Datawarehousing In Pharma


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Datawarehousing In Pharma

  1. 1. International Journal of Information Management 23 (2003) 259–268 Case study Data warehousing in decision support for pharmaceutical R&D supply chain Sarmad Alshawi, Isabel Saez-Pujol, Zahir Irani* Department of Information Systems and Computing, Information Systems Evaluation and Integration Network Group (ISEing), Brunel University Uxbridge, Uxbridge, Middlesex UB8 3PH, UK Abstract The expanding technology of data warehousing is providing organisations with a powerful decision support utility that can be effectively used to support supply chain activities throughout a business or industry. Pharmaceutical Research and Development (R&D) activity represents a unique type of information-based supply chain that utilises a huge amount of data and involves a large number of decision-making points along its stages. By analysing the processes of drugs R&D in a pharmaceutical case study (company unnamed), the authors identify the main types of internal and external information sources utilised by the principle decision-making levels within the drug R&D supply chain. A classification of the information sources and the decision-making levels is then presented. The paper also discusses how by integrating these information sources, data warehouse technology can facilitate effective decision support leading to a shortening of the drug development life cycle. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Decision support; Data warehouse; Pharmaceutical R&D; Supply chain 1. Introduction With the fast development of Information Systems during the last three decades, the amount of information available to the public has dramatically increased. In order to be able to capture and use this information to its full potential, organisations need to use the appropriate technology at their disposal to keep ahead of their competitors. Some of these tools will be facilitators for obtaining the right information at the right time, which could be critical to the evolution of the company and its competitiveness (Orr, 2000). *Corresponding author. Tel.: +44-1895-816-211; fax: +44-1895-816-242. E-mail address: (Z. Irani). 0268-4012/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0268-4012(03)00028-8
  2. 2. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 260 Many organisations are increasingly starting to recognise the potential of the information stored within their systems as well as the ability to capture, access and analyse information. Its distribution inside the enterprise as well as internal communication needs to be fast, accurate and efficient (Hackathorm, 1998). However, in practice, it is common that information is created, captured and stored in databases by different functions, in different ways and with little co- ordination throughout the different phases of supply chains (Boar, 1996). In more detail, data sharing throughout the different business areas can become a complicated process in multinational companies, where data is stored and accessed differently according to the culture, development of the economy and use of Information Systems across international locations. Some of the consequences of this lack of co-ordination are difficulties in accessing information which slow down the development process of the supply chain and create unnecessary rework, with the corresponding related cost (Jonathan, 1999). The use of a tool such as an integrated Data Warehouse could provide the solution to these problems. Modern data-warehousing tools provide the opportunity to store, access and distribute corporate data in an integrated way (Labio, Quass, & Adelberg, 1997; Cameron, 1998). Moreover, the analysis of historical data can support management decision making, therefore optimising company strategy (Tanrikorur, 1998; Thomsen, 1998). Supply chain management is understood as the integration of supplier, distributor, and customer logistics requirements into one cohesive process. Traditionally, this definition is used in manufacturing chains; however, this paper will focus on the process of supply chain management in Research and Development (R&D). In the pharmaceutical industry, before the drug is manufactured and launched into the market, approximately 7.5 years for the discovery and another 9.5 years for the development of the drug precede it. These 17 pre-manufacturing years are known as the pharmaceutical R&D supply chain. In some industries, using data in the right way is not only a means of obtaining competitive advantage, but also crucial to comply with regulatory constraints (Harrington, 1999), as the use of the wrong data could harm third parties. This is the case in the pharmaceutical industry, where using the wrong information could lead to human damage and other drastic consequences. This paper studies the role data warehousing can play in supporting decision making in the pharmaceutical R&D processes. It also highlights the importance of data warehousing in the speed of development of the drug’s life cycle. 2. Case study profile The research is based on the case study of the R&D divisions at a medium size multinational pharmaceutical organisation, whose operations are located in three different countries (UK, France and Spain). The research method followed consisted, besides general observations, of structured interviews and questionnaires directed at key employees throughout the R&D supply chain. The information obtained from the interviews and questionnaires was mainly related to their specific departments. The selection of the interviewees followed the guidance of the Head of R&D Informatics, who provided the contacts. An interview guideline was prepared and sent to the interviewees before the discussions. These guidelines were prepared following suggestions of Kimball (1996, 1998), and were approved by the Director of Data Management of the
