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Pharmaceutical Commerce - November 2021 Issue (PDF)

For all the good things AI, machine learning and big data have brought to the world of pharmaceuticals and the practice of healthcare, there is still a strong sentiment out there that these tools have yet to put a dent into the high rates of clinical trial failures. Documented for years, various numbers and odds have been cited. One that’s circulated of late finds that about 89% of novel drugs successfully advanced from mice-based studies fail in human clinical trials, with roughly half of the failures due to unanticipated human toxicity. 

Analogies attempting to put these numbers into perspective spring up on occasion, also in the context of the high cost of drug development, but this one I heard from Isaac Bentwich, a genomicist and serial AI life sciences entrepreneur, in a recent conversation kind of resonated: “Imagine a world where you’re in real estate, and you go to the best architect in the land, the best engineer in the land, and say, ‘I want you to build me a skyscraper,’ and they say, ‘we are the best in our trade, here’s what we’re going to do: we will help you build 10 skyscrapers, we will guarantee that nine will crumble, we don’t know which nine, and we will up the rent of the remaining building.'”

Bentwich, today founder and CEO of Quris Technologies, an AI platform company and the third tech outfit that he’s launched, acknowledges the dramatic effect, but says the mechanics are spot-on in describing the current state of drug development. Of course, risks and the unknown factor will likely always be inherent in clinical trials—failure is never too far away, with so many variables at play. But Bentwich and others believe the still-low drug success rates are due largely to the lack of efficient ways to predict which candidates will be effective in the human body. Not all biological data is created equal, the belief is, and according to some, current AI-pharma players still rely on traditional data points—limiting their value to the same endpoint.

That’s where this whole notion of next-gen AI enters the picture, in Quris’s case with what the company calls Bio-AI, a clinical prediction AI approach combining powerful deep learning with high throughput biological validation. Quris, focusing initially on rare genetic diseases that cannot be modeled in animals, works closely with “clinical trials on a chip”, a technology that emerged in academic, NIH and some industry research circles in recent years. Also called “patients on a chip,” it’s the idea of using miniature bioengineered models of human tissues and organ systems, along with nanosensors, to evaluate newly discovered drugs. Quris is developing a self-training AI platform to work with these devices, with the mission of better predicting clinical safety and efficacy for new drug candidates.

“So not in a sense that it replaces clinical trials but before going to clinical trials, you’re actually testing the drugs on a bunch of genomic variables,” Bentwich told me. “Take 1,000 drugs that have shown to be toxic, and take a thousand drugs that have shown to be safe and run each one of them on this platform of patient on a chip with the genomic variability. And then train the AI using this data. When you then run an unknown drug on the system, you ask the AI is this more like the safe ones that I’ve shown you or the unsafe ones?”

In securing access to thousands of patient genomes, Quris has an exclusive collaboration with The New York Stem Cell Foundation. The company is also working under the scientific guidance of industry visionaries such as Nobel Laureate Aaron Ciechanover and Robert S. Langer, co-founder of Moderna. Headquartered in Tel-Aviv and in the process of opening a lab in South Boston, Quris is preparing to launch a Phase I clinical trial next year for a drug using its Bio-AI prediction platform. The drug is for Fragile X syndrome, a trigger of autism and cognitive disability, and a notoriously difficult condition to treat.

“It’s not just sufficient to have powerful algorithms,” says Bentwich. “AI must have predictive value to be effective.”

Look for more of my conversation with Bentwich in a Q&A on our website in the coming weeks. Clinical trials on a chip is one example of emerging innovations in AI-powered data intelligence in pharma, some of which we explore in our 

. I remain impressed with efforts around natural language processing (NLP) to help characterize hard-to-diagnose diseases and improve screening rates for clinical trials.

And, as Ariel Katz, CEO of H1, notes in our “

, where he outlines the key trends that will shape medtech in 2022, wearables helped with the Covid-driven shift from in-person to in-home or remote clinical trial reporting, and enabled extended tracking of patients to generate more meaningful and longer-term data. Chances are good many of these new AI approaches will have equal staying power.

Michael Christel is Editorial Director of Pharmaceutical Commerce. He can be reached at

The promise of advanced AI in transforming the value picture in drug development

AI’s predictive power on the big stage

The biopharmaceutical and healthcare news of 2020 and much of 2021 has been dominated by Covid-19. The pandemic has been a driver and accelerator of dramatic changes that will shape the healthcare ecosystem in 2022 and beyond. The impact of Covid and other important trends and new legislation were widely discussed at this year’s MedTech Conference. Here are four that will have a major impact.

Patients demanded ownership of their healthcare data—and finally get it

In April 2021, the Interoperability, Information Blocking, and ONC Health IT Certification, went into effect. This rule requires that healthcare providers give patients access to their clinical information (e.g., consultation and treatment notes) without delay. By late 2022, those clinical notes must also be shared with patients’ health apps. While this might seem like a small change, it has a big impact on patients, who can now demand a copy of their healthcare record and are guaranteed to receive it in a timely manner.

In the past, it was the patient’s responsibility to ensure doctors had all their medical records. This put an enormous administrative burden on patients, who had to keep track of the information, chase down physical copies (e.g., x-ray images) and personally deliver them to healthcare providers (HCPs).

Apps can reduce this burden and the Cures Act now ensures that patients have unfettered access to their data and can easily share it with other HCPs (e.g., specialists, emergency doctors).

Patient data are better protected—and could start make patients money

The advantages and convenience of generating health data using electronic devices and health apps come at the price of privacy. While HIPAA privacy rules prohibit the disclosure of protected health information, it does not prohibit the commercialization of health data collected by fitness trackers, wearables and health apps. Currently, this data can be sold by the technology companies collecting it to health insurers, employers or third parties like Facebook without the consumer’s consent or knowledge.

At MedTech, Sen. Bill Cassidy (R-LA) discussed legislation that would enable patients to opt out of the selling/sharing of that data and not be left out of money shared during the exchange of their data. Similar legislation will give individuals control of the data they generate and usher in a wave of possibilities, not the least of which is the commoditization of that data.

Insurers have to provide accurate information about their physician network—or be fined

A recent study by The Centers for Medicare & Medicaid Services (CMS) found that almost 50% of providers listed in the directories of Medicare Advantage Organizations had at least one inaccuracy (e.g., wrong location or phone number) that can create barriers for members seeking services. As a consequence, CMS is now empowered to enforce hefty penalties for such errors, ensuring that patients experience fewer challenges in getting the care they need.

Clinical trials will be decentralized, longer—and more inclusive

Covid forced drug sponsors to change the way they undertake clinical studies; wearables helped with moving from in-person to in-home or remote reporting and also allowed extended tracking of patients to generate more meaningful and longer-term data. The trends toward decentralization of clinical trials and remote tracking are here to stay and will make it more feasible for underrepresented communities to enroll in clinical studies, adding much needed diversity.

Comprehensive databases support this trend by giving life science companies the tools they need to better plan clinical trials and expand their understanding of investigators and sites.

The common theme of these trends is easier access to more data will improve health outcomes for patients and help generate more meaningful data, but safeguards are necessary to protect patients’ sensitive health data.

