TECHNOLOGIES

DRUG DISCOVERY

TODAY

High-throughput, computer assisted,specific MetID. A revolution for drugdiscoveryIsmael Zamora1,*, Fabien Fontaine2, Blanca Serra2, Guillem Plasencia1

1Lead Molecular Design, S.L. Valles, 96-102 (Local 27), 08173, Sant Cugat del Valles, Barcelona, Spain2Molecular Discovery, Ltd., 215 Marsh Road, 1st Floor, HA5 5NE Pinner, Middlesex, United Kingdom

Drug Discovery Today: Technologies Vol. 10, No. 1 2013

Editors-in-Chief

Kelvin Lam – Simplex Pharma Advisors, Inc., Arlington, MA, USA

Henk Timmerman – Vrije Universiteit, The Netherlands

Metabolites: structure determination and prediction

One of the key factors in drug discovery is related to the

metabolic properties of the lead compound, which may

influence the bioavailability of the drug, its therapeutic

window, and unwanted side-effects of its metabolites.

Therefore, it is of critical importance to enable the fast

translation of the experimentally determined meta-

bolic information into design knowledge. The elucida-

tion of the metabolite structure is the most structurally

rich and informative end-point in the available range of

metabolic assays. A methodology is presented to par-

tially automate the analysis of this experimental infor-

mation, making the process more efficient. The

computer assisted method helps in the chromato-

graphic peak selection and the metabolite structure

assignment, enabling automatic data comparison for

qualitative applications (kinetic analysis, cross species

comparison).

Introduction

There are several aspects to be considered in the discovery and

development of a new potential drugs. Among them, drug

metabolism is one of the key factors that is routinely analyzed

during the drug discovery process [1]. Drug Metabolism

departments deliver a number of end-points to the discovery

*Corresponding author.: I. Zamora[19]–> (ismael.zamora@telefonica.net)

1740-6749/$ � 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ddtec.2012

Section editor:Gabriele Cruciani – University of Perugia, Italy.

teams, some of them related to the degradation of the com-

pound under study [2] and some others representing poten-

tial liabilities and toxicities [3,4]. In some cases, for example

clearance in in vitro systems, these data are used as a check

box; if the value obtained for a certain compound is above a

certain limit the compound is ‘green’ (it does pass the criteria)

and if not it is ‘red’ (does not pass the criteria). This binary

information is helpful to determine the quality of the che-

mical series under investigation in the discovery phase, but it

provides little insight in how to design compounds with

better properties, that is, clearance. The elucidation of the

chemical structure of the metabolites plays a role in both

types of end-points. The aim of this article is to present a new

technique to automatically process the experimental data

(mass spectra acquisition using HPLC or UPLC MS and MSMS

acquisition [5,6]), facilitating the structure elucidation and

moving the bottleneck of metabolite structure elucidation

from spectra interpretation to data acquisition [7]. By doing

so, metabolite identification can deliver faster answers to the

discovery teams, providing structural information together

with the clearance data, facilitating in this way the optimiza-

tion of the lead compound. This kind of metabolite identi-

fication has to be differentiated from the traditional role of

this field in late discovery or development, and may even be

called soft spot identification instead of metabolite identifi-

cation, since here the aim is to deliver the probable structure

.10.015 e199

Drug Discovery Today: Technologies | Metabolites: structure determination and prediction Vol. 10, No. 1 2013

for the major metabolite(s) and not to profile all possible

metabolites found.

Assisted metabolite structure elucidation: Mass-MetaSite

Mass-Metasite [8] is a computer assisted method for the

interpretation of LC–MSMS data that combines prediction

of a compound’s Site of Metabolism (SoM) [9–13] with the

processing of MS spectra and rationalization based on frag-

ment analysis. Mass-MetaSite uses as input the structure of

the compound together with different MS acquisition modes

such as MSE, and data dependent scan with either accurate or

nominal mass. The procedure consists of three steps: (a)

