Julian Upton is Pharmaceutical Executive’s European and Online Editor. He can be reached at jupton@mjhlifesciences.com

Julian Upton

The nation’s rapidly changing regulatory climate is creating a new landscape for R&D and manufacturing service providers

	One marker of the pace of change currently under way in China was highlighted at the FT’s annual Pharma and Biotech Conference in London in November. To the polling question, “When will China be a significant influencer of innovation?” half the audience answered 2025. This prediction might be surprising to those who have taken their eye off the ball, but the country’s intensive commitment in the last few years to a program of regulatory advancement is swiftly changing the Chinese pharma and healthcare market. So much so, one of the FT conference speakers, Christian Hogg, CEO of Hong Kong-headquartered biopharma company Chi-Med, proclaimed that 2025 is a “conservative” estimate for China’s breakthrough as the world’s pharma powerhouse. 

	And with a brace of regulatory changes that include deregulating the conduct of clinical studies; accelerating the drug and device approval process; promoting drug innovation and the growth of generics; strengthening the administration and supervision throughout product lifecycles; and enhancing the technical review infrastructure, China looks open for business in a way that did not seem feasible a few years ago. Back then,  international pharma companies were withdrawing from the country, having been obstructed by protectionist policies and a sketchy IP system.

	The scale of unmet need in China now “trumps protectionism,” Hogg told the FT audience. He added: “I’ve seen protectionism melt away over the last 10 years. The country has listened to the multinational companies out there [and] the regulatory advances have been amazing. It isn’t perfect yet, but it’s a lot better.” 

	The changes in China have resulted in “an extremely dynamic landscape for companies looking to enter the market,” says Dr. Chang Lee, vice president of consulting for Parexel in China. In October, the CRO announced the launch of a new China Advisory Service to help global biopharma companies “navigate all aspects of the country’s drug development process.” Lee told 

: “Four years ago, clinical trial approval from the Chinese FDA (now the National Medicinal Products Administration [NMPA]), took about 10 months. Now, by law, it is just 60 working days. Second, there’s a huge market from the Chinese reforming of the drug formulary coverage. It is now much easier for insurance companies to cover a drug, to get drug approval, and to get a drug on the national formulary. This has attracted pharma companies back to China.” 

	Roel de Nobel, vice president, global operations, clinical supply services at Catalent Pharma Solutions-which announced its plans in November to invest $2.5 million in a new, second clinical supply facility in Shanghai-told 

that, as a result of NMPA significantly shortening the lead time associated with granting a new clinical trial approval, “we have seen a significantly increased interest from multinational pharma in bringing their drug development activities into China.” He adds: “We expect this trend to continue, given China’s large and easily accessible patient pool, and supportive healthcare infrastructure.”

	As Lee noted, however, “the ability to quickly understand and adapt is critical to succeeding in this rapidly changing environment.” While the reforms have made the regulatory environment in China more accessible to pharma companies, barriers to entry still remain. These include government-mandated price controls in public medical insurance programs, complex price negotiation processes at national and province levels, complex distribution channels, infrequently updated formularies, and an absence of GMP-compliant manufacturing testing sites and GCP-compliant clinical sites.

	“In just a short time, China has grown from being an emerging market for companies looking to develop drugs to the second-largest biopharmaceutical market in the world,” says Paul Bridges, Parexel’s corporate vice president and worldwide head of consulting. “While many are aware of the opportunity that exists in the market, the challenges and hurdles to entry are generally not well understood.”

, “there are so many changes to regulations that the implementation of those changes will take time. It is too early to know how the newly formed NMPA will approve new drugs for national insurance coverage consistently. Also, there are limited local key opinion leaders to advise on China medical practice patterns and evaluate unmet medical needs.” As more R&D companies look to penetrate this market, he says, the need for advisory services will continue to grow. 

	A 2017 Research and Markets report predicted that the CRO industry in China-driven by the favorable policies, more spending on R&D, and an increase in the number of new drugs approved-will reach an estimated RMB116.5 billion ($17.1 billion) by 2021, with Chinese pharma R&D spending of up to $29.2 billion driving the development of the CRO industry from supply side. Currently, the top 10 global CRO companies, including Parexel, IQVIA, Covance, Syneos Health, and Charles River, hold a combined 45% market share in China, but the pace of regulatory change in the country has intensified demand for talent in this area. Lee told 

in October that “the biggest challenge for CROs in China currently is the lack of an adequate clinical research infrastructure, specifically; limited research sites; lack of qualified principal investigators and research staff; and inefficient institutional review board (IRB)/ethics committee (EC) processes, among other challenges.” 

