Check out the latest features and columns!

Applied Clinical Trials September 2023

Jim Kremidas, former executive director, Association of Clinical Research Professionals, shares his experiences as a patient in clinical trials.

Perspective and Hope From the ‘Other Side’

Senior leaders gather to share insights on the increased investment in real-world data to boost the quality and efficiency of drug development.

Executive Roundtable: The Rise of RWD in Clinical Research

Image Credit: © ipopba - stock.adobe.com

Biofourmis has announced the release of its expanded Digital Clinical Trials solution within the Biofourmis platform for biopharma and other life sciences companies.

The solution includes a technology-enabled, scalable platform and services that support digital tools enablement, trial decentralization, safety monitoring, personalized care, participant recruitment, and increased access to treatment options for patients. According to the company, it can help sponsors and researchers accelerate clinical development with remote data collection and novel digital endpoints from Biofourmis' library of biomarkers across multiple therapeutic areas.

The expanded solution offers a more holistic workflow, new data management automations, and integration of essential clinical trial documentation, participant scheduling, and data collection elements. Because the artificial intelligence (AI)-powered platform is configurable, study start-up timelines can be reduced. The connected platform integrates with 40+ devices, enabling study teams the freedom to select the right device for the right disease area.

Biopharma companies can also tap into Biofourmis' principal investigator (PI) network. The network includes experienced PIs from leading academic medical centers. Health systems that already use the Biofourmis platform for technology-enabled care delivery at home can expand their clinical research initiatives by leveraging the connected platform to include the Digital Clinical Trials functionality.

"Leveraging digital tools and automating and accelerating trial processes for both researchers and participants can increase the likelihood that endpoints will be reached sooner, which lowers costs and simplifies tasks for everyone involved," said Biofourmis COO Jaydev Thakkar in a press release. "More importantly, shorter trials with more meaningful results mean that groundbreaking new therapies can be discovered and developed faster to improve outcomes for patients worldwide."

Biofourmis Accelerates and Simplifies Drug Development with Expanded Digital Clinical Trials Solution

. (2023, September 18). Cision PR Newswire.

Articles

Tool offers a more holistic workflow, new data management automations, and integration of essential clinical trial documentation. 

Biofourmis Announces Expanded Digital Clinical Trials Solution

Vice President, Scientific eTech-Enabled Services, Calyx

Craig Mooney, VP, scientific eTech-enabled services, Calyx

One of the primary functions of an IRT system is the allocation of drug to participants in accordance with the randomized treatment. This is a zero-error tolerance process to ensure study integrity and patient safety. To achieve success in allocation, a robust supply management system is required. Quite simply, the right medication, for the right participant, at the right time, every time. Let’s explore these a bit deeper.

Like randomization, if allocation to study drug is not implemented according to the protocol the entirety of the study could be called into question. It would not be unusual given modern study designs for participants to have a crossover or other complicated dosing options. These add to the complications for implementation and ensuring the right medication maxim.

A common mistake—even made by experts—is not using the proper terminology when it comes to medication management and IRT systems (Figure 1). When terms are interchanged and conflated it leads to a lack of understanding. Not speaking in precise language can cause unnecessary complications and lead to teams drawing inaccurate conclusions. The three most often misused terms include:

These two terms can be reasonably interchanged. Allocation and assignment mean what the IRT system has chosen for the site to dose the participant. It is based on the study design and programmed algorithm. At the time of the transaction, it is what is intended, not what has reliably happened.

In a study where in-clinic dosing such as IV preparations are not required, dispensing means giving the medication to a participant to take home or, in a direct-to-patient scenario, delivering the medication to their home. This is the point when allocation/assignment turns into action. Therefore, it is improper to call an IRT transaction dispensing. Dispensing is done by study staff, not by IRT systems. Using this term conveys a level of certainty that does not exist without a confirmation transaction on the dispensation. Furthermore, it does not even confirm the participant was actively dosed.

The final step of the process is the participant being actively dosed with the treatment. As with dispensing, dosing moves from the theoretical of what the system has assigned/allocated to a practical act performed by study staff, the medication being consumed by the participant. The IRT system does not know for a fact that a participant was dosed unless a such a confirmatory transaction is part of the IRT system design.

Figure 1: Not using correct terminology during randomization creates confusion in the IRT system and increases clinical trial error risks.

Understanding the difference between these three distinct processes/terms puts you in a better position to comprehend what the data in each system really means. It can also guide you on the level of reconciliation required.

As one example, an IRT system can be the source of truth to ensure compliance and the most accurate data that will also support supply management. Another approach is to do only allocation/assignment in the IRT and use an EDC system to capture dispensation and dosing data. There are reasoned arguments for both. The decision is typically based on both the philosophical and practical approach a sponsor takes regarding how to manage this data flow . But whichever you choose, be precise in your language and understanding of the implications.

Allocation/assignment will only work if the medication is available where needed. Without a proper understanding of system workflows/inputs, logistics processes, and use/availability calculations, you will struggle to meet the baseline of the right medication, for the right participant, at the right time, every time.

Complications and details related to proper supply management are so fine that they cannot be covered adequately here. But one truth about drug supply management and calculations is that it is never an understanding of the number of participants, it is always about trying to predict when and where the participants will show up.

In an earlier article, we reviewed the randomization risks that could arise if an interactive response technology (IRT) system isn’t designed and/or implemented correctly. Here we address the consequences that trial sponsors could face if their IRT system isn’t adequately designed to handle the many and often complex drug allocation and trial supply aspects of their clinical trials.    

