) GEPPPGKP 777.86 5.14 389.70833 9.7 × 10
5
7.2 × 10
6
Figure 6.
Confirmation of M3 based on synthetic RMs. (
a
) Extracted ion chromatogram (EIC) of
m/z575.77238 (M3) in human liver microsomes incubated sample; (
b
) EIC of the RM BPC-157 (2–13)
free acid; (
c
) EIC of the RM BPC-157 (1–12) free acid; (
d
) MS/MS spectrum of m/z575.77238 (M3);
(
e
) MS/MS spectrum of the RM BPC-157 (1–12) free acid; (
f
) MS/MS spectrum of the RM BPC-157
(2–13) free acid.
Molecules 2023,28, 7345 10 of 16
Molecules 2023, 28, x FOR PEER REVIEW 10 of 18
4 BPC-157 (2–13) free acid
(M3) EPPPGKPADDAG 1150.19 5.16 575.77238 4.4 × 10
5
ND
5 BPC-157 (1–13) free acid
(M4) GEPPPGKPADDAG 1207.24 5.23 604.28312 5.4 × 10
6
5.9 × 10
4
6 BPC-157 (2–14) free acid
(M5) EPPPGKPADDAGL 1263.35 6.90 632.31442 ND 5.2 × 10
4
7 BPC-157 (1–14) free acid
(M6)
GEPPPGKPADDAG
L 1320.40 6.91 660.82515 1.8 × 10
5
2.0 × 10
6
8 BPC-157 (6–15) free acid
(M7) GKPADDAGLV 942.02 7.15 471.74818 ND 8.9 × 10
5
9 BPC-157 (2–15) free acid
(M8)
EPPPGKPADDAGL
V 1362.48 7.46 681.84862 3.7 × 10
7
9.4 × 10
6
10 M9 unknown see Table 1 8.03
755.36389
([M+2H]
2+
);
763.87708
([[M+H+NH
4
]
2+
])
1.0 × 10
6
;
5.2 × 10
6
ND
Figure 7. Metabolic profile of BPC-157 in two kinds of in vitro incubation models (HLM: human
liver microsomes; HSS9: human skin S9).
Interestingly, we also observed the signal of another suspected metabolite (BPC-157
(3–15) free acid, m/z 617.32733) in both incubation systems with high abundance (Figure
8, the human liver microsomes incubation system was taken as an example), which has
been reported as a metabolite of BPC-157 in Cox’s study [9]. However, some issues were
noted. The metabolite shared the same RT and peak shape with the parent and also existed
in the enzyme blank sample with higher intensity (Figure 8). In addition, a major product
ion (y13) produced by the MS/MS fragmentation of the parent showed the same m/z
(617.32733) and charge (+2) with the suspected metabolite (Figure 8e). Thus, it was
considered that BPC-157 was prone to in-source fragmentation, and the suspected
metabolite was speculated to be a fragment (y13) of BPC-157. As the amount of the parent
is reduced due to metabolic reactions, the amount of this fragment is also reduced,
accounting for the lower signal of this suspected metabolite in the incubation system than
that in the enzyme blank sample. Subsequent detection results of RM BPC-157 (3–15) free
acid confirmed this conjecture. The suspected metabolite has the same MS/MS fragment
as the RM BPC-157 (3–15) free acid (Figure 8f,g), which means that the two have the same
amino acid sequence, but the RT proved that they are not the same substance. The RTs for
the suspected metabolite and RM BPC-157 (3–15) free acid were 7.45 min and 7.57 min,
respectively (Figure 8b,d), which exceeded the RT-based qualitative criteria (0.1 min) for
Figure 7.
Metabolic profile of BPC-157 in two kinds of
in vitro
incubation models (HLM: human liver
microsomes; HSS9: human skin S9).
Molecules 2023, 28, x FOR PEER REVIEW 11 of 18
small molecules. In fact, in addition to the major fragment, several other minor in-source
fragments of BPC-157 that may be mistaken for degradation metabolites were also found.
Therefore, MS/MS experiments, as well as enzyme blank sample analysis, should be
carefully performed to eliminate false positive results, given the risk of misidentification
due to in-source fragmentation.
Figure 8. Identification of the suspected metabolite (BPC-157 (3–15) free acid, m/z 617.32733). (a) EIC
of BPC-157 in human liver microsomes incubated sample; (b) EIC of m/z 617.32733 in human liver
microsomes incubated sample; (c) EIC of m/z 617.32733 in enzyme blank sample; (d) EIC of the RM
BPC-157 (3–15) free acid; (e) MS/MS spectrum of the RM BPC-157; (f) MS/MS spectrum of m/z
617.32733 in human liver microsomes incubated sample; (g) MS/MS spectrum of the RM BPC-157
(3–15) free acid.
