KOL MANAGEMENT

WHITE PAPERKEY OPINION LEADER IDENTIFICATION AND SELECTION.

SCIENTIFIC

A PHARMA MATTERS REPORT.JANUARY 2009

Objectively identifying key opinion leaders (KOLs), scientific experts or clinical investigators can be an onerous task. In this white paper Thomson Reuters identifies the key issues and proposes a solution for optimal KOL identification and selection.

INTRODUCTIONPharmaceutical, biotechnology and medical devices companies enlist scientific experts as consultants to conduct basic research, assess the market, design and conduct clinical trials, and drive marketing and educational activities. These experts are often referred to as key opinion leaders (KOLs). In major therapeutic areas, the top KOLs are known in the industry through their celebrity and tenure. However other leading KOLs, whose scientific influence is apparent by the many times others have cited their work, are either less known or unknown to industry. This may be because these individuals are relatively new, may not headline conferences or speaking engagements, may be more interested in practicing than publishing, or may not have hundreds of articles published yet. These experts are the hidden gems in the pharmaceutical KOL mines. Objectively measuring the scientific credibility and influence of KOLs while focusing KOL selection for a specific purpose (e.g. primary investigator, product advocate) is a challenging proposition. This is further complicated by an emerging regulatory environment that demands transparency into the industry’s relationship with KOLs, including selection criteria and remuneration. According PharmaExec.com:

“Global KOLs, who publish in the New England Journal of Medicine or JAMA and speak at international conferences, are easily identified and well-known throughout… But showing up in Google is not enough. Companies must marshal other resources to reliably identify national, and especially regional, KOLs.” 1

This paper discusses the regulatory aspects of the KOL / industry relationship, proposes a primary means of determining KOL relevance, discusses methods for identifying KOLs to suit your business strategy, and proposes a solution for optimal KOL identification and selection.

PHARMA MATTERS | WHITE PAPER

For more information from Thomson Reuters on our pharmaceutical experts database Thomson Pharma KOLexperts, please visit thomsonreuters.com/products_services/scientific/kolexperts or email [email protected]

THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE

REgULATORY ASPECTSThe KOL / industry relationship has always been fraught with ethical pitfalls. In June 2008 a Congressional investigation revealed that a Harvard child psychiatrist, whose research was influential in growing the market for antipsychotic pediatric drugs, earned $1.6 million in consulting fees from multiple drug companies from 2000 to 2007. Much of this income was not reported to university officials2. In January 2009 a similar Congressional investigation revealed that a prominent spine surgeon, whose research was influential in promoting spinal products, received over $19 million in payments from a large medical device company. Once again, much of this income was not reported to university officials3. These and other incidents have driven a push for transparency into the KOL / industry relationship.

The bar for KOL / industry regulation was set in April 2003 by the US Department of Health and Human Services, Office of the Inspector general (OIg). The OIg issued its compliance guidance for pharmaceutical manufacturers stating that “Payments for research services [provided by KOLs] should be fair market value for legitimate, reasonable, and necessary services.” Five years afterwards, 92% of drug makers surveyed said the guidelines “significantly impacted” the structure of their medical affairs teams. For instance, many shifted medical science liaisons and thought-leader development teams away from commercial development. Meanwhile, 8% of drug makers surveyed indicated the guidelines caused a complete overhaul4.

Regulatory bodies are not the only ones seeking greater clarity into the KOL / industry relationship. The Association of the british Pharmaceutical Industry (AbPI) introduced new revisions to their code of practice, which must be implemented by November 1, 2008. These revisions state that “the criteria for [KOL] selection must be directly related to the identified need” and “payments must be reasonable and reflect fair market value.”

Other major industry bodies are also moving forward with their own guidelines. Notably, the Pharmaceutical Research and Manufacturers of America (PhRMA) recently updated its Code of Interactions with Healthcare Professionals; the updates take effect in 2009. Section 6, which covers the use of KOLs as consultants, states: “Decisions regarding the selection [of KOLs] as consultants should be made based on defined criteria such as general medical expertise and reputation, or knowledge and experience regarding a particular therapeutic area” and “the criteria for selecting consultants are directly related to the identified purpose and the persons responsible for selecting the consultants have the expertise necessary to evaluate whether the particular healthcare professionals meet those criteria.” These guidances are repeated in section 7 for the use of KOLs as speakers. Additionally, sections 6 and 7 provide that payments are fair market value, in line with the OIg’s and the AbPI’s guidances.


both regulatory bodies and industry associations are approaching the same conclusions: KOL selection and remuneration must be based on objective criteria including medical expertise and reputation. Subjective measures, such as “just knowing” whom the experts are in a therapeutic area, grow increasingly dangerous.

