Pharmaceutical research and development (R&D) is a challenging niche in which experts must find a balance between financial revenue and people’s well-being. It’s not a secret that drug discovery is a complex and expensive process. On top of that, the field of biotechnology is expanding progressively and developing at a rapid pace, which forces researchers and venture capitalists to fight numerous obstacles (e.g., competition, risks, delays, and costs). There are many experimental unknowns and ethical regulations that apply to the industry, which adds more to its complexity. At the same time, medical research is a precise science: there are clear data, statistical models, and well-defined stages of research and clinical trials. This transparency can help scientists and sponsors collaborate and commercialize biotechnology with the sole purpose to improve healthcare practices.
Nevertheless, sometimes even great ideas are never shared with the world as scientists cannot market their discoveries and products. As a result, some life-saving medications and treatments may have been lost in piles of documentation and insufficient funding. Thus, how can a novel discovery make its way to being manufactured, commercialized, and adopted in healthcare? The answer is in the successful approach and implementation of the net present value (NPV) and its alternatives (“Net Present Value – NVP”).
Industries worldwide rely on forecast-based valuations and predictions (“NPV vs. rNPV”). The net present value is one of the main indicators of the profitability of a project, which, in fact, is a preferred model by many investors. In simple words, the net present value approach is based on the present value of cash inflows and outflows over time and accounts for risk by discount rates. A positive net present value of a project means that the earnings will exceed the costs, which will justify the actual investment. Note that the main assumption is that money received today is more valuable than money received tomorrow, as it can be invested and profitable now (Stewart et al., 2001). Thus, the net present value model aims to calculate what tomorrow’s cash flow would be worth now.
To calculate the net present value of novel discovery, experts can follow an established formula that considers various factors (e.g., the net cash inflow during a certain net cash flow, total initial investment costs, discount rate, and the number of time periods). If a biotech company wants to market a drug, firstly, they’ll estimate the future cash flows and discount those eventual cash flows into a present value amount. Then, they’ll explore predictions and figures further. Although the field of life science is a specific area, financial decisions and marketing strategies are alike in other industries. Therefore, let’s set another example, which will help us understand the net present value model in real life. Imagine you want to buy a shop, and you’ve made all the required calculations, cash flow predictions, and discount rates – which gives you a sum of $250,000. The owner, however, is willing to sell their property for $150,000, which for you presents a positive net present value investment. As a matter of fact, if the owner is willing to sell for $150,000, then, this is a $100,000 net gain for you. Note that the net gain is the intrinsic value of the purchase or the investment. In other words, in this scenario, this is a good deal for the buyer. It’s not surprising that biotech companies also aim for good deals.
When it comes to biotechnology and pharmaceutical companies, though, revenue and investments cannot always follow a clear formula and positive predictions. As explained above, researchers and bio entrepreneurs face numerous challenges – sadly, a drug may never reach the market. At the same time, drug development is often well-regulated, following defined phases and rules. This helps experts employ statistical analyses, which can estimate failures, probabilities, and success rates (“NPV vs. rNPV”). As a result, biotechnology companies often rely on risk-adjusted net present value (rNPV) instead of the standard net present value model. Note that the net present value and the risk-adjusted net present value may coincide only when risk is estimated and, of course, eliminated.
The risk-adjusted net present value model is a more realistic approach, which accounts for delays, risks, and revenues – during the early research and development stages and through the actual development (Stewart et al., 2001). Just like with any other industry, only a fair evaluation will help companies market and sell their products, which can only benefit society.
Relying on assumptions can lead to errors, so apart from investment costs, discount rates, and revenue – risks and variables should be estimated. Experts agree that the risk-adjusted net present value model is a more realistic approach to biotech projects than the net present value, which can help bio entrepreneurs market a product and save lives. Nevertheless, medical research is full of obstacles, risks, and delays: many projects are limited to Phase I, data is highly secured, time frames are unclear, and post-discovery is rarely discussed. In fact, time is crucial. For instance, since any Phase III may last from three to five years, four years is a good frame for the risk-adjusted net present value model to be effective (Svennebring & Wikberg, 2013).