Getting IRT Right – Part 2: Study Drug Allocation and Supply Management 

Miranda Schmalfuhs is an Associate Editor for Applied Clinical Trials. She can be reached at 

Left to right: Robert Dougherty, Miranda Schmalfuhs, Dr. Dillard Elmore, and Evan Johnson

I recently had the pleasure of speaking on a panel at the recent Talk Health event in Philadelphia, PA at Le Méridien on September 11th, 2023. The panel, moderated by Evan Johnson, chief of staff at Watzan, and focused on what success looks like in audio from a publisher, vendor, and HCP perspectives. Speaking on the panel with me was Dr. Dillard Elmore, a practicing physician, and Robert Dougherty, president of Dougherty Media.

As members of the panel, we all use audio in professional and personal capacities. Outside of producing the 

, my leisure listening includes a few different podcasts hosted by some of my favorite comedians. Dougherty also uses audio often. “It's a lot easier for me to when I'm on the road to listen as opposed to read through certain things. But we as a company have found it's a different form of media consumption in a way to get your thought leaders information out quickly in a different format.” They do so typically in one-to-three-minute audio segments, which Doherty describes as ‘definitely very engaging’ seeing ‘much more engagement’ than the newsletters they’re currently producing.

Dr. Elmore shared that for him, the beauty of audio is that it's not selfish. “If you watch TV, you have to focus all of these senses. If you're studying through a textbook, you need all of your senses again. Whereas with audio, some of my fondest memories are spending time with family and friends, but listening to music. If I'm listening to an audiobook or medical podcasts, I can be mowing the lawn, do all my laundry, I can be cooking…You can have a conversation and still do other things.”

One of the biggest hurdles for getting started with audio is capturing quality audio. Some subjects won’t have headphones to plug into their laptops, let alone a professional microphone. Some basic practices to ensure usable audio would be to ideally have at least headphones with a microphone, but if not, to be in a quiet room that doesn’t provide an echo or have thin walls.

Beyond the quality of audio, making sure the subject is comfortable being interviewed is a key component to success. Typically for our podcast, we will have a pre-call with the guest where we speak about the topic and discuss the questions and direction the episode will take. Scheduling can also be a concern because the higher up in the c-suite the KOL is, the busier their schedule is and the harder it is it access them. A big concern is not wanting to be misquoted or misrepresented, having the questions ahead of time allows for the guest to be fully prepared when it comes time to record the episodes — and in turn, can help cut down on the editing process. Another tip here, encourage using bullet points versus drafting full responses to keep answers conversational and engaging.

The editing process for audio depends on the guest(s), and typically takes from one-to-three minutes per minute of audio. Certainly, a subject or host could go minutes without saying any filler words or making an error that requires edits. More tedious episodes with a less experienced subject, could take up to a few hours. It’s beneficial to create a standard procedure for editing to ensure you keep to a set schedule of posting content. For example, the 

 podcast has been posting biweekly, on Thursday mornings, at 9am EST, since its inception in 2018. Our listeners know exactly when and where they can find out content, and trust it will be there when they look to listen to it.

Between the preparation for the episode and the availability of the KOL, the process could take a couple of months or happen in two weeks, every episode will be different.

Dougherty said getting audio into e-newsletters and emails wasn't ‘too bad’ for his company and that the process is ‘pretty efficient.’ They interview physicians for a short time segment and after being combined with static banner advertisements, the e-newsletter goes out. Recipients then click to listen to the audio clip and end up on a dedicated landing page that also have those same static advertisements.

“They did really well, we saw a high engagement rate of 18-27% and 3-5% click through rates. They also act almost like a resource library…great KOLs people could go back to.” He explained that in some instances audio content that initially had 60 downloads/plays, would later reach 200+ plus while in the archive. Dougherty added that while their delivery rates for emails have always been strong, adding audio to their emails improved both their open and engagement rates significantly.

“It’s a different type of media. We’re using thought leaders to get information out about the message/brand, that you really couldn’t put in a banner advertisement.”

Dr. Elmore’s audio use begins soon after he wakes up. “I do the audio Bible for about 15 minutes, and while getting ready, I go through a swath of podcasts.” He shared that the typical podcast he’s looking for is less than 30 minutes, citing that even shorter listening times would be great. “I want to get to the point. I don't want my time to be monopolized by one thing. I would like to get a nice broad objective view of what's happening. Then when I'm having a conversation with say, a neurosurgeon, about my patient, I feel that I know [what they’re talking about] and there’s the ability to go deeper.” Other important qualities to grab this doctor’s attention: an engaging host presenting interesting content. “They could just make something that's basic, seem intriguing, and make you want to go down that rabbit hole. That's beautiful.”

In total, Dr. Elmore spends about two and a half hours each day, including his commute, consuming audio content. Admittedly, not all of Elmore’s audio is business, his leisure listening is reserved for sci-fi audiobooks during his commute. He added that his use of audio, that he says has been ‘like a renaissance’, has become such a large part of his life — it has decreased the amount of television that he watches.

“I just see [audio] being used more often across the board. I see a lot of pharma websites where you have the option to listen to a press release now,” explained Dougherty. Right now, a natural human voice or one of a recognized thought leader are still the best options, because you have all of the control. “Sophisticated areas of translation may be a little more difficult for artificial intelligence (AI) such as some of the generic terms and brand names…It’ll be interesting to see what AI does for that — I think it'll be commonplace in the very near future if it isn’t already.”

With AI programs getting better every day, some now even with your choice of accent, the wave of audio is going to be tough to beat.