Four trends shaping healthcare in 2022 and beyond

What’s Next for Med Tech?

Throughout the life of a clinical study, research associates, site coordinators, principal investigators and patients contend with an overwhelming volume of data: documentation, visits, calls, video meetings and emails. Executing, collecting, recording and filing trial documents may seem like a mundane part of the study process. However, the information within those documents can mean the difference between a trial that moves to the next phase and one that stalls out.

From interviewing and prescreening hundreds of potential patients to onboarding site staff, maintaining compliance and tracking patient progress, clinical trial teams face a host of competing priorities in need of their valuable time and skills. Neglecting any facet of the life of a study has consequences, none more so than the ability to recruit and retain the right patients to give a trial the best chance to succeed.

AI-powered support automation platforms offer solutions for patients and trial administrators, providing an interactive, informative interface, integrated cloud storage, intelligent document processing and a centralized knowledge base that keeps critical information accurate, accessible, and secure. Potential patients can learn about trials and start enrollment processes, while trial team members can complete patient selection, site compliance and trial analysis tasks more efficiently.

Improve the patient experience, starting with recruitment

Deloitte projects that all clinical trial stakeholders will move toward processes that center the patient experience in an effort to recruit and retain committed patients to support clinical trial goals and get more trials to the finish line. An AI-powered support automation platform can improve clinical trial recruitment and retainment with features that cater to potential patients while facilitating faster, more efficient recruitment selection for trial goals.

The recruitment process often starts with a website designed to give straightforward information to prospective patients who fit the criteria needed for a given trial. While a website with study information helps patients learn about the study itself, it doesn’t help research organizations glean any information about their prospective recruits. For that, companies often rely on site visitors to contact them via an email address on the website. Then, trial team members spend time reaching out to each prospect, responding to questions and interviewing the prospects, first to see if they qualify for the trial and then to learn whether they’re interested in participating and likely to stay with the trial. Since not all the interested candidates will qualify, and some will decide against participating, team members waste time and resources on those lengthy interviews.

By implementing an AI-powered conversational chatbot, organizations open up the line of communication on their recruitment websites. Prospective patients can ask questions about the study and get instant responses about a trial. The chatbot answers by leveraging an organization’s knowledge base, which is constantly updated to ensure accuracy. That means the chatbot gives the most current answer every time. If it can’t find an answer, it seamlessly escalates the request to a human expert who can answer a site visitor’s inquiry. The human expert’s responses are then added to the chatbot’s knowledge base, improving its abilities as it works.

While the chatbot informs site visitors, it also determines whether prospective patients fit trial criteria, saving team members hours of time and effort and providing them with a smaller pool of better choices for a trial. A chatbot learns about those prospective patients through guided conversations created and customized by trial administrators, giving the chatbot clear paths to follow as site visitors respond to its questions. An AI-powered chatbot can start the recruitment process, collect patient information and conduct prescreening questionnaires, making it easier for study coordinators to step in and complete the process.

Equip team members with actionable information, quickly

While much trial data is processed through electronic data capture, built-in system redundancies often mean humans are still manually sorting and moving records, leaving room for human error in e-filing and categorizing. And because each trial uses multiple clinical sites required to provide the same kinds of paperwork at regular intervals, the chances of sending documents to the wrong people or filing them in the wrong place are high. Too often, clinical trial documentation takes a slow journey from clinical site to research associate, finally landing on the desk (sometimes literally, as paper documents continue to plague the industry) of a trial assistant already working through an avalanche of papers.

Clinical research associates need documents to be timely, organized and accessible. That ideal scenario facilitates accurate reporting, follow up and next steps. Frictionless retrieval of accurate trial documentation, enabled by support automation, is essential to a clinical study’s compliance, safety and success.

The same chatbot that engages potential patients—answering questions and directing them to relevant information—also works for trial team members, mining an organization’s knowledge base to respond to inquiries about trial processes, contact information and site communication. Rather than searching for a specific document or data point in multiple places or interrupting a co-worker, trial administrators can ask the chatbot. It will guide them to the answer, whether it’s the expiration date on a medical license, an overview of a study population or a site coordinator’s phone number.

As part of an AI-powered support automation platform, the chatbot can also initiate customized workflows that use robotic processing automation (RPA) to streamline work processes. Workflows initiate a series of actions, ensuring the right team members, documents and processes are engaged—all while integrating with other systems to send emails, upload records or create action items. By integrating workflows, team members don’t have to think through the trajectory of a document each time they encounter a new one. They can work with a chatbot to follow a path that includes what needs to happen with a record, who should see it and what follow-up processes it triggers.

Intelligent document processing can function in tandem with workflows, allowing a trial team to create and follow templates for different kinds of documents, then batch upload trial records for processing. Machine learning classifies the documents against the customized templates, mines the data and pulls a human expert into the process to deal with any missing information. From there, trial teams can export the data, push the record onto its next step in a workflow or refine the template to capture information more precisely. Teams can verify the templates and provide feedback, so the system improves with use. The more documents are uploaded, the better the AI gets at sorting. And once records have been added, they become part of the knowledge base team members can access through the chatbot.

While a good support automation platform equips trial teams with the information they need, it must protect the security and privacy of the clinical work. Its deep knowledge base should have robust access controls that mirror the permissions trial team members have across applications to ensure the right people have access to the right information. That means if a team member asks the chatbot for information they can’t access, the chatbot won’t give it to them. In the same way, when a team member requests sensitive data, the system should provide it but keep the data private by not caching it in its server.

David Karandish is Founder and CEO of Capacity.

How AI-powered support automation can solve the data problem in clinical trial
management

Chatbot Potential in Clinical Research

A born Detroiter, Justin Hoss grew up with two loves (besides his sports teams): cars and his family’s mom-and-pop pharmacy in Hamtramck, MI, a little community surrounded by the Motor City. As the nature of most family-run businesses, starting at a young age, 10 or 11 as Hoss recalls, he began working at the pharmacy, offering “free elbow grease, as my mother used to call it.” But perhaps something else more lasting began to materialize on those days after school and on weekends sweeping the floors and helping count pills as a pseudo pharmacy tech. “It wasn’t just seeing my family filling scripts; I got used to understanding how things were ordered through distributors, all the software that was provided to local pharmacies, and then just built my passion for the life sciences and learning how drugs worked.”  

Though the seed was planted, years later, however, after graduating from the University of Michigan and starting his career in automotive consulting, Hoss faced an early crossroads moment: decide to stay on his current path or break into the healthcare space. He chose the latter; the rest is history. Today, Hoss is the national technology leader for life sciences at KPMG, where he is also a partner in its advisory practice with 25 years in business consulting and industry experience. “Healthcare doesn’t just stop at the healthcare provider,” says Hoss, who is based in North Chicago. “Healthcare is much more of a broader understanding of products and therapeutics in the pharmacy and how all that works within a value chain.”

 caught up with Hoss recently to discuss KPMG’s 2021 Global Pharma CEO Outlook survey—and the new trends gleaned from C-suite perspectives on cybersecurity and digital transformation in particular.

