Advancing Bioethics Performance Measurement and Public Reporting in the Pharmaceutical Industry: Evolving Metrics and Emerging Trends from the Good Pharma Scorecard

Transcript

• 00:00Good afternoon, and,
• 00:02welcome to everyone to our
• 00:06attention.
• 00:07This is Hi, Nice. I
• 00:10hate to interrupt these great
• 00:11conversations, but we have a
• 00:13great speaker. So we'll we'll
• 00:14get to it.
• 00:15Of course. So,
• 00:19welcome everyone to, our first
• 00:21research and progress presentation
• 00:24for twenty twenty six. It's
• 00:25great to see everyone here
• 00:26today, and it's great to
• 00:28have folks online as well.
• 00:31The CME code for today's
• 00:32meeting is five five six
• 00:35five nine
• 00:37five five six five nine.
• 00:39Just wanted to extend a
• 00:41congratulations to Gretchen Berlin who,
• 00:43presented her professorial,
• 00:46grand rounds this morning, at
• 00:48the department.
• 00:50It, was in recognition of
• 00:51our recent,
• 00:53promotion to professor. She just
• 00:55did a spectacular job, providing
• 00:57an overview of her
• 00:58career and many contributions,
• 01:01both here at Yale and
• 01:02nationally. So,
• 01:04just a great congratulations to
• 01:05her. If you missed it,
• 01:07there is a recording of
• 01:08it available on the department
• 01:09website.
• 01:13Okay. And,
• 01:15our weekly reminder about our
• 01:17retreats.
• 01:18Our next retreat is going
• 01:19to be February six, at
• 01:21the Yale West Campus, the
• 01:22professional development retreat.
• 01:24Abba Black has put together
• 01:26a wonderful program.
• 01:27She'll probably tell us more
• 01:29about it perhaps at next
• 01:30week's faculty meeting.
• 01:32And, it'd be great to
• 01:34see as many as many
• 01:35of you there as possible.
• 01:36It's always a wonderful
• 01:38experience that Abba puts together.
• 01:42Here's our weekly reminder, at
• 01:44least for this time of
• 01:45year, around the, FDAC,
• 01:48process.
• 01:49Again, we're at step one,
• 01:51which
• 01:52goes until February second. Now
• 01:54I heard from Vivian, I
• 01:55think, that so far,
• 01:57our response numbers are on
• 01:59the low side.
• 02:01But last year, we ended
• 02:02up with one hundred percent.
• 02:04I'm sure we'll get there,
• 02:06as well this year. So
• 02:09please don't forget to complete
• 02:10your FDAC documents and follow
• 02:12the steps
• 02:14on this slide.
• 02:16And then, next week, for
• 02:18our grand rounds,
• 02:20Ashley, Lussier
• 02:22is presenting on bronchiectasis,
• 02:24specifically
• 02:25when to think of it
• 02:26in primary care.
• 02:28And next Thursday at noon,
• 02:30we'll have our monthly section
• 02:32faculty and staff meeting, and
• 02:33I know there's a
• 02:35great agenda for that meeting
• 02:36as well. So,
• 02:37and we'll send out an
• 02:38announcement with that agenda early
• 02:40next week.
• 02:41Look forward to seeing everyone
• 02:42there.
• 02:44Here's our disclosure slide, and
• 02:46I have the privilege now
• 02:47of turning over the podium
• 02:49to your
• 02:50vice chief of research, Carrie
• 02:51Gross. Carrie.
• 02:56Thanks, Patrick, and happy New
• 02:57Year, everyone. And, yeah, again,
• 02:59I wanted to reiterate our,
• 03:01congratulations
• 03:02to Gretchen. It was a
• 03:03really an amazing grand rounds.
• 03:05And, actually, I feel like
• 03:06Jen Miller, our speaker today
• 03:08is,
• 03:10has followed footsteps that are
• 03:11illustrative of the importance of
• 03:13partnership and change from within
• 03:16as opposed to just changing
• 03:17from,
• 03:19from outside.
• 03:20Jen,
• 03:21I didn't realize you were
• 03:22a physics major undergrad.
• 03:25And then, after your under
• 03:27Jen Miller's
• 03:28undergraduate training, she proceeded to
• 03:31get graduate training in
• 03:35international health and business, but
• 03:36really then focusing more on
• 03:37bioethics where she went on
• 03:38to get her PhD in
• 03:40bioethics,
• 03:41from, let's get this name,
• 03:43the Pontypical University of Regina
• 03:46Apostolatorum,
• 03:47as well
• 03:48as post doctoral trainings,
• 03:50from both Duke
• 03:52and Harvard, and then was
• 03:53at NYU for quite some
• 03:55time. And we're very lucky
• 03:56to recruit her to Yale.
• 03:58So what I was alluding
• 03:59to when I was talking
• 04:00about Jen's skill and, in
• 04:03forging
• 04:04partnerships
• 04:05really focuses on this idea
• 04:07of, empiric bioethics to try
• 04:10to understand
• 04:11what different parties are doing
• 04:13with regard to the ethical
• 04:14conduct of research. And then
• 04:16unlike many of us in
• 04:17academia where we,
• 04:19kind of may focus on
• 04:21finding a problem, certain actors
• 04:23are not acting in a
• 04:25ethical or appropriate way. And,
• 04:28I think the tendency in
• 04:30academia is to focus on
• 04:31publishing the work. Like, Hey,
• 04:32look what we found. These
• 04:33guys aren't doing things right.
• 04:35The world should know about
• 04:36this and the world should
• 04:37just go, go fix it.
• 04:39So my job here is
• 04:40done. That's not at all
• 04:42the approach that Jen takes.
• 04:43She really forges partnerships with
• 04:45those who are engaging in
• 04:46and funding research
• 04:48and tries to work with
• 04:49them to find solutions
• 04:50to try to kind of
• 04:51cajole them,
• 04:53into not only partnering, but
• 04:55also moving in the right
• 04:56direction. I think it's really
• 04:57commendable and I'm really glad
• 04:59she's here to share,
• 05:00her prior work and and
• 05:02next steps. So
• 05:03without further ado, excited to
• 05:05introduce,
• 05:06associate professor Jen Miller to
• 05:08who is here to talk
• 05:09about advancing bioethics performance measurement
• 05:12and public reporting in the
• 05:14pharmaceutical
• 05:15industry, evolving metrics and emerging
• 05:17trends from the good pharma
• 05:18scorecard.
• 05:19Thank you, Jen.
• 05:32Thanks,
• 05:33doctor Gross, for that great
• 05:35introduction. It's really generous.
• 05:37And for the opportunity to
• 05:38present today
• 05:40on work to advance quality
• 05:41measures
• 05:43and benchmarks in the pharmaceutical
• 05:44industry focusing on their bioethics,
• 05:48performance
• 05:49through an initiative called the
• 05:50good pharma scorecard.
• 05:51But before I begin talking
• 05:53about the GoodPharma scorecard, I
• 05:54want to begin by acknowledging
• 05:56acknowledging and thanking the people
• 05:58in this room and on
• 06:00Zoom and beyond who have
• 06:01been so incredibly helpful and
• 06:03instrumental in the work that
• 06:04I'm gonna present,
• 06:05particularly Carrie,
• 06:07Gross and Joe Ross and
• 06:09Chris,
• 06:10Lee and Sakina and so
• 06:12many others.
• 06:14Hope you'll see your names
• 06:15on the screens, but I
• 06:15just also wanted to acknowledge
• 06:17you,
• 06:17before starting.
• 06:21I have, several disclosures
• 06:23which are on the slide.
• 06:26Okay. So for the there's
• 06:27a lot of new people
• 06:28in the room, so I'll
• 06:29begin at the beginning. What
• 06:30is the GoodPharma scorecard? So
• 06:32the scorecard is designed to
• 06:34be an index that rates
• 06:35and ranks pharmaceutical companies annually
• 06:38on their bioethics performance.
