LC-MSMS Identication of Small Molecules

University of Geneva Faculties of Medicine, Science & Computer Science MSc in Proteomics and Bioinformatics

LC-MSMS identiﬁcation of small molecules: making sense of the unknown

Author: Celine Moret Supervisor: Alexandre Masselot, Ph.D. Academic Year: 2009/2010 Declaration of Authorship

The author hereby declares that she compiled this thesis independently, using only the listed resources and literature.

The author grants to the University of Geneva permission to reproduce and to distribute copies of this thesis document in whole or in part.

Geneva, September 13, 2011 Signature Acknowledgments

This thesis would not have been possible without the help of many people. First, I would like to thank my professors of the Master, who allowed me to discover the fascinating field of Bioinformatics. I owe my deepest gratitude to Dr. Alexandre Masselot who gave me the opportunity of realising this thesis under his supervision. He never considered blindness as an obstacle, and even valued my different perspective. At each step, he was there to help me, motivate me, and teach me to welcome scientific challenges. I will never forget the friendly and stimulating working atmosphere at Genebio, and I would like to thank all previous and current collaborators. A special thanks goes to the SmileMS team: to Dr. Nicolas Budin and Anastasia Chasapi for their collaboration and support in every level; to Dr. Roman Mylonas for his endless patience and his everyday help to overcome accessibility issues by looking for alternative strategies. I am grateful to Pauline Carrara for her encouragements and her help in adapting inaccessible material. Finally, I would like to thank Christina Fasser my employer at Retina Suisse, as well as my family and friends for their support during my studies. I have a special thought for my godmother Beatrice to whom I dedicate this thesis. Abstract

Based on recent robust scoring models, liquid chromatography tandem mass spectrometry (LC-MSMS) is now a method of choice for small molecules iden- tiﬁcation. The most common method is to score one experimental spectrum versus a reference library spectra and eventually associate the experimental data with a molecule if the score is high enough. The aim of this thesis is to investigate a run content more globally and even to compare runs with each other, while conventional approaches score individual spectra in a run. For this purpose, we developed a clustering algorithm allowing to group similar spectra. Our approach was applied on two challenges associated with the intrinsic variability of LC-MSMS acquisitions.

• automatic library creation: after clustering spectra across a set of experiments, we built a library of ”representative” spectra. The goals were to see how such an automatic method performs compared to the usual human expert process and to evaluate if we could annotate common contaminants;

• molecules correlations within a run: in a typical LC-MSMS run, the vast majority of spectra are not elucidated and are usually neglected in an automatic process. The challenge was to increase this elucidation rate looking at how diﬀerent compounds are correlated together. We analysed a list of acquisitions made at Geneva University Hospital (HUG) and looked for such correlations (diﬀerent drugs, artefacts etc.)

The conclusions of this thesis oﬀer new perspectives for the interpretation of small molecules identiﬁcations by LC-MSMS.

Keywords LC-MSMS, small molecules

Author’s e-mail [email protected] Supervisor’s e-mail [email protected] Contents

List of Tables vii

List of Figures viii

Acronyms 1

1 Introduction 1 1.1 Small molecules ...... 1 1.2 Small molecules identiﬁcation methods ...... 2 1.2.1 Liquid chromatography tandem mass spectrometry (LC- MSMS) to identify small molecules ...... 3 1.3 Challenges associated with LC-MSMS ...... 5 1.3.1 Poor spectra reproducibility ...... 5 1.3.2 Reference libraries scarcity ...... 5 1.3.3 Elucidation rate ...... 5 1.4 Research goals ...... 8

2 Spectra clustering 9 2.1 Problematic ...... 9 2.2 Methods ...... 10 2.2.1 Data and library ...... 10 2.2.2 Clustering workﬂow ...... 10 2.2.3 Dealing with large data ...... 14 2.2.4 Clustering algorithm validation ...... 14 2.3 Results ...... 15 2.3.1 Cluster compacity ...... 15 2.3.2 Cluster discriminance ...... 16 2.4 Conclusion ...... 18 Contents vi

3 An automatic reference library building process 19 3.1 Problematic ...... 19 3.2 Methods ...... 20 3.2.1 Automatic library building workﬂow ...... 20 3.2.2 Spectrum selection process ...... 20 3.2.3 New library creation ...... 21 3.2.4 Automatic library validation ...... 21 3.3 Results ...... 23 3.4 Conclusion ...... 26

4 Seventy runs analysis: molecules recurrence and co-occurrence 28 4.1 Problematic ...... 28 4.2 Methods ...... 29 4.2.1 Library and data ﬁles ...... 29 4.2.2 Spectra recurrence analysis ...... 29 4.2.3 Correlations ...... 29 4.3 Results ...... 31 4.3.1 Spectra recurrence analysis ...... 31 4.3.2 Correlations ...... 33 4.4 Conclusion ...... 36 4.5 Achievements ...... 38 4.6 Perspectives ...... 39 4.6.1 Reference library building ...... 39 4.6.2 Elucidation rate ...... 39 4.6.3 Scoring ...... 40

Bibliography 43

A Freiburg library content I List of Tables

1.1 Main methodologies used for small molecule identiﬁcation . . .2 1.2 Main chromatographic methods used for HPLC ...... 4

2.1 Proportion of identiﬁed spectra in 70 runs ...... 9 2.2 Scores distribution in 30 clusters ...... 16 2.3 Summary of a single acquisition run clustering ...... 18

3.1 Comparative submissions against the Freiburg’ library and an automatic library ...... 24 3.2 Scores distribution against an automatic library ...... 25 3.3 Identiﬁcations against an automatic library ...... 25 3.4 Comparison between identiﬁcation scores against the Freiburg’ library and the automatic library ...... 26

4.1 Occurrence of four molecules across 15 runs ...... 34 4.2 Correlation between pairs of molecules across 70 runs ...... 35 List of Figures

1.1 Spectra variability in LC-MSMS ...... 6 1.2 Spectra reproducibility in GC-MSMS ...... 7

2.1 Clustering workﬂow ...... 12 2.2 Scores distribution in cluster 173 ...... 16 2.3 Scores distribution in cluster 184 ...... 17

3.1 Automatic library building workﬂow ...... 22

4.1 Spectra occurrence across 70 runs ...... 32 4.2 Identiﬁed spectra across 70 runs ...... 33 4.3 Spectra correlation ...... 37 Chapter 1

Introduction

1.1 Small molecules

Small molecules can be defined as low molecular weight organic compounds, which are, by definition, not polymers (Mylonas (2010)). Some typical examples include drugs, pesticides, dietary supplements and metabolites. Most of the analytes of biomedical, food and environmental interest have low molecular weight, usually less than about 1000 Da (Shankaran et al. (2007)). Their identification is very important in many fields such as (Mylonas (2010)):

• Metabolomics, for identiﬁcation and quantiﬁcation of metabolites.

• Pharmaceutical industry, for drug discovery, combinatorial chemistry, pharmacokinetics, drug metabolism, quality control

• Clinic, for drug testing

• Environment, for water quality, food contamination

• Geology, for the assessment oil composition

• Military, for the detection of toxic environments

• Forensics, for the study of drivers under drug inﬂuence

• Homeland security, for explosive detection

• Anti-doping, for the detection of doping products

• Space industry, for the analysis of collected material 1. Introduction 2

1.2 Small molecules identiﬁcation methods

As summarised in table 1.1, many methods exist to identify small molecules. Each one presents its own advantages and disadvantages, and the choice of a speciﬁc method depends on the laboratory needs.

Technology Advantages Drawbacks Immunoassays Ferrara et al. (1994) a) Rapid b) Easy to use a) Limited to specific Moeller et al. (2008) classes of molecules b) Antigen-antibody reac- tion not always specific NMR1 Garc´ıa-Pérez et al. a) High reproducibility a) Low sensitivity b) Not (2008) b) Non-destructive c) well adapted for screening Simultaneous quantifica- tion LC-UV2 Maurer (2004) a) Easy to use b) Wide a) Support from Bio- panel of compounds in- Rad discontinued b) Not cluded in shipped library very sensitive for specific c) plug and play system classes of molecules CE-MS3 Garc´ıa-Pérez et al. a) Rapid b) Wide panel a) Poor concentration (2008) of compounds detectable sensitivity b) Needs c) Good resolution special treatment for ionizable compounds GC-MS4 Maurer (2004) Vogeser & Seger a) Very large and a) Tedious sample prepa- (2008) machine independent ration b) No possible Moeller et al. (2008) libraries b) Reference automation c) Limited methods exist c) High to specific classes of sensitivity and specifity molecules LC-MSMS5 Vogeser & Seger a) High sensitivity and a) Requires substantial (2008) specificity b) Possible au- expertise and know-how tomation c) Rapidity b) Instrument dependent libraries c) Matrix effect

Table 1.1: Main methodologies used for small molecule identiﬁcation (Mylonas (2010)).

In this thesis, the interest will be focused on liquid chromatography tandem mass spectrometry, which will be presented in more details in the next section. 1. Introduction 3

1.2.1 Liquid chromatography tandem mass spectrometry (LC- MSMS) to identify small molecules

General workﬂow

Due to the capability of liquid chromatography tandem mass spectrometry (LC- MSMS) to identify low and high weight molecules, this technique is widely used in Proteomics (Chen & Pramanik (2009)), and has been used in small molecules identiﬁcation for some years. It presents the huge advantage of being able to identify nonvolatile and thermally labile compounds, in contrast with gas chromatography tandem mass spectrometry. LC-MSMS involves the following steps:

• Sample extraction

• liquid chromatography

• MSMS analysis

• computer analysis.

The sample extraction will not be explained in this thesis. The other steps are presented more extensively in the following paragraphs.

Liquid chromatography

Liquid chromatography is a technique for substance separation which prevents, in the LC-MSMS context, the molecules from reaching the mass spectrometer at the same time. Practically, a sample mixture is passed through a column packed with solid particles (stationary phase). With the proper solvents, packing conditions, some components in the sample will travel the column more slowly than others resulting in the desired separation. While there are several LC techniques as shown in table 1.2, small molecules are generally well separated thank to their diﬀering polarity (Mannhold (2008)), making Reverse Phase Chromatography the most appropriate choice. Reversed phase columns consist of a non-polar stationary phase. A C18 bonded silica is the most popular type of reversed-phase HPLC packing (Mannhold (2008)). The mobile phase is usually an aqueous blend of water with a miscible, polar organic solvent, such as acetonitrile or methanol (Mylonas (2010)). Polar molecules such as acids will elute faster, and will thus be injected early into the mass spectrometer. 1. Introduction 4

Chromatographic method Separation technique Normal phase (NP-HPLC) polar differences Reverse phase (RPC) polar differences Hydrophilic Interaction (HILIC) polar differences Size exclusion (SEC) molecule size Ion exchange molecular charge Bioaffinity complex building

Table 1.2: Main chromatographic methods used for HPLC.

Mass spectrometry

Mass spectrometry is a sensitive analytical technique which is able to quan- tify known analytes and to identify unknown molecules at the picomoles or femtomoles level (Staack & Hopfgartner (2007)). A mass spectrometer is an instrument which measures precisely the abundance of molecules which have been converted to ions. During mass spectrometry analysis, molecules are ﬁrst ionised and then separated in the mass spectrometer according to their ratio mass/charge (m/z). At this point, fragmentation can occur and a second analyser determines the m/z ratio of the resulting fragments. Ions are ﬁnally detected by a detector, and the obtained signal analysed by a computer.

Computer analysis with SmileMS

SmileMS is a platform for small molecules identifications developed by GeneBio. This software performs the last step of the small molecules identification workflow, the library search. In this step, all experimental spectra of a run are searched against a library and a result containing all the matches is generated (automated library search) (Mauron (2010)) Whereas spectra corresponding to the same molecule show great variations in peaks intensity fragments are generally conserved. In order to give less weight to the intensities while still taking them into account, SmileMS considers the rank of the intensity instead of its absolute or relative value as a robust metrics (Mylonas et al. (2009)). X-Rank, the SmileMS algorithm, is based on proba- bilistic calculations. This means it is trainable for specific conditions. When compared to the widely used dot product, X-Rank shows better performances. This is especially the case when the spectra data to identify is acquired under different conditions than the spectra library. 1. Introduction 5

All the small molecules identiﬁcations analyses performed in this thesis used SmileMS.

1.3 Challenges associated with LC-MSMS

1.3.1 Poor spectra reproducibility

Spectra obtained by tandem mass spectrometry tend to be poorly reproducible (Oberacher et al. (2009)). A same molecule can generate different spectra, varying in the peaks present and their intensity. As mentionned in (Mylonas (2010)), ionisation source and fragmentation source variability can partly ex- plain spectra disparity. Moreover, matrix effect, which are the alteration of ionisation efficiency by the presence of co-eluting substances, appear also par- tially responsible for such a variability. Figures 1.1 and 1.2 show spectra disparity in LC-MSMS compared to the reproducibility for the same spectra in GC-MSMS. Such variations, the presence of adduct or derived sub compounds, tend to generate spectra that are generally neglected or misinterpreted.

1.3.2 Reference libraries scarcity

Whereas large GC-MS libraries are available, few spectra libraries adapted to LC-MSMS exist (Mylonas (2010)). The scarcity of LC-MSMS spectral libraries can be partly explained by the poor availability of certain substances. The cost of building such libraries is also an important factor. In fact, while data acquisition can be rapidly performed, selecting the reference spectra to ﬁll the library is to be a time-consuming process relying on a human expert.

1.3.3 Elucidation rate

In a typical LC-MSMS run, the vast majority of spectra are not elucidated. This problematic will be further illustrated in chapter 4, where we show that, in the best case, only 7% of spectra are identiﬁed. The remaining spectra may consists of poor quality spectra, but also of metabolites or contaminants not stored in reference libraries. 1. Introduction 6

Figure 1.1: Spectra variability in LC-MSMS 1. Introduction 7

Figure 1.2: Spectra reproducibility in GC-MSMS 1. Introduction 8

1.4 Research goals

The aim of this thesis is to value usually neglected information in LC-MSMS analysis by looking at a run more globally and even comparing runs with each other. For this purpose, and to address the intrinsic variability associated with LC- MSMS acquisitions, we developed and validated a clustering algorithm allowing to group similar spectra. This tool makes it possible to investigate two challenges encountered in the LC-MSMS ﬁeld:

• We propose a workﬂow to automatically build a reference library, and we compare the performance of this method to the usual human expert process. Moreover, we study if such a workﬂow allows to annotate common contaminants.

• In a second step, we try to increase the elucidation rate of a LC-MSMS analysis, looking at how diﬀerent compounds are correlated together.

We believe that the results, which will be presented in this thesis, conﬁrm the need for a wider view on LC-MSMS runs and the value of usually neglected information. Chapter 2

Spectra clustering

2.1 Problematic

After LC-MSMS analysis, small molecules identification consists of comparing experimental spectra to a reference library containing spectra of known molecules. Such libraries therefore only include a limited number of spectra, and it is common to deal with spectra in a sample that do not match any of the molecules in the library. To illustrate this problematic, let us mention the example of 70 acquisition runs taken from the Toxicology Department of the HUG. They were submitted against a reference library, and the ratio of spectra matching against the library divided by the total number of spectra present in the run was calculated. Table 3.3 summarises our findings: as we can see, the maximum percentage of identified spectra was only equal to 7.2%, and in half of the runs, less than 3.7% of spectra were identified.

measure value minimum 0.01638 q1 0.02710 mean 0.03708 median 0.03716 q3 0.04353 maximum 0.07193

Table 2.1: Proportion of identiﬁed spectra in 70 runs: The ratio number of identiﬁed spectra divided by total number of spectra in the run was calculated for 70 run acquired on a Bruker ion trap machine at the Toxicology Department of the HUG. A score threshold of 0.3 was chosen. The mean, median, quartiles (q1 and q3) and the extreme values are reported. 2. Spectra clustering 10

To the extend of our knowledge, this part of a sample consisting of uniden- tiﬁed spectra has not been extensively studied, and we wanted to explore it more in depth. In this chapter we will focus on how spectra produced by a single acquisition run can be clustered together, suggesting that they could correspond to the same molecule. Moreover, we wanted to search for spectra, which did not match anything against the reference library, but which could be close to known molecules. These questions could be answered thanks to a clustering algorithm that we developed. We will present a workﬂow to build clusters, as well as validation tests.

2.2 Methods

2.2.1 Data and library

In this research, we worked with a selection of samples from the Toxicology Department of the HUG. They were acquired on a Bruker ion trap machine (amaZon), and the data ﬁles were in XML format. In order to identify their content they were submitted against the Freiburg library, built by Wolfgang Weimann at Freiburg University, Germany (Dresen et al. (2009)). It consists of an electrospray ionisation tandem mass spectrometry (ESI-MS/MS) library which contains over 50600 spectra of 10253 compounds relevant in clinical and forensic toxicology. It has been developed using a hybrid tandem mass spectrometer with a linear ion trap, analysing pure compound solutions-in some cases solutions made of tablets. The Freiburg library content can be consulted in appendix A.

2.2.2 Clustering workﬂow

State of the art

Clustering has been studied in peptide LC-MSMS identification, where experiments often generate millions of spectra that can be used to identify thousands of proteins in complex samples (Frank et al. (2007)). Analysing such large datasets poses a computational challenge, when searching millions of spectra against large protein databases, particularly if mutations and unexpected post- translational modifications (PTMs) are considered. Instead of repeating the identification process for each spectrum, it can be beneficial to perform this 2. Spectra clustering 11 process once and apply the results to all similar spectra. Tabb et al. (2003) demonstrated how clustering can accelerate analysis of runs, but at the cost of loosing some spectra identification. The MS2GROUPER algorithm improved the results by reducing by 20% the number of spectra that have to be searched and loosing 1% of peptides (Tabb et al. (2005)). In 2004, Beer et al. (2004) developed the Pep-Miner algorithm and applied it on 5000000 spectra. Although they demonstrated its usefulness in reducing running time and improving identification, this algorithm is not publically available and little is known on its clustering performance. Moreover, the clustering algorithm is based on retention time prediction, which can be difficult to calibrate especially when multiple runs are considered (Frank et al. (2007)). Clustering algorithm initially applied to Internet and database clustering have recently been adapted to MSMS analyses. Ramakrishnan et al. (2006), and Dutta & Chen (2007), proposed to use metric space embedding for MSMS database search and clustering. While these promising approaches offer a potential solution to the problem of clustering very large datasets, the applications of these new ideas were illustrated only with a related task of filtering candidates for database searches or for clustering with relatively small spectral datasets (Frank et al. (2007)). Frank et al. (2007) developed the MS-Clustering algorithm, an optimisation of the Pep-Miner algorithm allowing to analyse millions of spectra. Determining the similarity between spectra is a crucial step in spectra clustering. MS-Cluster uses a normalised dot product as similarity measure. This method has some drawbacks. The artificially high scores granted to spectra containing only few peaks can be mentioned (Mylonas (2010)).

