Recently, I had a conversation with a white female scientist and former colleague about the importance of diversity and inclusion (D&I) in drug innovation. She reminded me of common things that we had experienced during our time in large pharma. One was the clear difference in the treatment of white males versus women during succession planning discussions. White male candidates got the “opportunity to fail” or were given coaches to bridge gaps, whereas female candidates had to be perfect and already operating at the level to which they could be promoted. The second observation was that white males were invariably selected to lead major projects. She referred to it as the “White Mandemic in Large Pharma.” In the wider healthcare arena, the repeated refusal to appoint Dr. Janet Woodcock commissioner of the FDA is one of the most current visible examples of this “mandemic.”

In 1997, as the new EVP responsible for research and development at Hoechst Marion Roussel (which later merged with RhonePoulenc Rorer to form Aventis, later acquired by Sanofi), I introduced a number of changes. These were focused on leveraging the diversity of approaches in the three companies, Hoechst, Marion Merrell Dow, and Roussel Uclaf, to improve drug innovation. One of these changes was the focus on common mechanisms of action. This encouraged the profiling of new lead compounds in all relevant disease models irrespective of the geographic location or nationality of the scientist who had originally synthesized the drug.

Later, in 2000, with the announcement of the deciphering of the human genome, we introduced chemical biology platforms, such as kinase, protease, ion channels, and G protein-coupled receptor (GPCR) platforms that further encouraged collaboration among the three discovery centers and academic laboratories in the US, France, and Germany. These platforms were hubs of multidisciplinary communities of practice across the discovery centers and product development.

Today, the business case for D&I is even more compelling. For example, in 2019, 70% of the 3,000 drugs in Phase III clinical trials reportedly came from small biotech companies.

An analysis of companies’ 2017 annual reports by 

 found that only 10 out of 44 Pfizer compounds in clinical development came from in-house discovery and only two out of 18 J&J compounds came out of its own labs. In short, drug innovation is occurring increasingly more frequently in small biotech startups and academic laboratories.

How can large pharma improve D&I in its companies? First, each has to have the courage to make structural and organizational changes to foster equity and opportunity for everyone in its employ. Equity has to exist internally as well as characterize the interactions with external stakeholders. Employees who feel that they are not being treated equitably and experience few opportunities to advance within the company will be less motivated and less likely to create innovative solutions that inspire their peers.

Second, in addition to testing the rules and regulations for consistency with equity, they should ask three questions: What behaviors are rewarded? Who gets promoted? And, are the criteria for rewards and promotions transparent and offer everyone equal opportunity to achieve? This approach should be the same whether one is trying to ensure that every contributor and innovative idea has the same opportunity to be seen and heard in a multinational company as it is in a US-based organization with privileged groups.

Third, old excuses such as “the pipeline of such individuals does not exist” should be buried in the dustbin of history. The COVID-19 pandemic has shone light on the known health and opportunity disparities among underrepresented minorities. The paucity of Black clinical investigators and the lower percentages of Blacks participating in clinical trials were evident in this pandemic. This is particularly stark given the contribution of Dr. Kizzmekia Corbett in innovating the Moderna vaccine and the well-recognized fact that trusted advisors are the best persons to treat vaccine hesitancy. Large pharma can bridge these opportunity gaps by establishing collaborative research centers at select HBCUs. They will narrow the “opportunity gap” as was so poignantly identified by Kenneth Frazier, the only Black CEO in large pharma, who recently retired from Merck.

Systemic racism will be in retreat when equity and opportunity reinforce empathy, and replace the use of stereotypes.

Former professor at the Massachusetts Institute of Technology, where he was involved in founding the Center for Biomedical Innovation

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How can large pharma improve D&I in its companies?

Addressing Systemic Racism in Biopharma

Key to the success of any pharmaceutical product is a deep understanding of healthcare professionals (HCPs): Who treats the indicated patients, what drives their prescribing behaviors, which messages resonate with them, how do they interact via referral pathways, and who wields meaningful clinical influence? Management and sales teams pore over prescription and provider data to glean this intelligence, but reliance on these data alone could lead them to draw flawed or incomplete conclusions about the market potential or performance of their brand. Patient-level data is a crucial component of market and HCP assessment, providing a more comprehensive foundation for a successful product strategy.

The examples below illustrate how patient-level data can complement other data types to deliver unique context, nuance, and insight around market opportunities, effective messaging, networks of care, and spheres of clinical influence.

Accelerating recruitment and site selection

A large biopharma focused on immune modulators for pediatric patients needed to identify HCPs who could support a pharmacokinetic clinical study ahead of potential therapy approval. By using patient-level data, experts created a list of target HCPs treating the relevant patients who fit the key eligibility criteria, including age range, diagnoses, and treatment status. They likewise used patient-level data to map critical HCP affiliations to inform outreach, contacting HCPs seeing screening-eligible patients. In their case, patient-level data accelerated HCP recruitment and site selection for the trial.

A startup developing a new potential endocrine therapy wanted to understand its market potential, knowing it would face competition within the indication. Prescribing data suggested relatively low utilization of the competing drug, throwing into question the potential market value of its own product. Analysis of patient-level data painted a completely different picture of the market opportunity, by identifying net-new target patients who were untreated but had the relevant condition. Relying on prescription data alone—in essence, using the competitor’s results as a benchmark—could have led the company to vastly underestimate the true potential of its candidate therapy.

A small- to medium-sized biotech company had gaps in understanding its market basket prior to launching its flagship second-line therapy. As its therapy is indicated at a specific point in the patient treatment journey, the team needed high-resolution data to understand the prescribing potential of HCPs. An analysis from a legacy data aggregator did not accurately capture the connection between scripts and prescribers, offering an insignificant level of visibility into the company’s patient-level market share, especially for competitive therapies with multiple indications. But patient-level data provided unparalleled prescribing resolution, capturing up to 70% more patients and their relevant clinical signals. This patient-level resolution set up a successful launch of the therapy in a competitive market.

Company ‘Q’ was preparing to launch a gene therapy for patients with a rare disease who seldom survive past early childhood. Given the limited time frame during which the therapy is indicated (patients <2 years of age), the field team wanted to engage as many relevant HCPs as possible. Patient-level data enabled the company to locate a wider swath of clinicians across sub-specialties, ultimately identifying seven new physicians responsible for putting 17 patients on therapy within the first three months of launch. Insights from patient-level data enabled the company to differentiate its product in a market with two other competitive therapies.

A small biotech’s ultra-rare therapy, indicated for a familial disorder, was underperforming in the first year of launch. Patient-level data helped the company identify the full care teams for patients already diagnosed with the rare disease. The company engaged those care teams, including providers that had not previously been their main targets, to identify family members who may be at risk. In one case, it recognized that an HCP was seeing up to 10 at-risk individuals from two families with this rare hereditary disease.

A large biopharma company’s recently approved cell therapy is only available through certain academic settings with specialized expertise. The team needed to identify community HCPs seeing candidates for the therapy and educate them to direct those patients to the appropriate academic settings. Patient-level insights enabled the company to identify the right HCPs from community practices and to map its referral routes via actual observed patient relationships. Based on this knowledge, the company adjusted its engagement strategy to help HCPs streamline their patients’ journeys and close gaps in care.

A company with a late-line therapy for patients with liquid tumors wanted to engage HCPs at the right point in their patients’ treatment journeys. Using patient-level data, it could identify HCPs treating patients with competitive therapies, patients who were not responding to therapies, and patients who had already received multiple lines of therapy. The medical affairs field team connected with these HCPs to facilitate intervention at the precise stage of their patients’ journeys when this therapy is indicated

A biopharma company recently launched a drug for a disorder with multiple subtypes and variable clinical presentation. The clinical complexity can cause patients to experience long diagnostic journeys involving multiple HCPs. Patient-level data enabled a top-of-the-funnel view, revealing a more complete picture of patients’ encounters with the healthcare system through time and across settings. This perspective allowed the team to see patterns and signals in the patient journey more clearly and identify the entry points and sequences by which patients interacted with their target HCPs. This facet of HCP behavior would not be visible from the provider level.