• 06:40We started developing the index
• 06:42in around two thousand five,
• 06:43two thousand six when I
• 06:45was finishing my master's,
• 06:46in bioethics and beginning
• 06:48my PhD program. And we
• 06:50did this for a few
• 06:51reasons, but chief of them
• 06:52was at the time the
• 06:53medical literature,
• 06:55the media coverage, and the
• 06:57court cases,
• 06:58covering the industry were incredibly
• 07:00negative, focusing on
• 07:03mostly the ethics challenges, failures,
• 07:06and outright scandals. And so
• 07:07you can think about concerns
• 07:08ranging from worries about companies'
• 07:11honesty and truth telling about
• 07:13the safety and efficacy about
• 07:14products
• 07:15to deceptive marketing practices
• 07:18and then
• 07:19outright price gouging
• 07:21on products.
• 07:23And so in some ways,
• 07:24you can think about all
• 07:25of these concerns as being
• 07:27summarized as worries that companies
• 07:29put
• 07:30profits before people.
• 07:32And so while the literature
• 07:34and the media and the
• 07:35court cases can be pretty
• 07:37good at bringing to light
• 07:38certain problems,
• 07:39they don't answer necessarily four
• 07:42critical questions.
• 07:44One, the prevalence of the
• 07:46problem.
• 07:47Now is the transgression
• 07:48the product of an outlier
• 07:51company in an otherwise sound
• 07:53industry, a rogue employee in
• 07:54an otherwise good company?
• 07:56Is it a rear view
• 07:57mirror problem? I get this
• 07:59all the time. You academics,
• 08:00you use old data. Your
• 08:02studies are outdated. If only
• 08:03you were, you know, doing
• 08:04the most cutting edge data,
• 08:05you'd see that we had
• 08:06fixed this problem.
• 08:08Or is it actually a
• 08:09rotten barrel? Right? Is the
• 08:11problem genuine, current, and widespread
• 08:13and in need of reform?
• 08:16If there is a rotten
• 08:18barrel, the question is how
• 08:19do you fix it?
• 08:24And then what do good
• 08:25practices look like? What's the
• 08:26upward counterfactual?
• 08:27We
• 08:28as Carrie mentioned, we focus
• 08:30so much on what the
• 08:31bad looks like. We often
• 08:33don't focus enough on defining
• 08:35and conceptualizing
• 08:36what the good best practices,
• 08:37can and should look like,
• 08:39and then how effective our
• 08:40policies or regulations and laws
• 08:42have been at curving,
• 08:43the ethical lapses. So these
• 08:45four questions
• 08:47are what,
• 08:49inspired the founding of the
• 08:50GoodPharma scorecard.
• 08:53So the scorecard aims to
• 08:55do five things. First, set
• 08:57and communicate
• 08:58clear ethics goals for the
• 09:00sector. So define what good
• 09:01can look like, and then
• 09:03translate those goals into
• 09:05measures and outcomes that are
• 09:07observable, comparable, and publicly accountable.
• 09:11And then use them to
• 09:12evaluate and track progress on
• 09:14our goals
• 09:15annually and over time
• 09:17to recognize
• 09:18where there are any best
• 09:19practices so we can study
• 09:21and figure out how they
• 09:22were done and replicated across
• 09:24the sector, but importantly and
• 09:27most importantly, to catalyze change,
• 09:29better behaviors were needed for
• 09:31patients.
• 09:34So so far, we have
• 09:35five scorecards,
• 09:37three launched and two under
• 09:39development.
• 09:41The first one is focused
• 09:43on transparency in clinical research,
• 09:45the second one on data
• 09:46sharing practices in research,
• 09:48the third on representation in
• 09:50clinical trial enrollment,
• 09:51the fourth one launching this
• 09:53year is on access to
• 09:54medicines in low middle income
• 09:55countries,
• 09:56And then the fifth one
• 09:57that's really in the beginnings
• 09:58of development is on patient
• 10:00centricity in pharma.
• 10:02So I'll briefly touch on,
• 10:03all five of these today.
• 10:07But before I begin, I
• 10:08wanna say, like, is any,
• 10:09do the pharma companies even
• 10:10care about this? Because we
• 10:11we could build this, and
• 10:12they may
• 10:13never look at it.
• 10:15But they actually do pay
• 10:17very close attention to their
• 10:18scores.
• 10:19And so here you can
• 10:20see that it's in their
• 10:22annual reports,
• 10:25their CEO letters, their CSR,
• 10:28publications, their impact reporting, their
• 10:31ESG efforts,
• 10:32their SEC filings, which is
• 10:34really interesting,
• 10:38when they score well.
• 10:41And
• 10:42and importantly, we have been
• 10:43able to show progress, so
• 10:45upward movement on some key
• 10:46measures.
• 10:49Alright. So I'm gonna begin
• 10:50by going through the first
• 10:51two scorecards,
• 10:53the ones focused on transparency
• 10:55and data sharing in research.
• 10:57So in this set this
• 10:59part of the talk, I'll
• 11:00just go through four things,
• 11:01why we tackled this issue
• 11:03with the scorecard. It was
• 11:04the debut topic. The metrics
• 11:06we use to benchmark and
• 11:07track transparency over time, the
• 11:10trends we've been seeing over
• 11:11the last ten years in
• 11:12transparency, and then I'm gonna
• 11:14debut,
• 11:15the forthcoming rankings,
• 11:17which, I haven't sent to
• 11:18any of the coauthors yet.
• 11:20So all the errors are
• 11:20mine.
• 11:22So the reason we prioritized
• 11:24clinical trial transparency as the
• 11:26debut issue for the scorecard
• 11:28was that, at the time,
• 11:29there was pretty robust substantial
• 11:31evidence suggesting,
• 11:33a pervasive
• 11:34transparency problem. And here's just
• 11:36one paper,
• 11:37published by Monique Anderson, and
• 11:39you've probably all heard of
• 11:41Rob Califf, a former head
• 11:42of the FDA, showing that
• 11:44only twenty percent of clinical
• 11:46trials registered in clinical trials
• 11:47dot gov had public results,
• 11:50a year after their primary
• 11:52completion date. And even if
• 11:53you looked at, you know,
• 11:54sixty months later, years later,
• 11:56you could only find about
• 11:57half of the clinical trials,
• 11:59having public results in clinical
• 12:01trials dot gov.
• 12:06And so
• 12:08our group kinda stepped back
• 12:09and said, well, is this
• 12:10a problem? Well, yes. It's
• 12:11a it's a very big
• 12:12problem.
• 12:14We need that data for
• 12:16the quality of our medical
• 12:17evidence and patient care for
• 12:18innovation so we can learn
• 12:20and build upon the lessons
• 12:21of the scientists that go
• 12:22before us. But as an
• 12:23ethicist,
• 12:25it's also transparency is critical
• 12:27for honoring and protecting research
• 12:28participants.
• 12:29One of the key reasons
• 12:31that
• 12:32medical experimentation
• 12:33is justified is the potential
• 12:35to contribute to generalizable knowledge
• 12:37that can advance human health.
• 12:38So if you don't put
• 12:39the results in the public
• 12:41space, it's very hard to
• 12:42do that, and the whole
• 12:43ethics of the experiment comes
• 12:44into question.
• 12:48So what are we measuring
• 12:49in the transparency,
• 12:52scorecard? We're looking at
• 12:54five,
• 12:56sets of standards. One, we're
• 12:57looking to see whether clinical
• 12:58trials are registered
• 13:00in clinical trials dot gov,
• 13:03a registry maintained by the
• 13:04National Library of Medicine and
• 13:06at the NIH.
• 13:07We're looking to see if
• 13:08results are also reported in
• 13:10that same registry.
• 13:12We're looking to see if
• 13:13trials are published in the
• 13:14medical literature,
• 13:15and we're looking to see
• 13:16if, clinical trials comply with
• 13:18baseline
• 13:20US legal requirements for trial
• 13:21registration and results reporting.
• 13:24And then we're also evaluating
• 13:25companies' data sharing practices.
• 13:28The sample that we apply
• 13:30our metrics to are trials
• 13:32supporting FDA approval of novel
• 13:33drugs and novel biologics.