	In December, Parexel struck a new collaboration with Eli Lilly to launch a clinical research learning and development program in China to “bring high-value training opportunities to China’s clinical trial sites and investigators, enhancing the execution of local clinical trials.”

	There is growing competition for western companies, however, from the domestic CRO space, with companies such as WuXi AppTec, Pharmaron, Shanghai Medicilon, Hangzhou Tigermed Consulting, Joinn Laboratories, and Quantum Hi-Tech Biological reportedly pushing the development of the clinical outsourcing industry in China. In 2017, Quantum Hi-Tech acquired ChemPartner, one of China’s biggest chemistry service providers, and  state-owned Xinjiang Baihuacun entered the R&D market with a $270 million investment in Huawei Medical. 

	As China continues to attract overseas pharmas to expand their territories and capabilities in new drug R&D, “the leading Chinese CROs will be at forefront… gradually becoming their priority suppliers and important strategic partners,” wrote Jin Zhang in an October 2017 entry on her thefierceconsultant.com blog. While the preclinical CRO market is relatively small, things are changing, she added. “In light of the Chinese government’s support of drug innovation and the convergence between China and global medication standards, an increasing number of domestic pharma companies are slated to devote more resources and apply more capital to innovative medication development.” 

	Lee sees the traditional, clinical trial-focused CRO model developing into a “CRO plus” model in China. “For example, this might include CRO plus marketing data; CRO plus clinical lab; CRO plus contract manufacturing organization (CMO); CRO plus technology (e.g., AI, big data).” 

	On this front, the Shanghai-based giant WuXi AppTec (which accounted for 9.5% of the Chinese CRO market in 2016) has embarked on a program of national (and international) expansion, which, in 2017, saw its drug development business services merge with its active pharmaceutical ingredient (API) production business, STA Pharmaceutical. The firm announced that it would be building a new site in Changzhou, China, featuring nine large manufacturing facilities. According to published reports, by the middle of last year, WuXi  had reached a third of this target, with three commercial-scale facilities completed, making the company a sizable CMO as well.

	While, for many people, “outsourcing to China means WuXi” (as PharmaBlock Sciences CEO Haijun Dong was quoted as saying in 

last year), the need for contract manufacturing services in China is growing, with more biosimilars and innovative drugs entering the clinical pipeline but with most early-stage biologics developers in China lacking manufacturing facilities. 

	Vicky Qing Xia and Leo Cai Yang of BioPlan Associates wrote in 2018 in 

that, “China is demonstrating clear investment interest in participating in global markets for both innovative biologics and biosimilars produced at GMP quality levels.” They noted that before 2016, pharma contract manufacturing for domestic drugs “essentially did not exist in China,” because of a regulatory framework that precluded third-party/CMO manufacturing for either clinical trials or commercial production. In 2016, however, China established the pilot Market Authorization Holder (MAH) program, under which holders of a Chinese FDA biologics approval number had the option to manufacture the drugs or use a CMO. “It is widely expected that the regulatory hurdles for contract manufacturing of drugs in China will shortly be removed,” Qing Xia and Cai Yang added. (It is now believed that the MAH will not become law before 2020.)

	BioPlan Associates’ 14th Annual Report and Survey of Biopharmaceutical Manufacturing, which asked global respondents to evaluate their facility’s current plans for future international capacity expansion, revealed in April 2017 that China had for the first time beaten Germany and Singapore to fourth place as a potential outsourcing destination (the US remained the top destination). Qing Xia and Cai Yang concluded in 

that an expected rise in quality standards for manufacturing in China is “also likely to bring good news to vendors of new bioprocessing technologies, products, and services.” 