PRIMARY MEANS OF DETERMININg KOL RELEvANCEDetermining the expertise, reputation and influence of a scientific expert is easier said than done. The most famous attempt to identify the top three KOLs in the tremendously broad scientific fields of medicine, chemistry, physics and economics is Thomson Reuters’ well-publicized annual prediction of Nobel Prize winners. To understand the complexity involved, it is important to note that there are literally millions of scientists publishing. Narrowing the field to the highest elite still leaves at least 1,000 scientists5. Newsweek noted that “since Thomson Reuters started making predictions in 1989, there were only two years—1993 and 1996—when they failed to correctly predict at least one winner, and in some years they nailed two”6. In 2008 the Nobel Prize recipients for medicine and chemistry were correctly predicted, while the recipient for economics was one of those nominated by Thomson Reuters for the 2006 Nobel Prize. The reason that these predictions are so widely reported by media outlets, from The New York Times, to Forbes, to Nature to The Scientist, is because of the difficulty of making these predictions with such accuracy. It may be surprising, therefore, that the primary means of identifying Nobel Prize candidates so precisely is an age-old technique: citations. Why is citation analysis so effective as a primary means of prediction? According to David Pendlebury, Research Services, Thomson Reuters, “A strong correlation exists between citations in literature and peer esteem. Professional awards, like the Nobel Prize, are a reflection of this peer esteem.”

Pendlebury is not the only advocate of citation analysis to determine a scientist’s peer esteem. Jorge E. Hirsh asserts that “…while the total number of publications gives some indication of a scientist’s productivity, it says little about the quality of those publications. And while the total number of times a scientist’s papers are cited in other publications says something about their quality, those measurements can be suspect if a scientist has high-performing coauthors, few publications or a lifetime of mediocre work skewed by one or two highly cited papers.” Hirsh, professor of physics at the University of California, San Diego, developed the citation-based H-index in 2005 to measure a scientist’s productivity and impact. Hirsh defined the H-index as “A scientist has index h if h of his Np papers have at least h citations each, and the other (Np - h) papers have at most h citations each.” In other words, a scientist with an index of h has published h papers each of which has been cited by others at least h times. Additionally,

THOMSON REUTERS HISTORY OF PREDICTINg NObEL PRIZE WINNERS

Since 1989, Thomson Reuters has developed a list of likely winners in medicine, chemistry, physics, and economics. Those chosen are named Thomson Reuters Scientific Laureates - in recognition of the significant contribution their citations make to the navigation within the ISI Web of Science®.

For more information on Thomson Reuters 2008 Nobel Prize predictions, please visit scientific.thomsonreuters.com/nobel


Hirsh suggests that the H-index can be used more accurately in journal publication-oriented sciences such as biology than book publication-oriented sciences such as social science. Hirsh developed and tested the H-index using the Thomson Reuters ISI Web of Science publication database, showing a high level of correlation between a high H-index and scientists inducted into the US National Academy of Sciences, and Nobel Prize awardees7.

As medicine grows ever more specialized, it is often desirable to seek KOLs for granular therapeutic areas or indications. For drug development and marketing, for instance, it is more likely that KOLs specializing in non small cell lung cancer are sought than KOLs specializing in cancer in general. However the examples above of Nobel Prize prediction and the H-index address broad scientific fields. Can citation analysis work for the more specific needs of the life sciences industry? Matthew Wallace, a professor at the University of Ottawa, and Yves gingras, a professor at the University of Quebec, did their own study. They found that citation analysis, notwithstanding Thomson Reuters’ Nobel Prize prediction track record, was more difficult to do in broad fields due to lower citation count correlation. They asserted that “This can be explained not only by the growing size and fragmentation of the… disciplines, but also… by an implicit hierarchy in the most legitimate topics within the disciplines”8. In other words, by narrowing the fields (i.e. therapeutic areas) searched, especially when the fields are hierarchically organized, it should be possible to achieve better levels of accuracy for scientific expert selection.

