Deck The Halls With… Compensation Committees

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It’s that time of year again: Santa is preparing his list of naughty and nice biotech management teams, and taking his recommendations to the Compensation Committee.

As I’ve been sitting through several of these meetings already, I thought it would timely to share a few reflections on the topic.

Before sharing, I’ll add a warning: while analytics are important for benchmarking compensation vs the Joneses in the biotech next door, it shouldn’t drive the process.  The role of the Compensation Committee is to embrace the nuance of the situation, as every company and every member of management has different make-up, history, context, and story.  So here are a few general observations.

Compensation Philosophies: More Art Than Science

It seems obvious that a company’s Compensation philosophy should be aligned with its mission and its corporate culture.  But what does this mean?  High performance cultures have a variety of compensation structures that can work.

The primary goals of compensation are to attract, retain, and reward the professionals that drive the success of an organization.  In order to do the first two – attract and retain – you have to be competitive with other biotech and Pharma opportunities, hence the need for appropriate benchmarking.  The market for talent is fierce, and in some ways far tougher than the market for capital.  This market is the battleground where well-considered compensation philosophies can make a difference.

Comp philosophies also evolve with organizational size and status. Often in small startups, compensation approaches are more egalitarian in nature: most of the team members’ compensation is tied almost exclusively to corporate goals, since everyone (often) has a meaningful role in achieving those objectives.  Cash compensation may be relatively low, with “founding” members of the management team receiving much of their compensation in equity.  As companies mature, culture evolves, hierarchies form, HR consultants join the mix…  and things become more complicated.  Public biotech companies have even more complex issues, especially as “Owner” Directors (like VCs) often are exchanged for Independent Directors (here); ensuring strong, objective voices on a board is a critically important priority for all cases, but publicly traded companies in particular.

How does the philosophy translate in practice for a venture-backed biotech?  First, you need to figure out where you’d like to target and with what mix of salary, bonus, and equity arrows to put in your quiver.  Most of our early stage companies target the 50th percentile on cash compensation as a general rule, with equity being the primary long-term retention-and-reward compensation lever for most employees (here).  Cash bonuses are the norm in the biotech market today with a fairly wide range of possible targets .  Consider what resources you have (cash and equity) in what reliable quantities, and how you will invest these in your people.

Finally, the role of the Compensation committee itself changes over the life of a company: in most young companies, the Compensation committee merely “recommends” a proposal to the broader Board, whereas in more mature companies the committee charters adopt a more autonomous decision-making “authority” for a wide-range of compensation issues.  In between, you may have an experienced compensation committee Chair working with management and his/her fellow committee members to evolve the role and processes of the committee as the company matures.

Corporate Goals: Roadmap for value, measuring progress

To me, working with the CEO on goal-setting and goal-measuring is one of the most important contributions that a Board and its Compensation Committee can make.  What are the corporate objectives for the year, and how did we do against them?

Here are a few reflections on this particular theme:

  • Corporate goals should be aligned to creating shareholder value.  It’s easy to begin preparing a list operational items that lead to “box-checking” at year-end (“we did that, we did this…”), but sometimes these aren’t actually linked to creating value. Completing a Phase 2a study, or an IND-enabling tox package, isn’t value-creating for the company if they turn out negative. Getting the balance right here is key: successful execution is essential for successful outcomes, but the two aren’t the same – and, as a shareholder, the end result is the most important goal.
  • Goals need to be measureable with little room for argument.  Concrete goals that include numbers, key milestones, specific deliverables, etc are much preferred. Squishy goals like “engage in advanced BD discussions” or “prepare the market for our next fundraise” aren’t easily measured and open up a management team to criticism from persnickety board members (yes, those types do exist).  An alternative for squishy “BD progress” goal might be “receiving an initial term sheet” or something more measureable (like a term sheet with XYZ parameters). These make it hard to argue about whether you got one or not.  If it’s not in the cards to do something measureable this year, than don’t include it in the explicit goals.
  • Goals should target an outcome that balances uncertainty with confidence. I like to advise that goals should be “stretch” enough that they aren’t within the 95% confidence interval of the company’s expectations.  Teams should be confident that they would deliver on the overall corporate goals with only 75% or so probability.  Some goals will be more speculative or “stretch” – others less so.  But a set of sandbagged 95% confidence goals doesn’t do the company or its broader shareholders any good: we are all taking risks, we need to be aiming for stretch outcomes.
  • Weightings of the goals should be done at the “big bucket” level. We typically look for specific goals across Corporate Development, R&D (usually the biggest in science-led early stage companies), and Organizational/Execution topics, each with several sub-goals.  But when the weightings are applied, they are done with 3-5 major buckets. Getting down into the sub-goals of each bucket, and assigning specific weightings, conveys a micro-managing sense of accuracy, and it’s just not that helpful.  Point up the hill and charge, the reality of battle will change the details soon enough.
  • Corporate Goals are living documents and should be revisited in the face of new informationRelated to the last sentence above, setbacks and advances occur during the year and an on-the-ball Comp Committee takes this into account to revisit the goals quarterly with management in order to ensure the right things are being emphasized.  An FDA issue that pops up in March could dramatically alter the corporate R&D goals for the year, and this is meaningful (and early enough in the year) to warrant revisiting. This helps maintain the alignment between where management teams are focused and the explicit corporate goals of the business.
  • Goals should provide some flexibility for discretion by the board.  This flexibility can be created explicitly (like saving the final 10% of the weighting as Discretionary) or implicitly by directly tasking the Board to consider a broader set of factors beyond the agreed to goals themselves.  I tend to prefer the latter, but both approaches work. Further, Board discretion can help adjust the emphasis of within-bucket “weightings” where there may be 4-5 sub-goals in each section.

Cash Compensation: You can’t eat stock options

Cash-related compensation items are very important to get right, and without a competitive compensation approach on these aspects companies will struggle. This is where a constructive use of the right benchmarks (aka peer companies) can be helpful.

But to be constructive the benchmarks have to be carefully chosen and then appropriately adjusted as the fate of the peer companies and the target company changes. Once the benchmarks are determined and the data are prepared, it’s time for decision-making.  Considerations around executive tenure and contribution in specific roles, size and scale of the enterprise, geography and the local “market” for talent, and broader compensation package metrics (like equity) have to be considered as part of the overall discussion. Several good surveys exist (e.g., Radford, Thelander, Park Square, etc…), though many tend to leave a lot to be desired in terms of number of data points, granularity, and nuanced descriptors of the companies upon which they built the survey.

Many biotech companies do an inadequate job of factoring in all the nuanced considerations used to “vet” and “curate” the right benchmarks, and that leads to just jumping on the compensation escalator: “We’re here, the benchmarks are there, we need to be there”.  Without the nuance around roles, tenure, position/role descriptors, etc. this can become a frustrating exercise for both management teams and Boards – leaving everyone feeling unsatisfied by the process.  Data are data; they are not prescriptive.  This is where working with a compensation consultant who can recommend  benchmarks and appropriate data sources, and can be particularly helpful as companies mature.

Annual salary increases are an important end-of-year item many committees are discussing right now. When inflation was running higher, these were often called COLA (cost of living adjustment) increases.  Today they are more aptly called “merit increases”, and recently have tended to clock in at a rate of 2-4% in most private biotech companies I’m familiar with.  Typically it’s at the higher end of that range for strong performers, and at the low end (or none) for weaker performers.  A good way to do this is the set the midpoint – or the target – for the company and then adjust some employees up and some down from that target.  In practice, many early stage startups use an “”across the board” approach: everyone at the startup gets 3% salary increases, for instance. This approach is the easy answer, and can be the right answer as an emerging team gets a company off the ground – but over time this sort of quasi-socialist view of merit increases is clearly not the right approach for instilling a high performance culture in most circumstances.