Panel at recent Talk Health event at Le Méridien, in Philadelphia, PA, on September 11th, 2023, focused on what success looks like in audio from a publisher, vendor, and HCP perspectives.

Audio Success in Pharma

Katarzyna Cyranka, PhD, MA
Credit: University Hospital in Krakow

 naive to advanced diabetes technology is underlining the potential benefit of newer technologies in diabetes management.

Results of the study, which examined quality of life over 1-year after initiating use of an advanced hybrid closed-loop system, suggest use of diabetes technology was associated with notable improvements in quality of life.

“To the best of our knowledge, this is the first long-term follow-up study investigating the psychological well-being of adult people with T1D previously naïve to diabetes technology (treated with [multiple daily injections] and [self-monitoring of blood glucose]) who experience a direct switch to the [advanced hybrid closed loop] system with novel calibration-free sensors, and the sustainability of the obtained changes in their quality of life,” wrote investigators.

As the rate of advancement and endorsement among guidelines for diabetes technology continues to progress, optimal uptake of new technologies has become a focal point of research for many within the community. The current study was designed by a team of investigators from institutions in Poland led by Katarzyna Cyranka, PhD, MA, of the University Hospital in Krakow, as a 9-month observational continuation of a 3-month randomized controlled trial, with the aim of exploring the effect of 1-year of use of an advanced hybrid closed-loop system on quality of life, level of anxiety, and self-efficacy.

In the original trial, 41 patients were randomized to advanced hybrid closed loop therapy with the MiniMed 780G advanced hybrid closed-loop system or to remain on multiple daily injections and self-monitoring of blood glucose. Overall, 20 and 21 patients were randomized to the intervention and control arm, respectively.

A total of 37 patients completed the study. At 3 months, time in range increased from 69.3% (Standard deviation [SD], 12.3) at baseline to 85.0% (SD, 6.3) at 3 months in the advanced hybrid group, while remaining unchanged in the control group (treatment effect 21.5% [95% Confidence interval [CI] 15.727.3]; 

From the original study, 18 participants from the intervention group continued use in the 9-month observational continuation period. This cohort consisted of 10 men and 8 women, with a mean age of 40.9 (SD, 7.6) years. Of note, at month 6 of the study, patients were switched from the Guardian Sensor 3 to the Guardian Sensor 4 Continuous Glucose Monitoring system.

The primary outcomes of interest for the trial were change in quality of life, level of anxiety, and level of self-efficacy. Investigators pointed out quality of life was assessed using the Polish validated version of the ‘QoL-Q Diabetes’ questionnaire, level of anxiety was assessed using the State-Trait Anxiety Inventory (STAI), and self-efficacy was assessed using the General Self-Efficacy Scale (GSES).

Upon analysis, results pointed to significant increases in quality of life for the global QOL-Q Diabetes questionnaire score, with significant increases observed in as many as 11 of 23 areas of life. These areas included being physically active (

 <.01; Cohen d = 0.73), feeling in control of my body (

 = .01, Cohen d = .66), eating as I would like (

 <.01; Cohen d = .79), looking after or being useful to others (

 = .02; Cohen d = 0.65), being active with pets/animals (

 <.01; Cohen d = .95), being spontaneous (

 = .02; Cohen d = .67), and doing “normal” things (

Further analysis revealed use of the advanced hybrid closed-loop system was also associated with improvements in both state (

 .02; Cohen d = .60) anxiety as well as self-efficacy (

 = .03; Cohen d = .76). Investigators also pointed out there were no instances of pump discontinuation during the study.

“Evaluation of patients’ [quality of life] is a well-grounded indicator of the effectiveness of provided healthcare and may be helpful for health professionals and healthcare policymakers in their efforts to improve the well-being of patients,” wrote investigators.

Cyranka K, Matejko B, Juza A, et al. Quality of life in the course of a one-year use of an advanced hybrid closed-loop system in adults with type 1 diabetes previously naïve to advanced diabetes technology. 

. 2023;14:1210756. Published 2023 Aug 15. doi:10.3389/fendo.2023.1210756

Matejko B, Juza A, Kieć-Wilk B, et al. Transitioning of People With Type 1 Diabetes From Multiple Daily Injections and Self-Monitoring of Blood Glucose Directly to MiniMed 780G Advanced Hybrid Closed-Loop System: A Two-Center, Randomized, Controlled Study. 

. 2022;45(11):2628-2635. doi:10.2337/dc22-0470

A 1-year observational follow-up of a 3-month RCT provides insight into he benefits of advanced hybrid closed-loop system use in people with type 1 diabetes using multiple daily injections and self-monitoring of blood glucose.

Advanced Hybrid Closed-Loop Technology Could Improve Quality of Life in Type 1 Diabetes

Image Credit: Adobe Stock Images/Ruslan Batiuk

Belong.Life has officially launched Tara, a SaaS-based conversational AI cancer clinical trial matching platform for health providers and contract research organizations (CROs). According to the company, Tara is designed to accelerate the process of matching patients with eligible clinical trials, enabling increased recruitment at significantly lower costs. Following the launch of Dave, Tara is Belong’s second AI Health Mentor suite of solutions.

"Since our founding, Belong has provided clinical trial matching services to more than 30,000 patients," said Eliran Malki, CEO, co-founder, Belong.Life, in a company press release. "Tara is a natural extension of our services, helping to optimize clinical trial matching to benefit stakeholders with increased patient recruitment, improved randomization, and reduced costs and logistical challenges. Tara will enable more patients around the world to access clinical trials."

: Belong.Life Launches New Conversational AI SaaS Solution for Cancer Clinical Trial Matching and Recruitment. 

