Four Decades Of Hacking Biotech And Yet Biology Still Consumes Everything

Posted April 26th, 2017 in Drug discovery, Pharma industry, R&D Productivity | 1 Comment

Neural networks, cloud computing, deep learning, and in silico wizardry are on the cusp of disintermediating pharmaceutical drug discovery, cutting billions of billions off the industry’s cost of new drugs and reducing the time to get new medicines approved to just a few processing cycles. “Software eats biotech”, or so goes this new variant of a decades-old thesis. This time could be different – we could be at the singularity when “humans transcend biology” – but I don’t think so.

For reflection, here’s a quote about computer-aided drug discovery (CADD), highlighting its importance and impact:

 “Drug companies know they simply cannot be without these computer techniques. They make drug design more rational. How? By helping scientists learn what is necessary, on the molecular level, to cure the body, then enabling them to tailor-make a drug to do the job… This whole approach is helping us avoid the blind alleys before we even step into the lab…  Pharmaceutical firms are familiar with those alleys. Out of every 8,000 compounds the companies screen for medicinal use, only one reaches the market. The computer should help lower those odds … This means that chemists will not be tied up for weeks, sometimes months, painstakingly assembling test drugs that a computer could show to have little chance of working. The potential saving to the pharmaceutical industry: millions of dollars and thousands of man-hours”

What’s great about this quote is that you can hear its echo in current Silicon Valley tech-solves-biotech pitches, but it was from a Discover magazine article in August 1981 called “Designing Drugs With Computers”.  A couple months later Merck’s initial foray into CADD was featured in a cover article in Fortune magazine with a great cover image (right): “Next Industrial Revolution: Designing Drugs by Computer at Merck.”  These references highlight that over the past four decades, computer scientists have been applying their trade against hard drug R&D problems with new software. And if software has been truly eating biotech, then it’s been doing it very slowly – as there certainly seems to be plenty of biotech left to eat.

Don’t get me wrong, I’m not a Luddite on the role of in silico technology in improving R&D; quite the contrary, in fact, as I’m a big proponent and so is Atlas Venture where I’m a partner.  We’ve put our money behind the concept of CADD-inspired drug discovery many times. I co-founded Nimbus Therapeutics in 2009 with Ramy Farid of Schrödinger, one of the best science-driven CADD software shops in the world, and served as it’s acting CEO for several years (more here on the original launch of Nimbus). And either myself or my firm has been affiliated with many other companies in the CADD-inspired space, like Vitae (acquired by Allergan), SGX (acquired by Lilly), Avila (acquired by Celgene), Numerate, and a number of others.

We’re certainly advocates for the past impact and future promise of both major technology streams of CADD: structure-based drug design (think protein 3D-structures with drugs bound) and ligand-based drug design (think chemistry and quantitative structure-activity relationships, or QSAR). Over the past few decades, many approved drugs had significant CADD-enabled success, including “early” wins like the HIV protease inhibitors saquinavir, ritonavir, and indinavir in 1995-1996 over twenty years ago.

Nearly every successful small molecule drug discovery campaign I’ve been a part of has a healthy dose of CADD insight injected into it to help identify leads, as well as analyze various biophysical data (crystallography, NMR, spectroscopy) to explore the role of pharmacophores, solvation states, electrostatics/hydrophobicity, and more recently the PK/ADME space, including solubility and permeability. The prospective use of these novel CADD-enabled insights is possible today due to the significant improvements in the methodologies and computer power used for molecular dynamics simulations, free energy perturbation analyses, and quantum mechanics modeling of proteins and their ligands. These techniques enable “virtual” compound library screens, as well as better analysis and understanding of the findings from actual library screens (fragments, HTS, DELs).  And as computer processing power has scaled into the cloud, additional insight has the potential to be gleaned from these efforts going forward. Broadly defined, the CADD field has been a hugely important contributor to the state of modern drug R&D.

But here’s the critical takeaway: it’s only one of many contributors to overcoming the challenges of drug discovery today. There remains a wonderful abundance of artful empiricism in the discovery of new drugs, especially around human biology, and this should be embraced. Learning how to harness the latter while pursuing the benefits of CADD is critical.

To put it bluntly, we are far far away from a world where computers discover drugs, test them virtually in a cloud of robotic assays, and get them to patients with a few clicks of a mouse.