• 13:36And within those trials, we
• 13:37look at three samples. All
• 13:39of the trials supporting a
• 13:40product approval, so in an
• 13:41NDA or BLA,
• 13:43just the trials conducted in
• 13:44patients for the approved indication,
• 13:47and then a still narrower
• 13:48sample of just trials legally
• 13:50required to be registered and
• 13:51reported under US
• 13:55laws. In case anyone wants
• 13:57to know how we develop
• 13:58our metrics, we generally follow
• 14:00a four step process where
• 14:02we
• 14:03review the literature, we review
• 14:05the relevant guidelines in the
• 14:06area,
• 14:07We engage, stakeholders in consultations,
• 14:10focus groups, and interviews for
• 14:11their input, and we conduct
• 14:13bright spot analyses, which I'll
• 14:14talk about later when we
• 14:15can.
• 14:18So
• 14:19I'll go a bit through
• 14:20the development of the transparency,
• 14:23standards just so you can
• 14:24get a sense of how
• 14:25things are done.
• 14:26So in two thousand and
• 14:27fifteen, we published our first
• 14:29GoodPharma scorecard on clinical trial
• 14:31transparency, and we focused just
• 14:33on,
• 14:34novel drugs approved by the
• 14:35FDA in twenty twelve. And
• 14:37there, we looked at
• 14:41just the drugs sponsored by
• 14:42the twenty largest companies by
• 14:44their market caps. And we
• 14:45have only evaluated base looking
• 14:47at all the trials supporting
• 14:48product approval and just trials
• 14:50legally required to be,
• 14:52reported under US laws.
• 14:54And we only,
• 14:56were evaluating the drug. We
• 14:58didn't look at the company
• 14:59level yet.
• 15:00Then in twenty seventeen, we
• 15:02looked at twenty fourteen approvals,
• 15:03and we added a company
• 15:05ranking for the first time,
• 15:07mostly because companies
• 15:09were creating a badge and
• 15:11calling themselves ethical, and we
• 15:12never rolled anything up to
• 15:13the company level. So we
• 15:14we probably better look at
• 15:15that.
• 15:16And we added forty five
• 15:17different data sources. And this
• 15:19is because,
• 15:20companies were complaining after
• 15:25the entire globe, you would
• 15:26find that we scored a
• 15:27hundred percent.
• 15:29So we added forty five
• 15:30different clinical trial registries, and
• 15:32it was a complete
• 15:33waste of time. We only
• 15:34found one trial that was
• 15:36registered in a registry other
• 15:38than clinicaltrials dot gov, and
• 15:39it was one trial in
• 15:40a corporate registry. So we
• 15:42don't do that anymore.
• 15:45And then we added an
• 15:47analysis of just the trials
• 15:48conducted in patients for the
• 15:49approved indication.
• 15:51Then in twenty nineteen, we,
• 15:53added data sharing measures, and
• 15:55we added an amendment window
• 15:57where we said, look, companies,
• 15:59if you're really interested in
• 16:00changing, we'll give you thirty
• 16:01to sixty days, and you
• 16:02can improve your procedures, and
• 16:03we'll publish a pre and
• 16:04post score. And fifty percent
• 16:06of the low scoring large
• 16:07companies took us up on
• 16:08the amendment window.
• 16:10Jim, can you talk a
• 16:11little bit about this your
• 16:13communication and interaction with the
• 16:15companies? Because you're saying things
• 16:16like companies planting. Yeah.
• 16:18How did it get started
• 16:19that you actually ended up
• 16:21having a dialogue? Would you
• 16:23think
• 16:29Well, one was a, a
• 16:31conceptual commitment.
• 16:33So before taking the faculty
• 16:35position at NYU,
• 16:36I went down to study,
• 16:39regulatory governance with Ed Ballison,
• 16:40a historian at Duke, to
• 16:42try and see if you're
• 16:43gonna build a quasi
• 16:45public, quasi private governance system,
• 16:47what's the most effective model
• 16:48to do that? Is it
• 16:49a technocratic
• 16:51model, a bunch of experts
• 16:52in a room who come
• 16:53up with metrics?
• 16:55Is it some kind of
• 16:56multistakeholder
• 16:58engagement process? And it turns
• 17:00out, you know, looking through
• 17:01all these different models, that
• 17:03engagement of all affected parties
• 17:05is critical. So not just,
• 17:06you know, the ultimately, the
• 17:07patients, but the entity that
• 17:09you are hoping to change
• 17:11behavior. And so, conceptually,
• 17:14I was I was leaning
• 17:15towards engagement,
• 17:17and then
• 17:18they paid attention when the
• 17:19first scorecard came out.
• 17:23We started the dialogue. I
• 17:25don't know if that's a
• 17:25good enough answer. I have
• 17:26to think it through,
• 17:27but they're very engaged.
• 17:33I've been so busy bill
• 17:35building it. I haven't really
• 17:36asked, like, how did it
• 17:37get you know, how did
• 17:38it all work out?
• 17:41Alright. So in twenty twenty
• 17:42one, we added all sized
• 17:44companies, so not just the
• 17:46twenty largest by market cap,
• 17:47but the small and the
• 17:48medium sized companies, and we
• 17:49added biologics.
• 17:51In twenty twenty three, we
• 17:53added more metrics looking at
• 17:54representation and research. And then
• 17:56this year, we'll publish, the
• 17:57new scorecard.
• 18:01So how are we scoring
• 18:02today on
• 18:04clinical trial transparency?
• 18:07Well, for the first four
• 18:08scorecards
• 18:09that we did,
• 18:11the scores for the large
• 18:13companies were trending in the
• 18:14right direction,
• 18:16upwards.
• 18:18And,
• 18:19that was a statistically significant
• 18:21improvement every year.
• 18:24So with the new scorecard
• 18:26coming out, did this trend
• 18:28continue?
• 18:29So the new scorecard,
• 18:30looks at novel drugs and
• 18:32biologics approved by the FDA
• 18:34between twenty eighteen and twenty
• 18:35twenty one. So it's two
• 18:37hundred and eight drugs sponsored
• 18:38by a hundred and thirty
• 18:39six different organizations, a hundred
• 18:41and thirty five of which
• 18:42are industry,
• 18:43and sixty percent of which
• 18:44are based in the United
• 18:45States.
• 18:49For a variety of onco
• 18:50a variety of indications,
• 18:52the most common one being
• 18:53oncology,
• 18:55and they were approved based
• 18:56on around fourteen hundred trials,
• 19:00forty four percent of which
• 19:01were in patients for the
• 19:02approved indication,
• 19:04and thirty three percent are
• 19:05legally required to be,
• 19:07publicly reported under US laws,
• 19:10enrolling about three hundred and
• 19:11thirty thousand,
• 19:12patients around the world.
• 19:17So for the first time
• 19:18in the five cohorts
• 19:20of scorecards,
• 19:22large company
• 19:24median performance scores went down.
• 19:27However,
• 19:28it is not statistically significant.
• 19:33But
• 19:34if you match the companies
• 19:36across both the samples
• 19:39I'm laughing because Carrie asked
• 19:40if we did this in
• 19:41another paper.
• 19:42It is statistically significant, and
• 19:44you see that eleven
• 19:46of the large companies
• 19:48had,
• 19:50declines in scores, some substantial.
• 19:52Right there dropped twenty three
• 19:54points, Amgen twenty points,
• 19:57Novartis nineteen points, and the
• 19:59like.
• 20:03Now that's just the large
• 20:04companies. We actually look at
• 20:05two hundred and eight companies,
• 20:06and so here's how the
• 20:07full sample is scoring. So
• 20:09there's some good news. If
• 20:10you just look at trials
• 20:12conducted in patients for for
• 20:13the approved indication, you'll see
• 20:14that most trials are registered,
• 20:16ninety five percent, and most
• 20:17have publicly available results, eighty
• 20:19five percent. Seventy percent are
• 20:21reported in the regis in
• 20:22a registry,
• 20:23and sixty percent are published
• 20:25in the literature.
• 20:27But if you look at
• 20:28almost every other standard,
• 20:29there's
• 20:30substantial room for improvement.
• 20:32So only fifty percent of
• 20:33all trials supporting a product
• 20:35approval have public results,
• 20:37and only sixty two percent
• 20:39of applicable trials meet US
• 20:42baseline US legal requirements for
• 20:44transparency.