A new method

At best, usual clustering methods propose RT and precursor mass filter plus a dot product, which do not appear optimal to us as we have access to a much more robust scoring mechanism. In fact, X-Rank, the algorithm implemented in SmileMS (Mylonas (2010)) demonstrated better sensitivity, specificity and better robustness than dot product or derived scoring models. We therefore based our cluster algorithm on scoring, and we proceeded in the following way. As SmileMS offers a powerful scoring model to score spectra versus a library of reference spectra, the idea was simply to use an acquisition run as a reference library and score the run against itself. In a second step, based on 2. Spectra clustering 12

experimental spectra

1. insertion as ref library 2. scoring

SmileMS

list of matches between experimental spectra

clustering

clusters of spectra

Figure 2.1: Clustering workflow: experimental spectra from an acquisition run are inserted as reference library into SmileMS. The run is scored against itself. Spectra are then clustered based on obtained scores. the submission results we obtained, we grouped similar spectra together using our clustering algorithm described in figure 2.1. The different steps of this workflow are detailed in the following sections.

Submissions

The first step in our workflow consisted in identifying the content of a data file taken randomly from the HUG Toxicology Department. This process first involved the addition of this file in SmileMS as a new library of its own. The same file was then submitted against this added library. Both steps were performed using web services, allowing to use the power of the SmileMS platform from an external software. For both submissions, the main parameters were: 2. Spectra clustering 13

• a score threshold of 0.7: only matches between spectra with a higher score are considered. This value is rather conservative, in order to prevent unlikely matches;

• a ﬁlter on the precursor mass diﬀerence of ±0.2Da: this limit was chosen to take into account the instrument precursor mass accuracy.

At the end of this submission process, we had a list of all experimental spectra present in our data ﬁle, and the scores of the matches between pairs of spectra in our sample.

Clustering algorithm

Starting from a list of experimental spectra (annotated with the name of the corresponding molecule if known for later veriﬁcation), and knowing the scores between each pair of spectra, the goal of the clustering process is to group the spectra that are similar. At the beginning of this clustering process, no cluster is created. Each of the experimental spectra is considered sequentially. For each one, the algorithm checks if this spectrum is already present in a cluster. Three scenarios are then possible:

• the answer is no: a new cluster is then created and contains the considered spectrum, as well as its matching spectra.

• the answer is yes and only one cluster contains the considered spectrum: the matching spectra of the considered experimental spectrum are added to this cluster.

• the answer is yes and several clusters contain the studied spectrum: these clusters are merged. Moreover, the matching spectra of the considered experimental spectrum are added to the resulting cluster.

At the end of the clustering process, a test is systematically performed to ensure that no spectrum belonging to several clusters is found. If matches are symmetrical, redundancy is prevented with our clustering algorithm. It is important to understand that clustering is simply a partition of the set of all the spectra - every spectra is in one and only in one cluster at the end. We can note that, in addition to scores, retention time could also be used to calculate similarity between spectra, and it would be interesting to observe its eﬀect in a further study. 2. Spectra clustering 14

2.2.3 Dealing with large data

When applied on data files containing single acquisition runs, the clustering workflow appears fast, and no memory limitations are encountered. However, the study of several acquisition runs merged in the same data file required practical adjustment of two steps of our process because of memory constraints. First, the format of the data file which was inserted into SmileMS as a new library and then submitted against this library had to be changed. In fact, parsing a XML file in SmileMS (mandatory step in the library insertion process and submission process) involved the creation of a list of spectra objects, and then the storage of these objects in a database. In the case of a large data file, the number of generated objects was too important, leading to an ”out of memory” exception. Using SDF and mgf formats (for library and submission respectively) solved this issue because the creation of an individual spectrum object was immedi- ately followed by its storage in the database, preventing the generation of a too large number of objects. Practically, Groovy methods reading the XML files, converting them to SDF and mgf format and merging the individual files were written. The second step, which had to be slightly modified was the submission result file reading. In fact, its size being too large, the Groovy XmlSlurper could not manage it. Therefore, we had to parse the file manually, isolating the matches results for individual experimental spectrum, and use a XmlParser to parse the generated string.

2.2.4 Clustering algorithm validation

Two aspects of clusters, which we will call compacity and discriminance, were investigated in order to validate our algorithm meaningfulness.

Cluster compacity

Cluster compacity refers to the distance (scores in our case) between spectra belonging to a same cluster. In a given cluster, each spectrum was scored against the other cluster members, and the scores distribution was studied using R, an environment for statistical computing and graphics. The hist function allowed to obtain histograms 2. Spectra clustering 15 of scores, and the summary function to get a numerical description of data distribution by providing the following information:

• minimum value

• ﬁrst quartile (q1): cuts oﬀ lowest 25% of data

• median: cuts data set in half

• mean

• third quartile (q3): cuts oﬀ highest 25% of data, or lowest 75%

• maximum value

Cluster discriminance

Discriminance will refer, in this thesis, as the property of a cluster to contain only one molecule, or several very close molecules. In other words, a cluster will be considered as meaningful if it contains maximum one known molecule. In order to know which molecules were present in the clusters, we submitted spectra of these clusters against the Freiburg library (Dresen et al. (2009)).

2.3 Results

2.3.1 Cluster compacity

To evaluate scores between spectra in a cluster, we studied fifteen acquisition runs merged together and submitted to the clustering process. Thirty of the obtained clusters were scored against themselves and we analysed scores distribution in each cluster. In order to prevent a bias of the results, we of course did not take into account scores between a spectra and itself, which would be very high and artificially increase the mean score value for a cluster. Figures 2.2 and 2.3 are examples of scores distribution in two clusters: As we can notice, score values appear extremely high, meaning that our clustering algorithm indeed groups very similar spectra. These positive results were further confirmed by the distribution of all obtained scores within the thirty studied clusters. As shown in Table 2.2, 75% of the scores are greater than 0.78, and half of the scores appear superior to 0.86. 2. Spectra clustering 16

Scores distribution in cluster 173 10 8 6 Nb scores 4 2 0

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90

Score value

Figure 2.2: Scores distribution in cluster 173: As we can notice, most of the scores between pairs of spectra in this cluster are higher than 0.7, conﬁrming that our clustering algorithm is able to group very close spectra.

Routinely, much lower score thresholds are usually chosen to determine that a match between spectra is good. Therefore, the score values we obtained indicate that our clustering algorithm indeed allows to group very similar spectra.

measure value minimum 0.18 q1 0.78 mean 0.82 median 0.86 q3 0.91 maximum 0.91

Table 2.2: Scores distribution in 30 clusters: We can notice that 75% of scores are above 0.78, and 50% above 0.86, conﬁrming the validity of our clustering approach.

2.3.2 Cluster discriminance

In order to assess if the obtained clusters only contain one molecule or very closely related molecules, we analysed a single run acquisition. A description 2. Spectra clustering 17

Scores distribution in cluster 184 150 100 Nb scores 50 0

0.65 0.70 0.75 0.80 0.85 0.90

Score value

Figure 2.3: Scores distribution in cluster 184: As we can notice, most of the scores between pairs of spectra in this cluster are higher than 0.7, conﬁrming that our clustering algorithm is able to group very close spectra.

of the clusters containing known molecules is presented in table 2.3. First, and most importantly, we can notice that no cluster with more than one molecule was generated. Three molecules were identified in the studied run, each one appearing in a different cluster. Our clustering algorithm could successfully discriminate these molecules. The study of spectra, which were not associated with known molecules, appears extremely interesting. While the spectrum corresponding to methadone was alone in a cluster, unidentified spectra were present in clusters containing morphine and clomipramine. The precursor mass range in these clusters suggest that identified and unidentified spectra are probably very closely related (slightly chemically modified molecule for instance). Much more surprisingly, our single acquisition run revealed the presence of many unidentified spectra close enough to be clustered together. In total, 514 clusters were built. Three of them contained identified molecules, the remaining clusters were composed of unidentified spectra only. Most of them only contained one spectrum, but 18 clusters had at least two unidentified spectra. 2. Spectra clustering 18

nbSp mol nbMatches MozRange RtRange 10 Morphine 1 175.9 − 177 0.02 − 5.6 1 Methadone 1 272.9 0.94 19 Clomipramine 1 265.9 − 267 0.07 − 8.76

Table 2.3: Summary of a single run acquisition clustering: nbsp = number of spectra in the cluster; mol = molecules associated with the spectra present in the cluster; nbMatches = number of spectra in the cluster, which matched against the Freiburg library; MozRange = range of spectra mass precursor in the cluster; RtRange = range of spectra retention time in the cluster.

2.4 Conclusion

In this chapter we presented the development of a clustering algorithm for LC-MSMS spectra, based on SmileMS scoring. Validation tests showed the meaningfulness of this algorithm in two different ways. Cluster compacity revealed homogeneous clusters from a scoring point of view. Scores distribution in thirty clusters showed that 75% of scores were above 0.78, and 50% above 0.86. In routine analysis, much lower score thresholds are chosen to determine the significance of a score, and our results indicate a very high similarity between spectra of the same cluster. Cluster discriminance demonstrated that our clustering algorithm was able to separate molecules present in the studied sample as desired. This analysis also revealed 18 very interesting clusters, containing more than two unidentified spectra. Such spectra could be, for instance, contaminants or metabolites of drugs present in the sample. These findings lead us to wonder if these unidentified spectra are also present in other runs, opening the perspective to increase the elucidation rate in future analyses. In order to answer this question, it was necessary to develop a solution to store the unidentified spectra in a reference library, against which further runs would be searched. This approach will be presented in the next chapter. Chapter 3

An automatic reference library building process

3.1 Problematic

A reference library is a set of spectra associated with some characteristics (such as experimental protocol, acquisition parameters etc.), generally linked to a molecule identifier. After LC-MSMS analysis, experimental spectra are searched against such libraries, meaning that each experimental spectrum is compared to all reference spectra of the library, and a score is calculated. Building a reference library involves choosing a spectrum corresponding to a molecule, which is not a trivial task. Typically, for a 200-compound library, data acquisition is highly automated and takes approximately 1.5 days. Then, a human expert must select the best spectrum for each compound and store its association with a molecule. This second step can typically last a couple of weeks. Moreover, this selection process is often subjective, two experts do not always have the same opinion when electing the “best spectrum”. Finally, we must keep in mind that only spectra corresponding to known molecules are inserted into such libraries. In the previous chapter, we presented a clustering algorithm for experimental spectra. We were then interested to keep track of a ”representative spectrum” for each one of our clusters and use them to populate an automatic library. As many of our clusters do not contain known molecules, we wanted to investigate if these spectra (which could be contaminants, metabolites etc) were present accross different samples. Therefore, we developed an approach to automatically select a spectrum 3. An automatic reference library building process 20 from a cluster and insert it into a library. In order to validate our approach, we compared the submission of data files against a reference library and our automatic library.

3.2 Methods

3.2.1 Automatic library building workﬂow

Automatic library building is a direct application of our clustering procedure presented in the previous chapter. In fact, after the clustering step, an empty library is created in SmileMS and the ”most representative” spectrum of each cluster is chosen and inserted into this newly created library as shown in ﬁgure 3.1

3.2.2 Spectrum selection process

In each cluster, a representative spectrum must be selected. Frank et al. (2007) simply elected the best spectrum as the one with the highest signal-to-noise ratio. We do not want to select the ’best looking’ spectra, but the one which performs the best regarding the scoring mechanism. In a given cluster, for each spectrum, the sum of the scores between the considered spectrum and the other cluster members is calculated. The procedure is repeated for each spectrum of the cluster and the spectrum having the higher sum of scores is considered as the most representative spectrum of the cluster (being the closest to the other spectra in the cluster). If the cluster C is the set of n spectra s1 . . . sn, lets ˆ(C) be the best matching spectrum, i.e.:

n ( n ) X X sˆ(C) = sk such as sk · si = maxj=1...n sj · si (3.1) i=1 i=1

Our simple metric to chose a representative spectrum is based on scoring. This criteria would not necessary correspond to a human expert choice, but the goal is to select the most eﬃcient spectrum from the scoring point of view, not the human chemist’s one. We will present in section 3.3 how this choice performs. We could have built a consensus spectrum from the cluster (thus changing the functions ˆ(C) from equation 3.1) with ideas such as Mueller et al. (2007) 3. An automatic reference library building process 21 who kept only peaks present in a given number of spectra from the cluster, but our simple choice proved to be robust enough.

3.2.3 New library creation

Once a spectras ˆ(C) is elected from cluster C, it must be inserted into the library. Based on 20 acquisition runs from the HUG, a new reference library was created in SmileMS using web services, which allow to conveniently interact with the platform from external code. Such services also allow to insert, update and delete spectra into the new library. For this experiment, each run, stored as a file, was treated successively. We inserted the runs one after the other because we encountered memory problems at that time, even with the tricks to deal with large data presented in chapter 2. It means that we can have several times the same compound registrated from different runs. However, we accepted redundancy for these specific experiments because we wanted to encounter the wider variety of known molecules. Run acquisitions being patient samples, containing only few molecules, the number of considered runs was important. From a practical point of view, an automatic library contains the following information on inserted spectra:

• job ID

• spectrum ID

• MS level

• precursor mz

• retention time

• compound name (associated molecule, if any is available)

• title (job ID - spectrum ID)

3.2.4 Automatic library validation

In order to test the performance of our automatic library, we submitted 10 new ﬁles taken from the Toxicology Department of the Geneva University Hospi- tal against it and against the Freiburg’ library, the subset of Freiburg library molecules contained in the samples used to build our library. 3. An automatic reference library building process 22

experimental spectra

1. insertion as ref library 2. scoring

SmileMS

list of matches between experimental spectra

clustering

clusters of spectra

spectra selection

best spectra

insertion

automatic library

Figure 3.1: Automatic library building workﬂow: using our clustering algorithm described in ﬁgure 2.1, similar spectra of a run are grouped together. A representative spectrum of each cluster is automatically selected and inserted into a newly created library. 3. An automatic reference library building process 23

The aim of this experiment is to compare the molecules identified against both libraries. We expect that molecules identified against the Freiburg’ library will also be identified against the automatic library. This comparison tests involve that spectra inserted into the automatic library are annotated. Therefore, before filling the automatic library, spectra were submitted against the Freiburg library. A score threshold of 0.3 was chosen for all submissions. Our objective was, at that point, not to identify the most appropriate score, but to make sure that the identification against our automatic library was equivalent to the identification against the Freiburg’ library.

Let Stest be the set of spectra from the 10 test runs identiﬁed against

Freiburg’ library and the automatic library with a score ≥ 0.3. Let xauto(s) be the identification score of a spectrum s searched against the automatic library, and xfr(s) the identification score against the Freiburg’ library. Using R and Groovy, we quantified the performance of our automatic library by calculating the following metrics:

• nbcommons = kStestk, the number of common identiﬁcations between both

libraries, and nbmisses, the number of molecules identiﬁed in Freiburg’ library but not found in our automatic library.

• distribution of xauto(s), s ∈ Stest,

xauto(s) the distribution of the ratio rsim(s) = for all spectra s ∈ S. • xfr(s)

3.3 Results

Applying the strategy described in section 3.2, we built an automatic library based on twenty files. In order to test our approach, we submitted 10 runs against the Freiburg’ library and against our automatic library, and asked the following questions: How many common identifications do we have against both libraries? For these common identifications, how do the scores look like? Is xauto(s) similar to xfr(s) for these common identifications? As indicated in table 3.3, between 2 and 12 spectra matched against our automatic library and against the Freiburg’ library. More importantly, in half of the submitted runs, no missed identification was reported. In the remaining cases, one molecule was present in the samples used to build our library but was not inserted into it (and therefore not identified). 3. An automatic reference library building process 24

The analysis of scores (xauto and xfr) for these common identifications appears very interesting. Table 3.1 shows the details of three runs submitted against the two libraries. The molecules identified against both libraries are presented with their scores. We can notice that scores are extremely high, confirming a robust identification of these molecules. The distribution of {xauto(s)|s ∈ Stest} is presented in table 3.2. With a median of 0.9025, 50% of the scores appear superior to this value, confirming the validity of our spectrum selection choice.

The scores xauto(s) and xfr(s), for a given spectrum s, are not only high against both libraries, but also very similar. To confirm our impression, we calculated, for each molecule identified against both libraries, the ratio rsim(S) = xauto(s) . Table 3.4 summarises the distribution of obtained ratio. We can notice xfr(s) that 75% of the ratio are between 0.9506 and 1.069, therefore extremely close to 1, confirming the high similarity between the two compared scores. The remaining ratio, 25% of cases, are above 1.07, with a maximum value of 2.52, showing a tendency for the scores against our automatic library to be higher than the scores against the Freiburg’ library.

runid mol(s) xauto(s) xfr(s) 1 Citalopram 0.704 0.701 1 D4-Haloperidol 0.913 0.904 1 D4-Haloperidol 0.913 0.908 1 Clomipramine 0.913 0.864 7 D4-Haloperidol 0.913 0.902 7 Primidone 0.681 0.556 7 Primidone 0.750 0.721 8 Midazolam 0.913 0.899 8 Midazolam 0.913 0.912 8 D4-Haloperidol 0.913 0.912

Table 3.1: Comparative submission against the Freiburg’ library and an automatic library: 10 runs were submitted against both libraries. The example of three of these runs is presented, with runid ∈ {1, 7, 8}. Molecules (mol(s) is the compound associated to spectrum s in Freiburg Library) identi- ﬁed against both libraries are reported, with their score against the automatic library (xauto(s)) and against the Freiburg’ library (xfr(s)). 3. An automatic reference library building process 25

measure value minimum 0.3870 q1 0.8572 median 0.9025 mean 0.8558 q3 0.9130 maximum 0.913

Table 3.2: Scores distribution against an automatic library {xauto(s) | s ∈ Stest}: for all common molecules identiﬁed against the Freiburg’ library and our automatic library, the distribution of scores versus the automatic library is presented. The median of 0.9025 conﬁrms the validity of our spectrum selection choice.

runid nbcommons nbmissed 1 2 1 2 4 0 3 2 0 4 5 0 5 5 1 6 6 1 7 3 1 8 3 0 9 6 0 10 12 1

Table 3.3: Identiﬁcations against an automatic library: 10 runs were submitted against our automatic library and against the Freiburg’ library. The number of common matches between both libraries (nbcommons), and nbmissed the number of molecules present in Freiburg’ but not inserted into our library (and therefore not identiﬁed) are reported. 3. An automatic reference library building process 26

measure value minimum 0.9506 q1 1.006 median 1.013 mean 1.112 q3 1.069 maximum 2.052

Table 3.4: Comparison of identification scores between the Freiburg’ library and an automatic library: 10 runs were submitted against the Freiburg’ library and an automatic library. For each common identification xauto(s) s ∈ Stest, the ratio rsim(s) = was calculated. The distribution of rsim(s) xfr(s) is presented. We can notice that 75% of the ratio are between 0.9506 and 1.069, confirming that xauto(s) and xfr(s) are very close.

