With big data and advanced analytics progressing from fad to frontier status, Pharm Exec examines the readiness of the biopharma industry to take advantage of this promise. Can new digital health tools be harnessed to help advance new treatment approaches and aid decision-making? 

 (November 29, 2016), “is providing pharmaceutical companies with an opportunity to gain novel insights that can enhance and accelerate drug development.” From their vantage point, the authors explain that they “have seen how the pharmaceutical industry has leveraged big data for some potentially revolutionary advances and the challenges it has faced along the way.”

	Copping is a data scientist and global head of real-world data science analytics at Genentech; he and Li point to the company’s advances in the area in the last two years, building a big-data infrastructure (“a platform that can analyze billions of patient records in seconds”) and “aggressively recruiting and developing people with the requisite skills.” But the authors concede that “it will not be easy to learn how to tap the full potential of real-world data.” The tremendous opportunities generated by these advances are tempered by considerable resourcing and capability development challenges. 

	Indeed, not many pharma companies, certainly in the small or mid-size range, can boast of matching Genentech’s progress in the big data field. Those that can include Berg Analytics, which began investing  efforts into the idea of data-driven research as long ago as 2007. Berg co-founder and chief analytics officer, Slava Akmaev, told 

 that the company now has “a well-developed platform that we have used to populate our internal pipeline of therapeutic targets and diagnostics and guide the development of some of our flagship products.” One of those is a cancer drug already in Phase II trials. 

	But while Akmaev has observed “a lot of resolve in the last two years on the big pharma side to invest in big data analytics,” he says it’s difficult to gauge how quickly companies just starting in the space will be able to show that their artificial intelligence (AI) or big data platforms can deliver products to the market.

	For Steve Labkoff, executive director, medical affairs strategy, at Purdue Pharma and a fellow of the American College of Medical Informatics, a key barrier preventing pharma from launching successfully into big data optimization is its workforce. “There really aren’t enough well-trained people who understand how to deal with big data problems,” he told 

. “Organizations that are not that well informed think that big data is simply an IT problem and if you throw enough servers at it you are 

going to be able to solve your problem or get better insights. But that is about as naïve a perspective as you can get. It’s like going out and buying a high-performance car and getting your local mechanic to take care of it.”

	Labkoff believes organizations need to take a three-pronged approach to glean the true value of big data. “First, you definitely need IT people,” he says. “But, secondly, you need data scientists, or informaticians, who are a different breed and who understand the nature of where the data comes from and what it actually means at a granular level. For example, healthcare data needs to be put into a clinical context; if not, people start drawing conclusions that may be erroneous.” 

	The third leg includes the business leaders “who themselves have to have some degree of insight in terms of asking the right questions about the data,” says Labkoff. There are companies executing this approach. At Pfizer, Labkoff helped to establish “what was at the time probably the first healthcare informatics department in any pharma, back in 2003.” But, he adds, “Pfizer, for example, is a resource-rich company; it has the luxury of being able to build what it needs or to buy it in the market place. Other companies don’t have that luxury; they have to be far more selective, and they’re competing for a rare commodity.”

	Akmaev believes that the data scientist shortage has improved compared to five years ago. "The pool of candidates is growing," he says. “We see a lot of people with a quantitative background and experience leaving the genomics and next-generation sequencing organizations and opting to work more on the AI side of things, analyzing multi-omic data, for example.”â¨

	The wider problem, says Akmaev, is rooted in industry culture. “Looking at it from my background in mathematics, I feel that pharma is one of the least quantitative industries,” he says. “Adoption of advanced analytical tools, mathematical modeling and AI-based approaches to analyzing data has been slow compared with other industries such as finance and IT.” Akmaev notes that many leaders and

 decision-makers in the pharma space have backgrounds in the “non-quantitative sciences, such as biology, biochemistry, and medicine.” Compounding that is an ongoing skepticism of systems biology tools.

	“Pharma is still suffering from the hangover of the early 2000s, when we had the promise of the human genome,” says Akmaev. “There was a hype at that time that by using bioinformatics and mathematical methods, we can solve human disease problems, and it fell short of that.”

	If this “cultural barrier” requires wholesale change management, it is not just in pharma, but across the healthcare spectrum, says Dharmendra Sahay, managing principal at ZS. Trusting combined data that is generated from various sources “is going to require a mindset change not just on the part of pharma but physicians and patients as well.” According to Sahay, getting to a position of trusting the data lies in leveraging its partners to ensure the right platforms are in place to analyze unstructured data. This would ensure its provenance, accuracy and usability.

	Similarly, Joel Selanikio, MD, CEO and co-founder of Magpi, believes that technology holds the key. He told 

: “I’m not worried about a shortage of data scientists, as I think in the short-term the market is addressing that. What’s more interesting to me are the techniques that are being created, using AI and other technologies, to be able to put vast quantities of data to use-and which may render ‘data scientists’ obsolete.”

	As pharma considers its big data workforce options, the wearable devices space is pushing ahead regardless. With the consumer space rapidly expanding with data from wearables, Selanikio contends that “over time, more and more health data will be coming from outside the healthcare industry.” A 2016 research 

 by Validic reported that the next five years will see pharma and biotech firms turn in much larger numbers to digital health tools, including wearables, “to better collect, analyze and act on patient-generated health data.” The survey revealed that over 60% of 200 biopharma respondents said they have used digital health technologies in clinical trials, and over 97% plan to use these tools increasingly.

	A closer consideration of the pharma perspective, however, shows a more measured approach to patient-generated health data. Labkoff says: “I think wearables right now are where PDA (personal digital assistant) computers were in the mid-1990s, when the Apple Newton and PalmPilot came out. There was all this promise of what they could do, but they were basically 10 years too early; even Apple failed at this time. The jury’s still out on wearables; I would advise taking a watch-and-wait approach. You may be able to measure something 6,500 times a day, but that doesn’t mean that the information is going to be meaningful or useful.” 

	Akmaev agrees. “We need to make sure that this change in the industry is not overhyped and that it delivers three to five years down the road,” he says. “By that I mean we have tools and applications that can actually drive real clinical decision-making.” 

	While there is broad consensus that wearable devices become more ubiquitous in particular therapy areas, Steve Rosenberg, Oracle’s senior vice president and general manager of its Health Sciences Global Business Unit, says, “I don’t think we’re going to see the Wild West of wearables.” 

	Oracle is working with a handful of pharma companies that have experimented with various devices and are now trying to figure out their practical application in clinical trials. “Our role in this ecosystem,” says Rosenberg, “is not to decide what wearables should be used or what studies a certain device should be used in, but to provide the technology and the tools 

so our customers can make those choices and not have technology be the barrier to making those choices.” 

	The challenge for pharma is not so much the data from wearables but “when do we use the device to collect the data from a wearable standpoint,” he says. Devices generate an enormous amount of data but the data must be fit for purpose. 

	“You’ll see wearables being used where it makes sense to collect the data on a wearable, but pharma has to be more cognizant of convenience to the patient,” says Rosenberg. “Every time we put a wearable into a clinical trial, for example, we have to educate the site on how to train the patient to use it.” 