An oncology brand in launch mode knew that its patients often saw numerous specialists and needed to understand the landscape, unmet needs, and treatment dynamics within its market basket. Prescription data alone gave the team little insight into the patient journey. But patient-level data offered a fresh perspective on the brand’s market share by illuminating HCPs’ behavior: network influence, referral directionality, prescribing influence, clinical volume, industry engagement propensity, and more. It revealed far more clinicians along the patient care pathway who were not well captured in other aggregated data. Deeper coverage of secondary and tertiary influencers provided a more comprehensive view of product utilization and treatment dynamics that enabled the team to create a robust launch strategy.

A leading respiratory brand built its key opinion leader (KOL) list primarily based on providers’ publications, qualitative analyses, and specialty designations, which are often out of date. Using patient-level data, the team could identify new potential KOLs based on the patients HCPs saw and procedures they conducted. The team also looked at providers’ scientific and industry activities. All told, it added 1,365 HCPs to its KOL list, gained a fuller appreciation of the universe of influencers, and maximized the impact of its pulmonary therapy.

Pharma and biotech companies strive to understand HCPs so they can offer the insight and tools needed to help patients. While prescription and provider data are go-to sources, the picture they paint may be incomplete if it doesn’t incorporate comprehensive patient-level data. Starting with patients affords a much richer representation of patient journeys and helps pinpoint the HCPs who are best placed to influence them. From establishing disease burden and unmet need, to market launch, to evaluating comparative effectiveness, a better understanding of HCPs improves product strategy—and it starts with patient-level data.

GM of Commercial Life Sciences, Komodo Health

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Evaluating patient data in the context of complementary information can provide comprehensive market insights for product strategy.

Understanding the HCP: Patient-Level Data Proves Valuable 

COVID-19 changed everything. For clinical development, the pandemic highlighted the expanding role of real-world evidence (RWE) and the need to think about how data can be fit-for-purpose to meet drug approval and market-access standards. The moment demanded action, and the industry and researchers delivered.

In nine short months, the Pfizer-BioNTech COVID-19 vaccine was granted FDA approval. The 360,000-page application confirmed the safety and efficacy of the landmark vaccine and was approved in record time. However, this approval package wasn’t enough to answer the many questions that this pandemic posed and that were so critical in helping us understand how to treat and manage this devastating infection. We needed more, and we needed it quickly, to guide the decisions physicians, hospitals, and patients were making every day.

RWE—insights derived from data describing the usage and benefits of a medical product based on data collected outside of clinical trials through methods such as electronic health records or patient-generated data in home-use settings—provided these much-needed insights throughout 2020 and still today. Alongside FDA’s approval decision, the pandemic saw an explosion in programs and tools gathering data on COVID-19’s progression-tracking patient symptoms, treatments, and outcomes globally both from clinicians and patients. This new evidence has been critical in helping us better understand—in real-time—the pandemic’s course, the treatments being utilized, and how patients fared. While the needed, rigorous clinical trials were underway to test both the vaccines and numerous emerging treatments, RWE gave us an early view of the power and value of real-world data, and to the evolving approaches to collecting and evaluating it.

The pandemic also drove a seeming evolution in research design and approach—with key trials, like the RECOVERY trial in the UK, employing a more-traditional clinical trial design, but with real-time analysis and reporting to ensure that clinicians and patients benefitted from data and insights as they emerged. At the same time, the urgency of COVID-19 also spawned new collaborations and partnerships, bringing together key researchers, patient advocacy groups, drug developers, academia, and government entities to help drive results. The COVID Symptom Study is one such collaboration. An app-based, real-world COVID-19 registry initiated by researchers in the UK and US and with engagement from numerous universities, patient groups, and long-standing research studies, this innovative real-world study has gathered self-reported data from almost 5 million people in the UK and US.

This explosion of evidence during the pandemic has indeed highlighted the value of RWE and its ability to provide the clinical development industry with more diverse information and insights that will enable regulators to make decisions more quickly and confidently on the safety, efficacy, and effectiveness of therapies. With this boost in RWE’s generation and application, it’s become evident that it will play an increasingly important role in drug development, particularly in therapeutic areas such as rare diseases and cell and gene therapy, where research innovations are so urgently needed.

Traditional randomized, controlled clinical trials are essential in our drug evaluation process, allowing us to clearly see a product’s true effects. The strictly defined patient populations in a clinical trial are inherently homogeneous, allowing analysis of the product’s effect without any worry that other treatments or medical conditions contributed to the patients’ outcomes. But this clarity comes at a cost. We know that in the real world, life is much more complex, and our real assessments of outcomes—of longer-term safety and effectiveness—need to reflect this complexity. Typically larger and longer in duration than clinical studies, RWE studies provide more information and data to develop evidence and help regulators understand the outcomes of a product in routine care. RWE is uniquely able to help us understand that patients in the real world aren’t homogeneous, and that understanding their differences is the key to better treatment.

The stakeholders across our healthcare systems look at RWE through their respective lenses. Regulatory agencies want to know whether a product works and whether it is safe. Payers want to understand the product’s value—is it “worth it,” particularly compared to other treatments already available? And clinicians want to know which patients will benefit most from the new treatment. For patients, RWE represents a potentially more efficient and effective way to drive reimbursement for these critical new therapies.

What is most promising about RWE, though, is its power to help product developers get the right treatment to the right patients at the right time. With the rising cost of healthcare, we need to be more specific and directed in our approach to treating patients. The imperative for personalized medicine is in fact stronger today than ever before—for better understanding the genetic origin and markers of disease and their implications for patient outcomes, for understanding which patients simply won’t respond to certain therapies. It allows us to put patients at the heart of our approach, giving them the best chance at improvement, or even a cure.

External control arms, one of the more exciting applications of RWE today, have seen much interest in recent years. This approach, which uses patient-level data sourced from real-world retrospective studies, electronic health records, existing datasets, wearable technologies, and even control arms from other completed clinical trials, provides valuable real-time insights into the patient experience and outcomes. They can identify patient-population signals that may be undetected in randomized trials, and mimic randomized datasets providing flexibility for public health crises like COVID-19 or for diseases with smaller patient populations.

Importantly, external control arms can help accelerate a product’s path to market by eliminating the need to recruit and manage a separate control arm in a pivotal trial. Instead, the control arm is created from a source external to the trial; getting the data elsewhere, external to the trial itself, may cut the number of patients needed by as much as half, typically reducing both study timelines and costs. Importantly, studies with external controls are also often more attractive and appealing to patients. For patients with rare or severe diseases, clinical trials often represent a chance at treatment where other therapies are not available or effective, but at the risk of being randomized to either a placebo control or a potentially less effective treatment. Externally controlled studies, though, eliminate that risk; patients are guaranteed to receive the promising new therapy. 

While regulators have considered external control data in a number of studies and have applied it in their decisions in a growing number of reviews and approvals, they have mostly focused on rare diseases and oncology drug candidates to date. In these settings, a more traditional study design can be difficult to enroll, or the need for treatment may be so strong, randomizing to a control group may be simply unethical.

With this evolving landscape, there is clearly a growing recognition of the value of these data in regulatory decisions. Driven by the 21st Century Cures Act in the US, FDA is expected to soon release its guidelines for use of RWE in regulatory decisions; other agencies around the world, including China’s National Medical Products Administration (NMPA), have also already issued guidance to govern their decisions.

While our interest in RWE tends to gravitate to new therapies and to the critical regulatory approval environment, it also shows great promise for approved therapies, allowing the industry to answer long-term safety questions, ensure the right information is provided to payers, and better understand what happens when a product is introduced into the market.

