Diplomarbeit/Diploma Thesis

DIPLOMARBEIT/DIPLOMA THESIS

Titel der Diplomarbeit / Title of the Diploma Thesis “An evaluation of the accuracy of drug – related InChI & InChIKey on ChemSpider, DrugBank, PharmXplorer, PubChem and Wikipedia“

verfasst von / submitted by

Joachim Tscherny

angestrebter akademischer Grad / in partial fulfilment of the requirements for the degree of Magister der Pharmazie (Mag.pharm.)

Wien, 2018 / Vienna, 2018

Studienkennzahl lt. Studienblatt / A 449 degree programme code as it appears on the student record sheet: Studienrichtung lt. Studienblatt / Diplomstudium Pharmazie degree programme as it appears on the student record sheet: Betreut von / Supervisor: Univ.-Prof. Mag. Dr. Gerhard Ecker

Acknowledgments

Foremost, I would like to thank my supervisor Gerhard Ecker for the opportunity for realizing this project. Through these thesis, I was able to expand my knowledge extensively.

Furthermore, I would like to thank Daniela Digles, who especially supported me at the beginning of my work with the Knime Analytics Platform. I would also like to express my gratitude to Norbert Haider, who provided the data from PharmXplorer.

I would like to express my appreciation to the developers of the KNIME Analytical Platform – I could not have carried out the type of computational work without access to this software.

I would like to take this opportunity to thank my family, especially Mom, Dad and my sister Katharina, for the continuous and unconditional support they have given me throughout my duration of study.

And finally, I extend my personal gratitude to Martina for all the love, patience, and guidance she has given me during the last years.

“lucundi acti labores” (Marcus Tullius Cicero, Brutus 70)

III

Abstract

Freely available online resources such as ChemSpider, DrugBank, PubChem, and Wikipedia are widely used for obtaining information on drugs. For pharmacy students of the University of Vienna, PharmXplorer is a commonly used source of information. This project investigates whether the drug-related InChI & InChIKey are consistent in the databases ChemSpider, DrugBank, PubChem, and Wikipedia. On the other hand, a gold-standard dataset was created based on the data of the consistency tests, which were used to validate the databases ChemSpider, DrugBank, PubChem, PharmXplorer, and Wikipedia.

The workflow tool KNIME Analytics Platform was used to obtain InChI & InChIKey for all drugs approved in Austria from ChemSpider, DrugBank, PubChem, and Wikipedia. The consistency test showed that the total consistency is 79.34%.

The database validation revealed that PubChem performed best with a correctness of 96.59%, followed by DrugBank (96.07%), ChemSpider (93.88%), Wikipedia (92.83%) and PharmXplorer (83.94%).

All in all, whenever International nonproprietary names used to query InChI & InChIKey in four different databases automatically, this results in at least two different InChIs & InChIKeys in 20% of the cases.

Zusammenfassung

Frei verfügbare Onlineplattformen wie ChemSpider, DrugBank, PubChem und Wikipedia werden häufig genutzt um an Informationen über Arzneistoffe zu gelangen. Für Pharmaziestudenten der Universität Wien ist der PharmXplorer eine häufig genutzte Informationsquelle. Dieses Projekt untersucht, ob die von den Arzneistoffen zugehörige InChIs und InChIKeys in den Datenbanken ChemSpider, DrugBank, PubChem und Wikipedia konsistent sind. Auf der Grundlage der Ergebnisse des Konsistenztests wurde ein Goldstandard-Datensatz erstellt, der zur Validierung der Datenbanken ChemSpider, DrugBank, PubChem, PharmXplorer und Wikipedia diente.

Das Workflow-Tool KNIME Analytics Platform kam zum Einsatz, um die zugehörigen InChIs und InChIKeys aller in Österreich zugelassenen Arzneistoffen von ChemSpider, DrugBank, PubChem und Wikipedia zu erhalten. Das Ergebnis des Konsistenztestes brachte eine Übereinstimmung von 79.34% InChIs.

Die Validierung der Datenbanken unter Verwendung des Goldstandard-Datensatzes ergab, dass PubChem mit einer Korrektheit von 96.59% am besten abschnitt, gefolgt von DrugBank (96.07%), ChemSpider (93.88%), Wikipedia (92.83%) und PharmXplorer (83.94%).

Wenn der Internationalen Freinamen verwendet wird um automatisch in vier verschiedenen Datenbanken den zugehörigen InChI und InChIKey abzufragen, scheinen in 20% der Fälle mindestens zwei verschiedene InChIs und InChIKeys auf.