	In 2017, Boehringer Ingelheim was the first multinational company in China to set up a CMO project in line with global standards. With an eye on increased future demand, its China biopharmaceuticals site in Pudong, Shanghai, reportedly has a capacity of four 2000 L single-use bioreactors. Boehringer Ingelheim has been active in lobbying for MAH reform, and in January 2018, the company announced that BeiGene’s solid tumor treatment, tislelizumab, would be the first monoclonal antibody (mAb) to be produced at its Shanghai site, and the first to be made by a foreign CMO in China. Commenting at the time to BioPharma-Reporter.com, Diane Lam, marketing manager for the Swedish–Chinese contract manufacturer Wuxi Griffin, said that with “less than 10 Chinese CMOs complying to western GMP,” it was likely that more international CMOs would establish a presence in China. 

	de Nobel expects the growth of service providers in China, both international and domestic, to continue the foreseeable future. He told 

, “There is an opportunity for large global contract development and manufacturing organizations (CDMOs) to help prepare local Chinese pharma companies to expand beyond China in understanding global best practices, quality standards, and other regulatory requirements that they may need to follow outside of the country.” 

	He added: “The challenge is to be a global company that also establishes truly local operations that thoroughly understand the local language, regulatory, business, and cultural environment and the importance of building relationships within the country. To this end, localization of services is part of Catalent’s strategic focus for China and the APAC region.”

	At 6%, China’s healthcare spending as a portion of GDP still lags behind the US (17%) and Europe (10%). But, as Chris Lo pointed out in 

in November, this indicates that “the market is still primed for further expansion.” He added that, with the healthcare market expected to reach $2 trillion in value by 2030 (the country’s over-65 population will increase from 90.6 million people in 2017 to 143 million in 2027) and the official acknowledgement that foreign companies have a large role to play in meeting the country’s health objectives, “there’s never been a better time for the pharma industry to invest in China.”

	While risks remain there, Mark Mallon, AstraZeneca’s EVP of global product and portfolio strategy, medical, and corporate affairs, told the FT Pharma and Biotech Conference in November, “the opportunity to make a difference to vast numbers of people outweigh those risks.”

European and Online Editor. He can be reached at 

Articles

How the nation’s rapidly changing regulatory climate is creating a new landscape for pharmaceutical service providers

New Roads to China

Leaders at Novartis share how the big pharma is laying the foundations for a potential new operating model as a "medicines and data science company"

	At the beginning of 2018, Novartis CEO Vas Narasimhan spoke of reimagining Novartis as a medicines and data science company. The drugmaker was sitting on a “goldmine” of vast datasets, “built from having conducted countless studies in thousands of diseases.” Taking advantage of this “wealth of clean, curated, longitudinal, and interventional data,” Narasimhan explained, “has the potential to completely transform the way we develop medicines.” 

	Novartis had already begun signaling its commitment to the plan with a series of high-profile appointments of data science experts from outside the industry. In August 2017, the company hired Bertrand Bodson to the new role of chief digital officer. Bodson had no pharma background; he had previously served as chief digital and marketing officer for the UK retailer Sainsbury’s Argos, where he played a pivotal role in guiding the company’s digital transformation from a


	catalogue business “into a multichannel online powerhouse with more than a billion visits online annually.” Earlier, he had spent three years as Amazon’s senior group product manager. “The pharma space in particular is ripe for disruption,” Bodson commented in October. “Looking forward, data science will drive more of our decisions and more of our programs-including our medicines-will be digital.”

	Following more appointments of data experts from outside the industry in 2018, 

talked to two key players in Novartis’s push for digital transformation-Mimi Huizinga, vice president of strategic data and digital, and Miriam Donaldson, head of HR Digital-about how this influx of new talent is fueling a cultural change that is set to define the future of the drugmaker’s data science activities.

Can you outline Novartis’s mission to transform itself into a medicines and data science company?

Novartis associates are learning to be data curious and more agile in how they approach their work. We are seeking ways to use data and digital to support every aspect of the company-drug discovery, trial planning, manufacturing, commercial operations, talent management, and many more. It is truly a transformative time. 

Through our digital transformation, we aim to be the leading medicine company powered by data and digital. If you look at external research about what leads to successful digital transformations, a common theme is capability building. This is a priority for us as well. We are looking at capabilities across three different populations at Novartis: all associates, our leaders, and our digital practitioners. We are building different capability solutions for each, as they have different needs from general awareness and understanding of the potential of digital to leading teams who feel empowered to experiment, to having world-class data and digital expertise.