Citation analysis, such as a KOL’s total number of citations and average number of citations per publication, is a useful indicator of the KOL’s peer esteem, influence, productivity, credibility and expertise. However citation analysis is not a silver bullet. Other factors need to be considered to ensure optimal KOL / industry alignment.

METHODS FOR IDENTIFYINg KOLs TO SUIT YOUR bUSINESS STRATEgY

There are many decisions to make to ensure KOL selection optimizes your business strategy.

THE MARKETFirstly, consider the market. Are you creating a market, entering or increasing share of voice in an established market, or creating bridges between related markets? Creation of a genuinely new market is admittedly uncommon; however one need look only to recent times to find an example in Restless Leg Syndrome. Restless leg syndrome (RLS) is a neurological condition that is characterized by the irresistible urge to move the legs. Requip (Ropinirole), manufactured by glaxoSmithKline, was approved by the FDA in 2005 for treatment of RLS. This was accompanied by extensive disease awareness campaign in the US. KOL selection in new markets should be driven by publication prolificness. New


markets by their very nature will not be citation rich, and those supporting your product messages in highly acclaimed journals may serve as valuable product advocates. Publication count and journal impact factor are important metrics to help target scientific experts in the new market for help with pre-clinical and clinical development. Message alignment will also have to be considered in mid-clinical, regulatory and post market stages to support your marketing function.

Entering or expanding share of voice in an established market (e.g. diabetes) is a more common activity. When initiating such an endeavor, reaching out to ‘prestige’ leaders in the field is an important strategy. Prestige leaders are those who enjoy the esteem of their peers. In fact it is the collective wisdom of the scientific community that gives credence to both the scientific expertise and the influence of these individuals. The primary means of identifying the most prestigious KOLs is through citation analysis, namely overall citation count and average citation count per publication. How to use these to meet the more detailed aspects of your business strategy is discussed under the ‘KOL alignment’ heading further down in this paper.

In October 2008 the Journal of Clinical Investigation reported research that showed statins (used to decrease risk of heart attack) may prevent miscarriages in women with autoimmune syndrome9. While this may be one of the more unusual pairings of indications for a common remedy, opportunities abound to utilize a therapy in one area and extend it to another. A more common pairing is that of diabetes and obesity. Such pairings have the potential to fulfill many goals, from patent life extension, to off-label use considerations, to sales expansion. In the case of heart disease and miscarriage, it is likely prudent to rely on publication prolificness for KOL identification due to the relative strangeness of the pairing. In the case of diabetes and obesity, citation analysis for KOL identification will likely produce the most valuable results. In either case, the ability to target KOLs that bridge the gap between the therapeutic areas is paramount.

KOL ALIgNMENTSecondly, consider how to align KOLs with your drug development, growth and market penetration objectives, based on the current stage of your product’s lifecycle. KOLs fall into two broad categories: established leaders and rising stars.

In pre-clinical development, established leaders can help with their wealth of knowledge while rising stars may be able to point out novel and ‘out of the box’ approaches to obstacles. Protocol design may benefit in the same manner.


When moving forward with later-stage clinical trials, investigator selection requires a twofold approach that considers both recruitment and maximizing the impact of outcomes. This may call for a combination of rising stars and established leaders, with the latter often fulfilling a study chair role. It is interesting to note that according to CDER10, the average age of investigators receiving NIH grants in 2004 was about 42, which represents an age increase of a few years compared to the 1980s. This suggests that the NIH increasingly tends to favor scientists who are toward the beginning of their careers but also have 10 – 12 years of experience under their belt. From a KOL-alignment point of view, this could optimally be represented by a ‘seasoned’ rising star, or one who has a high average number of citations per paper, as well as a relatively high number of papers (more on this in the following paragraphs). The ability to function as an effective investigator cannot be determined from citation analysis alone of course; clinical trial experience must be taken into account as well. As a point of interest, according to a 2005 survey of 7,342 doctors by CenterWatch10, 54% had participated in 1 – 3 trials.