VII

VIII

Table of Contents

Acknowledgments ...... III

Abstract ...... V

Zusammenfassung ...... VII

Table of Contents ...... IX

List of Figures ...... XII

List of Table ...... XV

1 Introduction ...... 1

1.1 Motivation of the Thesis ...... 1

1.2 Statement of the problem ...... 1

1.3 Research Question ...... 3

1.4 Aim of the thesis ...... 3

2 Background Methodology ...... 4

2.1 The Internet – source of information ...... 4

2.2 Definitions ...... 4

2.2.1 ATC-Classification System...... 4

2.2.2 Molecule Representation ...... 7

2.2.3 Why InChI & InChIKey ...... 13

2.3 Databases ...... 14

2.3.1 ChemSpider ...... 14

2.3.1.1 Content of a ChemSpider entry ...... 14

2.3.1.2 Access ChemSpider webservices ...... 15

2.3.2 DrugBank ...... 17

2.3.2.1 Content of a DrugBank entry ...... 17

2.3.2.2 Access DrugBank Data ...... 18

2.3.3 PharmXplorer ...... 19

2.3.3.1 PharmXplorer information platform ...... 19

2.3.4 PubChem ...... 21

2.3.4.1 Content of a PubChem Compound entry ...... 21

2.3.4.2 Access PubChem: The PubChem API - PUG REST ...... 22

2.3.5 Wikipedia ...... 24

2.3.5.1 Use of drug information ...... 24

2.3.5.2 How Wikipedia works ...... 25

2.3.5.3 Is Wikipedia reliable...... 26

2.3.5.4 Content of a drug article ...... 27

2.3.5.5 Use of the Media Wiki API ...... 31

2.4 Used Tools & Software ...... 34

2.4.1 Knime ...... 34

2.4.2 Pywinauto ...... 34

3 Development of the Methods ...... 35

3.1 Preparation for retrieval of Drug Information from ChemSpider, DrugBank, PubChem and Wikipedia ...... 35

3.1.1 Retrieval of international nonproprietary names...... 35

3.1.2 Extraction of ATC codes from Austrian Medicinal Product Index ...... 42

3.2 Retrieval of Drug Information from ChemSpider, DrugBank, PubChem and Wikipedia ...... 45

3.2.1 Retrieval of Drug Information from ChemSpider ...... 45

3.2.2 Retrieval of Drug Information from DrugBank ...... 48

3.2.3 Retrieval of Drug Information from PharmXplorer ...... 51

3.2.4 Retrieval of Drug Information from PubChem ...... 54

3.2.5 Retrieval of Drug Information from Wikipedia ...... 57

3.3 Data preparation for consistency test ...... 61

3.3.1 Concatenate Data from ChemSpider, DrugBank, PubChem and Wikipedia 61

3.3.2 InChI to Boolean ...... 63

3.3.3 Binary Code – 16 Cases ...... 64

3.4 Consistency Test ...... 65

3.5 Creating gold-standard dataset ...... 67

3.6 Comparing InChI related InChIKey from Public Databases versus gold- standard dataset ...... 68

3.6.1 Comparing InChI related InChIKey from ChemSpider, DrugBank, PubChem & Wikipedia versus gold-standard dataset...... 68

3.6.2 Comparing InChI related InChIKey from PharmXplorer versus gold- standard dataset ...... 71

4 Results and Discussion ...... 72

4.1 Results of consistency Test ...... 72

4.2 Validation with gold-standard dataset ...... 74

4.3 Overview of Errors ...... 75

5 Conclusion and Outlook ...... 77

6 References ...... 79

7 Appendix ...... 82

7.1 List of Abbreviation ...... 82

7.2 Scripts ...... 83

7.2.1 Python script for batch conversion of .skc files to Mol files ...... 83

List of Figures

Figure 1 L-Alanine in a molfile V2000 format, screenshot from Wikipedia [8] ...... 8 Figure 2 SMILES string of Ciprofloxacin, adopted and modified from [11] ...... 9 Figure 3 InChI of (S)-Carboxy(chloro)methyl]azanium from InChI-Trust Technical FAQ [19] ...... 11 Figure 4 The InChI and InChIKey of (-)-Menthol drawn from [20] ...... 12 Figure 5 Screenshot of ChemSpider Entry for amlodipine CID 2077 [23] ...... 15 Figure 6 Screenshot of DrugBank entry for Amlodipine (small part) [25] ...... 18 Figure 7 PharmXplorer entry of hydrochlorothiazide [27] ...... 20 Figure 8 Screenshot of PubChem entry for amlodipine (small part) [29] ...... 22 Figure 9 Conceptual framework (=URL Path) PUG-REST services [30] ...... 23 Figure 10 Amlodipine as an example of a Wikipedia drug article ...... 27 Figure 11 Full single-drug template with extended fields [47] ...... 30 Figure 12 An example of setting up an API query in the MediaWiki API sandbox .... 31 Figure 13 Response of MediaWiki API in JSON format (fragment) ...... 33 Figure 14 Screenshot of ATC-index entry for A01BB from https://www.whocc.no/atc_ddd_index/?code=A10BB ...... 35 Figure 15 Workflow to retrieve International Nonproprietary Names; cyan highlighted ...... 36 Figure 16 Inside Metanode_2_ATC-Index- WHO-Crawler...... 37 Figure 17 Workflow snippet of Metanode_2_1_WHO_Crawler Part 1 ...... 37 Figure 18 Input of table creator node: ...... 38 Figure 19 The ready URL’s listed in the „URL-CALL” column ...... 38 Figure 20 Workflow snippet of Metanode_2_1_WHO_Crawler Part 2 ...... 39 Figure 21 Snippet of XML cell for https://www.whocc.no/atc_ddd_index/?code=A10BB generated via HTMLParser node ...... 39 Figure 22 Result of XPath Expression //dns:a ...... 40 Figure 23 Href result of //dns:a/@href ...... 40 Figure 24 Workflow snippet of Metanode_2_1_WHO_Crawler Part 3 ...... 41 Figure 25 ATC index English ...... 41 Figure 26 Screenshot from https://aspregister.basg.gv.at ...... 42 Figure 27 Inside Metanode_3_Austrian Medicinal Product Index: workflow to extract ATC codes ...... 43 Figure 28 Excel table input with the dataset of Austrian Medicinal Product Index .... 43 XII

Figure 29 Workflow to retrieve Drug Information from ChemSpider ...... 45 Figure 30 Generic Web Service Client settings to retrieve ChemSpiderID’s ...... 46 Figure 31 Generic Web Service Client settings to retrieve InChI, InChIKey, and Smiles from ChemSpider ...... 47 Figure 32 Segment from Table of Extracted Drug Information from ChemSpider .... 47 Figure 33 Workflow to retrieve Drug Information from DrugBank dataset...... 48 Figure 34 Column Filter settings to retrieve DrugBank Database_ID, InChI, InChIKey, Generic name ...... 49 Figure 35 Segment from Table of Drug Information from DrugBank ...... 50 Figure 36 Process of retrieving InChI & InChIKey from PharmXplorer ...... 51 Figure 37 Workflow “Retrieval of Drug Information from PharmXplorer”...... 53 Figure 38 Workflow to retrieve Drug Information from PubChem ...... 54 Figure 39 PUG REST URI for amlodipine to retrieve Drug Information from PubChem ...... 55 Figure 40 JSON Path node preferences to extract InChI, InChIKey and CID ...... 55 Figure 41 Segment from Table of Extracted Drug Information from PubChem ...... 56 Figure 42 Workflow to retrieve Drug Information from Wikipedia ...... 57 Figure 43 The API query string to retrieve the content of Wikipedia drugbox amlodipine ...... 57 Figure 44 API response structure for amlodipine ...... 58 Figure 45 JSON Path node setting to extract “*” property, title & pageid ...... 59 Figure 46 Extraction of InChI from Wikipedia Drugbox ...... 59 Figure 47 Extraction of InChIKey from Wikipedia Drugbox ...... 60 Figure 48 Segment from Table of Extracted Drug information from Wikipedia ...... 60 Figure 49 Fragment of concatenating table of retrieved data from ChemSpider, DrugBank, PubChem, and Wikipedia...... 61 Figure 50 Configuration of Row Splitter node to include standard InChIs ...... 61 Figure 51 Regular expression to include valid InChIKeys...... 62 Figure 52 Fragment of filtered concatenate table with InChI & InChIKey ...... 62 Figure 53 Fragment of Table with 1.265 Generic names and their related InChI & InChIKey ...... 63 Figure 54 Screenshot of overview if InChI database entry was found ...... 63 Figure 55 Workflow overview test for consistency ...... 65 Figure 56 Consistency test for case 16 – InChIKey included in all four databases ... 66