	The wearables phenomenon takes things back to the trust issue, and concerns about patient privacy have long been looming obstacles on the road to big data promise. “A lot of patient data is currently not identified and the patients have to allow its usage,” says Sahay. “They may allow the provider to use it, but may be suspicious of pharma using it.”

	Akmaev suggests the industry is perhaps getting stuck on this point. He explains: “Of course we need to maintain confidentiality and make sure none of the personal patient information is misused. But if you take it in perspective, I don’t know if the conversation about privacy in healthcare is much different than in other industries.” 

	For Labkoff, “The privacy issue shouldn’t be an obstacle; it’s just one facet of the problem to be solved. Big data doesn’t generally care about individuals’ data; most big data projects are looking at aggregate information to make a decision around a population, whether that is a population of doctors or patients or drug candidates.”

	Where the privacy issue does come into play, according to Rosenberg, is in genomics. “You may have a full genome sequence from a patient; that is personal, identifiable information. But while it might be individual, it’s not necessarily identifiable,” he says. “But there is hesitation in the academic research centers to share that data with others. Still, I think that when we get to the point when all that data is becoming mainstream, the privacy issues will be resolved.”

	If we see big data as residing on Gartner’s Hype Cycle somewhere near the “peak of inflated expectations,” then the next, inevitable move is downward into the “trough of disillusionment,” before recovering to a “plateau of productivity.” This may not be a negative thing; it could allow some pharma companies to take the “watch-and-wait” approach Labkoff advises with wearables before hurrying into costly big data projects that may ultimately have little value. 

	If big data is currently overhyped, there is consensus that it will deliver results in the long run. “I think it takes hype to get enough movement, to get pragmatism into the process,” says Rosenberg. “There’s not as much data as there is, for example, in banking applications. We are not talking about an unmanageable amount of data; it’s just a question of what it’s going to be used for. There’ll be pockets of success along the way. Two to three years from now you’ll see a pretty large proliferation of data, but until then the success will remain in certain therapeutics areas.” 

	Labkoff agrees that “we’re in the midst of a classic hype curve, definitely.” But, he adds, “there are very focused-use cases for big data, and it is making a big difference in certain places, such as genomics, patient recruitment, and virtual clinical trials. I don’t think it is going to suffer the slings and arrows of other ‘fads.’”

	The futurist Roy Amara once famously said that technology’s impact is overstated in the short term but understated in the long term. “You found that in the dot.com boom, and there is a bit of that with big data,” says Sahay. “If pharma and healthcare are going to expect big data results equivalent to what, say, Amazon or Uber are seeing, then they are going to be disappointed. But as the barriers are addressed in the medium to long term, the impact will be transformational.” 

 Online and European Editor. He can be reached at 

Articles

Features

With big data one of the hot healthcare topics of the moment, we gauge pharma’s response to the hype to see if the industry is poised to take advantage of the promise of advanced analytics.

Big Data: Is It Crunch Time for Pharma?

Amid the push for a more intelligent and predictive supply chain-focused on digitization strategies-life sciences companies are looking to keep pace. Overcoming challenges in mindset and culture may be the biggest hurdles in this process transformation.

	As the everyday world becomes increasingly engaged with connected devices-from Fitbits and Apple Watches to smart refrigerators and HAPIforks-and high-tech industries like electronics and banking continue to advance their operations with cyber physical systems and cognitive computing, the shift to full digitization is drawing the pharma supply chain into its inevitable gravitational pull. 

, a PwC Strategy& report by Dr. Marcus Ehrhardt and Peter Behner, published in October 2016, states that the “first wave of the digital revolution focused on using technology platforms and data analytics to better understand customers… [T]he next wave of the digital revolution is about transforming operations.” 

	True to pharma’s conservative style, some supply chain practitioners are eyeing with a degree of apprehension this encroaching era of “Industry 4.0”-originally an initiative introduced by the German government to link resources, information, objects and people, and now a globally accepted term representing “the fourth industrial revolution.” Practices at the logistics end of the supply chain, for example, remain, somewhat resistant to digitization. As PartnerSave’s Alan Kennedy told 

, pharma logistics is still “10 years behind in this area”; there is a lot of ground to be caught up. “The day-to-day reality for many in logistics is still one of traditional, paper-based systems,” he says. While there are valid reasons for this, and for other areas of pharma approaching Industry 4.0 with tentative steps, Ehrhardt and Behn’s report counters that such caution “is becoming a hindrance.” 

	For Ehrhardt, a leader in the life sciences strategy business within Strategy&, PwC’s strategy consulting group, digitization “holds tremendous potential in helping pharma companies transform their operations for improved supply chain performance and integrity.” He told 

, “Today’s pharma supply chains are characterized by long lead and throughput times, relatively low utilization levels, high inventory, limited end-to-end visibility, etc., which all add to their cost. While at the same time complexity and price pressure is increasing significantly, calling for more agility and productivity in pharma operations.” 

	Digitization can help pharma companies address these challenges, says Ehrhardt. “Pharma plants or production lines, for example, typically experience high levels of downtime. Through digitization, this downtime can be reduced significantly. Production lines with sensors providing operations data paired with innovative software solutions and analytics, machine-to-machine communication and machine-learning algorithms allow for seamless processes, predictive maintenance, and automatic corrective actions. We already see this being applied in electronic plants or airplane turbines, or heavy equipment for oil and gas production.  It’s already out there.” 

	The effort to realize this operational opportunity and successfully build a digital supply chain ecosystem requires a high degree of investment and a wide-ranging overhaul of supply chain processes, incorporating virtual supply chain control tools, cloud-based information architecture and a digitally enabled physical supply chain. But the necessary transformation that most commentators agree may be the hardest to achieve is a cultural one. Even Alessandro de Luca, chief information officer  for the healthcare business sector of Merck KGaA, Darmstadt, Germany-a drugmaker at the forefront of digitizing its pharma supply chain-agrees that the biggest challenge is “mindset.” De Luca, previously the company’s head of global supply chain network operations, told 

, “I keep repeating to everyone in my team and beyond that we need to change the mindset that says, ‘We’ve always done it this way in the past.’ This mindset is more of a challenge to supply chain digitization than the challenges coming from things like business processes, software development and technology.”

	De Luca is, nonetheless, convinced that transformation of the supply chain is fundamental. “The supply chain of the future should fully rely on digital solutions,” he says. “Our vision is to have a self-driving supply chain powered by artificial intelligence. As Tesla has developed self-driving cars, we want a self-driving supply chain ‘without pilots.’” De Luca envisions an intelligent supply chain that automatically balances supply with demand, with demand forecasting systems based on historical forecasts and predictive algorithms. “The role of demand planner will evolve,” he says. “Instead you will have demand architects. Same for the supply side; this role will also evolve as supply will be totally automated. This will be the first step toward full digitization.”

	For Kennedy, however, changing roles also equate to loss of jobs, especially at the logistics end. “There are social and internal consequences. We have a very fragmented, very conventional, very traditional industry when it comes to logistics, and here we are talking about cutting-edge technologies. Lots of people will see a downside to digitization, a loss of jobs, the loss of their own job.” 