Product advocacy efforts can benefit from established KOLs by the leader’s influence and broadly-reaching credibility. This can allow for tactical benefits in regulatory clearance activities or market penetration. However this can also have its shortcomings. Established leaders are well known and there are many companies ‘knocking at their doors’. The lead time to engage an established KOL may be 9 months or more. Use of established KOLs may also tend to be tactical in nature, again due to many suitors. Additionally established leaders will command higher consulting fees. In contrast, rising stars do not benefit from the visibility and tenure of the established KOL. but besides the rising star’s advantages of shorter or non-existent lead times and lower fees, budding KOLs present the opportunity to build strategic lifetime relationships: KOLs who will grow in tandem with the product. biomedical-focused bibliometric research, separately conducted by the University of Quebec11 and the Alfa Institute of biomedical Sciences12, showed that scientific impact per publication is highest while scientists are in their early 30s. Rising stars that can be engaged as KOLs shortly after this period may lead to significant value. Of course there is no reason to enlist only established leaders or only rising stars. The optimal KOL portfolio may be a mixture of both.

by taking some real-world examples for rheumatoid arthritis (RhA) over the course of the last 10 years, these concepts can be tangibly demonstrated. Table 1 shows the top 10 scientists by total publication count. Table 2 shows the top 10 scientists by total citation count.


RANKLAST NAME

FIRST NAME PUbLICATIONS

1 E P 601

2 b F 466

3 S J 404

4 T P 335

5 g S 331

6 b J 324

7 K T 299

8 K L 282

9 C M 252

10 H T 251

Table 1

RANKLAST NAME

FIRST NAME PUbLICATIONS CITATIONS CONCLUSION

1 E P 601 35843 Top leader

2 b F 466 31012 Top leader

3 S J 404 29181 Top leader

4 F M 140 27419 Leader

5 M R 149 25921 Leader

6 F D 195 24330 Leader

7 K J 224 23621 Leader

8 M L 198 22851 Leader

9 W A 42 22243 Rising star

10 L P 144 19652 Leader

Table 2

Data supplied from Thomson Pharma KOLexperts, a Thomson Reuters database

Table 1 holds the most prolific publishers. These KOLs would be good targets if RhA were a new market. by examination of Table 2, we see that only the top three KOLs are common to both tables. In other words EP, bF and SJ are both prolific and highly influential, and are therefore among the top KOLs in RhA. If RhA is in your marketspace, it is likely you would already be aware of these three. The examples in this paper look only at the top 10 for the sake of brevity, but in reality, companies may seek the top 100 – 300 as an initial list on which to focus. Therefore it may be likely that you would have found FM, MR, FD, KJ, ML and LP based on publication count alone, though you may not have been able to determine their influence. but it is unlikely that you would have identified WA, a rising star. WA has a relatively low publication count, but a remarkably high number of citations, especially when compared to his publication count.

Table 3 goes a step further and shows the top 10 scientists by average citation count per publication.


RANKLAST NAME

FIRST NAME PUbLICATIONS CITATIONS

AvERAgE CITATIONS / PUbLICATION CONCLUSION

1 S D 10 9793 979.30 Rising star

2 H g 12 10206 850.50 Rising star

3 F b 17 10939 643.47 Rising star

4 Z W 16 9131 570.69 Rising star

5 L J 13 7082 544.77 Rising star

6 D R 11 5928 538.91 Rising star

7 L L 18 9632 535.11 Rising star

8 W A 42 22243 529.60 Rising star

9 A N 13 6858 527.54 Rising star

10 v D 17 8018 471.65 Rising star

Table 3

Here we find the rising stars of RhA. It is highly unlikely that these KOLs could have been found by examination of total publication or citation count. In fact, WA is the only KOL in this table from Table 2. Who are these KOLs that have had such an impact on the scientific community with an average of only 17 publications? How can these individuals grow your RhA product in pre-clinical, clinical and post market?

bEYOND CITATIONSAlthough citation analysis is a primary means of identifying scientific experts and their alignments with your product strategy, there are other important factors that must be considered to correctly analyze citations and find the KOLs with the necessary skill sets.

Two of the concerns noted earlier in the report by Hirsh are a scientist’s having “high-performing coauthors or a lifetime of mediocre work skewed by one or two highly cited papers.” To address Hirsh’s first concern, it is possible to gain more clarity into the role of the KOL with respect to the publication by whether the author is listed first or last. Traditionally authors who are listed last are those who had a role in seeking the grant to fund the research, and/or were responsible for oversight. These individuals tend to be established KOLs. In contrast, authors who are listed first tend to be those who performed the actual research. Weighting the position of the author in the credits of the publication provides a fair assessment of the KOL’s role in his publications. To address Hirsh’s second concern involves a simpler solution: in addition to average citation count per publication, also consider the median citation count per publication.

Patent metrics may be important to gauge a KOL’s industry experience. As with publications, the inventor’s position in the patent credits traditionally points to his role.