XIII

Figure 57 Result table consistency test (fragment) ...... 67 Figure 58 Flowchart showing the process of generating a gold-standard dataset .... 68 Figure 59 Workflow overview comparing InChIKey from ChemSpider versus gold- standard dataset ...... 69 Figure 60 selected columns to prepare for comparison ...... 69 Figure 61 Rule Engine node configured to prove if InChIKey ChemSpider matches InChIKey Pharmaquiz gold-standard ...... 70 Figure 62 chart of total consistency ...... 73 Figure 63 Chart showing the correctness of InChI on public available databases in relation to the gold-standard dataset ...... 75 Figure 64 Error categories by percentage ...... 76

XIV

List of Table

Table 1 Main groups of First Level ATC code [5] ...... 5 Table 2 ATC-Levels of glibenclamide [6] ...... 6 Table 3 Multiple SMILES strings of ethanol [12] ...... 10 Table 4 URI parts pf PUG-REST request ...... 54 Table 5 16 Cases of InChI ...... 64 Table 6 result of consistency test in absolute numbers ...... 72 Table 7 result of the correctness of InChI on public available databases in relation to the gold-standard ...... 74 Table 8 Overview of error type ...... 76

XVI

1 Introduction

1.1 Motivation of the Thesis

Once upon a time, I read a drug article on Wikipedia about a selective competitive vasopressin receptor 2 antagonist called tolvaptan. Soon I fastened my eyes on the drugbox of the article and clicked randomly chosen on the linked DrugBank Identifier DB06212. After a few seconds, the DrugBank entry of tolvaptan opened in my web browser, and looked at and inspected the chemical structure of tolvaptan. I thought “Why did I see here a specified stereocenter? – According to Wikipedia tolvaptan is a racemate, 1:1 mixture of the S and R enantiomer – there should not be a defined stereocenter”. I scrolled down and compared the InChIs from the DrugBank and the Wikipedia and came to the same conclusion stereocenter specified vs. unspecified. I just found a difference comparing the InChI strings. So I thought it would be fascinating to search automatically for the generic name, e.g., tolvaptan in publicly accessible databases and extract the InChI from the databases and subsequently compare the InChI strings. If the strings would be different, there is probably a mistake. Through fortunate circumstances, I happened to talk to Professor Gerhard Ecker about this issue, and he immediately suggested that this point of interest would be an attractive master thesis topic. That’s how it all started. So the incentive of this project is to investigate if the chemical structure related InChI are consistent in public accessible databases. A literature research was done to check if there is already was an existing a free available goldstandard database to compare the InChI strings of the related topics.

1.2 Statement of the problem

The first literature research resulted in two basic findings: there is no goldstandard for the content of pharmaceutical structures, and the quality of internet chemistry has a wide range. Williams & Ekins published their study “A quality and call for improved curation of public chemistry databases” in Drug Discovery Today in September 2011. As the main result,

1 both researchers state that there is an urgent need to improve the quality of internet chemistry and limit the arising errors and wasted efforts in public online databases because they have become trusted valuable sources upon researchers rely for chemical structures and scientific information [1]. In 2012 a study conducted by Akhondi, Kors & Muresan showed that consistency of chemical identifiers and their corresponding MOL representation differ in large expanse between data sources (37.2% to 98.5%). Stereochemistry was shown to hava a great impact – when disregarded the consistency increased (84.8% to 99.9%) [2].

Williams, Ekins & Tkachenko (2012, p.686) stated in their publication “Towards a goldstandard: regarding quality in public domain chemistry databases and approaches to improving the situation”: “Unfortunately, for chemistry databases there are as yet no agreed upon standards and there is no freely available gold standard structure database which we can yet rely on. Despite the decades of experience that underpin the assembly of commercial molecule databases (e.g. Scifinder, MDDR, among others) primarily depending on skilled staff for curation and data checking, the delivery of online databases commonly appears to focus more on the development of the underlying cheminformatics architecture and platform rather than the delivery of a high quality resource of data.”[3].

1.3 Research Question

Out of the following listed problem subsequent research questions arise:

1. When generic names/ INN (approved in Austria) are searched for in publicly accessible databases such as ChemSpider, DrugBank, PubChem, and Wikipedia how consistent are their specific chemical structures?

2. Are the accessible public databases more valid than the PharmXplorer (e- learning platform – University Graz, Innsbruck, Vienna)?

3. Is it possible to extract a gold-standard out of the combined public accessible databases?

1.4 Aim of the thesis

The current thesis aims to investigate the consistency of the structural data in publicly accessible databases as well as the possibility to generate a gold-standard dataset by combining data from several public databases. If a gold standard dataset could be created, this might later be used for composing a chemistry learning application.

2 Background Methodology

2.1 The Internet – source of information

Nowadays people live in a fast moving world and use the Internet as a quick search tool for getting the information they are interested in, instead of consulting scientific books or researchers. According to Maurice de Kunder https://www.worldwidewebsize.com on Wednesday, February 21th 2018, there are at least 4.52 billion indexed Web pages [4]. This tremendous amount of information possibly originates from scientists and experts who publish their latest results. However, it could come from companies, whose attempt is to affect people to buy their specific products. Alternatively, it could happen not valid content gets spread as veritable information. For that reason, there is no certainty that pieces of information originate from reliable sources, or have been published by experts in their scientific research area. It gets problematic if people believe this information in the vital topic of health and especially on drugs they take.

2.2 Definitions

2.2.1 ATC-Classification System

The Anatomical Therapeutic Chemical (ATC) Classification System is controlled by the World Health Organization Collaborating Centre (WHOCC) for Drug Statistics Methodology [5] and is used for the classification of active ingredients of drugs. According to The World Health Organization “Introduction to Drug Utilization Research” published in 2003 (p. 32-33) “The ATC classification system divides the drugs into different groups according to the organ or system on which they act and according to their chemical, pharmacological and therapeutic properties. Drugs are classified in groups at five different levels. The drugs are divided into 14 main groups (first level), with two therapeutic/pharmacological subgroups (second and third levels). The fourth

4 level is a therapeutic/pharmacological/chemical subgroup and the fifth level is the chemical substance. The second, third and fourth levels are often used to identify pharmacological subgroups when these are considered to be more appropriate than therapeutic or chemical subgroups”[6].

First level The first level ATC code consists of one letter and indicates the main anatomical group. The 14 main groups of first level ATC code are displayed in Table 1.