What’s more, researchers and sponsors have started to apply various alternatives based on mathematical models, which can help them explore all the potential risks and benefits of a new product and its success rates. Some suggestions are to use numerous mathematical formulas, e.g., apply different probability rates during different research stages or analyze more than one compound for development (Svennebring & Wikberg, 2013). Apart from the risk-adjusted net present values, the payback method is another popular metric. However, the payback method doesn’t account for the time value of money, which means it cannot be applied to long-term clinical trials. The internal rate of return, on the other hand, is another financial alternative, which can be employed in biotechnology projects because it’s based on annual calculations and comparisons (“Net Present Value – NPV”). Unfortunately, many parties do not utilize proper methods and rely on unrealistic models and expectations, which leads to low quality, high prices, and poor healthcare practices. Sadly enough, revenue is the main motivator: mainly start-ups risk and invest in early projects, while big companies rely on safety.
The recent tendency for big pharma to invest in established products, which have passed the early stages of development, might be profitable but not progressive. What’s more, many standard risk-adjusted net present value models are way too optimistic. Thus, the stringent net present value approach (rpNVP) is a more effective model, which reflects the dynamics of large portfolios and leading pharmaceutical companies and investors (“Risk-adjusted NPV is Notoriously Fallible,” 2015).
To support long-term projects and rare diseases, experts can integrate Monte Carlo simulations to access factors, such as price, peak market share, accessible market matter, and most of all, various research and development budgets. The model is used to assess the probability of different outcomes and random variables in order to understand the impact of risk and uncertainty. Applying Monte Carlo simulations to the risk-adjusted net present value model gives numerous outcomes and probability distributions (incl. histogram plots and Tornado plots), which will help researchers and investors in the field of life sciences collaborate and support each other. In fact, the Monte Carlo method is a systematic analysis that is sensitive to multiple parameters and which is beneficial in the analysis of uncertainties – factors that can make this model a leading technique in the field of biotechnology.
Since healthcare is a complex process, embracing the whole picture and presenting realistic models can only benefit healthcare. Let’s say that a rare disease, such as non-small cell lung cancer, has been targeted. Then, novel treatments should be identified – which is the process of drug discovery. To be more precise, drug discovery starts with the target being identified and ends with the beginning of Phase I. After that, research focuses on drug development and optimizing the compounds and their properties (e.g., absorption, distribution, metabolism, elimination, and toxicology – ADMET). When compounds are synthesized effectively and can act as a novel drugs, research can continue, in both animals and humans. In addition, predictive models can support researchers in the further synthesis of compounds (Svennebring & Wikberg, 2013). As a matter of fact, drug manufacturing doesn’t stop there – aspects, such as post-discovery and marketing can determine if a drug will reach patients and become a leading product.
Therefore, when it comes to funding decisions, marketers and big pharmaceutical companies should realize that all four stages of research (Phases I, II, III, and IV) are worth funding as they can pay off. Revenue can be achieved only through applying effective net present value models and/or their alternatives, which target all aspects, risks, and outcomes of research.
To understand the problem of investment in the niche of biotechnology, let’s set an example that can help us put new life into financial formulas and net present value models. Imagine that a research organization has made a discovery that can help many people struggling with asthma and that could be worth millions. Their next step is clear: they’ll have to convince sponsors to invest in their research by utilizing a precise model (such as the risk-adjusted net present value model) and by considering various aspects (e.g., scientific progress, good management practices, and intellectual property). In fact, a beneficial suggestion is to start with the evaluation of the final step of the funding process – the royalty paid from the estimated annual revenue of the new medicine (Stewart et al., 2001.). In the scenario presented by Stewart and his colleagues, the annual market for asthma is approximately $5.8 billion. Nevertheless, since the competition in the sector is high, the share of the new drug will be around 5%; in other words, the annual gross return will be $290 million. Note that 60% usually goes to manufacturing and marketing companies, 5% is the royalty for the organization that discovered the drug, and 35% is to the biotechnology company that develops the drug. So, let’s say that a patent expert claims the new medicine will beat the competition in the field for the next 18 years. Still, companies need to consider the fact that it takes around eight years to conduct clinical trials and get the drug approved by the regulatory bodies. Consequently, it means that the potential payoff for the biotechnology company will be around 10 years (18 years protected from competition minus 8 years needed for research gives us ten years of revenue). The annual revenue is, as mentioned above, 35%, which in our example equals $100 million. In the end, the final payoff is $1 billion (10 years times $100 million each year gives us $1 billion of total revenue).