• 20:45And if you go up
• 20:46roll all this up to
• 20:47the company level, only twenty
• 20:49four percent of companies that
• 20:50we evaluated
• 20:51share the results of all
• 20:52of their clinical trials, and
• 20:54we're looking at six months
• 20:56post FDA approval of a
• 20:58product.
• 21:01And then if you look
• 21:02at Fadal Compliance, only thirty
• 21:04two percent of evaluated companies
• 21:06met baseline legal requirements for
• 21:08trial registration results reporting. So
• 21:10sixty eight percent of companies
• 21:11fail to meet baseline legal
• 21:13requirements.
• 21:14Just gonna leave that there
• 21:15for a second. That is
• 21:16a massive number.
• 21:20Jen, the
• 21:23what what changed
• 21:24between the over that one
• 21:26year? I mean, these are
• 21:28these are,
• 21:29scary numbers. But
• 21:31I don't know. So that's
• 21:33what we're going to start
• 21:34doing the qualitative research to
• 21:36to figure out. I know
• 21:37we stopped
• 21:39our benchmarking
• 21:40for a good five years
• 21:42because of resource constraints, and
• 21:43we were building new scorecards.
• 21:48I don't know. And some
• 21:49companies are voluntarily raising their
• 21:51own bars,
• 21:52returning plain language summaries to
• 21:53to patients, returning
• 21:55the
• 21:56data to trial participants so
• 21:58it can integrate into their
• 21:59electronic health records. So I'm
• 22:01not sure. So stay tuned
• 22:02till next year.
• 22:04It's a good plug. Thank
• 22:05you.
• 22:07And then so when we
• 22:09got,
• 22:09the companies
• 22:11together,
• 22:13we asked them, what do
• 22:13you think is going on?
• 22:14And they said, well, it's
• 22:15clearly the small companies. It's
• 22:16not the large companies' fault.
• 22:18But, really,
• 22:20the only the data sharing
• 22:22score is statistically different,
• 22:24by company size,
• 22:26not FEDAW compliance,
• 22:28or any of the other
• 22:29measures.
• 22:31So what do we look
• 22:32at for data sharing?
• 22:34We're looking at whether first
• 22:36companies have a policy committing
• 22:37to sharing, patient level data,
• 22:40the clinical study report, and
• 22:41the analyzable dataset supporting phase
• 22:43two and three trials for
• 22:44their product approvals.
• 22:46We're looking at whether they
• 22:47clearly explain how the data
• 22:49can be,
• 22:50requested,
• 22:51that they commit to sharing
• 22:52by six months after FDA
• 22:54approval of a product, and
• 22:55that they report the number
• 22:56of data requests they receive
• 22:58and whether they,
• 23:00approved or rejected the request.
• 23:04Jen, just real quick. These
• 23:05are only for FDA approved
• 23:06products. Isn't part of the
• 23:08approval also publish that phase
• 23:10two, phase three trial in
• 23:11their right? And it's usually
• 23:14The medical review has some
• 23:16information about a pivotal trial.
• 23:19Just not I. Not Definitely
• 23:21not the raw patient level
• 23:22data. Yeah.
• 23:27But I I like that
• 23:28you're saying, like, why can't
• 23:29we just ask everybody to
• 23:30go to the approval packages?
• 23:32That doesn't really happen, but
• 23:33in theory, some could get
• 23:34more information from them. We
• 23:36use them.
• 23:39So the takeaway from this
• 23:40side is that seventy percent
• 23:41of the companies we looked
• 23:42at
• 23:44don't even have a public
• 23:45policy committing
• 23:46to data sharing, right, which
• 23:48makes reproducibility
• 23:50a challenge or pulling data
• 23:51across trials for innovation.
• 23:54However, nineteen percent of companies
• 23:56were able to score a
• 23:57hundred percent on
• 23:58on all of our data
• 23:59sharing measures.
• 24:01And,
• 24:02eight companies scored
• 24:04a hundred percent across all
• 24:05of the transparency measures, the
• 24:07registration, results reporting, publication, legal
• 24:10compliance, and data sharing measures,
• 24:12suggesting it's possible to do
• 24:14this right.
• 24:15So now we're gonna go
• 24:16through and see
• 24:17how did they do it,
• 24:18especially the ones who have
• 24:19been consistent over time,
• 24:21why, and then understand why
• 24:23the the laggards are
• 24:25are either going down or
• 24:26or,
• 24:27not improving.
• 24:31So next steps, find out
• 24:32why transparency is decreasing.
• 24:36How are the bright spots,
• 24:38staying bright?
• 24:40Should we recalibrate,
• 24:42the transparency measures in some
• 24:44way? The expectations around transparency
• 24:47keep evolving, and we probably
• 24:48need to as well.
• 24:50Publish the scorecard results I
• 24:52just showed you and start
• 24:53gathering the data for the
• 24:54next,
• 24:55the next set of products.
• 25:00Alright. So now I'm gonna
• 25:01move on to the third
• 25:03scorecard. This one's focused on
• 25:04representation
• 25:05in clinical trial enrollment.
• 25:09I'll go over why representation
• 25:11is so important, what metrics
• 25:12we use to benchmark representation,
• 25:14the ten year performance trends,
• 25:16and then I'll preview the
• 25:17forthcoming
• 25:20scorecard.
• 25:22I think we all
• 25:24know that representation
• 25:25is a huge challenge in
• 25:27research,
• 25:28which is why we're tackling
• 25:29it. Women, older adults, racial
• 25:31and ethnic minoritized patients, and
• 25:32so many other groups are
• 25:34consistently underrepresented
• 25:35in research. Right? We tend
• 25:36to test new medicines
• 25:38on patient on healthy young
• 25:39white males that don't represent
• 25:41the patient population who ultimately,
• 25:44will use a product.
• 25:47Many of the foundational studies
• 25:48in this area have been
• 25:49done by our own faculty.
• 25:51In fact, this study dates
• 25:53back all the way to
• 25:54two thousand and four, and
• 25:55it was done by Carrie
• 25:56Gross. So thank you for
• 25:57pioneering in this field.
• 25:59But Joe Ross, Harlan Kremels,
• 26:01and so many others
• 26:03of us in this room
• 26:04have also, worked on this
• 26:05topic.
• 26:07Policy efforts to try and
• 26:09improve representation and research span
• 26:11at least forty years with
• 26:12limited impact
• 26:15and now with no to
• 26:16possibly even countervailing,
• 26:19federal support on the issue.
• 26:23We know why representation is
• 26:25important for the generalizability
• 26:26of results for everyone to
• 26:28have a fair opportunity to
• 26:30participate in research and for
• 26:31trust. Studies have shown that
• 26:33certain racial and ethnic minoritized
• 26:35patients, groups, and the clinicians
• 26:37who treat them are less
• 26:39likely to find trial evidence
• 26:41relevant for their care, are
• 26:43less likely to believe a
• 26:44drug will be effective for
• 26:45them, and are less likely
• 26:47to use a medicine when
• 26:48they're underrepresented in the dataset.
• 26:50Importantly, when the clinical trial
• 26:51is representative, the trust gap
• 26:53closes.
• 26:57So we followed our standard
• 26:59process for developing the metrics
• 27:00in this space.
• 27:02Interestingly, when you review the
• 27:03literature at the time and
• 27:04you review the guidelines,
• 27:06we found a lack of
• 27:07consensus on the best way
• 27:09to measure representation.
• 27:11Two key ways emerged, and
• 27:13the lack of consensus was
• 27:14highlighted by a NASEM report
• 27:16in twenty twenty two.
• 27:18And,
• 27:20we wrote in GEM Oncology
• 27:21about these two ways. The
• 27:23first one we called the
• 27:24country population based approach,
• 27:26which suggests that trial participant
• 27:29demographics
• 27:30should mirror the estimates of
• 27:31a country's population demographics. So
• 27:33for the US, that would
• 27:34mean always aiming to enroll
• 27:36six percent patients identifying as
• 27:38Asian, fourteen percent identifying as
• 27:39black, and the like,
• 27:41regardless of the condition you're
• 27:42studying, so a disease neutral
• 27:44approach.
• 27:46The other common approach is
• 27:48the what we call the
• 27:49condition based approach,
• 27:51which suggests that trial participant
• 27:53demographics should mirror those of
• 27:55the patient population with a
• 27:56targeted indication.