automatic detection of the chromatographic peaks related

to the parent compound and its metabolites; (b) structure

elucidation by proposing a potential metabolite structure

based on the fragmentation pattern for each peak detected

in the previous step and (c) for all the potential metabolite

structures compatible with the extracted fragment informa-

tion, a ranking is performed using the MetaSite SoM predic-

tion algorithm. Typically, 3 different acquisition files are

needed: (a) a blank file that is used to monitor the noise

and to distinguish signal from noise by comparison with the

other files; (b) a substrate file that is used to analyze the

fragmentation pattern of the substrate and (c) an incubation

file that contains all the products after incubation, from

either in vitro (e.g. using recombinant enzymes, liver micro-

somes, or hepatocytes) or in vivo (e.g. rat, mouse, dog) sys-

tems. The procedure is able to consider both phase I and

phase II metabolism and can be used to assign up to 3

generations of metabolites. In the chromatographic peak

detection step, assigned peaks are classified as to whether

they are related to first-generation metabolites, second or

higher generation metabolites, or result from a biotransfor-

mation not recognized by the software (hereafter referred to

as red peaks) or if the fragment ion could be obtained by

counter ion adduct formation or in-source mass loss.

The overall principle for the structural elucidation of meta-

bolites is a comparison of fragment ions from the parent

(assigned when the incubation time t = 0) and the metabo-

lites (t = t), identifying mass shifts corresponding to the mass

of the metabolite as well as common neutral loss when

appropriate. The program generates all possible metabolites

based on a predefined list of metabolic biotransformation

reactions. For the structural elucidation, hypothetical frag-

ments are generated by breaking each bond with the excep-

tion of aromatic, triple bonds, double bonds involving

heteroatoms and bonds to hydrogen atoms. To generate

the fragments, a maximum of four substrate bonds is broken

as default. A score is assigned to each metabolite that is

compatible with the data, and is based on the number of

matches/mismatches when comparing the fragmentation

pattern with the parent compound. This approach is applic-

able to the majority of metabolites (>80–90%), but needs the

assistance of a metabolite identification expert to validate

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and to address the most complex cases, such as chemical

reorganization of a product fragment which is currently not

addressed by the computational approach.

Validation

The Mass-MetaSite procedure described above has been vali-

dated using different data sources. Bonn et al. [8] published a

validation of the methodology using both MSE and MSMS

data for 20 publicly available sources and in-house com-

pounds. The authors manually analyzed the data prior to

applying the automatic procedure. For the 20 public com-

pounds, this manual analysis yielded a total of 92 assigned

metabolites which were used for validation of Mass-MetaSite

with MSE data. Markush representation was used for the

reactions that were ambiguous and the assignment from

Mass-MetaSite was considered a success if it was within the

Markush structure.

The original work was performed using Mass-MetaSite v1.0;

approximately 10% of the peaks were missing for both the

public and in-house data sets, due to missing reactions such

as amide hydrolysis or aromatic N-dealkylations, or second

generation metabolism. The same analysis on the public

compounds performed with MassMetaSite v 1.3 decreases

this number of missing peaks to 4%, since higher generations

of metabolites are now possible to process, and phase II and

hydrolysis reactions have been added to the database of

considered metabolic biotransformations.

In the original analysis of the public data set this included

68 metabolites from the MSMS data and 66 metabolites from

the MSE data. The assignment success rate was 76.5% and

77.6% using MSMS data and MSE data respectively, showing

that the quality of the MSE data was satisfactory and could be

used successfully with Mass-MetaSite. In the case of the in-

house compounds published by the same authors, alongside

previously reported metabolite identification, the automatic

assignment demonstrated a similar success rate with 76.7%

correctly assigned in the first rank. This type of validation

process has also been performed at different pharmaceutical

sites yielding similar performance and using different types of

acquisition modes (data not shown).

Analysis with different acquisition modes in order to illus-

trate the procedure with different acquisition modes, analysis

of verapamil is demonstrated in this study. First of all, a

detailed analysis of the study for verapamil using MSE acqui-

sition data is shown.

Figure 1 illustrates the extraction of the chromatogram

performed automatically by the Mass-MetaSite procedure.

The green peaks correspond to the metabolite of first genera-

tion, the brown ones to higher generation of metabolites and

the blue peak is the substrate or parent compound. In addi-

tion, Table 1 shows the information obtained about the peak

area, the m/z observed, the m/z difference compared with the

theoretical mass, and the Mass-MetaSite score. Each of these

Vol. 10, No. 1 2013 Drug Discovery Today: Technologies | Metabolites: structure determination and prediction

100

75

50

25

02.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2

Time

Sig

nal (

%) M6 -164

M5 -178M7 +2

M8 +2M10 -28 M13 -16

M12 +2

M16 -14

M9 -14 M11 -14 M14 +16M15 -14

M17 +16

Substrate +0

Drug Discovery Today: Technologies

Figure 1. MSE: UPLC–MS analysis chromatogram.

peaks is then processed by extracting the MS and MSMS

spectra, in order to assign the metabolite’s chemical structure

(Table 1) by a comparative analysis of the structure of the

theoretical fragments for the parent compound and the

metabolite (Table 1). In the example shown in Table 2 the

first 3 fragments are present in both the metabolite and the

parent compound indicating that the biotransformation

(demethylation in this example) could not occur in the

associated fragments, while in the last two fragments �14

mass units are lost, indicating that it should be in these

associated fragments where the metabolic reaction took

place.

