Biotech finance part 2: valuation methodologies and modeling considerations

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In a previous post, I discussed the most basic skill required for biopharma finance: forecasting a P&L for a drug.

In this post, I'll discuss the next step -- valuing biopharma companies. This is intended for people who understand basic finance and valuation but aren't too familiar with biotech. If you work in a biotech group, you probably already know this. Subsequent posts will cover topics that you'll need on the buyside (not sure when i'll get around to those however, this one was a bit longer than i anticipated).

How does valuing biopharma companies differ from valuing other companies?

Drugs have short and explosive life cycles, with new products growing from nothing to billions of dollars of high-margin revenue in just a few years, and then going to zero overnight when patents expire. Newly launched competing products can erode what once seemed like lasting franchises. Even $100B+ companies can lose 40% of their market cap almost overnight when one study of an unapproved product doesn't go as planned (look at Bristol Myers in summer 2016).

The drug industry is characterized by high growth, high profits, binary risk, and volatility. In many cases, significant portions of a company's valuation don't show up in financial metrics - late-stage unapproved drugs can be valued at tens of billions of dollars before they even enter the market.

To value biopharma companies, you need to do more than dig into the financials -- you need to dig into the products. And that means you need to get into the science.

But valuing products is a subject for another post. This post will be a "stepping stone" on the way from the traditional finance techniques you are familiar with to more science-oriented concepts you'll need to become familiar with in order to evaluate products. Specifically, in this post I'll provide an overview of the valuation methodologies commonly used in biopharma, and highlight a few biopharma-specific modeling considerations.

Valuation methodologies for biopharma companies

DCF / sum-of-the-parts

DCFs are tricky in any industry, as they are very sensitive to assumptions, and it can be hard to nail down key assumptions.

In biopharma, DCFs are even more difficult, as they layer on even more assumptions than DCFs for companies in other industries. Despite this lack of accuracy, they play a larger role in biopharma valuation than many other sectors, and are often the main method for determining a company's valuation, especially for earlier stage companies.

For many (if not most) pharma companies, significant value lies in the pipeline of unapproved drugs, or drugs at the earliest stages of commercialization. It is tough to use multiples for these products, as they don't generate revenues or profits. You can use forward multiples to try to capture the value of early-stage products, but that can get complicated quickly: do you use revenue or P/E multiples? What year's revenue or earnings do you apply the multiple to? Should you use an NTM multiple, or 2-year multiple, or discounted 5 year multiple? These answers will be different depending on the company.

You'll still use multiples based approaches, but rely a bit more on the DCF to give you more granularity. In some cases, you'll do a sum-of-the-parts DCF valuation, sometimes you'll do a SOTP and use different multiples for different products. If you are evaluating a $50B+ company, you can probably just use LTM and NTM P/E, but the earlier stage the company, the more you'll need to explore using revenue multiples, discounted forward multiples, 2+ year forward multiples, or some combination of those.

Forward P/E

In biopharma, P/E is used more often than EV/EBITDA. In most cases for larger, commercial-stage companies, LTM and NTM P/E are used, but often people will use a longer-term forward P/E or a discounted forward P/E for earlier-stage companies. The rationale for this is that a company may have valuable products that are either not launched or are in early stages of launch, so the products will generate little or no earnings for years, so using NTM P/E would not accurately capture the value of the products. So you pick a year in the future where these products become more mature and can be valued using P/E, then put a multiple on those forward earnings, then discount it back.

For example, if you have a product that you think will be a $1B revenue product in five years with 60% contribution margins, but that product is only expected to generate $10M in revenue next year, you might use earnings in year 5 for your P/E. Then you would look at comps, figure out the mean and median NTM P/E for those comps, multiply that by your year 5 earnings, and then discount that back four years (because you are using NTM P/E on year 5 earnings, so you are effectively using year 4 as your valuation date).

