Conversations with James F. Jordan: On the future of the biotech industry and why Proteomics is where it’s at.

James F. (Jim) Jordan is a long-time player in healthcare and life sciences. Along with his esteemed position at Carnegie Mellon University’s Heinz College, he plays pivotal roles in initiatives like StraTactic, the BIO Bootcamp, and the Healthcare Data Center. His corporate footprint includes senior positions in Fortune 100 companies such as McKesson Corporation and Johnson & Johnson.

As CEO of the Pittsburgh Life Sciences Greenhouse, he collaborated with numerous companies. An adept author, his notable works like “Innovation, Commercialization, and Start-ups in Life Sciences” and “The Intellectual Property Pyramid Assessment” are available on Amazon. Spanning academia to top-tier leadership, Jordan is a cornerstone in the healthcare domain.

He set aside some time to discuss the future of the health and biotechnology sectors with SCINQ.

How did Bio Bootcamp go this year?

The Bio Bootcamp went very well. One significant highlight was delving into cellular therapy, gene discovery, CRISPR, and other advanced topics. The importance of such bootcamps is increasing. In what I describe as the drug discovery pyramid, areas like ‘omics’, high-content screening, and system cell biology are where most of the inefficiencies lie. Only a tiny percentage emerges successfully from this process. Equipment companies are introducing faster, smaller, and cheaper tools, paving the way for startups to enhance drug pipeline productivity.

The contributions made at the Bio Bootcamp are vital. We’ve been conducting it for 17 years, and the curriculum offers a comprehensive guide. We educate participants on everything from drafting a business plan, negotiation techniques, and leveraging partnerships, to understanding patents, coding, setting prices, securing reimbursements, navigating regulatory pathways, managing boards, and fundraising. It’s a lot to take in, akin to “drinking from a firehose,” but such thorough coverage is crucial as there’s no other platform offering this knowledge.

On a personal note, I’ve authored a book on this topic. I’ve been engaged in both the corporate and startup sectors for years. About 15 years ago, I moved to Pittsburgh and collaborated with both a nonprofit and a for-profit venture capital firm. Our goal was to usher life sciences products from universities through the “valley of death” – a period marked by high failure rates, poor decisions, or inadequate planning. Our course addresses these gaps.

This year, I noticed a demographic shift in our participants. We saw business development representatives not only from renowned institutions like Mass General, Mayo Clinic, and USC Pittsburgh, which have established venture capital arms, but also from smaller hospital systems. This change was unexpected and intriguing. Their business model seems to revolve around investing in innovations that boost their productivity, a strategy I find quite brilliant.

It’s funny that you bring that up because hospitals are there on my list to discuss with you. Kind of like touching on all these things.

We observed an increased participation from smaller and international universities. While we’re accustomed to interactions with major universities, it’s heartening to see lesser-known institutions joining the fray, along with professors from all corners of the world.

When I converse with executives from smaller biotech firms, their CEOs, despite being accessible, frequently highlight the challenges on the business front. The scientific hurdles tend to get resolved, but the business side poses complexities.

Two significant themes resonated with me:

Understanding the contrast between corporate venture capital investment and company investment is crucial. Startups often squander resources by hiring a well-connected individual, hoping that person can secure them a deal. In reality, large firms involve a significant number of individuals in any given transaction, sometimes upwards of 200 people. The procedural knowledge of navigating these deals is more critical than knowing the right person. For instance, understanding the processes of firms like Merck or Sanofi is essential. I’ve emphasized this to our faculty repeatedly, and this year it seemed the participants grasped the message, especially after hearing stories about unsuccessful expensive hires.

Biotech startups often plan to sell their cell line to a larger company as they develop their drugs, as this approach can provide non-dilutive funding. However, many neglect to consider how these major corporations will engage with regulatory bodies like the FDA. At times, the larger company’s filing strategy with the FDA may disrupt the startup’s plans. Startups need to be more mindful of this aspect.

It’s essential that we address these nuances and prepare our participants for such intricate dynamics in the industry.