Table 1 Main groups of First Level ATC code Code Contents A Alimentary tract and metabolism B Blood and blood forming organs C Cardiovascular system D Dermatologicals G Genito-urinary system and sex hormones H Systemic hormonal preparations, excluding sex hormones and insulins J Antiinfectives for systemic use L Antineoplastic and immunomodulating agents M Musculo-skeletal system N Nervous system P Antiparasitic products, insecticides and repellents R Respiratory system S Sensory organs V Various

Table 1 Main groups of First Level ATC code [5]

Second level The second level consists of two digits and indicates the main therapeutic group. 5

Third level The third level consists of one letter and indicates the therapeutic/pharmacological subgroup.

Fourth level The fourth level consists of one letter and indicates the chemical/therapeutic /pharmacological subgroup.

Fifth level The fifth level consists of two digits and indicates the chemical substance.

Glibenclamide was chosen as an example to illustrate the structure of the complete ATC classification system, as shown in Table 2.

Table 2 ATC-Levels of glibenclamide [6] A Alimentary tract and metabolism (first level, main anatomical group) A10 Drugs used in diabetes (second level, main therapeutic group) A10B Oral blood-glucose-lowering drugs (third level, therapeutic /pharmacological subgroup) A10B B Sulfonamides, urea derivatives (fourth level, chemical/therapeutic /pharmacological subgroup) A10B B01 Glibenclamide (fifth level, a subgroup for chemical substance)

Table 2 ATC-Levels of glibenclamide [6]

2.2.2 Molecule Representation

The representation of molecules in a computer readable format can be done in many ways, but there exist significant differences. These differences exist mainly in:

• conformation information • human readability • length (characters per atom  data size) • stereochemical information • uniqueness

Structural Representations To represent molecules in a digital way the Chemical table file (CT File) is one possibility to solve this issue. The chemical table file belongs to the text-based chemical file formats, and there are several file formats in the family. The basic principle of these files is that the information about atoms, bonds, connectivity, and coordinates of a molecule are stored in form of a connection table [7]. An example of the MDL Molfile structure of L-Alanine is given in Figure 1. Inside the connection table, the stereochemical information is contained in the atom block and the bond block. Due to the fact that structural representation can relay conformational data, they are unique. The substantial memory requirement for structural representation is definitively their principal disadvantage. It does not matter whether in memory or on file, a structural representation always requires the provisioning of storage. In consequence of this fact, the use of structural representations as unique identifiers is questionable, especially with expanding information volumes (e.g. enlargement of characters per atom). The human readability is not a strength of a structural representation of a molecule because these structural files are complex to interpret. Although it is a human readable format that can be opened with any simple editor, it is very difficult to visualize the molecule without the use of special tools (visualization software), especially in 2D or the 3D environment.

Figure 1 L-Alanine in a molfile V2000 format, screenshot from Wikipedia [8]

String Representations To represent molecules as strings there exist several different formats. The most widely used string representation is undoubtedly the Simplified Molecular Input Line Entry Specification (SMILES) format [9]. It cannot be denied that the SMILES strings are human readable, and above all able to represent molecules in a very easy and fast way to use. (e.g., copy and paste a SMILES String from the Wikipedia info box to computer software like LigandScout [10] that allows creating three-dimensional pharmacophore models). In addition, creating a SMILES string can be done by anyone with a little practice. An example of a SMILE String is given in Figure 2.

Figure 2 SMILES string of Ciprofloxacin, adopted and modified from [11]

As can be seen in Figure 2, a SMILES String normally provides information about atoms types, bond orders, and connectivity. The representation of stereochemistry is possible but is unfortunately limited. Although the SMILES String offers many advantages, such as compact data size, the main disadvantage and the main reason that it is not eligible for this project is the point of uniqueness. For a molecule there exist usually many different ways to create a SMILES String. To demonstrate this issue different SMILES strings of ethanol are listed in Table 3.

CCO ethanol

OCC ethanol

C(O)C ethanol

[CH3][CH2][OH] ethanol

[H][C]([H])([H])C([H])([H])[O][H] ethanol

Table 3 Multiple SMILES strings of ethanol [12]

Therefore canonicalization algorithms have been developed to create one preferred unique SMILES string for each specific Molecule. The problem is that there is not ONE unique canonicalization algorithm [13]. Finally, different canonicalization algorithms lead to different SMILES string for the same molecule, and the uniqueness no longer exists. Various canonicalization algorithms are provided by software companies like Daylight Chemical Information Systems, OpenEye Scientific Software, MEDIT, Chemical Computing Group, MolSoft LLC [14].

InChI – International chemical Identifier The International chemical Identifier was developed under the patronage of the International Union of Pure and Applied Chemistry (IUPAC) [15]. In addition, NIST [16] and InChI-Trust [17] played a very important role in this project. IUPAC launched this project back in 2000 because at that time there was no open source and nonproprietary identifier available which made it possible to link chemical structures via the internet. The core of the InChI project is the InChI algorithm, which enables to convert the chemical structure of a molecule to a unique InChI. To accomplish this, the input of the chemical structure is transformed into a connection table, and then three fundamental steps are necessary to create a unique InChI. This includes normalization, canonicalization, and serialization [18]. The detailed description of the algorithm and other useful content is available at https://www.inchi- trust.org/downloads/.

To illustrate which parts the InChI consists an example is given in Figure 3.

Figure 3 InChI of (S)-Carboxy(chloro)methyl]azanium from InChI-Trust Technical FAQ [19]

As shown in Figure 3, the InChI consists of several layers. These layers provide several types of information. For instance, the main layer presents the chemical formula and describes how the different atoms are connected (canonical numbering), whereas the Stereochemical layer provides the stereochemical information. The InChI consists of different layers and thus, in principle, it is possible to generate different InChI strings for the same molecule with the InChI software if different settings are made (e.g., accounting for tautomerism or not). To solve this problem, the Standard InChI was launched in 2008. The Standard InChI takes the same details as the normal InChI into account but has a distinct advantage that it is truly unique. Whether it is an InChI or a standard InChI can be determined very easily in the main layer [InChI=1/’ (any InChI) or ‘InChI=1S/’ (Standard InChI)].

InChI was not designed for human readability and therefore it is logically barely readable for humans. Therefore human readability could be seen as a disadvantage.

Heller, McNaught, Stein, Tchekhovskoi, and Pletnev stated in their publication “InChI - the worldwide chemical structure identifier standard” that InChI is more like a bar code [20].

The length of InChI increases as the size of the structure increases. A very large structure with, e.g. many atoms (100+) and also many stereo centers results in a very long string and ultimately makes it impossible to search in a search engine successfully. To solve this issue, a shorter unique identifier was needed, and ultimately the InChIKey was developed [20]. The InChIKey is an SHA-256 hash based derivate of the InChI. A hash algorithm converts the InChI in a compact 27-character string. The InChI and InChIKey of (-)- Menthol are displayed in Figure 4.