  3. 3. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 261 organisation. The approach aimed to support consistency throughout all interviews and obtain the following information: The role of the departments in the R&D of a drug. * The business processes involved performing this role. * The types of data required and generated during the business processes as well as the tools * utilised to capture, access and store this data. The information obtained during the interviews was analysed using qualitative data analysis techniques recommended by Miles and Huberman (1984). The use of these analysis techniques ensured that the data obtained from the interviews was accurate. After the one-to-one, face-to- face interviews and questionnaire activities were completed, a report was drafted and given to the interviewees for their feedback, comments and the filling in of any gaps that may have resulted. Once all the departmental information had been analysed and documented, a cross-department analysis was performed in order to produce a high-level review of the internal and external information flow. This cross-department analysis was then reviewed by key employees in R&D that provided comments and feedback. 3. Pharmaceutical R&D Although most literature discusses supply chain management as the production of physical products, not all industries have the same supply chain characteristics. As Tanrikorur (1998) discusses, ‘‘every company has a uniquely different culture, environment and requirements’’. In certain industries, supply chains may also be non-physical, i.e. intellectual or informational (Crowley, 1997), where data or information is required and also produced while developing an intellectual concept or product. This is the case of R&D supply chains in the Pharmaceutical industry, where the time spent exchanging information in the discovery and the development of a drug concept can be extremely costly. Before a drug is manufactured and is made available to the public market, it follows a non-physical supply chain, where intellectual (rather than physical) information is required and produced. The R&D of a drug is initiated by scientists generating hypotheses for new compounds with both proprietary and public domain information. It continues through the discovery, development and testing of new drugs and finalises when the product receives marketing authorisation from the relevant regulatory authorities. In the pharmaceutical R&D supply chain, suppliers are the different sources of data and information required to develop the drug. These sources can be classified as external sources (e.g. Hospitals, Contract Research Organisations, consultants or the public sector) and internal sources (e.g. other departments or offices in the organisation). The R&D intellectual production of a drug is initiated with some basic research that will generate hypotheses for new compounds. It continues through the development and testing of new drugs and finalises when the product receives marketing authorisation from the relevant regulatory authorities. The customer is the Regulatory Agency to which registration of the drug will be submitted.
  4. 4. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 262 4. Phases of the drug research process Although it is not the purpose or scope of this paper to describe the particular details of the different stages in the discovery and development of a drug, we will provide a brief description of these activities in order to give the reader an insight of the processes. 5. Research process The research process of the drug R&D supply chain starts with the generation of hypotheses compounds and ends with the study of the compounds’ reactions against the disease. The different stages of the research are concept, screening, target identification, chemical lead and pharmacology. Concept: The first step of the discovery is defining the idea or concept of the drug that is going to be developed. Screening: During this phase thousands of potential candidate compounds are screened. The aim of this phase is matching the molecular structure of potential medicines with the patterns found in human genes. Screening can take up to 2.5 years to be completed. Target identification: This phase studies the effectiveness of the potential drug candidates against the specific disease. The tests are generally carried out for a length of 1.5 years. Chemical lead: The chemical lead identification consists on the mapping of the compound’s structure. A chemical name is chosen according to the activities of the compound. This is generally completed in 1.5 years. Pharmacology: Pharmacology is the detailed study of the way the compounds react towards the disease and other body functions. The study generally takes approximately 1.5 years. 6. Development process During the development of the drug, samples are tested on both animals and humans in different doses. The process includes the pre-clinical testing stage, followed by Phases I–IV. Pre-clinical testing: In this phase, laboratories evaluate the different compounds and the toxicological testing on animals is studied. The length of this process is approximately 3.5 years. Phase I: This phase consist on the testing of the drug on a high number of healthy volunteers (between 20 and 80 people). Phase I tests are generally carried out for a length of 1 year. Phase II: This phase studies the dose that is the most efficient and also determines side effects. The drug is administered to volunteer patients (between 100 and 300 people). The study generally takes approximately 2 years to complete. Phase III: This phase aims to verify the effectiveness of the drug by comparison to another drug or placebo. It also monitors adverse reactions from long-term use. The drug is administered to a large number of volunteer patients (between 1000 and 3000 people). The duration of the process is about 3 years.