2.3. Method Development and Validation for Doping Control of BPC-157
For the purpose of anti-doping screening, a sensitive and specific method was
established and validated for BPC-157 and the major metabolites in human urine in this
study. Mixed-mode weak cation exchange solid phase extraction (WCX-SPE) offers both
cation exchange and hydrophobic interactions that are frequently used for the detection
of small peptides in urine samples. Thus, based on our previously developed small
peptide detection method [28], minor optimizations were made to the WCX-SPE
procedure and chromatographic conditions to make them more suitable for the detection
of BPC-157 and the metabolites. The validation results are described below and
summarized in Table 3, fulfilling the requirements of WADA for routine analysis of small
peptides [29]. In addition, the results of validation parameters such as linearity, accuracy,
precision, recovery, and matrix effects also demonstrated the potential of the method for
quantitative analysis.
Table 3. Prominent results of the method validation
No. Compound LOD
(ng/mL)
Linearity
(ng/mL)
Repeatability
(n = 6, RSD%)
(ng/mL)
Accuracy (RE%)
(ng/mL) Recovery
(%)
Matrix
Effect (%)
1 5 20 1 5 20
1 BPC-157 0.01 0.02–50 1.53 1.93 2.17 -2.96 1.99 8.09 93.73 75.0
2 BPC-157 (2–8)
free acid (M1) 0.07 0.1–50 2.16 3.91 1.89 -0.15 -2.94 -0.63 109.48 74.0
Figure 8.
Identification of the suspected metabolite (BPC-157 (3–15) free acid, m/z617.32733). (
a
) EIC
of BPC-157 in human liver microsomes incubated sample; (
b
) EIC of m/z617.32733 in human liver
microsomes incubated sample; (
c
) EIC of m/z617.32733 in enzyme blank sample; (
d
) EIC of the RM
BPC-157 (3–15) free acid; (
e
) MS/MS spectrum of the RM BPC-157; (
f
) MS/MS spectrum of m/z
617.32733 in human liver microsomes incubated sample; (
g
) MS/MS spectrum of the RM BPC-157
(3–15) free acid.
2.3. Method Development and Validation for Doping Control of BPC-157
For the purpose of anti-doping screening, a sensitive and specific method was es-
tablished and validated for BPC-157 and the major metabolites in human urine in this
study. Mixed-mode weak cation exchange solid phase extraction (WCX-SPE) offers both
cation exchange and hydrophobic interactions that are frequently used for the detection of
small peptides in urine samples. Thus, based on our previously developed small peptide
detection method [
28
], minor optimizations were made to the WCX-SPE procedure and
chromatographic conditions to make them more suitable for the detection of BPC-157 and
the metabolites. The validation results are described below and summarized in Table 3,
Molecules 2023,28, 7345 11 of 16
fulfilling the requirements of WADA for routine analysis of small peptides [
29
]. In addition,
the results of validation parameters such as linearity, accuracy, precision, recovery, and
matrix effects also demonstrated the potential of the method for quantitative analysis.
Table 3. Prominent results of the method validation.
No. Compound LOD
(ng/mL)
Linearity
(ng/mL)
Repeatability
(n= 6, RSD%)
(ng/mL)
Accuracy (RE%)
(ng/mL) Recovery
(%)
Matrix
Effect
(%)
1 5 20 1 5 20
1 BPC-157 0.01 0.02–50 1.53 1.93 2.17 −2.96 1.99 8.09 93.73 75.0
2 BPC-157 (2–8) free acid (M1) 0.07 0.1–50 2.16 3.91 1.89 −0.15 −2.94 −0.63 109.48 74.0
3 BPC-157 (1–8) free acid (M2) 0.03 0.05–50 2.87 3.62 2.34 −3.91 −7.74 1.71 123.01 70.0
4 BPC-157 (2–13) free acid (M3) 0.07 0.1–50 3.55 2.33 2.5 3.87 0.03 3.02 116.28 75.4
5 BPC-157 (1–13) free acid (M4) 0.07 0.1–50 2.63 3.15 2.5 3.92 3.84 4.98 110.10 73.2
6 BPC-157 (2–15) free acid (M8) 0.11 0.2–20 2.2 1.77 1.39 5.92 0.81 7.81 93.79 78.9
2.3.1. Selectivity and Limit of Detection (LOD)
As the parallel reaction monitoring (PRM) mode with high resolution was used for
data acquisition, the signal interference in urine was eliminated to a large extent. Therefore,
no obvious interference was observed at the expected RTs in the twenty blank urine samples,
illustrating that the method was selective, and the false positive rate of this method can be
controlled effectively. Under optimized conditions, the method has satisfactory detection
sensitivity that is well below the minimum required performance level (MRPL) designated
for peptides by WADA (2 ng/mL) [
29
]. As shown in Table 3, the LOD for BPC-157 in the
present study was 0.01 ng/mL, which was significantly lower than that in the previous
study (0.1 ng/mL) [
9
]. In addition, this study involved the first detection of the metabolites
of BPC-157 with relatively low LODs (0.03~0.11 ng/mL).