	Kennedy points to a recent conference in China on Industry 4.0, where a speaker from the German-based logistics association BVL discussed the probability of dedicated jobs being replaced by robots or computers in the next few years. “He showed that freight forwarders have a 99% chance of losing their jobs because of new technologies,” says Kennedy. “There won’t be a need for them.”

	De Luca agrees that “automation will definitely replace repetitive tasks in human activities.” But, he says, “those who have the agility to reinvent themselves will survive and be on the leading edge. This is why we believe in diversity in every sense, including a blend of younger people working with the older generation. At Merck KGaA, Darmstadt, Germany, we are hiring younger people, while investing significantly in training development for older ones, in order to build the required skill sets to implement the newest technological solutions. This is how the mindset change can happen faster.”

	For supply chain practitioners resistant to cultural change, however, the regulatory considerations surrounding digitization may only compound the overriding sense of caution. As Ehrhardt told 

, the pharma industry historically has been cautious in adopting new technologies and making changes in its operations, due to the regulatory environment pharma companies operate in. Each change introduced in the production process of a product, whether it is going to be produced in a different manufacturing site or with different manufacturing equipment, have to be registered from a regulatory point of view for each affected product.

	These regulatory aspects, of course, add hurdles and cost to the business case of operations changes and often create push-back especially for older products or smaller volume products. But Ehrhardt insists that regulation “is not a ‘stop sign’ to the digitization of operations.” As long as a company does not change the production process itself in a way that it may affect product quality, “it’s less of a problem,” he says. Putting sensors on a machine to monitor operations and ascertain how long it has been running, for example, “is not really changing the machine or production process 

. A lot of operations digitization can be achieved without the problems of re-registering a product.”

	Kennedy, however, says that regulation presents “a conflict between individual companies and national needs.” Many of the far-reaching regulatory requirements in pharma “revolve around very decentralized, paper-based systems,” he says. 

	And changing that takes time. “The Luxembourg government, for example, launched the Single Window for Logistics (SWL) program in 2013/2014,” Kennedy explains. “This is well under way but it’s not programmed to finish until some time in the 2020s. Individual companies, however, may be looking to change their processes now, and therein lies the conundrum.” 

	Similarly, for Kennedy, the Declaration of Amsterdam, which is aimed at removing all the legislative barriers to rolling out vehicle automation, is “a good example of the global, regulatory picture being far removed from the experience of ‘the little guy.’”

	According to Ehrhardt, certain aspects of digitization are probably easier for smaller companies, as they typically have less complexity overall. “If you are a top-five global pharma company, it is a lot more difficult and a bigger commitment to say, ‘Let’s do digitization and roll it out straight away,’” he says.  “Pharma companies should approach the digitization of their operations in waves, starting with a pilot project, and then start to roll it out to sites, different regions countries, business partners and ultimately to the whole supply chain. This is what we’re starting to see. No one is really implementing broadly at this point, we’re still at the experimental stage.”

	Even for those wholeheartedly on board with the concept of supply chain transformation, the increased security threat surrounding digitization presents a conspicuous challenge. As digital processes become more sophisticated, so do the hackers and cyber criminals. Kennedy notes that “there will soon be more than 100 trillion sensors collecting data in the world, not all connected but increasingly they will be interconnected. The repercussions of something going wrong in a supply chain is exponentially compounded with an Internet of Things (IoT) approach.”

	Of course, there are risks and concerns: Could someone try to hack into your digital operations network, either to obtain information for competitive advantage or to engage in criminal blackmail activity? Could someone try to take control of your production? “These are not scenarios that are unthinkable,” says Ehrhardt. However, he adds, “There are very similar risks in the financial sector, and they are always striving to find solutions. I don’t see these risks as a showstopper. It does take effort and money  to secure systems, of course, but whatever you choose to do, I don’t see that anyone will stop this wave of digitization.” 

	De Luca asserts that, of course, pharma cannot allow any security or quality failures. “We cannot afford any breach, whether that relates to patient privacy, drug safety, quality, or production capabilities.” With safety, quality and efficacy factors always in mind, he says, “Pharma is always going to safely learn behind some of the other industries in terms of supply chain digitization, and is likely to be on the following path rather than the leading path. But with digital technology it can catch up faster.”

	From Kennedy’s perspective of caution, the obstacles facing supply chain digitization “aren’t issues that can’t be resolved, but they are issues that have to be resolved.” Key to making it work, he believes, is collaboration. “The reality is that companies have got to be much more prepared to work in cooperation,” he says. “We’ve seen this in the retail industry. Some of the big retail suppliers are now doing consolidated deliveries. For this, they all had to agree, which was almost counter-intuitive to them because they are all essentially in competition. But it’s the right solution. There shouldn’t, for example, be two half-full trucks heading toward the same supermarket to deliver their loads. Some industries have realized this, but this hasn’t happened on the pharma side.” 

	If pharma does not adopt such collaborative approaches, says Kennedy, digitization will be “a very piecemeal, long-haul adoption.” He believes that, for some of the smaller players at least, the best solution at this point is to “sit on the fence.” 

	“There is an air in some quarters of supply chain digitization being the next dot.com bubble,” Kennedy says. “It has such huge potential, but maybe it is not the right thing for everyone to do. Make the wrong decisions now and a company could be bearing some very serious consequences for their bottom line in the future.”

	De Luca agrees that a new perspective on partnership is essential. Merck KGaA, Darmstadt, Germany is “now looking at hardware and software providers not as providers but as real partners,” de Luca says. “We want to join with a few selected partners to develop tailored solutions in terms of technology, and together we can discover how to leverage the technology to drive our business better.” 

	De Luca’s commitment to full supply chain digitization will see Merck KGaA, Darmstadt, Germany achieve it, he believes, “in the next 18-24 months.” The wider pharma industry, he says, will get there in the next three to five years. 

	While Ehrhardt would not yet go so far as to say “digitize or die!”, he agrees that the next three to five years will see a lot of changes in the overall operations space in pharma. He warns that if companies don’t do anything, or fail to start setting up their organizations to build digital capabilities, it will be difficult for them to “jump on the bandwagon.”

	“Implementing new technology and, thus, transforming global operations is a complex task; it cannot be done in six months,” Ehrhardt says. “That’s why we urge leading players who want to stay at the forefront in the future to embrace it.”

	Those companies that seize the initiative, he adds, can give themselves a sustainable competitive advantage, operate with greater agility, cost-efficiency, and control, and ultimately provide better care for patients. 

Amid the push for a more intelligent and predictive supply chain, is it imperative for pharma companies to advance with their supply chain digitization strategies now-or is there still time to “wait and see?”

Setting Sights on the Smart Supply Chain

	“Big Money goes around the world,” is the first line of a Rush 

 that goes through my head whenever I hear the words “Big Data.” Here are the rest of the lyrics for the first verse-”Big money goes around the world, Big money underground, Big money got a mighty voice, Big money make no sound, Big money pull a million strings, Big money hold the prize, Big money weave a mighty web, Big money draw the flies.” And on from there. But it makes sense, in a way, that if you replaced the word Money with Data in each of these

 sentences, it would work. Big Data started out in pharma overhyped a few years ago, and according to many IT experts, hasn’t yet reached its potential. But it will.