If that sounds confusing, it's probably because it is a somewhat contrived approach. It makes theoretical sense, but in practice it is pretty squishy. What year's earnings do you apply the multiple to? In the above example it's year 5, but could it be year 4, or year 6? Let's say you want to look at year-5 earnings; should you use an NTM P/E on year 5 earnings and discount it back four years, or a 2-year forward P/E on year 5 earnings and discount it back three years? Or use an LTM P/E on year five earnings and discount it back 6 years? What comp set should you use?

Each of these could yield significantly different valuations, and there isn't really a "standard" methodology as far as I can tell. Your VP may end up asking you for 15 different permutations, all of which are semi-justifiable but none of which are that great.

Like all valuations, you'll use a combination of different methodologies, but these complexities make it a bit trickier to figure out "reasonable" multiples to use for a biopharma company.

Revenue multiples

Using revenue multiples can get around some of those issues and can be a "cleaner" way to use multiples to capture the growth potential of early-stage products. Typically a product will generate a meaningful amount of revenue before it generates a meaningful amount of profit, so you can put revenue multiples on earlier years than you can for P/E multiples (ie, if a product generates $100M revenue but only 20% contribution margins in year two, and "mature" margins are expected to be 50% starting in year 5, you may need to use year 5 for a P/E multiple but could potentially get away with year 2 for revenue multiple). However, if a company has high-growth products as well as material earnings from "mature" products, it's probably better to use P/E, or a sum-of-the-parts approach with different multiples for different products.

Of course it is still a very imperfect method. As discussed in the prior post, forecasting the sales trajectory of a new drug launch is very difficult. It's also not straightforward to pick which year's revenue to use - do you use 2-year forward revenue, 3-year, 4-year?

One way you can get around this is to use a "peak sales" multiple. Pick some comps that were bought at a similar stage to your company, figure out the peak sales of their main product(s) (assuming there are only a few major products), then divide equity value ("tech" value, assuming a company has no debt) by peak sales and there's your multiple. This way, you don't have to guess about the sales trajectory, and you don't have to worry about picking a year for your forward multiple. Of course the downside is that you may not have a great comp set, and this doesn't really account for how quickly / slowly your product will ramp.

Strategic / exit value

In the current market, many small or mid-cap companies are valued more on likelihood of getting acquired by big pharma than on conservative estimates of expected value of their cash flows. In the last five years pharma companies have shown extraordinary appetite to buy companies at very high premiums that can only be justified by very optimistic projections. In some cases, a pharma company will pay whatever it takes to get the deal done for assets that their science team deems highly strategic. So there has existed an "M&A thesis" that many investors follow, where they try to identify the next M&A candidate and then more or less value the companies based on a probability-adjusted discount to M&A comps.

You probably shouldn't use this technique much if at all, as it is difficult to justify with traditional fundamental valuation methodologies, but it is helpful to be aware of this because it reflects a current market reality. Many valuations are difficult to explain using traditional techniques, but that doesn't necessarily mean that they are wrong (at least in the short-term) - just that investors are betting on pharma's willingness to pay up for strategic assets.

If you look at models in equity research reports that use DCF or multiples methods to value companies, you will often see that there are a few pretty aggressive assumptions hidden in there. Without these aggressive assumptions, a reasonable DCF in many cases would get you a valuation below the current market price. In these cases, what's likely going on is that the market views the company as a takeout candidate.

VCs use a framework that is sort of similar to this when valuing early-stage companies. The idea is that you focus on the few assumptions that matter and "outsource" to the market assumptions that don't move the needle. You're giving up some precision in return for better accuracy. For example, probability of technical success at a given stage (preclinical development, Phase 1, etc) is probably the biggest determinant of value for early-stage companies, assuming there is a large enough market. Getting 50% smarter on probability of success will inform your valuation much more than getting 50% smarter on steady-state contribution margin. Consequently, startup valuation becomes more about diligencing technical risk than doing complex financial analysis (which is why most VCs are PhDs, not bankers). More on this in another post.