Beyond base salary, the other key component of most cash compensation packages is the “variable compensation” associated with a cash bonus.  Bonuses have a wide range amongst management teams, from zero to up to 50% at the high end for CEO’s of well-financed maturing private biotech companies.  The mix of base vs bonus in the aggregate compensation is typically part of the “compensation philosophy” discussion.

For some, bonuses are often viewed as merely “expected” parts of the annual paid-out compensation.  Many new biotech hires arriving from Big Pharma have learned to more or less expect their year-end bonuses at or near their targets – this “expectation” creates real tension if Boards decide not to pay them out based on missing goals and such.  I’ve yet to meet the Clark Griswold of Biotech on this topic
(recall the angst when Clark’s bonus was converted into the Jelly of the Month club), but I’m sure it’s happened.

Understanding the role of the cash bonus, and being explicit about it with management teams (new hires especially), is particularly important. Like many others, I personally favor the use of true “performance-based” cash bonuses for management teams as part of their variable incentive compensation. I think these should be linked tightly to achievement of (non-sandbagged) corporate goals. We often try to agree upon a threshold level of aggregate goal achievement, typically around 70%, below which the corporate component of the goals isn’t paid out at all. Above that threshold, it is paid out pro rata including supra-performance above 100%.  As most senior management in the biotech’s I’m involved with have their bonuses entirely linked to corporate goals, this can create a meaningful cash incentive around achievement of the corporate goals (hence the importance of measured, flexible, and real-time goals, as discussed above). More junior employees who may be 50:50 between corporate and personal goals “feel” the benefit of hitting the corporate goals as well but are also rewarded for individual contributions.

Equity – Currency of Entrepreneurship

Equity is the big incentive driver in startups of any sector, and biotech is no different.  As I described in an earlier blog (here), it’s the “currency” of entrepreneurship for startups and for scientific founders from academia.

Benchmarks for equity exist from the same surveys, although often in nonsensical ways.  The absolute number of shares or options granted for a specific level in a company is an irrelevant benchmark without the denominator.  Likewise the percentage a role should have of an existing option pool.  I’ve heard both recently and neither make sense.  The only equity metric that makes sense is what percentage of the company does an employee at that level typically own on a fully diluted basis.  This allows one to calculate an employee’s equity upside under different exit valuations in a meaningful way.  Price per share isn’t particularly useful in private companies as it’s an opaque measure.  Some of this changes as a company becomes public, but certainly applies to private biotechs.

Every position in a company – from C-level executives to Scientists to Assistants – should have a Board-approved range of expected equity ownerships for a given period of time or valuation, based on benchmarks and corporate compensation philosophy.  These should populate a pre-defined “equity matrix” with title/level and ownership ranges. Over time, as the company raises more capital and, hopefully, appreciates in value, the ranges in this matrix should change (percentages go down as company value goes up); it should also be a living document that is actively modified by the Compensation Committee over time – in particular around big value-creating events like BD deals, new financings, etc…

Vesting is another topic central to the equity discussion.  Historically, startup employees vest over time – typically monthly over four years.  Sometimes there’s a cliff after the first year of 25% (so hires that join and leave quickly don’t vest).  These time-based methods are the “market norm”, but I think leave much to be desired, as there’s nothing in them linked to value-creation.  An increasingly common trend is to use “performance milestone-based” vesting as a supplement to time-based vesting; having some portion of their stock grants vest at an IND filing or another critical value-creating event.  These vesting goals can span multiple years and serve as “equity bonuses” for superlative performance.