It was noted previously that if your goal is to find KOLs to design or execute clinical trials, clinical experience is of particular importance. Metrics such as how many trials in what phase the

Data supplied from Thomson Pharma KOLexperts, a Thomson Reuters database


KOL has been involved in, along with whether the KOL has been a primary investigator, will shed light on the KOL’s viability for contribution to clinical trials.

It was also noted previously that the ability to segment KOLs by their granular and hierarchical therapeutic area(s) of expertise will help you improve your KOL selection. This is an understatement; in fact, it will also prevent you from missing pivotal KOLs. If we take the indication of hepatitis C, for example, we might search for the following terms to determine which publications are related to hepatitis C: ‘hepatitis C’ OR ‘non-A non-b hepatitis’ OR ‘non A non b hepatitis’ OR ‘HCv’. While this will return some of the desired results, publications that are integral to hepatitis C but do not mention it specifically will be omitted. For example, publications dealing with aminotransferase or interferon that do not contain the terms ‘hepatitis C’ or ‘HCv’ would be disregarded, potentially causing you to ignore important KOLs. Searching for a drug name instead of an indication presents a similar dilemma, since the same substance often goes by different names. If therapeutic areas, indications and substances are hierarchically arranged, you can be assured that relevant experts will not slip through your fingers.

A roughly similar problem exists with author names. For example, if one publication is authored by Jay Smith, another is authored by J. Smith, and yet another is authored by Jeremy Smith, how can it be determined if J. Smith is Jay Smith, Jeremy Smith or some other person whose first name starts with J and last name is Smith? Resolving this is known as ‘author disambiguation’, and is a necessary process in order to accurately measure publication counts and citation counts of KOLs.

Another angle on attaining valid publication and citation metrics is de-duplication. Since it is safest to pull publications from many different sources such as PubMed, Medline, Biosis, Web of Science®, etc., it must be ensured that the same publication is not counted multiple times.

Another important facet of KOL selection is geography. besides physical proximity to a desired location, a KOL’s country of residence gives a good indicator of political, cultural and linguistic awareness and background. When advocating a product in Japan, it is likely beneficial to enlist a Japanese KOL, for example.

Last but not least, time will play an important role in your KOL selection. Specifically, publication counts, citation counts, clinical trials experience, patent experience, etc. vary over time. It may be of little value to find a KOL with high publication count, citation count, and average citation count per publication, if most of his publishing activity took place 10 years ago. The KOL may very well have retired! Having the ability to specify time periods on which to base your metrics will ensure the currency of your search results. To take it one step further, being able to see the progression over time of publication, citation and other metrics, from 10 years ago to 1 year ago, for example, will lend further transparency to a KOL’s activity trend.


SOLUTION FOR OPTIMAL KOL IDENTIFICATION AND SELECTION

Simply put, an optimal KOL identification and selection solution would fulfill the requirements outlined in the previous section. An ideal database would allow filtering and weighting on the following:

• Publication and patent information such as disambiguated authors, author position, and publication date, with hierarchically arranged therapeutic area, indication and drug terms

• Citation information for each KOL

• Clinical trials information, linked to the KOL

• Country of residence information for each KOL

Unfortunately the technology to achieve perfect author disambiguation and hierarchically arranged therapeutic area, indication and drug terms by computer algorithm alone does not exist. Therefore the database would require some level of manual data assessment and maintenance.

The metrics described in this paper have been primarily quantitative in nature, for the purpose of narrowing the list of potential KOLs to those best aligned with your objectives. However after identifying the most promising KOLs, you will need to ‘deep dive’ to carefully evaluate each before making contact. Therefore there must be a mechanism or service to provide detailed information on your potential KOLs such as contact information, education, affiliations, expertise, professional and agency-related activities, literature, news, meetings/symposium/associations, awards, grant history, clinical trial history and co-authorship (who has the KOL co-authored with and to what extent, to map influence).

Finally, but perhaps most importantly, the European Union, as well as some major countries, have privacy laws that forbid the compilation of databases of detailed information about individuals without their explicit consent. Therefore you will have to seek permission from each individual KOL to store or access his detailed information. This may be an obstacle. A pharmaceutical manufacturer may not want to approach a KOL directly to obtain consent for a variety of reasons, including the fact that the KOL may be enlisted by a competitor. Therefore it may be necessary to enlist a respected third party to perform this action.