Biopharma-specific modeling considerations

Beyond differences in valuation frameworks, there are a few finance and accounting idiosyncracies unique to biopharma. Many of these are very complex and technical, and you won't be expected to master them - you'll just need to know how to make reasonable assumptions and, in live deal or bakeoff situations, consult specialized lawyers, accountants or consultants for their take.

Terminal value and patent expiry

Most drugs have a terminal value of zero: when key patents expire, generics flood the market, and revenue drops 90%+ basically overnight. For these products, it is incorrect to model much if any terminal value. Rather, you can often model out sales every year until key patent expiry, then assume the drug is worth nothing.

For small molecule drugs, you should almost always assume that once patents expire, the drug is worthless. FDA has well-established regulations for enabling fast approvals of "generic" small molecule drugs.

For large molecules, however, "genericization" is less black and white. Due to the more complex nature of these molecules, it is harder to prove a large molecule is "biosimilar" to another large molecule. FDA only recently established a pathway for approval of "biosimilar" large molecules that are substitutable for pioneer large molecules, and this pathway is harder and more expensive than the pathway for small molecules. I'm not super current on the biosimilar world, but basically large molecules have longer and larger "tails" than small molecules when patents expire, so it is not always appropriate to forecast significant revenue erosion upon patent expiry. You need to look into the details of the particular drug you are forecasting.

So while you generally won't include a terminal value for products, sometimes investors place a terminal value on a platform (though you really need to exercise caution when doing this). The idea here is that a company has some fundamental scientific competency that makes it possible for them to discover and develop lots of new drugs, or a BD competency that gives them an advantage in identifying and licensing promising assets. So you could include a terminal value to account for the value of products the company has not invented / acquired yet, but is likely to invent / acquire in the future.

If you do this, you should have a good reason for it, and this should not be a big part of the value. The value of a drug increases exponentially as it advances in development. At the earliest stages, a drug candidate has a 1/10,000 chance or less of getting approved, thus very little value. Most of the value of a platform is determined by one or two products, and the value of the rest of the platform is typically almost meaningless.

"Biobucks"

The deal terms in the press releases around biopharma M&A or licensing deals are often pretty complex. You'll often see an "upfront" payment of some smallish amount, maybe combined with an equity investment, and then some development, regulatory or commercial milestones, and then royalty rates. You might also see that the licensor has agreed to pay x% of all development expenses for a particular product, or has an option to license rights to a few products in certain geographies, or even an option to acquire the whole company.

These contingent payments are referred to as "biobucks" (although often royalties are excluded from this). Nearly all partnering deals in biopharma have some sort of "biobucks". Biobucks are important because they enable companies to structure around risk. Sellers want to get paid if their products work, but buyers don't want to pay up for risky drugs that will most likely end up worthless. Biobucks enable companies to structure deals that bridge this gap.

However, this can make valuation more complex. For one, it adds another dimension of complexity to M&A comps. Often you'll have a column in your comp set for "upfront" value, and a column for "total" value. These numbers can be very far apart.

It also makes modeling more difficult in some cases. In addition to forecasting and valuing a P&L, you need to account for any deal-specific terms that alter the P&L or balance sheet. A few common terms to look out for:

  • Upfront fee: pretty self-explanatory, this is just cash that acquirers / licensees give a company in exchange for rights to an asset. This fee can be distributed to shareholders of the recipient company or re-invested into the company.
  • Equity investment: sometimes licensees will invest in a licensor's equity, and you'll need to account for this. How you account for this will depend on the structure of the transaction and the resulting ownership level.
  • Option payments: often licensees will pay the licensor for the option to license a particular product, or a particular set of rights (ie commercial rights in Greater China). Companies can also negotiate options to acquire entire companies at pre-negotiated terms.
  • Milestone payments: these will need to be accounted for, often as licensing revenue for the licensor or as a liability / expense for the licensee. Often you'll need to make some estimation as to the probability that these milestones occur. The details of this can vary depending on the particular deal, so take a look at whatever's available in the SEC filings and press releases.
    ** Development milestones: money paid upon achievement of certain clinical or preclinical milestones (IND filing, Phase 2 study initiation, lead candidate selection, etc)
    ** Regulatory milestones: typically FDA approval, can be other regulatory milestones (IND filing, End-of-Phase-2 meeting, or equivalent milestone in another country)
    ** Commercial milestones: these are one-time payments based on achievement of certain sales thresholds (annual or aggregate sales). These are distinct from royalties.
  • Profit / cost sharing: sometimes companies will enter into profit sharing agreements, or share development expenses. Sometimes a licensor will pay for development expenses up to a certain point, like Phase 1, and then the licensee will take over a percentage or all of the expenses. These are common terms but will vary based on the specific deal.

Modeling all of this can get pretty complicated, but it is important not to gloss over these terms. In some cases I've seen investors get this wrong and value a company as if they had nearly all the economics for a key asset, when in reality the company owns only a fraction of these rights. Don't do that :).

Discount rate

Discount rates in pharma are tricky, which is problematic because DCFs play such a large role in valuation. Traditional methods of calculating discount rates like CAPM don't really yield usable numbers in biopharma. The risks associated with biopharma companies are often unique to the company itself, and this can make it hard to compare risk and volatility across companies. Volatility for biotech stocks, especially pre-revenue companies, can be crazy and beta values are often useless.

In practice, investors will typically bucket companies into different groups based on development stage and size, and then apply progressively lower discount rates to larger companies. Figuring out the right discount rate when a big company buys a small company can be tricky - do you use the small company's discount rate, or the big company's discount rate? Should they be that different in the first place if the risks are diversifiable?

Taxes

This is a very complex area and for live deals or bakeoffs you may need to consult with lawyers and accountants. I'll just highlight a few pharma-specific issues, and then let you do further research to figure out how to model these factors.

The first issue is NOLs. Many pharma companies accrue a lot of net operating losses when they are developing their drugs, and these losses can be valuable tax shields. In M&A, you'll want to pay close attention to how NOLs are valued. This is an area where you'll want to figure out how your group typically handles this accounting, and get feedback from lawyers and accountants on important deals. For the purposes of this post, I'll just flag it as something to look out for.

The next major tax issue is understanding how various corporate structures and IP domiciles influence tax rate. Many pharma companies have historically domiciled much of their core IP in low-tax countries like Ireland, so profits from drugs using this IP is taxed at a very low rate. These laws are complex and rapidly changing, and when you need to get sharp on this, usually for a live deal, consult with lawyers.

IP domiciling and tax can be a really interesting and important area, and while it isn't expected you know much about this, if it piques your interest and you learn about it then it can be a nice way to differentiate your skill set.

IP

This is another very complex topic that is also very important, and one you aren't expected to know much about as an analyst (or probably even as an MD). Being wrong about IP is often a costly mistake, so if you are in a position where you need to make assumptions about IP in a live deal context, talk to a lawyer or consultant. It is also a highly technical intellectual rabbit hole, and if you find that you like this area, it can be a valuable way to differentiate your skillset.

When you are modeling drug revenues, it can be important to have good assumptions about patent expiry, and you probably can't get away with spending a couple grand on a lawyer for your pitchbook model. In these cases it's probably best to read equity research reports, company presentations or SEC filings to figure out where the market is bounding patent risk, and just run sensitivities on that.

Sensitivities

One major takeaway from all of this is that "garbage in, garbage out" applies even more in biopharma than for many other industries. You don't have historical financial information to anchor estimates for a substantial part of most businesses. There are a lot of assumptions, and they stack up very quickly. You need to be very diligent about your assumptions, and you will often have to make assumptions that you don't feel 100% confident about, because you just don't have data.