	Importantly, we want to encourage all of our associates to be curious, and feel comfortable around data and digital, and help us to see how these things can work better for us. We need to set them up for success to be part of that journey, so investing in their digital awareness and capabilities is really critical.

What are the challenges in achieving this transformation? And what advantages do you have?

: We want data and insights to drive our digital solutions, but healthcare data is difficult-the data is siloed with no single source of a complete, longitudinal record. This means that we have to deeply understand the data and its limitations to ensure we use the right dataset for the question at hand-and that our analytics help to minimize the risk of false conclusions from the inherent biases in the data. We are not able to understand patient barriers in the care journey as rapidly as we would like and that slows our development of potential digital solutions.

To lead a digital transformation, you have to be comfortable with failure. You have to be excited to experiment, learn from what works and what doesn’t and then act on it. You have to do it quickly and build solutions that you can scale. But that’s not the culture we’re starting from at Novartis. We have to help shape an environment where curiosity can really thrive and we have leaders who support their teams, not by telling them what to do but by creating a space where they can experiment and learn and not be frustrated if things don’t work the first time. Novartis is also transforming from being a collection of seven interdependent companies into one integrated company. This idea of building solutions that scale across Novartis is new for us. There’s a cultural aspect to this as well, we’re moving from rewarding individual heroes to teams that systemically solve problems and design innovations that scale.  

Novartis does have many advantages-our mission, our people, and our commitment to data and digital. As an MD and former epidemiologist, I joined Novartis because of the core focus on patients. That focus really clarifies how we make decisions and set priorities. There is a fantastic set of data, analytics, and digital experts within Novartis. We are working now to create more connectivity across the company. Finally, having leaders that understand the importance of data, analytics, and digital makes a tremendous difference.

You’ve made key new hires from companies such as Google and Amazon. Can you talk about the importance of hiring from other industries?

We approached this as choosing people with the portfolio of skills and experiences that will be important for the transformation. It started with the hiring of Bertrand Bodson as our first chief digital officer, who came from Amazon and retail, and had no experience in pharma prior to joining Novartis. For other key roles, such as our head of data strategy and head of data science, it was important to us that the folks we hired at this level had experience leading transformations. 

	That’s what led us to Raj Patil (head of data strategy) and Shahram Ebadollahi (head of data science and advanced analytics). Raj, for example, worked in the financial industry, which is also highly regulated and has some of the same challenges. He had successfully built data strategies in several banks and also worked at Google. Shahram helped to build the IBM Watson Health organization, a 4,000-person team. Through his work there, he experienced many of the use cases we are looking to address and has learned what works and what doesn’t. We believe that pairing that experience with our incredible in-house team of healthcare experts is going to be a powerful combination. Possibly most important with both of these leaders is that they have leadership styles consistent with the culture we aspire to and which we feel will engage our teams to work together in new ways to innovate in data and data science in close partnership with the business. 

	With the new talent we’re acquiring, especially in the data science area, the questions we ask ourselves when they come into the company are: How do we set them up for success? How do we help them to understand our organization while also asking them to help evolve it? We’re not trying to build a separate digital business, we want to transform our business using data and digital, which means integrating and influencing both what we are doing and how we do it. Specifically, we’re focusing now on how we elevate data science at Novartis, which in the past has maybe been viewed more as a supporting function to our clinicians, and other types of scientists and reposition the organization as a true strategic partner to the business.

What specific initiatives and policies are you implementing to drive technological and cultural change?

Broadly speaking, our activities fall into three buckets: data, analytics, and impact. For data, we are seeking to optimize the use of internal and external data while looking for new types of data that might provide value. We are working to organize the data and enable access to all appropriate employees while keeping data privacy top of mind. In the analytics bucket, we are automating our core analytics so that we can free up our analysts to think about the business issues and more complex analysis. We are also using artificial intelligence and learning how to industrialize AI models in our daily work. In order to see impact, we use design thinking concepts to ensure that results are meaningful and that visualization tools are intuitive. We also look to see how we can automate pull-through and build digital solutions that fit within an associate’s workflow and support the patient journey.

