Short-sighted financial strategy
Mundane, un-innovative science
The fear of taking big risks
A poorly-defined roadmap
Poorly-designed and managed clinical trials
Unlike the typical Silicon Valley startup that has a go-to-market strategy of just a few months, drug development takes years of intense cash burn to obtain regulatory approval. There is no sugar coating it. The statistics are bleak. Over 90% of pharmaceuticals that enter FDA clinical trials fail and the average cost of development is over $5B per drug (accounting for the costs of all the other failed attempts). These ridiculous figures can only be rivaled by the film industry’s remarkable rate of failure and cost of production. But like the film industry, it only takes a single blockbuster to wipe away all the sunk cost and make the early investors very, very happy. Counterintuitively, the key to success is rapid, iterative, and diligent failure. When embodying all three of these characteristics, failure loses its negative connotation and becomes the guiding light in an uncertain and obscure journey.
“Fail fast, fail often, fail forward”
Failure is the only way to optimize a process and ensure success on the next iteration. Failure does not have to incur a high cost – you can learn from the failures of others. This “diligent” failure relies on keen awareness of all the variables that go into the project and the ability to suspend emotion and analyze them objectively as the project develops. Fail fast. This process is scientific in nature, just like a researcher taking careful notes of his methods and results, willing to experiment by tweaking variables. Fail often. Below are common reasons why early-stage biotechnology companies fail and how to reduce those risks. Fail forward.
Forecasting R&D costs and cash flow is notoriously difficult for a biotech startup. Profitability is years in the making, and imminent clinical trial costs loom in the not-so-distant future . The predicted costs influence the amount of seed capital that will need to be raised to hit the pre-determined valuation inflection point/milestone (such as entering Phase 1 clinical trials). It is in the best interest of the founders and early shareholders to raise as little dilutive capital as possible (i.e. through the issuance of stock, stock options, or convertible debt) in the early days when the company’s valuation is low, and raise additional capital once the company’s value has risen, predicated on research and development and clinical trial success. Careful thought must be given to unexpected expenses and delays that could arise which could necessitate additional capital that renders the company cash-poor depending on their early fundraising strategy. It is good practice to raise more than you think you will need to hedge against these unpredictable events, but many founders are unwilling to do so in an attempt to hold on to their equity stake. Mistake #1.
Poor forecasting can be attributed to poor assumptions about the drug development process, such as R&D and clinical trial costs and timelines. Managers often assume that the resources invested in each stage of FDA clinical trials are independent, when in fact failure in early clinical trials can actually improve the probability of success (and reduce the cost and time) in later phases and reduce the overall cost of development (Remember, fail fast. The sunk cost at later stages can kill a company). Projects are often mistakenly viewed as “successes” or “failures”, when in fact value can be extracted from early failures and used to inform future experiments (e.g., perhaps a different route of administration or dose of the drug?). This iterative development model allows for the collection valuable data to improve the likelihood of success in future clinical trials. Obviously, this entire approach is predicated on the accurate documentation of time, costs, and failure rates of drug development projects. A wealth of data exists in the FDA clinical trial database and in the cybersphere. The intelligent manager does not rely solely on his individual failures, but also extracts information from the failures of others .
Failure is costly. There is no way around it. According to the Tufts Center for the Study of Drug Development, the average cost of reaching market approval for a single drug is ~$2.6B. There are many drivers of this enormous cost, mostly due to the large capital outlay needed to get each drug development project started ($1.4B of the $2.6B). The rest of the costs can be attributed to the cost of capital, or the opportunity cost of allocating resources to a failed project when those funds could have experienced a return on their investment somewhere else .
“Drug developers are taking action to rein in rising development costs, including increasing efforts to discover, validate, and use biomarkers, adopting new approaches to patient recruitment and retention, and implementing leading-edge project management practices, but they face strong headwinds, given the complexity of the problems they’re addressing.”