1. In your pharma CEO survey, results showed a 7% rise from last year and 9% from 2019 in CEOs reporting a “High” M&A appetite (see first chart below). What do you attribute for that increase, particularly amid the continued business impacts from the Covid-19 pandemic? Would you say the overall accelerated pace of digital adoption in pharma/healthcare is a major factor driving these decisions—whether confidence on the buyer side in their level of digital agility, or the growing appeal of digital health companies and medtech vendors as important acquisition targets?

I do think companies are a lot more confident, especially in the life sciences space, making what I would call more disruptive acquisitions. Many of these companies are flush with cash, so they’re always looking at what they can do from a portfolio standpoint, whether trimming portfolios or adding back. But what I think has changed recently, and you’re spot on, is there’s a lot more confidence in their digital agility, meaning being able to make quicker decisions using analytics on whether this is the right buy or not. It is more of a function of their business development groups really looking at the market, understanding where trends are happening and being a little bit more confident in leading with their own data and going, “okay, we should make this acquisition because it’s complementary to our portfolio or it adds additional digital services around it, brings us closer to the patient.”

I think that trend was happening pre-Covid; it just kind of got a push during the pandemic. But another aspect during Covid and in this kind of transitional Covid period is us as consumers talking [more] about life sciences. When was the last time you sat down with a friend of yours pre-Covid and talked about vaccine development? Those kinds of conversations are happening now at the dinner table. So as the public started to become more aware of life sciences, I think life sciences firms gathered a lot more confidence beyond just saying, “hey, I think this is going to be a strategic acquisition, a strategic add to our portfolio around a product or service”—but being a little more agile in their thinking and not overthinking some of these things.

2. Cybersecurity and data breaches have long been a concern of pharma companies, but with the emergence of digitalization and cloud computing, new concerns around cybercrime have been raised. In the KPMG survey, 57% of pharma CEOs claim their organizations are “well-prepared” for a future cyber attack, up 38% from last year; but, at the same time, only 11% say their companies are “very well-prepared,” the same figure as last year but down from 29% in 2019 (see second chart below). What dynamics need to change for CEOs to start feeling better about their organization’s ability to withstand and mitigate the effects of a cyber attack?

I’ve been in technology for 25 years and I don’t think you’re ever at the pinnacle, the North Star of being super prepared for anything that might happen from a cyber standpoint; it’s nice to be able to say that. I do think it’s become much more of a board-level discussion point, where in the past 10 years with some of the major breaches, it’s gotten a lot more visibility into not just the cyber aspect of it, but the trust aspect as well. If you think about healthcare and life sciences companies, we’re trusting them with a big part of who we are—our health records, what therapeutics we take, the things that keep us well.

From a confidence factor, I do believe that [pharma leaders] think that they’re well-prepared. But I also believe that there’s a move toward, it’s not just about cyber and the physical aspects of protecting your data and your IT systems, but there is also this overarching trust kind of view about, does a customer trust me, as a brand, to hold pretty sacred data about my health?

I think there’s a lot of, are we not only doing the right things physically to protect ourselves from cyber attacks, but also are we doing the right things to instill that trust with the consumer and the patient? Those are some of the reasons why that confidence factor has increased, because it’s not just about cyber, even though that’s what we always talk about.

An increasing board-level responsibility in understanding the reasons why cyber is important has been a positive factor of that. It’s not just a conversation that the technology groups are having with the CIO (chief information officer) and a couple key people on the audit committee. It’s much more of a broader board-level conversation. When more people understand why cyber’s important and more people are actually in the box around helping with the trust and the cyber factor, that confidence factor goes up. I think there’s a lot more positives just by the nature of how relevant and critical cyber has become as a board-level conversation; and not only at the board level, but the shareholders and stakeholders as well. It’s part of this overall ESG (environmental, social and governance) theme that’s going on.

3. There is the thought that the pandemic—and, for example, the rise in telehealth as the crisis took shape in 2020—created more of an opportunistic setting for cyber crimes. Do companies feel that there have been lessons that they’ve learned in data protection/compliance/governance while navigating Covid that will help them combat future cyber threats?

There was a lot of need for speed. But when you also have speed, sometimes you don’t have the security-by-design thought processes. But, honestly, if you start thinking about the pandemic specifically and people being opportunistic about telemedicine and teledocs and all that kind of stuff, there were some [threats] out there, but there wasn’t a ton. It does show me that leading into the pandemic, there was a lot of preparedness before it hit. But healthcare companies are among the No. 1 targets right now; it used to be mostly financial data organizations.

4. Your survey also touches on the concept of disruptive innovation, and, interestingly, weighs views of technology disruption as an opportunity or a threat, and if companies are being proactive disruptors themselves. Regarding the latter, 60% of CEOs, an increase of 18% from last year, agree that their organizations are actively disrupting the sector in which they operate, and only 8%, down from 20% in 2020, disagree. In that same vein, though a bit of a drop from last year but still significantly higher than in 2019, 59% of leaders agree that their companies see tech disruption as more an opportunity than a threat. What’s your take on these two trends and the factors behind them?

I think, finally, regardless of whether it’s in the CIO function or beyond the C-suite, it’s this commitment to embracing technology; they understand that technology is the strategy right now. If you’re truly talking about types of innovative business models, and changing the way the life sciences and pharma company value chain looks, you have to be able to harness the power of technology, not just for cost-efficiency purposes or the back-office function.

And so that means making these huge bets—not only with your own internal AI or data-analytics capabilities, and thinking about real-world evidence and doubling-down on that, but also thinking about innovative ecosystem partnerships where you can have these partnerships around technology that help you actually resolve a therapeutic state or some kind of challenge that you’re having. Organizations don’t mind that the technology discussion has left the CIO office and has become a value-chain conversation.

It’s funny, we did a previous study around digital, and [the sentiment] across time went from, we’re really big on digital in the life sciences to, “God, we hate digital, we got no value on it.” To now, it’s all about digital again. I think organizations finally discovered the true value because of the pandemic. It was the last push for those companies to get it—that digital transformation has little to do with the technology itself; it’s really about thinking about the value chain differently and how you actually interact with your end-customer. Life sciences firms are finally stopping the argument that it’s the payer that’s the customer, or it’s the distributor that’s the customer. No, it’s the actual patient that’s the customer.

So how do we push into that patient-care continuum and provide services around products that we already have in the market? What are those digital services that we can wrap around it? But in order to really do that, we have to rethink our value chain.

When companies first started talking about digital, it was much more functionally-lead. For example, digital transformation within finance or within commercial operations. Now, it’s really thinking about that whole value chain, and using digital technologies to do things like, how do I look at supply risk and can I do that in a digital twin modeling exercise, and predict where I’m going to have supply chain issues? Not just from a security standpoint, but what if another pandemic happens? Where am I going to have choke points? Where am I going to need to get different APIs in from different areas of the world? Where do I have to set up an API manufacturing plant to make sure I’m not dependent only on India and China? You can do that with digital technologies, you can model that without really messing with your supply network—and can try it out in real time.

All that stuff is being embraced because the C-suite is finally saying technology is our strategy—we are going to become tech firms in the future.