	I am working to promote a data curious culture in our US Oncology group. I work in all of the areas-data, analytics, and impact. I love seeing the “ah-ha” moments and hearing about how we helped someone think differently about a potential opportunity. My job is to ensure that when we build the data infrastructure or the analytics framework, that it will be able to meet our evolving needs three to five years from now. To do that, I have to understand our business strategy and be able to translate the commercial and medical leaders’ needs to our IT and analytics colleagues. On any given day, I may have conversations about our metadata management plan, detailed review of a Markov model we are bringing into production, brand level launch strategy, real-world evidence needs for a cost-effectiveness model, clinical trial feasibility, ideas for our next visualization product, and our website content management strategy. All of this makes my job challenging but very fun.

One of the things we’re doing in our capability-building is around teaching our leaders how to help teams who need to work differently, such as agile working, which has been really important in software development in other tech companies. It’s a completely different way of organizing teams and pursuing the work that needs to be done. This is one of the ways that we’re trying to get teams more comfortable with the rapid innovation cycles, customer-centric solutions, and building solutions we can scale. 

	For things this agile to work at Novartis, our leaders need to lead differently. We’re building a program that we call Digital Immersion for Leaders, which is a three to four-hour experience for teams. It’s kind of a “choose your own adventure,” where we build cases around different scenarios at Novartis. We show leaders how digital is already changing the work we are doing and provide them with some leadership challenges, so that they see the impact that they have on teams who are trying to innovate, and work in the way that we aspire to-and all while seeing how these solutions address their real business challenges. We launched a beta version of this program to our top 300 leaders back in September, and we will be rolling it out to the larger company over the next several months.

How will the next couple of years play out in terms of the transformation?

Now that we have our heads of data strategy and data science onboard, we are ramping things up quickly. So, I expect in 2019 that we’re going to be making a lot of hires in that space. And as our associates, leaders, and practitioners benefit from our investments in their capabilities and our culture and ways of working evolve, we think it will feel really exciting to work at Novartis. Almost every element of our business can be touched by this transformation, so that we can make smarter decisions based on data that help us bring meaningful medicines to patients faster. 

I have been amazed at how much we have accomplished since I joined in August 2017, but that work was largely foundational. We have really gained momentum over the last three months. We have a few tools deployed to the business and great engagement of our leaders. We are planning to launch an internal visualization tool [this month] that will provide deep insights to our commercial leaders. Our goal is that these tools will help the leaders ask better questions faster.

	We have industrialized one AI model and over the first half of 2019, we will be testing the limits of that model to understand when we need to refresh and rebuild. We will also place the outputs of a second AI model into pilot mode during that time.

	By mid-2020, our goal is that everyone in US Oncology will have a unique visualization product that supports their work and that stems from a common set of data and analytics.

European and Online Editor. He can be reached at 

Leaders at Novartis share how the big pharma is laying the foundations for a potential new operating model as a "medicines and data science company."

Future Pharma: Data Science as a Strategic Partner

, Director of the Health and Life Sciences Global Practice at SAS, about the growing role of big data analytics in pharma and how the industry is evolving to keep pace with it.

	PharmExec: Do you see life sciences companies advancing in the quest to keep up with and meaningfully manage the increasing flood of healthcare data?

Many companies now have strategies in place to deal with digital health data, and those that are most successful center around the patient and the science itself. They are following where the data is coming from and how it can help them move forward with clinical development or gain an understanding of what value their therapy brings to the market.

	The biggest pitfall is where companies develop analytical platforms on an “island”, with data lakes or warehouses being set up without any real purpose established beforehand. When that happens, it’s difficult to ensure that the people who really need those insights from the data can access them. What works better are smaller, iterative and experimental projects, which run with a very specific purpose and respond to specific problems, such as predicting the enrollment of patients in a clinical trial using certain algorithms. This is where the data analysis becomes valuable, because it's going to be used quickly and in a production context. You work your way back from the problem and then ask, “What data do I need and how can I collect that?” Those companies where the scientists and the technologists are working together on this are the most successful.

	A couple of years ago, companies began establishing in-house capabilities and warehouses and started bringing the data in. This is now maturing to the point where organizations understand what techniques they want to use and put in place different architectures and technologies for different purposes. But it’s about more than just a platform; it's also about the ability to govern the lifecycle of the data to make sure the information is always up-to-date and that you can trace the insights back to the original data and validate it. In that sense, I see a lot more realism coming along.