Figure 4 The InChI and InChIKey of (-)-Menthol drawn from [20]

As can be seen from Figure 4 the 27 characters long InChIKey is divided by two dashes into three blocks. The first 14 letters encode the molecular skelton (block 1). The second 8 letters encode stereochemistry and isotopes. In the third block, one letter indicates the number of protons (N stands for neutral).

2.2.3 Why InChI & InChIKey

The International Chemical Identifier was selected because it is freely available under an even less restrictive license than the GNU Lesser General Public License. Over that design, layout and algorithms which are described above are convincing. The main reason why the InChI was chosen is by far the point of uniqueness. Although InChI is not directly intelligible to a human reader, this is not relevent for the purpose of this thesis. The standard InChI can be converted to the 27 characters long InChIKey, and thus it is possible to check for consistency automatically.

2.3 Databases

2.3.1 ChemSpider

ChemSpider (www.chemspider.com) is a chemical database created from a hobby work by the chemist Antony Williams. In 2009 ChemSpider was acquired by the Royal Society of Chemistry which enabled a good infrastructure for support and access to even more data. ChemSpider manages to combine datasets from more than 400 sources to create a ChemSpider entry primarily based on the chemical structure. As far as possible it is always stated where the data came from. ChemSpider has been able to combine data from Wikipedia, the PubChem Chemical Entities of Biological Interest (ChEBI) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) [21]. As of July 2018 ChemSpider states on its homepage that ChemSpider now contains 67 million chemical structures of approximately 250 unique data sources [22]. According to Williams, some chemists describe ChemSpider as the Google for Chemistry and a Wikipedia for chemists [21]. ChemSpider offers a standard search and an advanced search option to access the corresponding ChemSpider entry. The standard search option allows searching for trade names, synonyms, register numbers and of course systematic names. Advanced search can be much more extensive. One can draw a chemical structure, looking for identifiers like the InChI or InChIKey, the molecular weight, calculated properties like logP and much more.

2.3.1.1 Content of a ChemSpider entry

If you get through the search or a cross-link to a ChemSpider entry, this entry always has a so-called unique ChemSpider ID. The focus of ChemSpider is certainly the chemical structure as mentioned above. Therefore, there is an image on the left side of the structure. To make it clear, the basic information is in the upper part. These include molecular formula, average mass, monoisotopic mass, and the ChemSpider ID. There is a screenshot of the ChemSpider entry of amlodipine in Figure 5.

Figure 5 Screenshot of ChemSpider Entry for amlodipine CID 2077 [23]

As can be seen in Figure 5 there is a more detail box which contains the InChI and InChIKey next to the systematic name and the smiles string. Below, there are several tabs like names and identifiers, properties, searches, spectra, articles, and vendors. For example, the vendor's tab lists very precisely where the data came from.

2.3.1.2 Access ChemSpider webservices

In order to gain access to the ChemSpider content through Knime, the Web Services Description Language (WSDL) is the medium of choice. ChemSpiderBlog provides a user manual at http://www.chemspider.com/blog/how-to-use-chemspider- webservices-from-knime.html. Essentially, two things are needed, the WSDL file with the specifications of the services and for certain operations with limited access an API key. The WSDL file can be downloaded directly from http://www.chemspider.com/blog/wp- content/uploads/2011/12/chemspiderSearchWSDL_no_soapencArray.zip. 15

The access token can be requested from the Royal Society of Chemistry. A more detailed description can be found in the methods section.

2.3.2 DrugBank

The DrugBank is a web capable drug database and freely available at www.drugbank.ca. DrugBank, as the name implies, specializes in pharmaceuticals and includes extensive data on molecular information, the mechanisms of action, interactions and their targets. DrugBank 1.0 was released in 2006, and since then the database has grown steadily. In 2018 DrugBank 5.0 contains all approved or previously approved drugs in Canada and the United States (2.358), as well as investigational drugs in Phase I, II, or III (4.501) [24].

2.3.2.1 Content of a DrugBank entry

If you get through the search or a cross-link to a DrugBank entry this entry always has a so-called unique DrugBank ID (Accession number) starting with DB followed by five digits. The focus of DrugBank is certainly on the pharmaceutical information. At the beginning of an entry, information is displayed whether it is a small molecule or a biological, approval status, and a brief explanation of the mechanism of action. It is extended by a picture of the structure and a list of other names (synonyms). The next section contains extensive information on product ingredients, product images, prescription products, generic prescription products, mixture products, unapproved/other products, international/other Brands, and categories. The next section contains the information that is necessary for this project. These are the chemical structure information that is also visible in the example of amlodipine in Figure 6 [25].

Figure 6 Screenshot of DrugBank entry for Amlodipine (small part) [25]

Furthermore, the DrugBank entry provides extensive information on pharmacology, interactions, clinical studies, pharmacoeconomic, properties, targets, enzymes etc. In this project, however, everything focuses on the InChI.

2.3.2.2 Access DrugBank Data

Although DrugBank offers an API, it is not available for free. Fortunately, DrugBank offers records for download as SDF. These DrugBank datasets are released under Creative Commons Attribution-NonCommercial 4.0 International License. The data was therefore extracted from the freely available sdf file and is described in detail in the methods section.

2.3.3 PharmXplorer

The PharmXplorer is an online accessible multifunctional learning management system which provides a database with comprehensive data of pharmaceutical relevant information. The project was launched in 2003 by the Universities of Graz, Innsbruck, and Vienna as part of the "New Media in Education" campaign of the Ministry of Education [26].

PharmXplorer is divided into different platforms that access the same databases and are integrated into an overall system:

• Information platform • Learning platform • Continuing education platform

In the case of this project, only the Information platform for approved drugs is relevant.

2.3.3.1 PharmXplorer information platform

The PharmXplorer information platform provides information about approved drugs in Austria including chemical, physical, pharmaceutical, and pharmacological properties. From the PharmXplorer entry of hydrochlorothiazide in Figure 7 can be seen that an image of the chemical structure and an ATC code is present. To get to the desired entry of a drug several different search queries are available. It is possible to search directly for the drug name, by indication group, or ATC code [27].

Figure 7 PharmXplorer entry of hydrochlorothiazide [27]

A point that distinguishes the PharmXplorer from all other databases is that students partly provide the content. At the university of Vienna, pharmacy students of pharmaceutical chemistry participate in a seminar where they are divided into groups to expand an entry of a certain drug and keep it up to date. The contents are mutually controlled by the student and then uploaded to the platform. The PharmXplorer has no InChI and InChIKey and does not have an application programming interface, so the retrieval of drug information from PharmXplorer was fundamentally different (detailed in the methods part).