There seems to be a notable overlap between startup entrepreneurs and those who initiate or manage university labs. In many ways, university labs can be likened to small businesses; they often operate with specific budgets and need to make hiring decisions to sustain operations. What are some of the essential skills required to run a small business or a lab?

University policies on intellectual property (IP) differ widely, and startups need to be meticulous when navigating these policies. Some institutions claim that any research or innovation produced in their labs is owned by the university. Often, people spin off an idea, return to their university lab to develop it further, and inadvertently use investor funds to enhance the university’s IP. It’s crucial to be vigilant in such scenarios.

One valuable skill that researchers acquire from these labs is the ability to formulate robust hypotheses and test protocols. Many of these labs operate under a basic version of good laboratory practices. They are familiar with preventive maintenance, calibration, procedural setups, and documentation. This exposure sets a foundation for their future endeavors.

However, what many researchers lack is an understanding of scaling, validation, equipment qualification, and establishing consistent protocols. Additionally, they often aren’t well-versed in the FDA’s expectations regarding acceptable models for their research category. For instance, I’m associated with the Cancer Prevention Research Institute of Texas. This organization has an out-of-state advisory board that evaluates investment prospects. In our recent review of 20 proposals, a recurring issue was the use of animal models that didn’t align with industry standards. While such models might be suitable for publishing in journals like Nature, they won’t necessarily facilitate progress to clinical trial phases, leading to wasted time and resources.

Similarly, there’s a stark difference between a grant given to a university and an SBIR/STTR grant. When it comes to intellectual property, academic pressures can sometimes lead professors astray. They’re often under pressure to publish and may rush to their university’s office to patent a significant finding, only to realize later that they had already presented the information publicly, say in Australia two years prior. Such prior disclosure renders the idea unpatentable.

Hospital business models vary by country, but many hospitals historically operate with low profit margins. This tight financial situation can eventually impact patient care and has ramifications in various areas. Is the current hospital business model fundamentally flawed? Moreover, do DRGs play a detrimental role in this scenario? I’ve heard numerous complaints about diagnostic related groupings from various professionals.

Diagnostic Related Groupings (DRGs) are intricately linked with ICD codes, which are diagnostic and procedure codes that dictate payment. A single DRG can encompass multiple ICD codes, making the system more nuanced than it may initially seem. Moreover, physicians receive payments based on a separate set of codes: the CPT code or the hick-picks system.

It’s evident that the healthcare payment infrastructure is multifaceted and broken. To illustrate this, I often compare the system to credit cards. Some establishments refuse certain cards due to a mere half a percentage point difference in transaction fees. Yet, our current system permits insurance companies to retain up to 20% of funds, with the actual retention around 15%. The discrepancy is glaring.

In developing any efficient system, it’s essential to involve those who understand its foundational rules from the onset. In the medical context, these are the clinicians. Once a system is structured, the onus should be removed from the medical professionals, ensuring that they focus primarily on patient care. Currently, this isn’t the case, and this system’s inefficiencies persist.

Moreover, the entire US reimbursement system is riddled with complexities and inefficiencies. Years ago, companies had to invest heavily, between $250,000 to $300,000 per practice, for seamless operations. The advent of cloud computing has brought some relief, but challenges remain. For example, during a recent podcast interview, a doctor mentioned managing 31 unique contracts, each with its pricing system and method of documentation.

Hospitals face similar challenges. They maintain a ‘charge master,’ which lists the perceived cost of services. However, with numerous contracts, predicting revenue becomes an onerous task. Even a hospital’s chief financial officer would confess that forecasting revenue is daunting due to the unpredictable nature of costs and reimbursements. This ambiguity, coupled with the vast resources devoted to managing reimbursements—often at the expense of direct patient care—highlights the systemic issues plaguing our healthcare system.

So how would you begin to fix that? 

There was an interesting paper years ago that proposed a straightforward solution to our complex tax system: a flat tax rate of five cents on the dollar for everyone. This approach would not only increase tax revenue but also reduce the need for legions of lawyers and financial professionals. Of course, considering the vested interests that profit from the current reimbursement system, such a change seems unlikely.