5. So as more pharma CEOs consider their companies digital disruptors, they must be seeing proof in action—these technologies paying off value-wise, correct?

Well, think about the Covid vaccine itself—the race for a vaccine we had. You can debate all you want, but the use of analytics, AI, machine learning, real-world evidence—and ultimately platform-based therapies, which a lot of the [new]vaccines are based off of—all that happened because of technology. We wouldn’t have been able to have something like 16 vaccine candidates in the course of eight months. Obviously there was a lot of money thrown at it and a lot of collaboration; I’m not saying it was technology only, but technology was one of the catalysts. It was like, wow, we can actually start seeing it come together.

6. Are these trends also illustrative of senior leaders, in integrating data science and big data into their decision-making, increasingly weighing competitor and customer perspectives?

People are understanding that in order to really be digitally-enabled, a lot of the stakeholders that are in this value chain—whether it be regulators, patient advocacy groups, or pharmacies—they all have to be part of the chain. Not that long ago, your front door to your healthcare was your primary care physician. Now your front door is virtual telemedicine, it’s a platform-based service that helps you with your diabetes care, for example; it’s the pharmacy where you can get your vaccines.

There’s multiple front doors to your healthcare now. What has changed is the demand for all these digital services from a consumer experience standpoint.

7. As you mention in a KPMG report on AI, 81% of life sciences executives want their company to be even more aggressive in adopting AI, and 93% say these tools have the potential to make their organizations more efficient. But you also bring up the disconnect at times in who is leading the AI charge inside these companies, citing a wall between what IT thinks is happening and the actual business execution on the clinical R&D and commercial sides with the technology. What can be done to bridge those internal gaps?

The disconnect, I think, is happening because of the nature of what IT historically used to be and where innovation actually happened within the life sciences or pharma company. If you think about drug discovery and R&D, and coming up with a new large or small molecule, that’s innovative, right? So that’s where the innovation used to happen, and IT was really a support-enablement function [to that]. I still think there’s some history that says IT is not doing much AI because you’re only seeing it from their four walls or what IT is responsible for, versus what’s happening in the drug discovery and R&D space of a large pharma company.

At the same time, I believe that a bridge is forming kind of naturally—that there will be a time when we’ll go to a pharma company and say, “where’s the IT department?” and you won’t be able to see it; it won’t be as tangible as it is today. It will be very blended and integrated with the business operations within the value chain. It’s not going to be this separate function; it’s going to be included with cloud technologies, automation, AI, all that kind of stuff. It’s going to be a part of the connective tissue of the value chain itself. That’s a natural evolution and I think that bridge is already being built. 

8. How much does the continued emergence and creation of new tech-focused C-suite positions at pharma companies, such as chief data officers, chief innovation officers, chief digital officers, etc., play into that equation?

It’s part of that. I’m a big believer in disrupting from within, otherwise you get disrupted from external forces and you’re on your heels trying to react to it. I wrote this article around five years ago that asked, “CIOs, are you ready to disrupt yourself?” Because chief information officer is not enough. If you’re just providing information and you’re expecting the database to take that information and create innovative products or provide insight, there’s no role for you in the future. Then, around the time that article came out, you started seeing CDOs, chief data officers; and new CTOs, chief technology officers. That trend is happening.

9. So you’re seeing adjustments being made not just fundamentally in IT function, but on the C-level operational side, as alluded to?

On the C-level side, it’s becoming so much more tech savvy. The business folks that are coming in with MBAs, they all know how to use technology. They start working in sales and marketing or commercial operations or quality control within a pharma company, and they’re already looking for innovative ways to resolve problems using technology. The whole federated technology model within life sciences firms is being bred based on the talent that’s coming into those organizations at a young age.

But because of that, they’re also competing for talent against Google, Amazon and Facebook, and the next innovative startup. So how do you compete with them in order to basically say, “you’re going to have a [leadership] path here?”

It’s not just a talent issue, it’s also the opportunity issue that they’ll be able to apply the digital tools that they’ve learned.

10. How do you view the potential of advanced analytics and data mining capabilities, including the use of predictive modeling, to generate reliable real-world data to really benefit patient and physician engagement—across all aspects of treatment and disease progression?

I am very bullish on use of AI, advanced analytics and predictive model, not only for patient/healthcare professional (HCP) engagement—a current example is the “check-in process” that the CDC is running when you get your Covid vaccine shots, but also in the use of current therapeutics for different therapies and disease states. We are seeing the “real-world” use of RWE/D in COPD (chronic obstructive pulmonary disease), RA (rheumatoid arthritis), diabetes, heart disease and other aliments in both patient engagement, but also identifying ‘tweaks” to either their wellness plans, type of therapy or amount/type of the drug/biologic.

What these firms’ data scientists are finding is small tweaks based on personalized medicine on the information gathered via RWD can go a long way to treating the patient as an individual vs. a patient population.

A conversation with Justin Hoss, technology leader for life sciences, KPMG

The Pulse of Digital in Pharma

The application of real-world data (RWD) for pharmaceutical brand marketing has evolved from the traditional model of executing on historical data, to AI-driven strategies that allow brand teams to execute on evidence-based insights, drawn from real-time data powered by predictive analytics at the point of care (POC). This gives pharma brand teams the ability to proactively alert physicians about relevant treatment information based on dynamic data about their patient’s journey.

As the healthcare industry continues its accelerated adoption of digital solutions —largely spurred on by the Covid-19 pandemic—improving patient care and adherence through technology must start with optimized insights at the POC. Reimagining RWD from how it’s been traditionally leveraged by pharma is key.

There is a vast opportunity for life sciences companies to use AI-driven solutions to bridge the gap from data to insights to execution, bringing everything together into one seamless closed loop. The application of predictive analytics, using machine learning (ML) methods applied to RWD, can speed time-to-therapy, and support positive outcomes by enabling life sciences to help healthcare professionals (HCPs) identify patients who may be qualified for specific therapies, by raising awareness of qualification parameters and patient access pathways, as well as identifying early indicators of non-adherence among patient populations in real-time. AI-driven solutions also enable personalized HCP engagement programs based on up-to-date demographics, disease and care milestones of their specific patient panels.

This kind of technology advances POC communication by layering AI-driven algorithms on top of real-world datasets to solve high-impact awareness, access and adherence challenges, optimizing the feedback loop between life sciences and providers, ultimately supporting better patient outcomes.


Digitizing access is the gateway to adherence

The quest to solve drug awareness, access and adherence challenges is less elusive now than it’s ever been, thanks to technology solutions at the POC. What’s new is how the life sciences industry and its partners are applying new technologies to proactively support and engage patients.

AI-driven predictive analytics can help pharma brands execute more intelligent digital commercialization strategies to simplify therapy initiation by presenting HCPs with a fully electronic option for enrollment, benefits verification, prior authorization and patient support onboarding. This proactive approach enables pharma manufacturers to support patients by removing an obstacle in their journey and helping them get started and stay on their doctor’s recommended course of therapy.