  5. 5. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 263 Phase IV: Following Phase III, a process of review and approval by the Food and Drug Association (FDA) begins. It can take up to 2.5 years for the FDA to authorise the launch of the drug into the market. 7. Data warehouse and pharmaceutical supply chain integration Most big pharmaceutical companies such as Eli Lilly (Cronin, 1997) and Pfizer (Richards, 1999) have initiated data-warehousing integration projects during the last 5 years. The aim of these projects is to harmonise data-warehousing tools throughout the international locations to support the easy retrieval and storage of data as well as being able to share information internally and externally. As Bernard (1996) puts it ‘‘improving data flows internally within the organisation as well as externally to R&D partners and regulatory agencies will dramatically shorten product-to- market times’’. For these projects to be successful, an in depth examination of the variety of databases currently utilised, detailed data requirements and business processes analysis needs to be performed for each phase of the supply chain (Birkhead & Schirmer, 1999). Also, the impact of the different regulatory constraints on the supply chain need to be understood. An example of these constraints is the achievement of Regulatory Compliance for Electronic Records and Signatures (21 CFR Par 11) imposed by the FDA. The difficulty of these projects increases with the fact that most phases of the drug development are divided into different business departments across countries with different cultures, languages and regulations that need to be also considered. In some industries, using data in the right way is not only a means of obtaining competitive advantage, but also crucial to comply with regulatory constraints (Harrington, 1999), as the use of the wrong data could harm third parties (Leiheiser, 2001). This is particularly the case in the pharmaceutical industry, where using the wrong information could lead to human damage and other drastic consequences. Shortening time-to-market is becoming a critical IT function in the pharmaceutical industry (Hibbard, 1998). Data-warehousing integration seems to be the key to success of improving the quality and speed of data sharing and communication across the different phases of the drug R&D supply chain. The data warehouse receives information from both internal and external sources and compiles the information required by the different R&D phases. Departments working in R&D may play both roles: internal information source and DW information customer. Once the intellectual production is finalised with the regulatory preparation of the dossier, the Regulatory Agency receives the final product, which is in this case the marketing authorisation application. Moreover, the Regulatory Agency also plays the role of external information source when providing guidance and regulatory advice to the organisation. The amount of data required and generated by the different phases of the drug R&D supply chain and the cost involved are extensive. It is a two-way flow: data is made available by suppliers to the customer and also from the drug developers to the suppliers as a means to communicate and distribute information. As currently no common and integrated repository of data is available across the R&D supply chain, unnecessary time is spent in communicating, exchanging and trying to obtain the required information from the related source. Moreover, as different sources keep their own type of storage, data quality is also compromised. Again, an integrated data warehouse
  6. 6. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 264 would not only speed the access to data but also improve the quality and consistency across the R&D supply chain. 8. Identified information sources During the research for this study, a total of 32 different types of information sources have been identified. The variety of source types supports the initial suggestion that individual databases could not handle the analysis of external and across-business units’ data, as information is provided from a wide variety of sources and data formats. A data warehouse could then be the tool facilitating the integration of the different type of sources. The sources have been classified according to their relationship with the organisation. They are considered to be external if they are not part of the company. Internal sources are departments, individuals or other offices that are part of the company (R&D and non-R&D), independent of the country of their location. A total of 13 main types of external sources providing a wide range of information to the R&D divisions were identified. Also, 19 high-level types of internal sources have been identified, of which three can be considered to be non-R&D information sources, five R&D Support information sources and 11 R&D intellectual production information sources. Non-R&D sources are departments internal to the organisation that are outside the R&D domain but that interact with R&D departments to exchange information. R&D support sources provide support or services to the whole R&D supply chain to ensure that the organisation runs smoothly and to optimise future and strategic decisions. This includes departments taking care of the R&D intellectual production phases, and are classified by the two main divisions in the intellectual production: the Discovery and the Development divisions. 9. Classification of decisions Some of the potential decisions that a data warehouse could support during the drug R&D processes have been identified with the analysis of the research results. These decisions could be classified according to the four decision-making levels presented by Laudon and Laudon (1998) namely strategic, management control, knowledge-level and operational control. This classifica- tion also combines the way the decision affects the different levels of the supply chain (internal and external sources, the R&D divisions and the ultimate submission to the Regulatory Agency). The following organisational decisions are typical examples: At a Strategic level, a data warehouse could provide information supporting decisions on which concepts or compounds should be researched in the future. It could also support decisions on which market to target for regulatory submissions. Historical information obtained from external sources would be essential for this type of strategic decision, making a data warehouse the ideal information/data storage to be used as a support system. At a Management control level, a central repository of data would provide management with high-level project information and would support awareness of current project status. Consequently, the data warehouse would support management decisions on how to proceed with departmental projects, being able to plan the time and required resources more accurately.