In countries like France, there’s a common misconception that they only have a government-run healthcare system. In reality, while there is a central government system, private insurance also exists. However, private insurers in such countries typically operate on the backbone of the primary government system, streamlining the process.

Transitioning to a different topic, let’s consider the healthcare challenges faced by general practitioners. Take, for example, a doctor who has a patient pool of a thousand individuals. Among them, two have a specific medical condition, say pancreatitis. This condition, if not treated timely, can lead to severe outcomes like pancreatic cancer. Recognizing and intervening at the right time is crucial. However, if two patients, one with pancreatitis and another with psoriasis, walk into a clinic, their treatments would differ significantly based on their individual health profiles. With such a vast patient base, how can one doctor ensure personalized treatment plans for everyone? This is where Artificial Intelligence can play a pivotal role. I’ll delve deeper into the role of AI in healthcare later on.

Given the choice between generic antibiotics and more expensive brand-name or newer antibiotics, many hospitals opt for the former due to cost considerations. This poses a challenge for antibiotic developers. Their business model seems counterintuitive; if their antibiotics are effective, they eliminate the very infections they treat, thereby reducing their own demand. This contrasts with the chronic disease medication sector where long-term treatment ensures consistent revenue. Is there a sustainable business model that can support antibiotic developers, given these unique challenges?

There’s a complex interplay between pharmacy benefit managers and the genuine need for specific branded drugs. Pharmacy benefit managers often drive generics, profiting significantly from this distribution. An example that brings this to life is the case of a 42-year-old Parkinson’s patient. The branded drug worked wonders for her, but the generic did not. However, due to constraints, she couldn’t access the branded drug, highlighting the issue where bureaucratic systems override clinical judgment.

There’s a pressing need to leverage precision medicine and AI to make informed decisions. Bureaucratic algorithms shouldn’t replace a doctor’s clinical judgment.

Europe has taken steps to approve drugs based on outcomes. This approach rejects drugs that offer minimal improvement over existing solutions, ensuring only significantly better drugs make it to the market. For a drug industry moving forward, companies will need to ensure their new treatments offer a tangible difference to be accepted.

Moreover, the evolving pharmaceutical landscape is now leaning towards faster, more efficient drug discovery. The industry needs to adopt a “fail early, fail often” approach, focusing on making drug discovery more productive. Startups can play a significant role in streamlining this process. In the future, big pharma companies will likely shift their primary focus towards marketing and clinical trials, with lesser emphasis on organic discovery. This evolution underscores the importance of training more scientists and professors in this nuanced field. The industry has many unwritten rules, which is why resources like books and courses that demystify these aspects are invaluable.

How will AI change healthcare?

AI tools are emerging as innovative products in the market. For example, real-time information retrieval tools like Bard and ChatGPT are revolutionizing the way users access and interact with vast data pools.

These AI tools are not just standalone products but can be integrated to enhance and streamline existing healthcare processes. Hospitals can leverage AI for efficient administration tasks such as patient scheduling, monitoring population health, and more. In surgical spaces, AI’s integration is evident with robotic assistance. The case of augmented reality in spinal fusion surgeries exemplifies how AI can provide surgeons with more precise tools, ensuring better patient outcomes. The challenge of realignment post patient movement can be addressed with AI, offering surgeons an accurate representation to aid their judgment.

Looking ahead, AI is poised to redefine healthcare business models. The ‘hospital-at-home’ model is gaining traction, shifting the care paradigm from centralized hospitals to decentralized, patient-centric care. This model encourages treatments in more comfortable settings for patients, like their homes, while still ensuring they receive the best care. Another aspect is chronic disease management. For conditions like congestive heart failure or schizophrenia, AI can monitor patient behaviors, treatment adherence, and physiological markers. By tracking these parameters, AI can preemptively identify deviations or potential flare-ups, ensuring timely interventions. This not only improves patient outcomes but also translates to significant cost savings in the long run.