	Let’s talk about the hype. In a recent discussion I had with technology and clinical trials experts, we struggled over using the term “Big Data.” Basically, we agreed that everyone hates the term because of the hype factor, but used it because people understand the term as it is referenced in journals and the like. 

	In this issue, our European Editor Julian Upton 

looks at big data and pharma’s response

 to see if the industry is poised to get past the hype and take advantage of its promise. 

	“I think it takes hype to get enough movement, to get pragmatism into the process,” Steve Rosenberg, Oracle’s SVP and general manager of its Health Science Global Business Unit, told Upton. “There’s not as much data as there is, for example, in banking applications. We are not talking about an unmanageable amount of data; it’s just a question of what it’s going to be used for. There’ll be pockets of success along the way. Two to three years from now you’ll see a pretty large proliferation of data, but until then the success will remain in certain therapeutics areas.”

	Further, Upton interviews Berg Analytics Co-founder and Chief Analytics Officer Slava Akmaev, who says, there has been “a lot of resolve in the last two years on the big pharma side to invest in big data analytics, it is difficult to see how quickly companies just starting to move into the space will be able to show that their artificial intelligence (AI) or big data platforms can deliver products to the market.”

	Before we get to AI, however, let’s discuss what has to happen to data in the next two to three years. In the technology panel referred to above, and included in this 

, David Evans, Managing Director, Accenture, LLP, said that getting to AI/Cognitive Computing is not an easy thing. He explained: “In machine learning, it has to have a gold standard of large volume and high quality endpoints to learn or determine decisions. You still have to build the big database using high-quality data in order for the machine to interpret what the unknown is so that it can learn.” This is how Big Data relates to AI. Without large volumes of high-quality data in a consistent format (achieved through standards), the pattern recognition, decision support, signal detection, etc., is not achievable. 

	Evans believes that data can also come from the Connected Computing sector-defined as all the Internet of things (IoT), wearables, connections, smartphones, sensors, etc., that offer real-time data everywhere. “As the connected universe expands, you will have large volumes of high quality data to go to Big Data, to inform analytics,” Evans said. “But currently, we have a 

	As Rosenberg noted, there will be successes along the way, in certain therapeutic areas. And for that we can look directly to next-generation sequencing (NGS) data that characterize tumors and immune systems in the area of immuno-oncology. In this issue, Brandi Davis-Dusenbery, Senior Vice President of Science & Product for Seven Bridges Genomics, 

 biopharmaceutical executives have to de-silo information to take advantage of full potential of these NGS data sets, which can be useful in multiple settings and be used more in analytic decision-making. 

	While we learn to accept that Big Data is not hype, that pharmaceutical companies are starting on the path to high quality, shareable data that gets them to AI and better decisions, executives too will have to look at the costs. Investing in Big Data is the path to the future. Maybe the path doesn’t lead directly to Big Money, but it will lead to big changes in how drugs are developed and delivered. 

From the Editor

No longer just hype, investing in big data is the path to the future for pharma, even if big returns aren't a direct result.

Big Money and Big Data

	Amid all the hustle and bustle at the recent JP Morgan Healthcare Conference, the industry’s largest annual confab for hopeful dealmakers, 

, with support from health communications agency Russo Partners, convened a roundtable discussion with CEOs from seven emerging biotechs to explore the prevailing issues and challenges facing the biopharmaceutical sector in 2017. Does true innovation-or the potential of new technologies and novel science in transforming medicine-still hold the trump card to opportunity in today’s turbulent and uncertain healthcare, business, and regulatory climates? Staying above the fray will be key to pushing hopeful new biomedical discoveries to the clinical finish line.

We wanted to get a well-rounded perspective on the state of biotech at the moment from a diverse representation of companies in this space. Could you describe what is unique about your respective organizations?  

: We are a clinical-stage organization that is advancing a therapeutic biofiltration device that has a specific affinity to bind circulating viruses and cancer-promoting exosomes, based on the unique structure that resides on the surface of these disease targets. We are in a feasibility study with the FDA-a modular study that allows us to branch off into multiple viral targets. The exosome field is emerging on both the therapeutic and diagnostic fronts. Beyond our efforts to target tumor-derived exosomes, we have been advancing a candidate exosomal marker to detect

encephalopathy (CTE) in living patients. A former teammate of mine (Joyce played football at the University Maryland and briefly with the Denver Broncos in 1984-85) passed away in 2008, and he became the second person diagnosed with CTE by the Boston University CTE Center. Based on our knowledge of exosomes, we were invited to participate in the first NIH study of CTE. The most high-risk group to suffer from CTE is former NFL players. 

	In this study, we demonstrated that exosomal tau, which we call a TauSome, might provide a means to monitor the abnormal accumulation of tau protein in the brain, which is the hallmark of CTE. We were able to demonstrate that TauSome levels were significantly higher in subjects who played in the NFL as compared to controls from the same age group. We also observed that cognitive decline seemed to correlate with high TauSome levels. 

CHRIS ANZALONE, Arrowhead Pharmaceuticals:

 We are a RNA interference (RNAi) company focused right now primarily on hepatitis B and alpha-1 antitrypsin deficiency. We also have a program, ARO-F12, for thrombosis and angioedema. We’ve got some oncology programs as well. 

 We’re a regenerative medicine company focused on esophageal cancer, a silent killer. The disease afflicts about half-a-million people annually worldwide. In the US, there are 17,000 new cases reported each year and 15,000 deaths from the disease. The standard of care for a patient is to undergo an esophagectomy, in which all or part of the esophagus is removed. This leads to the need for the patient to undergo a gastric pull-up, which is predictably medieval. The alternative is a colon interposition, in which the colon is used to replace the esophagus. The five-year survival rate for these patients is 15%. We are focused on improving the therapeutic options for these patients. 

	Our Cellframe technology uses the patient’s own mesenchymal stem cells that are adipose-derived from a biopsy. We isolate and extend the mesenchymal stem cells and put them onto a synthetic scaffold. The implant is placed where the tumor was removed. After 21 days in the patient, we remove the synthetic scaffold, leaving only a regenerated, fully functioning esophagus. We hope to file an IND (investigational new drug) with the FDA this year, as well as proceed into human clinical trials. 

Fundamentally, we are using lipid nanoparticles to deliver drugs more effectively. That is, we can create more targeted drugs as well as those designed for longer duration. The areas we cover include oncology, where we can use these nanoparticle delivery systems to not only package many small molecules but also coat these nanoparticles with antibodies on the surface. Not only is the drug going to the tumor site, or where we may want the drug to go in a tissue, but it can also be directed into a specific compartment within the cell. We’re looking to target specific molecules in specific compartments as well; that is another program still at the discovery stage. In the area of pain management, we have one program currently in the clinic in osteoarthritis.