Finally, a comparison between different acquisition

modes and incubations is shown. Since the analysis is

presented for incubations performed at different concen-

trations and systems, they may yield quantitatively differ-

ent results. The aim of the study is to assess whether the

different systems are qualitatively the same, and detect the

same major metabolites, since this will be of help for the

medicinal chemist in designing new compounds based on

experimental data. For the determination of the major

metabolites or the metabolic soft spots, it is not necessary

to assign all metabolites and quantify them, but to identify

the major routes of compound degradation. Therefore,

different incubations and MS techniques should yield simi-

lar major metabolite interpretation. In this verapamil study

five different techniques were used using four different

incubation conditions:

(a) MSE: 5 mM of the parent compound was incubated in

Human Liver Microsomes, using the time point closer to

the half life previously measured. The analytical techni-

que used UPLC with a QTof detector in a Waters Synapt

system, following the same experimental procedure

described by Bonn et al. [8,14].

(b) MSMS: 5 mM of the parent compound was incubated

in Human Liver Microsomes, using an incubation time

of 45 min. The analytical technique used UPLC with a

QTof detector in a Waters Synapt system, following the

same experimental procedure described by Bonn et al.

[7,8].

(c) Orbitrap: 10 mM of the parent compound was incubated

in Human Liver Mircosomes [8], using an incubation

time of 30 min. The analytical technique used HPLC with

a LTQ-Orbitrap XL detector [15].

(d) QTof-DDS: 1 mM of the parent compound was incubated

in Human Liver Mircosomes [8], using an incubation

time of 30 min. The analytical technique used HPLC with

an Agilent 6450 QTof detector [16].

(e) Iontrap: 1 mM of the parent compound was incubated in

recombinant cytochrome P450 3A4 using an incubation

time of 45 min. The analytical technique used HPLC with

a LTQ-XL instrument [17]. With the exception of the Ion

Trap/CYP3A4 approach (e), all cases yielded the seven

most abundant metabolites of verapamil, as determined

by MS chromatographic peak area.

Only when the percentage of the area is lower than 1% are

differences between the identified metabolites, indicating

that any of these tested systems are useful to determine

the soft spots of verapamil. In the case of the Ion-Trap/

CYP3A4 analysis there are 2 metabolites missing among

the seven major ones; this could be due to the fact that

the incubation was performed in a recombinant enzyme,

as opposed to Human Liver Microsomes that were used for

the other four analyses, or it could be a limitation in the data

derived from this non-accurate mass type of acquisition

(Table 1) [18]. The structural elucidation of the metabolites

are similarly independent from the detector used. Neverthe-

less, the number of fragment peaks used for the structure

assignment and the scoring for each of the structure varies

from one methodology to another, making this approach

platform independent and enabling the comparison among

different instruments.

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Table 1. Metabolites found in the different incubations tested

Name RT m/z Formula m/z Diff

(ppm)

Mass

score

SMILES MSE MSMS Orbit QTof

DDS

Ion

trap

Parent 3.13 455.2926 C27H38N2O4 �3.48 N#CC(CCCN(C)CCc1ccc(OC)c(OC)c1)(C(C)C)c2ccc(OC)c(OC)c2 45 – 30.4 20.5 12.4

M6 �164 2.26 291.2077 C17H26N2O2 �1.37 429 N(C)CCCC(C#N)(C(C)C)c1ccc(OC)c(OC)c1 22.2 26.4 30.2 23.6 50.7

M16 �14 3.06 441.2743 C26H36N2O4 2.41 534 c1cc(CCNCCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc(OC)c1OC 20.9 28.5 20.8 23.1 19.9

M14 +16 2.92 471.2866 C27H38N2O5 �1.59 476 N#CC(CCCN(C)CC(O)c1ccc(OC)c(OC)c1)(C(C)C)c2ccc(OC)c(OC)c2 2.6 5.8 1.2 1.4 1.4