2.3.2. Linearity, Repeatability, and Accuracy
The linearity of the method was investigated by preparing a series of spiked urine
samples ranging from 0.02 to 50 ng/mL (0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, and 50 ng/mL)
for all targets. Calculated with an external standard method, BPC-157 and BPC-157 (1–8)
free acid (M2) showed good linearity in the range of 0.02~50 ng/mL with R
2
> 0.999; BPC-
157 (2–8) free acid (M1), BPC-157 (2–13) free acid (M3), and BPC-157 (1–13) free acid (M4)
showed good linearity in the range of 0.1~50 ng/mL with R
2
> 0.999; and BPC-157 (2–15)
free acid (M8) had a linear range of 0.2~50 ng/mL with R
2
> 0.999 (Table 3). Compared
with a previous report [
9
], the detection method for BPC-157 in this study showed a wider
linearity as well as for its major metabolites. Meanwhile, the RSD% for repeatability
in the six replicates of urine samples varied from 1.39% to 3.91%, and the RE% ranged
from
−
7.74% to 8.09% for all analytes at three concentration levels (1, 5, and 20 ng/mL),
meeting the quantitative requirements and demonstrating the quantitative possibilities of
the established method.
2.3.3. Recovery, Matrix Interference, and Carryover
Although the previous study indicated that the recovery of BPC-157 on the WCX
cartridge is poor (11.4% at 2 ng/mL) due to its acidic isoelectric point [
9
], the WCX SPE-
based method developed here provided outstanding recovery for both BPC-157 and its
metabolites. As shown in Table 3, all targets evaluated were extracted with a recovery rate
higher than 90% at 2 ng/mL. In addition, the matrix had a slight ion suppression effect for
all the targets (21.1~30.0%), and no carryover was observed after the analysis of a spiked
sample at a concentration of 8 ng/mL.
2.3.4. Reliability and Sample Extract Stability
According to the results of LOD (Table 3), all target analytes could be detected and
identified in ten samples at 2 ng/mL with the operation performed on different days by
different analysts, demonstrating that minor variations in the experimental conditions do
Molecules 2023,28, 7345 12 of 16
not affect the results. Re-analyzing the batch of ten previously prepared urine samples at
2 ng/mL at time intervals of 48 h (autosampler, 10
◦
C) and 5 days (
−
20
◦
C) showed that all
the targets can be detectable in 100% of the samples. Thus, the sample extract is considered
stable under the instrument autosampler (10 ◦C) for 48 h and −20 ◦C for 5 days.
3. Materials and Methods
3.1. Chemicals and Reagents
Mixed-gender human liver microsomes (10-Donor Pool, total protein concentration of
20 mg/mL) and mixed-gender human skin S9 (3-Donor Pool, total protein concentration
of 1.89 mg/mL) were purchased from BioreclamationIVT (Westbury, NY, USA). Phosphate
buffered saline (PBS, pH 7.2~7.4) was obtained from Sangon Biotech Co., Ltd. (Shanghai,
China). BPC-157 (HPLC purity > 99%) and (Deamino-cysl, val4, D-arg8)-Vasopressin (DCVDV,
internal standard, ISTD) were purchased from Alta Scientific Co., Ltd. (Tianjin, China).