Furthermore, adding another dose of realist-skepticism, as these software tools have come online in the industry over the past couple decades, R&D productivity has largely gone downwards, not upwards. Correlation is not causation, but many in silico techniques haven’t panned out in making better drugs. More QSAR doesn’t always help if the data inputs aren’t ideal, they just make the piles bigger (and reduce the signal to noise ratio). More crystallography and computational work around inappropriate structures, unnatural conformers, or inaccurate protein homologues doesn’t help drive a discovery campaign in the right direction.

And lots of snake-oil charlatans have “sold” the promise of their variant of CADD over time. Many supposed “algorithms” and in silico platforms aren’t based on sound science. Over-fitting of data is a common mistake: these make great retrospective case studies (“we would have found this drug far faster than the actual chemists”), but often fail to deliver on actual prospective projects. A quick or clever piece of code doesn’t get you a breakthrough medicine. Sadly, though, lots of the claims the field makes substitute science-based conclusions with hype-based proclamations. Hype has been the bane of CADD’s reputation. Further, the field has done itself a disservice in many respects by adopting esoteric yet seductively impressive nomenclature like conducting “Metropolis Monte Carlo simulations” of “Grand Canonical Ensembles” (I mean, come on, who wouldn’t want to invest in that?). Charlatans often exploit this nomenclature to good effect, especially with naïve investors, but in the end its been lots of over-promise, under-deliver from this vein of CADD. The proliferation of Silicon Valley buzzwords today in drug discovery seem eerily familiar to prior snake oil speak. These and other false promises have left burnt R&D program budgets and unhappy investors in their wake.

But if current structure- and ligand-based approaches to CADD are “just” contributors to drug discovery efforts (albeit important ones), what else drives success and why can’t we model it? What’s stopping a computer from clicking its way to new medicines?

The answer is that instead of “software eats biotech”, the reality of drug discovery today is that biology consumes everything.

The primary failure mode for new drug candidates stems from a simple fact: human biology is massively complicated. Drug candidates interfere with the wrong targets or systems leading to bad outcomes (“off-target” toxicity). They interfere with the right targets but with the wrong effects (“on-target” or mechanism-based toxicity). They are most often promiscuous and interact with lots of things, some known and many unknown. Beyond their target pharmacology, drugs interact with the human body in countless ways, rendering them ineffective or worse (absorption, distribution, metabolism, excretion being four important ones). And, of critical importance, the biology might just not work at ameliorating a specific disease, improving mortality, or elevating quality of life – we often pick the wrong target to interrogate, which is a (the) major cause of attrition in Phase 2 and beyond. To make it more wonderful in its challenges, variation amongst patients (and, even more so, species!) in how biology manifests also leads to added complexity, both good (insightful) and bad (unfortunate). In fairness, even when drugs are approved, we don’t know everything about it.

Biology also drives the costs and timelines in drug R&D, as well as many of the headaches – as it still takes a lot of sweat, time, and money to figure all that biology out. Bespoke cell-based assays tackling new biology can be hard to source or create/validate, and take time. Further, much of research-stage biology is like pregnancy, as even if you throw more money and people at it you can’t speed it up significantly. Formal IND-enabling studies just take time; a 28-day GLP tox study takes months from start to final report no matter how much you are willing to pay for it. Long term mouse efficacy models are by definition long (months). Phase 1 healthy volunteer studies, like single- and multiple-ascending dose studies, are also hard to speed up if you need to ensure safety between doses as you explore new human biology.

Importantly, better chemical equity can help address this biology more expeditiously. It’s well appreciated that the best predictor of success in drug discovery is the quality of the starting chemical equity (the “hits”). Initiating a hit-to-lead or lead optimization project with a big ugly molecule and hoping to shrink it or dial out its liabilities isn’t usually a high probability gamble. CADD can often support getting to or selecting “better” starting points for these drug discovery efforts. Yet even with good chemical equity, it still takes time, money, and the contributions of lots of thoughtful scientists to advance these successfully. Many “good” molecules have broken on the anvil of biology.

It’s worth highlighting a few examples about biology defeating or obstructing CADD-inspired discovery, though the list of programs could be very very long. T-cell kinase ZAP70 has been attacked by CADD since mid 1990s (here), and yet there are no approved drugs against it. MAPK/p38 is another well-trodden CADD target: dozens of publications out there about CADD success stories against p38 with new and improved binders and the like; yet, clinical development is a veritable graveyard for these programs, as figuring out the safe and effective biology of these projects remains a challenge. Or take renin inhibition – after years of great CADD-enabled discovery, the first program got approved but only to find out in subsequent Phase III that drug development wasn’t kind (see #16 in the FDA’s recent roster of failures).

