Archives For January 2019

A Forward-Looking Statement

January 12, 2019

Talking about the future with assurance takes faith. For those in a business setting it is often preceded with carefully crafted legalese, so-called safe harbor or forward-looking statements, that seek to come clean on just how uncertain, about the future, we truly are. Yet a future unimagined is a future unrealized. It is our power to predict, or at least the desire to do so, that sets humans apart.

A collective peek into the future of healthcare is attempted each year at the JP Morgan (JPM) Conference and this year’s January gathering was no different. Apropos to the fragility of predictions, it (thankfully) rained much less than had been forecast. None the less, all the conventionally well-prepared biotech executives had their, just in case, umbrellas at the ready.

This year at JPM I had the privilege to guide a discussion at the WuXi Global Healthcare Conference (v-restream begins at 148 minutes) on the role that advanced artificial intelligence (AI) and machine learning (ML) are hoping to play in healthcare. It was fascinating.

The conference had begun with R&D leaders expressing fundamental concerns about the implied limits, derived from algorithm training bias, of AI/ML in Rx discovery. To unpack this in greater detail our conversation focused initially on the key raw material “data”. Swiftly moving beyond the generalities of “big data”, into a focused discussion on what is commonly missing in opportunistic datasets. On the fact, that all too often the “available big data” are neither of sufficient quality nor in an optimal format to generate insightful AI or ML derived observations – and how when we elect to make data compromises we do so at great risk. Because the same supra-human abilities which enable AI/ML algorithms to detect subtle signal in large multi-dimensional datasets makes them equally (and hauntingly) good at detecting, then erroneously focusing on, artifactual noise.

Even in relatively mature fields, like population genomics, we are only just beginning to collect the right data needed to enable scaled AI/ML derived insights. The same for preclinical, clinical and even behavioral data. Building scale, quality, and continuity between carefully collected datasets has essentially just begun.

In addition to recent attention on improved data collection, rapid progress is just beginning on the analytical side. Some of which are derived from customized hardware that process image-based data with greater and greater ease. Software and algorithms, particularly in deep learning approaches, are now progressing rapidly too – with new benchmarks being eclipsed daily.

Yet the raison d’etre of the session was on value creation. On the possibility that AI/ML applications could enable smarter drug development, perhaps making R&D processes faster or cheaper and on ways in which real-world evidence could cross-correlate additional behavioral, contextual, and treatment-related insights – collectively leading to even more value-advantaged medical solutions.

Yet, our starting point is somewhat grim. Even after decades of trying (without any AI/ML help), we have little to demonstrate that developing innovative medical solutions is getting any faster nor have we reduced our rate of failure. In fact, the data would suggest, that on an ROI basis, as an industry, we are getting swiftly worse.

Can these AI/ML tools help? It is too early to be certain, but the trends are encouraging. As with many new tools, much trial and error await. Some will seek to use these methods with the wrong data and follow inferred fantasies to false lands. Others will seek to use them in model settings for which we have insufficient correlation and causation linkage. But transformative progress seems inevitable. Using built-for-purpose datasets, filling the air gaps between those data to facilitate longitudinal forecasting, then integrating -omics, personal context, and individual behavior data with long term intervention or prevention settings are when and where the first big contributions await. The ability to eliminate costs (and steps) associated with “business as usual”, will require not just technology advances but ecosystem changes involving regulators, policy, etc. But these too are likely as more and more evidence of value accrues from platforms built on sound datasets and validat(able) models.

Predicting the future is a fool’s bargain. But with some assurance (i.e. faith), one has to imagine that the healthcare contributions of AI and ML will (eventually) be material. And beyond a blind faith in technology, we will need equal attention on insightful ethics, regulation, and policy. Data ownership, dynamic consent, privacy, security, and data use remuneration all will prove to be as central as any chipset or AL/ML algorithm. For the world that we seek to serve, will expect that our industry understands their needs and personally defined interests.  This near-future will demand that we earn, honor and respect the profound trust bestowed on those who work with the personal health and wellness data-diaries of the world. This is a setting in which the Hippocratic oath primum non nocere (first, do no harm) must always remain front and center.

JPM at 20

January 6, 2019

Habits are useful things. Behavioral patterns that are etched into our brains and upon which we rely; they are the autopilots of our lives. Habits increase the efficiency of our mind, freeing up the neural bandwidth needed to capture and process the new. For many of us, heading off to Union Square in early January is a deeply embedded ritual. A habit that has become as mindless as tying our shoes.

For those arriving from a distance, JPM will include a flight filled with well-known faces. Those for whom San Francisco is home will experience a temporary invasion of overdressed, swift footed, wing-tipped (or some, but still way too few high-heeled) biotech immigrants. A flash mob that will bring with them commensurate surge pricing on everything imaginable. While this January habit simplifies our preparation, with its ease, in seeps complacency. Expectations of the similar narrow our mental aperture and bring with them the risk of overlooking the new.