While RWE has shown great promise and has already contributed significantly to our drug development and commercial landscapes, challenges to its use and application do remain. There is a misconception that it’s always going to be faster, easier, and cheaper to provide insights with existing data, but we know that’s not always the case. While it can offer important insights and may be able to do so more quickly or less expensively, we also need to be cognizant that we are still advancing our understanding of how best to work with these data, and how to confront some of the challenges and limitations of this approach.

In recent years, our ability to access existing sources of real-world data has exploded, with new ways to utilize electronic medical record (EMR) and other data seemingly coming online each week. But access doesn’t always equal application. We recognize today that there is no single dataset that will answer all questions, and a single dataset may not even be adequate to answer a single, specific question. Commonly today, we’ve found that multiple data sources are required to ensure the evidence generated is robust.

Indeed, we continually need to evaluate the structures, completeness, and quality of the data and ensure they are fit-for-purpose for the analysis required and the questions we are trying to answer. It’s not unusual today to see that a hybrid approach is needed—collecting data from existing datasets while also gathering data prospectively from sites to ensure that specific, critical data fields are captured with the completeness, depth, and accuracy needed to support the study analyses.

There is also the ongoing need to understand the differences between types of data and ensure transparency in the assessments made on the data relevancy and quality. It is important for sponsors to work with the right partners to find the right approach to meet their objectives.

RWE is no longer an innovation. It is the “here and now,” central to how the industry and stakeholders need to approach drug development and to ensure that products are best supported throughout their full lifecycle. Aligning evidence-generation planning with both the development and commercialization pathways is key to ensuring patients truly have access to innovative therapies, today and in the future.

Senior Vice President, Real-World Evidence & Access, 

Vice President of Real-World Evidence Strategy, both with Parexel

COVID may have given real-world evidence the boost it needed to become routine, especially in areas that demand research innovation and advancement.

Industry Game Changer: RWE Impacts on Drug Development  

’s 20th Annual Pharmaceutical Industry Audit, covering the Year of the Pandemic. We present a unique financial performance analysis of the top 18 publicly traded biopharmaceutical companies based on 2020 sales revenue. As in our previous Audit, this total of 18 organizations reflects the acquisitions of Celgene by Bristol Myers Squibb (BMS) and Allergan by AbbVie. The Audit is focused on a number of financial performance metrics, particularly critical metrics such as growth in shareholder enterprise value, enterprise value to sales (EV/S), and return on invested capital (ROIC). Regarding the last metric, we introduced the impact of weighted average cost of capital (WACC) in September 2019. WACC adjusts ROIC by assessing the difference between ROIC and WACC.

This year’s Audit relies on secondary reported information for the 2019–2020 time period. The metrics are also weighted reflecting their relative importance in assessing a company’s performance. Some metrics are more important than others. For example, sales growth is important, but sales growth can occur as a result of mergers and acquisitions and in-licensing. So it takes a back seat to more critical metrics such as ROIC, which measures how well a company is managed, including margin management (the profit and loss or income statement) and asset management (the use of assets on the balance sheet), and most importantly, whether ROIC exceeds the cost of capital (the cost of debt and the opportunity cost of stock equity, which are reflected by WACC).

Two metrics are included that are not weighted: sales, general, & administrative (SG&A) expenses, or overhead; and profit per employee.

The higher a company performs on a metric is reflected in a ranking based on the number of points it receives per metric. The highest metric for this year’s Audit is 18 based on the number of firms in our Audit. For example, if a company places 17th (second highest) on the critical metric EV/S, it receives 51 points on the metric (17 rank x weight of 3). In another example, if a company comes in at a ranking of five (five places from the bottom), on the metric gross margin (pricing power), its total points would be 10 (5 rank x weight of 2).

Basic indices are the growth of the US economy and inflation. An organization has to be able to grow faster than the US economy: around 3% in 2019 and higher than inflation as measured by the Consumer Price Index (CPI), about 2% for 2019. Other important benchmark metrics for 2020 include:

Dow Jones Medical Equipment Index: up 22%

S&P Composite 1500 Health Care Equipment & Supplies Industry Index: up 17%

Table 1 shows sales in US dollars along with sales growth for 2019–2020. It is good to grow, especially organically, compared to just acquiring companies. But that’s easier said than done for companies at high sales levels such as Johnson & Johnson (J&J), Novartis, and Roche.

The average dollar sales for our 18 companies was $35.9 billion in 2020, vs. $31.3 billion the previous year: a growth rate increase of 12.9%, outpacing the US economy growth rate (3.4%) and inflation (1.4%). Our 18 pharmas also outperformed the Fortune 500 for 2020 (Fortune 500 sales growth: 3.72%). The key metric in Table 1 is sales growth, with BMS increasing the fastest, due in part to the Celgene acquisition, which was completed in November 2019. AbbVie’s sales growth comes in second, again, with lingering impact of its acquisition of Allergan in 2019. Of our 18 companies, four showed a sales decrease for the pandemic year.

This is the first of the three crunch metrics; EV/S and ROIC are the others. There are other worthy performance metrics (e.g., corporate responsibility, sustainability, the best places for women and minorities to work, etc.), but our focus is financial performance and enterprise value and growth. Did a company create or add to shareholder (enterprise) value or destroy shareholder value?

EV is the sum of an organization’s market capitalization; then add in debt and adjust for cash and other current assets. Simply put, EV is the market value or market capitalization of a company. At present, two firms are fighting it out for the highest company value on earth: Microsoft and Amazon, both at company market caps of more than $1 trillion.

Table 2 shows the pharma with the highest EV is J&J at $471.5 billion, with an average growth for the year of 11.6%. The company that grew its EV the most was Viatris (the Mylan plus Pfizer’s Upjohn spin-off), with a spike of 101%. To put that in perspective, the average EV for our 18 pharmas in 2020 was $176.8 billion, up 12.8%; average EV jumped 18.9% the previous year. Only three companies dropped in this metric.

Enterprise, or shareholder, value for this year’s pharma crop averages $176.8 billion, a 12.8% increase over 2019’s $156.3 billion. This is lower than 2019’s EV growth of 19%.

EV and EV growth are very important performance metrics. EV/S supplements that metric by assessing which firms are still climbing 

 so-called “value” stocks—stocks for widow and orphans that, if not growing in upside market cap, still pay noteworthy dividends and are solid businesses.

Table 3 lists EV/S. The average EV/S for 2020 is 4.6, the same as 2019. At the top is Novo Nordisk at 7.6, an increase from the drugmaker’s 5.7 EV/S in 2019. Coming in a close second is Eli Lilly at 7.1. Only seven pharmas posted increases in EV/S, compared to 12 in 2019. The higher the EV/S ratio, the more likelihood the company’s performance is going to get better.

Yes, there is net-net and list price vs. net price, but at the end of the day, there is gross margin, which is tantamount to markup. As Warren Buffet would call it, “the moat around your castle.” Gross margin reflects an organization’s power to maintain or, even better, increase price.

This metric represents total revenue minus cost of goods sold from the income statement. This is quintessential margin management: how price is managed while simultaneously managing operating costs to produce net income. The higher the gross margin is, the more a company is able to cover operating expenses, including SG&A. Table 4 below shows Novo Nordisk at the top with a gross margin of 81.8%, a slight decrease from 2019, which followed another price decrease in 2018. The biotechs rule pricing as they have ever since the first 

 Industry Audit in September 2002. But note the significant drop in gross margin average for our 18 pharmas in recent years, from 77.7% in 2018 to 68% in 2019, and for 2020, down to 67%. Nine firms were able to increase their gross margin in 2020, with Lilly, Novartis, Gilead, Biogen, and Amgen raising it the most.

Staying with margin management, Table 5 shows pre-tax operating income, or profit to sales. Again, the higher the gross margin is, the more that contributes to improving pre-tax operating income. Operating income consists of total revenues minus cost of goods sold and minus operating expenses related to a firm’s typical business. It excludes one-time gains and losses, dividend income, and interest income.