In addition to doing the usual sensitivities, spend extra time thinking through 1) which assumptions move the needle the most, 2) your "confidence intervals" for each assumption and 3) where you differ most from the market and your conviction level around that (you should do all of this for any valuation work you do, but it is especially important for biopharma). This is probably less important for banking than for the buyside, but it's never a bad idea to be extra diligent about your assumptions.

Selecting a comp set

Especially for pre-revenue companies, you won't select comps based on sector, size and other financial metrics. You determine your comp set based on factors like disease being treated (the more specific the better), stage of development, type of drug (what is the molecular target of the drug, mechanism of action, how good is the data), and commercial factors like price, number of patients who are candidates for the drug, and sales channel / reimbursement (are you selling to hospitals, primary care physicians, dermatologists, etc). CapIQ and FactSet aren't great for this, although you can use tools like Evaluate Pharma, or just do a ton of googling.

How to learn this

If you can find a fairness opinion for a relevant biopharma deal, that can be a helpful resource. Equity research reports can also be useful as a framework, although you should obviously take a critical look at their models. A helpful exercise for learning is looking at a bunch of ER models, seeing where they have divergent assumptions, and then developing your own view on the fundamentals and appropriate valuation methodologies.

Putting your money where your mouth is can also be a great incentive to learn quickly. Treat it like bitcoin -- only invest as much as you can afford to lose, and expect a wild ride. And beware of shorting - it can work, but many stocks are hard to borrow, and it is not unheard of for companies to get acquired for 300-600%+ premiums, so you may lose more than you bargained for.

What next?

Eventually, you'll start asking questions that are critical to your valuation but that you don't have answers to. Will FDA approve this drug? How likely is it that the upcoming Phase 2 studies are positive? Will a generic company invalidate a key patent next year? Is this drug better than a competing drug? Answering these questions is at the heart of the work you will do on the buyside, and requires learning enough science to have intelligent discussions with physicians and scientists. You don't need a PhD or MD to do this, but there is a fairly steep learning curve. I'm not aware of many good articles / books on these topics, so I may write other posts on these in the future if I can find time.

Comments (17)

Nov 16, 2018

Great post. A lot of very useful stuff in here. That said, I have to quibble with a few points:

On discount rates:

The discount rate should only capture market risk and not subject entity risk. Ideally, the subject entity risk should be captured in the defined scenarios. Biopharma forecasting is scenario-based forecasting. The valuation analysis should consider multiple IP scenarios in the situation where there's significant IP risk (not sure about the strength of a formulation patent?). The same is true for other, non market risk factors (R&D, regulatory, etc).

Also, the proper discount rate is always the target's discount rate (not your own).

On not needing a PhD or MD:

I have to disagree here. At the end of the day, your forecasts must be supported, if not provided, by someone with the proper technical background. You simply cannot understand (and therefore adequately quantify) the regulatory and development risk without an understanding of the product's chemical/clinical attributes and benefits. Sure, at an early stage, you can rely on IMS analogs, statistical benchmark studies and/or ER reports, but all of those are estimates that are too high level for an actual transaction - they don't consider the entity specific attributes. In addition, you simply wont understand the true clinical benefit of the product relative to the competition and thus will have difficulty understanding physician prescription dynamics (i.e., the uptake curve) as well as the appropriate marketing strategy.

At the end of the day, the forecasts really come from the PhD's at any reputable organization.

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Most Helpful
Nov 16, 2018

Thanks for your comments, those are fair points. My main point about the discount rate is that in many cases it is hard to get useful betas in biotech, esp for low liquidity stocks, and that while the market risk premium is supposed to reflect non diversifiable risk, in biotech the market risk premium in reality (incorrectly) often includes risk that is actually diversifiable. This shows up in many M&A scenarios. But i was sort of sloppy in that section adn your points are fair

Think we will agree to disagree on the MD / PhD point. You are absolutely right that you need to understand a drug's chemical and clinical attributes to estimate probability of success and market adoption. But i dont agree you need an MD or PhD to do that. I know investors at all levels from analyst to partner at all kinds of funds (11-figure AUM hedge funds to the most prestigious early stage venture funds) who don't have an MD or PhD. The most successful biotech entrepreneur I know is an MBA, not a PhD or MD, and he is a better judge of the value of science than any PhD I know (and the PhD VCs I know tell me the same thing). I have a friend who is a pharmacology Phd and runs a VC backed hematology company, and she says that she knows finance people with no science background who know more about hematology than she does (and she knows a lot about hematology).