M9 �14 2.78 441.2761 C26H36N2O4 �1.7 590 C(#N)C(CCCN(C)CCc1ccc(OC)c(OC)c1)(C(C)C)c2ccc(O)c(OC)c2 1.9 9.6 4.5 4.4 –

M11 �14 2.84 441.2742 C26H36N2O4 2.57 473 Oc1ccc(CCN(C)CCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc1OC 1.5 2.7 2.9 0.6 –

M12 +2 2.87 457.2707 C26H36N2O5 �0.93 570 O(C)c1cc(ccc1OC)C(C#N)(CCCNCC(O)c2ccc(OC)c(OC)c2)C(C)C 1.2 4.3 2 1.3 4.3

M5 �178 2.2 277.1894 C16H24N2O2 7.84 419 C(C)(C)C(C#N)(CCCN)c1ccc(OC)c(OC)c1 0.9 2.1 1.7 1.4 4.4

M8 +2 2.67 457.2708 C26H36N2O5 �1.18 581 OC(CN(C)CCCC(C#N)(C(C)C)c1ccc(OC)c(OC)c1)c2ccc(O)c(OC)c2 0.9 4.1 – 2 1

M15 �14 2.92 441.2743 C26H36N2O4 2.23 614 Oc1ccc(CCN(C)CCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc1OC 0.6 – 0.6 – –

M2 �259 0.73 196.1326 C11H17NO2 5.91 618 c1(CCNC)ccc(OC)c(c1)OC 0.5 – – – –

M10 �28 2.8 427.2617 C25H34N2O4 �4.79 487 c1(OC)cc(ccc1OC)C(C#N)(CCCNCCc2ccc(O)c(OC)c2)C(C)C 0.3 1.6 3.7 2.8 1.6

M7 +2 2.46 457.2717 C26H36N2O5 �3.23 492 c1cc(CCN(O)CCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc(OC)c1OC 0.3 0.9 – 0.3 –

M17 +16 3.21 471.2853 C27H38N2O5 1.19 534 N#CC(CCCN(C)CCc1ccc(OC)c(OC)c1)(c2ccc(OC)c(OC)c2)C(C)(C)O 0.3 0.9 0.8 1.4

M4 �178 1.86 277.1927 C16H24N2O2 �3.8 444 COc1cc(ccc1O)C(C#N)(CCCNC)C(C)C 0.2 – 0.5 0.3 4.4

M1 �289 0.44 166.0858 C9H11NO2 5.93 136 c1(CCNC)ccc( O)c(c1) O 0.2 – – – –

M13 �16 2.88 439.2603 C26H34N2O4 �1.43 367 c1cc(CC NCCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc(OC)c1OC 0.2 – – – –

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Vol. 10, No. 1 2013 Drug Discovery Today: Technologies | Metabolites: structure determination and prediction

Table 2. Selection of fragments that help in the M16-16 metabolite structure elucidation

Sub. obs.

m/z

Sub. cal.

m/z

Sub. m/z

diff. ppm

Substrate Metabolite D Met. obs.

m/z

Met. calc.

m/z

Met. m/z

diff. ppm

150.0664 150.0681 11.42 +0 150.0670 150.0681 7.25

165.0869 165.0916 28.22 +0 165.0892 165.0916 14.30

260.1637 260.1651 5.33 +0 260.1652 260.1651 �0.50

303.2030 303.2073 14.09 �14 289.1921 289.1916 �1.86

455.2889 455.2910 4.48 �14 441.2750 441.2753 0.71

Metabolite formation: kinetic data analysis. The fact that the

spectra can be processed automatically in a single format type

enables the possibility to perform the analysis of a large

number of data samples [19]. Therefore the influence of

different experimental variables, like time or matrix, can

be performed without much additional effort. When study-

ing the variable time of incubation it is possible to analyze not

only the disappearance of the parent compound over time

(that determines the clearance) but also the appearance of the

metabolites. This information is relevant in the drug discov-

ery process when a compound is metabolized very quickly,

having a high clearance, and the structure of the parent has to

be modified in order to optimize this property. In this case,

the information about the structure of the metabolite that is

formed first may lead to an improved understanding of the

metabolic pathway, helping to guide the design of new

compounds to overcome this issue. The process becomes

simpler if all data are acquired under the same conditions

and processed in an automatic fashion. The program has to

follow the chromatographic peak at the same retention time,

with the same mass, and thanks to the automatic structure

assignment, the same metabolite structure.