	Smaller biotech or pharma companies with smaller budgets to license commercial real-world data sets can be more innovative, because they have to be more creative and look for more automation. Automation is definitely a big driver in making the best use of the data and the best use of AI-whether that’s natural language processing or image analysis or newer techniques. A company may want to use text analytics and natural language processing, for example, to help them detect serious adverse events in pharmacovigilance reports. While big pharma companies have armies of people to manually filter out the meaningful signals, small companies don't have access to that and so they want to deploy AI techniques on those big data sets. That leads to more efficient ways of looking at information and helps ensure that their scientists are looking at high priority problems.

	How does SAS continue to differentiate itself from its competitors in offering data analytics solutions?

	For us, nothing happening around big data is  new, because we have always dealt with these types of data. What 

 changed is the size and variety of data. We have expanded our platform in recent years, so we can now analyze video, image, and text data, and more importantly, combine these information silos with different types of clinical and patient data. And as we’ve seen more organizations using and deploying different types of analytics, we understand that we need to work in a hybrid environment, where we embrace different technologies, cloud and open source programming languages, offering the control and choice when a user needs it.

	But it’s not just about the different types of data, it's also the ability to compare different datasets. As more data becomes available that requires analysis, we keep investing and evolving our capabilities. There's a lot of data in isolation that is not standardized; this can be the biggest hindrance to deploying AI in analytics broadly. Pulling that data into a unifying platform where it can be controlled and governed is where we feel we make a tangible difference for the industry.

	What problems remain in terms of navigating different policies and different data sets from around the world?

	I think there are two conflicting trends. There is more policy and more data than ever before, and if you look at the different healthcare systems that pharma companies are interested in, there's a lot of divergence. Some countries are way ahead in standardizing their healthcare data; others have more fragmented digital healthcare systems. Variation in how data is structured and how it relates to one another is a big problem. The absence of mature healthcare standards is a big problem. There are mitigating techniques-knowledge graphs, linked data-but, unfortunately, these do not solve everything, and they have their own inherent limitations. The likely solution here will come from regulatory, technical and domain standards bodies like HL7, NCI, WHO and CDISC working together more strongly.

	But the other positive trend is that globalizing policies are also driving harmonization. One example is the EU’s GDPR, which is having an effect globally in the way that the people are thinking about patient privacy.  One of the downstream effects will be data harmonization and standardization. The FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles that were initiated in academia are also becoming more important. Pharma organizations and industry representative bodies like EFPIA are doing a lot to make their clinical trial data available for external academic researchers. But there's a lot of work to be done in making data available across different stakeholders in the healthcare ecosystems and increase collaboration.

	What data and AI trends do you see emerging in the near future?

	In a couple of years, no clinical trial will be run without first consulting real-world data. It will be part of the whole clinical development effort, from modeling and simulation and predicting where you need to go with your trial in terms of geography and therapeutic area, to really understanding what the medicine under investigation does for patients. Using that kind of real-world data, generated from an abundance of sources -video data, demographic data, claims data, financial data-will help companies understand more about the average patient and create therapies that improve patient outcomes and are impactful for society.

	Another trend I anticipate will involve more data from n-of-1 trials, or single-patient trials, which are randomised controlled crossover trials in a single patient. They investigate the efficacy or side-effect profiles of different interventions, with the goal determining the optimal or best intervention for an individual patient using objective data-driven criteria. The goal is that every patient can have a specific therapy geared towards their genetic background and their disease, and patients can be matched with the right trial and the right therapies. These n-of-1 trials offer the opportunity to gather a lot of data-genomics data, proteomics data-and do a lot of real-time analytics.

	One more thing to watch is the application of AI and analytics to streaming data, such as video. Streaming analytics will become more pervasive. Take a hospital that is using a robot for a surgical procedure, for example. A lot of video data comes from that procedure. Streaming analytics can be applied to that video data to help support the physician as he or she performs the surgery. In a similar manner, streaming analytics can be used for clinical development. It will help companies to trim down and keep just the data and information that is relevant. Not every data point is valuable and needs to be stored for eternity.  

Pharm Exec speaks to Mark Lambrecht, Director of the Health and Life Sciences Global Practice at SAS, about the growing role of big data analytics in pharma and how the industry is evolving to keep pace with it.