A final topic here on equity is the subject of “reloading” the equity ownership of employees in private biotech companies with new grants after a dilutive financing.  These are more appropriately called equity “top-ups” in later stage biotechs.  Raising $20M on a $30M pre-money will dilute everyone by 40% before new grants are issued.  How much of that drop in ownership should be reloaded with these new grants?  I’m very opposed to full anti-dilution mechanisms (bringing a management team right back to where they were), as this approach makes the recipient indifferent to dilutive impact of capital raises.  As a very early stage investor, I prefer management teams aligned with me: all shareholders should face dilution, at least to some meaningful extent, with a new financing. Obviously balancing that with the equity benchmarks from comp surveys becomes the challenge: if the company’s management team was at their target of the “XXth percentile” in equity before a financing, without a full reload they must by definition be below that after the financing.  This circular logic is the challenge most Compensation Committees have to manage with regard to reloading of equity grants.

Naughty and Nice

Compensation Committees face the important and tough task of translating the overarching compensation philosophy into the practical details of salary, bonus, and equity grants, among other items.  It’s not a task to be taken lightly as a sound approach is critical in the war for talent.  And winning that war is not a matter of being merely naughty or nice, but of success and failure for our emerging biotech startups.

 

Special thanks to HR strategist Nancy Arnosti and Casey Lindstrom for their helpful suggestions on this post.

 

 

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A Billion Here, A Billion There: The Cost Of Making A Drug Revisited

Posted in Pharma industry, R&D Productivity | 3 Comments

The cost of making a new drug has grown to nearly $2.6B, according to the latest and greatest from Tufts Center for the Study of Drug Development (here).  That’s a big and almost unfathomable number.  Critics immediately pounced on it, calling anyone who believed it a flatlander (here), and suggesting that the cost, with failures, was closer to $150M.  Tufts’ number appears incredulous, but the critics paltry number even more so.

Last time I weighed in on this subject back in 2011, an article in Slate suggested that the cost of a drug was $50M.   I put out a model so that readers could “choose their own adventure” and play with the assumptions around drug R&D (here).  It provoked some good commentary and engagement.

Although not much has been shared from the Tuft’s study, other than a backgrounder and a slide deck, I thought I’d review the assumptions required for the modeling.  The estimates used by Tufts in prior analyses have already been independently verified by others (here), and this blog won’t be nearly as rigorous.

At first principles, there are several items that need to be factored in: direct costs of moving a drug forward, paying for failures along the way, and the time value of money (forgoing other investments). Since they haven’t shared their model, I’ve built a quick-and-dirty version using their public assumptions to recreate in a ballpark fashion their $2.5B drug cost estimate.  The distribution of costs (30-33% of spending into pre-clinical phases) is similar to their report.  Here is my “estimated” model that you can download and play with (here), with a snapshot below:

Tufts Estimated Model_2014

Observations on the key assumptions:

  1. Attrition: Rates Of Program Success.  The numbers the Tuft’s report uses are very much in line with other recent publications from the BIO organization in the January 2014 issue of Nature Biotechnology (here), McKinsey’s 2010 anatomy of attrition (here), AstraZeneca’s recent 2014 lessons learned in Nature Reviews Drug Discovery (here), or Eli Lilly’s 2010 review of the “grand challenge” in the same journal (here).   So it’s hard to argue with these estimates.  A general consensus set of attrition numbers leads one to cumulative probability of around 8% give or take for getting a Development Candidate to approval.  Obviously first-in-class drugs have higher rates of failure, especially in Phase 2a, but the blended numbers Tufts uses are very reasonable.
  2. Timelines: Duration of Each Phase.  Again, in line with attrition rates, its hard to argue with their timelines that suggest, on average, drugs take ~8 years from first-in-man studies to approval.  There are clearly some that are faster (e.g., Zelboraf, Xalkori, and Imbruvica all went from FIM to approval in less than five years, here) and some are slower (e.g., many of the 2011 FDA approvals were discovered in the 1990s here).  So Tufts timelines are reasonable representations of the average.
  3. Costs: Direct Spend on Projects.  These assumptions are very big factors in the model, as one might guess.  This is where using the average is potentially distorting, and probably the weakest part of any broad industry analysis like this.  Tufts doesn’t reveal the exact costs estimates, but they apparently built their cost assumptions from 106 drugs from ten large pharmaceutical companies first tested in the clinic between 1995-2007 that advanced through the various phases.  Based on that approach, these cost assumptions are undoubtedly biased towards Big Pharma programs targeting broader diseases, as the targeted/orphan theme didn’t receive as much Big Pharma attention pre-2007 as it does today.