3.4 Conclusion

In this chapter, we presented an approach to select a representative spectrum from a cluster of MSMS spectra. Our choice was to take advantage of the robust SmileMs scoring to elect the best performing spectrum. We then built an automatic library with the selected spectra using twenty acquisition runs. Our approach was tested by submitting 10 files against our home-made library and against the Freiburg’ library. We reported the number of common identified molecules, as well as the number of missed identifications, taking into account the molecules that were encountered in the 20 files used to build the library. Few missed identifications were noticed (only one in half of the runs), explained by the non insertion of the encountered molecules into our library because another spectrum from the cluster was selected. In spite of the small size of our automatic library, between 2 and 12 common molecules per run could be identified. The scores of these common molecules are extremely high against the automatic library (in 50% of cases, they are above 0.90), and very close to scores against the Freiburg’ library. These results confirm our spectrum selection choice as well as the validity of our approach. Our workflow allowed to create a personalised library, and to automate an otherwise time-consuming process. We of course used a limited number of acquisition runs, and could therefore identify a small number of molecules. We worked with samples taken randomly from the HUG in “real-life” condi- 3. An automatic reference library building process 27 tions, and containing few molecules. By spiking molecules separately - protocol usually followed to build a library and lasting one day and a half - we could encounter all molecules present in the Freiburg library and therefore build a useful library. Moreover, we encountered memory limitations, preventing us from using large data files (merged runs) and leading to a probable redundancy in our library. Optimising the code could probably solve this issue and thus prevent redundancy. In the next chapter, a smaller but non redundant library will be built in order to study unidentified spectra. In chapter 2 we showed their large number in an acquisition run, and looking for recurrence of such spectra across runs will be of particular interest. Chapter 4

Seventy runs analysis: molecules recurrence and co-occurrence

4.1 Problematic

In chapter 2, we showed that LC-MSMS analysis of runs obtained from the Geneva University Hospital leads to the identification of some known molecules. However, it is very common to face experimental spectra, which do not correspond to any of the reference spectra stored in the Freiburg library. In chapter 3, we presented a workflow to build a reference library with such unidentified spectra, and we are now interested in investigating the recurrence of unidentified spectra across runs. Does a particular unidentified spectrum appear in many different runs or is it isolated in one run? In a second step, we will adress the question of correlations between the presence of certain spectra. In other words, is a particular spectrum present in a run more often than by chance if another spectrum is also present? Such correlations could bring very useful information such as confirming the presence of a particular molecule using the presence of another molecule. Moreover, recognising unidentified spectra thank to their correlation with other spectra could increase the elucidation rate. In order to answer these questions, we generated an automatic library based on 15 files. Seventy runs were then submitted against this library, and the recurrence and correlation of unidentified spectra were studied. 4. Seventy runs analysis: molecules recurrence and co-occurrence 29

4.2 Methods

4.2.1 Library and data ﬁles

A reference library was automatically created as described in chapter 3. This library was filled with spectra selected, after clustering, from a SDF file containing 15 runs from the HUG. In order to build a robust library, a score threshold of 0.7 was chosen. Seventy runs from the HUG were submitted against this automatic library in order to study the recurrence of spectra across these runs. To visualise the results, an Excel sheet was generated, representing the exhaustive list of identified spectra across the seventy runs and the number of time they were encountered in each run.

4.2.2 Spectra recurrence analysis

The recurrence of spectra across seventy runs was represented graphically using R. The obtained curve was then ﬁtted in order to ﬁnd the distribution of the number of occurrence of a molecule across 70 runs.

4.2.3 Correlations

Hypergeometric distribution

After submitting 70 runs against our automatic library and studying molecule recurrence, the central question was to test if correlations between molecules exist. In other words, are there pairs of molecules which are present together in several runs more often than by chance? In order to answer this question, we calculated the probability of our observations.

Considering N runs, observing molecule A (molA) in nA runs, molecule

B(molB) in nB runs, we can count the number of time molecules are seen together in the same run nA∩B. This observable nA∩B follows a hypergeometric distribution. A hypergeometric experiment typically consists in drawing without replace- ment n marbles from an urn containing N marbles in total, NA of which are of interest. If X is a random variable following a hypergeometric distribution. Equation 4.1 describes the probability that X = k success (Lecoutre (2002)): 4. Seventy runs analysis: molecules recurrence and co-occurrence 30

NAN−NA k n−k PX (X = k) = N (4.1) n

Across N = 70 runs, if molA is present in nA runs, molB in nB runs, the probability of observing both molecules in k runs is given by equation 4.2. It is important to note that this formula could directly be applied, because the distribution of identified spectra across the 70 runs was homogenous. There were very few runs with a very low or a very large number of identified spectra, as shown in figure 4.1, and we did not have to take this element into account.

nB 70−nB k nA−k PX (X = k) = 70 (4.2) nA Therefore, the probability of seeing the two molecules in more than k runs follows: min(nA,nB ) nB 70−nB X l nA−l P (X ≥ k) = 70 (4.3) l=k nA

Bonferroni correction

Large numbers of significance tests may be difficult to interpret because if we go on testing long enough we will inevitably find something which is “significant.” We must beware of attaching too much importance to a lone significant result among a mass of non-significant ones (Bland & Altman (1995)). If we test a null hypothesis which is in fact true, using α = 0.05 as the critical significance level, we have a probability of 0.95 of coming to a not significant (correct)) conclusion. If we test two independent true null hypotheses, the probability that neither test will be significant is 0.95 · 0.95 = 0.90. If we test 20 such hypotheses the probability that none will be significant is 0.36, and thus the probability of observing at least one significant test (by chance) is equal to 0.64. Generally, with κ the number of independent tests at the α significance level, the probability of observing no significance differences is (1 − α)κ. By decreasing α enough, we can make the probability that none of the separate tests is significant equal to 0.95. The Bonferroni adjustment proposes to calculate a new significance level α0 by dividing α by κ as shown in equation 4.5 (Bland & Altman (1995)) In the present situation, 10260 spectra are compared to each other. As we do not consider the comparison of a spectrum against itself and as comparing 4. Seventy runs analysis: molecules recurrence and co-occurrence 31

spectra si with sj or sj with si is the same, the number of tests κ is given by equation 4.4 and the obtained α0 in the present analyses in equation 4.6.

1260 ∗ 1259 κ = = 793170 (4.4) 2

α α0 = (4.5) κ

0.05 α0 = = 6.3e−8 (4.6) 793170

Probability calculation

Practically, in order to determine if two molecules are correlated across 70 runs, we performed the following steps:

• we generated an exhaustive list of all identiﬁed molecules across the 70 runs;

• we calculated, for each possible pair of molecules within this list the probability of observing them together in more than k runs using the hypergeometric distribution;

• if the obtained probability was inferior to the signiﬁcance level α0 calculated using the Bonferroni adjustment, we considered the pair of molecules as correlated.

A graphical representation of the obtained correlations was performed using Groovy. The links between correlated molecules were drawn, allowing to visualise possible groups of correlated molecules.

4.3 Results

4.3.1 Spectra recurrence analysis

How often does a spectrum occur accross all runs?

We were ﬁrst interested in investigating the recurrence of spectra across 70 runs taken from the HUG. Does a particular spectrum appear very occasionally or is it frequently present in diﬀerent patients samples? To answer this question, we submitted 70 runs against an automatically built library containing eight 4. Seventy runs analysis: molecules recurrence and co-occurrence 32

Figure 4.1: How often does a spectrum occur accross all runs? Distri- bution of molecules occurence across 70 runs: An exhaustive list of identified spectra across 70 runs was built. For each one, the number of runs where it was measured is marked with circles and the fitting curve from equation 4.7 is shown by the red curve. compounds and many unidentified spectra. In total, 1260 spectra were identified across the 70 runs. We found out that although many spectra appear only in a few runs, some are frequently present across runs, as shown in figure 4.1. Equation 4.7 describes the distribution of occurrence (n) for a molecule across 70 runs.

1260 f (n) ∼ (4.7) occ 1.91n−1.75 The observed recurrence of spectra across runs conﬁrms the interest of stor- ing unidentiﬁed spectra in an automatic library for further submissions. In fact, their detection can highly increase the elucidation rate in new runs. 4. Seventy runs analysis: molecules recurrence and co-occurrence 33

Identified spectra across 70 runs 30 20 Nb runs 10 0

0 100 200 300 400

Nb spectra

Figure 4.2: How many spectra per run? Distribution of the number of identiﬁed spectra across 70 runs: 70 runs taken from the HUG were submitted against an automatic library. For each one, the number of identiﬁed spectra was reported.

How many spectra per run?

The 70 submissions against our automatic library is a demonstrative illustra- tion. Globally, between 50 and 150 spectra were identiﬁed in each run, in spite of the small size of our library (ﬁgure 4.2).

4.3.2 Correlations

While looking at the recurrence of spectra across runs, we observed spectra very often present in the same runs and absent from other runs as illustrated in table 4.1. This observation of very similar presence pattern lead us to be interested in possible correlations between the presence of pairs or groups of spectra. The question was to determine if the presence of molA can influence the probability of observing molB in the same run. In order to answer this question we calculated, for each pair of molecules, the probability of observing molA in nA runs, molB in nB runs, and both molecules in more than k runs. Our first impression was confirmed numerically, and we found pairs of correlated molecules. Table 4.2 shows some examples. Even more interestingly, the graphical representation of correlations between pairs of molecules lead to the identification of groups of correlated molecules, as illustrated in figure 4.3. 4. Seventy runs analysis: molecules recurrence and co-occurrence 34

m1 m2 m3 m4 run1 ···· run2 ···· run3 ···· run4 •••• run5 ···· run6 ··•• run7 ••·· run8 ••·· run9 ··•• run10 ••·· run11 ••·· run12 ···· run13 ···· run14 ···· run15 ·•••

Table 4.1: Occurence of four molecules across 15 runs: the presence (•) or absence (·) of 4 molecules (m1 . . . m4) across 15 runs is reported. We can notice that m1 and m2 have a very similar presence pattern. m3 and m4 are often present together in the same runs and show a diﬀerent pattern from m1 and m2. 4. Seventy runs analysis: molecules recurrence and co-occurrence 35

id molA na id molB nB nA∩B pV alue 9784 25 7207 25 25 1.55e-19 3761 30 9679 32 30 8.96e-18 4026 51 7948 51 51 1.58e-17 5816 23 9678 22 22 2.68e-17 8988 33 5817 36 33 7.12e-17 8988 33 3761 30 30 9.86e-17 8988 33 9679 32 31 2.25e-16 9679 32 4485 33 31 2.25e-16 7305 50 7931 51 50 3.15e-16 5817 36 9679 32 32 6.77e-16 8988 33 4485 33 31 3.52e-15 5817 36 4485 33 32 2.01e-14 3872 13 5892 13 13 2.11e-14 3872 13 3873 13 13 2.11e-14 5892 13 3873 13 13 2.11e-14 3761 30 4485 33 29 2.75e-14 8988 33 309 30 29 2.75e-14 136 21 4274 22 20 2.88e-14 7457 26 4802 24 23 3.27e-14 3761 30 5817 36 30 3.52e-14

Table 4.2: Correlations between pairs of molecules across 70 runs: given that molA is present in nA runs, molB in nB, the pV alue of observing molA and molB in more than nA∩B is calculated based on equation 4.3. 4. Seventy runs analysis: molecules recurrence and co-occurrence 36

In some of these groups, spectra have a precursor mass difference of 1 Da, suggesting a lack of precision during data acquisition. In other clusters, a mass shift is evident and could be explained by the presence of the same molecule with different chemical modifications or by the presence of several molecules.

Mixt groups

Some interesting clusters are composed of identified and unidentified spectra, allowing to associate unidentified spectra to molecules, therefore increasing the elucidation rate. Finally, we find clusters containing two drugs, such as methadone and nordiazepam. Dr. Marc Fathi, from the Toxicology Department of the HUG confirmed the meaningfulness of our results by explaining that it is common for patients treated with methadone to take psychotrope agents like benzodi- azepines.

4.4 Conclusion

In this chapter, we were first interested in the recurrence of spectra across 70 runs taken from the Geneva University Hospital. We could show that some unidentified spectra are very commonly found in patients samples and are therefore worth to be stored in an automatic library. In fact, detecting these recurrent spectra can highly increase the elucidation rate in further analyses. Moreover, spectra constantly present in runs could also be useful for retention time calibration, instead of adding internal standards. Retention time is used in LC-MSMs analyses as additional information to increase the confidence in the identification (Mylonas (2010)). Retention time can be either already known from previous experiments or predicted based on physicochemical properties of molecules. However, even in fixed conditions, retention time can show important variations from acquisitions to acquisitions. A solution is to use the relative RT, involving the interpretation of molecules RT compared with internal standards RT. Using constantly present spectra could simplify laboratory protocols by avoiding the utilisation of internal standards. Even with a small automatic library, due to memory constraints, we were able to identify recurrent spectra, which have interesting future applications. Focusing on overcoming the memory limitations will allow to build much more 4. Seventy runs analysis: molecules recurrence and co-occurrence 37

(a) (b)

Figure 4.3: Spectra correlation: two molecules spectra are linked together if the p-value of their co-occurrence is lower than α0. For each molecule, its precursor mass (m/z) and the number of runs in which it is matched is reported inside the node, while the number of runs in which both molecules occur is printed along the edge. Yellow color indicate that the spectrum has also been identified against the Freibug library. In figure (a), we can see the correlation with an error on the precusor mass attribution. In (b), precursor mass are close, but either the error tolerance was too small or the spectra were too different to have been clustered together. In (c), we see different molecules (artefacts?) correlated with Clomipramine. In (d), we can notice how two different molecules are appearing together, namely methadone and nordiazepam, which is meaningful from the toxicologist point of view. 4. Seventy runs analysis: molecules recurrence and co-occurrence 38 complete automatic libraries and therefore increase the utility of such approaches. The second question of this chapter dealt with correlations between molecules. Are some pairs of molecules present together across runs more often than randomly? We were able to find a positive answer, and we could find correlations between pairs of molecules. Even more interestingly, we could cluster groups of molecules. The clusters containing both identified and unidentified spectra are of particular interest. In fact being able to associate unidentified spectra with the name of a molecule already brings more information on the content of a run. Moreover, these unidentified spectra could be good candidate of drug metabolites and are therefore worth to be studied. Finally, the presence of unidentified spectra could have an influence on scoring by helping to confirm the presence of the associated molecule. chapterDiscussion

4.5 Achievements

In this thesis, we developed a MSMS spectra clustering algorithm based on SmileMS scoring. In order to assess the meaningfulness of our approach, we studied clusters compacity and discriminance. Compacity refers to a cluster ability to contain close spectra (based on scoring). The distribution of scores in 30 obtained clusters revealed that 75% of scores between pairs of spectra were above 0.78, much higher values than those routinely considered. Cluster discriminance consists in a cluster capacity to contain spectra corresponding to the same molecule or to very closely related molecules. An acquisition run analysis showed that the three molecules present in the sample were separated in three different clusters, therefore confirming the validity of our algorithm. Clustering experiments also revealed the presence of interesting clusters composed of unidentified spectra, which could be metabolites or contaminants. In a second step, based on our clustering algorithm, we proposed a workflow to automatically build a reference library containing known molecules and unidentified spectra. We decided to choose the most efficient spectrum from a SmileMS scoring point of view as the representative spectrum for a cluster. Submission of 10 runs against an automatic library revealed that molecules present in the files used to build the library were identified most of the time. Finally, we built an automatic library and investigated spectra recurrence 4. Seventy runs analysis: molecules recurrence and co-occurrence 39 across runs, as well as co-presence between spectra. We could confirm the recurrence of certain spectra across multiple runs. We also discovered pairs, and even groups, of spectra, which presence were strongly correlated.

4.6 Perspectives

4.6.1 Reference library building

Whereas large GC-MS libraries are available, few libraries adapted to LC- MSMS exist (Mylonas (2010)). Library building is a time-consuming and costly process because it requires a human expert to select the best spectrum for a compound. The workflow we presented allows to automate this process, therefore avoiding the requirement for an expert and building a library in a couple of hours instead of several weeks. We decided to rely on SmileMS scoring to select the most representative spectrum for a cluster. The obtained results were positive, but further tests comparing our simple method of spectra selection to consensus spectra generation (Mueller et al. (2007)) would be informative. An improvement of the spectra library workflow could also be to store strongly correlated spectra. In the present work, we worked with only few samples taken randomly from the Geneva University Hospital, and a limited number of molecules were encountered and inserted in our library. By following a usual protocol (i.e. spiking hundreds molecules separately), we could apply our clustering and spectra selection algorithms on a rich run and build a routinely useful library containing known molecules and unidentified spectra.

4.6.2 Elucidation rate

As illustrated in this thesis, the vast majority of spectra of an acquisition run consists of unidentified spectra. We could show that the percentage of identified spectra in samples from the HUG only reaches 7% in the best case. We found out that some unidentified spectra frequently appear in patients samples, and their storage in a library could dramatically increase elucidation rate. Moreover, using our molecules co-presence analysis approach, we can associate these recurrent unidentified spectra with a known molecule, giving us a clue on their identity. 4. Seventy runs analysis: molecules recurrence and co-occurrence 40

4.6.3 Scoring

In LC-MSMS analysis, a molecule is currently identified if an experimental spectrum corresponds to a reference spectrum present in a library. Knowing the correlations that can exist between the presence of several molecules could be extremely valuable information to take into account during the scoring process. Let us imagine the case of a molecule, which presence is strongly correlated with its metabolite. The reliability of a molecule identification in a patient sample would be increased if the metabolite is also detected. Similarly, if several molecules are associated in a treatment (prescribed together to increase their therapeutic effect for example), the detection of some of them can suggest or confirm the presence of the others. The findings of correlations between molecules and their integration to the scoring process could bring another dimension to the identification of small molecules. Further experiments should be lead to investigate the pertinence of such an approach. Analysing literature, we believe that the approach we undertook, from the clustering part to library building and spectra correlation, is original and would be of great interest to be embedded in a LC-MSMS identification platform such as SmileMS. Bibliography

Beer, I., E. Barnea, T. Ziv, & A. Admon (2004): “Improving large-scale proteomics by clustering of mass spectrometry data.” Proteomics 4(4): pp. 950–960.

Bland, J. & D. Altman (1995): “Multiple signiﬁcance tests: the bonferroni method.” Bmj 310(6973): p. 170.

Chen, G. & B. Pramanik (2009): “Application of lc/ms to proteomics studies: current status and future prospects.” Drug discovery today 14(9-10): pp. 465–471.

Dresen, S., M. Gergov, L. Politi, C. Halter, & W. Weinmann (2009): “Esi-ms/ms library of 1,253 compounds for application in forensic and clinical toxicology.” Analytical and Bioanalytical Chemistry 395(8): pp. 2521–2526.

Dutta, D. & T. Chen (2007): “Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search.” Bioin- formatics 23(5): p. 612.

Ferrara, S. D., L. Tedeschi, G. Frison, G. Brusini, F. Castagna, B. Bernardelli, & D. Soregaroli (1994): “Drugs-of-abuse testing in urine: statistical approach and experimental comparison of immunochemical and chromatographic techniques.” J Anal Toxicol 18(5): pp. 278–91.

Frank, A., N. Bandeira, Z. Shen, S. Tanner, S. Briggs, R. Smith,& P. Pevzner (2007): “Clustering millions of tandem mass spectra.” Journal of proteome research 7(01): pp. 113–122.

Garc´ıa-Perez´ , I., M. Vallejo, A. Garc´ıa, C. Legido-Quigley,& C. Barbas (2008): “Metabolic ﬁngerprinting with capillary electrophore- sis.” J Chromatogr A 1204(2): pp. 130–9.