13
C
6
,
15
N
2
-BPC-157 (labeled BPC-157, HPLC purity > 98%) was purchased from Synpeptide
Co., Ltd. (Nanjing, China). BPC-157 (2–15) free acid, BPC-157 (1–13) free acid, BPC-157 (2–13)
free acid, BPC-157 (1–12) free acid, BPC-157 (3–15) free acid, BPC-157 (1–8) free acid, and
BPC-157 (2–8) free acid were synthesized by Alta Scientific Co., Ltd. (Tianjin, China). MgCl
2
was from Beyotime Biotechnology (Shanghai, China). Nicotinamide adenine dinucleotide
phosphate (NADPH) was purchased from Vetec (Shanghai, China). Acetonitrile (ACN),
methanol (MeOH), and formic acid (FA) were of LC-MS grade and were obtained from Thermo
Fisher Scientific (San Jose, CA, USA). Milli-Q purified water (MerckMillipore, Vimodrone,
Milan, Italy) was used for sample preparation, reference material dilution, and LC mobile
phase preparation. Mixed-mode weak cation exchange cartridges, Oasis WCX (30 mg, 1 cc),
were purchased from Waters (Milford, MA, USA). Protein LoBindtubes (1.5, 2, and 5 mL) were
obtained from Eppendorf (Hamburg, Germany). A positive pressure SPE system “Biotage
Pressure+48” was purchased from Biotage Trading Co., Ltd. (Uppsala, Sweden).
3.2. Standard Solution Preparation
BPC-157 and labeled BPC-157 were reconstituted in PBS at a concentration of 0.4 mmol/L,
respectively, and stored at
−
80
◦
C for long-term use. MgCl
2
solution (1 M) was diluted
with PBS at a concentration of 0.1 mol/L. NADPH solution, which needed to be prepared
immediately before use, was reconstituted in PBS at a concentration of 0.1 mol/L.
Standard and ISTD stock solutions were prepared with the solvent mixture of H
2
O/ACN/
FA (49.5/49.5/1, v/v/v) in LoBind tubes and stored at
−
80
◦
C. A series of mixed standard
solutions (including BPC-157 and the five metabolites) at levels of 2, 5, 10, 20, 50, 100, 200, 500,
1000, 2000, and 5000 ng/mL were prepared for method validation. The working solution of
ISTD was prepared at a concentration of 1
µ
g/mL. Blank urine samples were obtained from
healthy volunteers (both male and female) without any known medication administration.
3.3. In Vitro Metabolic Incubation of BPC-157 and Labeled BPC-157
The
in vitro
metabolism study was performed with mixed-gender human liver micro-
somes (the final protein concentration was 1 mg/mL) and human skin S9 (the final protein
concentration was 0.189 mg/mL). In detail, 10
µ
L of the standard solution of BPC-157 or
labeled BPC-157 was added to the human liver microsomes incubation system (consisting
of 160
µ
L of PBS solution, 10
µ
L of liver microsomes solution, 10
µ
L of MgCl
2
solution
and 10
µ
L of NADPH solution) and the human skin S9 incubation system (consisting
of 180
µ
L of PBS solution and 10
µ
L of human skin S9 solution), respectively. Then, the
incubation system was vortexed for 30 s followed by incubation at 37
◦
C for 2 h. Afterward,
the sample solution was fortified with 200
µ
L of frozen acetonitrile and centrifuged for
10 min at 12,000 rpm. Then, 100
µ
L of the supernatant was diluted with 400
µ
L of water
and transferred into HPLC vials for analysis by UHPLC-HRMS. In all series of incubation
experiments, enzyme blank (without human liver microsomes and NADPH/human skin
S9) and substrate blank (without BPC-157/labeled BPC-157) samples were prepared to
allow for differentiating metabolic from incubation artifact reactions.
Molecules 2023,28, 7345 13 of 16
3.4. Urine Sample Preparation
In total, 15
µ
L of ISTD solution and 5
µ
L of FA were added to 1.5 mL of urine sample
successively. Then, samples were vortexed automatically for 3 min and centrifuged for
5 min at 3500 rpm. The extraction of target analytes in urine samples was performed on a
positive-pressure SPE system. First of all, WCX SPE cartridges were activated with 1 mL of
MeOH and balanced with 1 mL of water. Then, urine samples (1 mL) were loaded onto the
cartridges and washed with 1 mL of water. The target analytes were eluted with 1 mL of
elution reagent (75% ACN and 5% FA in water) directly into the LoBind tubes. The eluates
were evaporated to dryness under a nitrogen stream at 38
◦
C. Then, 100
µ
L of water with
10% ACN and 0.2% FA was added to the tube for reconstitution. Samples were vortexed
and centrifuged for 10 min at 12,000 rpm, and the supernatants were transferred into vials
for analysis.
3.5. Instrument Parameters and Data Processing
All LC-MS experiments were performed on a Vanquish Flex system coupled with an
Orbitrap Explories 480 mass spectrometer (ThermoFisher Scientific, Bremen, Germany).