One example of how AI-driven RWD marketing tools at POC helps patients obtain the therapies they need is in the area of specialty medications. The increasing availability and use of these therapies has exposed unique barriers to prescription fulfillment processes and patient access. This has created complicated healthcare delivery workflows resulting in hurdles that impact a provider’s ability to optimally prescribe, and for patients to receive specific therapy. A recent physician survey by the American Medical Association

 found that prior authorization process delays have a significant effect on patient outcomes, with 90% of physicians surveyed reporting that the prior authorization process has somewhat or significant negative impact on clinical outcomes, and 30% reporting that this process has led to a serious adverse event for a patient in their care.

For conditions requiring specialty medication, AI-based solutions can reduce the provider burden and improve patient care. Predictive analytics simplifies the prescribing process and helps patients access the specialty drugs they need by addressing the enrollment and approval of specialty drugs at the earliest possible point in the prescribing process. It also ensures that key enrollment and affordability information is delivered based on the disease state or benefits profiles of each provider’s patient population in real-time.

A novel approach to identify real-time needs

For years, the life sciences industry has been executing on RWD insights months after the data is collected—in a retrospective, rather than prospective needs-based manner. Typically, RWD and predictive analytics are leveraged by pharma marketers to predict the influence of a physician on prescribing trends and anticipate future prescribing needs and volumes based on past physician behavior. Using this model, life sciences sales and marketing teams determine which HCPs may need what information about their treatments.

Evidence-based physician engagement uses a novel approach to understanding which HCPs need specific information, because it doesn’t rely on past physician behavior to predict future need. Instead, it uses patient characteristics like disease progression and coverage information to identify current needs among the patient populations being seen by specific HCPs. This can be seamlessly integrated at POC, including in the electronic health record (EHR).

The benefits of real-time, evidence-based engagement include:

Bridge the months-long time gap that manufacturers have historically faced between when data sets are created, and when they can execute on that data.

Enable pharma brand teams to employ a predictive, proactive approach to execution on data insights at POC, bringing it all into a seamless closed loop.

Facilitate contextual engagement between manufacturers and HCPs at the POC, which can speed patient access to therapy and support adherence that can have a positive impact on outcomes.

To truly capitalize on this untapped value in the short run, life sciences companies need to focus on identifying steel thread use-cases where value can be returned quickly on these projects. One real-world evidence (RWE) use case that is currently being utilized by a top pharma manufacturer is to provide visibility to doctors when Medicare patients’ treatment plans are at risk of lapsing due to loss of coverage. An AI-driven RWD engine is helping the manufacturer determine when to notify physicians that patients in their panel may be eligible for financial assistance. This helps ensure that patients who qualify for therapy can continue to follow their physician’s preferred treatment plan without interruption, due to new and unexpected out-of-pocket costs.

Leveraging AI-driven data throughout the patient care journey

The biggest untapped opportunity is the use of RWD and AI to reach providers and patients with more timely and relevant information at critical junctures throughout the patient care journey, at the POC and beyond. AI-directed, real-time HCP marketing raises awareness of treatment benefits to give patients a timely start on therapy while personalized digital patient support programs help patients stay on therapy. This allows the pharma industry to close the loop and interact with HCPs and patients by enabling care-focused engagement throughout the patient care journey. The ability to leverage actionable, AI-driven, integrated data at the POC and beyond is key to unlocking streamlined communication and processes around timely therapy initiation to reduce abandonment, and design personalized, successful adherence programs to help patients stay on their doctor-recommended course of therapy.

The future of AI-driven solutions for life sciences

As the healthcare industry continues trending toward interoperability in the coming years, being able to message HCPs and patients at various points throughout the care journey will become the standard. HCPs and patients will be inundated with messaging, and the value-driving lever will move from message volume to message quality—measured by relevance and specificity. That’s where RWD and AI will play a key role by enabling precision in messaging—identifying smaller subsets of HCPs and patients that are exactly the right audience for each brand, at exactly the right time for that message to affect behavior—supporting timely therapy initiation and driving adherence.

The real value brands can add to the patient’s care journey lies not just in knowing which HCPs to message, but also how those HCPs operate in their EHRs and when those physicians are seeing patients who qualify for therapy. AI will be used to glean insights beyond those visible solely in claims data. It will be combined with additional sources of data to compensate for the gaps in conventional RWD—overcoming the time-lag in most RWD datasets, and identifying patients who are eligible for therapy changes even before they show that tendency in their claims history.

In short, AI-driven solutions will change the way we think about the entire value proposition of life sciences messaging.

Mike Rousselle is Vice President of Data Product, OptimizeRx.

https://www.ama-assn.org/system/files/2021-04/prior-authorization-survey.pdf

AI-driven strategies for pharma brand marketing teams to help bridge
the gap from data intelligence to execution

RWD Benefits at the Point of Care

The ongoing evolution of computing and cloud capabilities, modeling and simulation methodologies and data analytics techniques continues to shape how the pharma/life sciences industry carries out all of its essential activities—from upstream drug development and regulatory approval to downstream commercialization and lifecycle-management efforts beyond patent expiry.

“By augmenting proprietary data with external sources of both real-world data (from payer claims databases, electronic health records [EHRs] systems and more) and aggregate data available in published clinical trial results, companies can dramatically increase their confidence in the most critical drug-development decisions and drive greater efficiency,” says Matt Zierhut, PhD, vice president, integrated drug development for Certara, a biosimulation software and technology company.

“Whether it be a subpopulation where there is believed to be a particular advantage for the treatment, or a related setting outside of the original target population where benefits have been observed, strategically tapping into available external sources may help drug sponsors to validate clinical beliefs that were initially based on anecdotal evidence before making big investments in a new direction,” adds Roman Casciano, senior vice president and head of Certara’s evidence and access consulting business.

The goal of data-analytics efforts is to not only improve the operational efficiency of all phases of the clinical trial, “but to increase the probability of high-validity clinical findings that can support product-development decision-making and define all of the most promising therapeutic indications and the right patient populations to target, and then optimize the protocols to improve the odds of success for that particular trial,” says Javier Jimenez, MD, PhD, executive VP, real-world evidence and late phase, Syneos Health, a contract research organization (CRO).

Another important use for advanced data analytics is to help ensure that clinical trials produce higher-validity, regulatory quality data, by sorting and classifying data-entry errors, outliers, inconsistencies and misreported adverse events that could undermine clinical development findings, notes Niranjan Kulkarni, PhD, senior director, consulting services for CRB, an integrated solutions provider to advanced technology industries, specializing in the life sciences markets.

A key challenge for high-cost prescription therapies has always been how to translate what happened in the trial to the actual outcomes that occur once the therapy becomes available, in order to justify its price. “Today, stakeholders no longer need to just model various scenarios hypothetically—they can actually track the product’s clinical results in real-world use and conduct studies to validate more widespread clinical outcomes over time,” says Jimenez.

Such studies can demonstrate how the product performs on its own, how it performs in comparison with competing products in the same therapeutic space and how it performs in patients who are decidedly different from those enrolled in the underlying trial. 

Similarly, by identifying patterns over time in actual prescribing and related healthcare data (particularly with regard to off-label use among real-world patients), biopharma manufacturers can sometimes identify promising—previously unknown—uses for the medication. When such additional indications can gain regulatory approval, the effort helps to provide additional clinical benefit for patients and ongoing revenue to the company. In September, FDA released draft guidance

 that describes how real-world evidence (RWE) can support label-expansion discussions.

