Part one of a very insightful analysis by Kelvin Stott showing Pharmaceutical IRR will hit 0% by 2020. Why isn’t translational success rising given better research methodologies and richer datasets?

Productivety graph

By Kelvin Stott, Director, R&D Portfolio Management, Basel, Switzerland

Like many industries, Pharma’s business model fundamentally depends on productive innovation to create value by delivering greater customer benefits. Further, sustainable growth and value creation depend on steady R&D productivity with a positive return on investment (ROI), in order to drive future revenues that can be re-invested back into R&D. In recent years, however, it has become clear that Pharma has a serious problem with declining R&D productivity.

Various analysts (notably Deloitte and BCG) have tried to measure Big Pharma’s R&D productivity in terms of the internal rate of return (IRR) on investment, but in each case the analysis is highly complex and convoluted (and thus subject to doubt), as it depends on many detailed assumptions and forward-looking forecasts at the individual product level. Here I apply a far simpler, much more robust methodology to calculate Pharma’s return on R&D investment, which is based only on reliable and widely available high-level data on the industry’s actual historic P&L performance. This analysis confirms the steady decline reported by others, but here I also explore the underlying drivers and make concrete projections, which suggest that the entire industry is on the brink of terminal decline.

A simple robust method to measure R&D productivity / IRR

Pharma’s business model essentially involves making a series of investments into R&D and then collecting the return on these investments as profits some years later, once the resulting products have reached the market. However, the situation is complicated by the fact that both investments and returns are phased over many years for each product, and not all products make it to market; in fact, most products fail to reach market at all and they fail at different times and costs during their development.

Now we can greatly simplify this picture by considering only the average return on investment across the industry as a whole, which is what interests us in any case. We simply assume that all profits in any given year come from investments made within a single previous year, where the gap between these two years represents the average investment period, from the midpoint of R&D investment to the midpoint of returns at peak sales. As it happens, this average investment period is relatively stable and well-defined, as it is largely driven by a fixed standard patent term of 20 years, as well as a historically stable R&D phase lasting roughly 14 years from start to finish. Thus, the average investment period is about 13 years, from the midpoint of the R&D phase after 7 years, plus another 6 years to reach peak sales before loss of exclusivity.

There is one potential argument against this method, which is that the later phases of R&D tend to cost many times more than the earlier phases. However, we must also remember that we need to invest in many more projects at the earlier phases than we invest in at the later phases, due to natural attrition within the R&D pipeline. Thus, the total R&D investment is actually distributed quite evenly throughout the development timeline. And, as I show below, the calculated return is not very sensitive to this single assumption in any case.

Before we use this simple method to calculate the return on investment, there is one more small but important detail to remember: The net return on R&D investment includes not only the resulting profits (EBIT), but also the future R&D costs. This is because future R&D spending is an optional use of profits that result from previous investments.

So now we can calculate the average return on investment (IRR) as the compound annual growth in the value of past R&D investments to the value of resulting profits (EBIT) plus future R&D costs, as illustrated here with industry P&L data from EvaluatePharma:

P&L Composition of global Pharma sales

Now we get the following simple formula to calculate the Internal Rate of Return (IRR) on Pharma R&D in any given year x:

IRR(x) = [(EBIT(x+c)+R&D(x+c))/R&D(x)]^(1/c) – 1

Where c is the average investment period of 13 years.

Return on investment in Pharma R&D is rapidly declining

Applying this simple formula across multiple years of P&L data from EvaluatePharma, we see the following downward trend, which is fully consistent with reports published by both Deloitte and BCG:

Return on Investment in Pharma R&D

Now the scariest thing about this analysis, is just how robust, consistent and rapid is the downward trend in return on investment over a period of over 20 years. But moreover, these results confirm that return on investment in Pharma R&D is already below the cost of capital, and projected to hit zero within just 2 or 3 years. And this despite all efforts by the industry to fix R&D and reverse the trend.

I mentioned earlier that this analysis is based on one assumption, the average investment period which is quite stable and well-defined, but here below we see that the results are not sensitive to this single assumption in any case. The downward trend is just as clear, as is the projected IRR of 0% by 2020:

Return on INvestment in PharmaR&D2

So what is driving this trend, and why haven’t we been able to do anything about it?

Law of Diminishing Returns

Many different causes and drivers have been suggested to explain the steady decline in Pharma R&D productivity, including rising clinical trial costs and timelines, decreasing success rates in development, a tougher regulatory environment, as well as increasing pressure from payers, providers, and increasing generic competition, however there is one fundamental issue at play that drives all these factors together: The Law of Diminishing Returns.

As each new drug improves the current standard of care, this only raises the bar for the next drug, making it more expensive, difficult and unlikely to achieve any incremental improvement, while also reducing the potential scope for improvement. Thus, the more we improve the standard of care, the more difficult and costly it becomes to improve further, so we spend more and more to get diminishing incremental benefits and added value for patients which results in diminishing return on investment, as illustrated here:

Standard of Care

But why does the analysis above suggest a linear decline that will hit 0% IRR by 2020? Shouldn’t the decline slow down and curve away so that it never reaches 0% IRR?