• 27:58These two approaches
• 28:00can yield wildly different enrollment
• 28:02goals, so it's really important
• 28:03for us to think through
• 28:04which one we were gonna
• 28:05use.
• 28:06And, ultimately, we settled on
• 28:08the condition based approach.
• 28:11So we're benchmarking
• 28:12three types of,
• 28:15three groups of measures. First,
• 28:16we're looking at transparency.
• 28:18Can we even tell the
• 28:20demographics of trial participants? The
• 28:22sex, the age, the race,
• 28:24ethnic identity.
• 28:25And we're looking in clinical
• 28:26trials dot gov and publications.
• 28:28Then we're looking at representation.
• 28:30Does the population,
• 28:32represent the patient population's demographics
• 28:34with the targeted indication aiming
• 28:35to represent at least eighty
• 28:37percent?
• 28:38And then fair inclusion is
• 28:39both looking at both transparency
• 28:41and representation.
• 28:43Looking at seven measures for
• 28:45transparency and five for representation.
• 28:50And so what did we
• 28:50find? We piloted,
• 28:52the metrics and published them
• 28:54in twenty twenty three in
• 28:55BMJ Medicine.
• 28:57This paper was led by
• 28:58Tanvi Varma. She was a
• 28:59med student here at the
• 29:00time, and now she's graduated.
• 29:06Oh, yes. Anne went on
• 29:07to make it onto the
• 29:08Forbes thirty under thirty list
• 29:09for her work with the
• 29:10GoodPharma scorecard. I'm really excited
• 29:11about that.
• 29:14She's amazing.
• 29:15So the pilot sample looked
• 29:17at the products approved by
• 29:18the FDA for an oncologic
• 29:20indication between twenty twelve and
• 29:22twenty seventeen. That was fifty
• 29:23nine novel cancer therapeutics
• 29:26sponsored by twenty five different
• 29:27companies
• 29:29for a variety of oncological
• 29:31indications based on sixty four
• 29:32pivotal trials enrolling about thirty
• 29:34thousand different patients around the
• 29:35world. It's a median of
• 29:36three hundred and twenty six
• 29:37participants per,
• 29:39pivotal trial.
• 29:42So what we found is
• 29:43while a hundred percent of
• 29:44trials reported the sex of
• 29:46participants, so we could tell
• 29:48the number of women or
• 29:49older, or men in,
• 29:51enrolled,
• 29:54we could not
• 29:55often tell the age of
• 29:57trial participants or the racial
• 29:58and ethnic identity of participants.
• 30:01Further, not only was it
• 30:02not transparent, it was not
• 30:04representative.
• 30:08And to some degree, representation
• 30:10of women also needs to
• 30:11be improved.
• 30:15Interestingly,
• 30:16one company did meet all
• 30:18of our measures, United Therapeutics.
• 30:20This company is a public
• 30:22benefit corporation.
• 30:24They were started in nineteen
• 30:26ninety six, and then in
• 30:26twenty twenty one changed their
• 30:28legal structure to be a
• 30:29PVC.
• 30:32So while no other company
• 30:33scored a hundred percent, some
• 30:35scored a hundred percent on
• 30:36different measures,
• 30:39and we introduced a rating
• 30:40for the first time. So
• 30:41there wasn't may not be
• 30:42a big difference between number
• 30:43one and number two, number
• 30:44three, number four. So we're
• 30:45grouping top twenty five percent
• 30:47above the median and then
• 30:48unrated as everyone below the
• 30:50median score.
• 30:53So we just, finished writing
• 30:55up the results,
• 30:56the first draft, for the
• 30:57forthcoming scorecard,
• 30:59and it looks at novel
• 31:01oncology products approved by the
• 31:03FDA between twenty eighteen and
• 31:04twenty twenty three. So it's
• 31:06seventy five,
• 31:07products
• 31:08sponsored by fifty two different
• 31:10companies
• 31:10based on eighty pivotal trials,
• 31:12enrolling about twenty thousand
• 31:14patients. And what are we
• 31:16finding? On the company level,
• 31:18you'll see that almost all
• 31:19the companies reported
• 31:20the sex of,
• 31:24all their pivotal trial participants.
• 31:27However, only eighty three percent
• 31:29reported the age of participants
• 31:30and seventy one percent the
• 31:32racial and ethnic identity of
• 31:33all pivotal trial participants.
• 31:35And then in terms of
• 31:36representation, you'll see that only
• 31:38seventy one percent of companies
• 31:39adequately represented women,
• 31:41forty two percent adequately represented
• 31:43older adults age sixty five
• 31:45and older,
• 31:46and only four percent adequately
• 31:47represented racial and ethnic minoritized
• 31:49patients.
• 31:52And then obviously goes down
• 31:53for fair inclusion with only
• 31:55two percent of companies. That's
• 31:56one company
• 31:57fairly including,
• 31:59all analyzed racial and ethnic
• 32:01minoritized patients, and that one
• 32:02company is Jazz Pharmaceuticals.
• 32:06And quick question. How do
• 32:07you think about international trials?
• 32:09So, like, say there's a
• 32:10pivotal trial and half of
• 32:13the participants were enrolled in
• 32:14the US and half of
• 32:16them were Yeah. Yeah.
• 32:18When you think about, like,
• 32:19the composition of race and
• 32:21ethnicity of people in that
• 32:23trial,
• 32:24how do you
• 32:26the US?
• 32:28As you know, Carrie, we
• 32:29use global enrollment.
• 32:31Yeah. So this is one
• 32:33of the pushbacks from
• 32:35companies. They'll say, well, if
• 32:36only you had looked at
• 32:37our US enrollment, you'd see
• 32:38that we are
• 32:39perfectly representative.
• 32:40But the median US enrollment
• 32:42is around eight
• 32:43patients per trial.
• 32:47So that's
• 32:49not even feasible to really
• 32:51do.
• 32:52So we use global enrollment
• 32:54to benchmark against US
• 32:56demographics.
• 32:58If you have a better
• 32:59way of doing this,
• 33:01let us
• 33:02know.
• 33:03I'm all ears.
• 33:10So
• 33:11so what how how do
• 33:13we think about those two
• 33:14scorecards?
• 33:15Are are,
• 33:18are things improving?
• 33:20And, yes, the good news
• 33:21is they are improving for
• 33:22transparency.
• 33:24For both older adults and,
• 33:27racial and ethnic minoritized patients,
• 33:28transparency reporting of, demographics has
• 33:31improved.
• 33:34However, nothing has improved for
• 33:35representation or fair inclusion.
• 33:38And so that's leading me
• 33:39to ask,
• 33:40what more can we do
• 33:42to try and improve representation?
• 33:45And so one idea, and
• 33:47I'm curious to hear what
• 33:48you think,
• 33:49is to build leading indicators
• 33:51into the scorecard.
• 33:53Right? We use what economists
• 33:54might call a lag indicator,
• 33:56an outcome measure. Did you
• 33:57or did you not enroll
• 33:58a representative sample in your
• 33:59clinical trial? But another type
• 34:01of measurement is a leading
• 34:03indicator that evaluates the steps
• 34:05put in place to achieve
• 34:06the outcome that we're trying
• 34:08to drive.
• 34:09So we've been exploring what
• 34:10types of leading indicators we
• 34:12might want to add to
• 34:13the scorecard, and we've been
• 34:15doing this by,
• 34:17conducting
• 34:18a review of prominent guidelines
• 34:21and
• 34:22a bright spot analysis.
• 34:25I'm gonna briefly go through
• 34:26what we're finding
• 34:28in in these analyses.
• 34:29So we published the the,
• 34:32guideline review in May of
• 34:33twenty twenty four in BMJ
• 34:35Medicine.
• 34:37We reviewed eight different guidelines,
• 34:40from the FDA, EMA, World
• 34:41Health Organization,
• 34:43different groups. And what we
• 34:44found is twelve
• 34:46recommended strategies for advancing representation.