At the top in this metric is Novo Nordisk with pre-tax operating income of 41.7%. The average figure for 2020 was 16%, a significant four-digit drop from 20.3% in 2019. Only seven of our 18 companies saw their pre-tax operating incomes increase for the year.

Gross margin and pre-tax operating income have to do with margin management; sales to assets has to do with asset management. If a firm is at $70 billion in sales, for example, it won’t be doubling revenue anytime soon. If the company has also curtailed SG&A and disposed of assets, it won’t be cutting operating expenses in half anytime soon either. Then it turns to asset management to do a better job making use of, not necessarily owning, assets.

When you multiply profit to sales (pre-tax operating income) by sales to assets (asset management), you get a far more important measure: return on assets. A company can have a relatively low profit margin with a relatively high sales-to-assets ratio that will result in a better performance in terms of ROIC.

As seen in Table 6, Novo Nordisk’s sales-to-assets ratio is 0.9. For every dollar invested in assets, Novo Nordisk gets back 90 cents. Roche comes in at No. 2, getting back 70 cents for every dollar invested in assets. In our latest Audit, only three companies rank above average regarding how productive they are in managing their assets.

Now we come to the mother of all metrics: ROIC. ROIC is net income left over to shareholders as a percent of debt and common stock. According to longtime organizational consultant Mark Van Cleaf in 

, the best measure of business performance is ROIC—how much is a company generating on its capital investments, plant and equipment, minus the cost of that capital, debt, and equity? Combine this with our relatively new addition to the Audit: weighted average cost of capital (WACC). According to Van Cleaf, management should be providing value that exceeds its cost of capital. For example, two organizations can have a ROIC of 10%, but company 1 has a WACC of 12% while company 2 has a WACC of 7%. The first firm is destroying shareholder value while the second one is creating shareholder value.

Prior to the mid-1980s, the conventional wisdom was that debt had a cost (the rate you pay for borrowing), but issuing stock was free. But what if the peer groups an organization competes with have a higher ROIC than the company? Even though the firm shows a profit, it is destroying shareholder value if its profit is below its peers’ average. What return could an investor get by investing in an organization of equal risk? This was the brainstorm of Stern Stewart, a consulting firm based in New York City.

Our source for WACC relies on three years of income statements and balance sheets to arrive at a firm’s WACC percentage.

Table 7 below shows Novo Nordisk far out in front with a ROIC of 66.5%. Lilly ranks second at 31.6%. Average ROIC decreased to 14.5%, from 16.13% in 2019. Only one-third of our 18 companies increased ROIC for 2020.

To avoid overstating the impact of ROIC, and double-counting its value, we use the ROIC number to come up with the rankings on this metric. But the inclusion of WACC in Table 7 tells an interesting story: The greater the difference between ROIC and WACC, the greater the return to shareholders. Aside from Novo Nordisk’s outsized ROIC, the firm with the most impressive creation of shareholder wealth for 2020 was Lilly, with a 26.4-point difference between ROIC and WACC. Roche was next with a 24.5% differential.

This metric is not weighted in the rankings. It is interesting, however, to show how profitable the pharma sector is. Biogen produces the most profit per employee of our 18 pharmas, at $439,600 per employee, as indicated in Table 8 below.

SG&A (see Table 9 below) is another metric that does not impact the rankings. Nevertheless, it is still important because the expenses in this category are necessary to run an organization and constitute routine spending such as rent, salaries, advertising, marketing, legal, and more. Laying off and firing workers costs money in severance before it begins to pay off. It’s also important to keep in mind that this is a one-year comparison. A company can be making investments in advertising, training its sales force, etc., and that will pay dividends down the road. One swallow does not a season make.

Opportunities exist to cut advertising costs by being more efficient with agencies and revamping supply chains. Also, SG&A can increase, but it is the goal for sales growth to grow faster, thereby lowering the SG&A-to-sales ratio. Ballooning overhead leaves a firm inefficient and less productive than it otherwise could be.

The average SG&A/sales ratio for 2020 is 41.5%, the same figure it was for 2019. Take, for example, AstraZeneca and its SG&A ratio of 57%, which is about 16 points higher than average. What if the organization could get its SG&A/sales ratio down to the average (41%)? That would result in about $2 billion that could be assigned to bringing a new drug to market, or returned to shareholders.

Table 10 reveals the winner of this year’s Audit: Novo Nordisk, which secured the top spot for the third consecutive year. Lilly finished a close second, followed by Amgen, AstraZeneca, and AbbVie rounding out our “Fab 5.”

Worth noting is the impressive run Lilly had in 2020. The drugmaker was the only company on our list to show positive growth in every one of the seven weighted metrics.

Finally, I want to return to a performance discussion we touched on in the past: The Dr. Bill Trombetta Hall of Fame for Outstanding Management Performance. In a few installments of the Audit over the years, we focused on two measures: profit to sales multiplied by sales to assets = profit to assets, a crude version of the more powerful performance metric ROIC. I want to go back to such a year-capsule evaluation but in terms of a new metric: The PSE Metric—­profit grows faster than sales and sales grow faster than expenses (SG&A). I am calling this “Dr. Bill Trombetta’s Annual 

 Pharmaceutical Industry Audit ‘Trifecta.’”

What does it profit a company whose sales growth is exceeded by its growth in SG&A (overhead, burden)? And what if sales growth is lower than the growth in profits? Again, it would be unfair to penalize or reward an organization for its SG&A spend in a given year; the variable cost spend could be an investment that will pay off in the near future or a sign of overhead bloat.

But in a given year, if a company can grow its sales faster than SG&A spend and grow the organization’s profits faster than the growth of its sales, this is worth shouting out.

Sales should grow faster than SG&A spend over time. And profit growth should exceed sales growth. What does it mean if revenue grows faster than profit? It suggests that the company is profiting less from each dollar of sales revenue. Perhaps sales growth is growing in strategic business units or product lines that carry lower profit margins. It also could mean increasing competitive pressure.

Given the rationale above, here are Dr. Bill Trombetta’s top Trifecta Performers, in no particular order, for 2020: GlaxoSmithKline, Sanofi, AstraZeneca, Gilead, and Lilly. Hats off to these five pharmaceutical manufacturers for a well-managed financial performance for 2020.

Bill Trombetta, PhD, is Professor of Healthcare Strategy & Marketing at St. Joseph’s University Haub School of Business in Philadelphia. He can be reached at 

Our latest numbers crunch on biopharma financial performance—this one assessing growth and value-generation during the heart of COVID-19—reveals a mix of the familiar and new entrants into the top spots in seven business-critical metrics.

20th Annual Industry Audit: Year of the Pandemic 

When you think about technology and pharma, the industries might seem bipolar. The first stirs images of speed while the second holds a reputation of slowness. However, as the field of data science grows, these industries are noticing the potential of doing business together and unconventional partnerships are being forged.

While some companies have tried to enter the pharmaceutical business on their own, such as Amazon’s launch of Amazon Pharmacy in 2020, others have found working together might be a better route. Tech may be drawn to a relatively recession-proof pharma industry that relies on vast amounts of data, but pharma is equally enamored by tech’s ability to accelerate its business. Both Google and Microsoft have created divisions specifically centered on the life sciences. Combined with novel technologies and a biotech boom fueling innovation, companies of all sizes on both ends are now finding ways to collaborate.

According to Jay Galeota, Kallyope CEO and 

editorial advisory board member, the industry is at a “unique confluence of three macro trends that are going to shape the next decade of biotech and healthcare, drug discovery, and development.” The first trend centers around biology and an unprecedented ability to understand and manipulate complex systems. The second is around the use of computational analytics and the widespread availability of high-powered often AI-enabled analytical capabilities. The third is advances in materials sciences to create previously impossible modalities, like introducing genes into cells through materially mediated mechanisms, or using lipospheres or microspheres to more accurately deliver therapeutics.