 Chromocell is a 15-year-old company. We started with a platform technology, called Chromovert. Chromovert allows us to create hard-to-express human receptors, such as for taste, pain, heat, etc. We used these receptors to do high-throughput screening to find new molecules that stimulate or block these receptors. For example, finding a molecule that stimulates the sweetness receptor, or a molecule that blocks the pain receptor. Initially, we focused on taste receptors and worked with consumer-goods companies to find new sweetness or saltiness enhancers. While we built our flavors business, we used a portion of the income to go into pain research, focused on a target called NAV1.7, a pain receptor. 

	The interesting thing about NAV1.7 is that it’s highly validated genetically and it’s a non-opioid receptor. We developed that receptor and did a high-throughput screen to find a molecule that blocks the NAV1.7 pain receptor. After development of the molecule, in 2015, we got closer to the stage where we thought we had a valuable asset. We partnered this program with Astellas Pharma and entered into clinical trial with our new pain medication in 2016.

We are strictly in small-interfering RNA (siRNA). We now have the most robust siRNA clinical pipeline in the world. We have two products in Phase III trials and three in Phase II. We also received the approval to start dosing patients in India and China. We had to teach the authorities there what “siRNA” is. They told us that it would be difficult, but we overcame the difficulties and are dosing patients there. Two out of the five indications are for delayed graft function (DGF) for kidney injury and three are for  ischemic optic neuropathy (ION), which is a rare disease, and age-related macular degeneration (AMD) and glaucoma. 

We are a company that is focused exclusively on regenerative medicine. We have developed an off-the-shelf stem cell therapy-stem cells in a vial. Unfortunately, bone marrow transplantation, which has been around since the late 1950s, is a complicated, messy, and not very scalable procedure. What we do is take a special class of stem cells, which we can isolate from young, healthy, consenting donors. We have shown that these cells have very robust growth properties. We can expand them to produce the equivalent of millions of clinical doses with a small amount of material from a single donor, and can do this consistently across donors. We have also demonstrated clinically in six different programs that we can administer these cells like type O blood. We don’t have to tissue-match or immunosuppress the patient we put the cells into. 

	Our most advanced program is headed into a pivotal Phase III study. We recently got FDA authorization to conduct that trial under a special protocol assessment (SPA). Our lead indication is in treating ischemic stroke. The study will be conducted in the US, Canada, and Europe. In parallel, we’ve got a partnership in Japan to take advantage of the new regenerative medicine accelerated regulatory approval pathway there. We also have Phase II programs for treating damage from myocardial infarction and treating acute respiratory distress syndrome.

Regarding the US, specifically, do you feel the recent sentiments expressed by the Trump administration on the industry in areas such as pricing and overseas manufacturing will fuel significant volatility in the biotech space this year? 

 I think it’s going to be somewhat unstable. You have an administration whose background is not in this industry. There was some early dialogue around the question of who will be the representatives in the new administration providing oversight for the biotech industry. A lot of those questions haven’t been answered yet. There are a lot of unknowns, such as who is going head the FDA? 

One of the things that we’ve seen consistently over the past few decades is that the market will always reward innovation, regardless. If you develop a solution that is addressing an unmet medical need-where patients really need it-and do it in a way that demonstrates safety, real therapeutic effectiveness, and practicality, then the market embraces that and innovative organizations get rewarded for that. 

The president has discussed slashing drug prices extensively. People can look at the statistics on what it costs to commercialize a drug, from R&D through approval. Whether it’s the Tufts University statistic of $1.6 billion or somebody else’s statistic that says it’s over $2 billion, none of those take into account the cost of the failures. From a pure business standpoint, the new administration or others who don’t follow this space need to understand what the real cost and barriers to entry are and that those barriers are a factor in determining pricing.

 The flip side of that coin is that it’s incumbent upon all of us to educate the broader public and, likely, the administration as well that Valeant and [former Turing CEO] Martin Shkreli are not our industry. Those are not representative of what we do. The vast majority of us are doing things that are not just innovative but are potentially revolutionary from a health standpoint. They are not incremental gains; they are switches on and off. Those have costs-and most of those fail. The extent that we can help people understand this, the better off we are.  

I have the impression that the mood was becoming a little friendlier than last year-it was going up. I thought maybe the election outcome actually played a part in that-that people thought that price controls were a little bit more associated with Hillary Clinton. That was my conclusion, but now I’ve been 

proven wrong on that. I think the president tweets out things and then he oftentimes comes back with other ideas. I wouldn’t over-interpret any of this until the dust settles.  

In addition to innovation, we need to focus on the price and the outcomes of our evidence-based medicine. At the end of the day, our value is in solving unmet medical problems. I fear that the industry’s reputational challenges are going to get in the way of a small innovative company trying to move a program forward, because they are associated with areas or individuals that, unfortunately, may act as a barrier.

What are the things your respective leaderships think about or do actively-on a short- or long-term basis-to reinforce or improve the industry’s reputational componen

As a small company, Biostage hopes to improve the reputation of the industry by focusing on innovative technology, making wise business decisions, and learning from our peers. We hope to continue to make a difference in patients’ lives. 

 The organizations that we might be members of, whether it be the Biotechnology Innovation Organization (BIO) or the Alliance for Regenerative Medicine (ARM), or others, they don’t just include companies. In the case of ARM, it’s also disease foundations, patient groups, a broad community of people that all want the same thing. They want safe, effective medicines to be developed and made available, and, therefore, are working together. One of the ways in which you can do that and make it impactful is to utilize different vehicles and platforms. For example, through the use of video, you can tell a story to personalize and humanize just how debilitating or devastating a particular disease indication might be and how innovation can actually help address that.  

	So, part of this is just good, old-fashioned communication using sophisticated, new-age technology platforms to help us do that on a more effective and comprehensive basis. Because, frankly, that’s one of the things we all need to be devoting a little bit more time and effort toward-to help the outside world understand just how important and significant the work that we’re doing really is.

I think we all need to use the new tools to effectively communicate. I believe that one of the problems we have as an industry is that people have misconceptions about the industry. One idea may be that the patient side become more active-patient advocacy groups and the doctors who use the products we develop. We are innovating, but we are also creating profits, and, thus, our message might not resonate as much. Maybe people that benefit from the innovative products can step up more and help us communicate the message.

 Every company expresses patient interest in a different way, but perhaps we need more dedicated people working in this area. Patient-centric approaches to business, such as collaborating with patient advocacy groups, are critical. They help give the business a deeper purpose.

Stemming off this whole discussion of reputation, does the industry need to do a better job, perhaps, in regulating itself when it comes to things like pricing? Is that something that’s even possible? 

 I think I can provide a different perspective, coming from Taiwan or Asia. When looking at the overall market, you start to see the money flow when you’re talking about capital markets. We are actually seeing a lot of capital flowing into the US right now. That will help drive up the whole market this year. Second, even though Trump talked about pricing and things like that, if you think about what is really driving the innovation, it’s actually about how you can get better pricing in the US. Anyone, no matter where they are, who wants to make an impact in the healthcare industry, they come to the US. I’m personally optimistic about this year and going forward, based on the capital market, the money flow, and what is driving this industry. Trump can tweet whatever he wants, but I think these are the key drivers.

Why do you think more money will come from the Far East this year compared to last year?        