• 34:51I'll just mention three. For
• 34:53example,
• 34:54broadening eligibility
• 34:55criteria. Right? So more trials,
• 34:57more patients can,
• 35:00can qualify for participation when
• 35:01appropriate,
• 35:02expanding site locations beyond
• 35:05the large academic medical centers
• 35:07that we traditionally use that
• 35:08are on the coasts
• 35:09to include
• 35:11community centers,
• 35:12educating communities and patients to
• 35:14increase awareness about trial opportunities.
• 35:21In the bright spot analysis,
• 35:23we
• 35:24went through our dataset to
• 35:25see if there were any
• 35:26bright spots. Was anyone getting
• 35:28this right? There were a
• 35:29few. And then we went
• 35:30and interviewed them to see
• 35:32how they thought
• 35:33but what were the factors
• 35:34driving their success.
• 35:37And we,
• 35:39published aim one,
• 35:41in May.
• 35:43So we looked at a
• 35:44hundred and eleven products
• 35:46sponsored by seventy companies
• 35:48and a hundred and twenty
• 35:49one pivotal trials, and we
• 35:51found thirty one bright spot
• 35:52trials,
• 35:54nineteen of which were sponsored
• 35:55by a large company.
• 35:57And so we interviewed thirteen
• 35:58teams from eight large companies.
• 36:00We hit saturation, so we
• 36:02stopped at, thirteen.
• 36:05And
• 36:07seven of the strategies that
• 36:08the bright spots are using
• 36:09are in the guidance already,
• 36:11although they added a lot
• 36:12more details to it. So,
• 36:13for example, instead of just
• 36:15saying use more diverse sites,
• 36:16they said exactly how they're
• 36:17doing that. They're leveraging, for
• 36:18example, an NCI
• 36:21designated cancer center that they're
• 36:23using. So instead of just
• 36:23coming to Yale,
• 36:25Smilo every time, they're gonna
• 36:26go to Bridgeport, right, in
• 36:27any of our affiliations.
• 36:30And they had a few
• 36:32other strategies. I'm only gonna
• 36:33mention three.
• 36:37These three strategies,
• 36:38were used by the company
• 36:39that sponsored the most bright
• 36:40spots.
• 36:42One, they had CEO
• 36:44commitment and involvement,
• 36:45so top down initiatives.
• 36:47They had a dashboard with
• 36:49real time performance measurements that
• 36:51elevated
• 36:52representation goals to the same
• 36:53level of the other corporate
• 36:54goals, like
• 36:56first patient enrolled into a
• 36:57trial, time to trial completion,
• 36:59time to regulatory submission of
• 37:00the product.
• 37:02And then they had awards
• 37:03and recognitions, including financial bonuses
• 37:06tied to representation
• 37:07goals.
• 37:10And, of course, visibility for
• 37:11the laggards across the entire
• 37:13company.
• 37:17So this the rest, if
• 37:19you wanna if you wanna
• 37:20see all the other bright
• 37:20spots, you have to read
• 37:21the paper, which we're submitting
• 37:22hopefully this week.
• 37:25And now I wanna move
• 37:25on to the fourth scorecard
• 37:27focused on access to medicines
• 37:28in low middle income countries.
• 37:32This scorecard was started based
• 37:34on an exploratory
• 37:35study that we published in
• 37:37twenty twenty one,
• 37:39where we asked two simple
• 37:40questions. One, where are drugs
• 37:42tested for FDA approval on
• 37:44the country level, And do
• 37:45those countries who participate in
• 37:47trials for FDA approvals get
• 37:49market access to those products?
• 37:51Does the company submit and
• 37:53receive marketing authorization to sell
• 37:55those products in those countries?
• 37:58The sample was products approved
• 38:00by the FDA, novel drugs
• 38:01in twenty twelve and twenty
• 38:02fourteen, just sponsored by large
• 38:04companies.
• 38:05So a small sample.
• 38:07It was thirty four,
• 38:09novel drugs
• 38:11approved based on eight hundred
• 38:12ninety eight trials. We looked
• 38:13at all the trials, not
• 38:14just pivotal.
• 38:16Each drug was tested in
• 38:17a median of twenty six
• 38:19different countries,
• 38:20twenty high income, six upper
• 38:22middle, and a median of
• 38:23one low middle income country.
• 38:25And what we found of
• 38:26the seventy countries participating in
• 38:28the clinical trial supporting FDA
• 38:30approval of those products, only
• 38:31seven percent got market access
• 38:33to the products they helped
• 38:35test within one year of
• 38:36FDA approval. And even if
• 38:37you looked at five years,
• 38:39seven years later, that number
• 38:40only went up to thirty
• 38:41one percent.
• 38:44And then, unsurprisingly, approvals were
• 38:46higher and faster in high
• 38:47income countries.
• 38:52Canada,
• 38:53most of Europe,
• 38:54full access or almost full
• 38:56access. In contrast, Africa,
• 38:58zero
• 38:59access to the products they
• 39:00help test.
• 39:03In ethics, this is arguably
• 39:05exploitation.
• 39:07Right?
• 39:08Research is not ordinarily supposed
• 39:10to be conducted in a
• 39:11population that doesn't stand to
• 39:13benefit
• 39:14from the intervention
• 39:15with access to the intervention.
• 39:18This is enshrined in the
• 39:19Helsinki declaration,
• 39:20in so many of our
• 39:21ethical guidelines.
• 39:23So when I presented this
• 39:25data at a very large
• 39:28conference with pharma companies,
• 39:30I was on a panel
• 39:31with a general counsel of
• 39:33major company, and she's she
• 39:34said,
• 39:36to review, we are a
• 39:37problem. You old you academics
• 39:39use old data. If only
• 39:40you looked at the new
• 39:40data, you'd see we fixed
• 39:41this problem.
• 39:43So we updated the study
• 39:44to see, is this still
• 39:45a problem?
• 39:48And the new study
• 39:51alright. Let me just take
• 39:52a minute and acknowledge Chris
• 39:53Lee, who's here, a med
• 39:54student who was the first
• 39:56author on this paper. Thank
• 39:57you, Chris, for all your
• 39:58hard work.
• 40:00So the updated
• 40:03study for this paper, which
• 40:04is Chris is leading, is
• 40:05looking at,
• 40:07novel drugs approved by the
• 40:08FDA between twenty fifteen and
• 40:09twenty eighteen.
• 40:11So it's, a hundred and
• 40:13seventy two drugs sponsored by
• 40:15seventy five different companies based
• 40:16on eight hundred and eighty
• 40:17five trials.
• 40:20So the first thing to
• 40:21see is that these hundred
• 40:23and seventy two products were
• 40:24tested in eighty nine different
• 40:25countries,
• 40:27A median of sixteen
• 40:29countries
• 40:30per drug,
• 40:31fifty five percent of which
• 40:32are high income, twenty six
• 40:34percent upper middle, thirteen percent
• 40:35lower middle, and six percent
• 40:37low income. So this is
• 40:38where trials
• 40:39take place or took place
• 40:40for this sample for FDA
• 40:42approvals. And so now the
• 40:43question is, do they get
• 40:44access, physical access?
• 40:47If you're in Canada or
• 40:48Western Europe, you're getting pretty
• 40:50high access, but not perfect.
• 40:52I didn't I thought I
• 40:53didn't expect
• 40:54people from Canada to get
• 40:55really upset about these numbers.
• 40:57I got a lot of
• 40:57interesting emails.
• 40:58They why isn't it a
• 41:00hundred percent?
• 41:02But then if you look,
• 41:03Africa is still at the
• 41:04bottom.
• 41:09And so is anything improving
• 41:11as suggested?
• 41:13Yes.
• 41:14For high income countries,
• 41:17but not for upper middle
• 41:18or lower middle.
• 41:20And, in fact, access is
• 41:21declining in several geographic regions,
• 41:24namely Asia and the Middle
• 41:26East.
• 41:29So now for this scorecard,
• 41:32we have a variety of
• 41:34step next steps. But one
• 41:36of them is that we're
• 41:36partnering with the Yale and
• 41:38the World Fund,
• 41:39to start the Yale Medicines
• 41:40Access Network, which we're calling
• 41:42Y Man.
• 41:43And that's with Jeremy,
• 41:45here. And I just wanna
• 41:46thank Jeremy for your participation.