AI/Data Trends to Watch: Mark Lambrecht, SAS

While the increasing importance of real-world evidence (RWE) is widely acknowledged, the dramatic shift required by biopharma companies to embed and secure an RWE capability effectively across the organization is still a work in progress

Second Annual Real-World Evidence (RWE) Benchmarking Survey

, published earlier this year, the authors highlighted how the proliferation of healthcare data, advancing technology and analytics capabilities, and an increased regulatory/pricing focus on value showed how “the use and importance” of RWE in the life sciences industry had evolved in just 12 months. More specifically, this year’s report pointed to how “RWE initiatives are increasing at the executive level,” not just in regard to generating evidence, but also in supporting other research, corporate, and commercial objectives. Almost all (90%) of the survey’s respondents, Deloitte reported, “have either established or are currently investing in building RWE capability for use across the entire product life cycle,” with 70% building or increasing their internal RWE capabilities. “As a result,” the authors wrote, “RWE spending on talent and technology in the future is anticipated to increase.”

	While the increasing use and importance of RWE is widely acknowledged, the shift required by biopharma companies to embed and secure an RWE capability effectively across the organization is still a work in progress. For Qin Ye, associate principal and global RWE lead at ZS Associates, in developing effective strategies, pharma companies still face a big hurdle “to overcome product-development-centric business models and be a lot more connected with the value they’re trying to bring to market and the problem they’re trying to solve for their stakeholders.” 

Addressing this, he says, involves a fundamental culture shift. Companies tend to have a need to compartmentalize their functions to gain the necessary focus and efficiency, but that is at the expense of a more holistic approach. While pharma companies need talented teams with specialized training and knowledge, these highly skilled teams tend to base a lot of their decision-making on their experiences in the past. 

	“It can be difficult to change that mindset to one that is more data-driven,” says Ye. 

	In helping companies implement an effective RWE strategy, ZS sees “the change management of culture shift as the key priority.” A lot of pharma companies currently have a siloed approach to gaining access to and leveraging data. “Looking end to end,” says Ye, “the question should be, how do you leverage data to help you to make decisions and better position your product from the very beginning of the development process?”

	This view is echoed by Saama Technologies, a clinical data integration platform company, who this year partnered with Informa Pharma Intelligence’s Citeline to bridge clinical trial information to RWE. Nekzad Shroff, Saama’s VP of field product management, told 

, “The adoption of RWE by pharma leaders is a different problem from developing the actual capabilities involved. Adoption requires a shift to an exploratory mindset. There needs to be a tolerance of ambiguity and imperfect data to be able to actually interpret and gain insights in spite of data gaps.” He says that, with challenges and fears around data quality and security and the constraints of data sharing, “some companies would rather have siloed data than shared data.”

	But Shroff believes the situation is slowly being overcome, as pharma organizations are building standards around how real-world data (RWD) is generated as well as interpreted. Similarly, the days when decision-making based on RWE “required an advanced understanding of mathematical statistical techniques as well as computer programming” are also changing, slowly. With increasing access to the “democratization” of these data sources and platforms providing actionable insight, Shroff says, companies “are now getting used to relying on these metrics to enhance their decision-making.”

	Once that happens, this mindset starts to become pervasive and embedded into day-to-day business processes. And when that culture shift begins, “analytics tools can definitely help,” Shroff explains, “with IT playing a big part in providing a platform with the right set of capabilities, access, and security levels.”

	Many large pharmas have identified the gaps in their real-world data and evidence capabilities and have the funding to fill them, bringing more data scientists into the talent pool. But, Ye warns, even when a company recruits talented data scientists, it can be difficult to leverage that talent, and equally difficult for the newcomers to gain an understanding of the business process. “It’s a merger of old and new, of two different scientific capabilities,” he says, “and that can be challenging if you don’t focus also on building the process transformation and enabling technologies. Just having new talent come into your team structure in a people-only approach will not solve the problems.”

	Karim Damji, Saama Technologies’ senior vice president of marketing, has seen big pharma “running into talent issues.” For companies trying to expand their in-house data-analytics capabilities, there are questions around the talent associated with achieving this the right way, he says. “Pharma companies have been doing it the hard way for a very long time. The tools and technologies that are available to other industries have

 not been widely adopted by pharma, simply because of a fear of taking that legal risk associated with doing something novel.” 