No. 0% IRR corresponds to breaking even and getting exactly your original investment back, but as anyone who has worked in Pharma will know all too well, you can easily lose all your original R&D investment as most drugs fail without making any return at all, so the minimum theoretical IRR is in fact negative 100%. There is no reason why the IRR should stop declining before it reaches 0%, or even -100%, besides the limited patience of investors.

To further illustrate how the Law of Diminishing Returns applies to Pharma R&D, let us consider a limited set of 200 potential drug development opportunities defined by a random exponential distribution of expected costs (investments) yielding an independent random exponential distribution of expected values (returns) after an average investment period of 13 years. The expected IRR of each opportunity is given by the formula:

IRR = [eReturn/eCost]^(1/13) – 1

Now we rank and prioritize all these potential opportunities by their expected IRR over time, just as we select and prioritize drug development projects by their expected return on investment in the Pharma industry, and this is what we get:

Expected IRR of Prioritized Opportunites-1

Notice how the midsection of the IRR plot of prioritized opportunities follows a perfectly linear downward trend that passes right through 0% IRR, which is exactly what we have seen with our analysis of Pharma R&D productivity above!

And most incredibly (in case you were wondering), it does not matter how the values and costs are distributed. The midsection of the IRR plot of prioritized opportunities follows a perfectly linear downward trend through 0% IRR regardless of how they are distributed:

IRR Plot of Opportunities

The implications of this are profound and yet, in hindsight, blindingly obvious:

Return on investment in Pharma R&D is declining because that is precisely how we prioritize investment opportunities over time.

In essence, drug discovery is rather like drilling for oil, where we progressively prioritize and exploit the biggest, best, cheapest and easiest opportunities with the highest expected returns first, leaving less attractive opportunities with lower returns for later. Eventually, we are left spending more value than we are possibly able to extract:

R&D Diminishing Returns

Implications and projections for the Pharma industry

Now given that the steady decline in return on investment in Pharma R&D follows the Law of Diminishing Returns as the natural and unavoidable consequence of how we prioritize R&D investment opportunities, where does that leave the industry?

We can simply extrapolate the robust linear downward trend in IRR, and then apply the same formula we used above to calculate IRR based on past performance in reverse, to predict how the industry will evolve in the future. This is what we get:

IRR Projection of global Pharma sales

This seems incredible, but remember that this is not some arbitrary bleak forecast. It is the direct mathematical result of the Law of Diminishing Returns which we have already seen in our analysis above, and which we have been able to exactly replicate by prioritizing a limited set of random investment opportunities.

So what is going on here? Can this really happen?

Pharma’s broken business model

The situation is illustrated nicely by this schematic here below:

Pharmas Broken Business Model

What we have here is an industry that is entering a vicious cycle of negative growth and terminal decline as its fundamental business model has run out of steam by the Law of Diminishing Returns: Diminishing R&D productivity and return on investment leads to diminishing growth in sales. Eventually, growth turns negative and sales start to contract. Decreasing sales then limits the amount of money available to invest back into R&D, which causes sales growth to decline even further. And so on, until the industry is gone altogether.

This principle is further illustrated here, showing how value creation is turning negative:

Pharma LifeCycles

Industry life cycles and regeneration

So can this happen? Will Pharma really shrink out of existence, and is there anything we can do to stop it?

In short, yes, it can and will happen. Pharma as we know it will shrink out of existence, and no, there is nothing we can do to stop it. We know this because the steady decline in IRR is an unavoidable consequence of prioritization, and has continued despite all our efforts to slow, stop and reverse the decline to date.

We should not be surprised by this. All industries and business models follow the Law of Diminishing Returns, and many industries have come and gone through history. In fact, the Pharma industry itself sprouted out from the terminal decline of the chemicals and dye industry as it was slowly commoditized. Out of the ashes grows the new.

And therein lies the only real hope for the Pharma industry — or at least the companies and hundreds of thousands of people working within it.

Just as the Pharma industry evolved from the chemicals industry, and the Biopharma industry has evolved from the Pharma industry, the Pharma and Biopharma industries together will evolve into something quite different, most likely continuing the historic trend of increasing complexity towards more complex biological solutions to pressing healthcare problems, such as cell & gene therapy, tissue engineering and regenerative medicine:

Pharma's Increasing Complexity

But who really knows?

What is clear is that Pharma (and Biopharma) will not be around forever, and Darwin’s theory of evolution applies to companies and industries just as much as it applies to the species of life:

It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change.

Indeed. Adapt or die!

Disclaimer: The views expressed herein are my own personal opinions, and are not intended to represent those of my employer.

Original Source: LinkedIn