• 41:47So Yale,
• 41:49in the world, they they
• 41:50called out for an application,
• 41:51and they got eighty,
• 41:53applicants. And we were one
• 41:54of the nine,
• 41:56awarded projects. So Jeremy Schwartz
• 41:58and I are now building
• 41:58out this this network,
• 42:01which you haven't seen this
• 42:03logo, but here you go.
• 42:06We can change it.
• 42:08Thank you, chat.
• 42:10I also
• 42:11chat doesn't confirm that this
• 42:13isn't somebody else's
• 42:15material, so please,
• 42:17everyone check the copyrights.
• 42:20So what are we trying
• 42:21to do with why, man?
• 42:24We're trying to partner the
• 42:27the bright spot countries, if
• 42:28there are any, with the
• 42:29low the the dark spots
• 42:30so that they can learn
• 42:31from each other and
• 42:33and maybe even negotiate together,
• 42:35to fix this problem.
• 42:36So first step is we
• 42:37need to identify the bright
• 42:39spot countries, and we're working
• 42:40on that,
• 42:41as well as, the lower
• 42:43performing ones. And then we're
• 42:44we're starting to host monthly
• 42:46meetings with the ministers of
• 42:47health and the,
• 42:49clinical trialists and the heads
• 42:50of the oncology departments in
• 42:52a selection of countries,
• 42:54to figure out what's working,
• 42:56but also the barriers.
• 43:00And, we're supposed to host
• 43:02a big meeting with everyone
• 43:03at Yale
• 43:04around May, and we're working
• 43:06on that too.
• 43:07We need help if anyone
• 43:08wants to get involved.
• 43:10So for year one for
• 43:11Y Men, we're gonna focus
• 43:13on Africa. And if you
• 43:14look at the appendix of
• 43:15the paper,
• 43:16that we just published, you'll
• 43:17see that we scored the
• 43:19countries
• 43:20and
• 43:22somewhat ranked them, but
• 43:25but within the regions.
• 43:27And you'll
• 43:28there may be some bright
• 43:29spots emerging,
• 43:31Ethiopia and Uganda, but the
• 43:33sample sizes are really small.
• 43:35So we're expanding our analysis,
• 43:38to to confirm which groups
• 43:40will be engaging first.
• 43:42And then,
• 43:44secondly,
• 43:46we have a grant to
• 43:47study
• 43:48not just whether countries are
• 43:49getting
• 43:51physical access to a product,
• 43:52but also affordability,
• 43:55supplies, distribution,
• 43:56you know, broader access issues,
• 43:58in five countries in Africa,
• 43:59Uganda, Zambia, Kenya, Rwanda, and
• 44:01Nigeria.
• 44:02This team includes Thomas Walter,
• 44:05who's also a med student
• 44:06here,
• 44:07at Yale. And for this,
• 44:08we're conducting a systematic review
• 44:10of the literature to understand
• 44:11barriers and facilitators for access
• 44:13to novel oncology products in
• 44:14these five countries. But, importantly,
• 44:16we're interviewing,
• 44:18on the ground, the clinicians,
• 44:22some patient groups, ministers of
• 44:24health again, and the heads
• 44:25of the oncology departments
• 44:26clinics.
• 44:31It's too early to share
• 44:33findings, and we don't have
• 44:35enough time. So
• 44:37that'll be next year. And
• 44:38then talking,
• 44:39moving off to the last
• 44:41scorecard, this one is just
• 44:43at the beginning. You see
• 44:44how long it takes to
• 44:45develop these things. This one
• 44:47is
• 44:48focused on the concept of
• 44:50patient centricity
• 44:52and developing
• 44:54quality measures that can be
• 44:55both implemented and,
• 44:57assessed.
• 44:59We're using our traditional mixed
• 45:00methods, our literature review. We're
• 45:02interviewing clinicians. Some of you
• 45:03in this room have participated
• 45:04in the interviews. Thank you.
• 45:06Patient interviews. And we're also
• 45:08interviewing investors,
• 45:10to see how they think
• 45:11about these things in case
• 45:12they're a lever for change.
• 45:15And then we're developing a
• 45:16framework, and then we'll engage,
• 45:18stakeholders for feedback in in
• 45:20the refinement process.
• 45:24I just wanna say that
• 45:26patient centricity is
• 45:28I'm not sure if it's
• 45:29a well developed construct, but
• 45:30it is a developed construct
• 45:32in medicine and in the
• 45:34context of the doctor patient
• 45:35relationship,
• 45:36arguably, right,
• 45:38having its
• 45:39its,
• 45:40roots in the Hippocratic oath.
• 45:42But then the term was
• 45:44coined in nineteen sixty nine
• 45:45by Ina
• 45:46Ballant. It, first time it
• 45:48appeared in literature, and she
• 45:49used the concept
• 45:51of patient centricity to call
• 45:52for a shift for clinicians
• 45:54from an illness oriented type
• 45:55of care to a patient
• 45:57centered care, which for her
• 45:58meant seeing the whole person
• 45:59and not treating
• 46:01a person as a a
• 46:02diseased organ.
• 46:05Then in two thousand, Mead
• 46:07and Bauer developed
• 46:09methods
• 46:10for they defined the concept
• 46:12and methods for assessing implementation.
• 46:15And then today, as of
• 46:16two thousand and one, patient
• 46:17centricity is considered one of
• 46:19six dimensions
• 46:20in health care quality.
• 46:23So aiming
• 46:24I'm not sure if we're
• 46:25aiming to do this, but
• 46:26I'm aiming to explore
• 46:27whether it's possible to do
• 46:29something similar in the pharmaceutical
• 46:31context.
• 46:34So we've
• 46:36finished extracting data from the
• 46:38literature,
• 46:39and I'm not gonna go
• 46:41through. I'm just gonna give
• 46:42you just a sense of
• 46:43where we are in this
• 46:44process.
• 46:45Patient centricity for pharma is
• 46:47a pretty new concept.
• 46:49Almost eighty percent of the
• 46:50articles are published in the
• 46:52last ten years on patient
• 46:53centricity.
• 46:55The term
• 46:57in the context of the
• 46:58pharma didn't exist until nineteen
• 46:59ninety three
• 47:00when David Forrester,
• 47:03wrote some guidelines
• 47:04for interactions between medical residents
• 47:07and
• 47:08industry. He was worried that
• 47:09the prescribing practices could be
• 47:11corrupted
• 47:12with,
• 47:13with, industry engagement.
• 47:16The literature is
• 47:19predominantly
• 47:20opinion based.
• 47:21It's not
• 47:22rigorous research,
• 47:24and it's driven by industry
• 47:26in terms of funding and
• 47:27authorship,
• 47:28which I found
• 47:31I don't know if I
• 47:31found it surprising, but it
• 47:32was interesting.
• 47:34And there's no agreement on
• 47:35how to define patient centricity,
• 47:37but everyone agrees it's really
• 47:38important to define.
• 47:41And there's only two definitions
• 47:43that surface more than once.
• 47:45One seems to think patient
• 47:47centricity in pharma is about
• 47:48a patient regulating the flow
• 47:49of information to and from
• 47:51them and being able to
• 47:52exercise choice consistent with their
• 47:54preferences, and another one links
• 47:56it with engaging patients
• 47:59to achieve a positive outcome
• 48:00for them.
• 48:02Not gonna dissect those for
• 48:03you. But this slide just
• 48:05says there's a wide scope
• 48:06of
• 48:08topics people think you should
• 48:09tackle if you're gonna tackle
• 48:10patient centricity in pharma.
• 48:14So that's a really
• 48:17shallow sort of horizontal view
• 48:19of the scorecards.
• 48:21Three launched,
• 48:23one coming out this year,
• 48:24and one very much under
• 48:25development.
• 48:26We've come a long way,
• 48:27yet there's so much more
• 48:28to do,
• 48:29to translate ethical requirements and
• 48:31commitments into measures and outcomes
• 48:33that are observable,
• 48:34comparable,
• 48:35and publicly accountable so we
• 48:37can improve things for patients.
• 48:38Thank you.
• 48:46Hey. Questions from the audience?
• 48:51Good job, Ken. Thank you,
• 48:52Adam. Who funds you to
• 48:54do all of this?