September 13, 2023. Accessed September 14, 2023. https://www.prnewswire.com/news-releases/belonglife-launches-new-conversational-ai-saas-solution-for-cancer-clinical-trial-matching-and-recruitment-301926197.html

Solution designed to accelerate the process of matching patients with eligible clinical trials.

Belong.Life Creates Conversational AI SaaS Solution to Support Cancer Clinical Trials

Alan Kalton, SVP, commercial strategy, Aktana

The average cost for a pharmaceutical company to bring a new drug to market in the US has reached a staggering $2 billion, according to a recent study by Deloitte.

 The path is made even more challenging by a series of additional obstacles. From growing regulatory complexity to the difficulty of communicating the nuanced benefits of specialized therapies to niche audiences, launching a new product can feel like Sisyphus pushing a rock uphill. It’s not surprising that more than one third of all product launches fail, often resulting in serious (if not catastrophic) revenue losses.

As if product launches weren’t already daunting enough, there are several factors on the horizon that will intensify challenges in the years ahead. The Inflation Reduction Act, signed into law last year, will empower the federal government to negotiate lower prices on a number of key drugs. When that process begins in this fall, revenues will likely drop for many companies. In addition, the industry is facing the prospect of losing more than $200 billion in annual revenue from patents that are set to expire. As a result, nearly 200 drugs (including 69 blockbusters) will lose exclusivity by 2030. For example, Johnson & Johnson’s top earner, Stelara, which treats plaque psoriasis, psoriatic arthritis, Crohn’s disease, and ulcerative colitis, will lose US exclusivity this year along with AstraZeneca’s star respiratory medicine, Symbicort.

This grim reality has the potential to impact almost every major pharmaceutical company, forcing many to rush to replenish their research pipelines and skillfully manage future product launches to replace lost revenue.

All of this is happening amid strong financial headwinds, including a weakening biotech investment sector, a decrease in available capital, general banking woes, and overall economic uncertainty. The entire sector is feeling a tightening grip around their budgets, driving companies to trim the proverbial fat, streamline processes, and get serious about improving commercial effectiveness.

These uneasy factors mean that life sciences companies are faced with a dichotomy: as launching new products grows more difficult, it also becomes more critical to shore up finances. Like it or not, companies need to wade into these choppy waters to remain profitable. As such, what can the industry do?

Part of the solution can be found in data and analytics. Today’s data analytics technologies enable companies to collect, manage, analyze, and synthesize more data and information faster than ever. Traditionally, it has taken companies months to uncover important insights on the effectiveness of a launch strategy midstream and then another few months to pivot—far too late to properly tune a strategy that might be faltering. It doesn’t have to be that way anymore. Now, companies can make informed, data-driven responses nearly in real time, which can make all the difference in launch success.

As the life sciences industry seeks to chart a course through these turbulent times, key industry figures have been weighing in with their expert insights. Among them is Andree Bates, PhD, chairman, CEO, and founder of Eularis, a company specializing in harnessing the power of artificial intelligence (AI) to boost efficiencies within the biopharma and healthcare sectors. In an exclusive interview, Bates emphasizes the crucial role of AI in confronting today’s multifaceted market challenges.

“AI-driven strategies are the key to solving today’s complex market problems,” she asserts. “In fact, life sciences companies should be doing this in general—not just in the face of the patent cliff or the current challenging environment.”

As the pharma industry looks to navigate these difficult waters, here are three ways forward.

More than ever, pharma companies need to develop broader product pipelines so that they can position themselves for the market as quickly as possible. Advanced AI and large language model (LLM) analytics can help organizations accelerate clinical research and get the most out of the work that they’ve already completed.

“There are so many different areas where technology can speed up processes to save time and money in R&D, and clinical trials in particular,” says Bates. “AI and machine learning can predict the outcome of a trial before it even starts, for instance. Theoretically, in 10 years, the whole process of getting a new product to market could go from 10–15 years to only one to three years using these technologies.”

One example of an organization taking advantage of AI in R&D is GRAIL, a healthcare company that focuses on early detection of cancer. GRAIL recently introduced Galleri, the first multi-cancer early detection test that can find a shared cancer signal across more than 50 types of cancer. Through AI-driven, next-generation sequencing and machine learning algorithms, Galleri can isolate cell-free DNA and analyze its methylation patterns to detect if a cancer signal is present. If it is, then the test predicts the cancer signal origin to help guide diagnostic evaluation.

This unique combination of AI, rich data, and quality practitioners has resulted in a breakthrough test that could potentially impact millions of lives in the years ahead—and it’s a prime example of leveraging advanced analytics to speed the research process.

Companies cannot leave anything on the table in the current economy. Brand teams must double down on cultivating relationships with existing customers and continue to develop loyalty ahead of a product losing exclusivity. Identify the practitioners who are most loyal. The physicians who have been loyal prescribers for years and have seen positive results in their patients are more likely to forgo the generic alternative when it becomes available. The more life sciences companies embrace these high-volume prescribers, the better results they will see, both prior to losing exclusivity and after.

It’s worth noting, too, that extending prescribing behavior even for just one week beyond patent expiration and before a physician starts to prescribe the generic due to consumer demand can make a difference. Every little bit helps. Extra loyalty-building efforts now could be worth millions of dollars in revenue down the road.

AI-based next-best-action (NBA) solutions help supercharge loyalty-building efforts by unearthing valuable insights about how to best tailor each engagement with healthcare professionals (HCPs). For example, in Italy, one Aktana customer had invested substantially in developing loyalty-building customer journeys. The pharma company used Aktana’s contextual intelligence platform to design and choreograph these programs via the NBA engine, delivering the recommended actions through sales and marketing automation systems that personalized experiences for HCPs. As a result, the company increased strengthened relationships with their HCP customers.”