Lecoutre, J. (2002): Statistique et probabilit´es. Dunod. Bibliography 42

Mannhold, R. (2008): Molecular drug properties: measurement and prediction. Vch Verlagsgesellschaft Mbh.

Maurer, H. H. (2004): “Position of chromatographic techniques in screening for detection of drugs or poisons in clinical and forensic toxicology and/or doping control.” Clin Chem Lab Med 42(11): pp. 1310–24.

Mauron, Y. (2010): “Lc-msms identiﬁcation of small molecules: Smilems, a small molecule identiﬁcation platform applied to clinical toxicology.” .

Moeller, K. E., K. C. Lee, & J. C. Kissack (2008): “Urine drug screening: practical guide for clinicians.” Mayo Clin Proc 83(1): pp. 66–76.

Mueller, L., O. Rinner, A. Schmidt, S. Letarte, B. Bodenmiller, M. Brusniak, O. Vitek, R. Aebersold, & M. M ”uller (2007): “Superhirn–a novel tool for high resolution lc-ms-based peptide/protein proﬁling.” Proteomics 7(19): pp. 3470–3480.

Mylonas, R. (2010): “Lc-msms identiﬁcation of small molecules; x-rank, a robust library search algorithm.” .

Mylonas, R., Y. Mauron, A. Masselot, P. Binz, N. Budin, M. Fathi, V. Viette, D. Hochstrasser, & F. Lisacek (2009): “X-rank: a robust algorithm for small molecule identiﬁcation using tandem mass spectrometry.” Analytical chemistry 81(18): pp. 7604–7610.

Oberacher, H., M. Pavlic, K. Libiseller, B. Schubert, M. Sulyok, R. Schuhmacher, E. Csaszar, & H. K ”ofeler (2009): “On the inter-instrument and inter-laboratory transfer- ability of a tandem mass spectral reference library: 1. results of an austrian multicenter study.” Journal of Mass Spectrometry 44(4): pp. 485–493.

Ramakrishnan, S., R. Mao, A. Nakorchevskiy, J. Prince, W. Willard, W. Xu, E. Marcotte, & D. Miranker (2006): “A fast coarse ﬁltering method for peptide identiﬁcation by mass spectrometry.” Bioinformatics 22(12): p. 1524.

Shankaran, D., K. Gobi, & N. Miura (2007): “Recent advancements in surface plasmon resonance immunosensors for detection of small molecules of biomedical, food and environmental interest.” Sensors and Actuators B: Chemical 121(1): pp. 158–177. Bibliography 43

Staack, R. F. & G. Hopfgartner (2007): “New analytical strategies in studying drug metabolism.” Anal Bioanal Chem 388(7): pp. 1365–1380.

Tabb, D., M. Thompson, G. Khalsa-Moyers, N. VerBerkmoes,& W. McDonald (2005): “Ms2grouper: group assessment and synthetic re- placement of duplicate proteomic tandem mass spectra.” Journal of the American Society for Mass Spectrometry 16(8): pp. 1250–1261.

Tabb, D. L., M. J. MacCoss, C. C. Wu, S. D. Anderson, & J. R. r. Yates (2003): “Similarity among tandem mass spectra from proteomic experiments: detection, signiﬁcance, and utility.” Anal Chem 75(10): pp. 2470–2477.

Vogeser, M. & C. Seger (2008): “A decade of hplc-ms/ms in the routine clinical laboratory–goals for further developments.” Clin Biochem 41(9): pp. 649–62. Appendix A

Freiburg library content

CompoundName CASNumber Formula MolWeight SpectrumCount (-)-Trans-delta-9-THC carboxylic acid A C22H30O4 358.2144 4 17alpha-Hydroxyprogesterone 68-96-2 C21H30O3 330.2194 4 17-alpha-Methyltestosterone 58-18-4 C20H30O2 302.2245 4 2-Amino-5-chlorobenzophenone 719-59-5 C13H10ClNO 231.045 4 2-Amino-5-nitrobenzophenone 1775-95-7 C13H10N2O3 242.0691 8 2-Benzyltetronic acid 3734-22-3 C11H10O3 190.0629 4 2-Hydroxyethylflurazepam 20971-53-3 C17H14ClFN2O2 332.0727 4 3,4-Dimethoxyphenethylamine 120-20-7 C10H15NO2 181.1102 4 3,4-Methylenedioxyamphetamine 13673-99-9 C10H13NO2 179.0946 4 3,4-Methylenedioxyethylamphetamine 82801-81-8 C12H17NO2 207.1259 4 3,4-Methylenedioxymethamphetamine 42542-10-9 C11H15NO2 193.1102 4 3,5-Diiodotyrosine 66-02-4 C9H9I2NO3 432.8671 4 3-Hydroxybromazepam 13132-73-5 C14H10BrN3O2 330.9956 4 3-Methylfentanyl C23H30N2O 350.2358 4 4-Benzamidosalicyclic acid 13898-58-3 C14H11NO4 257.0688 8 4-Methylumbelliferyl acetate 09.05.2747 C12H10O4 218.0579 4 5-(p-Methylphenyl)-phenylhydantoin 51169-17-6 C16H14N2O2 266.1055 4 5-Aminosalicylic acid 89-57-6 C7H7NO3 153.0425 4 6-Chlorothymolsulfonic acid 83732-70-1 C10H13ClO4S 264.0222 4 6-Mercaptourine 50-44-2 C5H4N4S 152.0156 8 6-O-Monoacetylmorphine 2784-73-8 C19H21NO4 327.147 4 7-Aminoclonazepam 4959-17-5 C15H12ClN3O 285.0668 4 7-Aminodesmethylflunitrazepam 894-76-8 C15H12FN3O 269.0964 4 7-Aminoflunitrazepam 34084-50-9 C16H14FN3O 283.112 4 7-Aminonitrazepam 03.02.4928 C15H13N3O 251.1058 4 8-Chlorotheophylline 85-18-7 C7H7ClN4O2 214.0257 8 8-Hydroxyquinoline 148-24-3 C9H7NO 145.0526 4 9-Hydroxyrisperidone C23H27FN4O3 426.2067 4 Acebutolol 37517-30-9 C18H28N2O4 336.2049 4 Acecarbromal 77-66-7 C9H15BrN2O3 278.0266 8 Aceclidine 827-61-2 C9H15NO2 169.1102 4 Aceclofenac 89796-99-6 C16H13Cl2NO4 353.0221 8 Acemetacin 53164-05-9 C21H18ClNO6 415.0822 4 Acenocoumarol 152-72-7 C19H15NO6 353.0899 8 Acepromazine 61-00-7 C19H22N2OS 326.1452 4 Aceprometazine 13461-01-3 C19H22N2OS 326.1452 4 Acetaminodantrolene 41515-09-7 C16H14N4O4 326.1015 4 Acetazolamide 59-66-5 C4H6N4O3S2 221.9881 4 Acetiamin 28008-04-0 C16H22N4O4S 366.1361 4 Acetylaminonitroprophoxybenzene 553-20-8 C11H14N2O4 238.0953 8 Acetylsalicylamid 487-48-9 C9H9NO3 179.0582 8 Acetylsalicylic Acid 50-78-2 C9H8O4 180.0422 4 Aciclovir 59277-89-3 C8H11N5O3 225.0861 8 Acipimox 51037-30-0 C6H6N2O3 154.0378 4 Acrivastine 87848-99-5 C22H24N2O2 348.1837 4 Actinoquinol 15301-40-3 C11H11NO4S 253.0408 8 Adefovir 106941-25-7 C8H12N5O4P 273.0626 4 Adenine 73-24-5 C5H5N5 135.0544 8 A. Freiburg library content II

Adenosine 58-61-7 C10H13N5O4 267.0967 8 Adrenalone 99-45-6 C9H11NO3 181.0738 8 Ajmaline 07.12.4360 C20H26N2O2 326.1994 4 Alachlor 15972-60-8 C14H20ClNO2 269.1182 4 Alclometasone-17,21-dipropionate 66734-13-2 C28H37ClO7 520.2227 4 Alcuronium 23214-96-2 C44H50N4O2 666.3933 4 Alfuzosine 81403-80-7 C19H27N5O4 389.2063 8 Alimemazine 84-96-8 C18H22N2S 298.1503 4 Alizapride 59338-93-1 C16H21N5O2 315.1695 8 Allopurinol 315-30-0 C5H4N4O 136.0385 8 Almitrine 27469-53-0 C26H29F2N7 477.2452 4 alpha-Hydroxyalprazolam 37115-43-8 C17H13ClN4O 324.0777 4 alpha-Hydroxymidazolam 59468-90-5 C18H13ClFN3O 341.0731 4 alpha-Hydroxytriazolam 37115-45-0 C17H12Cl2N4O 358.0388 4 Alprazolam 28981-97-7 C17H13ClN4 308.0828 4 Alprenolol 13655-52-2 C15H23NO2 249.1728 4 Alprostadil 745-65-3 C20H34O5 354.2406 4 Altretamine 645-05-6 C9H18N6 210.1592 4 Alypin 963-07-5 C16H26N2O2 278.1994 4 Amantadine 768-94-5 C10H17N 151.136 4 Ambenonium 115-79-7 C28H42Cl2N4O2 536.2684 4 Ambroxol 18683-91-5 C13H18Br2N2O 375.9785 4 Ambucetamide 519-88-0 C17H28N2O2 292.215 4 Amcinonide 51022-69-6 C28H35FO7 502.2366 4 Ametryn 834-12-8 C9H17N5S 227.1204 4 Amezinium 30578-37-1 C11H12N3O 202.098 4 Amidopyrin 58-15-1 C13H17N3O 231.1371 4 Amidotrizoic Acid 117-96-4 C11H9I3N2O4 613.7696 4 Amiloride 2609-46-3 C6H8ClN7O 229.0478 4 Aminodantrolene 14663-28-6 C14H12N4O3 284.0908 4 Aminoglutethimide 125-84-8 C13H16N2O2 232.1211 4 Aminophenazone 58-15-1 C13H17N3O 231.1371 4 Aminopromazine 58-37-7 C19H25N3S 327.1769 4 Aminoquinuride 3811-56-1 C21H20N6O 372.1698 4 Aminorex 2207-50-3 C9H10N2O 162.0793 4 Amiodarone 1951-25-3 C25H29I2NO3 645.0236 4 Amiphenazole 490-55-1 C9H9N3S 191.0517 4 Amisulpride 71675-85-9 C17H27N3O4S 369.1722 4 Amitriptylin 50-48-6 C20H23N 277.183 4 Amitriptylinoxide 4317-14-0 C20H23NO 293.1779 4 Amitrole 61-82-5 C2H4N4 84.0435 4 Amlodipine 88150-42-9 C20H25ClN2O5 408.1451 4 Ammoidin 298-81-7 C12H8O4 216.0422 4 Amobarbital 57-43-2 C11H18N2O3 226.1317 4 Amorolﬁne 78613-35-1 C21H35NO 317.2718 4 Amoxapine 14028-44-5 C17H16ClN3O 313.0981 4 Amoxicillin 26787-78-0 C16H19N3O5S 365.1045 8 Amphetamine 300-62-9 C9H13N 135.1047 4 Amphotericin B 1397-89-3 C47H73NO17 923.4878 4 Ampicillin 69-53-4 C16H19N3O4S 349.1096 8 Amprolium 121-25-5 C14H19ClN4 278.1298 4 Amrinone 60719-84-8 C10H9N3O 187.0745 4 Anilazine 101-05-3 C9H5Cl3N4 273.9579 4 Antazoline 91-75-8 C17H19N3 265.1578 4 Apalcillin 63469-19-2 C25H23N5O6S 521.1369 8 Apomorphine 58-00-4 C17H17NO2 267.1259 4 Apophedrin 7568-93-6 C8H11NO 137.084 4 Apraclonidin 66711-21-5 C9H10Cl2N4 244.0282 4 Aprinidine 37640-71-4 C22H30N2 322.2408 4 Arecoline 63-75-2 C8H13NO2 155.0946 4 Astemizole 68844-77-9 C28H31FN4O 458.2481 4 Asulam 3337-71-1 C8H10N2O4S 230.0361 4 Atenolol 29122-68-7 C14H22N2O3 266.163 4 Atorvastatin 134523-00-5 C33H35FN2O5 558.253 8 Atraton 1610-17-9 C9H17N5O 211.1433 4 Atrazine 1912-24-9 C8H14ClN5 215.0937 4 Atrazine-desethyl 6190-65-4 C6H10ClN5 187.0624 4 Atropine 51-55-8 C17H23NO3 289.1677 4 Atropinmethylbromid 2870-71-5 C18H26NO3 304.1912 4 Atropin-N-octylbromide 5843-82-3 C25H40NO3 402.3008 4 Axeen 2537-29-3 C10H14N2O4 226.0953 4 A. Freiburg library content III

Azapropazone 13539-59-8 C16H20N4O2 300.1586 8 Azatadine 3964-81-6 C20H22N2 290.1782 4 Azathioprine 446-86-6 C9H7N7O2S 277.0381 8 Azelaic Acid 123-99-9 C9H16O4 188.1048 4 Azelastine 58581-89-8 C22H24ClN3O 381.1607 4 Azidamphenicol 13838-08-9 C11H13N5O5 295.0916 4 Azidocillin 17243-38-8 C16H17N5O4S 375.1001 8 Azinphos methyl 86-50-0 C10H12N3O3PS2 317.0057 4 Azintamid 1830-32-6 C10H14ClN3OS 259.0546 4 Aziprotryne 4658-28-0 C7H11N7S 225.0796 4 Azithromycin 83905-01-5 C38H72N2O12 748.5085 4 Azoluron 4058-90-6 C12H14N4O 230.1167 4 Azosemide 27589-33-9 C12H11ClN6O2S2 370.0073 4 Aztreonam 78110-38-0 C13H17N5O8S2 435.0518 8 Bacampicillin 50972-17-3 C21H27N3O7S 465.1569 4 Baclofen 1134-47-0 C10H12ClNO2 213.0556 4 Bambuterol 81732-65-2 C18H29N3O5 367.2106 4 Bamethan 3703-79-5 C12H19NO2 209.1415 4 Bamifylline 2016-63-9 C20H27N5O3 385.2113 4 Bamipin 4945-47-5 C19H24N2 280.1939 4 Barbital 57-44-3 C8H12N2O3 184.0847 4 Barverin 1639-79-8 C29H43N5O3 509.3365 4 Beclamide 501-68-8 C10H12ClNO 197.0607 4 Beclometasone dipropionate 08.09.5534 C28H37ClO7 520.2227 4 Befunolol 39552-01-7 C16H21NO4 291.147 4 Bemetizide 1824-52-8 C15H16ClN3O4S2 401.027 4 Benactyzine 302-40-9 C20H25NO3 327.1834 4 Benazolin-ethyl 25059-80-7 C11H10ClNO3S 271.0068 4 Bendamustine 16506-27-7 C16H21Cl2N3O2 357.101 4 Bendiacarb 22781-23-3 C11H13NO4 223.0844 4 Bendroflumethiazide 73-48-3 C15H14F3N3O4S2 421.0377 4 Benfluorex 23602-78-0 C19H20F3NO2 351.1446 4 Benfotiamin 22457-89-2 C19H23N4O6PS 466.1075 8 Benodanil 15310-01-7 C13H10INO 322.9807 4 Benorilate 5003-48-5 C17H15NO5 313.095 4 Benperidole 2062-84-2 C22H24FN3O2 381.1852 4 Benproperine 2156-27-6 C21H27NO 309.2092 4 Benserazid 322-35-0 C10H15N3O5 257.1011 8 Bensultap 17606-31-4 C17H21NO4S4 431.0353 4 Bentiromide 37106-97-1 C23H20N2O5 404.1372 8 Benzatropine 86-13-5 C21H25NO 307.1936 4 Benzethonium 121-54-0 C27H42NO2 412.3215 4 Benzocaine 94-09-7 C9H11NO2 165.0789 4 Benzoctamine 17243-39-9 C18H19N 249.1517 4 Benzododecinium 139-07-1 C21H38N 304.3004 4 Benzoxonium 19379-90-9 C23H42NO2 364.3215 4 Benzoylecgonine 519-09-5 C16H19NO4 289.1314 4 Benzquinamide 63-12-7 C22H32N2O5 404.2311 4 Benzthiazide 91-33-8 C15H14ClN3O4S3 430.9834 8 Benzthiazuron 1929-88-0 C9H9N3OS 207.0466 4 Benzydamine 642-72-8 C19H23N3O 309.1841 4 Benzylpenicillin 61-33-6 C16H18N2O4S 334.0987 4 Benzyltectronic acid 3734-22-3 C11H10O3 190.0629 8 Berberine 2086-83-1 C20H18NO4 336.1235 4 Betaine 107-43-7 C5H11NO2 117.0789 4 Betamethasone 17-valerate 2152-44-5 C27H37FO6 476.2574 4 Betamethasone 21-phosphate 360-63-4 C22H30FO8P 472.1662 8 Betamethasone-17-benzoate 22298-29-9 C29H33FO6 496.2261 4 Betaxolol 63659-18-7 C18H29NO3 307.2147 4 Bethanidine 55-73-2 C10H15N3 177.1265 4 Bevonium 5205-82-3 C22H28NO3 354.2069 4 Bezafibrate 41859-67-0 C19H20ClNO4 361.108 8 Bezitramide 15301-48-1 C31H32HN4O2 493.2603 4 Bicalutamide 90357-06-5 C18H14F4N2O4S 430.061 4 Bidesmethylcitalopram C18H17N2OF 296.1324 4 Bietamiverine 479-81-2 C19H30N2O2 318.2307 4 Bifonazole 60628-96-8 C22H18N2 310.1469 4 Biotin 58-85-5 C10H16N2O3S 244.0881 4 Biperiden 514-65-8 C21H29NO 311.2249 4 Bisacodyl 603-50-9 C22H19NO4 361.1314 4 Bisoprolol 66722-44-9 C18H31NO4 325.2253 4 A. Freiburg library content IV