When the currency starts to fluctuate locally, you want to go to much more stable and stronger currency. And if you are talking about tax reduction in the US, if you look at a big corporation, right now the corporate tax is 35%. If you just go down 10%, everybody’s EPS, in general, is going to increase 20% automatically. That’s if your gross still stays the same.

Are there other regions where you are seeing solid industry growth in the early stages of 2017? 

When I think about growth within our area of esophageal cancer, it comes down to epidemiology. This disease is 10 times more prevalent in the Asian population. Food and diet in regions outside the US and Western Europe is a contributing factor to a much higher incidence of esophageal cancer. Therefore, we are focusing on the Asian market due to the increased prevalence of the disease in those populations.

: The epidemiology is definitely a big consideration when you are thinking about moving into a particular geographic region or about incorporating clinical activities in those areas. A region that I have been very impressed with and spent a lot of time in, is Japan. One of the thrusts of Abenomics is to invest more in healthcare and particularly innovative technologies like regenerative medicine. Japan, like a lot of other countries around the world, is experiencing an unprecedented transition where we are seeing a massive expansion of the elderly segment of the population. A lot of people haven’t really taken the time to assess what that is going to mean from a healthcare economic perspective. 

	The reality is that as we get older, we can spend eight to 10 times more annually on healthcare-related expenditures than we do when we are younger and healthy. Japan has the worst demographic profile of any developed country and probably any country, period. They recognized this because they’ve got a national healthcare system, and they started doing things that have created a much more favorable environment, designed to promote innovative healthcare solutions in the areas where they need them the most. Within the last couple of years, Japan has implemented new regulatory frameworks designed to expedite development, not just for regenerative medicine therapies but other forms of innovative therapies, which I think have a more broad-based benefit in terms of shortening the clinical development path, making it more concise, more efficient, and yet doing it in a way that protects and ensures patient safety and well-being.

	If you can create better efficiency, which some of the provisions of 21st Century Cures Act in the US are designed to do, and if some of these other regulatory initiatives have the desired impact in that regard, you can get a shorter and less expensive development cycle. Ultimately that benefits everybody-the companies, the patients, and the investors.   

One of the most challenging things that management of a therapeutic company faces is the question of what regions of the world are they going to pursue. In our case, we had opportunities to initiate human studies early on in India and other regions. The regulatory barriers were lower in those regions. There was never any assurance that the FDA was going to give us clearance to initiate studies in the US, so we focused on collecting data overseas. It was very valuable. That data got us to the point where the FDA approved studies for us to move forward and advance our technology in the US.  

	Then you have the dilemma of thinking about, “Well, am I going to focus on US clinical progression?” If you are a publicly traded company, you have to evaluate and consider the market’s long-term response to the path you choose. How is Wall Street going to value you? Some companies may choose to say, “We want to focus on early product commercialization”-a lower-barrier entry-and can be successful in doing that, with the plan of coming back and 

advancing things in the US. But then they find out that Wall Street starts to value them on a revenue earnings model once their product is approved overseas. And, thus, their post-approval shrinks to levels below that of when they were progressing clinically. 

	It’s a very challenging decision. It’s a decision that most investors don’t understand. You need to evaluate where the primary market is, what’s the largest market, what is the reimbursement climate, and where the value is. If you are a company that is doing something that could attract large organizations that might want to acquire your business, if you are not advancing clinically in the US, there is probably not much interest. I have seen colleagues go overseas, never to be seen again. And I have seen companies here in the US take therapeutic candidates through the FDA approval process and fail. 

 I disagree with one of the things you said. I think, dependent upon where in the world a company’s clinical development is happening, you can still get a lot of interest from Wall Street and still be taken seriously from acquirers. It depends on where it goes. It’s been our experience that the way the FDA is structured now, that-at least for us-there is little reason to do early stage clinical development in the US. It’s much faster ex-US. It’s easier for FDA to have it done ex-US and then come back here for pivotal studies.  

 Nevertheless, if you develop a really innovative drug for an unmet medical need, if you don’t get FDA approval for the clinical studies, you will not go to a country like India. Getting US approval is still the gold standard. You can conduct some studies outside the US and return to the US later. But if you want to get approval worldwide, you had better do it in the US first.  

We have trials done in Japan, China, Taiwan, Europe, and the US. I always tell people the fastest way is through the Taiwan FDA or the China FDA or through the NDA (new drug application) process with the US FDA first. Because, with a lot of these other countries, there are not as many experienced reviewers, which can make the process very indecisive and go in circles.

In the US, to what level do you believe the “Cures” legislation, when implemented, will truly help achieve the changes sought in streamlining the regulatory process and ultimately accelerating drug development?

It will be interesting to see how other government agencies become involved after the Cures Act. Before a company begins discussions of reimbursement, the FDA will provide vouchers and incentives to companies that focus on tropical diseases and pediatric diseases. I’m hesitant to encourage other government agencies to define innovation in drug 

development through incentives. There need to be some incentives, but this is a cautious area.

Cures was an important step in the right direction, but I don’t think it did everything we need. The next phase in the legislative landscape is going to be the PDUFA reauthorization and putting some additional provisions and mechanical things in place that will help us continue that trend toward hopefully a clearer, more predictable, and more efficient regulatory framework for all of us. There’s been a particular challenge at the agency with combination products. Cell therapies in the context of materials is problematic. So, addressing those areas of inefficiency to advance the boundaries is still a priority for many people.

To that end, is the merging of different review departments within FDA a wish during a product evaluation-so that you don’t have 16 people in the room, so to speak?  

The FDA is right to have a delineation of departments to regulate a device versus a biologic. This enables regulators to stay ahead of the technology and make an informed decision about the potential of the product.

	The difficulty comes when the FDA evaluates products that address rare or orphan diseases. Today, there are thousands of orphan diseases identified around the world. With that in mind, there is an important social question: should we focus our efforts on treatments for just those rare diseases or focus our efforts on treatments for broader disease states? This social question also applies as technology and big data change.

Regulatory burden aside, what are some of the main scientific challenges your companies face in advancing your technologies forward? Is it achieving new modalities of medicines, like stem cell or RNA therapies? Is it finding new targets?

 We have enough science to fill all of our lifetimes. It is translating that into clinical programs and then translating that into marketable products. We are at this amazing time in science where we can innovate extremely rapidly with animal models, 

, etc. But there is still this massive bottleneck, in part because it’s complicated now to get into clinical studies, but also because of the capital necessary to support it.

 I couldn’t agree more. There’s plenty of science and technology out there. It’s more about choice-where are you going with it? The reimbursement factor is important as well. You go into something with big hopes, but very early on in the process, you have to consider the market outlook; does the product you are trying to develop really compare sufficiently above the what’s out there? That may sound easy to do at first glance, but particularly in my company’s area-pain, it’s so broad and risk profiles come into play. The reimbursement aspect can also put you in a situation where you believe in that molecule or that therapy but just can’t be sure whether it will get reimbursed or whether the insurer understands that it should be reimbursed. These are all factors that you need to take into consideration.

