Michael Christel

Casey McTigue, managing director of professional services at SRS Acquiom, which provides support for M&A transactions across various sectors, discusses the firm’s recently released study on milestone payment and earnout trends in life sciences M&A, including during Covid; their influence on sales and development milestones; how parties account for transaction disputes; and the potentially shifting power dynamic in pharma dealmaking post-pandemic.

Podcasts

Administrator on how pandemic has affected milestone events—the so-called “biodollars”—in life sciences’ deal structures

M&A Milestones: The Covid Impact

AJ Loiacono, CEO of Capital Rx, which doubles as pharmacy benefit manager (PBM) and health tech firm, dives into his inspiration for entering the PBM arena, the company’s formula for providing more transparency into drug pricing and the importance of simplifying complex administrative and workflow barriers between pharmacies, payers and other entities to keep up with the future of healthcare.

Software company on mission to infuse efficiency into the pharmacy benefits system

Solving the Drug Pricing Conundrum

David Coman, CEO of Science 37, a decentralized clinical trials platform company, discusses his organization’s recent public debut via the SPAC route, how that’s powering its mission to enable universal and diverse access to clinical research, and the potential of “agile clinical trial” designs in disrupting the future drug development landscape for the better.

The latest episode of 

Capital infusion, Nasdaq debut ignite ‘agile’ approach to attracting patients without bounds

Diversity and Disruption in Clinical Research

Pouria Sanae, CEO of ixlayer, a technology company, explains the obstacles surrounding the distribution of remote lab tests pre-, during and post-pandemic, the shift to at-home Covid screenings, the logistical challenges in collaborating with different US regions, and the concept of the “pre-patient” model for preventative care.

A digital health company’s quest to improve the delivery and scaling of lab testing

Diagnostic Testing During a Pandemic

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

Articles

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

AI’s predictive power on the big stage

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