While these elements have existed independently in the traditional biology and drug development space, the analytics/IT/computational analytics space, and the device space, the revolution is in seeing them converge at a pace that’s faster than ever before. And now there are novel companies emerging that recognize this opportunity, whose discovery development platforms are dependent on some of these nontraditional approaches. Innovative biotechs are recognizing that traditional drug discovery and development is no longer enough; they need access to world-class data and analytics, and novel approaches to enable more targeted therapeutic delivery.

The team at Kallyope is integrating multiple sophisticated technologies to provide insights into gut-brain communication—a complex biological process—to target multiple diseases of high unmet need in a fundamentally new way.

“Our approach would not have been possible even five years ago,” says Galeota. “We are combining leading science in biology and chemistry with advanced capabilities in computational analytics and statistical genetics, together with new approaches for the study of human biology on the bench to discover and translate our novel programs. For example, we are utilizing organoids, which are essentially mini-guts in a test tube, to test the effectiveness of various compounds on specific targets. That’s pretty remarkable. This is an example of what is possible when you bring all of these approaches together.”

Though the potential for partnership has been around for decades, the conflicting philosophies of the scientific process and technology development have been a fundamental hurdle. “In the tech world, there is generally an engineering mindset with the belief that if you build it, they will come,” says Galeota. “In science, it’s almost the exact opposite: First, do no harm. In order for pharma to accept something new, it has to have been validated, peer reviewed, pressure tested, and replicated before widespread adoption occurs, which of course is a good thing since safety is paramount, but it does create a basic challenge in merging these fields.”

Galeota is beginning to notice an appreciation for the value of the scientific process for validation and legitimacy on the tech side. On the science side, there’s also an increasing acceptance of tech, for example in new ways to conduct clinical studies and an acknowledgement that the traditional ways of validating something scientifically are sometimes too slow for technology’s current pace.

“Tech partners that understand the scientific process, including the need to be rigorous in their research and to demonstrate in a controlled way the application of their technology may be the most successful partners for pharma,” he says. “For tech companies that want to be life science tech companies or seek to be partnered with innovative biotechs, it’s important to understand how to work with established scientific channels, like medical societies, the publication and presentation process, and peer review. An appreciation for the value of these elements will give them a leg up.”

Another area where pharma and tech need to converge is around speed. Pharma is generally more heavily regulated than tech, so there needs to be a willingness from tech to accept that as part of the process. It’s not a high-speed Silicon Valley tempo where things move at lightspeed; it’s a slower, more methodical orientation. Conversely, pharma is concerned that if novel approaches are adopted that aren’t thoroughly vetted and properly executed, there could be setbacks, the need to rework tasks, or new approaches might not be accepted at all.

“The ability to collaborate with various stakeholders is one way to bridge the gap, so that stakeholders collectively work to agree on what makes sense,” says Galeota. “Having a mutual desire to move in this direction is the common ground that they all share. Trying to do that in a way that’s thoughtful and collaborative and takes into account the various concerns that the different constituencies have would be an important aspect of continuing to advance in this direction.”

The war on COVID-19 has forced enormous collaboration at never-before-seen levels. One area of pharma that could benefit from tech collaboration is in improving the efficacy of already proven therapeutics that could be limited due to toxicities and adverse experiences. For instance, some people might not be able to take a medicine that will help them because the ancillary effects are too debilitating or risky. Some examples are disease areas like CNS-related diseases, depression and anxiety, schizophrenia, immuno-oncolytics, and anti-inflammatories, where effective treatments are frequently dose limited due to side effects and/or adverse experiences.

“If we’re able to find innovative ways to apply the kinds of technologies we’ve been talking about to enable these known interventions to either get the medicine to the target site in ways that are more efficient, which can reduce the systemic exposure levels, or find novel ways to administer directly to the point of interest, that would be beneficial,” he says.

Pharma should also look to tech to develop technologies that can enhance compliance of its therapies. While the industry may excel at scientific innovation, it should outsource its technologic needs to companies with that expertise. Managing medical conditions that are chronic and require the activity of patients, such as diabetes, high blood pressure, and arrhythmias, are possible areas for pharma to start collaborating in digital health.

“My favorite example for this is Roche’s acquisition of mySugr, a startup developing an award-winning diabetes application,” says Bertalan Meskó, MD, Director of The Medical Futurist Institute. “Now the company offers its diabetes medication with a useful smartphone app that is a more attractive package for healthcare providers.”

Partnering to create technological products that people like to use can be incredibly useful to pharma. “Pharma companies are pretty bad at doing that,” says Meskó. “This is justified by the fact that only around half of patients worldwide comply with the therapy they received. At the same time, Apple, Google, and Amazon keep on making products people depend on and use for years. This is a quality that would be beneficial for pharma companies to tap into.”

As the pharmaceutical industry gradually grows more dependent on advanced digital technologies, it can become more efficient and cost-effective, adds Meskó. These are technologies that big technology companies have immense experience with, while pharma companies simply don’t. Thus, tech is realizing an untapped business opportunity to help develop products consumers like to use. It is a niche to be filled.

To see how pharma and tech companies are already helping each other, read our five examples of Collaboration in Action below.

, Jan van de Winkel, MD, CEO of Genmab called out the importance of new partnerships between biopharma and data science companies to help catalyze breakthrough innovation and more rapidly develop medicines for indications where speed is critical.

One of the company’s most recent innovative partnerships was with data sciences company Tempus. Genmab worked closely with Tempus as a sequencing provider for some of its clinical trials, and the partnership naturally evolved from that relationship. It stemmed from a shared goal to improve the lives of cancer patients with both companies working to explore novel treatment concepts and biomarkers, and the rapid integration of translational data into drug development.

“By building teams that are working together side by side instead of siloed, we have been able to create a unique approach to a research partnership, all with the goal of designing and building better therapeutics,” says Kate Sasser, Senior Vice President and Global Head of Translational Research at Genmab. “Tempus is focused on building the world’s largest molecular and clinical data sets, and we are focused on our goal of developing the best antibodies to transform cancer therapy; therefore, we are perfect partners to move our mission forward as fast as possible.”

Collaborations have always been a big part of Genmab’s mission. In addition to partnerships with biopharma companies, Genmab works with top-tier research institutions. In 2020, the company partnered with the Princeton Catalyst Initiative (PCI) on research projects in specific areas that unlock approaches in translational cancer research, antibody science, and data science. It also was awarded the Dutch Prix Galien last year for its work with private research institutes in the Netherlands to develop robotization to perform large-scale PCR (polymerase chain reaction) screening for COVID-19.

“By leveraging data science, computational biology, and machine learning, our goal is to develop more personalized therapies and bring the right antibody to the right patient at the right time,” says Sasser. “We also intend to utilize data science and digital technologies to maximize our antibody discovery process, do intelligent trial design, communicate to physicians and patients in a more personalized way, and ultimately make a bigger difference in the cancer therapy lifecycle. We believe cross-industry partnership between tech and biotech will lead the innovation that can ultimately benefit the patients.”

Genmab has been investing in digital technology and data science in research and development for several years now. Its scientists and leaders leverage data and digital to drive translational research and precision medicine. Sasser expects the next wave of benefits will be found in 

 drug discovery and development, biomarker identification, digital pathology, and innovative clinical trial design.

“The most successful collaborations happen when each side comes with a shared vision to improve the lives of patients,” she says. “The supercharged, rapid innovation can happen when that passion is also matched with expertise, a get-it-done attitude, and a willingness to aim high.”

Adaptive Biotechnologies: Information Sharing Increases Ability to Pivot

Chad Robins, CEO and co-founder of Adaptive Biotechnologies, also called out his experience with tech partner Microsoft (see below) in 

. The collaboration of the two Seattle-based companies, which began in 2018, combines Adaptive’s advanced sequencing technology and Microsoft’s large-scale machine learning and cloud computing capabilities to map the T-cell immune response to many different diseases. The terabytes of data generated by this partnership and other Adaptive research, is accelerating an innovative era of medicine the company calls immune medicine.