This scenario is way too optimistic, though. It’s not a secret that often biotechnology companies and investors have no sufficient revenue. Factors, such as costs, risks, and time, can interfere with the presented models and expected earnings. Clinical trials may require additional costs and filings; they also are subject to many errors and risks. In fact, a study may never reach Phase III of a clinical trial and some drugs may never reach the market. Thus, risk-adjusted costs need to be considered. Time is also a major risk for success or failure. As explained above, money received today is more valuable than money received tomorrow. Note that the amount tomorrow’s money may lose in value annually is the so-called discount rate. Since clinical trials are time-consuming, discount rates can significantly affect investments and biotech companies. When we apply all these factors to the example given by Stewart and colleagues, the new drug designed to treat asthma has a lower value, of around $18 million. These figures are more realistic and can be helpful in any investment decisions (e.g., if a company will pay milestones or a royalty on sales).
There’s no doubt that discount rates are an influential factor. Yet, while precise models can help experts evaluate risks and promote their products, many big pharma and biotech companies admit that teams often base their decisions on uncertainty. A survey, based on interviews with 44 CEOs in the field, revealed that 21% used simple cost-plus models and 12% made a guess (Svennebring & Wikberg, 2001). Sadly, in a field where real lives and patients’ well-being may suffer, uncertainty is unacceptable.
Therefore, apart from the discounted cash flow model used to overcome obstacles, there are various practical options, which a company may employ to become more flexible and successful (“Basics of Valuation”)
We should mention that in the field of life science, the option to abandon is quite common. However, the dynamics in the field are way too complicated, and it’s not easy to terminate a project. In the end, revenue is not the main goal but improved healthcare and patients’ well-being.
Financial decisions should be based on clear formulas, realistic expectations, and transparent predictions. The net present value model is a common approach, which helps sponsors decide if an investment will be successful. Note that a successful investment means that earnings will succeed costs and a dollar today is more valuable than a dollar tomorrow. At the same time, drug research and development are challenging fields; clinical trials are prone to errors, risks, delays, and unexpected costs. Thus, biotech companies are encouraged to use more sophisticated models, such as risk-adjusted net present value, in order to predict risks and additional expenditures. Note that there are alternative models which suggest using different predictive values for the different stages of research and for the different compounds of a novel drug. In fact, the Monte Carlo simulations model offers a complete understanding of the process and its success rates. Of course, pharmaceutical companies should also develop effective strategies in case an investment turns out to be unsuccessful.
At the same, biotech start-ups and big pharma giants should consider the specific field of their work. Drug discovery and development is not only a scientific experiment or a financial endeavor; it’s not about the reputation of the research institution or the revenues of the investors either. Drug research and development has always been about people’s well-being and improved healthcare. Sponsors need to encourage Phase I studies and embrace risks because a trial may prove to be beneficial from a medical point of view. In the end, finding a balance between well-being and profit is hard but not impossible.
Basics of Valuation. Retrieved from file:///C:/Users/Owner/Downloads/9783642108198-c1.pdf
Net Present Value – NPV. Retrieved from https://www.investopedia.com/terms/n/npv.asp
NPV vs. rNPV. Retrieved from http://www.avance.ch/newsletter/docs/avance_on_NPV_vs_rNPV.pdf
Svennebring, A., & Wikberg, J. (2013). Net present value approached for drug discovery. Springerplus, 2. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622797/
Stewart, J., Allison, P., & Johnson, R. (2001). Putting a price on biotechnology. Retrieved from https://www.nature.com/bioent/2003/030101/full/nbt0901-813.html
Risk-adjusted NPV is Notoriously Fallible (2015, October 26). Retrieved from https://www.alacrita.com/whitepapers/pharma-and-biotech-valuations-divergent-perspectives/
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