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WHITE PAPER KEY OPINION LEADER IDENTIFICATION AND SELECTION.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.SCIENTIFIC
INTRODUCTION Pharmaceutical, 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. 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@example.comPHARMA MATTERS | WHITE PAPER
REgULATORY ASPECTSThe KOL / industry relationship has always been fraught withethical pitfalls. In June 2008 a Congressional investigationrevealed that a Harvard child psychiatrist, whose research wasinfluential in growing the market for antipsychotic pediatricdrugs, earned $1.6 million in consulting fees from multipledrug companies from 2000 to 2007. Much of this income wasnot reported to university officials2. In January 2009 a similarCongressional investigation revealed that a prominent spinesurgeon, whose research was influential in promoting spinalproducts, received over $19 million in payments from a largemedical device company. Once again, much of this income wasnot reported to university officials3. These and other incidents havedriven a push for transparency into the KOL / industry relationship.The bar for KOL / industry regulation was set in April 2003 bythe US Department of Health and Human Services, Office of theInspector general (OIg). The OIg issued its compliance guidancefor pharmaceutical manufacturers stating that “Payments forresearch services [provided by KOLs] should be fair market valuefor legitimate, reasonable, and necessary services.” Five yearsafterwards, 92% of drug makers surveyed said the guidelines“significantly impacted” the structure of their medical affairsteams. For instance, many shifted medical science liaisons andthought-leader development teams away from commercialdevelopment. Meanwhile, 8% of drug makers surveyed indicatedthe guidelines caused a complete overhaul4.Regulatory bodies are not the only ones seeking greaterclarity into the KOL / industry relationship. The Associationof the british Pharmaceutical Industry (AbPI) introduced newrevisions to their code of practice, which must be implementedby 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 theirown guidelines. Notably, the Pharmaceutical Research andManufacturers of America (PhRMA) recently updated its Codeof Interactions with Healthcare Professionals; the updatestake effect in 2009. Section 6, which covers the use of KOLsas consultants, states: “Decisions regarding the selection [ofKOLs] as consultants should be made based on defined criteriasuch as general medical expertise and reputation, or knowledgeand experience regarding a particular therapeutic area” and “thecriteria for selecting consultants are directly related to the identifiedpurpose and the persons responsible for selecting the consultantshave the expertise necessary to evaluate whether the particularhealthcare professionals meet those criteria.” These guidancesare repeated in section 7 for the use of KOLs as speakers.Additionally, sections 6 and 7 provide that payments are fairmarket value, in line with the OIg’s and the AbPI’s guidances. THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE
THOMSON both regulatory bodies and industry associations areREUTERS HISTORY approaching the same conclusions: KOL selection andOF PREDICTINg remuneration must be based on objective criteria includingNObEL PRIZE medical expertise and reputation. Subjective measures, such asWINNERS “just knowing” whom the experts are in a therapeutic area, growSince 1989, increasingly dangerous.Thomson Reutershas developed a list PRIMARY MEANS OF DETERMININg KOL RELEvANCEof likely winners inmedicine, chemistry, Determining the expertise, reputation and influence of aphysics, and scientific expert is easier said than done. The most famouseconomics. Those attempt to identify the top three KOLs in the tremendously broadchosen are namedThomson Reuters scientific fields of medicine, chemistry, physics and economics isScientific Laureates Thomson Reuters’ well-publicized annual prediction of Nobel- in recognition Prize winners. To understand the complexity involved, it isof the significant important to note that there are literally millions of scientistscontribution their publishing. Narrowing the field to the highest elite still leavescitations make to thenavigation within the at least 1,000 scientists5. Newsweek noted that “sinceISI Web of Science®. Thomson Reuters started making predictions in 1989, there wereFor more only two years—1993 and 1996—when they failed to correctlyinformation on predict at least one winner, and in some years they nailed two”6.Thomson Reuters In 2008 the Nobel Prize recipients for medicine and chemistry2008 Nobel Prize were correctly predicted, while the recipient for economics waspredictions, please one of those nominated by Thomson Reuters for the 2006 Nobelvisit scientific.thomsonreuters. Prize. The reason that these predictions are so widely reportedcom/nobel 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,PHARMA MATTERS | WHITE PAPER