There is a lot of debate right now about how do you appropriately reimburse so-called curative therapies. Some of these are designed to be a one-time therapy that has a long-lasting therapeutic impact in a way that’s not contemplated under the historical third-party payer reimbursement dynamic, which is “we are going to pay for you to take this pill for an extended period of time, or forever.” 

	To use some of the pediatric orphan indications, for example, where you can treat a child and cure a disease that might otherwise be progressive and debilitating, that has decades-long impact; but, yet, you’ve only got a small population and you still have the significant cost and development challenges to face. How do you figure out a reimbursement dynamic that’s going to accommodate all of that? The reality is we still don’t know. There is discussion going on between industry, third-party payer groups, and sponsors and innovator organizations like ARM and BIO to try to get to a better place on this issue.

Developing an innovative drug, because of the state of biology today, is like going to a casino and just playing roulette. The rate of failure is still very high. It’s true that there are more technologies, and from time to time, like in every science, you have one breakthrough, such as CRISPR/Cas9, for example, and that moves everything forward. There are certain drugs today that you have  to  test diagnostically before administration in humans. These are the directions

What’s the catalyst for these personalized approaches? Is it eliminating a failure faster? Is it artificial intelligence that can help make better and earlier predictions of effectiveness?  

 Our ability to assess and analyze genetic heterogeneity has improved by leaps and bounds over the past decade. I’m not saying it’s perfect, but we now have much more comprehensive knowledge about the human genome and some of the things that are important from a clinical perspective. It hasn’t been implemented to the point where we can routinely anticipate and characterize those types of things clinically. But the technology has clearly advanced in that direction.  

 Think about how interesting it would be if you could obtain regulatory answers quicker or understand whether your product is performing well sooner. In the case of a biotech company that is burning $5 million a month, what if you could get your answer six months earlier? That’s a lot of money saved. 

If you can help people get the answers faster in particular areas, there’s a lot of value in that. Achieving clarity faster and more efficiently is important.

There is an analysis of drugs on the market that compares their original indications to their marketed indication, showing that likely more than half were approved in a different indication than originally planned. By predicting effectiveness, or lack thereof, earlier, we can improve drug development. Some people say it’s not that the drug fails; it’s that the company fails the drug. 

Given these dynamics, how would you assess the state of emerging research areas such as stem cells, regenerative medicine, RNA technologies, etc.? 

I think that regenerative medicine, cell therapy and other advanced therapies, like gene therapy, are in a positive place right now. Contrary to a lot of the narrative out there, the FDA actually has significant knowledge and willingness to engage with companies and help give them guidance, much more than it did 10 or 15 years ago when this field was really just beginning, and in its early stages. This doesn’t mean that everything operates perfectly. 

	But I think that if you look at the capital flow into regenerative medicine and advanced therapies over the past several years, it’s risen dramatically. There’s a growing recognition and appreciation that this is one of those areas that could really transform medicine as we know it, particularly for some major problematic conditions where there are no effective solutions and no good current standard of care.  

	The challenge now in this space, as more of us are entering into late-phase clinical development, is to see those discoveries translated and see a few compelling cases go to the finish line. That would create a kind of tidal-wave phenomenon, if you will, pushing in the right direction. I think we are almost at that point.

 If you look at RNA, especially siRNA or microRNA, the antibody is part of the sophisticated immune system, which was discovered even before the innate, or primitive, immune system. And we now know the role that RNA is playing in our body. One of the biggest surprises after the mapping of the human genome was that we have almost the same number of genes as the mouse. So, what is the difference? The difference is the regulation of the genes-and RNA is the major regulator. 

	The human race is able to take the antibody, which is a part of our immune system, and manipulate it to produce efficient drugs for its own advantage. Similarly, we are going to do that with the primitive immune system of siRNA. It’s just a question of time. Once the first siRNA hits the market, you will see an avalanche. 

Facing mixed market signals, biotech companies look to rise above the fray in advancing new-wave discoveries to the clinical finish line.

Biotech Roundtable: The Quest for Translation

	President Donald Trump has upended the global pharmaceutical industry by pressuring US manufacturers to reduce overseas production and to curb imports in order to boost jobs at home. While industry leaders are anxious to avoid price controls and to gain tax breaks and patent protections from the new administration, Trump’s “America First” mantra threatens pharma manufacturing and marketing arrangements around the world.

	According to FDA officials, the US imports about 50% of drugs from Europe, Asia, and other regions. And up to 90% of active pharmaceutical ingredients (APIs) for US production of finished dosage forms come from overseas, largely from China and India. At the same time, the US exports large quantities of drugs and biotech therapies to Europe and other regions; exports totaled $25 billion in 2015, surpassed only by Germany, Switzerland, and Belgium. 

	In his first weeks in office, Trump pulled out of the Trans-Pacific Partnership agreement, halted US involvement in the Trans-Atlantic Trade & Investment Partnership, and threatened to cripple the long-established North American Free Trade Agreement (NAFTA). Even though respected economists around the world have blasted these moves as likely to raise product costs and reduce investment in the US, the new administration anticipates benefits for workers and for consumers. Peter Navarro, head of Trump’s new National Trade Council, predicted important gains from the “unwinding and repatriating in the international supply chains” in ways that reduce imports, including components for finished products. 

	The White House also has threatened to levy a border tax on imports, a devastating prospect for trading partners on all sides. Such a policy could lead to higher product costs at home and abroad. Ironically, some members of Congress look to reduce outlays on prescription drugs by authorizing imports from Canada of medicines that lack competition sufficient to keep prices under control. One has to wonder if Trump would veto such a measure. 

	White House pressure, thus, is prompting some US firms, such as Amgen and Lilly, according to press reports, to rethink plans to build or expand plants overseas. Many firms have been investing in manufacturing operations in low-tax, low-cost countries such as Ireland, Singapore, India, and China, as global markets for medical products have expanded.

	Such moves stand to save money while also facilitating sales and distribution to newly-industrialized and developing nations. And some foreign manufacturers, such as Indian drugmaker Aurobindo Pharma, are looking to expand production in the US to better serve that lucrative market, particularly for sterile injectables that have to meet high quality manufacturing standards. 

	In return for supporting Trump’s economic policies, industry leaders are looking for significant tax reform that facilitates repatriation of income. Pharma companies predict that a “tax holiday” for bringing back funds held overseas could help create 350,000 jobs in the US over 10 years. However, analysts note that such expansion in employment did not occur following a similar tax change in 2004, and that companies more likely would use repatriated cash to fund mergers and acquisitions-which often lead to reorganizations and layoffs. 

	Some economists believe that a new-jobs tax credit will do more to stimulate hiring than changes in how the US taxes corporate profits. 

	Pharma companies also are cozying up to Trump to gain administration protection of patents in other countries, which may erode with the demise international trade agreements. And industry would like to head off Trump price-cutting proposals, such as permitting Medicare negotiation of drug prices. In recent weeks, the White House has been quieter about accusing drug companies of “getting away with murder” in jacking up prices, but such reticence could change quickly. 

’s Washington correspondent. She can be reached at 

Columns

Attack on treaties and imports threatens international operations.