  7. 7. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 265 At a Knowledge-level, a data warehouse would support knowledge sharing and distribution across departments and locations. It could also support general awareness, which could have an excellent indirect impact on the individual and team performance. Time spent on rework would be reduced increasing the consistency across the organisation and optimising standardisation, which are essential for quality improvement. Finally, at an Operational level, a data warehouse could provide details on key information about previous projects. This data could optimise decisions on how to proceed with future projects of similar characteristics, avoiding rework, saving time and also reducing the cost of the research. Decisions on the type of population to be used for clinical trials could also be supported by the data stored in the data warehouse providing information on previous projects’ approach and success. Table 1 presents a summary of the above and other decisions that a data warehouse could support at the drug R&D supply chain. 10. Concluding comments The drug R&D supply chain is a long and expensive process. A drug may take up to 17 years before regulatory authorities approve it for manufacture and marketing. During these years, the different stages include the discovery phases of concept, screening, target identification, chemical lead and pharmacology as well as the development pre-clinical phases. Data warehousing can become a key success factor in the pharmaceutical industry. It is expected that an integrated data- warehousing approach will facilitate communication, improve the accuracy of the data and therefore will shorten the expensive and lengthy life cycle of drug development. However, the current lack of experience in this area makes such integration projects the biggest challenge for the pharmaceutical IT departments. During the research undertaken by the authors, 32 different type of information sources were identified, from which 13 were external and 19 internal sources to the organisation. This variety of source types suggests that no single database could handle the amount and diversity of data that these information sources provide. Only a data warehouse would have the capacity and capability to store, relate and analyse the information, so that it can support decision making at the different organisational levels. Some of the cost-related benefits associated with employing data warehouse technology would include the reduction of IT storage tools and the required IT knowledge and support to maintain them. Also, the reduction of time to access information, improvement of data quality and improved productivity would support shortening the time to market. Shortening the drug time to market provides a pharmaceutical organisation with high competitive advantage against their competitors, as it increases the time that the drug is on the market with patent protection. Such an advantage would also enhance relationships with the Regulatory Agency, which would be a customer-related benefit. Some of the strategic benefits include improvements in knowledge sharing, accuracy and expansion of regulatory knowledge and general pharmaceutical market awareness. Finally, data warehousing provides the organisation with the required information to support decision making at the different organisational levels: strategic, management control, knowledge- level and operational control. In this paper, some examples of the type of decision making that a
  8. 8. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 266 Table 1 Decision making at R&D R&D supply chain Strategic Management Knowledge-level Operational control decision making control Information Internal Supports Supports analysis Supports Supports tracking sources decisions on of the knowledge of departmental departmental departmental sharing across projects, which strategic performance departments supports management, decisions on such as location which tasks to in the building, perform next mergers between departments, etc. External Supports Supports decision Supports and Supports the decisions on how making on how to improves selection of to proceed in the improve external knowledge CROs/hospitals future in order to communications sharing across the reduce the cost of with partners company as obtaining previous information (e.g. information negotiating with searches can be CROs, etc.) shared from project to project and from office to office Supports choice of pharmaceutical consultants providing reports on their previous quality of work Supports strategic Supports Supports the decisions on how management decision on to deal with control on external source projects (increase company choice according or decrease the expenses in to quality of use of consultants obtaining information and and CROs to knowledge and service work on projects information or develop in- house resources— open new company laboratories, etc.) Production Research Supports Supports Supports Supports analysis decisions on decisions at an reducing time and on the which areas of the organisational cost of projects by performance of market provide level on which knowledge the research tools an opportunity of offices should sharing across used and
