What Are The Most Productive Pharmaceutical Companies?Pharmaceutical R&D Efficiency Review 1999-2018

Last week I published an article on the three academic and industry groups that perform detailed analysis of the pharmaceutical industry, evaluate R&D productivity, and look for best practices. In order to improve, we do need to understand where we are and where we are going. If I am making a claim that I can discover a novel target for a broad disease, design a novel molecule, complete Phase 0 human clinical trial, and go into Phase I in under 30 months – how much impact am I making and how does it compare to the traditional approaches in terms of the probability of getting there, speed and cost? How do you compare to the peers? And are you doing the right thing? To answer these questions we do need to have specialist industry observers, and to set the stage I got to talk to Prof. Alexander Schuhmacher and Prof. Oliver Gassmann of the Competence Network for Life Science Innovation at the Institute of Technology Management at University of St. Gallen about their recent papers covering pharma R&D productivity. What comes next is a more detailed analytical piece that many of the pharmaceutical companies will not exactly like.

During the COVID-19 Pandemic we got to observe how the various pharmaceutical companies utilized their R&D, partnering, and market access capabilities to develop and market vaccines, and drugs. It was very surprising to see the established vaccine and anti-viral drug vendors like GlaxoSmithKline (GSK) fail to deliver either vaccine or a drug, while the other giants with smaller vaccine and antiviral franchises performed spectacularly well.

For example, Pfizer was not even on the list of Statista’s top 10 anti-viral pharmaceutical companies but due to the heroic efforts and actions of their CEO, Dr. Albert Bourla, Pfizer made history in both categories by partnering on BioNTech vaccine and developing PAXLOVID, a small molecule viral 3CL protease inhibitor, where they had a starting point since the original SARS scare. His bold actions quickly propelled Pfizer to the top of the anti-viral Olympus.

But COVID-19 is just one example. Many companies just did not want to pivot from their current R&D programs. When the COVID-19 pandemic just stated many companies with significant capabilities decided not to pivot to or allocate substantial resources to the COVID-19 programs. When it just emerged, many proposals to the board of directors were met with “This will be another SARS. You will spend the money but it will go away”. I am sure that many companies did not pursue anti-COVID-19 programs for this reason and at some companies, including ours these projects got substantially delayed.

From what I hear from the market is that Dr. Bourla let go the strategy advisors that were recommending against going after COVID and mobilized all internal R&D resources to get PAXLOVID on the market setting many industry records and saving millions of lives – a heroic achievement worthy of a Nobel Peace Prize. So we can clearly see that in the face of adversity, the pharmaceutical companies can mobilize and deliver spectacular results. Fortunately, we can read the public side of the story in Dr. Albert Bourla’s new book, “Moonshot”.

As pharmaceutical companies begin to rely on internal data science and AI, how do they decide the amount of expenditure required for efficient use of R&D resources? A comprehensive analysis of the productivity in the pharmaceutical industry will help streamline the industry into cutting waste and developing meaningful new molecular entities (NME’s).

The August 2021 edition of Drug Discovery Today contained an article titled “R&D efficiency of leading pharmaceutical companies – A 20-year analysis” that was written by heavy-hitting academics and industry experts: Alexander Schuhmacher, Oliver Gassmann, and Lucas Wilisch of University of St Gallen, Michael Kuss of PricewaterhouseCoopers, Andreas Kandelbauer of Reutlingen University, and Markus Hinder of Novartis Institute of BioMedical Research.

In this study, the group took 20 years-worth of data from 1999 to 2018 comprised of financial, drug output and bibliographic data, to conduct a qualitative and quantitative comparative analysis of 14 leading pharmaceutical companies to identify success factors for R&D efficiency.

The group also compared the new molecular entities (NME’s) going through the pipelines of leading companies segmented into one of three ways: through internal R&D, through M&A, and through licensing.

The Olympus of New Medical Entities in Big Pharma R&D 1999-2018

Pfizer came in as a clear leader in total R&D productivity over 20 years followed by MSD (Merck US), and Novartis.

New Medical Entities Through Internal R&D 1999-2018

Despite delivering the highest total number of drug approvals, most of the NMEs developed and approved by Pfizer came through M&A – 29 in total. Only 8 NMEs came through internal R&D. When it comes to the internally-developed drugs, the clear leader was Novartis with 20 NMEs followed by GSK with 13, and then by a tie between MSD and Bristol Myers Squibb (BMS) with 10 each.