Hirsh suggests that the H-index can be used more accuratelyin journal publication-oriented sciences such as biology thanbook publication-oriented sciences such as social science. Hirshdeveloped and tested the H-index using the Thomson ReutersISI Web of Science publication database, showing a high level ofcorrelation between a high H-index and scientists inducted intothe US National Academy of Sciences, and Nobel Prize awardees7.As medicine grows ever more specialized, it is often desirable toseek KOLs for granular therapeutic areas or indications. For drugdevelopment and marketing, for instance, it is more likely thatKOLs specializing in non small cell lung cancer are sought thanKOLs specializing in cancer in general. However the examplesabove of Nobel Prize prediction and the H-index address broadscientific fields. Can citation analysis work for the more specificneeds of the life sciences industry? Matthew Wallace, a professorat the University of Ottawa, and Yves gingras, a professor atthe University of Quebec, did their own study. They found thatcitation analysis, notwithstanding Thomson Reuters’ Nobel Prizeprediction track record, was more difficult to do in broad fieldsdue to lower citation count correlation. They asserted that “Thiscan be explained not only by the growing size and fragmentationof the… disciplines, but also… by an implicit hierarchy in themost legitimate topics within the disciplines”8. In other words, bynarrowing the fields (i.e. therapeutic areas) searched, especiallywhen the fields are hierarchically organized, it should be possibleto achieve better levels of accuracy for scientific expert selection.Citation analysis, such as a KOL’s total number of citations andaverage number of citations per publication, is a useful indicator of theKOL’s peer esteem, influence, productivity, credibility and expertise.However citation analysis is not a silver bullet. Other factors need to beconsidered to ensure optimal KOL / industry alignment. METHODS FOR IDENTIFYINg KOLs TO SUIT YOUR bUSINESS STRATEgYThere are many decisions to make to ensure KOL selectionoptimizes your business strategy.THE MARKETFirstly, consider the market. Are you creating a market, enteringor increasing share of voice in an established market, or creatingbridges between related markets? Creation of a genuinely newmarket is admittedly uncommon; however one need look onlyto recent times to find an example in Restless Leg Syndrome.Restless leg syndrome (RLS) is a neurological condition that ischaracterized by the irresistible urge to move the legs. Requip(Ropinirole), manufactured by glaxoSmithKline, was approved bythe FDA in 2005 for treatment of RLS. This was accompanied byextensive disease awareness campaign in the US. KOL selectionin new markets should be driven by publication prolificness. New THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE
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 ALIgNMENT Secondly, 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.PHARMA MATTERS | WHITE PAPER
When moving forward with later-stage clinical trials, investigatorselection requires a twofold approach that considers bothrecruitment and maximizing the impact of outcomes. This maycall for a combination of rising stars and established leaders,with the latter often fulfilling a study chair role. It is interesting tonote that according to CDER10, the average age of investigatorsreceiving NIH grants in 2004 was about 42, which represents anage increase of a few years compared to the 1980s. This suggeststhat the NIH increasingly tends to favor scientists who are towardthe beginning of their careers but also have 10 – 12 years ofexperience 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 inthe following paragraphs). The ability to function as an effectiveinvestigator cannot be determined from citation analysis aloneof course; clinical trial experience must be taken into account aswell. As a point of interest, according to a 2005 survey of 7,342doctors by CenterWatch10, 54% had participated in 1 – 3 trials.Product advocacy efforts can benefit from established KOLs bythe leader’s influence and broadly-reaching credibility. This canallow for tactical benefits in regulatory clearance activities ormarket penetration. However this can also have its shortcomings.Established leaders are well known and there are manycompanies ‘knocking at their doors’. The lead time to engage anestablished KOL may be 9 months or more. Use of establishedKOLs may also tend to be tactical in nature, again due to manysuitors. Additionally established leaders will command higherconsulting fees. In contrast, rising stars do not benefit from thevisibility and tenure of the established KOL. but besides therising star’s advantages of shorter or non-existent lead timesand lower fees, budding KOLs present the opportunity to buildstrategic lifetime relationships: KOLs who will grow in tandemwith the product. biomedical-focused bibliometric research,separately conducted by the University of Quebec11 and the AlfaInstitute of biomedical Sciences12, showed that scientific impactper publication is highest while scientists are in their early 30s.Rising stars that can be engaged as KOLs shortly after thisperiod may lead to significant value. Of course there is no reasonto enlist only established leaders or only rising stars. The optimalKOL 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 canbe tangibly demonstrated. Table 1 shows the top 10 scientistsby total publication count. Table 2 shows the top 10 scientists bytotal citation count. THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE
LAST FIRST RANK NAME 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 LAST FIRST RANK NAME 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.PHARMA MATTERS | WHITE PAPER