With CADD, predicting affinities easy, but the “answer” in drug discovery isn’t just a protein and ligand with predicted binding affinities or specific activities – it’s making a drug that can go all the way to patients on the market.

Beyond just biology consuming everything (which isn’t yet nor soon to be model-able), the wonderfully powerful “big” computing approaches like AI, machine learning, neural networks, and such technologies all suffer from a range of technology issues when applied to the drug discovery challenge today: perennial “garbage in, garbage out” concerns around the “training” data (even the large sets are full of noise) and how these relate to and capture the complexity of biology; black box algorithms that are hard to understand or deconvolute (a common problem with learning methods right now, as described here) as veins of future biological exploration; and, a dominating model-first, “under-the-lamppost” myopia from CADD practitioners that often misses the value of empiricism and serendipity in science. This often shows up as “we shouldn’t make those compounds [or tackle that target] because the model says they won’t work…” (That’s precisely why they should be made – to continually test the model!). Melanie Senior has an upcoming In Vivo article coming out in early May worth reading.

All of these things, coupled with the vast wonderful complexity of biology, give me great confidence in saying that CADD won’t be mouse-clicking its way to drugs in the absence of any lab coats any time soon.

Three final observations.

CADD delivers the best when its applied in an integrated manner with drug discovery teams.  CADD can’t contribute without being a key member of the team, but similarly it can’t do it all by itself. Integrating high quality CADD approaches into teams of seasoned drug hunters who know when and how to apply the in silico insights (or ignore them) is critical. Assembling high performing teams that include experienced veterans in R&D is critical to creating value, and that includes both chemists and biologists – greybeard chemists who have sniffed solvents for decades, and biologists who actually poured their own sequencing gels. It may come as a surprise to Silicon Valley, but most of these drug hunters have real PhDs (and don’t lie about them) or MDs, or both, plus years if not decades of R&D experience. The judgement, insight, and scar-tissue they bring to bear on drug discovery programs is of real value. Sure, their “acquired smarts” involve extensive pattern recognition, which in theory can be replicated by computer code and AI, but it also involves creativity, putting programs in position for positive serendipity, understanding what fork on the road to take, and how to extract the “right” insight from empiricism rather than simply chasing patterns. Silicon Valley’s love affair with both youth and hyped-optimism often ignores this, but does so at its own peril.

Nimbus’ Acetyl-CoA carboxylase (ACC) program, which Gilead just shared exciting Phase 2 data on, exemplified this experienced team model. Rosana Kapeller, Nimbus’ CSO, built and led a great scientific team, including the ACC program leader Gerry Harriman and early development lead Wes Westlin, among many others. External consultants like Jim Harwood, an experienced veteran from Pfizer on the ACC target itself, as well as a broad range of other collaborators were engaged. This scientific team collaborated closely with our founding partners at Schrödinger. We conducted a virtual screen of the ACC binding pocket, informed by a natural ligand and solvation energetics, and identified a singleton hit that led to an exciting series. Optimizing that series involved significant integration of in silico structural insights, but also a vast set of non-CADD activity inputs like cell-based assay data, efficacy, PK/AMDE, and tox screening that informed our modification/optimization choices. To drive liver uptake, an important and defining feature to the program today, moieties were designed by Gerry and others to facilitate its active transport into liver, largely without computational insight. Undoubtedly the broader CADD inputs were helpful in driving this program, but like most successful drug discovery campaigns there were lots of non-CADD elements contributing in major ways to its success (including the very innovative translational approach and fructose challenge model in healthy volunteers). The summary message is that even for a CADD-based company like Nimbus, successful drug discovery isn’t yet hackable by computers alone.