Bitertanol 55179-31-2 C20H23N3O2 337.1789 4 Bopindolol 62658-63-3 C23H28N2O3 380.2099 4 Bornaprine 20448-86-6 C21H31NO2 329.2354 4 Bosentan 147536-97-8 C27H29N5O6S 551.1838 8 Brallobarbital 561-86-4 C10H11BrN2O3 285.9953 4 Brimonidine 59803-98-4 C11H10BrN5 291.0119 4 Brivudine 69304-47-8 C11H13BrN2O5 332.0007 4 Brodifacoum 56073-10-0 C31H23BrO3 522.083 8 Bromacil 314-40-9 C9H13BrN2O2 260.016 8 Bromazepam 1812-30-2 C14H10BrN3O 315.0007 4 Bromazine 118-23-0 C17H20BrNO 333.0727 4 Bromchlorophen 15435-29-7 C13H8Br2Cl2O2 423.8268 4 Bromhexine 3572-43-8 C14H20Br2N2 373.9993 4 Bromocriptine 25614-03-3 C32H40BrN5O5 653.2212 4 Bromopride 4093-35-0 C14H22BrN3O2 343.0895 4 Bromperidol 10457-90-6 C21H23BrFNO2 419.0896 4 Brompheniramine 86-22-6 C16H19BrN2 318.0731 4 Bromural 496-67-3 C6H11BrN2O2 222.0003 4 Bromurone 3408-97-7 C9H11BrN2O 242.0054 4 Brotizolam 57801-81-7 C15H10BrClN4S 391.9498 4 Brucine 357-57-3 C23H26N2O4 394.1892 4 Bucetin 1083-57-4 C12H17NO3 223.1208 4 Buclosamide 575-74-6 C11H14ClNO2 227.0713 4 Buclosamide 575-74-6 C11H14ClNO2 227.0713 4 Budesonid 51333-22-3 C25H34O6 430.2355 4 Budipine 57982-78-2 C21H27N 293.2143 4 Bufexamac 2438-72-4 C12H17NO3 223.1208 8 Buflomedil 55837-25-7 C17H25NO4 307.1783 4 Bulbocapnin 298-45-3 C19H19NO4 325.1314 4 Bumadizone 3583-64-0 C19H22N2O3 326.163 4 Bumetanide 28395-03-1 C17H20N2O5S 364.1092 4 Bunazosin 80755-51-7 C19H27N5O3 373.2113 4 Bunitrolol 34915-68-9 C14H20N2O2 248.1524 4 Buphenine 447-41-6 C19H25NO2 299.1884 4 Bupivacaine 2180-92-9 C18H28N2O 288.2201 4 Bupranolol 14556-46-8 C14H22ClNO2 271.1339 4 Buprenorphine 52485-79-7 C29H41NO4 467.3035 4 Buprenorphine-D4 136781-89-0 C29H37D4NO4 471 4 Buprofezin 69327-76-0 C16H23N3OS 305.1561 4 Buspirone 36505-84-7 C21H31N5O2 385.2477 4 Butalamine 22131-35-7 C18H28N4O 316.2263 4 Butalbital 77-26-9 C11H16N2O3 224.116 4 Butallylonal 1142-70-7 C11H15BrN2O3 302.0266 4 Butamirate 18109-80-3 C18H29NO3 307.2147 4 Butanilicain 3785-21-5 C13H19ClN2O 254.1185 4 Butaperazine 653-03-2 C24H31N3OS 409.2187 4 Butetamate 14007-64-8 C16H25NO2 263.1884 4 Butinoline 968-63-8 C20H21NO 291.1623 4 Butizide 2043-38-1 C11H16ClN3O4S2 353.027 4 Butoxycaine 3772-43-8 C17H27NO3 293.199 4 Cabaril 63-25-2 C13H14N2O 214.1106 4 Cabergoline 81409-90-7 C26H37N5O2 451.2947 4 Cabral 553-69-5 C13H14N2O 214.1106 4 Cafaminol 30924-31-3 C11H17N5O3 267.1331 4 Caffeine 58-08-2 C8H10N4O2 194.0803 4 Calteridol 121915-83-1 C17H32N4O7 404.227 4 Camazepam 36104-80-0 C19H18ClN3O3 371.1036 4 Camylofine 54-30-8 C19H32N2O2 320.2463 4 Candesartan 139481-59-7 C24H20N6O3 440.1596 4 Canrenoic Acid 4138-96-9 C22H30O4 358.2144 8 Capecitabine 154361-50-9 C15H22FN3O6 359.1492 8 Caproylresorcinol 70807-24-8 C12H16O3 208.1099 8 Captodiame 486-17-9 C21H29NS2 359.1741 4 Captopril 62571-86-2 C9H15NO3S 217.0772 8 Carazolol 57775-29-8 C18H22N2O2 298.1681 4 Carbachol 51-83-2 C6H15N2O2 147.1133 4 Carbamazepine 298-46-4 C15H12N2O 236.0949 4 Carbamazepine 10,11-epoxide 36507-30-9 C15H12N2O2 252.0898 4 Carbaril 63-25-2 C12H11NO2 201.0789 4 Carbazochrom 69-81-8 C10H12N4O3 236.0909 4 Carbendazim 10605-21-7 C9H9N3O2 191.0694 4 A. Freiburg library content V

Carbenoxolone 5697-56-3 C34H50O7 570.3556 8 Carbimazole 22232-54-8 C7H10N2O2S 186.0462 4 Carbinoxamine 486-16-8 C16H19ClN2O 290.1185 4 Carboxin 5234-68-4 C12H13NO2S 235.0667 4 Carbutamide 339-43-5 C11H17N3O3S 271.099 8 Carbuterol 34866-47-2 C13H21N3O3 267.1582 4 Carisoprodol 78-44-4 C12H24N2O4 260.1736 4 Carprofen 53716-49-7 C15H12ClNO2 273.0556 8 Carteolol 51781-06-7 C16H24N2O3 292.1786 4 Carticain 23964-58-1 C13H20N2O3S 284.1194 4 Carvedilol 72956-09-3 C24H26N2O4 406.1892 4 Cefaclor 70356-03-5 C15H14ClN3O4S 367.0393 4 Cefadroxil 53994-73-3 C16H17N3O5S 363.0888 4 Cefalexin 15686-71-2 C16H17N3O4S 347.0939 4 Cefamandole 34444-01-4 C18H18N6O5S2 462.078 4 Cefapirin 21593-23-7 C17H17N3O6S2 423.0558 4 Cefazedone 56187-47-4 C18H15Cl2N5O5S3 546.9612 4 Cefepime 88040-23-7 C19H24N6O5S2 480.1249 4 Cefetamet 65052-63-3 C14H15N5O5S2 397.0514 4 Cefixime 79350-37-1 C16H15N5O7S2 453.0412 4 Cefmenoxime 65085-01-0 C16H17N9O5S3 511.0514 8 Cefodizime 69739-16-8 C20H20N6O7S4 584.0276 4 Cefotaxime 63527-52-6 C16H17N5O7S2 455.0569 4 Cefotiam 61622-34-2 C18H23N9O4S3 525.1035 4 Cefpirome 84957-29-9 C22H22N6O5S2 514.1093 4 Ceftazidime 72558-82-8 C22H22N6O7S2 546.0991 4 Ceftizoxime 68401-81-0 C13H13N5O5S2 383.0358 4 Ceftriaxone 73384-59-5 C18H18N8O7S3 554.046 4 Cefuroxime 55268-75-2 C16H16N4O8S 424.0688 4 Celiprolol 56980-93-6 C20H33N3O4 379.2471 4 Cerivastatin 145599-86-6 C26H34FNO5 459.2421 4 Cetirizine 83881-52-1 C21H25ClN2O3 388.1553 4 Cetobemidone 469-79-4 C15H21NO2 247.1572 8 Chinine 130-95-0 C20H24N2O2 324.1837 4 Chlorambucil 305-03-3 C14H19Cl2NO2 303.0792 4 Chloramphenicol 56-75-7 C11H12Cl2N2O5 322.0123 4 Chlorazanil 500-42-5 C9H8ClN5 221.0468 4 Chlorbenzoxamine 522-18-9 C27H31ClN2O 434.2124 4 Chlorcyclizine 82-93-9 C18H21ClN2 300.1393 4 Chlordiazepoxide 58-25-3 C16H14ClN3O 299.0825 4 Chlordimeform 6164-98-3 C10H13ClN2 196.0766 4 Chlorfluazuron 71422-67-8 C20H9Cl3F5N3O3 538.9629 4 Chloridazon 1698-60-8 C10H8ClN3O 221.0355 8 Chlormadinone Acetate 302-22-7 C23H29ClO4 404.1754 4 Chlormequat 999-81-5 C5H13ClN 122.0736 4 Chlorophacinon 3691-35-8 C23H15ClO3 374.0709 4 Chloropyramine 59-32-5 C16H20ClN3 289.1345 4 Chloroquine 54-05-7 C18H26ClN3 319.1815 4 Chlorothiazide 58-94-6 C7H6ClN3O4S2 294.9488 4 Chlorphencyclan 5632-52-0 C18H28ClNO 309.1859 4 Chlorphenethiazine 2095-24-1 C16H17N2SCl 304.08 4 Chlorpheniramine 132-22-9 C16H19ClN2 274.1236 4 Chlorphenoxamine 77-38-3 C18H22ClNO 303.1389 4 Chlorpromazin-D3 136765-28-1 C17H16ClD3N2S 321 4 Chlorpromazine 50-53-3 C17H19ClN2S 318.0957 4 Chlorpromazine Sulfoxide 969-99-3 C17H19ClN2OS 334.0906 4 Chlorprothixene 113-59-7 C18H18ClNS 315.0847 4 Chlorthenoxazine 132-89-8 C10H10ClNO2 211.04 4 Chlorzoxazone 95-25-0 C7H4ClNO2 168.993 4 Ciclacillin 3485-14-1 C15H23N3O4S 341.1409 8 Cicletanine 89943-82-8 C14H12ClNO2 261.0556 8 Ciclonium Bromide 29546-59-6 C22H34NO 328.264 4 Cilastatin 82009-34-5 C16H26N2O5S 358.1562 8 Cilazapril 88768-40-5 C22H31N3O5 417.2263 8 Cimetidine 51481-61-9 C10H16N6S 252.1157 8 Cinchocaine 85-79-0 C20H29N3O2 343.2259 4 Cinchonidine 485-71-2 C19H22N2O 294.1732 8 Cinchonine 118-10-5 C19H22N2O 294.1732 8 Cinnarizine 298-57-7 C26H28N2 368.2251 4 Cinoxacin 28657-80-9 C12H10N2O5 262.0589 4 Ciprofloxacin 85721-33-1 C17H18FN3O3 331.1332 4 A. Freiburg library content VI

Cisapride 81098-60-4 C23H29ClFN3O4 465.183 4 Citalopram 59729-33-8 C20H21FN2O 324.1637 4 Citiolone 1195-16-0 C6H9NO2S 159.0354 4 Clarithromycin 81103-11-9 C38H69NO13 747.4768 4 Clemastine 15686-51-8 C21H26ClNO 343.1702 4 Clemizole 442-52-4 C19H20ClN3 325.1345 4 Clenbuterol 37148-27-9 C12H18Cl2N2O 276.0796 4 Clibucaine 15302-10-0 C15H20Cl2N2O 314.0952 4 Climbazole 38083-17-9 C15H17ClN2O2 292.0978 4 Clindamycin 18323-44-9 C18H33ClN2O5S 424.1798 4 Clobazam 22316-47-8 C16H13ClN2O2 300.0665 4 Clobenzepam 1159-93-9 C17H18ClN3O 315.1138 4 Clobetasol-17-propionate 25122-46-7 C25H32ClFO5 466.1922 4 Clobetason-butyrate 25122-57-0 C26H32ClFO5 478.1922 4 Clobutinol 14860-49-2 C14H22ClNO 255.1389 4 Clofedanol 791-35-5 C17H20ClNO 289.1233 4 Clofenamide 511-46-6 C6H7ClN2O4S2 269.9535 4 Clofexamide 1223-36-5 C14H21ClN2O2 284.1291 4 Clomethiazole 533-45-9 C6H8ClNS 161.0065 4 Clomiphene 911-45-5 C26H28ClNO 405.1859 4 Clomipramine 303-49-1 C19H23ClN2 314.1549 4 Clonazepam 1622-61-3 C15H10ClN3O3 315.041 4 Clonidine 4205-90-7 C9H9Cl2N3 229.0173 4 Clopamide 636-54-4 C14H20ClN3O3S 345.0913 8 Clopenthixol 982-24-1 C22H25ClN2OS 400.1376 4 Clopidogrel 113665-84-2 C16H16ClNO2S 321.059 4 Cloprednol 5251-34-3 C21H25ClO5 392.139 8 Clorindione 1146-99-2 C15H9ClO2 256.0291 4 Clotiapine 2058-52-8 C18H18ClN3S 343.0909 4 Clotiazepam 33671-46-4 C16H15ClN2OS 318.0593 4 Clotrimazol 23593-75-1 C22H17ClN2 344.108 4 Cloxiquine 130-16-5 C9H6ClNO 179.0137 4 Clozapine 5786-21-0 C18H19ClN4 326.1298 4 Cocaine 50-36-2 C17H21NO4 303.147 4 Codeine 76-57-3 C18H21NO3 299.1521 4 Colchicine 64-86-8 C22H25NO6 399.1681 4 Coniine 458-88-8 C8H17N 127.136 4 Corticosterone 50-22-6 C21H30O4 346.2144 4 Cortisone 53-06-5 C21H28O5 360.1936 8 Cotinine 486-56-6 C10H12N2O 176.0949 4 Coumatetralyl 5836-29-3 C19H16O3 292.1099 4 Croconazole 77175-51-0 C18H15ClN2O 310.0872 4 Cromoglicic acid 16110-51-3 C23H16O11 468.0692 8 Crotetamide 6168-76-9 C12H22N2O2 226.1681 4 Cyamemazine 3546-03-0 C19H21N3S 323.1456 4 Cyanazine 21725-46-2 C9H13ClN6 240.089 4 Cyclamic acid 100-88-9 C6H13NO3S 179.0616 4 Cyclicine 82-92-8 C18H22N2 266.1782 4 Cyclobenzaprine 303-53-7 C20H21N 275.1673 4 Cyclodrine 52109-93-0 C19H29NO3 319.2147 4 Cyclopentamine 102-45-4 C9H19N 141.1517 4 Cyclopentobarbital 76-68-6 C12H14N2O3 234.1004 4 Cyclopentolate 512-15-2 C17H25NO3 291.1834 4 Cyclophosphamide 50-18-0 C7H15Cl2N2O2P 260.0248 4 Cyclovalone 579-23-7 C22H22O5 366.1467 4 Cyproheptadine 129-03-3 C21H21N 287.1673 4 Cytarabine 147-94-4 C9H13N3O5 243.0855 4 D10-Psilocin C12H6D10N2O C12H6D10N2O 214 4 D3-Atropin C17H20D3NO3 292 4 D3-Benzoylecgonine 115732-68-8 C16H16D3NO4 292 4 D3-Cocaine 65266-73-1 C17H18D3NO4 306 4 D3-Codeine 70420-71-2 C18H18D3NO3 302 4 D3-Doxepine C19H18NOD3 282 4 D3-Ecgonine methyl ester 136765-34-9 C10H14D3NO3 202 4 D3-LSD 136765-38-3 C20H22D3N3O 326 4 D3-Methadon 60263-63-0 C21H24D3NO 312 4 D3-Morphine 67293-88-3 C17H16D3NO3 288 4 D3-Morphine-3-beta-glucuronide C23H24D3NO9 464 4 D5-Amphetamin 65538-33-2 C9H8D5N 140 4 D5-Diazepam C16H8ClN2OD5 289 4 D5-Fentanyl 118357-29-2 C22H23D5N2O 341 4 A. Freiburg library content VII

D5-MBDB C12H12D5NO2 212 4 D5-MDA 136765-42-9 C10H8D5NO2 184 4 D5-MDEA 160227-43-0 C12H12D5NO2 212 4 D5-MDMA 136765-43-0 C11H10D5NO2 198 4 D5-Metamphetamine 60124-88-1 C10H10D5N 154 4 D7-7-Aminoflunitrazepam C16H7D7FN3O 290 4 D7-Flunitrazepam C16H5D7FN3O3 320 4 D9-Methadone C21H18D9NO 318 4 Dacarbazin 04.03.4342 C6H10N6O 182.0916 4 Danazol 17230-88-5 C22H27NO2 337.2041 4 Dapiprazole 72822-12-9 C19H27N5 325.2266 4 Dapsone 80-08-0 C12H12N2O2S 248.0619 4 Daunorubicin 20830-81-3 C27H29NO10 527.1791 4 Debrisoquine 1131-64-2 C10H13N3 175.1108 4 Deflazacort 14484-47-0 C25H31NO6 441.2151 4 Dehydrocholic acid 81-32-2 C24H34O5 402.2406 4 Delorazepam 2894-67-9 C15H10Cl2N2O 304.017 4 Demecarium 56-94-0 C32H52N4O4 556.3988 4 Demeclocycline 127-33-3 C21H21ClN2O8 464.0986 4 Demeton-O 298-03-3 C8H19O3PS2 258.0513 4 Demeton-O-methyl 867-27-6 C6H15O3PS2 230.02 4 Demeton-S-methylsulfone 17040-19-6 C6H15O5PS2 262.0098 4 Demoxepam 963-39-3 C15H11N2O2Cl 286.0509 4 Denaverine 3579-62-2 C24H33NO3 383.246 4 Desallylflurazepam C15H10N2OFCl 288.0465 4 Desipramine 50-47-5 C18H22N2 266.1782 4 Desmedipham 13684-56-5 C16H16N2O4 300.111 4 Desmethylcitalopram C19H19N2OF 310.1481 4 Desmethylclobazam 22316-55-8 C15H11ClN2O2 286.0509 8 Desmethylclomipramine 303-48-0 C18H21ClN2 300.1393 4 Desoximethasone 382-67-2 C22H29FO4 376.2049 4 Desoxycortone 21-(3-phenylpropionate) 14007-50-2 C30H38O4 462.277 4 Desoxycortone enantate C28H42O4 442.3083 4 Detajmium 33774-52-6 C27H42N3O3 456.3226 4 Dexamethasone 50-02-2 C22H29FO5 392.1999 4 Dexamethasone 21-Acetate 1177-87-3 C24H31FO6 434.2104 8 Dexamethasone 21-isonicotinate 2265-64-7 C28H32FNO6 497.2213 4 Dexfenfluramine 3239-44-9 C12H16F3N 231.1234 4 Dextromethorphan 125-71-3 C18H25NO 271.1936 4 Dextromoramide 357-56-2 C25H32N2O2 392.2463 4 Dextropropoxyphene 469-62-5 C22H29NO2 339.2198 4 Diaveridine 5355-16-8 C13H16N4O2 260.1273 4 Diazepam 439-14-5 C16H13ClN2O 284.0716 4 Diazoxide 364-98-7 C8H7ClN2O2S 229.9916 8 Dibenzepin 4498-32-2 C18H21N3O 295.1684 4 Dibutyl adipate 105-99-7 C14H26O4 258.1831 4 Dichlorophen 97-23-4 C13H10Cl2O2 268.0057 4 Diclofenac 15307-86-5 C14H11Cl2NO2 295.0166 8 Dicycloverine 77-19-0 C19H35NO2 309.2667 4 Didanosine 69655-05-6 C10H12N4O3 236.0909 8 Dienestrol 84-17-3 C18H18O2 266.1306 4 Dienogest 65928-58-7 C20H25NO2 311.1884 4 Diethazine 60-91-3 C18H22N2S 298.1503 4 Diethylcarbamazine 90-89-1 C10H21N3O 199.1684 4 Difenoconazole 119446-68-3 C19H17Cl2N3O3 405.0646 4 Difenoxuron 14214-32-5 C16H18N2O3 286.1317 4 Difenzoquat 43222-48-6 C17H17N2 249.1391 4 Diflorasone diacetate 33564-31-7 C26H32F2O7 494.2116 4 Diflucortolone 09.06.2607 C22H28F2O4 394.1955 4 Dihydralazine 484-23-1 C8H10N6 190.0966 4 Dihydrocodeine 125-28-0 C18H23NO3 301.1677 4 Dihydroergocristin 17479-19-5 C35H41N5O5 611.3107 4 Dihydroergokryptine 25447-66-9 C32H43N5O5 577.3264 8 Dihydroergotamine 511-12-6 C33H37N5O5 583.2794 4 Dihyprylone 77-03-2 C9H15NO2 169.1102 4 Dilazep 35898-87-4 C31H44N2O10 604.2995 4 Diltiazem 42399-41-7 C22H26N2O4S 414.1613 4 Dimefuron 34205-21-5 C15H19ClN4O3 338.1145 8 Dimephenopam 17279-39-9 C11H17N 163.136 4 Dimetacrine 4757-55-5 C20H26N2 294.2095 4 Dimethachlor 50563-36-5 C13H18ClNO2 255.1026 4 A. Freiburg library content VIII