“We are collaborating with Roche/Genentech to use our immune medicine platform to screen, identify, and characterize T-cell receptors against shared cancer antigens from the blood of healthy donors,” says Robins. “Genentech will use these T-cell receptors to develop and manufacture ‘off-the shelf’ cell therapies.”

While mapping T-cell receptors to antigens specific for diseases, like Lyme disease, with the help of Microsoft, Robins wondered if the same process could be applied to COVID-19 to identify its unique immune response signature. He called the Corporate Vice President of Research and Incubations at Microsoft, Peter Lee, and asked if his company would be willing to make a massive investment in this mission without any clear benefit to the company other than being part of a much-needed global solution.

“They were all in,” says Robins. “We shared a similar commitment to being part of the solution. We rapidly expanded our research and began to collect data from the blood samples of people infected with COVID-19 to sequence the genomes and T-cell receptors.”

Soon Providence St. Joseph Healthcare, Illumina, and Labcorp were brought in. The team analyzed the immune responses of thousands of patients, creating ImmuneCODE—the largest open database of immune responses to any disease. It also launched a research tool for vaccine developers, immunoSEQ® T-MAP COVID, to measure the T-cell immune response to vaccines in development, as well as the strength and duration of that response. This work also enabled the launch of T-Detect™ COVID, an early-entrant T-cell test for people to detect past or recent COVID-19 infections.

“COVID has highlighted the importance of partnerships and collaborations between technology and biopharma companies,” says Robins. “The ability to share information quickly as a collective working together allowed for greater impact. And that translates beyond a global pandemic to the diseases many suffer from day to day. What we were able to do with our COVID projects would not have been possible five years ago. As tragic as the pandemic is, we were perfectly positioned through our partnership to tackle this, and now with the insights we have from COVID, we are even more prepared to use this research and data to propel the next generation of diagnostics for a range of diseases.”

Adaptive’s original work with Microsoft continues and it is actively exploring other disease areas, including autoimmune disorders and oncology. In addition, the company is working with a variety of biopharma partners to develop tools across its three core units—life science research, clinical diagnostics, and drug discovery.

Microsoft: Helping Pharma Accelerate Solutions During a Difficult Time

To get the tech perspective of Microsoft’s collaboration with Adaptive, we asked Corporate Vice President of Research and Incubations Peter Lee for his insights.

“About three years ago, we were ramping up our healthcare and life sciences efforts at Microsoft,” says Lee. “We came across a startup called Adaptive Biotechnologies that had just published a paper on DNA in T-cell receptors that can predict, very accurately, what kind of antigen that specific T-cell would bind to. Through various conversations with Adaptive’s Chief Scientist Harlan Robins about this work and how Microsoft could use its technology to advance results, we developed a strategic partnership.”

The ultimate goal of the relationship is to figure out how to decode the language of the immune system—how it detects and coordinates attacks on disease—and then help Adaptive translate that natural diagnostic and therapeutic capability into clinical products using the power of Microsoft Azure, AI, and machine learning.

When Adaptive CEO Chad Robins approached Lee about switching gears to apply the technology to COVID, Lee initially had some reservations about taking on another project, but ultimately saw the value in pursuing it.

“Our research timeline showed we were still more than a year away from our initial goal, and so I personally had some questions about taking our joint project in this direction even though the need for new COVID-19 mitigations was severe,” he says. “Chad Robins, however, really encouraged us all to take on this new direction. We tried it, and it worked in just a matter of weeks. It truly was a real testament, not only to the Adaptive team and our team at Microsoft, but to the whole science community that’s been responding to COVID-19. We were thrilled to be able to get T-cell samples from our medical partners, including Providence, Labcorp, and Illumina to retarget our machine learning pipeline.”

Through the partnership, Microsoft provided its computer science and large-scale machine learning expertise through a dedicated team of researchers, and the technical cloud infrastructure for the sequencing and training of Adaptive’s data with its machine learning models. Adaptive is now rapidly generating tremendous amounts of data.

“With our AI models and cloud-scale computing power, we’ve been able to truncate what would have taken years’ worth of work analysis down to a matter of weeks, accelerating the process significantly to create T-Detect COVID,” says Lee.

Though the companies’ focus was on their COVID-19 work throughout the past year, they are now returning to their broader program.

In January, Google and Boehringer Ingelheim decided to partner to apply quantum computing to pharma research and development. The collaboration combines Boehringer Ingelheim’s expertise in the field of computer-aided drug design and 

 modeling with Google’s resources as one of the leading developers of quantum computers and algorithms. It is the first time Google has partnered with a pharmaceutical company in quantum computing.

“Google’s Quantum AI team partnered with Boehringer Ingelheim to focus on researching and implementing cutting-edge use cases for quantum computing in pharmaceutical R&D, specifically including molecular dynamics simulations,” says Shweta Maniar, healthcare & life sciences industry strategy executive, Google Cloud.

As pharma companies increasingly look to technology to empower innovation and fuel progress, increased collaboration between tech and pharma can help accelerate the drug discovery process. For example, Schrödinger is collaborating with Google Cloud to further increase the speed and capacity of its physics-based computational platform for drug discovery and materials science. This partnership effectively triples Schrödinger’s previous throughput and pushes the capacity to the equivalent of the world’s most powerful supercomputers.

Moderna is also working with Google Cloud and Looker, its analytics and business intelligence solution, to break down data silos for researchers across the company, providing a consistent single source of truth. Looker improved visibility across entire studies through simple visualizations of hardset data, allowing teams from multiple sectors to share a common viewpoint and easily access each other’s work. Eliminating the hours needed to manually input these siloed data sets allowed Moderna to provide its clinical operations team with a single view to make real-time informed decisions about enrollment and trial execution.

And Accumulus Synergy, a nonprofit working to develop an information and data exchange platform aimed at transforming how drug innovators and health authorities worldwide interact, is building a suite of cloud-based technology solutions on Google Cloud that both health authorities and biopharma companies can utilize to enhance their data and information exchange experiences to help bring safe and effective medicines to patients faster and more efficiently.

“Personalized medicine and customized treatments, fueled by data-driven insights, are becoming a reality for the industry,” says Maniar. “Technology provides compute, data, and AI/ML [artificial intelligence/machine learning] underpinnings that enable progress in this area, and we expect the opportunities to advance personalized care to grow dramatically in the next five to 10 years. We [must continue to] lay the foundations for future success and open up new opportunities to improve medicine, while meeting regulatory requirements and ensuring the data is secure and protected. As we do so, Google continues to hire industry experts who can articulate opportunities and challenges from the specific perspective of the life sciences industry so that we can build and deploy truly helpful technology.”

GlaxoSmithKline: Investing in Machine Learning Leads to Explosive Growth

Computation is key to the work of GlaxoSmithKline’s AI group. To support its compute power, the company teamed up with artificial intelligence company Cerebras, which produces the largest silicon chip in the world, specifically designed for machine learning.

GSK has seen explosive growth in the amount of complex data being generated both within and outside GSK, and this data requires sophisticated analysis. AI helps decode the data in order to learn from it and advance drug discovery. GSK has established a world-class in-house group over the past two years to achieve this. Cerebras hardware allows GSK’s AI researchers to build larger machine learning models at a faster rate. In particular, GSK uses the Cerebras hardware for building models based on DNA sequences.

“We know targets with genetic evidence are twice as likely to succeed than those that aren’t,” says Kim Branson, Senior Vice President and Head of AI-ML at GSK. “AI and machine learning are helping us to maximize the genetics and genomics data we have amassed in R&D through partnerships, including with UKBiobank, the Broad Institute, and 23andMe, to improve drug discovery and double our development success rates. Our goal is to bring innovative medicines and vaccines to more patients, with greater precision and scale.”