An integrative CADD approach also helps enable “virtual” biotech business models. Today’s ecosystem of globally-distributed CROs and research partners also enables companies to apply “the best of CADD” without having to build a ton of infrastructure. We’ve been zealots for “virtual” biotech without their own wet labs for years (here, here, here) – about half of our portfolio fits this description. In some ways, the “cloud biology” concept espoused of late in Silicon Valley is a riff off this same theme – virtual, distributed biotech models where experiments get done remotely. A big difference in practice, though, is that lab coats still exist in the ecosystem for the vast majority of assays and activities in state-of-the-art virtual biotech models today. High quality partners like Charles River Labs, Evotec, ChemPartner, Wuxi, and many others are key to this model. Lab coats (and scientists in them) are especially required in areas of novel science where bespoke assays or unique approaches need to be developed, validated, and expanded; in fact, hybrid models are likely to be the solution for many of those new areas of biology (see Mike Gilman’s post on the subject). Laboratory science may have gone increasingly virtual – as in distributed remotely – but it’s not yet fully automated for robots and AI just yet. Maybe work at firms like Transcriptic and Emerald Cloud Labs will change that over time, but it’s not likely broadly applicable in the near term. I’m guessing my kids (13, 12, and 9 years old) will still be able to find a career with a lab coat if they wanted, but maybe my grandkids won’t. You can be sure the future scientists of my kids’ age will need to appreciate how to apply the best CADD approaches if they are to be discovering the drugs of the future.

Putting science-first with CADD, rather than hype, is key to delivering the expected returns to patients and shareholders.  As noted above, overhyped CADD solutions over the past four decades have damaged the credibility of the field. Promises of huge savings in time and money back in 1981 have failed to be delivered by CADD, in general, to date. Silicon Valley may thrive on the hype-cycle, which is clearly at work today in digital healthcare, but at the end of the day in biotech the real and only currency of long-term value is new medicines. The ever-improving CADD technology suite will continue to contribute to that currency, but we need to avoid the over-promising of the past. Silicon Valley thinks they’ve got the solution and with a flick of the switch will cure Pharma R&D of its productivity troubles. That’s not going to happen. But if lots of money chases the premise that software-eats-biotech and a proliferation of startups ensues, what will happen is lots of folks will lose lots of money. Instead of blaming biotech for those future woes, I hope they will blame their software. Doubtful.

Don’t misinterpret this post, I’m a believer in CADD and the power of big data to drive positive contributions to drug discovery. But I’m also a realist, and an investor who needs to make a return on our investments within my lifetime – and in general returns correlate with discovering and developing high impact new medicines.

Unyielding biology remains the biggest obstacle to new drugs and remains enigmatic in many aspects. The two arms of empiricism, creativity and serendipity, still play critical roles in our quest to unravel human biology. Machines are more than welcome to join us in that quest, but let’s not forget the full constellation of experienced human contributors required to deliver those new medicines.


The Inescapable Gravity Of Biotech’s Key Clusters: The Great Consolidation Of Talent, Capital, & Returns

Posted March 21st, 2017 in Bioentrepreneurship, Biotech financing, Boston Cluster, Talent | 8 Comments

Two key geographic clusters dominate the biotech landscape today. These two areas, Boston and San Francisco, combine a unique blend of biomedical science, venture capital, entrepreneurial talent, risk-taking culture, and geographic density. Other regions have some or all of these elements, but not in the same magnitude or momentum that Boston and San Francisco have today – and the gap is just getting bigger.

Last year, GEN ranked Boston #1 and San Francisco #2 in their biotech clusters report (here). Others have covered these clusters and the rivalry between them (here), as the Economist did with “Clusterluck” earlier in 2016. But rather than draw distinctions between them, I’d like to focus on these key clusters relative to the rest of the biopharma ecosystem.


Relative to the US biotech scene, Europe is often viewed as a laggard in the biopharma space from an entrepreneurial perspective. Of course, there are notable exceptions – like Actelion’s incredible success – but, by and large, there’s limited funding, limited R&D-veteran entrepreneurship and risk-taking, and limited prospects for scaling companies. While there is great science across many world-class research institutions in Europe, the commercialization of their science into local startups and emerging biopharma companies remains a challenge.

Two relevant analyses were captured in the data-rich report from HBM Partners on the M&A environment which highlight the essence of this challenge from a venture perspective: US-based biotechs drove faster exits (here) with higher investment return multiples (here) than their EU counterparts.

These data are striking.  But how much of this outperformance was driven by the two key clusters in Massachusetts and San Francisco? (I’ll refer to them as the “key clusters” from now on). In an attempt to answer this, I’ve worked with Pitchbook to assemble some data on these two key clusters relative to the rest of the US, as well as Europe.

The quick conclusion is that the rest of the US should likely use the Euro as its biotech currency; outside the key clusters, the rest of the US biotech sector performs and looks a lot like the European ecosystem.

As shown in the charts below, examining either M&A or IPO events between 2013-2016, biopharma startups from the key clusters were roughly two years faster at the median to get to that exit outcome (left chart): 7-7.5 years to M&A or IPO versus 9-9.5 years.  The quartile ranges are shown as well.  Similar timeline differentials are evident in prior time periods over the past decade as well.