Dimethomorph 110488-70-5 C21H22ClNO4 387.1237 4 Dimetindene 5636-83-9 C20H24N2 292.1939 4 Dimetotiazine 7456-24-8 C19H25N3O2S2 391.1388 4 Dimetridazole 551-92-8 C5H7N3O2 141.0538 4 Dinoprost 551-11-1 C20H34O5 354.2406 4 Dioxethedrin 497-75-6 C11H17NO3 211.1208 8 Diphenamid 957-51-7 C16H17NO 239.131 4 Diphenhydramine 58-73-1 C17H21NO 255.1623 4 Diphenidol 972-02-1 C21H27NO 309.2092 4 Diphenylpyraline 147-20-6 C19H23NO 281.1779 4 Dipiproverine 117-30-6 C20H30N2O2 330.2307 4 Diponium 58875-33-5 C20H38NO2 324.2902 4 Diprophylline 479-18-5 C10H14N4O4 254.1015 4 Dipyridamole 58-32-2 C24H40N8O4 504.3172 4 Disopyramide 05.09.3737 C21H29N3O 339.231 4 Distigmin 15876-67-2 C22H32N4O4 416.2423 4 Disulﬁram 97-77-8 C10H20N2S4 296.0509 4 Dixyrazine 2470-73-7 C24H33N3O2S 427.2293 4 D-Norpseudoephedrine 492-39-7 C9H13NO 151.0997 4 Dobutamine 34368-04-2 C18H23NO3 301.1677 8 Dolasetron 115956-12-2 C19H20N2O3 324.1473 8 Domperidone 57808-66-9 C22H24ClN5O2 425.1618 8 Dopamin 51-61-6 C8H11NO2 153.0789 8 Dopexamine 86197-47-9 C22H32N2O2 356.2463 4 Dormovit 1146-21-0 C12H14N2O4 250.0953 4 Dorzolamide 120279-96-1 C10H16N2O4S3 324.0272 8 Dosulepin 113-53-1 C19H21NS 295.1394 4 Doxapram 309-29-5 C24H30N2O2 378.2307 4 Doxazosin 74191-85-8 C23H25N5O5 451.1855 4 Doxepin 1668-19-5 C19H21NO 279.1623 4 Doxorubicin 23214-92-8 C27H29NO11 543.174 4 Doxylamine 469-21-6 C17H22N2O 270.1732 4 Drazoloxon 5707-69-7 C10H8ClN3O2 237.0305 4 Drofenine 1679-76-1 C20H31NO2 317.2354 4 Dropropizine 17692-31-8 C13H20N2O2 236.1524 4 Drospirenone 67392-87-4 C24H30O3 366.2194 4 Ecgonin-D3 C9H12D3NO3 188 8 Ecgoninemethylester 01.09.7143 C10H17NO3 199.1208 4 EDDP 66729-78-0 C20H24N 278.1908 4 EDDP-D3 136765-23-6 C20H21D3N 280 4 Eldoral 509-87-5 C11H17N3O3 239.1269 4 Embutramide 15687-14-6 C17H27NO3 293.199 4 Emepronium 3614-30-0 C20H28N 282.2221 4 Emetine 483-18-1 C29H40N2O4 480.2988 4 Emodin 518-82-1 C15H10O5 270.0528 4 Enalapril 75847-73-3 C20H28N2O5 376.1998 4 Endomide 4582-18-7 C17H28N2O2 292.215 4 Enoximone 77671-31-9 C12H12N2O2S 248.0619 4 Ephedrine 299-42-3 C10H15NO 165.1153 4 Epinephrine 51-43-4 C9H13NO3 183.0895 8 Eprazinone 10402-90-1 C24H32N2O2 380.2463 4 Eprosartan 133040-01-4 C23H24N2O4S 424.1456 4 Erythromycin 114-07-8 C37H67NO13 733.4612 4 Esculin 531-75-9 C15H16O9 340.0794 8 Esmolol 103598-03-4 C16H25NO4 295.1783 4 Estazolam 29975-16-4 C16H11ClN4 294.0672 4 Etafedrine 48141-64-6 C12H19NO 193.1466 4 Etafenone 90-54-0 C21H27NO2 325.2041 4 Etamiphyllin 314-35-2 C13H21N5O2 279.1695 4 Etamivan 304-84-7 C12H17NO3 223.1208 4 Ethacridine 442-16-0 C15H15N3O 253.1215 4 Ethacrynic Acid 58-54-8 C13H12Cl2O4 302.0112 8 Ethambutol 74-55-5 C10H24N2O2 204.1836 4 Ethaverine 486-47-5 C24H29NO4 395.2096 4 Ethenzamide 938-73-8 C9H11NO2 165.0789 4 Ethionamide 536-33-4 C8H10N2S 166.0564 8 Ethyl glucuronide C8H14O7 222.0739 4 Ethyl glucuronide-D5 C8H9D5O7 227 4 Ethyl sulfate C2H6O4S 125.9986 4 Ethyl sulfate-D5 C2HD5O4S 131 4 Ethylbenzhydramine 642-58-0 C19H25NO 283.1936 4 A. Freiburg library content IX

Ethylparathion 56-38-2 C10H14NO5PS 291.033 4 Etidocaine 36637-18-0 C17H28N2O 276.2201 4 Etifelmin 341-00-4 C17H19N 237.1517 4 Etilamfetamine 457-87-4 C11H17N 163.136 4 Etilefrine 709-55-7 C10H15NO2 181.1102 4 Etiroxate 17365-01-4 C18H17I4NO4 818.7336 8 Etodolac 41340-25-4 C17H21NO3 287.1521 8 Etodroxizine 17692-34-1 C23H31ClN2O3 418.2023 4 Etofenamate 30544-47-9 C18H18F3NO4 369.1187 4 Etofibrate 31637-97-5 C18H18ClNO5 363.0873 4 Etofyllinclofibrate 54504-70-0 C19H21ClN4O5 420.12 4 Etofylline 519-37-9 C9H12N4O3 224.0909 4 Etoloxamine 1157-87-5 C19H25NO 283.1936 4 Etomidate 33125-97-2 C14H16N2O2 244.1211 4 Etophylline 519-37-9 C9H12N4O3 224.0909 4 Etoposide 33419-42-0 C29H32O13 588.1842 8 Etryptamine 2235-90-7 C12H16N2 188.1313 4 Eucatropine 100-91-4 C17H25NO3 291.1834 4 Exemestane 107868-30-4 C20H24O2 296.1776 4 Famciclovir 104227-87-4 C14H19N5O4 321.1437 4 Famotidine 76824-35-6 C8H15N7O2S3 337.0449 4 Famprofazone 22881-35-2 C24H31N3O 377.2467 4 Fedrilate 23271-74-1 C20H29NO4 347.2096 4 Felbamate 25451-15-4 C11H14N2O4 238.0953 4 Felodipine 72509-76-3 C18H19Cl2NO4 383.0691 8 Fenarimol 60168-88-9 C17H12Cl2N2O 330.0326 4 Fenbutrazate 4378-36-3 C23H29NO3 367.2147 4 Fendiline 13042-18-7 C23H25N 315.1986 4 Fenetylline 01.08.3736 C18H23N5O2 341.1851 4 Fenfluramine 458-24-2 C12H16F3N 231.1234 4 Fenfuram 24691-80-3 C12H11NO2 201.0789 4 Fenofibrate 49562-28-9 C20H21CLO4 360 4 Fenoprofen 31879-05-7 C15H14O3 242.0942 4 Fenoterol 13392-18-2 C17H21NO4 303.147 4 Fenoxazoline 4846-91-7 C13H18N2O 218.1419 4 Fenpipramide 77-01-0 C21H26N2O 322.2045 4 Fenpiprane 3329-14-4 C20H25N 279.1987 4 Fenproporex 15686-61-0 C12H16N2 188.1313 4 Fentanyl 437-38-7 C22H28N2O 336.2201 4 Fenticonazole 72479-26-6 C24H20Cl2N2OS 454.0673 4 Fexofenadine 83799-24-0 C32H39NO4 501.2879 4 Finasteride 98319-26-7 C23H36N2O2 372.2776 4 Flavoxate 15301-69-6 C24H25NO4 391.1783 4 Flecainide 54143-55-4 C17H20F6N2O3 414.1378 4 Fleroxacin 79660-72-3 C17H18F3N3O3 369.13 4 Flocoumafen C33H25F3O4 542.1704 8 Floctafenine 23779-99-9 C20H17F3N2O4 406.1139 4 Fluanisone 1480-19-9 C21H25FN2O2 356.19 4 Flucloxacillin 5250-39-5 C19H17ClFN3O5S 453.0561 8 Fluconazole 86386-73-4 C13H12F2N6O 306.104 4 Fludrocortisone Acetate 514-36-3 C23H31FO6 422.2104 4 Flufenamic Acid 530-78-9 C14H10F3NO2 281.0663 8 Flumazenil 78755-81-4 C15H14FN3O3 303.1019 4 Flunarizine 52468-60-7 C26H26F2N2 404.2064 4 Flunitrazepam 1622-62-4 C16H12FN3O3 313.0862 4 Fluocinolone Acetonide 67-73-2 C24H30F2O6 452.201 4 Fluocinonide 356-12-7 C26H32F2O7 494.2116 4 Fluocortin Butyl 41767-29-7 C26H35FO5 446.2468 8 Fluocortolone 152-97-6 C22H29FO4 376.2049 4 Fluorouracil 51-21-8 C4H3FN2O2 130.0178 4 Fluoxetine 54910-89-3 C17H18NOF3 309.134 4 Flupentixol 2709-56-0 C23H25F3N2OS 434.1639 4 Fluphenazine 69-23-8 C22H26F3N3OS 437.1748 4 Flupirtine 56995-20-1 C15H17FN4O2 304.1335 4 Fluprednylidene 21-Acetate 1255-35-2 C24H29FO6 432.1948 8 Flurazepam 17617-23-1 C21H23ClFN3O 387.1513 4 Flurbiprofen 5104-49-4 C15H13FO2 244.0899 4 Flurochloridone 61213-25-0 C12H10Cl2F3NO 311.0091 4 Fluspirilen 1841-19-6 C29H31F2N3O 475.2435 8 Flutamide 13311-84-7 C11H11F3N2O3 276.0721 4 Fluticasone Propionate 80474-14-2 C25H31F3O5S 500.1844 4 A. Freiburg library content X

Fluvastadine 93957-54-1 C24H26FNO4 411.1845 8 Fluvoxamine 54739-18-3 C15H21F3N2O2 318.1555 4 Fominoben 18053-31-1 C21H24ClN3O3 401.1506 4 Fomocain 17692-39-6 C20H25NO2 311.1885 4 Formetanate 22259-30-9 C11H15N3O2 221.1164 4 Formylsulfamethine 795-13-1 C13H14N4O3S 306.0786 8 Fosinopril 98048-97-6 C30H46NO7P 563.3011 8 Fuberidazole 3878-19-1 C11H8N2O 184.0636 8 Furalaxyl 57646-30-7 C17H19NO4 301.1314 4 Furaltadone 139-91-3 C13H16N4O6 324.1069 4 Furazolidone 67-45-8 C8H7N3O5 225.0385 4 Furosemid 54-31-9 C12H11ClN2O5S 330.0077 4 Gabapentin 60142-96-3 C9H17NO2 171.1259 4 Galantamine 357-70-0 C17H21NO3 287.1521 4 Gallopamil 16662-47-8 C28H40N2O5 484.2937 4 gamma-Hydroxybutyric acid 502-85-2 (sodium salt) C4H8O3 104.0473 4 gamma-Hydroxybutyric acid-D6 C4H2D6O3 110 4 Gemcitabine 95058-81-4 C9H11F2N3O4 263.0717 4 Gemﬁbrozil 25812-30-0 C15H22O3 250.1568 4 Gepefrine 18840-47-6 C9H13NO 151.0997 4 Gestodene 60282-87-3 C21H26O2 310.1932 4 Gibberelic acid 77-06-5 C19H22O6 346.1416 4 Gitoxigenin 545-26-6 C23H34O5 390.2406 4 Glafenin 3820-67-5 C19H17ClN2O4 372.0876 4 Glibenclamide 10238-21-8 C23H28ClN3O5S 493.1438 4 Glibornuride 26944-48-9 C18H26N2O4S 366.1613 4 Gliclazide 21187-98-4 C15H21N3O3S 323.1303 4 Glimepiride 93479-97-1 C24H34N4O5S 490.2249 8 Glipizide 29094-61-9 C21H27N5O4S 445.1783 4 Gliquidone 33342-05-1 C27H33N3O6S 527.2089 8 Glisoxepide 25046-79-1 C20H27N5O5S 449.1732 8 Glucosamine 3416-24-8 C6H13NO5 179.0793 4 Glymidine 3459-20-9 C13H15N3O4S 309.0783 4 Granisetron 109889-09-0 C18H24N4O 312.195 4 Grepaﬂoxacin 161967-81-3 C19H22FN3O3 359.1645 4 Griseofulvin 126-07-8 C17H17ClO6 352.0713 4 Guafecainol 36199-78-7 C16H27NO4 297.194 4 Guaifenesin 93-14-1 C10H14O4 198.0892 4 Guanabenz 5051-62-7 C8H8Cl2N4 230.0126 4 Guanacline 1463-28-1 C9H18N4 182.1531 4 Guanethidine 55-65-2 C10H22N4 198.1844 4 Guanfacine 29110-47-2 C9H9Cl2N3O 245.0122 8 Guanoxan 2165-19-7 C10H13N3O2 207.1007 4 Halcinonide 3093-35-4 C24H32ClFO5 454.1922 8 Halofantrine 69756-53-2 C26H30Cl2F3NO 499.1656 4 Halofuginone 55837-20-2 C16H17BrClN3O3 413.0141 8 Halometasone 50629-82-8 C22H27ClF2O5 444.1515 4 Haloperidol 52-86-8 C21H23ClFNO2 375.1401 4 Haloperidol-D4 136765-35-0 C21H19ClD4FNO2 379 4 Haloxyfop ethoxyethyl ester 87237-48-7 C19H19ClF3NO5 433.0903 4 Hapargosid C24H30O11 494.1788 8 Heptaminol 372-66-7 C8H19NO 145.1466 4 Heroin 561-27-3 C21H23NO5 369.1576 4 Hesperidin 520-26-3 C28H34O15 610.1897 4 Hexacarbacholine 306-41-2 C18H40N4O4 376.3048 4 Hexachlorophene 70-30-4 C13H6Cl6O2 403.8498 4 Hexamethonium 60-26-4 C12H30N2 202.2408 4 Hexamidine 3811-75-4 C20H26N4O2 354.2055 4 Hexazinone 51235-04-2 C12H20N4O2 252.1586 4 Hexobendine 54-03-5 C30H44N2O10 592.2995 4 Histamine 51-45-6 C5H9N3 111.0796 4 Histapyrrodine 493-80-1 C19H24N2 280.1939 4 Histidine 71-00-1 C6H9N3O2 155.0694 4 Homatropine 87-00-3 C16H21NO3 275.1521 4 Homofenazine 1256-01-5 C23H28F3N3OS 451.1905 4 Hordenine 539-15-1 C10H15NO 165.1153 4 Hydralazine 86-54-4 C8H8N4 160.0748 4 Hydrochlorothiazide 58-93-5 C7H8ClN3O4S2 296.9644 4 Hydrocodone 125-29-1 C18H21NO3 299.1521 4 Hydrocortisone 50-23-7 C21H30O5 362.2093 4 Hydrocortisone 21-acetate 50-03-3 C23H32O6 404.2198 4 A. Freiburg library content XI

Solucortef 125-04-2 C25H33O8 461.2175 4 Hydrocortisone buteprate 72590-77-3 C28H40O7 488.2774 4 Hydroﬂumethiazide 135-09-1 C8H8F3N3O4S2 330.9908 4 Hydromorphone 466-99-9 C17H19NO3 285.1364 4 Hydroxychlorquine 118-42-3 C18H26ClN3O 335.1764 4 Hydroxymethylpyridine C6H7NO 109.0527 4 Hydroxyzine 68-88-2 C21H27ClN2O2 374.1761 4 Hymecromone 90-33-5 C10H8O3 176.0473 8 Hyoscyamine 101-31-5 C17H23NO3 289.1677 4 Ibogaine 83-74-9 C20H26N2O 310.2045 4 Idarubicin 58957-92-9 C26H27NO9 497.1685 4 Idoxuridine 54-42-2 C9H11IN2O5 353.9712 4 Ifosfamide 3778-73-2 C7H15Cl2N2O2P 260.0248 4 Imiclopazine 7224-08-0 C25H32ClN5OS 485.2016 4 Imidapril 89371-37-9 C20H27N3O6 405.1899 4 Iminostilbene 256-96-2 C14H11N 193.0891 4 Imipramine 50-49-7 C19H24N2 280.1939 4 Imiquimod 99011-02-6 C14H16N4 240.1374 4 Imolamine 318-23-0 C14H20N4O 260.1637 4 Indanazoline 40507-78-6 C12H15N3 201.1265 4 Indapamine 26807-65-8 C16H16ClN3O3S 365.06 4 Indinavir 150378-17-9 C36H47N5O4 613.3628 4 Indomethacin 53-86-1 C19H16ClNO4 357.0767 4 Indoprofen 31842-01-0 C17H15NO3 281.1051 4 Indoramin 26844-12-2 C22H25N3O 347.1997 4 Inosine 58-63-9 C10H12N4O5 268.0807 8 Iobitridol 136949-58-1 C20H28I3N3O9 834.8959 4 Ioglicic acid 49755-67-1 C13H12I3N3O5 670.7911 4 Iopodic acid 5587-89-3 C12H13I3N2O2 597.8111 4 Ipratropium 22254-24-6 C20H30NO3 332.2225 4 Iprazochrome 7248-21-7 C12H16N4O3 264.1222 8 Iprindole 5560-72-5 C19H28N2 284.2252 4 Irbesartan 138402-11-6 C25H28N6O 428.2324 4 Isoaminile 77-51-0 C16H24N2 244.1939 4 Isocarboxazid 59-63-2 C12H13N3O2 231.1007 4 Isoconazole 24168-96-5 C18H14Cl4N2O 413.986 4 Isoniazide 54-85-3 C6H7N3O 137.0589 4 Isoprenaline 7683-59-3 C11H17NO3 211.1208 8 Isoproturon 34123-59-6 C12H18N2O 206.1419 4 Isothipendyl 482-15-5 C16H19N3S 285.1299 4 Isoxicam 34552-84-6 C14H13N3O5S 335.0575 8 Isoxsuprine 395-28-8 C18H23NO3 301.1677 4 Isradipine 75695-93-1 C19H21N3O5 371.1481 4 Kavain 500-64-1 C14H14O3 230.0942 4 Ketamine 6740-88-1 C13H16ClNO 237.092 4 Ketazolam 27223-35-4 C20H17ClN2O3 368.0927 4 Ketoconazole 65277-42-1 C26H28Cl2N4O4 530.1487 4 Ketoprofen 22071-15-4 C16H14O3 254.0942 4 Ketorolac 74103-06-3 C15H13NO3 255.0895 4 Ketotifen 34580-13-7 C19H19NOS 309.1187 4 Khellin 82-02-0 C14H12O5 260.0684 4 Labetalol 36894-69-6 C19H24N2O3 328.1786 4 Lacidipine 103890-78-4 C26H33NO6 455.2307 8 Lactitol 585-86-4 C12H24O11 344.1318 4 Lamivudine 134678-17-4 C8H11N3O3S 229.0521 4 Lamotrigine 84057-84-1 C9H7Cl2N5 255.0078 4 Lauroguadine 135-43-3 C20H36N6O 376.295 4 Leﬂunomide 75706-12-6 C12H9F3N2O2 270.0616 4 Lercanidipine 100427-26-7 C36H41N3O6 611.2995 4 Levallorphan 152-02-3 C19H25NO 283.1936 4 Levobunolol 47141-41-4 C17H25NO3 291.1834 4 Levocabastine 79516-68-0 C26H29FN2O2 420.2213 4 Levodopa 59-92-7 C9H11NO4 197.0688 8 Levomepromazine 60-99-1 C19H24N2OS 328.1609 4 Levopropylhexedrin 6192-97-8 C10H21N 155.1673 4 Lidocaine 137-58-6 C14H22N2O 234.1732 4 Lincomycin 154-21-2 C18H34N2O6S 406.2137 4 Liothyronine 03.02.6893 C15H12I3NO4 650.79 8 Lisinopril 76547-98-3 C21H31N3O5 405.2263 4 Lisurid 18016-80-3 C20H26N4O 338.2106 4 Lobeline 90-69-7 C22H27NO2 337.2041 4 A. Freiburg library content XII