Further, NBA platforms support personalized HCP engagement executed across all customer-facing teams—sales, marketing, and medical—at scale. This is critical. NBA technologies not only deliver critical insights about HCPs’ preferences but also empower the entire commercial organization to orchestrate an expertly coordinated customer experience in real time. It’s the key to delivering a better, synchronized HCP experience that makes them repeat prescribers. It’s no longer a nice-to-have thing but a necessity.

“Here’s one example of the power of intelligence solutions. We used AI to analyze the value of all commercial engagement channels used for a product with only three years left under its patent,” says Bates. “By prioritizing the channels and messages that had the best results, the product doubled its market share in only six months, essentially helping to maximize revenues for the remaining patent life.”

Life sciences companies that are exploring new indications for products going off-patent can also use AI to identify new HCPs and begin to cultivate those relationships early. Advanced NBA solutions can then help companies strategically coordinate pre-commercial sales and marketing efforts with medical science liaison engagement of leading physicians to pave the way toward a more effective product launch post-regulatory approval and establish long-term loyalty with influential key opinion leaders and digital opinion leaders.

3. Adopt a data analytics program that informs customer-facing teams more accurately

Once a product has gone to market, companies must be able to quickly and effectively pinpoint their target customers to maximize value. Being able to effectively analyze and synthesize enormous amounts of data can often mean the difference between success and failure. This delicate and time-consuming process—the who, what, when, where, and how of commercializing a product—can be greatly improved with data analytics and AI-powered intelligence systems. For example, Eularis helped a top 10 pharmaceutical company plan the launch of a new drug like their existing blockbuster, but with a new “mode of action” and without eroding the blockbuster’s market share.

“We used both big and small data, including claims data, patient registry data, and market research data to map how the target disease space was changing as well as how the patient journey and treatment approaches were evolving with the new market entrants,” explains Bates. “We did this analysis across all relevant therapeutic areas for the drug.”

Among many other analyses, Eularis also mapped each disease area for all existing drugs in the market, to identify the strongest opportunity spaces for the new drugs.

“Through this detailed work—aided by AI—the pharmaceutical company was able to create highly effective sales and marketing strategies using the insights gained. The new drug was launched and performed exceedingly well, reaching around $3 billion in sales in the first year without cannibalizing the original blockbuster,” says Bates.

Such comprehensive AI-driven data analysis, done in coordination with machine learning and human analysis, made a big difference. Without the data, technology, or human input, this meticulous work would not have been possible, potentially leaving large amounts of revenue on the table.

Additionally, NBA platforms help sales, marketing, and medical teams target the right HCPs in the way that they like to be contacted and with the content that will move them to action. Such platforms can help companies wade through data to provide solutions that improve the customer experience. NBA systems that learn, adapt, and coordinate effective execution allow commercial teams to adjust strategic priorities and chart new pathways for execution in real time. For example, facing the imminent launch of a new vaccine, one global pharma company working with Aktana used an NBA platform to optimize launch strategy execution in real time. Although reps were armed with call plans and targets for their new product, it was difficult to plan out a strategic route without an established visit pattern to guide activity. By delivering NBA suggestions and insights directly within sales reps’ daily workflow, the teams optimized their schedules, effectively incorporated new targets into existing routes, and surfaced the right messaging for every HCP detail. As a result, sales reps who used the platform outperformed reps who did not by more than 30%.

Companies should be investing in technology now that will provide real-time launch feedback and enable continuous improvement in preparation for the deluge of patent expiry that’s coming. Only AI-powered platforms empower teams with ongoing, up-to-the-minute insights that inform adjustments to a strategy with a continuous feedback loop: “insight, action, learn, adjust.” Rinse and repeat.

Over the next decade, pharmaceutical companies will have to contend with a host of obstacles standing in the way of their success. While there is much that these companies will have to do to mitigate the challenges before them, fully embracing AI, machine learning, and large-language model analytics will provide a way forward. Integrating these technologies into every phase of their business—from pipeline development to sales and marketing—will empower companies to weather the storm of waning patent exclusivity and emerge stronger than before.

“The life sciences industry needs to accelerate their adoption of AI and generative AI, including LLMs, across all areas—from R&D to commercial,” says Bates. “Urgently, too, companies should embrace these technologies for product launch success. AI tells teams exactly who to engage and when, how to engage them most meaningfully, and what content motivates them to prescribe. That’s the magic formula.”

Corvino, B., Jaeger, J., Elsner, N., Wagh, M. Rethinking Market Access. Deloitte. March 9, 2022. https://www2.deloitte.com/xe/en/insights/industry/life-sciences/pharmaceutical-market-access.htm

Integrating artificial intelligence and advanced analytics throughout operations offers pharma companies a pathway to offset impending drug patent expirations.

Fortifying Defenses Pre-Patent Cliff

Image Credit: Adobe Stock Images/freshidea

Partnering with a mission to champion diversify within the clinical trials sector, The Association of Diversity in Clinical Trials (AOD) has inked an agreement to partner with BlueCloud by HealthCarePoint.com. According to a company press release, BlueCloud’s main objective is to improve clinical research efficiency while prioritizing patient well-being.

"We enthusiastically welcome BlueCloud by HealthCarePoint.com to our community," stated Diana Foster, President, AOD. "Their innovative approach seamlessly aligns with our dedication to fostering inclusivity and equality in clinical trials. Together, we're reshaping the clinical research landscape, ensuring each patient's viewpoint and experience is valued and considered."