As we gather our things, board our planes, trains and surge priced Ubers to rejoin as a community for the 37th J.P. Morgan 2019 Healthcare Conference, this year could be one to reboot our expectations: to discard a little of the known and in so doing make space for the unexpected.

Over the last several Decembers, I’ve habitually reminded myself of key milestones that the biotech industry has traversed since the first JPM or, as it was previously known, H&Q. Perhaps you will also find these historical stepping stones intriguing.

JPM at the beginning

JPM at 5

JPM at 10

JPM at 15

This year our Flashback takes us to 2001, when the conference would reconvene for its 20th consecutive year. 2001 was to be the second year of the new millennium: a year that had moved past the threatened calamities of the Y2K “divide by zero disaster” but not the bubble that had preceded it. 2000 was the year that ended the first irrational chapter of internet/tech investing, washing way billions of dollars invested businesses. Biotech had ridden this market wave as well, ending 2000 with the largest number of biotech IPOs (56), breaking the previous record of 47 in 1996. By the end of 2001, only six new biotech companies had found a home on public markets. Independent of this market malaise, several new transformative medicines were approved in 2001 such as Gleevec (approved in just 2 months by the FDA), Remicade, Tenofovir, all three of which remain mainstay treatments today. Science charged on as well. Just eleven years after its start, and 4 years before the originally stated completion date, the first draft human genome sequence was released in dual publications, one in Nature from the 20 institutions publicly funded consortium led by Eric Lander alongside one in Science generated by the for-profit team led by Craig Venter’s Celera Genomics.

Only during a few days of our lives do we witness the historical. These days we vividly remember where we were, who we were with, and maybe even how we were dressed. In 2001, one such day began for my team with an early breakfast. At a hotel in San Francisco, with investment bankers we watched in shock as United Airlines Flight 11 plunged into the World Trade Center north tower. Seventeen minutes later, with black smoke billowing from the first impact, UA 175 vanished into the sister tower. About 50 minutes later the second tower to be hit collapsed, followed soon by the other. More than 3,200 innocent lives were lost. A black swan day that completely overturned our agenda. Those meetings, nor the subsequent offering would ever happen. Carefully planned business milestones were rightly overshadowed by the tragic loss of life. Short on cash in a paralyzed market, we pivoted and sold that company to another in need of late-stage assets. Lesson learned and memories etched, the team moved on.

But even before the fateful events of 9/11, the most serious global economic slowdown in 20 years had begun. The U.S. economy had entered into a recession, sending the European Union into a sharp economic contraction. The Asian economy, which had been slowly recovering from the 1996 financial crisis, also slowed its pace of growth. The Latin American economy was staggering with political disturbances beginning in Argentina. The world and its markets seemed fragile and rightly so – as true then as it is today.

So, as we head off to JPM, let’s reflect on the year just completed. In 2018, over 70 biotech companies have gone public on global exchanges, raising a cumulative $8.3 billion. This eclipses the previous peak in 2014, during which 88 biotechs raised $6.3 billion in IPOs. On average, 2018’s biotech IPOs each raised more than $116 million, with a median raise of $98 million. In 2014, those figures were $73 and $58 million, respectively. According to R.W. Baird, biotech investors also poured $23 billion into 210 follow-on offerings during the first 11 months of 2018. That said, 30 of the US-based 2018 IPO class are now trading below their opening price and the NASDAQ Biotechnology Index ended the year with a loss of 14%. iShares Nasdaq Biotechnology ETF, which is benchmarked to the NBI, ended at a loss of 13%. These numbers stand in context to the broader markets where the Dow Jones Industrial Average lost 7.1%, the S&P 500 shed 4.5% (including dividends), and the NASDAQ Composite ended down 4.8%.

Back in healthcare, 59 new medicines were approved by the FDA this year, compared to 46, 22, 45 in 2017, 2016 and 2015 respectively. These 2018 approvals are an impressive collection of novel and some perhaps even curative treatments. As an industry, we have much to be proud of in 2018, yet much left to do.

In now what has become perhaps the most impressive JPM habit, our industry’s innovators are certain again this year to report on even more stunning advances. New treatments for diseases for which not long ago patients had no hope. Yet we must aspire for more. We must embrace the increasingly grave diagnosis of the global healthcare system itself. The innovations we invent save lives. Yet they are extremely expensive to create. Their resulting price must both sustainably support the required investment as well as be linked to the validated value to the patients they have helped. As we have tirelessly worked to serve those with medical needs, we must now double down to reinvent the healthcare system on which we all equally depend. Bringing similar ingenuity to the table to more effectively demonstrate the utility and value of the solutions we provide.

See you there, and bring an umbrella!