Innovative, disruptive science is the first criterion that must be considered when valuing a clinical-stage biotech company. If the science is not exciting enough, then it is probably predicated on some known therapeutic mechanism that another company already developed. Riding the coat-tails is a sure-fire way to produce a drug product that will never see commercial success (if it even makes it through clinical trials) The experimental drug asset must address a high unmet need in a unique and superior way to available treatments, even if that means segmenting the disease into subtypes and indicating the drug for a patient subset that may be more receptive to treatment (for example, dividing a cancer patient population into groups based upon the presence of a specific biomarker, and testing the efficacy of the drug within each subset individually instead of grouping all of the patients together). While this decreases the addressable market, it may increase the likelihood of successful clinical trials and commercial viability. It is better to be known as the drug that “reduced mortality in HER-2 positive metastatic breast cancer in women under the age of 65 by 75%” than the drug that “showed 5% improvement in tumor reduction in all breast cancer patients when combined with the standard of care chemotherapy”.
“Make sure the technology addresses a high unmet need, with a first mover advantage that allows enough time for potential project holds against direct and indirect competitors.”
The degree of innovation is relative and based upon the current standard of care and emerging technologies in the disease area of interest. Fierce competition is what drives the rapid pace of technologic advancement. A strong tell that your science is truly innovative is if the biotech community extremely critical of it (not openly, of course, but behind your back at industry conferences!). The smart founder/manager identifies the most hostile companies and brainstorms possibly synergies. Counterintuitive… but why fight when you can ally? Competitors are potential strategic partners and licensees of your technology. A partnership distributes risk and increases the probability of clinical trial success by combining resources.
“If you don't fail, you're probably not really innovating.”
Innovators often struggle with being critical of their own science. Ideas are currency in the academic community, and the value of that currency is often based upon perception. When you spend decades researching a single protein or gene and develop a potential therapy based on that knowledge, you can be very reluctant to be open and honest about observed failures. Perhaps the protein is not as important to the growth of the cancer being studied, or the gene of interest isn’t actually expressed in that cancer subtype like it was assumed to be? It can be exceedingly difficult to let go, but it is critically important to evaluate the central biological hypothesis early on before capital is allocated and tremendous cost is sunk into the project. Sunk cost fallacy is notorious for poor decision making.
The successful innovator must have a willingness to experiment but must be highly disciplined . One method to increase objective observation and openness to criticism is to launch multiple drug development projects in parallel (referred to as a “drug development pipeline”), distributing the risk and making an individual failure sting less. Prioritization of experiments is critical to allocating capital efficiently, and once chosen, tangible goals and milestones must be well-delineated. Free-range “science projects” are certain death for the clinical-stage biotech company. Scientists, after all, love to do science, and the bigger picture of bringing the experimental drug to market is often lost amongst researchers .
“It’s in our personalities to try and preserve a company or any other endeavor we’ve worked on, you see it every day. But it’s the false positives that cost the industry so much money and act as a drag on finance and opportunity”.
Investing your time, money, and soul into biotech research and development is a sublime exercise in risk management. Founders of early-stage, pre-revenue life sciences companies are the unsung heroes of the medical field. No one hears about them because by the time their drug candidate(s) are noteworthy they have been acquired by Big Pharma for further development and commercialization. Glance at the PR Newswire and you will get a glimpse of what goes on behind the curtain. Bold decisions must be made to get to the point of a strategic partnership with or acquisition by a large pharmaceutical company. These decisions must be clear-cut and stood-by until their outcome can be assessed (a difficult exercise, as the results of executive decisions in this industry can be 5-10 years later). Failure is likely, but temerity is a requisite for grand success.
“Success is rare and hence, almost by definition, it comes from outliers who make outlier-type decisions. Failure lurks when the modus operandi of a team is to settle on a compromise rather than making bold choices. Regression to the mean is the enemy of success.”
The habit of risk-averse decision making must be avoided at all costs. The culture of the company must have a tolerance for failure but no tolerance for incompetence. Wild ideas should be discussed and seriously entertained. Consensus can kill the outliers, so special diligence should be given to the most “out-there” ideas . The key to maintaining a risk-seeking strategy but avoiding pitfalls is to establish a risk-mitigation plan. Such a plan may include third-party evaluation of the core of the science before further investment, intermediate steps to assess the mechanism of action of the drug and predict efficacy in patient populations, multiple pre-clinical studies to determine the pharmacokinetics and safety of the drug before attempting human clinical trials, regular re-evaluation of the path of development and goals/milestones as the project develops, the use of Big Data and machine learning to predict the outcomes of clinical trials before conducting them , frequent communication with the FDA in the form of pre-IND meetings, and maintaining relationships with VCs and early investors.