CITATIONS1 Kashif Chaudhry and Anne Love, “Key Opinion Leaders Interactions with

Pharma.” PharmaExec.com, October 1, 20052 gardiner Harris and benedict Carey, “Researcher Fails to Reveal Full Drug

Pay.” The New York Times online, June 8, 2008: U.S.3 Armstrong, burton, “Spine surgeon received $19 million in payment over five

years from Medtronic.”, The Wall Street Journal, January 16, 20094 Cutting Edge Information (http://www.cuttingedgeinfo.com)5 David Pendlebury, Thomson Reuters Scientific, Research Services6 Sharon begley, “The Nobel Prizes: Place Your bets.”, Newsweek online,

October 3, 2008: Lab Notes7 J.E. Hirsch, University of California, San Diego, “Does the h index have

predictive power?”, Proceedings of the national Academy of Sciences of the United States of America, November 15, 2005

8 Matthew L. Wallace and Yves gingras, “Why it has become more difficult to predict Nobel Prize winners: a bibliometric analysis of Nominees and Winners of the Chemistry and Physics Prizes (1901-2007)”, Cornell University Library online, August 19, 2008: Physics > Physics and Society

9 guillermina girardi, Hospital for Special Surgery in New York, “Statins may help avoid some miscarriages”, Journal of Clinical Investigation as reported by United Press International online, October 13, 2008: Home / Health News

10 Lamberi et. al., “State of the Clinical Trials Industry.”, CenterWatch, 200711 Yves gingras et. al., “The Effects of Aging on Researchers’ Publication and

Citation Patterns”, University of Quebec, October, 200812 Falagas ME, Ierodiakonou v, Alexiou vg., “At what age do biomedical

scientists do their best work?”, Alfa Institute of biomedical Sciences, December, 2008

CONCLUSIONLife sciences organizations enlist KOLs for a variety of important purposes, including pre-clinical and clinical development, as well as marketing and education. KOL identification, selection and remuneration are subject to significant regulation and must be based on objective criteria. Citation and publication analysis, combined with patent and clinical trial information, is a proven way to not only provide the desired objectivity, but also target the KOLs best aligned with product strategy and geographies. A KOL identification and selection enabling system that is able to provide these metrics, the functionality to filter, weight and visualize this data, a method to obtain detailed KOL information, and a mechanism to ensure compliance with privacy laws, may be key to your company’s success.


NOTES

THOMSON PHARMA® KOLexpertsTHE AuTHORiTATivE, ObjEcTivE PHARMAcEuTicAL ExPERTS dATAbASE

A premier tool supporting the pharmaceutical, and biotechnology industry that gives users the ability to objectively identify, rank and verify KOLs and experts in the life sciences.

ImAgE CoPyRIgHT: CORbIS

SCIENTIFIC

AbOUT THOMSON REUTERS

Thomson Reuters is the world’s leading source of intelligent information for businesses and professionals. We combine industry expertise with innovative technology to deliver critical information to leading decision makers in the financial, legal, tax and accounting, scientific, healthcare and media markets, powered by the world’s most trusted news organization.

Our Scientific knowledge and information is essential for drug companies to discover new drugs and get them to market faster, for researchers to find relevant papers and know what’s newly published in their subject, and for businesses to optimize their intellectual property and find competitive intelligence.

AbOUT THOMSON PHARMA® KOLExPERTS

Thomson Pharma KOLexperts is based on the respected, objective scientific databases developed by Thomson Reuters, including the citation indexes first developed by Dr Eugene garfield in 1955 and now accepted as one of the major standards of scientific ranking. The content is expertly indexed, editorially-maintained, and fully up-to-date.

NOTE TO PRESS:

To request further information or permission to reproduce content from this white paper, please contact:

Susan besawPhone: +1215 823 1840Email: [email protected]

To find out more information on Thomson Pharma KOLexperts please visit thomsonreuters.com/products_services/scientific/kolexperts

Copyright © 2009 Thomson Reuters

Scientific Regional Head offices

Americas Philadelphia +1 800 336 4474 +1 215 386 0100

Europe, Middle East and Africa London +44 20 7433 4000

Asia Pacific Singapore +65 6411 6888Tokyo +81 3 5218 6500

For a complete listing of Scientific offices, visit: scientific.thomsonreuters.com/contact

PH0811238 C

KOL MANAGEMENT

Documents

Transcript of KOL MANAGEMENT