  9. 9. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 267 Table 1 (continued) R&D supply chain Strategic Management Knowledge-level Operational control decision making control competitive deal with each of geographical decisions on how advantage the phases of the locations and also to improve them research process across departments Supports Supports project Supports Supports decisions on awareness and knowledge on optimisation of whether specific planning (time regulatory decisions on doses drug research and resources) constraints administrated projects are worth reducing in vitro and developing any possibilities of in vivo and the further (providing project failure type of chemical previous and biological company research test that need to of abandoned be performed projects or other pharmaceutical companies information) Supports Supports the decisions on clinical candidate which other selection concepts and providing detailed compounds information on should be the compounds researched Development Supports strategic Supports Supports Supports decisions on the decisions at an reducing time and optimisation of type of drug and organisational cost of projects by decisions on doses market the level on which knowledge administrated company wants offices should sharing across and type of to target in the deal with each of geographical population for long/medium/ the phases of the locations and also clinical trials short term development across process departments. It also increases the knowledge on the research phases and previous phases of the drug development Supports the Supports project Supports decision on when awareness and knowledge on to submit the planning (time regulatory filing of a patent and resources) constraints reducing possibilities of project failure
  10. 10. S. Alshawi et al. / International Journal of Information Management 23 (2003) 259–268 268 DW could support have been identified during the analysis. A data warehouse could, e.g. provide the market information required to optimise the time the organisation decides to file the compound of a patent. This is a very important decision, as it will have a direct impact on the amount of time the drug will be in the market with patent protection. The longer the drug will be in the market patent protected, the higher the organisational revenue will be. References Bernard, S. (1996). Make way for the next world. Pharmaceutical Executive, 50 (Advanstar Communications, Inc.) Birkhead, N., & Schirmer, R. (1999). Add value to your supply chain. Transportation & Distribution, 15(9), 51. Boar, B. (1996). Understanding data warehousing strategically. In R. Barquin, & H. Edelstein (Ed.). Building, using and managing the data warehouse, 1997 (pp. 277–299), New Jersey: Prentice-Hall PTR. Cameron, D. (1998). Do you really need a data warehouse? Direct Marketing, 61(2), 43–45. Cronin, M. J. (1997). Getting drugs to market fast. Fortune, 10, 263. Crowley, A. (1997). Delivering a healthy dose of sales data. PC Week, 14(49), 53–54. Hackathorm, R. (1998). Rouging the Web for your data warehouse. DBMS, 11(9), 36–37. Harrington, L. H. (1999). Focus on pharmaceuticals: Put good ideas to work. Transportation and distribution, 41(9), 41. Hibbard, J. (1998). Research gains from IT boom—Top drug makers depend on technology for more than just speeding production. Information Week, 700, 217. Jonathan, R. (1999). Pfizer’s prescription for a healthy supply chain. Enterprise Systems Journal, 14(10), 26. Kimball, R. (1996). The data warehouse toolkit. Practical techniques for building dimensional data warehouses. New York: Wiley. Kimball, R. (1998). Professional boundaries (the data warehouse manager’s job). DBMS, 11(8), 14–15. Labio, W., Quass, D., & Adelberg, B. (1997). Physical database design for data warehouses. Proceedings of the International Conference on data engineering, Dallas, USA. Laudon, K. C., & Laudon, J. P. (1998). Management information systems. New Jersey: Prentice Hall. Leiheiser, R. (2001). Data quality in health care data warehouse environment. Proceedings of the 34th Hawaii International Conference on System Sciences, Hawaii, USA, IEEE computers. Miles, M. B., & Huberman, A. M. (1984). Qualitative data analysis: A sourcebook of new methods. Newbury Park, CA: Sage Publications. Orr, K. (2000). Data warehousing technology. The Ken Orr Institute, URL: Richards, J. (1999). Pfizer’s prescription for a healthy supply chain. Enterprise Systems Journal, 14(10), 26. Tanrikorur, T. (1998). Enterprise DSS architecture: a hybrid approach. DM review online, URL: http:// Thomsen, E. (1998). Smart decision support systems. Database programming design (pp. 59–61). Professor Zahir Irani is the Director of Postgraduate Studies in the Department of Information Systems and Computing, Brunel University (UK). Having worked for several years as a project manager, Professor Irani retains close links with industry, and is a non-executive director to a leading engineering company. He consults for international organisations such as Royal Dutch Shell Petroleum, DERA, BMW and Adidas, and has also taken part in UK-Government funded trade missions to the Middle-East and Gulf region. Professor Irani leads a multi-disciplinary group of International PhD students that research information systems evaluation and application integration.