Investing in a startup biotech company is investing in a dream – a dream to discover, innovate, and eradicate the scourges and plagues on human health. Dreams are intangible and amorphous, but the people conceiving them are very real. The value of a dream is predicated on the probability that the dreamer can rip this nascent idea from the realm of consciousness and thrust it into the realm of possibility. The seasoned biotech investor knows that investing in early-stage therapies is really placing a bet on the company’s management and advisors. A strong, deep management team with the ability to recruit top talent is the single-most important factor when determining whether an opportunity is worth further consideration. Market size, barriers to entry, research and development milestones, regulatory hurdles, etc. are only relevant once the management has been thoroughly vetted.
Avoid people who operate with the process-focused approach of a big pharma or without the set of skills needed to operate in a dynamic biotech startup
It is easy to assess the competence of individual directors, officers, and advisors to the company. But strong teams feed off synergies between the members, complementing each other’s skillsets and bringing unique perspectives that can either jive or instill dissent. There is only one question that needs complete consensus amongst management: what is the path to exit? Regardless of how each individual conceives of the path to exit, the entire group must be on-board with the overall strategy to ROI (this applies to early-investor VCs too). Experienced advisors make the trajectory much more linear, avoiding expensive and time-consuming detours .
Hiring mistakes happen and people get placed in roles that they are woefully incompetent for. Sometimes these mistakes act as catalysts to allow the under-qualified to rise to the occasion and bring tremendous value to the company due to their naïve perspectives. The company’s barometer for performance must be calibrated to detect underperforming hires that are not seizing their moment to shine and address the issue early on. Cultivating a culture where hires are given the opportunity to make mistakes but demonstrate consistent improvement is critical to maintaining company morale and incentivizing honest discussion about expectations and performance. Unfortunately, many managers are so fearful of creating an overly “nice” culture that they send under-performers straight to the chopping block. The truth is that you will never find the perfect employee for the position at a reasonable price. Startup biotech companies have limited cash to pay multiple 6-figure salaries and rely on stock option issuance to align incentives and recruit top talent.
“The people who often do the best are the ones that are good at dealing with failure.”
Hierarchies are intrinsic to the human psyche and social organization - it is human nature to determine our standing in the group and identify authority figures to respect. In corporate culture, this top-down organization with rigidly defined roles and authority brings about a sense of comfort and duty among the ranks. But in a lean startup that relies heavily on collaboration individual accountability, this framework is extremely inefficient. Culturally “flat” organizations often outperform the traditional, hierarchical model because they place the expert knowledge to the forefront of decision making. This second-generation culture encourages individuals to discuss their “truth” with more senior members without fearing recourse for overstepping their role, allowing for open discussion and criticism of the top rank (much like a lab meeting in an academic setting). Can a laboratory assistant challenge the Chief Scientific Office? Is the CEO aware of the day-to-day work that goes on in the lab space? Culturally flat organizations can frame the ultimate goal from a 30,000-foot vantage point while also engaging at the microscopic level. This allows for both C-level executives to become well-versed in the scientific details of the drug discovery and development and researchers able to understand the corporate strategy and high-level decision making . A holistic understanding of key management activities (e.g., basic drug discovery, GMP drug development, clinical trial design, etc.) by everyone in the organization is a hallmark of a well-organized startup biotech company.
Lack of clear-cut milestones delineated far in advance of capital deployment is a common cause of failure. Running out of funding or delaying the drug development process through inefficiencies can spell disaster for an early-stage biotech company that has yet to prove the clinical efficacy of its drug candidate(s), reliant solely on investor dollars, and is far away from an exit opportunity. It is critically important that the large, early financiers (such as VCs) agree with the roadmap to minimize conflict and encourage further investment as the company experiences success. The company’s capital raising strategy must work in concert with their forecasted development goals; it behooves companies to raise enough money to comfortably hit their next valuation “inflection point” (such as completing Phase 1 clinical trials), allowing them raise capital at a higher valuation (i.e., less dilutive to early shareholders) to accomplish the next milestone (such as completing Phase 2 clinical trials). Forecasting should be subject to stress-testing – what happens if we fail to accomplish the next milestone? Will we have enough cash runway to pivot? This activity can be anxiety-inducing, but it cannot be avoided.