: Association of Diversity in Clinical Trials Welcomes BlueCloud by HealthCarePoint.com as a Supporting Partner. 

September 12, 2023. Accessed September 12, 2023. https://www.prnewswire.com/news-releases/association-of-diversity-in-clinical-trials-welcomes-bluecloud-by-healthcarepointcom-as-a-supporting-partner-301922885.html

Partnership hopes to champion diversity, inclusivity, and innovation within the clinical trials sector.  

BlueCloud by HealthCarePoint.com Accepted as New Supporting Partner for Association of Diversity in Clinical Trials 

Michelangelo, who was born in 1475, built models for his sculptures from wax and clay. The Wright Brothers, circa 1906, reportedly built a few models of the first airplane in their bicycle shop. And the Model T, so the story goes, was so named because of the 19 models (A-S) that preceded its launch in 1908.

One could argue that these geniuses created models and prototypes because they saw value in investing that time: Where were the flaws in their design? The sweet spots? How could they make their creation more beautiful, efficient, useful? The use of a model, logic would dictate, could help eliminate or minimize the risk of failure in the end result. In the above examples, the end results speak for themselves.

Of course, designing a Ferrari isn’t the same as designing a monoclonal antibody; the latter will behave differently because of human variables.

Variables aren’t anything to fear, say experts in the field of simulation; certainly they aren’t reason enough for the $1.48 trillion pharma industry

 to forgo simulation of clinical trials, or to patient-test the protocol.

Until the pivotal trials begin, lots of time exists to learn about the drug and how patients will respond to protocol demands, experts say.

Once the trials start, a sponsor is trusting fate. If one of the usually two FDA-required Phase III trials fail, says Mark Lovern, senior vice president, Certara, “you’re basically up a creek without a paddle.”

Of the differing types of simulation companies portrayed in this article—predicting drug performance in people, designing optimal protocols, or inviting patients to play-act roles in clinical trials—all say their businesses are growing and repeat customers are the norm. The case studies are impressive, highlighting money saved and FDA approvals achieved.

At Medidata, Alicia Staley, vice president, patient engagement, reports that its role-playing studio for patients—who often leave trials because of the onerous demands placed on them—has become so popular with sponsors that within two years of operation it is now a revenue producer.

Exploristics, CorEvitas, and Certara all report growth; in some cases, significant growth.

However, while significant growth is reported here, industry-wide adoption may be lacking, specifically because of time and money.

Reasons vary from a potential fear of adding more time to pre-trial investigation; queasiness over leaning too heavily on AI results; unwillingness to change long-existing exploratory and analytical procedures; and problems with communication between internal company silos. These are the reasons, those interviewed say, for why the use of simulations is still the exception, and not the norm.

Mike Rea, founder of IDEA Pharma, says the few R&D professionals he has spoken with about relying on AI-based simulation results are very wary. “I get the strong sense from R&D that they are skeptical about how far technology can drive things at the moment with AI, including simulations,” he says. AI, adds Rea, helps create ideas and run various scenarios, “but may not reliable enough to make a decision.”

While AI may not be an iron-clad decision, simulation can help point a sponsor in the right direction. Controversies abound over the efficiency and productivity of pharma R&D. In a recent analysis of 16 industry members between 2001 and 2020, the group collectively launched 251 new drugs, representing 46% of all Center for Drug Evaluation and Research (CDER)-related FDA approvals within the time period. The average total R&D expenditure per new drug was $6.16 billion.

Despite any gains that pharma might make in terms of extending lives or making those lives more livable, it is the remarkable length of time that trials take and the stunning financial outlay involved in taking a molecule from lab to regulator define pharma’s measure of success, or not. Simulators think 

trial models can help decrease time and money spent with more accurate patient selection, a more honed indication, and subsequent endpoint. “Anything you can do in the planning phase will help,” says Lovern. “You can use a model as a framework.”

“What unites people who are interested in simulations is the feeling that too much of the decision-making in drug development” is made by the medically trained who rely on their gut feelings and past experiences to make major decisions on drug development and subsequent trials, says Sam Miller, head of strategic consulting, Exploristics, a provider of biosimulation software and biostatistics services.

In short, more quantification, less qualification in the decision-making process.

PubMed doesn’t provide extensive help in assessing whether simulation of any part of the clinical trial process is, or isn’t, a keeper. There were no studies that assessed any type of simulation’s overall value. Wrote Krishnaswami et al. in 2020: “The discipline needs to continue to evolve from one‐off case studies to a paradigm of systematic, best practices‐driven approaches to improving decision-making across the [drug discovery, development, regulatory approval, and clinical utilization] continuum.”

In a recent study, researchers simulated the effects of a central nervous inhibitor called HSK3486.

 Designed as an anesthetic, HSK3486 was developed in 2019 to guide model-informed drug development (MIDD) on certain populations like infants and the elderly. HSK3486 was designed to have better anesthetic properties and less systemic exposure than the widely used propofol, which tends to stay in the liver. Using MIDD to test for exposure, the researchers found that predicted systemic exposure went up somewhat in those with hepatic impairment and the elderly, which was similar to the data gathered in a subsequent clinical trial.

FDA supports MIDD; it has done so since 2018. The agency runs what it calls its MIDD-paired meeting program, so sponsors and agency experts can discuss different modeling approaches. A successful modeling application, writes the agency, can raise the odds of a drug’s approval and help determine the right dosages for various populations “in the absence of dedicated trials.”