• 48:55Not enough people.
• 48:59So we've been doing this
• 49:00for ten years,
• 49:01and, originally,
• 49:02it was not funded. Then
• 49:04we had some grants from
• 49:06the Arnold Foundation for five
• 49:07years.
• 49:08Then we leveraged grants from
• 49:09the FDA, Oncology Center for
• 49:11Excellence.
• 49:12We've leveraged, like, other grants,
• 49:14the spot analyses.
• 49:17And we're launching a program
• 49:18with a membership
• 49:19where pharma companies could participate
• 49:21a little bit in some
• 49:22of this.
• 49:24That's controversial,
• 49:26and we've taken funding very
• 49:28small amounts from five companies.
• 49:30But it's less than ten
• 49:32percent of the overall funding
• 49:34and two individual donors.
• 49:38Good question.
• 49:40Yeah. Do you have you
• 49:41considered with your YMed approach
• 49:43expanding it beyond the oncology
• 49:45maps? Because I feel like
• 49:47a town founder is gonna
• 49:48be the resources
• 49:49to do that in the
• 49:50country. So some people
• 49:53like, other med medical devices
• 49:55that have been tested, like,
• 49:57vaccines, I think we talked
• 49:58about prep.
• 49:59Have you thought about expanding
• 50:01beyond the oncology formulary?
• 50:04Ideally, yes. But we're so
• 50:05in the beginning of the
• 50:07oncology.
• 50:08Jeremy, what do you think?
• 50:10Yeah. I mean, we
• 50:12oncology is a very,
• 50:14yeah, I think as Sarah's
• 50:15suggesting, it's a it's a
• 50:16challenging one to tackle because
• 50:18the resources
• 50:19surrounding
• 50:20the medicines themselves are so
• 50:22sparse and complicated. Mhmm. The
• 50:24testing and everything. Yeah.
• 50:29It's it's too late to
• 50:31pivot,
• 50:32but we can add.
• 50:35So maybe we can talk
• 50:35about that
• 50:37later.
• 50:39Yes. Yeah.
• 50:41What would you what in
• 50:42your mind would could or
• 50:44should pharma companies do to
• 50:45expand availability
• 50:47of treatments across the globe?
• 50:50Well, we're starting so humbly,
• 50:52right, with the most basic
• 50:53thing, which is if you're
• 50:55testing
• 50:55a first of all, you
• 50:56should be testing your products
• 50:58in hot spots, right, where
• 51:00the disease burden is high.
• 51:01And then you should absolutely,
• 51:02at a minimum, be submitting
• 51:03products for regulatory approval in
• 51:05those countries so that there's
• 51:06physical access. And that's the
• 51:08first problem we're starting we're
• 51:10trying to tackle. And then
• 51:11we're gonna tackle
• 51:12affordability,
• 51:14adequate supplies, and distribution from
• 51:16there.
• 51:17But we can't even tackle
• 51:18that given
• 51:21the challenges with the
• 51:22first problem.
• 51:24Yeah. I think with the
• 51:26low income,
• 51:28countries,
• 51:29have you looked at the
• 51:30issue of profit? Because Of
• 51:32what?
• 51:33Profit.
• 51:35Because in those countries, a
• 51:36lot of people can't afford
• 51:37the drugs at the
• 51:39going market rate, and they
• 51:41have to be significantly
• 51:43subsidized
• 51:43by either by the government.
• 51:45And health insurance is not
• 51:47as robust as in
• 51:49the high income countries. So
• 51:51maybe Yeah. That's exactly yeah.
• 51:53Exactly.
• 51:54That's the that's one of
• 51:55the exact questions on the
• 51:57interview guide and then trying
• 51:59to tease out if there's
• 52:00a subsidy needed exact you
• 52:02know, by who should be
• 52:03paying for it, how much
• 52:04does it need to be,
• 52:05is it a proportion, is
• 52:06it
• 52:08so if you want, I
• 52:09can show you what we're
• 52:10finding
• 52:11afterwards.
• 52:14But we're still awash in
• 52:15data. We haven't,
• 52:17you know, wrapped our heads
• 52:18around the next steps.
• 52:20Steve.
• 52:22Just thinking about transparency
• 52:24and representativeness.
• 52:30If the companies change their
• 52:31practices so that they all
• 52:33got really good scores around
• 52:35transparency and represent
• 52:39would you expect
• 52:41different
• 52:42or better
• 52:44jobs
• 52:45to be
• 52:46improved,
• 52:47and what's the grounds for
• 52:48that expectation?
• 52:50How do you define better?
• 52:53Well, one way to think
• 52:54about it might be better
• 52:55targeted,
• 52:58But,
• 53:00I'm I'm just wondering,
• 53:02I I I guess the
• 53:04the wonderment is most easy
• 53:05to express in terms of
• 53:07transparency.
• 53:09If all that stuff is
• 53:10not getting published or published
• 53:12Mhmm. Would that change what's
• 53:14getting approved? Mhmm. Would that
• 53:16change the warning labels? Would
• 53:17that result in drugs being
• 53:18better targeted or better marketed?
• 53:22In other words, you you
• 53:23want the transparency is wonderful
• 53:25from an ethical point of
• 53:26view, sort of on its
• 53:27own. It's a good in
• 53:28itself in some way. Mhmm.
• 53:29Like there to be payoff
• 53:30for patients at the end.
• 53:32You you'd like to think
• 53:33that with
• 53:34full information,
• 53:36it would inform
• 53:38prescribing practices. Right? And so
• 53:40you would think that if
• 53:41information
• 53:43that was negative, right, came
• 53:44to light,
• 53:45that that would inform I
• 53:46I I I would I'm
• 53:48an empiricist. I need to
• 53:49see I need to measure,
• 53:50right, the the the counterfactual.
• 53:52Like, if things were better,
• 53:53what would it look like?
• 53:54But I would suspect strongly
• 53:56that it would change practice.
• 53:57Right? And there are cases,
• 53:58and we're having that guest
• 53:59speaker,
• 54:01who wrote the book no
• 54:01more tears come,
• 54:03yes, come to the talk
• 54:04that the program for biomedical
• 54:05ethics is hosting in February.
• 54:07What's the date, Sarah? Are
• 54:08you
• 54:09The eighteenth, I believe?
• 54:12Thank you. Sarah's,
• 54:13also director of the program,
• 54:15and,
• 54:17there's an author. Gardner Harris
• 54:19wrote a book called No
• 54:20Martyrs, a big expose on
• 54:22j and j, and it's
• 54:23case after case after case
• 54:24of studies not being published,
• 54:27that showed ethic, safety challenges,
• 54:30for drugs. And after those
• 54:32studies came to light, practices
• 54:33did change. But those are
• 54:34an you know, those are
• 54:36multiple ends of ones.
• 54:38But I would suspect but
• 54:39I don't know. Right? What
• 54:41do you think?
• 54:43I I would suspect that
• 54:44it would change prescribing pracs.
• 54:45Yeah. Negative,
• 54:47or or Or full information.
• 54:49Yeah.
• 54:50Published just there are lots
• 54:51of studies that weren't published
• 54:52because they showed no
• 54:54result from the drug and
• 54:56so on. And if it
• 54:56turned out that they're cherry
• 54:57picking studies that have best
• 54:59results for their Mhmm.
• 55:00Some Mhmm. All that stuff
• 55:02came out, I would expect
• 55:03that to affect. Right.
• 55:05I was very surprised to
• 55:06hear that
• 55:07from you,
• 55:09that that
• 55:11lack of representativeness
• 55:12had,
• 55:14sort of payoff in trust
• 55:16at the level of the
• 55:16doctor patient. Mhmm. Mhmm. How
• 55:19how do they even know?
• 55:21I think that was a
• 55:22controlled experiment. Yeah.
• 55:24Yeah.
• 55:27Our hour is up. Thank
• 55:28you for, your questions. So
• 55:30there's a couple of questions
• 55:31in the chat, and I'll
• 55:32just encourage
• 55:33those,
• 55:35email you directly to those
• 55:36questions if that's okay. Great.
• 55:38Yes. That's okay. So we
• 55:39can do that. Thank
• 55:42you.