Cerebras provides the hardware to support much of GSK’s work, including analyzing vast amounts of genetic and genomic data that it needs in its research. GSK has already used the CS-1 computer to analyze DNA sequences and predict outcomes of specific genetic mutations.

GSK also works closely with NVIDIA, including with its new powerful supercomputer, the Cambridge-1, dedicated to healthcare and R&D. This will contribute additional computational power and state-of-the-art AI technology to GSK’s drug discovery process.

As the demand grows for data engineers who can operate at the intersection of computer science, pharma R&D, and medicine, GSK recognizes the need for talent development. The company has created academic collaborations to support PhD programs at a number of universities. In addition, it runs a two-year AI Fellows program out of its London AI hub.

“These partnerships are essential, and GSK has put technology at the heart of our R&D strategy for both vaccines and medicines,” says Branson. “We’ve generated more data in one quarter of 2020 than we had in our entire 300-year history—much of this from our genomics experiments.”

Though pharma is still in the nascent stages of AI being used in its R&D, GSK believes it will be increasingly important given its ability to produce and analyze large amounts of in-depth data. The priority is integrating all of the information to discover targets that are more likely to be successful in the clinic and become registered medicines, and in parallel, work to find companion diagnostics to improve the patient experience once medicines are approved.

“AI is a core aspect of our strategy and will be absolutely critical to our success,” says Branson. “We are working hard to deploy it at scale. Partnerships that contribute to our progress in this space are extremely valuable. What improves the likelihood of a successful tech-pharma relationship is being in sync on culture and values, clarity on deliverables, strong alliance and contracting teams on both sides, and frequent communication, with a willingness to continually examine what’s working and adapt.”

Pharm Exec’s Senior Editor. She can be reached at

Unconventional collaborations between biopharma and tech are starting to take off.

Opposites Attract

In its 2021 industry Global Data Management Benchmark study, EDM Council—a global nonprofit established “to elevate the practice of data management as a business and operational priority”—highlights the establishment in pharma of the Chief Data Officer (CDO) position. The study suggests that this development “indicates that the majority of organizations in the sector are already deriving value from data, trusting it to power their businesses.” But the report also notes that most CDOs have been established only recently and, for a number of companies, this function will take time to “stabilize.”

Indeed, the “newness” of the CDO role, in the life sciences industry at least, means that many companies are still in the process of defining it. Where the financial sector installed CDOs 10–12 years ago in response to the global financial crisis and the need for stricter controls around the sector’s data, EDM Council says that 52% of CDOs in its 2021 pharma survey had been appointed only in the last year or so. But already the CDO is moving up the organizational chart and becoming a pivotal position, Mike Meriton, co-founder and chief operating officer of EDM Council, tells 

. “What we’re seeing is an evolution,” he says. “The first CDOs reported to the Chief Information Officer (CIO). But as the position evolves, CDOs are serving the supply chain; they’re connecting with the business side; they’re interacting with the risk, compliance, and audit functions to make sure data is used ethically and appropriately. They also work with operations and the other parts of the business, such as manufacturing.” Ultimately, all of the parts of the business are going to be data driven; for this reason, the CDO in pharma is likely to report to the Chief Operating Officer (COO), who oversees both IT and operations, says Meriton. “In some other industries,” he adds, “we’re finding that the CDO reports to the CEO.”

Defining the role and scope of the CDO is a work-in-progress. The title itself is open to variation, spanning such iterations as Chief Data and Analytics Officer (CDAO), Chief Data and AI Officer, Chief Information Security and Data Officer, and Chief Data Strategist and Solutions Officer. Otsuka’s CDAO, Tifani McCann, generally defines a CDAO role as “harnessing the company’s data, whether it’s generated or acquired, and converting that data into actionable insights, leading to valuable business impact.” She tells 

, however, that the specific CDAO role definition depends on the business strategy, needs, and vision of the respective company. McCann appreciates how broadly Otsuka defines the role. She leads a centralized data and analytics organization covering R&D, commercial, and enterprise functions. “It’s not just about efficiencies or creating a functional home—which is important to attract, engage, develop, and retain talent—but having that ability to provide objectivity and create more elegant and impactful solutions,” she says. “When you have a line of sight across R&D, Commercial, and Enterprise, you’re able to share knowledge and tools. We have had a couple of great-use cases where we’ve been able to answer different business questions across these domains with the same tools, with the same data.”

McCann’s title reflects EDM Council’s observation that CDOs “will mature as they move up the organization chart and into a broader, more business-oriented context, with the function over time including data and analytics.” The “traditional pathway” sees a CDO dealing with just the core data supply chain—asking, for example, “Where is the data originating from? Is it trusted? Is it fit for purpose?”—while another executive “worries about the analytics around that data,” says EDM’s Meriton. “But there’s a high dependency between the quality and provisioning of data and the quality of the analytics that are derived from the data, so having the data and analytics functions separated is not a good formula for assuring reliable business outcomes.”

For Shahram Ebadollahi, questions such as how to manage data in a unified way, how to make it fluid and easily accessible across the value chain, and how to consistently derive value from data as a strategic asset serve as the impetus for his role at Novartis. But as his title—Chief Data and AI Officer—suggests, “Those questions aren’t enough. Companies then say ‘Okay, now I have my data sorted out, what do I do with it? What kind of insights can be extracted from it?’ This is where AI, in the broad sense, comes into play.”

Ebadollahi sees several key angles to his role. “One is building and enabling the underpinnings of becoming a data science or data-driven company. That includes the technology and talent underpinnings and building the central capabilities,” he says. For instance, the company set up the Novartis AI Center about two years ago; it now has “AI and machine learning (ML) experts working, with domain experts and other types of scientists, on different programs, challenges and use cases from drug development, operations, and enabling functions to commercial. These are lasting capabilities that will remain to power core parts of the business.”

Another aspect of Ebadollahi’s role is making Novartis “more agile with respect to decision-making, thanks to the vast and diverse amounts of data that has been cleaned and curated and continues to evolve.” That involves “embedding AI and data science in the workflow across all operations, across the value chain, to enable every associate, our experts who are on the front line, no matter what they’re doing, to be able to make data-driven decisions, without necessarily being a data scientist.” He adds, “When we say, ‘make Novartis a data science-based company,’ it has to become the new way of working within the company.”

You could be forgiven, given their range of responsibilities, for confusing the newly minted Chief Data Officer role with the tasks of the not-much-older Chief Digital Officer—not least because they are both “CDOs.” “In fairness, both these roles are under development,” says Meriton. “And we can add another C-suite role into this mix: the Chief Information Officer. Indeed, the CIO might argue, ‘I have responsibility for data and digital. Why are these two new C-suite officers showing up?’”

The CIO role has been operating now for four or five decades; it’s the oldest of the three roles by a very large margin. “Virtually every company in the world has a CIO,” says Meriton. “They predominantly look at the technical landscape in which a company executes its strategy, both internally and externally. Their job is to make sure all the pieces are sustainable, joined together, and that core functions like security access are in place technically.” To use an analogy, Meriton continues, “the CIO is concerned with the pipes; that is, making sure they the pipes are trusted and sustainable. The Chief Data Officer, however, worries about the water—i.e., the data—flowing through those pipes—the lifeblood, if you will.” (Interestingly, Otsuka’s McCann offers the same analogy, but with oil instead of water. “That shows how much I value data,” she says.) The Chief Data Officer is concerned with the content, the underlying data, and how companies execute, generate, and collect data around their internal processes. Everything from drug discovery to manufacturing and distribution has a “super-high data dependency,” says Meriton. As for Chief Digital Officers, he continues, “they look at the digital experience of various key stakeholders as they interact with the company. What is that experience, and how can it be sped up, improved, and automated so that a company can better predict the requirements of the customer and end consumer and better meet their needs?”