Today, there is growing collaboration among the traditional epidemiology community, data scientists, drugmakers and the regulatory community, who are working to develop methodologies that reduce bias in findings derived from data-analytics effort, says Jimenez, who adds: “The goal is to be able to design and carry out targeted studies involving real-world data (RWD) that are able to replicate the findings in a clinical trial, and to be assured that the data-driven clinical findings are real—not biased perhaps because of how the population was selected or due to other confounding factors.”

Improving clinical trial design and recruitment

The randomized clinical trial (RCT) process—as valuable as it is—remains inherently flawed, mainly due to the limited overall scope of any trial, in terms of the number and type of patients involved, trial duration and the clinical endpoints studied. “RWD studies can be very helpful in contextualizing results from clinical trials, especially where the trial population does not adequately reflect the diversity of real-world patients taking the medication,” says Aniketh Talwai, delivery lead for Medidata Acorn AI. Recent work by that company with clients in the cardiovascular space to determine the real-world, age-stratified risk levels for some common agents, “found that they can vary quite a bit from the results published for the trial population,” he adds.

“Today, RCTs, coupled with advances in computing power, accessibility and recording of additional data modalities via medical imaging and EHRs that are available for trial participants, and innovations in statistics, machine learning (ML), and big data analytics can help generate a completely new environment for drug exploration and validation,” adds Yvonne Duckworth, PE, associate, senior lead automation engineer, Pharma 4.0 SME for CRB. Such efforts can also be used to quantify and prioritize unanswered clinical questions in the absence of published evidence, she says.

However, not all types of RWE have equal evidentiary value. So-called research-grade RWE—which refers to high-accuracy evidence that is developed when complete medical records are linked with outcomes using advanced technologies—is required “to help clarify endpoints that would be of interest,” says Anand Shroff, co-founder and president of Verantos, an advanced RWE company based in Menlo Park, Calif.

Such efforts “help drug sponsors to improve trial design and pressure test common flaw areas,” adds Lucas Glass, VP and global head for IQVIA’s Analytics Center of Excellence. He notes that such efforts support the ability to:

Estimate the target study population and evaluate which inclusion and exclusion criteria are best.

Compare potential study endpoints to recent similar trials to highlight similarities and differences that may help the drug sponsor to identify a competitive advantage to pursue or to better align efforts with emerging trends.

Identify extraneous and missing procedures (extraneous procedures can be costly and impose unnecessary burden on sites and patients, while missing procedures can result in failure to achieve statistical significance on a primary or secondary endpoint).

Examine patients’ familiarity with significant or invasive procedures and identify sources of patient burden that may create barriers to recruitment and retention.

Conduct sub-population analysis to better understand and predict how different patient cohorts will respond to new therapies.

Analyze previously published RCT data to predict the probability of technical and regulatory success.

Using an appropriate data-analytics strategy, drug sponsors can also “merge databases from different sources to filter out patients that do not meet basic requirements for the clinical trial,” says Duckworth. “Patterns in different studies can be analyzed using artificial intelligence (AI) techniques to evaluate risk factors and effectiveness of the intervention, help to create a general prediction model for the patient and assess the cost effectiveness of the treatment.”

According to Talwai, Medidata Acorn AI is interrogating its compiled immuno-oncological trial records to identify risk factors for key treatment-related adverse events (TRAEs). “The findings can then be translated into a trial protocol that is stratified by baseline patient risk,” he adds.

One of the perennial challenges in harnessing RWD is fragmentation across payer systems, EHR systems’ diagnostic lab-testing records and other desirable sources of data. “One big advance in practice that looks poised to help finally overcome this barrier is the advent and real-world implementation of privacy preserving tokenization approaches, which can help to bridge patient data across silos in a compliant way,” says Talwai. “We’ve seen this successfully implemented at scale recently through one pro-bono, public-private Covid-19 consortium involving nearly a dozen companies

This effort brought together Covid-related records for millions of patients across claims, EHR and consumer data sources over a multi-year period, “which would have been immensely challenging to execute even a short while ago,” contends Talwai.

Similarly, the design of the clinical trials carried out in pursuit of the Covid-19 vaccines “was driven directly by patient-level data and public health data that was studied to target areas with high early infection rates,” explains Jimenez of Syneos Health. “Efforts to recruit patients more effectively really showed how making the best use of available data guided the decision-making and helped sponsors to evaluate the benefit of the vaccine in the trial. This helped to accelerate those trials much more than anyone anticipated.”

Poor adherence to therapy within a clinical trial can undermine the caliber of the study findings and reduce actual clinical outcomes in real-world use. “In a typical trial that spans many sites, sponsors are sort of rolling the dice when it comes to ensuring that participants are actually taking their medications,” says Rich Christie, MD, PhD, chief medical officer at AiCure. The company is using AI and ML tools, along with digital biometric markers (discussed ahead), to confirm that patients enrolled in a trial are staying on therapy. The resulting insights allow for faster human intervention from trial operators to keep the study on track. And Christie notes that now that AiCure has tracked more than a million doses for its clients, “we have the scale of data that allows us to go from just knowing who’s taking their medications to predicting— with accuracy—which types of patients are likely to take them or not. This enables better real-time interventions.”

When matching potential patients with specific trials, the use of digital biomarkers that are captured “in the wide part of the funnel” during screening efforts can help to narrow down the field to enroll the best participants. Examples include using audio and video tools to assess hand tremors in patients with Parkinson’s disease, or to assess cognitive changes based on voice and language in Alzheimer’s disease patients. “I think there will be a lot of real impact in this area in the next few years,” says Christie.

Similarly, advanced data analytics are being used to improve trial site identification by studying the prevalence/incidence of the target patient population in different countries, notes Glass of IQVIA. He says that in one recent client engagement, for example, the trial had very challenging inclusion/exclusion criteria whereby patients had to have been on a new therapy for 12 months prior to being eligible for the study. “We were able to see a comprehensive landscape of how much of that medicine was flowing through all hospitals globally over the past 12 months and avoid sites that were not yet prescribing the medication at scale,” says Glass.

Addressing knowledge gaps in trial results

Many clinical trials, on their own, don’t have a sufficient sample size to use advanced data-analytics techniques to conclusively identify patterns or signals, but could be used for hypothesis generation,” says Terri Madison, PhD, MPH, senior vice president and general manager, evidence and access, Certara. “However, we have several examples where we are either combining indication-specific data across trials, or across trials plus RWD sources, and then using AI, simulation and other advanced analytics techniques to strengthen understanding of a disease and the potential impact of therapy.”

By way of example, Madison notes that in one rare, serious genetic disorder, AI has helped to identify factors associated with earlier disease onset and rapid disease progression— critical information that can then be used to fine-tune future trial inclusion/exclusion criteria and help in targeting trial participants who have the factors associated with rapid disease progression. “Such efforts would theoretically lower the sample size requirements (due to a larger anticipated effect size) or could reduce study duration (by enriching the trial with patients anticipated to have worse/accelerated disease-related outcomes),” says Madison. “This could also enable accelerated approval in certain patient subgroups, thus strengthening the investment via earlier commercialization of the therapy.”