This is because ultimately, you do diligence by talking to people. To properly evaluate a drug you need PhD-level expertise on 5-10 different topics, and no single person has all of that expertise. Even if they did, evaluating science involves so much uncertainty that you can't rely on a single person's assessment. Your job as an investor is to identify key risks, solicit the opinions of leading experts to better understand those risks, then synthesize that info and make business judgments. An investor should rely on others for technical expertise, but rely on him / herself for judgment and decision making

You need a baseline level of scientific and clinical knowledge in order to rapidly learn enough about a given area to identify key risks and have intelligent conversations with domain experts, but beyond that baseline level, technical knowledge is less marginally useful than 1) ability to learn quickly 2) ability to conduct efficient, productive, unbiased interviews with domain experts and 3) good judgment and critical thinking skills

If you do a PhD in molecular biology, cell biology or pharmacology, you will have some but not all of that baseline level of technical knowledge, but you don't need to do a PhD to get that knowledge (however you need to work very hard and be very self-directed to learn this stuff w/o a phd).

The reason that an MD / PhD is a good filter for hiring biotech investors is that there aren't that many non phd-mds who do the work to get up to speed on science, so the false negative rate for the filter is pretty low.

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Nov 16, 2018

Solid response. Thanks for the clarification. I'm digesting point #2 but it's well articulated. I work(ed) in pharma corp dev. Everything basically came from the technical experts. The valuation analysis was just the quantified summary of their findings. The external consultants had PhDs as did the internal SMEs.

No one would trust me to build an uptake curve but you're right that, over time, with enough experience, one could presumably get there. I just see it as a massive up-hill battle. I know PhDs that have gotten things wrong. Overlooked certain attributes that led to previously unforeseen regulatory hurdles, particularly on the generic side (bioequiv hurdles) and lost their jobs as a result of it.

Thanks again.

Nov 16, 2018

I think it's a little more nuanced than you've characterized here....

PhD's/MD's, in earning their degrees, focus on very, very specific fields. For example, a nephrologist probably wouldn't have particularly useful insight on an opthamology therapy. Unless they happen to be an "expert" (i.e. did their thesis in) in the same scientific/therapeutic field/area as the product being evaluated (very low odds), the PhD doesn't really give them much of a leg up on the average joe who can read up on the pathway, pharmacokinetics, therapy development history (i.e. why other things failed/succeeded), etc. Furthermore, no PhD/MD is so cutting edge that they are experts on all of the new therapies that are being explored in their field (and as such their ability to gauge probabilities of success, adoption, etc. is still handicapped). If they were capable of doing this much better than the average bear, then as a class, they would be much better biotech investors. Plenty of PhD's/MD's have lost tons of money betting on drug candidates just as plenty of people without those degrees have made tons of money betting on drug candidates.

I want to be clear here.....I'm not advocating that having those degrees provides no advantage at all, but I wouldn't say they are required for success.

Nov 23, 2018
Esuric:

The discount rate should only capture market risk and not subject entity risk. Ideally, the subject entity risk should be captured in the defined scenarios. Biopharma forecasting is scenario-based forecasting. The valuation analysis should consider multiple IP scenarios in the situation where there's significant IP risk (not sure about the strength of a formulation patent?). The same is true for other, non market risk factors (R&D, regulatory, etc).

Also, the proper discount rate is always the target's discount rate (not your own).