Global Pharma Deals with US Trade Overhauls

	Since the immuno-oncology market is rapidly growing and is projected to reach $34 billion by 2024, the biopharma industry needs to be prepared to take full advantage of this opportunity. To successfully push cancer immunotherapy development forward, drugmakers should use precision medicine approaches in order to manage the complexity of cancer and the heterogeneity of patient populations. In particular, next-

generation sequencing (NGS) data that characterize tumors and immune systems are crucial to all phases of drug development, from target discovery to clinical trials. 

	Given the importance of NGS data across development pipelines and portfolios, biopharma companies need to de-silo data storage and analysis throughout their organizations to improve efficiency and productivity. Strategies to achieve this include integrating NGS data in cloud computing environments and deploying data harmonization approaches to make the data useful.

	The effective use of NGS data enables innovation during the preclinical stages of drug development. In the discovery phase, genomic data can pinpoint genes or genetic networks of interest. For target identification or validation, genome-wide association studies can demonstrate links between genetic markers and clinical outcomes. Alternatively, genomic data can be directly integrated into a therapeutic approach. For instance, companies developing neoantigen vaccines must tailor the therapy to each patient using their genomic data. Because each individual has a unique repertoire of tumor neoantigens, generating an effective vaccine requires performing DNA sequencing on their tumor and normal cells.

	Additionally, biomarkers based on NGS data contribute to the success of immunotherapy clinical trials. A 2016 study released by the Biotechnology Innovation Organization (BIO) showed that using selection biomarkers in clinical trials increased the overall likelihood of drug approval from 8.4% to 25.9%. Biomarkers measured using NGS data can provide a comprehensive picture of a patient’s tumor or immune system; DNA sequencing can measure the tumor mutational landscape or the repertoire of T or B cell receptors, while RNA sequencing can characterize levels of immune signaling molecules in the tumor microenvironment. Such biomarkers can be used to design clinical trials and select participants likely to respond to a therapy. In addition to predicting therapeutic efficacy, these biomarkers can be used throughout a clinical trial to monitor adverse events.

	As researchers continue to find applications for NGS data across the immunotherapy development pipeline, biopharma executives are faced with the challenge of integrating data access and analysis throughout their companies. Conventionally, biopharma data are siloed in various ways. Independent research divisions each generate NGS data and perform 

 analyses on them. Even when data are produced by collaborating teams, silos can arise from physical distance, such as when data are collected at separate clinical trial sites. Additionally, data collected with different methods or formats cannot be readily used together. Although different cancer types were traditionally considered separate silos, researchers are increasingly understanding the similarities between them, including common underlying mutations.

	Because NGS data sets can be useful in multiple settings, integrating them across the company eliminates redundancies and inefficiencies. Just as Toyota employed the “Lean” philosophy to reduce waste in their manufacturing processes, biopharma companies can streamline drug development by de-siloing NGS data. This strategy can have a positive impact on all fronts, both within and outside the company.

	Integrating NGS data among internal and external research groups prevents redundancy, promotes collaboration, and sparks new discoveries. Establishing a centralized repository of NGS data facilitates access for all internal research groups, regardless of therapeutic area or development phase. In this way, ideas from individual product pipelines can cross-pollinate other areas of the overall portfolio. There can also be feedback between drug development phases; for example, clinical trials can provide patient biomarker data for researchers to mine for new preclinical discoveries. Integrating NGS data storage and analysis also promotes collaboration with external groups, including academic laboratories and contract research organizations (CROs).

	By de-siloing NGS data, biopharma executives can help their teams not only manage internal data, but also combine them with public data to make analyses more robust. Massive public genomic data sets such as The Cancer Genome Atlas (TCGA) enable drug discovery. Last year, Loxo Oncology received the FDA breakthrough therapy designation for their drug larotrectinib. The treatment inhibits tropomyosin receptor kinase fusions in metastatic cancers, a molecular target that researchers identified using TCGA data. The discovery power of integrated proprietary data can be extended through analysis alongside massive public data sets.

	Leading biopharma companies are increasingly deploying NGS data storage and computation on the cloud to promote productivity and efficiency. When companies store integrated NGS data in a centralized infrastructure, multiple research groups can access the same data. For example, data collected in multi-center clinical trials can be stored and analyzed in the cloud by all collaborators, preventing costs in redundant storage and time spent transferring data among researchers for analysis. Moreover, researchers can analyze proprietary data stored on the cloud in the context of massive public cancer genomics data sets that are also hosted on cloud environments. 

	When data analysis tools are brought to the cloud, workflows become reproducible and scalable. If multiple internal research divisions and external groups use the same standardized tools, collaboration and reporting to regulatory bodies become easier. Further, computation on the cloud is elastic; computational resources are optimized and allocated to analyses based on their scale, enabling faster processing.

	Because putting NGS data on the cloud does not automatically make them useful, data harmonization strategies are also necessary. Companies should apply data standards across their organization to promote consistent data formatting and labeling. In particular, systematic metadata are key to gaining insights from NGS data. Metadata properties describing sample characteristics such as tumor histology, radiation therapy, and patient demographics must be coordinated before researchers can combine data sets and analyze them together. 

	Another consideration for data integration is how to effectively operate under different regulations for data transfer and consent. The globalization of drug development means that companies conduct research under multiple regulatory regimes. Understanding them informs strategic decisions, such as in which countries should companies generate and analyze NGS data so that the data can be widely used.

	Biopharma companies must recognize the full potential of NGS data in the field of immuno-oncology. Because NGS data drive innovation ubiquitously throughout the immuno-oncology development pipeline, de-siloing data across the company enables productivity and efficiency. One powerful method to integrate this data is through cloud computing for storage and analysis. 

	Beyond leveraging cloud technology, it is crucial that a company’s leadership ensures their organizational data harmonization strategies are extracting the most value from their NGS data. Together, these approaches can help the industry deliver on the promise of NGS data and precision immunotherapies. 

PhD, is Senior Vice President of Science & Product at Seven Bridges Genomics. She can be reached at 

How integrating next-generation sequencing data can advance efforts in immuno-oncology research.

Precision Medicine and Big Data

	The late 1990s saw the emergence of interim management onto the life sciences stage- then a novel approach to help solve a wide-ranging set of scenarios where not having the right people, when and where you needed them, was no longer an acceptable excuse for failure.

	Almost 20 years on, what we see today is a genuine success story-a concept that has grown into a widespread and well-accepted way of doing business. The explanation is simple: the product delivers at a cost and an ease of acquisition that is acceptable to the marketplace.   

	The life sciences sector has continued to change at an exhilarating pace. Not surprisingly, interim management has similarly had to evolve. 

A look at how pharma and biotech interim management has evolved in the last 20 years.

Pharmaceutical Executive-03-01-2017

Big Data: Is It Crunch Time for Pharma?

Setting Sights on the Smart Supply Chain

Big Money and Big Data

Biotech Roundtable: The Quest for Translation

Global Pharma Deals with US Trade Overhauls

Precision Medicine and Big Data

Changing Faces: The Interim Manager

Pharmaceutical Executive, March 2017 Issue (PDF)

Country Report: Ireland