Human clinical trials fail for all sorts of reasons, some of which can be avoided. Failures can arise from biologic issues such as lack of efficacy and reporting adverse events that halt the clinical trial early. It is estimated that 57% of clinical trials that failed in Phase 3 were due to problems with the efficacy of the drug. Unfortunately, the lack of positive results may not be attributed to lack of therapeutic effect of the drug, but may actually be caused by a poorly designed, underpowered” clinical trial. Safety issues also become apparent in larger Phase 3 trials despite being absent in small Phase 1 and 2 studies. Hwang et al estimated that 17% of Phase 3 trials failed due to safety, even though no adverse events were reported in Phase 1 and 2 . It is important that safety be examined at each successive stage of clinical trials even if it is not the prime objective. The cost of failing at a later stage can be astronomical. Fail fast.
Non-biologic causes for failure can largely be avoided with appropriate planning and diligence Hwang et al estimated that 22% of failed Phase 3 trials were due to lack of funding. Phase 3 trials can cost upwards of $40,000 per patient and involves thousands of patients, so it is no wonder why the average cost of bringing a single drug to market is $5B when accounting for all of the sunk cost into the 99% of drugs that failed. Patient recruitment and eligibility is another important, non-biological consideration that must be planned well ahead of a study. Patients with too many comorbidities should be excluded from a study due to their increased likelihood of reporting safety issues that have nothing to do with the drug being tested. These patients may be forced to withdraw from the study early, forcing the company to extend the duration of the study and incur additional costs. However, overly-stringent inclusion criteria can make it difficult to recruit a large enough patient population to derive meaningful results, and delayed clinical trials due to overly narrow eligibility criteria can come at a high price . Keeping the patients from withdrawing from the study is critically important to ensuring that the sample sizes are large enough to determine statistical significance. Remuneration may help keeping patients engaged, but surveys indicate that being paid gives the patient the impression that the trial is high-risk. Overall, it is important to create a culture of accepted, mitigated risk and the opportunity for constant improvement regarding clinical trial design, with the end goal of optimizing the entire drug development process. Fail forward.
Failure can be seen through an optimistic lens. Provided that you were diligent and made bold decisions based upon core principles, the cause of failure can largely be circumstantial or, more informatively, point to a failure in your methodology. The latter is adaptable and critically important to growth. This “diligent failure” is a consequence of being bold, innovative, and competent. A good example of failing forward can be found in clinical trial design. Trial design is multifactorial and must be conceptualized using a framework. Variables include recruitment time, patient retention, patient eligibility criteri,a the endpoints and outcomes being measured, the size of the trial needed to generate statistically significant data, the meaningfulness of the endpoints and translatability into overall patient outcome (does that tumor biomarker actually indicate the presence or absence of the cancer?), and the schedule of patient observation/follow-up, etc. This is incredibly complex, but the investigator must start somewhere. The process begins with collecting as much data as possible and building out a conceptual framework that places weight on the aforementioned variables. The intelligent investigator does not have to learn from his mistakes alone… the advent of Big Data has given rise to predictive algorithms that can make sense out of massive datasets and lend insight into the trial optimization. With a well-described conceptual model of trial design developed, the investigator can begin the clinical trial knowing that any failures can be noted and used to revisit how the model was devised. This is an iterative learning process that is the same logic applied to artificial intelligence and adaptive learning algorithms. Failure can be a tool to be used to construct a structural framework reinforced by constant feedback. Wielded correctly, failure is nothing more than a step in the right direction. Fail fast. Fail often. Fail forward.
“Failure in biotech should be celebrated, especially good failures. Good failures are those where the right de-risking experiments were done and the team’s development strategy was sound but unfortunately there was an unforeseeable technical failure.”
 Hwang T.J., Carpenter D., Lauffenburger J.C., Wang B., Franklin J.M.,Kesselheim A.S. Failure of investigational drugs in late-stage clinical development and publication of trial results. JAMA Intern. Med.2016;176:1826–1833 [Link]
 Campbell M.K., Snowdon C., Francis D., Elbourne D., McDonald A.M., Knight R., Grant A. Recruitment to randomised trials: strategies for trial enrollment and participation study: the STEPS study. Health Technol. Assess. 2007;11:105. iii-ix [Link]
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