AvERAgE LAST FIRST CITATIONS / RANK NAME NAME PUbLICATIONS 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 starTable 3Data supplied from Thomson Pharma KOLexperts, a Thomson Reuters databaseHere we find the rising stars of RhA. It is highly unlikely thatthese KOLs could have been found by examination of totalpublication or citation count. In fact, WA is the only KOL in thistable from Table 2. Who are these KOLs that have had such animpact on the scientific community with an average of only 17publications? How can these individuals grow your RhA productin pre-clinical, clinical and post market?bEYOND CITATIONSAlthough citation analysis is a primary means of identifyingscientific experts and their alignments with your productstrategy, there are other important factors that must beconsidered to correctly analyze citations and find the KOLswith the necessary skill sets.Two of the concerns noted earlier in the report by Hirsh are ascientist’s having “high-performing coauthors or a lifetime ofmediocre work skewed by one or two highly cited papers.” Toaddress Hirsh’s first concern, it is possible to gain more clarity intothe role of the KOL with respect to the publication by whether theauthor is listed first or last. Traditionally authors who are listed lastare those who had a role in seeking the grant to fund the research,and/or were responsible for oversight. These individuals tend tobe established KOLs. In contrast, authors who are listed first tendto be those who performed the actual research. Weighting theposition of the author in the credits of the publication provides afair assessment of the KOL’s role in his publications. To addressHirsh’s second concern involves a simpler solution: in addition toaverage citation count per publication, also consider the mediancitation count per publication.Patent metrics may be important to gauge a KOL’s industryexperience. As with publications, the inventor’s position in thepatent credits traditionally points to his role.It was noted previously that if your goal is to find KOLs to designor execute clinical trials, clinical experience is of particularimportance. Metrics such as how many trials in what phase the THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS 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.PHARMA MATTERS | WHITE PAPER
SOLUTION FOR OPTIMAL KOL IDENTIFICATIONAND SELECTIONSimply put, an optimal KOL identification and selection solutionwould fulfill the requirements outlined in the previous section. Anideal 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 KOLUnfortunately the technology to achieve perfect authordisambiguation and hierarchically arranged therapeutic area,indication and drug terms by computer algorithm alone doesnot exist. Therefore the database would require some level ofmanual data assessment and maintenance.The metrics described in this paper have been primarilyquantitative in nature, for the purpose of narrowing the listof potential KOLs to those best aligned with your objectives.However after identifying the most promising KOLs, you will needto ‘deep dive’ to carefully evaluate each before making contact.Therefore there must be a mechanism or service to providedetailed information on your potential KOLs such as contactinformation, education, affiliations, expertise, professional andagency-related activities, literature, news, meetings/symposium/associations, awards, grant history, clinical trial history andco-authorship (who has the KOL co-authored with and to whatextent, to map influence).Finally, but perhaps most importantly, the European Union,as well as some major countries, have privacy laws that forbidthe compilation of databases of detailed information aboutindividuals without their explicit consent. Therefore you will haveto seek permission from each individual KOL to store or access hisdetailed information. This may be an obstacle. A pharmaceuticalmanufacturer may not want to approach a KOL directly to obtainconsent for a variety of reasons, including the fact that the KOLmay be enlisted by a competitor. Therefore it may be necessary toenlist a respected third party to perform this action. THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE
CONCLUSION Life 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. CITATIONS 1 Kashif Chaudhry and Anne Love, “Key Opinion Leaders Interactions with Pharma.” PharmaExec.com, October 1, 2005 2 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, 2009 4 Cutting Edge Information (http://www.cuttingedgeinfo.com) 5 David Pendlebury, Thomson Reuters Scientific, Research Services 6 Sharon begley, “The Nobel Prizes: Place Your bets.”, Newsweek online, October 3, 2008: Lab Notes 7 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, 2007 11 Yves gingras et. al., “The Effects of Aging on Researchers’ Publication and Citation Patterns”, University of Quebec, October, 2008 12 Falagas ME, Ierodiakonou v, Alexiou vg., “At what age do biomedical scientists do their best work?”, Alfa Institute of biomedical Sciences, December, 2008PHARMA MATTERS | WHITE PAPER
NOTES THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE
ImAgE CoPyRIgHT: CORbISTHOMSON PHARMA® KOLexpertsTHE AuTHORiTATivE, ObjEcTivEPHARMAcEuTicAL 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.SCIENTIFIC