You seem a little out of your depth here. There are a lot of legitimate reasons reasons why biotechs would have differing discount rates. If you think an AMGN deserves the same WACC as an INCY then you are completely neglecting portfolio diversification, regional diversification, and so on. These are fundamental differences that need to be accounted for that exist completely outside of pipeline/IP/launch scenario analyses.

Also, always using the target WACC is NOT always appropriate, especially when the acquisition itself would make the company less risky (i.e., the big pharma buyer has a plug and play sales force that is ready to go)

Esuric:

On not needing a PhD or MD:

I have to disagree here. At the end of the day, your forecasts must be supported, if not provided, by someone with the proper technical background. You simply cannot understand (and therefore adequately quantify) the regulatory and development risk without an understanding of the product's chemical/clinical attributes and benefits. Sure, at an early stage, you can rely on IMS analogs, statistical benchmark studies and/or ER reports, but all of those are estimates that are too high level for an actual transaction - they don't consider the entity specific attributes. In addition, you simply wont understand the true clinical benefit of the product relative to the competition and thus will have difficulty understanding physician prescription dynamics (i.e., the uptake curve) as well as the appropriate marketing strategy.

At the end of the day, the forecasts really come from the PhD's at any reputable organization.

I think other people have addressed this so I won't beat the dead horse too much, but I've been on hundreds of doc calls and you would be surprised how ignorant physicians can be on certain topics (even within their own field). Most docs don't even know how much on-market drugs cost, let alone what a theoretical drug should cost. You are going to get 100x more useful info just talking to a payor (if drug shows X, then could it justify price Y)

Nov 23, 2018
cakepie:
Esuric:

The discount rate should only capture market risk and not subject entity risk. Ideally, the subject entity risk should be captured in the defined scenarios. Biopharma forecasting is scenario-based forecasting. The valuation analysis should consider multiple IP scenarios in the situation where there's significant IP risk (not sure about the strength of a formulation patent?). The same is true for other, non market risk factors (R&D, regulatory, etc).

Also, the proper discount rate is always the target's discount rate (not your own).

You seem a little out of your depth here. There are a lot of legitimate reasons reasons why biotechs would have differing discount rates. If you think an AMGN deserves the same WACC as an INCY then you are completely neglecting portfolio diversification, regional diversification, and so on. These are fundamental differences that need to be accounted for that exist completely outside of pipeline/IP/launch scenario analyses.

Also, always using the target WACC is NOT always appropriate, especially when the acquisition itself would make the company less risky (i.e., the big pharma buyer has a plug and play sales force that is ready to go)

You don't seem to understand the mechanics of discount rates or the content of my comment. Discount rates reflect market risk. As a best practice, idiosyncratic risk should be accounted for in the forecasts of a scenario based model. Adjusting the discount rate is technically incorrect and doesn't adequately account for the variables mentioned. Now, in no way does that mean that AMGN should have the same discount rate as INCY. There are plenty of market risk factors that would lead to varying beta's between the two (but it doesn't have to do with "portfolio" or "regional" diversification).

And yes, the target discount rate is always the appropriate discount rate. That's just basic stuff. A "plug and plays sales force" by a big pharma player would impact the valuation via synergy estimates and not through the discount rate.

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Nov 16, 2018

I agree. I think having at least a BSc or MSc in this industry is very very important and useful but full PhD is not fully needed.

Additionally, I would like to add 1 more point but not sure where you would put it in the valuation. When forecasting things like drug valuations or sales, an important factor is ability to get reimbursement (particularly for the EU). This is key because you may have the most amazing drug in the world, but if HTAs only approve reimbursement for a niche population, your sales drop significantly and thus impact your value (this is particularly important if you are a small to mid-cap company).

To advocate the importance of knowing the science, I would also advocate to the importance of knowing the scientific process.