Today, model-based meta-analysis (MBMA) is a popular meta-analytic technique that incorporates pharmacological principles (such as dose-response) and outcome changes over time to yield longitudinal data. It is being used to synthesize rich aggregate datasets from prior clinical trial publications into more easily understood predictive models. MBMA, experts say, can be used to better define efficacy and safety targets that are needed to achieve differentiation, says Zierhut of Certara.

Specifically, according to Certara’s Zierhut, MBMA is helping drug developers to:

Reduce trial size and trial duration (lowering costs).

Increase treatment effect precision at same costs (increasing confidence).

Enable more precise assessment of relative effects for a new drug versus key competitors without needing a risky and costly head-to-head trial.

Decide whether to advance or kill their own investigational drugs—before needing to gather definitive data from expensive Phase III trials.

By analyzing consolidated insights from past clinical trials, stakeholders can also convert inherent uncertainty into statistical risk that can better support decisions about resource allocation and portfolio optimization,” adds Talwai of Medidata Acorn AI.

AiCure’s Christie believes the biggest factor to truly moving the needle in using predictive analytics to advance clinical and commercial objectives is to be able to do it at scale.

Making use of unstructured data in EHR systems

While mountains of data are collected throughout routine healthcare activities today, extracting the most relevant information is not that straightforward. Today, by some industry estimates, unstructured data constitutes up to 80% of information available in EHR systems.

“Natural language processing (NLP) can help users to understand the unstructured data in medical records and thus help investigators to truly understand a patient’s health status,” says Shroff of Verantos. For example, it is difficult to look at the structured portion of the medical record and understand if a patient suffering from asthma has symptoms consistent with severe asthma over time. “However, NLP can inspect the unstructured data and help make that determination,” he says, adding: “An ML algorithm can also detect comorbidities based on a number of data points, and a deep learning algorithm can predict patient outcomes based on extensive training and validation.”

“When similar variables are collected for multiple patients across geographies, the data quickly becomes complex and abundant,” adds Kulkarni of CRB. “Handling such a large number of variables and deciphering patters is not a trivial task.” He says the data-reduction technique known as principal component analysis (PCA) is helping current pursuits. “The literature is rich with applications of PCA to identify common or underlying root causes in clinical settings, as well as for defining pricing strategies,” says Kulkarni.

Creating new opportunities for existing drugs

One particularly lucrative opportunity for data analytics in pharma is the ability to mine existing RWD related to both on- and off-label prescribing, and earlier trial data—and identify clinical findings that may allow existing drugs to be reevaluated as treatment options for entirely new clinical indications. This can be done sometimes in different patient subpopulations within the same disease category, and other times, in entirely new therapeutic spaces.

In October, a study funded by the National Institutes of Health (NIH)

 found that a commonly available oral diuretic pill (bumetanide) may be a potential candidate for an Alzheimer’s treatment for those who have a particular genetic profile. Specifically, NIH researchers looked at EHR data sets from more than five million people and split them into two groups—adults over 65 who display a certain genetic signature of interest (a form of apolipoprotein E gene called APOE4) and took bumetanide, and a matching group who did not take the oral diuretic. The analysis showed that those who had the genetic risk and took bumetanide had a 35%-to-75% lower prevalence of Alzheimer’s disease compared to those not taking the drug, according to NIH. Ongoing studies are underway.

Meanwhile, in 2019, Pfizer made headlines when it received FDA approval for Ibrance (palbociclib; in combination with an aromatase inhibitor or fulvestrant) as a first-line treatment option for men with hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2) metastatic breast cancer. The novelty of this additional indication was that regulatory approval was based solely on studies of EHR and other post-marketing data—IQVIA Insurance database, Flatiron Health Breast Cancer database and the Pfizer global safety database—related to male patients who had the same form of biomarker-directed breast cancer, and were being treated with Ibrance off-label due to an unmet medical need and a lack of alternative treatment options. No additional clinical trials were carried out.

UK company Healx focuses on using AI methodologies (via its Healnet AI drug-discovery platform) to help identify novel therapy options and combinations of existing drugs that can help patients with rare diseases. In October, Healx received investigational new drug (IND) approval from the FDA,

 along with an orphan-drug designation, for the Phase IIa clinical study of HLX-0201 (initially approved as a non-steroidal anti-inflammatory drug) for the treatment of Fragile X syndrome—the world’s leading inherited cause of autism and learning difficulties, for which there are currently no treatment options available.

HLX-0201 was identified as a potential treatment for Fragile X syndrome by Healx’s omic-based drug-matching methods, “which compare the gene expression profile for a disease with the gene-expression profiles from Healx’s curated drug database to find entirely novel connections and disease pathways between the two,” explains the company. Several other compounds identified by Healx’s AI methods are also progressing toward the clinic, with the ultimate aim of finding a combination with synergistic mechanisms of action. Recruitment for participants in a clinical trial is set to begin in the coming months.

The power and promise of predictive modeling

Thanks to the availability of vast data sources and novel data analytics tools, predictive modeling and simulations are also gaining traction among drug developers, to investigate a variety of queries. “Predictive modeling based on RWD can be used to make decisions at different levels—prescriptive analytics, predictive analytics and business-intelligence analytics,” says Kulkarni. “The type of data that are required varies according to the objective—for instance, in some cases, the analysis requires wide data (ample data related to a sufficiently large population); in other cases, deep data (a significant amount of data about one patient) is required.”

So-called artificial neural network (NN) learning models also “do a particularly good job of memorizing, generalizing and generating recommendations, as long as the available data is sufficiently wide and deep, adds Kulkarni. “Such models allow the user to enter certain inputs and contexts i.e., input to the EHR notes in natural language), and the models produce outputs in a hierarchical manner [as actionable recommendations],” he explains. “The wide and deep model when ‘memorizing’ will rely on frequency of certain keywords, features and the underlying ‘intent’, and use linear model (i.e., logistic regression) to determine an output.”

One promising application for using simulations is to test the validity of tapping data coming from various wearables/digital health monitors—as surrogates of expensive and labor-intensive, gold-standard measures for the same endpoints, notes Madison of Certara, adding: “This could potentially save millions of dollars on future trials, if the right wearable proves to be as good as the current gold-standard measure for gathering key data during the trial.”

Simulations based on RWD are also being used to evaluate what is the optimal treatment sequencing in crowded therapeutic spaces such as multiple sclerosis, where knowing which early/first-line therapies, or the optimal sequencing of therapies, can produce the best overall prediction of long-term delay in progression, “This could have a huge impact for patients, and for payer decision-making,” says Madison.

Increasingly today, there is growing focus on tying drug pricing and formulary decision-making to value—and in terms of actual clinical outcomes in real-world settings, not just early findings produced in the trial. When data analytics efforts are able to demonstrate better outcomes for different patient populations, “the two data sets become the basis for conducting drug-pricing negotiations,” says Shroff of Verantos. “In the future, we expect to see real-world evidence being more rigorously produced in line with recent FDA guidance and becoming the basis for value-based contracting arrangements between pharma and payers.”