New Medical Entities Through Mergers and Acquisitions 1999-2018

Unsurprisingly, most of the approved NMEs in pharma pipelines come through mergers and acquisitions. For example, in 2009 Pfizer acquired another pharmaceutical giant founded in 1860, Wyeth bringing in a large portfolio of drugs. 2009 was a big year for M&A. Same year, Roche acquired Genentech, which had a celebrated portfolio of NMEs several of which came through Genentech’s strong internal biology capabilities. So naturally, the top three leaders in M&A over the past two decades were Pfizer, MSD and Roche.

New Medical Entities Through Licensing 1999-2018

Another way to advance internal R&D is through licensing. Many pharmaceutical companies in-license the molecules from biotechnology companies. Recognizing promising assets in early stages also requires strong internal capabilities in business development, chemistry, and biology. Here, we have a tie between GSK and MSD with 7 successfully-developed NMEs each, closely followed by Novartis and Sanofi.

Surprisingly Average R&D Performance by AstraZeneca 1999-2018

One finding of this study that surprised me the most was surprisingly average performance by AstraZeneca. Over the past decade, we saw the many celebrated papers by their R&D leadership explaining the internal R&D best practices. We even attempted to replicate the popular “5R Approach” by developing 5R-compatible AI pipelines. However, it scored 7th out of 14 in internally-developed NMEs, 5th in NMEs through M&A, and 11th in licensing.

I also noticed that the recent announcements of AI-partnership achievements are not very impressive either. 2+ years from April 2019 announcement of a target discovery partnership to lead identification in December 2021 that generated a lot of hype in social networks, seems to be closer to industry average using traditional approaches. This does not mean AstraZeneca’s 5R approach or partnership practices do not work or are overhyped. However, looking at the numbers from the Schuhmacher and Gassmann paper, and the AstraZeneca numbers seem to be surprisingly average as they have a perception of being the most productive. In fact, in my popular 2020 article, which was abundantly shared by AstraZeneca itself, it did look like AstraZeneca held the leading position in AI-powered drug discovery and development.

Total Number of Publications 1999-2018

Furthermore, the study compared the leading pharmaceutical companies by the number of scientific publications during this period and showed the leadership of Pfizer (24,564), followed by GlaxoSmithKline (GSK) and Merck & Co. (22,727 and 15,556 respectively).

Impact Factor of Publications 1999-2018

In terms of cumulative impact factor (CIF), GlaxoSmithKline topped the chart with 104,047, with Pfizer coming in second place with a CIF of 101,825. Roche came in third place with a CIF of 95,603. But I guess that the average impact factor is a more important metric and here Gilead and Amgen, that have the lower total number of papers, are clearly in the lead.

The analysts found that between 1999 and 2018, the R&D investments of the 14 leading pharmaceutical companies increased from US$49.2 billion in 1999 to US$ 87.1 billion in 2018, and that they launched 270 of the 602 NMEs approved throughout the industry.

The study also revealed that the more a company invested in R&D in the past 20 years, the higher was their output (expressed as approved NMEs or as cumulative impact factor). In brief, this paper proves that higher R&D investments are associated with higher R&D output.

To learn more about the study, I reached out to two members of the group – Alexander and Oliver – and asked them about the study and their take on how to build up an R&D ecosystem.

What are some of the key findings that are important in this paper?

Alexander: Really surprising for us is the finding the bigger the company [its R&D organization] was, the higher the R&D output was. This is what we described as ‘economies of scale in pharmaceutical R&D’ in the paper – if you think about it, it makes sense because pharma R&D can leverage from size (resources, competencies, technologies).

Oliver: Yes, and for those companies whose R&D organization is not big enough to profit from economies of scale, it’s all about building an R&D ecosystem that can mimic the difference in size. In consequence, for some pharmaceutical companies it is advantageous to pursuit of size with M&A, while other should focus stronger on collaborations and strategic alliances with peers in terms of sharing data, competencies, and technologies in open innovation manner.

In this context, the future threat for pharmaceutical companies is not just within the industry itself, but also coming from outside the pharmaceutical sector. In one of our recent publications we described that large parts of the value creation will be captured by new players (new pharma entrants) – all in front the Tech Giants. For example, we recently saw Apple getting a first FDA approved smart healthcare device – a disruptive technology coming from the outside (see hereto Clayton Christensen’s innovator’s dilemma). Google/Verify and other tech companies are also entering the pharma/healthcare market. Already today and even more in the future, value creation will come from data and business models will be more data-driven – the only solution for pharma is: Build partnerships with peers to profit from economies of scale and collaborate with tech companies to leverage from economies of scope.