In terms of value, biopharma startups in the key clusters have outperformed both on the median upfront M&A and the median IPO pre-money valuations during 2013-2016 (right chart). The average dramatically skews the distribution upwards in the key clusters (due to big outliers). Again, this differential in value also exists during other time windows of the past decade.

Holding periods and valuations at exit are reasonable but only directional proxies for investor returns. The actual returns depend on pricing of each of the venture rounds, as well as timing of stock sales, etc.  But it’s reasonable to assume based on these data that biotech returns in the two key clusters have outperformed other regions.

Why is differential in performance happening?

Biotech historians in the future might call it the “Great Consolidation of Talent and Capital.” While Silicon Valley quickly emerged in Tech a few decades ago as the nexus of all things IT and venture capital, in biotech it’s been far more geographically egalitarian in the past.  San Francisco and Boston were clearly important leaders in the early decades of the field, but so were other biotech clusters: Seattle, San Diego, Raleigh, Philly/NJ, Colorado, etc…  Most of these also had legacy Pharma or big Biotech footprints that were important for cultivation of talent.  And great firms grew out of places well beyond Boston and San Francisco: early winners like Immunex, IDEC, Centacor, Medimmune, and Celltech, just to name a few – and firms like Celgene (San Deigo, NJ) and Amgen (Thousand Oaks) – all born in other geographies.

In recent years, this has changed – Boston and San Francisco are now the preeminent biotech clusters.  And their gravity in the ecosystem is only getting stronger.

Beyond having great science and the right “pixie dust” in the local environment, two fundamentally important ingredients to the success of any cluster are capital and talent – and both are aggregating into the two key clusters.


Over the past decade, startups in the key clusters have been consuming ever-greater portions of the global biotech venture capital pie.

As shown in the chart below, since 2012, these two key clusters have increased their share by more than 50%, now securing nearly half of the global venture capital funding budget for biotech. In the US alone, depending on the data source, the two clusters are now receiving 60-70% of the country’s venture pie. Both the rest of the US and the EU have shrunk on a relative basis as a response.  The percent change in absolute dollars reflects this – nearly 130% increase in five years into the two key clusters, with the other regions relatively flat or down. Looking back to 2007, most of the change has occurred in the past five years – the distribution of capital across geographies were nearly the same between 2007-2012.

On top of the flow of venture capital, other funding sources are also consolidating into these geographies. Take NIH funding, for instance: California and Massachusetts rank first and second in terms of total NIH funding to its institutions.  And Massachusetts ranks a far-and-away first with regards to NIH funding per capita, nearly 3x higher than most other strong states (like CA, NY, PA, NJ, etc). Five of the top six NIH-funded independent research hospitals are in the Boston area (here).  Fund flows like these further contribute to the consolidation of biomedical activity into the key clusters.

However, it’s important to call attention to the disconnect between where scientific discoveries are made and where startups are formed. Many of the new companies that get created or launched in the two key clusters do not have scientific roots in those regions. As an example from our own portfolio here at Atlas Venture, despite nearly all of our startups being located/formed in Cambridge MA, the founding science is sourced from all over the globe: Unum came from Singapore, AvroBio from Toronto, Padlock from Florida, Quartet from EPFL in Switzerland, Delinia from San Francisco, etc… About a third of our new startups have roots or connections into Boston’s research institutions, a third with institutions across the rest of the country, and a third outside of the US. So the concentration of capital doesn’t mean scientific sources have shrunk; in fact, it’s increasingly clear to us that science competes on a global stage and we need to access the best substrate wherever it may be – but put the startup where it can take advantage of the benefits of a key biotech cluster.


It’s much harder to quantify the talent metrics, but one only has to see all the cranes in Cambridge MA to know that big things are happening here. Nearly every major biopharma company has a research footprint in the region.  Same goes for the Bay Area, though much more spread out around the region.

To get a sense for the consolidation of talent, here’s a chart that attempts to capture the change in biopharma R&D employment in the three geographic groupings.  The key clusters have seen R&D employment grow by 30% in past decade, versus shrinking in the other major biopharma states (like PA/NJ). Europe, according to their pharma trade group, is flat – though I suspect the metric is actually down in Pharma R&D organizations and up in the CRO R&D world.  As a macro point, these data reflect the intuitive sense we have of recruiting talent from other regions into Boston: with regards to R&D teams, prior Pharma hubs are shrinking rapidly while Boston is growing. We’ve even recruited a few sun-loving San Diego biopharma vets to move to the Boston market recently.