2.3.4 PubChem

PubChem is a publicly acessible database containing chemical substances and their biological activities. In 2004 PubChem was launched as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH), and in 2015 it had more than 60 million unique chemical structure entries. On top of that, there are over one million biological assay descriptions, covering about ten thousand unique protein target sequences. In general, PubChem is made up of three interconnected databases:

• PubChem Substance • PubChem Compound • PubChem BioAssay

PubChem Substance contains chemical information, PubChem Bioassay contains biological activity data. Both databases are filled with data from more than 350 contributors. The sources are listed at https://pubchem.ncbi.nlm.nih.gov/sources. The PubChem Compound database contains only unique chemical structures that are generated from the other two databases based on a standardization procedure. More information about the standardization procedure can be found in the publication “PubChem Substance and Compound Databases” by Kim et al. Nucleic Acids Research, 2016, Vol. 44. [28]. Logically, the PubChem Compound database was used for this project because it offers all the necessary information.

2.3.4.1 Content of a PubChem Compound entry

If you get through the search or a cross-link to a PubChem Compound entry this entry always has a so-called unique PubChem Compound Identifier (CID). The PubChem Compound entry starts with a brief overview with basic information such as the CID, chemical names, molecular formula, molecular weight, InChIKey as well as a brief description of how to use the substance if known.

Of course, a picture of the chemical structure is displayed (2D structure as well as 3D conformer). Figure 8 shows a screenshot part of the PubChem Compound entry of amlodipine.

Figure 8 Screenshot of PubChem entry for amlodipine (small part) [29]

As can be seen from Figure 8 in the index "content" the information provided by PubChem is very extensive - from point one 2D structure to point 18 Information source. Furthermore, it can be seen that the information needed for this project, the InChI, is under point 3 "names and identifiers".

2.3.4.2 Access PubChem: The PubChem API - PUG REST

PubChem offers with the PUG (Power User Gateway) REST, a web interface for accessing PubChem data and services. The documentation of the PubChem API is available at https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest. A tutorial document on

PUG-REST is also available at https://PubChemdocs.ncbi.nlm.nih.gov/pug-rest- tutorial. The conceptual framework (= URL path) of the PUG-REST services is displayed in Figure 9 [30]. The PUG-REST is built around three unique PubChem Database identifiers. The three identifiers are SID for substance, CID for compounds, and AID for assays. The PUG-REST request consists of prolog, input, operation, and output. The PUG-REST request is submitted to the PubChem server. The first step consists of converting the input part into a unique database identifier. If this step was successful, the next step operation could be executed. During the operation step, different tasks could be processed, and the results are returned to the requester in the desired output format (examples are shown in Figure 9). If the request cannot be successfully completed error messages are returned.

Figure 9 Conceptual framework (=URL Path) PUG-REST services [30]

2.3.5 Wikipedia

According to its own database entry (accessed on February 21th, 2018), Wikipedia is defined as follows “Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. Wikipedia is the largest and most popular general reference work on the Internet and is named as one of the most popular websites. The project is owned by the Wikimedia Foundation, a non-profit which "operates on whatever monies it receives from its annual fund drive” [31].

2.3.5.1 Use of drug information

Which search engines are used for searching information about drugs? Laurent & Vickers (2009) evaluated in their study the ranking of different data sources in the search results of a number of well-known search engines as Google [32], Google UK [33], Yahoo [34] and MSN [35]. The English Wikipedia was ranked among the first ten results in 75 - 85% of search engines and keywords from MedlinePlus, NHS Direct Online, and the National Organization of Rare Diseases.

Another result states the Wikipedia results exceeded MedlinePlus and NHS Direct Online in most of the cases excepting using Google UK. Furthermore, it was shown that Wikipedia pages corresponding to the search results read more often than the Medline plus topics [36].

Two years later Law, Mintzes & Morgan (2011) published their study evaluating the sources of online information about prescription drugs by evaluating search results of Bing [37], Google Canada & USA and Yahoo when consulting the names of the most dispensed prescribed drugs in the US (brand and generic names). Three quarters of the first result for both brand and generic names (Google USA) were linked to the National Library of Medicine. Opposite, Wikipedia was the first result for about 80% on the other three search engines (Bing, Google Canada, and Yahoo). On these sites, more than two third of brand name searches were led to industry sponsored sites. Opiates, benzodiazepines, antibiotics, and antidepressants were the Wikipedia pages 24 with the highest number of hits and visits. The authors state Wikipedia and the National Library of medicine are highly ranked among online drug searches. They also suggested that efforts to improve the quality of online data should focus on drugs, for which Internet users are searching the most [38]. To sum up, both studies showed Wikipedia is a widely accessed health information source.

Brokowski & Sheehan (2009) published a study which showed about 80% of pharmacists use the internet as a source for obtaining drug information. Between February 2nd – March 14th, 2009 1.067 questionnaires were completed and sent back to the authors. 11 questionnaires had to be excluded due to the fact that they were from pharmaceutical students. 35% of practicing pharmacists were using Wikipedia to obtain drug related information. 19% stated they considered Wikipedia as a trustworthy source. 12% would indicate Wikipedia to other pharmacists and 7% would recommend it to consumers or patients. The majority of pharmacists reported that they use Wikipedia to search indications of drugs. The authors reported concern because of the fact that 28% of the interrogated pharmacists had no knowledge of how Wikipedia content was created [40]. From all of the three previously mentioned studies, it can be deduced that a great number of people (medical students, pharmacists etc.) rely on Wikipedia drug related information. Due to this fact it is essential that the data contained within is correct, valid and up to date.

2.3.5.2 How Wikipedia works

There was an international project aiming to collaboratively create an encyclopedia that contains the entirety of human knowledge. Wikipedia is one of the projects coming up [41].

Everybody accessing the Wikipedia website can create and modify specific content. This approach is called collaborative editing. Today, Wikipedia is available in 298 languages, thirteen of which contain over one million articles. The English Wikipedia ranks first on the list with almost 5.6 million articles (11.7%) [42].