Lofepramine 23047-25-8 C26H27ClN2O 418.1811 4 Lonazolac 53808-88-1 C17H13ClN2O2 312.0665 4 Loperamide 53179-11-6 C29H33ClN2O2 476.223 4 Loracarbef 76470-66-1 C16H16ClN3O4 349.0829 4 Loratadine 79794-75-5 C22H23ClN2O2 382.1447 4 Lorazepam 846-49-1 C15H10Cl2N2O2 320.0119 4 Lormetazepam 848-75-9 C16H12Cl2N2O2 334.0274 4 Lornoxicam 70374-39-9 C13H10ClN3O4S2 370.9801 8 Losartan 114798-39-9 C22H23ClN6O 422.1621 4 Lovastatin 75330-75-5 C24H36O5 404.2562 4 Loxapine 02.10.1977 C18H18ClN3O 327.1138 4 Lysergide 50-73-3 C20H25N3O 323.1997 4 Mafenide 138-39-6 C7H10N2O2S 186.0462 4 Maprotiline 10262-69-8 C20H23N 277.183 4 Mazindol 22232-71-9 C16H13ClN2O 284.0716 4 Mebendazole 31431-39-7 C16H13N3O3 295.0956 4 Mebeverine 07.06.3625 C25H35NO5 429.2515 4 Meclizine 31884-77-2 C25H27ClN2 390.1862 4 Meclofenamic acid 644-62-2 C14H11Cl2NO2 295.0166 8 Meclofenoxate 51-68-3 C12H16NO3Cl 257.0818 4 Mecloxamine 04.06.5668 C19H24ClNO 317.1546 4 Meclozine 569-65-3 C25H27ClN2 390.1862 4 Medazepam 06.12.2898 C16H15ClN2 270.0923 4 Mefenamic acid 61-68-7 C15H15NO2 241.1102 8 Mefenorex 17243-57-1 C12H18ClN 211.1127 4 Mefexamide 1227-61-8 C15H24N2O3 280.1786 4 Meﬂoquine 53230-10-7 C17H16F6N2O 378.1166 4 Mefruside 7195-27-9 C13H19ClN2O5S2 382.0423 8 Melatonin 73-31-4 C13H16N2O2 232.1211 8 Melitracen 5118-29-6 C21H25N 291.1987 4 Meloxicam 71125-38-7 C14H13N3O4S2 351.0347 4 Melperone 3575-80-2 C16H22FNO 263.1685 4 Melphalan 148-82-3 C13H18Cl2N2O2 304.0745 4 Mepacrine 83-89-6 C23H30ClN3O 399.2077 4 Meperidine-D4 53484-73-4 C15H17D4NO2 251 4 Mephentermine 100-92-5 C11H17N 163.136 4 Mepindolol 23694-81-7 C15H22N2O2 262.1681 4 Mepivacaine 96-88-8 C15H22N2O 246.1732 4 Meprobamate 57-53-4 C9H18N2O4 218.1266 4 Meptazinol 54340-58-8 C15H23NO 233.1779 4 Mequitazine 29216-28-2 C20H22N2S 322.1503 4 Mescaline 54-04-6 C11H17NO3 211.1208 4 Mesoridazine 5588-33-0 C21H26N2OS2 386.1486 4 Mesuximide 77-41-8 C12H13NO2 203.0946 4 Metaclazepam 65517-27-3 C18H18BrClN2O 392.0291 4 Metamfepramone 15351-09-4 C11H15NO 177.1153 4 Metamitron 41394-05-2 C10H10N4O 202.0854 8 Metamizol 68-89-3 C13H17N3O4S 311.0939 8 Metamphetamine 537-46-2 C10H15N 149.1204 4 Metazachlor 67129-08-2 C14H16ClN3O 277.0981 4 Metenolone acetate 434-05-9 C22H32O3 344.2351 4 Metformin 657-24-9 C4H11N5 129.1014 4 Methabenzthiazuron 18691-97-9 C10H11N3OS 221.0622 4 Methadone 76-99-3 C21H27NO 309.2092 4 Methamphetamine-D11 152477-88-8 C10H4D11N 160 4 Methamphetamine-D8 136765-40-7 C10H7D8N 157 4 Methanthelinium 53-46-3 C21H26NO3 340.1912 4 Methaphenilene 493-78-7 C15H20N2S 260.1347 4 Methapyrilene 91-80-5 C14H19N3S 261.1299 4 Methaqualone 72-44-6 C16H14N2O 250.1106 4 Methazolamide 554-57-4 C5H8N4O3S2 236.0037 8 Methfuroxam 28730-17-8 C14H15NO2 229.1102 4 Methocarbamol 532-03-6 C11H15NO5 241.095 4 Methohexital 151-83-7 C14H18N2O3 262.1317 4 Methohexital-D5 160227-45-2 C14H13D5N2O3 267 4 Methoprotryne 841-06-5 C11H21N5OS 271.1466 4 Methotrexate 59-05-2 C20H22N8O5 454.1713 4 Methyl nicotinate 93-60-7 C7H7NO2 137.0476 4 Methyldopa 555-30-6 C10H13NO4 211.0844 8 Methylephedrine 552-79-4 C11H17NO 179.131 4 Methylphenidate 113-45-1 C14H19NO2 233.1415 4 A. Freiburg library content XIII

Methylprednisolone 83-43-2 C22H30O5 374.2093 8 Methylscopolamine 155-41-9 C18H23NO4 317.1627 4 Methylthiouracil 56-04-2 C5H6N2OS 142.02 8 Methysergide 361-37-5 C21H27N3O2 353.2103 4 Metipranolol 22664-55-7 C17H27NO4 309.194 4 Metixene 02.02.4969 C20H23NS 309.1551 4 Metoclopramide 364-62-5 C14H22ClN3O2 299.14 4 Metofenazate 388-51-2 C31H36ClN3O5S 597.2064 4 Metoprolol 37350-58-6 C15H25NO3 267.1834 4 Metronidazole 443-48-1 C6H9N3O3 171.0643 4 Metsulfuron-methyl 74223-64-6 C14H15N5O6S 381.0743 8 Metyrapone 54-36-4 C14H14N2O 226.1106 4 Mexiletine 31828-71-4 C11H17NO 179.131 4 Mezlocillin 51481-65-3 C21H25N5O8S2 539.1144 4 Mianserin 24219-97-4 C18H20N2 264.1626 4 Miconazole 22916-47-8 C18H14Cl4N2O 413.986 4 Midazolam 59467-70-8 C18H13ClFN3 325.0782 4 Midodrine 42794-76-3 C12H18N2O4 254.1266 4 Milrinone 78415-72-2 C12H9N3O 211.0745 8 Miltefosine 58066-85-6 C21H46NO4P 407.3164 4 Minocycline 10118-90-8 C23H27N3O7 457.1849 4 Minoxidil 38304-91-5 C9H15N5O 209.1276 4 Mirtazapine 61337-67-5 C17H19N3 265.1578 4 Mizolastine 108612-45-9 C24H25FN6O 432.2073 4 Moclobemide 71320-77-9 C13H17ClN2O2 268.0978 4 Modafinil 68693-11-8 C15H15NO2S 273.0823 4 Mofebutazone 68693-11-8 C13H16N2O2 232.1211 8 Molindone 7416-34-4 C16H24N2O2 276.1837 4 Molsidomine 25717-80-0 C9H14N4O4 242.1015 4 Monocrotophos 6923-22-4 C7H14NO5P 223.0609 4 Monolinuron 1746-81-2 C9H11ClN2O2 214.0509 4 Monuron 150-68-5 C9H11ClN2O 198.0559 4 Moperone 1050-79-7 C22H26FNO2 355.1947 4 Morantel 20574-50-9 C12H16N2S 220.1034 4 Morazone 6536-18-1 C23H27N3O2 377.2103 4 Morphin-3ßD-glucuronide-D3 C23H24D3NO9 464 8 Morphine 57-27-2 C17H19NO3 285.1364 4 Morphine-3-beta-D-glucuronide 20290-09-9 C23H27NO9 461.1685 4 Moxaverine 10539-19-2 C20H21NO2 307.1572 4 Moxisylyte 54-32-0 C16H25NO3 279.1834 4 Moxonidine 75438-57-2 C9H12ClN5O 241.073 4 N,N-Diethyl-m-toluamide 134-62-3 C12H17NO 191.131 4 N2-Ethylguanin C7H9N5O 179.0807 8 Nabumetone 42924-53-8 C15H16O2 228.115 4 Nadolol 42200-33-9 C17H27NO4 309.194 4 Naftidrofuryl 31329-57-4 C24H33NO3 383.246 4 Naftifine 65472-88-0 C21H21N 287.1673 4 Nalidixic acid 389-08-2 C12H12N2O3 232.0847 4 Nalorphine 62-67-9 C19H21NO3 311.1521 4 Naloxone 465-65-6 C19H21NO4 327.147 4 Naltrexone 16590-41-3 C20H23NO4 341.1626 4 Nandrolone 434-22-0 C18H26O2 274.1932 4 Naphazoline 835-31-4 C14H14N2 210.1156 4 Napropamide 15299-99-7 C17H21NO2 271.1572 4 Naproxen 22204-53-1 C14H14O3 230.0942 4 Natamycin 7681-93-8 C33H47NO13 665.3047 4 N-Despropylpropafenone C18H21NO3 299.1521 4 Nebivolol 99200-09-6 C22H25F2NO4 405.1751 4 Nefazodone 83366-66-9 C25H32ClN5O2 469.2244 4 Nefopam 13669-70-0 C17H19NO 253.1466 4 Nicametate 3099-52-3 C12H18N2O2 222.1368 4 Nicardipine 5598-32-5 C26H29N3O6 479.2056 4 Nicergoline 27848-84-6 C24H26BrN3O3 483.1157 4 Nicotinamide 98-92-0 C6H6N2O 122.048 4 Nicotine 54-11-5 C10H14N2 162.1156 4 Nifedipine 21829-25-4 C17H18N2O6 346.1164 4 Nifenazone 2139-47-1 C17H16N4O2 308.1273 4 Niflumic acid 4394-00-7 C13H9F3N2O2 282.0616 8 Nilvadipine 75530-68-6 C19H19N3O6 385.1273 4 Nimodipine 66085-59-4 C21H26N2O7 418.174 4 Nimorazole 6506-37-2 C9H14N4O3 226.1065 4 A. Freiburg library content XIV

Nimustine 42471-28-3 C9H13ClN6O2 272.0788 4 Nisoldipine 63675-72-9 C20H24N2O6 388.1634 4 N-Isopropylsalicylamide 551-35-9 C10H13NO2 179.0946 8 Nitrazepam 146-22-5 C15H11N3O3 281.08 4 Nitrendipine 39562-70-4 C18H20N2O6 360.1321 4 Nizatidine 76963-41-2 C12H21N5O2S2 331.1136 4 MBDB 103818-46-8 C12H17NO2 207.1259 4 N-Methylephedrine 552-79-4 C11H17NO 179.131 4 Nomifensine 24526-64-5 C16H18N2 238.1469 4 Nonivamide 2444-46-4 C17H27NO3 293.199 4 Norbuprenorphine 78715-23-8 C25H35NO4 413.2566 4 Norbuprenorphine-D3 C25H32D3NO4 417 4 Nordiazepam 1088-11-5 C15H11ClN2O 270.0559 4 Nordiazepam-D5 65891-80-7 C15H6ClD5N20 275 4 Norephedrine 14838-15-4 C9H13NO 151.0997 4 Norepinephrine 51-41-2 C8H11NO3 169.0738 4 Norethisterone 68-22-4 C20H26O2 298.1932 4 Norethisterone acetate 51-98-9 C22H28O3 340.2038 4 Norfenefrine 536-21-0 C8H11NO2 153.0789 4 Norfentanyl C14H20N2O 232.1575 4 Norfloxacin 70458-96-7 C16H18FN3O3 319.1332 4 Normorphine 466-97-7 C16H17NO3 271.1207 4 Norpropoxyphene 3376-94-1 C21H27NO2 325.2041 4 Norpseudoephidrine 492-39-7 C9H13NO 151.0997 4 Nortriptyline 72-69-5 C19H21N 263.1673 4 Noscapine 128-62-1 C22H23NO7 413.1474 4 Nuarimol 63284-71-9 C17H12ClFN2O 314.0622 4 Obidoxime 7683-36-5 C14H16N4O3 288.1222 4 Octopamine 104-14-3 C8H11NO2 153.0789 4 Ofloxacin 82419-36-1 C18H20FN3O4 361.1437 4 Olanzapine 132539-06-1 C17H20N4S 312.1408 4 Olsalazine 15772-48-2 C14H10N2O6 302.0538 4 Omeprazole 73590-58-6 C17H19N3O3S 345.1147 4 Ondansetron 99614-02-5 C18H19N3O 293.1528 4 Opipramol 315-72-0 C23H29N3O 363.231 4 Orciprenaline 586-06-1 C11H17NO3 211.1208 4 Ornidazole 16773-42-5 C7H10ClN3O3 219.041 4 Orphenadrine 83-98-7 C18H23NO 269.1779 4 Oxadixyl 77732-09-3 C14H18N2O4 278.1266 4 Oxamyl 23135-22-0 C7H13N3O3S 219.0677 4 Oxatomide 60607-34-3 C27H30N4O 426.2419 4 Oxazepam 604-75-1 C15H11ClN2O2 286.0509 4 Oxcarbazepine 28721-07-5 C15H12N2O2 252.0898 4 Oxedrine 94-07-5 C9H13NO2 167.0946 4 Oxeladin 468-61-1 C20H33NO3 335.246 4 Oxetacaine 126-27-2 C28H41N3O3 467.3147 4 Oxiconazole 64211-45-6 C18H13Cl4N3O 426.9812 4 Oxilofrine 365-26-4 C10H15NO2 181.1102 4 Oxitriptan 08.09.4350 C11H12N2O3 220.0847 8 Oxitropium 30286-75-0 C19H26NO4 332.1861 4 Oxomemazine 3689-50-7 C18H22N2O2S 330.1401 4 Oxprenolol 6452-71-7 C15H23NO3 265.1677 4 Oxybuprocaine 99-43-4 C17H28N2O3 308.2099 4 Oxybutynin 5633-20-5 C22H31NO3 357.2303 4 Oxycodone 76-42-6 C18H21NO4 315.147 4 Oxyfedrine 15687-41-9 C19H23NO3 313.1677 4 Oxymetazoline 1491-59-4 C16H24N2O 260.1888 4 Oxymorphone 76-41-5 C17H19NO4 301.1314 4 Oxypendyl 17297-82-4 C20H26N4OS 370.1827 4 Oxypertine 153-87-7 C23H29N3O2 379.2259 4 Oxytetracycline 79-57-2 C22H24N2O9 460.1481 4 p-(Aminomethyl)benzoic acid 56-91-7 C8H9NO2 151.0633 4 Papaverine 58-74-2 C20H21NO4 339.147 4 Paracetamol 103-90-2 C8H9NO2 151.0633 4 Paraoxon 311-45-5 C10H14NO6P 275.0557 4 Paroxetine 61869-08-7 C19H20FNO3 329.1427 4 Penbutolol 38363-40-5 C18H29NO2 291.2198 4 Penfluridol 26864-56-2 C28H27ClF5NO 523.1701 4 Pentamidine 100-33-4 C19H24N4O2 340.1899 4 Pentobarbital 76-74-4 C11H18N2O3 226.1317 4 Pentobarbital-D5 52944-66-8 C11H13D5N2O3 231 4 A. Freiburg library content XV