Syneos Health’s Jimenez believes the ability to study genetics and genomics information to better differentiate likely responders from non-responders has the potential to not just inform study design and trial enrollment but shorten the duration of the study, and thus the costs; reduce patient burden; and speed time to market to address unmet clinical need. “And payers are keenly interested in this approach, because it is in their best interest to pay for products that will have a higher probability of clinical success in specific patients,” he says.

“The question isn’t whether a solid data-analytics strategy can support commercialization efforts—it is an absolute necessity, because without a doubt, the real-world situation is not as neat and tidy as one might expect if we were to read the treatment guidelines or listen in on the latest advisory board meeting,” adds Casciano of Certara. “Our development programs and pivotal trials only give us a very narrow view of the heterogeneous treatment context.”

Thus, Casciano continues: “It is essential to be able to take what we have learned from our clinical research and apply it into real-world situation with all of its nuance. This is an impossibility without the proper insights from a well-executed RWD program, and, frankly, no responsible payer would give favorable access to a new treatment without an assessment of the likely consequences in their population.”

It is important to understand whether certain real-world patients that will get the treatment will be in any appreciable way different from those studied in the clinical trial programs. “if so, does this mean we should expect better or lesser results when compared to the trials?” says Casciano. “Without the answers to these kinds of questions, entering into a complex pricing or reimbursement arrangement would be nothing more than a leap of faith.”

Pfizer is so confident in the real-world capabilities of its lung cancer therapy Xalkori (criotinib), for patients with metastatic non-small cell lung cancer whose tumors are anaplastic lymphoma kinase (ALK) or ROS1 positive, that in October, the pharma giant announced a new outcomes-based contracting arrangement called the Pfizer Pledge Warranty Program.

 Through the program, the company will refund eligible patients for out-of-pocket expenses, and will reimburse payers (including private insurance, employer-sponsored health plans or Medicare Part D) if patients suspend their prescription (due to clinical reasons) before the fourth 30-day supply is dispensed. Refunds to the payer plan will be up to the cost of the first three bottles (30-day supply) of Xalkori—a maximum of $19,144 for each bottle, or an aggregate maximum of up to $57,432—according to the company.

“Similar to drug-pricing negotiations, payers are looking for evidence that a new therapy delivers additional benefits to their members before including the therapy in their formulary,” says Shroff. “Pharma can support these discussions by measuring outcomes of a cohort over time and comparing them against outcomes of another cohort which is phenotypically similar but on different treatments.”

Similarly, while formulary structures can be an effective way to help payers control spending, they don’t serve all patients equally well. Consider specific patients (or categories of patients) who don’t benefit from the top-tier formulary therapies, but are forced to cycle through them before getting to a more ideal treatment option.

“This adds costs and stress, and delays can impact quality of life, worsen disease progression and engender wasted costs,” says Talwai of Medidata Acorn AI. “Insights from RWD and pooled clinical trial data can play an invaluable role here in identifying those patient subpopulations for whom preferred agents, drug classes and/or mechanisms of actions may have inferior efficacy or safety profiles.” This combined intelligence, she says, can thus lighten the burden on patients and healthcare providers serving these underserved treatment segments.

“At the end of the day, data-analytics efforts should be used to bridge the endpoints from clinical trials to real-world value and to help address remaining knowledge gaps,” adds Christie of AiCure.

When it comes to big data in the pharma and life sciences industry, the old adage applies: Go big or go home.

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory

https://www.nih.gov/news-events/news-releases/precision-medicine-data-dive-shows-water-pill-may-be-viable-test-alzheimers-treatment

https://healx.io/ind-fragile-x-clinical-trial/

https://www.xalkori.com/pfizer-pledge-warranty-program

Ongoing strides in data mining, data analytics, modeling and simulation are
opening up new and more definitive opportunities to apply data-driven insights
to improve everything from upstream drug discovery and clinical trials, to the
downstream go-to-market strategy and post-patent lifecycle management

Pharma & Big Data: New Heights Beckon

In recent years, the pharmaceutical industry has undergone significant digital and omnichannel transformations. Amid the global Covid-19 pandemic, the pace of this transformation has accelerated. Fewer face-to-face interactions between pharma reps and healthcare professionals (HCPs) forced many companies to cram five years of digital transformation into one year. For pharma companies, the call for truly mature omnichannel capabilities is growing louder.

In response, many drug firms have shifted explicitly to formal omnichannel strategies over the past few years. They have brought in customer experience (CX) experts, mapped out customer journeys and hired significantly from outside of pharma. However, building omnichannel capabilities into an organization’s DNA is much more challenging than buying tools or hiring. Manufacturers face both regulatory and organizational constraints when undertaking these efforts. For companies looking to expand their omnichannel toolkit, a marketing data hub (MDH) is a great tool to help build a solid data foundation.

An MDH is a two-way data exchange that enables both marketing operations and analytics. This data repository should contain records of all marketing interactions with customers and serves as the basis for omnichannel marketing coordination. It is both an operational data store and an analytics data mart.

On the marketing operations side, an MDH helps coordinate real-time marketing activities in conjunction with traditional data sources like customer master lists or preference lists. It acts as a two-way data exchange between channel touchpoints, generating list feeds for a range of outbound marketing and re-marketing activities—be it email campaigns, targeted display ads or even webinar invites. Here, coordinated automation is a priority.

On the analytics side, an MDH should serve up cleaned and harmonized data, supporting an array of analytics activities, from the channel and campaign-level performance to segmentation exercises, marketing mix and machine learning. Literally and figuratively, an MDH is the central data hub around which omnichannel execution and analytics revolve.

While MDHs, traditional data warehouses (DWs) and customer data platforms (CDPs) serve similar functions, they differ significantly in complexity, time-to-build and more.

DWs are often read-only databases intended to support reporting, business intelligence and analytics. DWs tend to be IT-centric projects with correspondingly long development times. Due to perennial scope creep and constantly changing business needs, the success rate of DW projects over the past 20 years has been shockingly low.

In contrast, CDPs provide digital coordination and customer identity resolution in specific digital spaces. CDPs also add some level of marketing automation and insights. CDPs are digital-first, working well for fast-paced e-commerce or consumer-oriented organizations with relatively straightforward customer activity flow. Pharma, however, must consider a range of privacy concerns, fragmented data inputs, overlapping lines of authority and the added complexity of rep-driven marketing. For these reasons and more, pharma can face challenges in adopting CDPs successfully.

How to best plan out and implement a marketing data hub as a key tool to harnessing today’s omnichannel transformation in pharma

Pharmaceutical Commerce - November 2021

Pharmaceutical Commerce - November 2021 Issue (PDF)

AI’s predictive power on the big stage

What’s Next for Med Tech?

Chatbot Potential in Clinical Research

The Pulse of Digital in Pharma

RWD Benefits at the Point of Care

Pharma & Big Data: New Heights Beckon

Marketing Data Hubs: When Operations and Analytics Collide

CSA a Game Changer for the Life Sciences?

The Key Force in CGT Commercialization

Aligning Medical Information and PV

Preserving Drug Sampling Activity