Certara, says Lovern, has enabled 100 novel drugs, found using physiological based pharmacokinetic modeling and simulation, to obtain label claims in lieu of clinical trials. Most are for drug-drug interactions, he says.

An Exploristics’ case study also shows simulation success. A biotech working on a molecule for a rare disorder came to Exploristics to see the probability of this molecule in a particular indication. It was showing possible efficacy in a few different ones. The biotech was looking for a Phase II trial. It had been told it needed 1,000 patients.

Miller says they ran many assumptions regarding various endpoints and different patient groups to recruit. All those scenarios showed less than 50% of probable success. So it pared down the indication and honed the endpoint to mortality. The result: a new enrollment of 180 patients, and at least 80% success rate. The results showed benefit to patients by enabling a trial that otherwise was unfeasible, says Miller.

Computer power and data retrieval have made simulation possible. Before the 20th century, the technology didn’t exist for researchers to look at bodily processes, AKA pharmacokinetics, to measure the minute amounts of drug present in blood and tissues, says Lovern. It wasn’t until the 1980s that computing power enabled analysis of the large quantities of data needed to inform predictive pharmacological models.

However, even with the technology, data at scale, remains elusive. It’s either proprietary or outrageously expensive to get from a provider. Jane Myles, program director, Decentralized Trials & Research Alliance, says that even within a pharma company itself, the silos don’t share. “Our computational vision is limited to that silo,” she notes.

Exploristics and Certara know about data limitations. Both mostly rely on public sources for data and engage teams of scientists to scour PubMed and the like. Certara uses about 30 data scientists. And Exploristics, says Miller, increases its data pool in a singular way. Its clients agree that the company can access the simulated information to add to future simulations. So far no one has said no, he says. According to Miller, Exploristics’ business has grown at least 30% year over year for some time.

Myles says audience members attending one session at the Drug Information Association (DIA) Annual Meeting in June were asked if they incorporated patient voice into their protocols. Fewer than 40% was the response—a far different percentage than found in the DIA-Tufts Center for the Study of Drug Development patient engagement studies conducted a few years ago. Asked a similar question, 65% of those surveyed said they are “investing in patient-centric initiatives in drug development.”

The following are good examples that our interviewees shared in regard to patient-centric simulations.

Five years ago, Medidata wanted to know how being patient-centered would impact clinical trial design. The company’s so-called design studio was the result. The studio, Staley tells 

, is an “opportunity for [the in-house] team to bring problems into the studio.”

The studio is in operation every month; Medidata has a set group of patient advocates and others they can call for specific disease states. Product managers, designers, and engineers bring their problems to the patient team. And these aren’t all new problems—some have been worked and reworked over the years.

One tool that has received such attention is eConsent. The patient advocates—all of whom have some disability—go through a mock scenario and assess every aspect: ease of setting up an account, language comprehension, even if the screen is presented in an “empathetic way.”

Some patient advocates have left their mark, says Staley. A lung cancer patient advocate that she worked with had been in seven lung cancer trials and had received 60 brain scans. The patient had contrast dye (gadolinium) in her brain, of which there are no clear safety guidelines to determine a maximum exposure to gadolinium, notes Staley. The clinical trial teams had not paid attention to this aspect of the patient’s care plan. A team will not act, says Staley, if information isn’t in front of them. “No one is overseeing all the data points,” she points out.

What patient simulations provide is control, notes Staley. “What we are delivering to the market is a team of patients who have looked at it, taken it apart, and put it back together. That is a cost savings right there, that gets back to industry.”

At CorEvitas, Stephanie Terrey, senior director, patient experience, has a team that gathers recruited patients, study coordinators, the principal investigators, and other physicians. An outreach manager is responsible for patient recruitment. Terrey says the protocols are at the almost finished stage. Once the patients and site staff role play key aspects of the protocol, debrief interviews are conducted with all participants and the feedback is “analyzed and then [CorEvitas] reports to the client,” she explains.

Terrey says there have been times when the patient response has influenced protocol change, such as reducing the number of visits, adding a car service, or easier samples drop-off; even the trial’s design from traditional to decentralized.

In another recent simulation between two principal investigators, Terrey relays that one spoke with empathy, the other went straight down the list of requirements. “We are doing the same protocol, but how patients responded was very different, which is valuable to inform site selection and training,” she says.

A decade ago, at Roche, Myles and colleagues built an “imaginarium” for Roche colleagues who were designing trials. Once a team had assessment schedules and the primary endpoint was established, imaginarium staff looked for real patients. It also brought in important team members—key opinion leaders, clinical scientists, and biostatisticians.

The team also included a graphic artist, for visual depictions done in real time. Myles says, “It was so useful, he would be listening for different things, for example, the emotion in the room.”

But those who were most surprised by what they learned from patients in the two-year patient imaginarium were the biostatisticians and clinicians. “They didn’t know how much they were asking of patients,” says Myles, for example, such as staying in an infusion suite for 12 hours.

Even communication among simulators can be difficult. Lovern says oftentimes, bioengineers and statisticians are speaking a language the decision-makers do not understand. The “modeling nerds” have the information, but they can’t communicate that meaningfully.

“It is like they use different vocabularies,” he says. “The engineers have to explain things in non-tech terminology, or even better, in a therapeutic context. This way, the physician can say, ‘I understand what you’re trying to say and why it’s important to the decision that I am trying to make.’”

With the successes and challenges of simulations, it still comes down to time and money. For industry, time is a business driver. Staley, a three-time cancer survivor, says, for the patient, time is life. Industry must move faster for patients.

 is a freelance writer for medical, clinical trials, and pharma information.