As Novartis’ Chief Data and AI Officer, Ebadollahi reports to the Head of Customer and Technology Solutions. He says, “My peer is the Chief Technology Transformation Officer (CTTO), or, you could say, the CIO of the company. I have another peer who looks at digital innovation, asking, ‘What are the novel approaches to use digitization of the various types of businesses?’ Where I come in is the methodologies, the technologies, and solutions that need to be developed. Bringing the thinking, capabilities, and talent that needs to be in place to develop and deploy them across the company to achieve maximal impact.” All these tasks go hand in hand “but with a very focused angle from each of us.” Ebadollahi adds, “These are team sports, so to speak.”

McCann says: “A strong partnership is required across all these roles.” But she observes that the interplay of these partnerships has evolved over time. “In the 20th century, data was heavily managed by the IT function. In the 21st century, organizations started to realize that data is one of their most valuable assets and created roles dedicated to harnessing and leveraging the data. That’s when the CDO and CDAO roles started to evolve.” Inevitably, some organizations were left with some “gray areas” in between. Says McCann, “At Otsuka, the CIO and I have a very strong partnership. We work together to clarify as much of that gray area as possible.”

“There is much agreement that the CDO/CDAO is a critical role,” says Otsuka’s McCann, “but not necessarily agreement on the role’s definition.” As such, CDOs can encounter some frustrations, stemming from their organization’s sometimes unclear understanding of their activities. There can be, for example, a “limited understanding of all the complexity and the value of the work CDOs do to clarify that complexity,” says McCann. The CDO’s responsibilities are often thought of in two ways, both of which are critically important. “There are what people refer to as the defensive activities, which focus on data management, stewardship, privacy security, and creating reliable data,” explains McCann. “And then there’s the offensive side, where you’re providing insights, using more advanced analytics, and generating potential for revenue impact.” She says that senior leaders tend to focus on the latter, “without maybe appreciating all of that data wrangling that’s going on behind the scenes.”

Novartis indicated its shift to a data science company several years ago, and “there is now a huge amount of awareness about data, a lot of receptivity from the business,” says Ebadollahi. The cultural shift at Novartis has moved on from one that had the support of “early adopters” to one where “the aperture is more open and curious, and more and more people are looking to those early adopters to see what can be achieved.” Still, Ebadollahi believes the company is just in the middle of its journey. “I would say it still requires maybe two or three years to get to a point where we can say that Novartis is completely data-driven, with data science embedded throughout.”

For new and next-generation CDOs, the balance between technical expertise and the commitment to cultural integration will be vital. In comparing “Chief Data Officer 1.0 with Chief Data Officer 2.0,” Meriton says, “CDO 1.0 was more internally focused and a little more regulatory oriented. CDO 2.0, however, is looking at the full picture of business enablement and how to get the company to be data-driven. They have to be excellent communicators with a broader point of view.” Looking forward to the era of Chief Data Officer 3.0, Meriton says “what we will eventually see is the concept of data literacy being part of a company’s culture, not limited to the few hundred or few thousand data practitioners in the company.”

For the CDO, Ebadollahi concludes, “technology is just part of the story” in changing how a company operates. “It is how you transform the way of working based on these technologies and how you embed them into the day-to-day workflow.” Ultimately, he adds, “it is about ceasing to work the old way and embracing a data-driven mindset.”

Pharma/Life Sciences Global Data Management Benchmarking Report 2021.

is Pharm Exec’s European and Online Editor. He can be reached at

While there is broad agreement that the Chief Data Officer plays a critical part in pharma, a definitive description of the role is still evolving.

Defining the Chief Data Officer

When you’re fueled by a passion for what you do, it’s sometimes difficult to give up getting your hands dirty. Imvax Chief Executive Officer John Furey understands that. After amassing nearly 20 years of experience in various pharma roles culminating in the chief operating officer position at Spark Therapeutics, he decided to step back and share his vast knowledge in advisory roles to various companies. But it wasn’t long before he realized his impact could be much greater in a direct operational role.

“Nearly a year into that journey, I realized that I still had an interest and a passion for another operational role,” says Furey. “I felt I could take the experiences of what I had learned in big pharma and at Spark Therapeutics, and put my energies into a project that had a meaningful impact for patients.”

In searching for that next step, Furey had two criteria: The opportunity had to have great science and great people.

Spark Chairman Steve Altschuler, who had become an investor in Imvax just months earlier, knew Furey could be an asset to the company. He invited him to an advisory role at Imvax, to help the team build the early clinical data that had been developed at Jefferson University Health System.

As Furey spent time with the Imvax team, it became clear that the data were compelling in a high unmet need in glioblastoma. In addition to the science, he developed a rapport with co-founders David Andrews, MD, who was the head of neuro-oncology at Jefferson and a pioneer of the science associated with Imvax, and Arthur Howe, chief financial officer. With both his search criteria met, Furey began discussions with Altschuler, Andrews, and Howe about joining the company in a more hands-on role as CEO and “was only too glad to join Imvax,” he says.

When Furey began in September 2019, the company consisted of just five people in a rented office in the Philadelphia neighborhood of Chestnut Hill. Two years later, it has expanded to include nearly 40 people. It has strengthened its team across key areas, including clinical, research and development, and technical operations. It has moved into its own purpose built office and laboratory space at The Curtis Center in Center City Philadelphia and is about to complete construction on a manufacturing suite there, too. Imvax has become a fully integrated biotech company in the span of two years.

Furey enjoys having day-to-day interaction with the team, patients, and various stakeholders versus working with the operational teams to strictly provide insight in an advisory role. Yet he still is able to practice his advisory muscle as a board member for Sensorion and Adaptimmune, and enjoys that balance. He’s also vice chair of the Board of Trustees for Neumann University, just outside of Philadelphia, and an Editorial Advisory Board member for 

“I don’t think I would have had the same experience in an advisory role as I’ve had in the direct operational role as CEO,” says Furey. “Everything I had considered that I would like to do in one more operational role, I’m experiencing at Imvax now.”

Imvax’s extraordinary growth over the past two years has brought the company to its current inflection point, and Furey has been hard at work building the necessary capabilities to get the organization to the next level. He identifies two critical areas for the company’s growth over the next two to three years.

The first is taking the Phase I investigator study and initiating Imvax’s first company-sponsored Phase II trial in glioblastoma. This will be a significant and pivotal undertaking for the company over the next 12 to 24 months.

The second is derived from Imvax developing its own laboratory and scientific capabilities. Signs are emerging through robust early preclinical data that there is potential to build out a platform in other solid tumors, deploying the same technology used for the glioblastoma study.

“We’re seeing significant signals in ovarian, liver cancer, pancreatic cancer, and we are also seeing equivalent and emerging preclinical signals in other solid tumors,” says Furey. “But what I’m feeling very positive about is that we’re able to take the technology where we have human data in GBM [glioblastoma multiforme], and now utilize it to provide an alternative for patients in a number of other solid tumors and develop a platform company.”

As Furey and Imvax navigate this journey from single program to platform company, there are many challenges. First, for any biotech company making this pivot, the operational component of running a clinical trial for the first time can be daunting. To facilitate that, Imvax is building a strong clinical operations and development team and has a solid relationship with its clinical research organization.

The second challenge is translating the early preclinical signs into investigational new drugs and to start conducting multiple Phase I trials at the same time. Furey says Imvax has “a problem of privilege” in that it’s realizing many positive early preclinical signals and hopes to be able to develop an organization that can execute on all of its opportunities. The company has recently invested resources into standardizing its chemical, manufacturing, and controls (CMC) and unique technology approach.

Finally, CEOs of early stage companies are always confronted with the issue of investment—raising money and financing the company. That’s another task Furey faces in the coming 12 months. But knowing that he and his team understand all the challenges before them and that they are putting the right resources in place gives him confidence to succeed.

Imvax CEO John Furey taps into tried-and-true powers of hope and clarity as he moves the clinical stage biotech from single program to platform company.

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