Would it be true to suggest that the larger you are as a company, the more acquisitive you are too?

Alexander: Neither yes nor no. We see both models in the industry – one based on knowledge creation and the other on acquisition. Just to name to examples: Novartis has built a very strong and successful R&D organization that leverages from its internal competencies, while Pfizer was very successful with acquiring R&D portfolios (e.g. Wyeth, Warner Lambert, Pharmacia). So, the important question is, how can you leverage from the resources you have and which R&D models suits best to the resources at hand? There is not a simple answer to this complex question.

Looking at individual companies and how they ranked in the paper, were there any surprises when you put this data together and you compared that to the analyst ratings of those companies and what people perceive?

Alexander: As scientists, we are always surprised about the outcomes of our research. Indeed, it looks like that some companies use their R&D resources more efficient than others or have built R&D models that enable them to perform more efficiently.

As we have used data from 20 years, our results are meaningful. For example, a simple calculation is to take the cumulative R&D expenditures of a pharma company over the 20 years and divide it by the number of drugs it launched in that period. You’ll see that top 20 pharma companies have (clearly) different R&D efficiencies.

With respect to ratings and rating’s companies; this is not our home turf. We are scientists interested in analyzing and understanding specific settings or type of setting as well as generalizing from it. We do not give any company-specific analysis or even investment recommendations. For example, one of our foci is on R&D efficiency, as it is an industry-specific type of challenge. And we analyze and try to understand this complex field and aim at drawing conclusions that are generalizable for the whole industry.

Do you think that findings in this paper should incentivize people to merge, to acquire, to become even bigger? Because if this statement holds true that the bigger you are, the more efficient you are, it probably kind of postulates the mergers and acquisitions pathway for pretty much any pharma company.

Oliver: In very general terms, yes this paper is a template for M&As. But M&As are not new to the industry. Over the past years, we saw several big M&A transactions and countless acquisitions of small biotechs. What might be new to the industry is the acquisition of tech start-up companies that bring along the highly needed digital competencies for a data-driven R&D. Or it is the thinking in R&D ecosystems that include traditional biotech and pharma players but also new partners from other industries. In consequence, we foresee pharma going away from the more traditional path of open innovation with research collaborations and licensing to a new network-based model that is more agile, more smart and may provide better R&D efficiency.

In consequence, those companies who like to address and build up an R&D ecosystem in a more modern way have to think of what I call a ‘win-win-win’ formula, which means you need to get a win for the users, for the partners, and for your own company. At the end, it’s really a question of to whom do you create value for? It’s not only that you have to think the great value for the patient or for your customers and for yourself, but also thinking actually stronger for what could you do to be more attractive for your partners in the ecosystem?

About Profs Alexander Schuhmacher and Oliver Gassmann

Prof. Dr. Alexander Schuhmacher graduated in biology at the University of Konstanz (Germany), in pharmaceutical medicine at the University of Witten/Herdecke (Germany) and made its PhD in molecular biology at the University of Konstanz; he is also a graduate of the Executive MBA program at the University of St. Gallen (Switzerland). Alexander holds a full professorship in life science management at the Technische Hochschule Ingolstadt (Germany). His research focus is on biopharmaceutical innovation management with a specialization on R&D efficiency, artificial intelligence and open innovation. Prior to that, Alexander worked 9 years as professor at Reutlingen University (Germany) and 14 years in various R&D positions in the pharmaceutical industry.

Prof. Dr. Oliver Gassmann is a professor for technology and innovation management at the University of St.Gallen, one of Europe’s leading business schools. He is managing director of the Institute of Technology Management. Until 2002 he worked for Schindler and led its Corporate Research as VP Technology Management. He is co-founder of the BMI-Lab which focusses on business model innovation. His research lead to a revolutionary method of how to design new business models: The Business Model Navigator. Oliver has published over 300 publications and several books on management of innovation. His book ‘The Business Model Navigator’ by Hanser and Financial Times Publishing has been called as a ‘sensation’ by the leading German newspaper F.A.Z. and became rapidly a bestseller. He is one of the most cited innovation researchers, the most published author in R&D Management.

https://www.forbes.com/sites/alexzhavoronkov/2022/04/18/what-are-the-most-productive-pharmaceutical-companiespharmaceutical-rd-efficiency-review-1999-2018/

Jinggo B Danuarta

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