The area for that chromatographic peak can then be

plotted against the time of incubation, assuming that the

same metabolite has the same mass response over the differ-

ent incubation samples. Alternatively, the area according to

the UV data (if available) can be displayed to compare mass

and UV signals. Figure 2 shows the metabolite time profile for

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Drug Discovery Today: Technologies | Metabolites: structure determination and prediction Vol. 10, No. 1 2013

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08

1.0E+09

1.2E+09

0 20 40

Peak

Are

a

Time (min)

Mass spectra

0

50000

100000

150000

200000

250000

0 20 40

peak

Are

a

Time (min)

Ultra-violet spectra (2 54 NM)M16 -14

M12 +2

M14 +16

M9 -14

M2 -259

M5 -178

M6 -164

M10 -28

Substrate

Drug Discovery Today: Technologies

Figure 2. Kinetic analysis of metabolite identification data.

the major metabolites of verapamil. The differences observed

between the peak areas assigned from the UV and MS data are

due to the different response of the metabolites in the detec-

tor system. In any case the concentration of the metabolite is

not directly correlated with the signal shown. In order to

perform a more accurate quantitative analysis a calibration

curve should be measured using pure metabolite, and an

internal standard should also be used to avoid injection-to-

injection differences, however in the discovery phase the

pure metabolite is usually not available. Nevertheless, for

verapamil, the two major metabolites identified are the N-

demethylation and the N-dealkylation, which are observed as

major signals by two independent detector methods (UV and

MS); this indicates that there is a high probability that these

two are the relevant metabolites to consider in compound

design.

Conclusion

The study of the metabolic transformations is a key factor for

successful drug discovery, enabling the design of compounds

to reduce clearance, analysis of the differences across species

and also the investigation of those metabolic transformations

that may lead to reactive metabolites and therefore toxicity.

Computational approaches such as MetaSite have previously

been validated for the prediction of the major metabolites

through the Site of Metabolism prediction. These approaches

are used during the drug discovery process in the lead opti-

mization and generation phases to design compounds with

an improved metabolic profile. Nevertheless, the SoM or

activity contribution approaches are typically used when

the compound is not available for detailed metabolic experi-

mental analysis. When the compound is available, it is com-

mon to directly perform the incubation with the metabolic

enzymes and analyze the resulting data using mass spectro-

metry and a computer assisted metabolite structure elucida-

tion approach like Mass-MetaSite.

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Mass-MetaSite has been demonstrated to yield 77% success

in the elucidation of the structure of the metabolites using

different acquisition modes. For the detection of the major

metabolites several mass spectrometry techniques have been

compared concluding similar results.

Therefore the system is platform independent enabling the

comparison between different types of instrumentation, by

converting the different raw data into the internal format and

performing the relevant algorithms on this uniform format to

elucidate the structure of the metabolites. The software

assigns a score to each proposed metabolite structure, giving

a degree of confidence to the structure assignment.

Moreover, the computer assisted and systematic mass

spectra interpretation is complemented by the use of the

MetaSite soft spot prediction approach. The experimental

data in combination with the prediction provides two levels

of solutions. The first is directly from the interpretation of

the mass spectra data that may assign the structure to a

Markush type of compound, where typically there is certain

degree of unspecificity in the exact atomic position where

the metabolic reaction takes place. The second level of

solution adds the MetaSite SoM prediction, refining the

Markush structure to the specific structure that is most

probable.

Another interesting application of an automatic and sys-

tematic analysis is that all of the data is in a common format,

enabling the analysis of multiple experimental samples, for

example performing a kinetic analysis of the metabolite

formation. The combination of the computer assisted assign-

ment of the structure of the metabolite and the time-course

for the formation of metabolites is a powerful tool to help in

the compound design to overcome metabolic issues. The

approach illustrated in this article to analyze time-course

data can be easily extended to analyze metabolite structures

from different matrixes enabling for example automatic

metabolite profiling across different species.

Vol. 10, No. 1 2013 Drug Discovery Today: Technologies | Metabolites: structure determination and prediction

This new technique can revolutionize the way the meta-

bolite data is reported to the discovery team, since the bottle-

neck is no longer in the human interpretation of the mass

spectra, but moved to the acquisition time. Therefore, an

increasing number of compounds may be analyzed, making it

possible in the near future to report the major metabolites for

every compound in a lead optimization structural series,

helping to transform the metabolic clearance information

into design knowledge.

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