Pentoxifylline 06.05.6493 C13H18N4O3 278.1378 4 Pentoxyverine 77-23-6 C20H31NO3 333.2303 4 Perazine 84-97-9 C20H25N3S 339.1769 4 Pergolide 66104-22-1 C19H26N2S 314.1816 4 Periciazine 2622-26-6 C21H23N3OS 365.1561 4 Perindopril 82834-16-0 C19H32N2O5 368.2311 4 Perphenazine 58-39-9 C21H26ClN3OS 403.1485 4 Pethidine 57-42-1 C15H21NO2 247.1572 4 Phenacetin 62-44-2 C10H13NO2 179.0946 4 Phenazone 60-80-0 C11H12N2O 188.0949 4 Phenazopyridine 94-78-0 C11H11N5 213.1014 4 Phencyclidine 77-10-1 C17H25N 243.1987 4 Phenelzine 51-71-8 C8H12N2 136.1 4 Phenethylamin 64-04-0 C8H11N 121.0891 4 Phenindione 83-12-5 C15H10O2 222.068 8 Pheniramine 86-21-5 C16H20N2 240.1626 4 Phenmedipham 13684-63-4 C16H16N2O4 300.111 4 Phenobarbital 50-06-6 C12H12N2O3 232.0847 4 Phenobarbital-D5 72793-46-5 C12H7D5N2O3 237 4 Phenothiazine 92-84-2 C12H9NS 199.0455 4 Phenoxymethylpenicillin 87-08-1 C16H18N2O5S 350.0936 8 Phenprobamate 673-31-4 C10H13NO2 179.0946 4 Phenprocoumon 435-97-2 C18H16O3 280.1099 4 Phentolamine 50-60-2 C17H19N3O 281.1528 4 Phenylbutazone 50-33-9 C19H20N2O2 308.1524 4 Phenylephrine 59-42-7 C9H13NO2 167.0946 4 Phenylpropanolamine 14838-15-4 C9H13NO 151.0997 4 Phenyltoloxamine 92-12-6 C17H21NO 255.1623 4 Phenytoin 57-41-0 C15H12N2O2 252.0898 4 Pholedrine 370-14-9 C10H15NO 165.1153 4 Phthalylsulfathiazole 85-73-4 C17H13N3O5S2 403.0296 8 Physostigmine 57-47-6 C15H21N3O2 275.1633 4 Pilocarpine 92-13-7 C11H16N2O2 208.1211 4 Pimozide 2062-78-4 C28H29F2N3O 461.2278 4 Pindolol 13523-86-9 C14H20N2O2 248.1524 4 Pioglitazone 111025-46-8 C19H20N2O3S 356.1194 4 Pipamperone 1893-33-0 C21H30FN3O2 375.2322 4 Pipemidic acid 51940-44-4 C14H17N5O3 303.1331 4 Piperacillin 61477-96-1 C23H27N5O7S 517.1631 4 Piprozolin 17243-64-0 C14H22N2O3S 298.1351 4 Piracetam 7491-74-9 C6H10N2O2 142.0742 4 Pirbuterol 38677-81-5 C12H20N2O3 240.1473 4 Pirenzepine 28797-61-7 C19H21N5O2 351.1695 4 Piretanide 55837-27-9 C17H18N2O5S 362.0936 8 Piritramide 302-41-0 C27H34N4O 430.2732 4 Piroxicam 36322-90-4 C15H13N3O4S 331.0625 4 Pizotifen 15574-96-6 C19H21NS 295.1394 4 Polythiazide 346-18-9 C11H13ClF3N3O4S3 438.9708 4 Practolol 6673-35-4 C14H22N2O3 266.163 4 Prajmalium 35080-11-6 C23H33N2O2 369.2542 4 Pramipexole 104632-26-0 C10H17N3S 211.1143 4 Prazepam 2955-38-6 C19H17ClN2O 324.1029 4 Prazosin 19216-56-9 C19H21N5O4 383.1592 4 Prednisolone 50-24-8 C21H28O5 360.1936 8 Prednisone 53-03-2 C21H26O5 358.178 8 Prenylamine 390-64-7 C24H27N 329.2143 4 Prilocaine 721-50-6 C13H20N2O 220.1575 4 Primaquine 90-34-6 C15H21N3O 259.1684 4 Primidone 125-33-7 C12H14N2O2 218.1055 4 Procainamide 51-06-9 C13H21N3O 235.1684 4 Procaine 59-46-1 C13H20N2O2 236.1524 4 Prochlorperazine 58-38-8 C20H24ClN3S 373.1379 4 Procyclidine 77-37-2 C19H29NO 287.2249 4 Profenamine 522-00-9 C19H24N2S 312.166 4 Progesterone 57-83-0 C21H30O2 314.2245 4 Promazine 58-40-2 C17H20N2S 284.1347 4 Promethazine 60-87-7 C17H20N2S 284.1347 4 Prometryn 7287-19-6 C10H19N5S 241.1361 4 Propafenone 54063-53-5 C21H27NO3 341.199 4 Propallylonal 545-93-7 C10H13BrN2O3 288.0109 4 Propham 122-42-9 C10H13NO2 179.0946 4 A. Freiburg library content XVI

Propiconazole 60207-90-1 C15H17Cl2N3O2 341.0697 4 Propionylpromazine 3568-24-9 C20H24N2OS 340.1609 4 Propipocaine 3670-68-6 C17H25NO2 275.1885 4 Propiverine 60569-19-9 C23H29NO3 367.2147 4 Propranolol 525-66-6 C16H21NO2 259.1572 4 Propyphenazone 479-92-5 C14H18N2O 230.1419 4 Prothiopendyl 303-69-5 C16H19N3S 285.1299 4 Protionamide 14222-60-7 C9H12N2S 180.0721 8 Protriptyline 438-60-8 C19H21N 263.1673 4 Proximpham 2828-42-4 C10H12N2O2 192.0898 4 Pseudoephedrine 90-82-4 C10H15NO 165.1153 4 Psilocin 520-53-6 C12H16N2O 204.1262 4 Pyranocoumarin 518-20-7 322 4 Pyribenzamine 91-81-6 C16H21N3 255.1735 4 Pyridoxine 65-23-6 C8H11NO3 169.0738 4 Pyrilamine 91-84-9 C17H23N3O 285.1841 4 Pyrimethamine 58-14-0 C12H13ClN4 248.0828 4 Pyritinol 1098-97-1 C16H20N2O4S2 368.0864 8 Pyrvinium 548-84-5 C26H28N3 382.2283 4 Quetiapine 111974-72-2 C21H25N3O2S 383.1667 4 Quinapril 85441-61-8 C25H30N2O5 438.2154 4 Quinaprilat 82768-85-2 C23H26N2O5 410.1841 8 Quinidine 56-54-2 C20H24N2O2 324.1837 4 Quinine 130-95-0 C20H24N2O2 324.1837 4 Ramifenazone 3615-24-5 C14H19N3O 245.1528 4 Ramipril 87333-19-5 C23H32N2O5 416.2311 4 Ranitidine 66357-35-5 C13H22N4O3S 314.1412 4 Raubasine 483-04-5 C21H24N2O3 352.1786 4 Raupin 482-68-8 C19H22N2O2 310.1681 4 Reboxetine 98769-81-4 C19H23NO3 313.1677 4 Remoxipride 80125-14-0 C16H23BrN2O3 370.0892 4 Repaglinide 135062-02-1 C27H36N2O4 452.2675 4 Reproterol 540-63-54-6 C18H23N5O5 389.1699 4 Reserpin 50-55-5 C33H40N2O9 608.2732 4 Rifampicin 13292-46-1 C43H58N4O12 822.405 4 Riluzole 1744-22-5 C8H5F3N2OS 234.0074 4 Risperidone 106266-06-2 C23H27FN4O2 410.2118 4 Ritodrine 26652-09-5 C17H21NO3 287.1521 4 Rizatriptan 145202-66-0 C15H19N5 269.164 4 Rocuronium 119302-91-9 C32H53N2O4 529.4005 4 Ropinirole 91374-21-9 C16H24N2O 260.1888 4 Ropivacaine 84057-95-4 C17H26N2O 274.2045 4 Rosiglitazon 122320-73-4 C18H19N3O3S 357.1147 4 Salbutamol 18559-94-9 C13H21NO3 239.1521 4 Salicylamide 65-45-2 C7H7NO2 137.0476 8 Salmeterol 89365-50-4 C25H37NO4 415.2722 4 Salsalate 552-94-3 C14H10O5 258.0528 4 Scopolamine 51-34-3 C17H21NO4 303.147 4 Sebuthylazine 7286-69-3 C9H16ClN5 229.1094 4 Secbutabarbital 125-40-6 C10H16N2O3 212.116 4 Selegilin 14611-51-9 C13H17N 187.136 4 Serotonin 50-67-9 C10H12N2O 176.0949 4 Sertindole 106516-24-9 C24H26ClFN4O 440.1779 4 Sertraline 79559-97-0 C17H17Cl2N 305.0738 4 Sibutramin 106650-56-0 C17H26ClN 279.1753 4 Sildenaﬁl 139755-83-2 C22H30N6O4S 474.2049 4 Simazine 122-34-9 C7H12ClN5 201.0781 4 Simvastatin 79902-63-9 C25H38O5 418.2719 4 Sotalol 959-24-0 C12H20N2O3S 272.1194 4 Spirapril 83647-97-6 C22H30N2O5S2 466.1596 8 Stanozolol 10418-03-8 C21H32N2O 328.2514 4 Sulfabenzamide 127-71-9 C13H12N2O3S 276.0568 8 Sulfacetamide 144-80-9 C8H10N2O3S 214.0412 4 Sulfaclomide 4015-18-3 C12H13ClN4O2S 312.0447 8 Sulfadiazine 68-35-9 C10H10N4O2S 250.0524 4 Sulfadicramide 115-68-4 C11H14N2O3S 254.0725 8 Sulfadoxine 2447-57-6 C12H14N4O4S 310.0735 8 Sulfaethidole 94-19-9 C10H12N4O2S2 284.0401 8 Sulfaguanidine 57-67-0 C7H10N4O2S 214.0524 4 Sulfalene 152-47-6 C11H12N4O3S 280.063 4 Sulfamerazine 127-79-7 C11H12N4O2S 264.068 4 A. Freiburg library content XVII

Sulfamethizole 144-82-1 C9H10N4O2S2 270.0245 8 Sulfamethoxazole 723-46-6 C10H11N3O3S 253.0521 8 Sulfamethoxypyridazine 80-35-3 C11H12N4O3S 280.063 4 Sulfanilic Acid 121-57-3 C6H7NO3S 173.0146 4 Sulfapyridine 144-83-2 C11H11N3O2S 249.0571 4 Sulfaquinoxaline 59-40-5 C14H12N4O2S 300.068 8 Sulfasalazine 599-79-1 C18H14N4O5S 398.0684 4 Sulfathiazole 42-14-0 C9H9N3O2S2 255.0136 8 Sulfinpyrazone 57-96-5 C23H20N2O3S 404.1194 4 Sulindac 38194-50-2 C20H17FO3S 356.0881 4 Sulpiride 15676-16-1 C15H23N3O4S 341.1409 4 Sultiame 61-56-3 C10H14N2O4S2 290.0395 8 Sumatriptan 103628-46-2 C14H21N3O2S 295.1354 4 Suxibuzone 27470-51-5 C24H26N2O6 438.179 8 Tacrine 321-64-2 C13H14N2 198.1156 4 Tadalafil C22H19N3O4 389.1375 4 Talinolol 57460-41-0 C20H33N3O3 363.2521 4 Tamoxifen 10540-29-1 C26H29NO 371.2249 4 Tebuconazole 107534-96-3 C16H22ClN3O 307.1451 4 Telmisartan 144701-48-4 C33H30N4O2 514.2368 4 Temazepam 846-50-4 C16H13ClN2O2 300.0665 4 Teniposide 29767-20-2 C32H32O13S 656.1563 4 Tenocixam 59804-37-4 C13H11N3O4S2 337.019 4 Terazosine 63590-64-7 C19H25N5O4 387.1906 4 Terbinafine 91161-71-6 C21H25N 291.1986 4 Terbumeton 33693-04-8 C10H19N5O 225.1589 4 Terbutaline 23031-25-6 C12H19NO3 225.1364 4 Terbuthylazine 5915-41-3 C9H16ClN5 229.1094 4 Terbutryn 886-50-0 C10H19N5S 241.1361 4 Terconazole 67915-31-5 C26H31Cl2N5O3 531.1803 4 Terfenadine 50679-08-8 C32H41NO2 471.3137 4 Tertalolol 34784-64-0 C16H25NO2S 295.1606 4 Testosterone 58-22-0 C19H28O2 288.2089 4 Testosterone benzoate 2088-71-3 C26H32O3 392.2351 4 Tetracaine 94-24-6 C15H24N2O2 264.1837 4 Tetracycline 60-54-8 C22H24N2O8 444.1532 4 Tetramethrin 7696-12-0 C19H25NO4 331.1783 4 Tetrazepam 10379-14-3 C16H17ClN2O 288.1029 4 Tetroxoprim 53808-87-0 C16H22N4O4 334.1641 4 Tetryzoline 84-22-0 C13H16N2 200.1313 4 THC 7683-36-5 C21H30O2 314.2245 4 THC-COOH 23978-85-0 C21H28O4 344.1986 8 THC-OH 366557-05-08 C21H30O3 330.2194 8 Thebacon 466-90-0 C20H23NO4 341.1626 4 Thenalidine 86-12-4 C17H22N2S 286.1503 4 Theobromine 83-67-0 C7H8N4O2 180.0646 4 Theodrenaline 13460-98-5 C17H21N5O5 375.1542 4 Theophylline 58-55-9 C7H8N4O2 180.0646 4 Thiabendazole 148-79-8 C10H7N3S 201.036 4 Thiamazole 60-56-0 C4H6N2S 114.0251 4 Thiamine 59-43-8 C12H17N4OS 265.1123 4 Thiazaflurone 25366-23-8 C6H7F3N4OS 240.0292 8 Thiazinamium 2338-21-8 C18H23N2S 299.1581 4 Thiethylperazine 1420-55-9 C22H29N3S2 399.1802 4 Thiobutabarbital 2095-57-0 C10H16N02O2S 228.0932 4 Thiodicarb 59669-26-0 C10H18N4O4S3 354.049 4 Thiofanox 39196-18-4 C9H18N2O2S 218.1088 4 Thioguanine 154-42-7 C5H5N5S 167.0265 4 Thiopental 76-75-5 C11H17N2O2S 241.101 4 Thiopropazate 84-06-0 C23H28ClN3O2S 445.159 4 Thioproperazine 316-81-4 C22H30N4O2S2 446.181 4 Thioridazine 50-52-2 C21H26N2S2 370.1537 4 Thiothixene 5591-45-7 C23H29N3O2S2 443.1701 4 Thiram 137-26-8 C6H12N2S4 239.9883 4 Thonzylamine 91-85-0 C16H22N4O 286.1793 4 Thymopentin 69558-55-0 C30H49N9O9 679.3653 4 Tiagabine 115103-54-3 C20H25NO2S2 375.1326 4 Tiapride 51012-32-9 C15H24N2O4S 328.1456 4 Ticlopidine 55142-85-3 C14H14ClNS 263.0535 4 Tiemonium 144-12-7 C18H24NO2S 318.1527 4 Tilidine 20380-58-9 C17H23NO2 273.1728 4 A. Freiburg library content XVIII

Timolol 26839-75-8 C13H24N4O3S 316.1569 4 Tinidazole 19387-91-8 C8H13N3O4S 247.0626 4 Tiocarlide 910-86-1 C23H32N2O2S 400.2184 8 Tiracizine 83275-56-3 C21H25N3O3 367.1895 4 Tizanidine 51322-75-9 C9H8ClN5S 253.0188 4 Tocainide 41708-72-9 C11H16N2O 192.1262 4 Tolazamide 1156-19-0 C14H21N3O3S 311.1303 8 Tolazoline 59-98-3 C10H12N2 160.1 4 Tolbutamide 64-77-7 C12H18N2O3S 270.1038 4 Toliprolol 2933-94-0 C13H21NO2 223.1572 4 Tolmetin 26171-23-3 C15H15NO3 257.1051 4 Tolnaftate 2398-96-1 C19H17NOS 307.103 4 Tolpropamine 5632-44-0 C18H23N 253.183 4 Tolycaine 3686-58-6 C15H22N2O3 278.163 4 Topotecan 123948-87-8 C23H23N3O5 421.1637 4 Torasemide 56211-40-6 C16H20N4O3S 348.1256 8 Tramadol 27203-92-5 C16H25NO2 263.1884 4 Tranexamic acid 1197-18-8 C8H15NO2 157.1102 4 Tranylcypromine 155-09-9 C9H11N 133.0891 4 Trapidil 15421-84-8 C10H15N5 205.1327 4 Trazodone 19794-93-5 C19H22ClN5O 371.1512 4 Triadimefon 43121-43-3 C14H16ClN3O2 293.0931 4 Triadimenol 55219-65-3 C14H18ClN3O2 295.1087 4 Triallate 2303-17-5 C10H16Cl3NOS 303.0018 4 Triamcinolone 124-94-7 C21H27FO6 394.1791 8 Triamterene 396-01-0 C12H11N7 253.1075 4 Triasulfuron 82097-50-5 C14H16ClN5O5S 401.056 8 Triazolam 28911-01-5 C17H12Cl2N4 342.0439 4 Trifluperazine 117-89-5 C22H23F4NO2 409.1664 4 Trifluperidol 749-13-3 C22H23F4NO2 409.1664 4 Triflupromazine 146-54-3 C18H19F3N2S 352.1221 4 Trihexyphenidyl 114-11-6 C20H31NO 301.2405 4 Trimazosin 35795-16-5 C20H29N5O6 435.2117 4 Trimethobenzamide 138-56-7 C21H28N2O5 388.1998 4 Trimethoprim 738-70-5 C14H18N4O3 290.1378 4 Trimipramine 739-71-9 C20H26N2 294.2095 4 Tripelennamine 91-81-6 C16H21N3 255.1735 4 Triperiden 14617-17-5 C21H29NO 311.2249 4 Triprolidine 486-12-4 C19H22N2 278.1782 4 Tritoqualine 14504-73-5 C26H32N2O8 500.2158 4 Tromantadine 53783-83-8 C16H28N2O2 280.215 4 Tropisetron 89565-68-4 C17H20N2O2 284.1524 8 Trospium 10405-02-4 C25H30NO3 392.2225 4 Tryptamine 61-54-1 C10H12N2 160.1 4 Tulobuterol 41570-61-0 C12H18ClNO 227.1076 4 Urapidil 34661-75-1 C20H29N5O3 387.2269 4 Valacyclovir 124832-26-4 C13H20N6O4 324.1546 4 Valdecoxib 181695-72-7 C16H14N2O3S 314.0725 8 Valsartan 137862-53-4 C24H29N5O3 435.227 8 Vardenafil 224785-90-4 C23H32N6O4S 488.2205 4 Venlafaxine 93413-69-5 C17H27NO2 277.2041 4 Verapamil 52-53-9 C27H38N2O4 454.2831 4 Vetrabutine 04.09.5974 C20H27NO2 313.2041 4 Viloxazine 46817-91-8 C13H19NO3 237.1364 4 Viminol 21363-18-8 C21H31ClN2O 362.2124 4 Vincamine 1617-90-9 C21H26N2O3 354.1943 4 Vinpocentine 42971-09-5 C22H26N2O2 350.1994 4 Viquidil 84-55-9 C20H24N2O2 324.1837 4 Warfarin 81-81-2 C19H16O4 308.1048 4 Xantinol 437-74-1 C13H21N5O4 311.1593 4 Xipamide 14293-44-8 C15H15ClN2O4S 354.0441 8 Xylometazoline 526-36-3 C16H24N2 244.1939 4 Yohimbine 146-48-5 C21H26N2O3 354.1943 4 Zimeldine 56775-88-3 C16H17BrN2 316.0575 4 Zolpidem 82626-48-0 C19H21N3O 307.1684 4 Zopiclone 43200-80-2 C17H17ClN6O3 388.105 4 Zotepine 26615-21-4 C18H18ClNOS 331.0797 4 Zuclopenthixol 53772-83-1 C22H25ClN2OS 400.1376 4 1-Mdpba 107447-03-0 C11H15NO2 193.1102 4