So while the attrition and time estimates seem reasonable, the big question in my mind is whether cost numbers in this range are valid representative estimates for the drug industry at large?

At first glance they don’t seem far off from reality for a mid- to large-sized Pharma company or a reasonably large indication requiring a few thousand patients to get approved.  The model suggests direct single program costs for an average drug of $400-500M to get to approval.  Seems credible, at the average, though well in excess of the costs to bring an orphan drug with a 75-patient package to approval (like the Increlex approval in Laron Syndrome), and well shy of the primary care mega-drug requiring 20-30,000 patient exposures before approval. But since less than half of the new drugs approved by the FDA are from Big 20 Pharma, if their R&D numbers are skewed towards the bigger firms, then they probably aren’t the right cost assumptions to use for making generalizations about the entire industry.

To further pressure test these cost assumptions, lets do a quick top-down and bottom-up comparison of the R&D spend.

  • Top-down.  The industry spends ~$100B in R&D each year (including the $70M or so from Big 20 Pharma, $25B from all other public companies in biopharma, and the $5B a year spent by VC-backed biotechs, highlighted here).  About since 25% of this aggregate number goes to Life Cycle Management of existing products (e.g., new indications and Phase IV studies), that leaves about $75B spend on “new drug R&D”.  At 25-40 FDA NME approvals per year, that’s about $2-3B weighted cost per NME approval – in the ballpark of the Tufts analysis and of Matt Herper’s 2012 Forbes piece using a similar top down approach (here).
  • Bottom-up.  The PhRMA organization publishes the number of medicines in the industry pipeline – 5408 drugs according to the recent report (here), not inclusive of discovery and preclinical.  The phases of these medicines are in the pie chart on the cover of the report.  Using the annualized cost estimates by phase in my estimate of the Tufts “model” above, and ignoring the thousands of drugs being studied in discovery and preclinical phases, implies a bottom-up annual spending by the industry of at least ~$200B.

The big 1.5x discrepancy between this top-down (what the industry actually spends) and bottom-up (implied costs accrued by drugs per phase) analysis suggests a few things: either the numbers on the industry’s pipeline from PhRMA for are vastly inflated or include non-NME programs, or that the development cost estimates I’ve used to recreate the Tufts model aren’t fully reflective of the entire big-and-small Pharma industry.  My guess is that its far more of the former than than latter, but both of these conclusions probably have some merit.

Lastly, its worth noting that like any industry-wide general analysis, the assumptions required to derive the “average cost to bring a drug to approval” require fitting drug R&D into a normal distribution (certainly not right) and using single point values that frankly aren’t that instructive.  So in short, these models can only be specifically wrong and generally right.  But they are better than nothing, and frame up the scale of the challenge.

As a second final note, it’s worth mentioning that the tax-shielding benefit of R&D spending isn’t included in the model from Tufts to my knowledge.  If you assume a 25% effective rate, the numbers are in Tufts estimates can be reduced by a quarter; the numbers are still large, but it’s probably fair to include the these in a fully capitalized financial model of drug costs.  Tax inversion deals would, of course, reduce the magnitude of tax-shielding by R&D; good thing they are going the way of the dodo.

No analysis is perfect.  If Tufts estimate is off the mark for the entire industry, it doesn’t appear off by a huge amount, and certainly not the order of magnitude implied by the critics. Most practitioners in the field agree on the general rather than specific conclusion: the cost to bring a drug to market is big – very big – especially when accounting for all the failed attempts.  If we want to reduce this number, the solution is simple – just do things better, faster, and cheaper.  Back to work.

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