2.3.5.3 Is Wikipedia reliable

As previously stated a great number of people are using Wikipedia to find information regarding topics as health, disease, and drugs. In case users rely on this web page concerning such vital issues, the lack of confidence in data arises concern. There have been numerous published studies on this topic, and in each, a different approach and method were used to evaluate the quality of Wikipedia data. Koppen, Phillips & Papageorgiou (2015) analyzed the reference sources, which are quoted at the bottom of each Wikipedia article. They pooled a small sample of 22 drugs (very small subset), which appeared on the Food Drug Administration’s (FDA) MedWatch [43] website during a pre-defined period of seven months. Drugs are featured on MedWatch when there are concerns reported about its safety or whenever drugs are recalled [44]. Due to this fact, the suggestion was that any piece of information reported on MedWatch should be relevant and important. That is why the study measured how long it took until the MedWatch citation was added to the references of the article – thus how long it took for the content of the article to be updated with the newly available information. As a result, it was found that, on average, these references appeared 5.9 days after the MedWatch alert was published. Wikipedia collaborators most commonly cited peer- reviewed journal articles (49.2%) and newspaper articles (12.0%). It was also pointed out the lack of evidence based guidelines in the references, as these contain important drug information for physicians. The authors concluded that due to the nature of the information sources referenced, Wikipedia could be useful to healthcare providers in gaining insight into the amount and nature of the information that their patients may access. However, this information may not be up-to-date [45]. Kraenbring et al. (2014) analyzed the accuracy and completeness of drug information of Wikipedia (German & English version) in comparison to standard textbooks of pharmacology. Additionally, references, revision, and readability were evaluated. For 100 curricular drugs, which were retrieved from the German standard textbooks of general pharmacology and were compared with the Wikipedia articles in both languages. The accuracy of drug information in Wikipedia was 99.7% (± 0.2%) compared to the textbook data in both languages. There was a difference between the two languages, as this score for the German site was 83.8% while on the English drug pages it was 91.1% at the degree of completeness, which means the amount of

26 information present in the standard textbooks also present on Wikipedia. The quoted references were also evaluated: peer-reviewed academic publications (24.5%), textbooks (22.9%) and scientific databases and prescribing information (36.4%) were cited most commonly, and potentially biased sources such as news media (online or print) accounted for only 3.2% of the references. The authors suggested Wikipedia is an accurate and comprehensive source of drug- related information for undergraduate medical education [46].

2.3.5.4 Content of a drug article

Independent of the language all Wikipedia pages are structured in a smilar. Figure 10 shows an example of a Wikipedia page.

Figure 10 Amlodipine as an example of a Wikipedia drug article The main menu is located on the left hand side; there are different options available. Under the header Article, the main text of the article is found in the middle of the web page. Most of the drug pages contain on the right hands a table called infobox. For chemicals, this box is called chembox whereas for a drug it is referred to as drugbox or infobox drug. Drugbox was used in the current thesis for gaining data on drug information.

The drugbox is structured into subsections, including: structural representation of the molecule, IUPAC name, clinical data (e.g. tradename, link to Drugs.Com, routes of administration, legal status), pharmacokinetic data (e.g. bioavailability, elimination_half-life), identifiers (e.g. CAS number, ATC code) chemical and physical data (e.g. molecular formula, molecular weight, InChI, InChIKey, density, melting point). A full single-drug template with extended fields is shown in Figure 11. For this thesis, InChI and InChIKey had the major importance.

{{Infobox drug | drug_name = | INN = | type = | IUPAC_name = | image = | width = | alt = | image2 = | width2 = | alt2 = | imageL = | widthL = | altL = | imageR = | widthR = | altR = | caption = | pronounce = | tradename = | Drugs.com = | MedlinePlus = | licence_CA = | licence_EU = | DailyMedID = | licence_US = | pregnancy_AU = | pregnancy_AU_comment = | pregnancy_US =

| DrugBank = | ChemSpiderID = | UNII = | KEGG = | ChEBI = | ChEMBL = | NIAID_ChemDB = | PDB_ligand = | synonyms = | chemical_formula = | C= | H= | Ag= | Al= | As= | Au= | B= | Bi= | Br= | Ca= | Cl= | Co= | F= | Fe= | Gd= | I= | K= | Li= | Mg= | Mn= | N= | Na= | O= | P= | Pt= | S= | Sb= | Se= | Sr= | Tc= | Zn= | charge= | molecular_weight = | SMILES = | Jmol = | StdInChI = | StdInChI_comment = | StdInChIKey = | density = | density_notes = | melting_point = | melting_high = | melting_notes = | boiling_point = | boiling_notes = | solubility = | sol_units = | specific_rotation = }}

Figure 11 Full single-drug template with extended fields [47]

2.3.5.5 Use of the Media Wiki API

Magnus Manske developed MediaWiki as a free and open-source wiki software. It is written in PHP programming language and enables access to Wikipedia, Wiktionary and Wikimedia Commons [48]. MediaWiki offers an API, which is called MediaWiki web service API. The documentation of the API is available at http://www.mediawiki.org. Additionally, users can tryout all queries quickly at the provided API sandbox at https://www.mediawiki.org/wiki/Special:ApiSandbox. Figure 12 displays an example of how the sandbox works.

Figure 12 An example of setting up an API query in the MediaWiki API sandbox

Based on the example above, the parameter action was set to query, so that the API call will request information from Wikipedia, and the format parameter was set to json (JavaScript Object Notation), an open standard file format commonly used for the asynchronous browser–server communication because its organized hierarchically. With action=query, additional parameters have to be set, some of them optional. For instance, the titles parameter (in the action = query tab) in this case was set to amlodipine. This means that the call is requesting information about the Wikipedia article titled “amlodipine”.

These titles can be derived from the URI of a Wikipedia article, for example: https://en.wikipedia.org/wiki/Amlodipine

Instead of the title, the pageids or revids can be selected, but only one at a time. Both IDs are assigned to each page and revision, respectively. Wikipedia calls each version of an article a revision, and with each update, a new revision of the article is created and stored. As a result, older versions of the pages can also be accessed via the MediaWiki API. The revisions are specified in the prop parameter (query tab), which tells the API that “revisions” property of the amlodipine page is being called for, the consequence being that revisions tab opens. In the revisions tab, rvprops is set to content.

Clicking on the “Make request” button results in that the API displays the URI of the GET request used for this query:

/w/api.php?action=query&format=json&prop=revisions&titles=Amlodipine&rvprop=co ntent

All information which was selected in the sandbox is included, but in order to be able to use the query in a web browser, the API endpoint also needs to be specified. https://en.wikipedia.org/

This endpoint ensures that the MediaWiki knows that the English language Wikipedia is being requested.

The response of the MediaWiki is hierachically structured in JSON format, and a fragment of the response is shown in in Figure 13.

Figure 13 Response of MediaWiki API in JSON format (fragment)

The API request can be made directly by pasting the query into the web browser or any other tool, e.g., in this case, the workflow tool Knime.

