Objectively Identifying Hidden Gem KOLs

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
scientific.pharma@thomsonreuters.com

PHARMA MATTERS | WHITE PAPER

REgULATORY ASPECTS
The 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.

THE AUTHORITATIvE, ObJECTIvE PHARMACEUTICAL ExPERTS DATAbASE

THOMSON both regulatory bodies and industry associations are
REUTERS HISTORY approaching the same conclusions: KOL selection and
OF PREDICTINg remuneration must be based on objective criteria including
NObEL PRIZE medical expertise and reputation. Subjective measures, such as
WINNERS
“just knowing” whom the experts are in a therapeutic area, grow
Since 1989, increasingly dangerous.
Thomson Reuters
has developed a list PRIMARY MEANS OF DETERMININg KOL RELEvANCE
of likely winners in
medicine, chemistry, Determining the expertise, reputation and influence of a
physics, and scientific expert is easier said than done. The most famous
economics. Those attempt to identify the top three KOLs in the tremendously broad
chosen are named
Thomson Reuters
scientific fields of medicine, chemistry, physics and economics is
Scientific Laureates Thomson Reuters’ well-publicized annual prediction of Nobel
- in recognition Prize winners. To understand the complexity involved, it is
of the significant important to note that there are literally millions of scientists
contribution their publishing. Narrowing the field to the highest elite still leaves
citations make to the
navigation within the
at least 1,000 scientists5. Newsweek noted that “since
ISI Web of Science®. Thomson Reuters started making predictions in 1989, there were
For more
only two years—1993 and 1996—when they failed to correctly
information 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 chemistry
2008 Nobel Prize were correctly predicted, while the recipient for economics was
predictions, please one of those nominated by Thomson Reuters for the 2006 Nobel
visit scientific.
thomsonreuters.
Prize. The reason that these predictions are so widely reported
com/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,


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 MARKET
Firstly, 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 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.


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.


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.


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 star
Table 3
Data supplied from Thomson Pharma KOLexperts, a Thomson Reuters database

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 CITATIONS
Although 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


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.


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, 2008


NOTES


ImAgE CoPyRIgHT: CORbIS

THOMSON PHARMA® KOLexperts
THE 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.

SCIENTIFIC

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Objectively Identifying Hidden Gem KOLs

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