LC separations were carried out on a reversed-phase BEH C18 column (100 mm
×
2.1 mm,
1.7
µ
m; Waters, Milford, CT, USA) at 30
◦
C with a flow rate of 0.25 mL/min. The mobile
phases were ultrapurified water (eluent A) and ACN (eluent B), both containing 0.1% of
FA. The gradient elution profile was as follows: 0–1 min, 3% B; 1–10 min, 3% to 50% B;
10–13 min, 50% to 95% B; 13–15 min, 95% B; 15–15.1 min, 95% to 3% B; 15.1–20 min, 3%
B. Samples were stored at 10
◦
C in the autosampler prior to analysis and the injection
volume was fixed at 5
µ
L. The mass spectrometer was operated with a heated electrospray
ionization (HESI) source in positive ion mode. The spray voltage was 3.8 kV, the capillary
temperature was 320
◦
C, and the auxiliary (AUX) gas heater temperature was 380
◦
C. The
nitrogen sheath and AUX gas flow rates were set to 40 and 10, respectively. For the full scan
mode, the resolution was set at 60,000 (m/z200), the mass range was 100 to 1500 m/z, and
the S-lens radio frequency (RF) level was set to 40. For parallel reaction monitoring (PRM)
mode, the resolution was set at 15,000 (m/z200), and the isolation window was 2 m/z.
The optimized normalized collision energies (NCEs) for each analyte are summarized
in Table 4.
Table 4. LC and MS parameters of the target analytes.
No. Compound Molecular Formula RT
(min) Charge Precursor Ion 1Product Ion 1,2 NCE
1 BPC-157 C62H98 N16O22 7.45 2 710.35936 617.32733
651.81873
558.78784 30
2BPC-157 (2–8) free acid (M1) C33 H52N8O10 5.01 2 361.1976 352.19183
248.14960
398.23975 15
3BPC-157 (1–8) free acid (M2) C35 H55N9O11 5.14 2 389.70833 296.67606
592.34412
187.07118 15
4
BPC-157 (2–13) free acid (M3)
C49H75 N13O19 5.16 2 575.77238 462.72519
827.38922
924.44135 20
5
BPC-157 (1–13) free acid (M4)
C51H78 N14O20 5.23 2 604.28312 511.25076
511.75076
462.72427 15
6
BPC-157 (2–15) free acid (M8)
C60H95 N15O21 7.46 2 681.84862 681.84860
1114.51404
568.79956 15
1
Theoretical m/zvalues;
2
the first product ion was used for screening, and the three product ions were used
for confirmation.
The instrument was operated using Xcalibur 4.5.474.0, and the data were processed
using TraceFinder 5.1 (Thermo Scientific, Waltham, MA, USA). Compound Discoverer 3.1
(Thermo Scientific, USA) was used for the ion extraction with the following key parameters:
Molecules 2023,28, 7345 14 of 16
(1) ion peak signal intensity
≥
50,000; and (2) ions detected as [M+H]
+
, [M+2H]
2+
, and
[M+3H]
3+
. For automatic
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
m/zpair picking, the resulting molecular
features were exported into Excel (Microsoft, Redmond, WA, USA) and then analyzed
using a custom program implemented in Python 3.10.
3.6. Method Validation
The analytical method developed for qualitative and quantitative analysis of BPC-157
and its main metabolites in human urine using WCX-SPE combined with UHPLC-HRMS
was validated according to WADA technical documents.
3.6.1. Selectivity and LOD
Twenty blank urine samples (males and females) were analyzed in two batches to
evaluate the ability of the method to differentiate target analytes from endogenous matrix
interferences in the sample, and interference at the stated RT shall not appear in all samples
for the product ions shown in Table 4. Ten urine samples (males and females) fortified with
the analytes at desired concentrations (0.02, 0.1, 0.2, 0.5, 1, and 2 ng/mL) were analyzed by
two analysts over different days for the estimation of LOD, and an S/N of the product ion
peak larger than 3 was considered as detected. The LOD is defined as the concentration where
a 95% detection rate was reached according to a sigmoid fitting function (R version 4.1.0).
3.6.2. Linearity, Repeatability, and Accuracy
Linearity was assessed by analyzing urine samples fortified to final concentrations of 0.02,
0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, and 50 ng/mL for all target analytes. The concentrations were
plotted against the peak area of target analytes, and the regression coefficient was calculated.