As this implies, for startup biotechs, larger biopharma companies are the lifeblood of the talent flows (prior blog on topic is here). Most of our early stage startups are led by teams with experiences inside of larger R&D organizations. The serial cycle of biotech entrepreneurship – starting companies, recruiting talent, discovering new medicines, and getting acquired (or going public) – is accelerated in high density clusters. The entrepreneurial diaspora enabled by biotech M&A is just much larger and more vibrant in these clusters (here). As shown by the data above, faster timelines, more acquisitions, and more R&D talent flows just add more and more water to the millrace, powering the wheel of the biotech mill churning out startups in these key clusters.

As these data suggest, the great consolidation of talent and capital into the two key clusters has clearly been happening in the past decade – and shows no signs of abating any time soon. While the prior return metrics of time and value are clearly lagging indicators of an ecosystem, as they reflect the value creation pace and trajectory in the past few years, metrics around talent and capital flows into startups will most definitely shape the future of these ecosystems.

So what are the implications of this consolidation for different stakeholders in the ecosystem?  Here are a few thoughts.

If you are a sector or civic leader in one of the key clusters, some simple advice: don’t get complacent. Make sure the local infrastructure doesn’t fail you at the most critical moment. Congestion, commutes, and chaos can only lead to an exodus over time of those interested in a better quality of life. Further, building capacity to grow will be important; scarce lab space has already driven rents in Cambridge to outlandish levels. We’re seeing startups begin to move back out to the Rt 128 corridor. While still part of the greater Boston ecosystem, those locations are less hyper-connected to the benefits of the density and proximity of the Kendall biotech scene. Lastly, make sure you keep importing ideas and talent into the cluster from around the world or you risk becoming insular; ideas are global, talent is mobile – so keep focusing on bringing them into the region.

If you aren’t in a cluster today, different stakeholders might think about this challenge differently. A few themes:

  • Can’t beat ‘em, join ‘em. One school of thought would suggest that rather than fighting the consolidation, work to benefit from it.  If you are an academic investigator interested in founding a new company, and you want to maximize its chances of success, you might consider putting the startup in a key cluster and create virtual links back to your lab elsewhere in the US or Europe (we’ve done this successfully many times). You’ll need connections into those clusters, but some creative use of email and social media can usually get you that. If you are a tech transfer executive, you might also work to build connectivity with venture creation firms in the key clusters.
  • Give them an offer they can’t refuse. The alternative for economic development minded folks in other regions is to enable your local startups with “extra” advantages. Provide R&D credits or funding, like Texas did with CPRIT grants – these significant funding infusions lower the cost-of-capital for young startups and let them progress with less equity investment (since its less available outside of clusters). Unclear whether these are sustainable in the long run, but they could help prime the pump. It’s also important to cultivate more anchor tenants to remain or build in the region; easier said than done, but the revolving door of talent from larger biopharma into smaller companies is a crucial component to a successful cluster. Along those lines, facilitating the return of your regions biopharma diaspora could help; many exec’s in the key clusters hail from other parts of the country or world, and some wouldn’t mind returning “home” like former California investor JD Vance recently did with Ohio. I’m sure tax deductions to successful returnees would spur lots of interest. At the end of the day, in the face of this ongoing consolidation, regions need to figure out how to give their startups a leg up to compete without a major disadvantage.
  • Be the big fish in the small pond. This is a riff off the age-old contrarian investor thesis. Much like biotech has been a recent contrarian bet in venture capital amongst many LP’s, creating or investing in startups outside the key clusters could offer some advantages – fewer competitors for the regions talent and capital, even if scarce, means that you could attract more of them. And there’s less threat of losing talent if there’s few other places for them to go. Further, operating costs are lower – both people (salaries) and fixed costs (rents, etc) are often far less expensive outside the clusters. Most of these other regions remain under-appreciated, and a local champion might get a reasonable cut of the “best” ideas in the smaller pond.  But you’ll need to figure out how to evolve these businesses into global competitors over time – and one idea is to have a satellite office inside one of the major clusters to access the talent and capital advantages of those markets (which we’ve done with numerous startups in the recent past, like here).

The current trends around capital and talent flows strongly suggest that Boston and the Bay Area will be the preeminent biotech clusters for the foreseeable future – and the global biotech startup scene needs to figure out how to adapt to that reality.