Global Pharmaceutical Industry - Statistics & Facts. Statista.com. July 14, 2023, 

https://www.statista.com/topics/1764/global-pharmaceutical-industry/#topicOverview

Schuhmacher, A.; Hinder, M., von Stegmann Und Stein, A.; Hart, D.; Gassmann, O. Analysis of Pharma R&D Productivity - a New Perspective Needed? 

https://pubmed.ncbi.nlm.nih.gov/37506762/

Krishnaswami, S.; Austin, D.; Della Pasqua, O.; et al. MID3: Mission Impossible or Model-Informed Drug Discovery and Development? Point-Counterpoint Discussions on Key Challenges. 

https://pubmed.ncbi.nlm.nih.gov/31955417/

Zhang, M., Yu, Z.; Liu, H.; et al. Model-informed Drug Development: The Mechanistic HSK3486 Physiologically Based Pharmacokinetic Model Informing Dose Decisions in Clinical Trials of Specific Populations. 

https://pubmed.ncbi.nlm.nih.gov/37313580/

FDA, Model-Informed Drug Development Paired Meeting Program (July 31, 2023). 

https://www.fda.gov/drugs/development-resources/model-informed-drug-development-paired-meeting-program

Patient Engagement Study Findings. DIA. 2018, 

https://engage.diaglobal.org/DIAPatientEngagement.html

Experts weigh in on the latest advances and adoption trends.

The Pulse of Modeling and Simulation in Clinical Trials

Patient Centricity Data Manager, Experience Optimization Team, IQVIA

Director, Patient Recruitment Strategy, IQVIA

Jill Guary, Patient Centricity Data Manager, Experience Optimization Team, IQVIA

Shana Hull, Director, Patient Recruitment Strategy, IQVIA

Sergio Sánchez-Gambetta, Director, Design Analytics, IQVIA

Patients and their caregivers know their unique needs and challenges better than anyone else. Though clinical trial sponsors cannot always anticipate the right path forward, actively listening to patients to understand which (and how) specific protocol elements can hinder or enhance engagement can help shape trial design. A comprehensive “patient empathy mapping” strategy can implement several key methodologies to help sponsors, their contract research organization (CRO) partners, and study teams gauge which patient-friendly elements— whether traditional, hybrid, or decentralized—can or should be considered in trial design and may be beneficial to the current target population.

Uncovering areas of perceived burden and patient preferences creates a strong foundation that can be adjusted and updated during trial design to best meet patient needs and to maintain their interest as valued partners in the process. This can be achieved through a multipronged approach, including activities described ahead, that provides the sponsor with a holistic scope of intelligence to better prioritize which trial design elements patients truly need or want.

Direct surveys: Capturing the patient voice

Hearing directly from patients and caregivers for real-time insights into participation motivators and barriers is critical to successfully preparing for patient-first trial design. Survey groups may be found in proprietary, company-specific patient communities and registries, advocacy groups, online communications organizations that reach rare-disease populations, and similar databases.

Because patient populations vary in therapeutic need, age, geography, race/ethnicity, and socio-economic status, surveying provides a holistic view of what barriers impact which subpopulations more and how to prioritize solutions accordingly.

For example, to learn from patients with cardiovascular disease or related risk factors about their experiences with and opinions of trials, IQVIA surveyed more than 600 screened and qualified patients (primarily 60-years-old and older with cardiovascular disease or risk factors) to determine potential participation deterrents. Trial length, number of required study visits, and mode of medication administration (e.g., oral, injection, etc.) were not priority concerns. Instead, financial motivators (e.g., participation compensation, lack of treatment fee, etc.) were the top-cited factors for deciding to participate. For patient enrollment strategies, this may mean prioritizing how we address financial barriers, such as including the patient stipend and related details in the informed consent form and ensuring that site coordinators can walk participants through financial concerns as part of site education during consent.

When gauging how decentralized a trial may need to be to ensure participation, the survey found that more than half of respondents recognized that virtual trial solutions like telehealth may reduce participation burden, and 82% said they’d be comfortable using a mobile device to participate. However, only 15% of respondents preferred in-home visits by a medical professional over in-person visits for routine study assessments, which require travel to and from sites. So, while we recognize the convenience of these decentralized trial components, it may not be a priority for this specific patient group. Rather, nearly half (49%) of the respondents said they would like travel assistance to and from site visits, which again emphasizes the value placed on financial support.

There are more than a billion unique non-identified patient records currently available. By leveraging an extensive breadth of real-world data (RWD) (e.g., claims data, medical records, etc.), sponsors can gauge how potential patients may view trial procedures based on their current experiences and usual disease care. These insights can show how many patients are familiar with trial procedure and how frequently they may experience it. Comparing that to how many times these procedures may be required within a trial, which could be more frequently, should provide a better understanding of the patient’s level of willingness to participate.

While drafting trial protocols, understanding patients’ willingness to participate based on what they believe is expected of them can improve recruitment and retention. By leveraging patient-centric insights gained through surveys, virtual, or face-to-face focus group discussions, RWD, and more, it is possible to apply analytics to assess patient burden based on trial design aspects that impact willingness to participate in studies or may require more aggressive mitigation.

 Using those analytics, protocol elements can be scored to quantify patient burden. Using burden-scoring for each element, sponsors can examine whether protocols are at the expected level of burden when compared to trial protocols for a similar phase, therapeutic area, disease, etc.

Approaching protocol design and operational planning with patient empathy. 

Featured Content

Applied Clinical Trials September 2023

Perspective and Hope From the ‘Other Side’

Executive Roundtable: The Rise of RWD in Clinical Research