Take some of the new Alzheimer's drugs in development against Tau proteins. It is very likely that populations of patients that get these new drugs will require the presence of Tau to be confirmed by Tau-PET scan or Tau-CSF analysis. In some EU markets, PET scans that are more in-depth than FDG PET are not reimbursed and very very very few people pay OOP. On top of that, in some of the poorer regions of a country it may be harder to obtain radio ligands for those scans. And if you take Tau-CSF, many labs will wait for full ELISA samples to start running tests meaning that if you REQUIRE this test for drug eligibility, you can lead to an increase in national costs and waiting times which can slow down your market entry, adoption and general deployment.

I would put the aforementioned paragraph under valuation considerations. Just the $0.02 of someone who works in the strategy side of the industry

Nov 16, 2018

Those are all really good points. I think that even the best investors are still trying to figure out how to model pricing, especially in the EU but increasingly in the US. That's interesting about the tau products -- was not aware that PET scans might be required. That seems challenging for pharma companies.

My thinking on the structure for this post was that i'd just provide an overview of valuation frameworks and techniques, and then talk about how to diligence assumptions within those frameworks at a later point. You make a good point that pricing / reimbursement is a key topic to cover -- I think a lot of people (including me) are realizing how quickly the drug pricing world can change.

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Dec 3, 2018

Thanks for the quality post!

What's your take on Moderna and their IPO?

"well thank god your feelings aren't a fucking priority here"

Dec 5, 2018

I'll be on the sidelines for the moderna IPO but not necessarily because I have a view on the company, but bc I'm not sure that the return on time invested in doing the research required to get conviction will be worthwhile compared to other opportunities. They have so many products, many of which are at early stages of clinical work and could be needle-movers, and it would just take a lot of time to dig into all of them. Plus I'd want to dig into their delivery tech and RNA platform and compare it to others, and that would take a ton of time too

At such a rich valuation for an early stage clinical company i cant really justify spending much time on it bc there are other interesting things out there. of course I could miss out on a nice return but i can live with that

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Dec 3, 2018

Thanks OP, this is a super helpful post.

I have a question regarding forecasting reimbursement rates - I was looking at an orphan drug company that just launched a product two years ago. The drug is a huge success and American government programs and insurance companies all had no problem giving the drug very favorable reimbursement rates. However, the company is still negotiating with a few EU countries about a good reimbursement rate and there's very little information on when the negotiations will resolve.

A large portion of the patient population is in these EU countries so the reimbursement rate has material impact on revenue. In uncertain forecasting situations like this, would you just keep the current reimbursement rate or are there any other approaches you would recommend? Thank you again.

Dec 5, 2018

That's a good question and one that I think a lot of investors and companies are struggling with. I think it's fairly safe to assume that EU pricing would be at a discount to the US and take longer to finalize, though off the top of my head I don't know what specific discount or delay to model. I'd look at comparably priced orphan drugs that target a similiar size patient population, and look at when they got EU coverage compared to US, and what discounts they faced. You can look at NICE reviews for England, they are usually very comprehensive and show detailed cost benefit analyses, and many other OECD countries follow NICE's lead. NICE is much more conservative than US payers. You can look at ICER reviews for a sense of the US perspective

I was looking at a Novartis investor presentation the other day, they spend a good amount of time on the gene therapy they bought from Avexis. Slide 48 shows a few super-expensive orphan drug pricing comps. Spinraza, a competitor to AVX-101 that launched a few years ago, is an interesting case study.
It is one of if not the most expensive drugs in the world (i think $750K in first year and $350K / year thereafter) but did $880M in its first year of launch. Not sure about geographic breakdown -- it is approved in Japan and EU -- but assume its mostly US. However NICE has not yet endorsed it in the UK even at a discount to the US as NICE thinks they are too expensive

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Dec 7, 2018

Great point on the NICE reports - I'll definitely dig into those.

Thank you again OP. Your post has answered many of my questions about biotech valuations. Definitely the most comprehensive guide I have seen.

Dec 3, 2018
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