2.4 Used Tools & Software

2.4.1 Knime

Knime is an open source workflow tool first released by a team of developers from Silicon Valley headed by Michael Berthold at the University of Konstanz in 2006 [49]. Knime (derived from Konstanz Information Miner) provides an environment where the user can visually assemble and customize the flow of analysis. In Knime the central units are called nodes. The nodes can perform many tasks:

• Read in data • Modify data • Transform data • Visualize data • Output Data

The nodes can be connected to others. A simple workflow starts with a node that reads in data. Then it will be connected to a next node that processes data. The processing node is then connected to an output node, which completes a simple workflow. [50] This allows automated data analysis without extensive programming or scripting knowledge or even none at all.

2.4.2 Pywinauto

Pywinauto [51] is a software that allows you to automate tasks normally performed by humans directly by mouse and keyboard input. So Pywinauto is a graphical user interface (GUI) automation software. The software developer Mark Mc Mahon coined the development since 2006. The focus was laid on the graphical user interface (GUI) of Windows and the project utilized the strength of the programming language Python. Since 2015, an open source community has been responsible for further development and maintenance, with lead maintainer Vasily Ryabov. Pywinauto is listed in github at http://pywinauto.github.io/. There, all information, updates, and instructions can be found to optimally use pywinauto in practice.

3 Development of the Methods

3.1 Preparation for retrieval of Drug Information from ChemSpider, DrugBank, PubChem and Wikipedia

In order to retrieve the InChI and InChIKey from ChemSpider, DrugBank, PubChem and Wikipedia, a list of International Nonproprietary Names (INN) was needed in the first place. Furthermore, all drugs which are listed in the Austrian Medicinal Product Index were needed to be matched with the INN.

3.1.1 Retrieval of international nonproprietary names

Due to the fact that in the Austrian Medicinal Product Index ATC codes are included, it was searched for a solution to link ATC codes with INN.

Figure 14 Screenshot of ATC-index entry for A01BB from https://www.whocc.no/atc_ddd_index/?code=A10BB

Figure 14 shows that a fourth level ATC-Classification entry from WHO ATC-Index includes level one to level four and all fifth Level codes with all drugs related to the superior fourth level. The “name” of fifth level ATC-code is almost equal to the International nonproprietary name. Therefore, a WebCrawler in Knime was built to get the INN and ATC codes. Figure 15 shows the workflow that was used to retrieve International nonproprietary names of approved drugs.

Figure 15 Workflow to retrieve International Nonproprietary Names; cyan highlighted

In “Metanode_1_ATC-Index-Hauptverband“ an ATC index in the German language is read in via File reader node. The German ATC index is included in the “Elektronischer Erstattungskodex” which was downloaded from http://www.hauptverband.at. All Fourth level ATC codes were extracted and sent to the next Metanode “Metanode_2_ATC- Index- WHO-Crawler”.

Meta nodes look like a single node, although they can contain many nodes and like in “Metanode_2_ATC-Index- WHO-Crawler” even more meta nodes (shown in Figure 16) [52].

Figure 16 Inside Metanode_2_ATC-Index- WHO-Crawler

Inside Metanode_2_ATC-Index- WHO-Crawler the Metanode_2_1_WHO_Crawler is responsible for retrieving the ATC Index.

Figure 17 Workflow snippet of Metanode_2_1_WHO_Crawler Part 1

Figure 17 shows how the URL’s for the crawler is generated. The Fourth Level ATC codes are coming in through the WrappedNode Input and joined via Cross Joiner node with Table creator node. (Input of table creator node shown in Figure 18)

Figure 18 Input of table creator node:

The “$$$$” of the Pre-URL http://www.whocc.no/atc_ddd_index/?code=$$$$ are replaced via String Manipulation node to the particular fourth level ATC code. A small sample is shown in Figure 19.

Figure 19 The ready URL’s listed in the „URL-CALL” column

The “URL-CALL” column was finally sent to the HttpRetriever node. The HttpRetriever node sent an HTTP GET request to the respective URL (i.e. http://www.whocc.no/atc_ddd_index/?code=A10BB), and the results were provided as HttpResult cell Type. According to (accessed February 13th, 2018) “The HttpResult type bundles the actual binary content of the result, status code and all response headers” [53]. In the next step, the HttpResult was sent to the HtmlParser node shown in Figure 20.

Figure 20 Workflow snippet of Metanode_2_1_WHO_Crawler Part 2

The HtmlParser node converted the HTTPResult into XML cells (shown in Figure 21).

Figure 21 Snippet of XML cell for https://www.whocc.no/atc_ddd_index/?code=A10BB generated via HTMLParser node

The conversion in XML cell allowed in a further step to select via XPath node through an XPath query which was set to “//dns:a” specific select the HTML tag. The ungrouped results of the expression “//dns:a” is shown in Figure 22.

Figure 22 Result of XPath Expression //dns:a

The data was once again supplied to the XPath node, wherein the XPath query was set to //dns:a/@href to get specifically the hyperlinks stored in href. (Href result is shown in Figure 23).

Figure 23 Href result of //dns:a/@href

The Data was sent to RowSplitter node; this node allows row filtering according to certain criteria. The Matching criteria were set to only include rows of the Href column whose pattern matches hyperlinks with the structure of “*http://www.whocc.no/atc_ddd_index/?code=*”. The filtered url’s had a similar structure to http://www.whocc.no/atc_ddd_index/?code=A01AA01&showdescription=yes

At first, the http://www.whocc.no/atc_ddd_index/?code= part was removed via String Replacer node. So finally all requested ATC codes were received. According to the above example, the extracted Fifth Level ATC code is A01AA01. At this stage of the thesis, merely all ATC codes have been collected, with the descriptions of the ATC codes were still missing. The workflow overview for extracting ATC description is shown in Figure 24.

Figure 24 Workflow snippet of Metanode_2_1_WHO_Crawler Part 3

To extract data corresponding to HTML Tag hrf XPath query was set to //dns:a[contains(@href, '')]. The data were further processed, i.e. remove “Show text from Guidelines” via RowSplitter node. Full ATC Index in English was received (snippet of ATC-index shown in Figure 25).

Figure 25 ATC index English

3.1.2 Extraction of ATC codes from Austrian Medicinal Product Index

In order to extract ATC-Codes from Austrian medicinal product index, at first, the dataset had to be downloaded. The complete dataset was downloaded from https://aspregister.basg.gv.at/ October 19th, 2017. At that time 17.781 human and veterinary medicinal products were listed.

Figure 26 Screenshot from https://aspregister.basg.gv.at

The workflow of ATC code extraction was performed in Metanode_3_Austrian Medicinal Product Index (previously shown in Figure 15). The dataset from the Austrian Medicinal Product Index was read in Inside Metanode_3_Austrian Medicinal Product Index via ExcelReader node shown in Figure 27 (Excel table input is shown in Figure 28).