	In the meantime, Damji adds, companies liked Google, Apple, Facebook, Amazon, and Netflix have been “gobbling up all the talent that pharma needs in terms of expanding internally.” 

	That may be changing, as some firms are making high-profile moves to draw that talent into the pharma realm. Ye welcomes this, as such recruits are used to “thinking outside the box, have lots of experience dealing with a wide variety of data, and transforming how that data can be leveraged.” What’s more, he says, when these “outsiders” bring with them a “very humble mentality to engage with and learn life sciences, it helps to create a more impactful collaboration.” 

	However, says Shroff, for pharma, it is a question of solving the issue “non-traditionally.” Instead of pushing to hire more data scientists, Saama also looks at “the other side of the equation.” That is, “How can we enable the existing employees and executives of companies to start to make sense of some of this complexity without needing an advanced degree in data science?” He adds, “That’s not easy, because sometimes there’s a reluctance to take insights from a system that you don’t understand in and of itself.”

	While pharma grapples with these organizational questions, there is also the issue of navigating the myriad-and growing-data sources available for leverage. The industry should avoid simply “rushing in and buying data from the biggest data set in the market,” says Ye. 

	“Companies should start from their business need and their questions. While they do need some baseline data for the area they are focused on, they should have a more comprehensive view of the things they want to transform, and from that they can decide what specific data sources are needed.”

	For Damji, amid the myriad data sources, a key challenge for pharma is around longitudinal information and patient centricity. But it’s not just about looking at all the RWD sources and creating an intra-longitudinal view of the patient from a data source. “Creating inter-longitudinal views of the information is a bigger challenge,” Damji says. “And this is where I think modern aspects are being developed outside of pharma. Technology assets, to be precise, will significantly help mine that and stitch it together.” (See graphic; click to enlarge).

	Although Shroff points to a current lack of standards covering the use of RWE, he sees more standards being created around the interchangeability and exchangeability of data sets as the space evolves.

	“Once we standardize how patient records are used for insight generation and how technologies like blockchain can store that kind of data in a secure way, we start to get more consistent around generation, use, and interpretation,” he says. Further FDA involvement will see data sets become much more streamlined, Shroff notes, “as opposed to right now, where companies are looking at anything they can get their hands on.”

	As the rise of patient data continues apace, with more wearable devices continuously measuring the attributes that pharma companies are interested in, Shroff believes that companies will start to go straight to patients for an increasing number of data points, rather than going through the traditional trial route. “A lot of pharma’s questions are answerable through observational studies and through custom data sets that they’re able to create. So, there will need to be creativity on how to get to the patients and how to get patients to agree to share their data.” 

	There is work already being done in this area. Shroff points to California, where, by 2020, patients will be able to consent to have their own data used for a specific purpose without actually going through a third-

party consent mechanism. Shroff predicts there will be a consolidation of the companies that provide this kind of data.

	“As standards become more established, the large data providers will start to snap up the data assets wherever they exist, because providing the right data to the right people is going to be a business in itself,” he says. “We’ll see new industries looking at how that data can be monetized and used for competitive purposes. All of that will start to happen on a much grander scale than we’re seeing today.” While this will be a fragmented landscape that consolidates slowly, Shroff says that the winners and losers will be determined by the data’s ease of use and adaptability. 

	Meanwhile, the winners and losers in pharma’s quest to embed meaningful RWE strategies will be determined by their preparedness to make these dramatic organizational shifts, requiring trust-building and change management, along with investments in data sources, partnerships, and technology. According to ZS’s Ye and Abhay Jha, principal and R&D technology lead, it drills down to establishing a cross-functional operating model that considers how different teams

 leverage data, with a clear action plan to measure ROI, leadership support, and the appropriate level of technology to support a data-driven model (see sidebar). 

	“Without those driving factors in place,” they say, “efforts can remain motionless, which is where many pharma companies find themselves today.” 

While the increasing importance of real-world evidence (RWE) is widely acknowledged, the dramatic shift required by pharma to embed meaningful and holistic benefits from this capability is still a work in progress.

Author | Julian Upton

Articles

New Roads to China

Future Pharma: Data Science as a Strategic Partner

AI/Data Trends to Watch: Mark Lambrecht, SAS

Securing a Winning Strategy for Real-World Evidence