Overall, AI’s impact on healthcare is multi-dimensional, touching upon products, processes, and business models. Its evolution in this space promises a future of more efficient, effective, and patient-centric care.

Now, regarding gene editing and gene therapy, can you briefly describe the current state? How do you envision its future evolution, and what are some obstacles to its advancement?

Certainly, advancements in the realm of genomics and proteomics are signaling a seismic shift in how we understand and tackle diseases.

The initiation of the Human Genome Project exemplifies how collaboration and government intervention can accelerate scientific progress. While the initial stages of this venture seemed daunting, technological advancements rapidly accelerated the mapping of the entire human genome, revealing an intricate network of information that forms the basis for life as we know it.

Now, as we delve into the world of gene editing, the foundational knowledge gained from the Human Genome Project provides invaluable insights. Gene editing technologies like CRISPR have started rewriting the rules, with the potential to correct genetic disorders and tackle previously untreatable diseases.

However, while our understanding of genomics has expanded, our grasp on proteomics – the study of the set of proteins expressed in an organism – remains relatively nascent. Given that proteins execute most of the functions in our cells, their importance cannot be overstated. Hence, forging collaborations and pooling resources, akin to what was done with the Human Genome Project, is imperative for us to better grasp the intricacies of proteomics.

Complementing these endeavors is the rise of bio-printing and synthetic biology. The capability to create realistic models of human tissues and organs not only offers more accurate testing grounds for new treatments but also potentially mitigates the ethical concerns surrounding animal testing. The future promises the ability to replicate complex human systems, drastically refining our preclinical testing strategies.

In essence, as we stand on the precipice of these scientific breakthroughs, embracing collaboration, investing in technology, and championing innovation will undoubtedly expedite our journey towards revolutionizing healthcare and understanding the enigma that is the human body.

How common Do you think Gene therapy will be In the future?

This is just my opinion. In my view, understanding proteins may be even more crucial in the long term. The question then becomes, what impact will editing a gene have on proteins? Will it affect multiple proteins, potentially thousands or tens of thousands? The question is whether this is a beneficial, upstream approach or if we need to focus on being more targeted and selective regarding which individual proteins we choose to modify. To me, it seems premature to focus solely on gene editing given our current level of knowledge and resources. However, I believe there is a pressing need for a proteomics project to better understand the complex interactions between genes and proteins.

What is the state of proteomics right now?

Just plodding along at a slow pace, I mean, if so, again, I think the main problem here is similar to the human genome, that creating proteomic discovery equipment isn’t profitable.

And you must go through distributors because there are countless places to sell them, right? So it’s not like Johnson & Johnson selling a product and sending representatives to hundreds of hospitals. Instead, you send a representative to a single university or hospital to sell a few pieces of equipment. That won’t generate enough returns. Therefore, I assume there are people who are struggling to keep up. When I look at the current state of affairs, I see that Thermo Fisher Scientific has recently introduced Orbitrap Fusion Mass Spectrometry, which represents progress over existing technology. However, judging a protein’s structure based on how it separates in a gel is still an antiquated method. We should be at the electron scanning microscope stage by now, but there’s no financial incentive to pursue it. Eventually, I suspect that proteomics will become the weak link in the chain of biomedical research, thanks to the management theory known as the tightrope theory. There’s a peg in the back holding everything back, and I believe proteomics is that peg.

Will artificial intelligence (AI) accelerate the advancement of proteomics? Possibly not. Someone has to invest in it first. As an illustration, starting with minimally invasive surgery required a substantial initial investment, but once that happened, computers and AI were able to revolutionize the industry. However, someone had to initiate that investment. Similarly, we may eventually reach a point where AI enhances the process of interpreting large amounts of proteomic data, but until then, there’s little motivation for anyone to invest in the field. Let’s take stock of all the proteomic companies and determine how many exist. Most of them are large distributors rather than small firms pushing the envelope and developing the next breakthrough. I believe AI will only be applied to improve the process when there’s sufficient profit potential. Unfortunately, venture capitalists and angel investors aren’t currently interested in supporting proteomics research.

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