Six replicates of urine samples spiked at low, middle, and high three different concentration
levels (1, 5, and 20 ng/mL) were prepared and assayed for the evaluation of repeatability
and accuracy. Repeatability was evaluated by calculating the RSD% of the peak area of the
target analytes in six replicates. Accuracy was calculated based on the established calibration
curves and expressed as the relative error in percent (RE%), RE% = (measured concentration
value—actual concentration value)/actual concentration value * 100(%).
3.6.3. Recovery, Matrix Interference, and Carryover
The recovery of the method was evaluated at a concentration of 2 ng/mL. One set
of ten urine samples was fortified with target analytes prior to extraction and compared
to another set of the same ten urine samples fortified with target analytes after extraction.
The ratio of the peak areas in two sets for each analyte was calculated. In addition, ten
blank urines and ten aliquots of water were prepared before adding the standard solution
to a final concentration of 2 ng/mL to evaluate the interferences due to co-eluting matrix
components. The mean peak area of ten spiked urines was compared to the mean peak
area of spiked water for each analyte. The risk of carryover was investigated by analyzing
one urine sample spiked at 8 ng/mL, followed by consecutive analysis of two extracted
blank samples, and the results were evaluated with regard to the presence of target signals
in the blank sample.
3.6.4. Reliability and Sample Extract Stability
The reliability was assessed to ensure the production of consistent results in the case
of routine variations in the experimental conditions, such as different analysts and different
days. Accordingly, ten representative urine samples spiked at 2 ng/mL were prepared and
analyzed in two batches with five samples/batch by two analysts over different days. All
target analytes must be detected and identified in ten samples. Sample extract stability was
evaluated by re-analyzing the batch of ten previously prepared urine samples at 2 ng/mL
at time intervals of 48 h (autosampler, 10
◦
C) and 5 days (
−
20
◦
C). The loss of detection
response was allowed, while the detection rates for all analytes were required to be 100%.
Molecules 2023,28, 7345 15 of 16
4. Conclusions
In summary, a stable isotope labeling-based nontargeted strategy combined with
UHPLC-HRMS was first proposed for the metabolism analysis of small-molecule dop-
ing agents and demonstrated via its application in the deeper
in vitro
metabolic profil-
ing of a new peptide doping BPC-157. Based on the similar chromatographic behavior
and fixed mass differences of isotope pairs, a complete workflow including automatic
13
C
0
,
15
N
0
-
13
C
6
,
15
N
2
m/zpair picking was developed, which realized the rapid and effec-
tive screening of targets from a large amount of mass spectral information. As a result,
extensive metabolism was observed in the human liver microsomes and human skin S9
incubation systems with an obvious difference, and one metabolite produced by a novel
metabolic pathway plus eight metabolites produced by the conventional amide-bond break-
ing metabolic pathway was discovered. The five main metabolites were identified with
the aid of synthetic RMs. Furthermore, a sensitive and specific method for the detection of
BPC-157 and the five main metabolites in the human urine samples was developed and
validated in accordance with the criteria of WADA for the first time, which was also capable
of quantitative analysis for relatively high recovery, ideal accuracy, and precision. To our
knowledge, the novel metabolic pathway was first discovered for BPC-157 and can possibly
provide new insights into the biotransformation of not only BPC-157 but also other small
peptides. The
in vitro
metabolic profile could provide a further reference for the
in vivo
metabolism of BPC-157 and improved analytical targets for doping control.
Author Contributions:
Conceptualization, T.T., J.J. and Y.S.; Formal analysis, Y.W.; Funding acquisi-
tion, T.T., J.J. and Y.S.; Investigation, X.D.; Methodology, T.T. and J.J.; Supervision, Y.S.; Validation,
T.T., J.J. and Y.L.; Visualization, Y.S.; Writing—original draft, T.T. and J.J.; Writing—review & editing,
X.D. and Y.S. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Research Project of Shanghai University of Sport, grant
number 2023STD026; Research Project of Shanghai University of Sport, grant number 2023STD018;
Research Project of Shanghai University of Sport, grant number 2023STD017; Ministry of Science and
Technology of the People’s Republic of China, grant number 2020YFF0304501.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki, and the protocol was approved by the Ethical Committee of Shanghai University of Sport,
Shanghai, China (102772020RT106, 27 October 2020).
Informed Consent Statement: Not applicable.
Data Availability Statement: Data will be made available on request.
Acknowledgments:
The authors wish to thank anonymous reviewers for their constructive comments
on the presentation of this article.
Conflicts of Interest: The authors declare no conflict of interest.
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