When looking for Investors in the Life Science sector, entrepreneurial scientists and start-up companies have to deal with an unavoidable questions: ‘What is actually the appropriate valuation of my idea or business?’ Venture capitalists may hesitate investing in biotechnology if bioentrepreneurs fail to provide or accept realistic estimates of the value of their technologies. One of the underlying reasons is that there is often little intuition into what biotech companies are worth and numbers sometimes can seem very arbitrary. Furthermore, owing to the complexity and specificity of scientific knowledge, it can be challenging and time-consuming to evaluate the technological and scientific risks associated with an early-stage Biotech company.

Traditionally, the typical instruments applied for valuation in the biomedical biotech area are based on the Discounted Cashflow (DCF) analysis and the Net Present Value (NPV) model. These approaches require revenue and growth projections as well as projections of potential market share. In addition, the net price of the future drug, the costs per clinical trial and market access are further parameters normally considered. Based on these calculations, assumptions regarding price, peak market share and accessible market have the greatest impact on the venture valuation. By following this type of analysis, investors usually focus more on parameters relevant for the commercial phase of a product rather than the R&D phase.

Importantly, additional risk adjustments can be applied to the NPV calculations by modifying  future cash flows based on the probability of a drug progressing from one development stage to the next, resulting in a risk-adjusted NPV (rNPV). However, reference data for the determination of the attrition risks are usually calculated from historical information on the success rate in each development phase for products of a similar category (e.g. type of disease) – without taking into account pre-clinical data quality and integrity.

At least for the early-stage companies (i.e. before clinical Proof-of-Concept), this is quite surprising and risky as only robust and high-quality pre-clinical data can build a solid foundation for future success of drug R&D projects. Several steps within the R&D process are covered by GxP-based quality procedures (e.g. GLP, GCP, GMP, etc.) that aim to protect the integrity of research. However, these same standards cannot be applied to the basic and pre-clinical areas of drug discovery, and, consequently, biotech companies can differ widely regarding the quality of data sets generated. Thus, pre-clinical quality of research data is crucial and should be taken into account if the venture valuation is done before the Proof-of-Concept in Phase II is delivered. It is therefore highly recommended to analyze the likelihood that a given set of preclinical data is robust enough to support a successful clinical drug discovery project.

Uncertainties regarding data quality and robustness can be reflected by superimposing Monte Carlo (MC) simulations on the rNPV calculations, which returns a range of possible outcomes and importantly, the probability of their occurrences – rather than providing only a single return on investment figure, like the rNPV. In reality, only a small minority of drug development projects have positive cash flows (in case a projects reaches beyond the pre-registration phase) and most scenarios have, in fact, a negative rNPV (in case one of the clinical trials yielded a negative result). In contrast to the standard rNVP, further advanced models (e.g. risk-profiled MC valuations) indeed place the focus on clinical phase I/II failures as the most probable outcome. Hence, the costs and lengths of phase I/II trials become the most critical parameters with the highest impact on valuation.

Furthermore, for projects, where data robustness and probability of reproducing preclinical data is low, most of the rNPV range will shift towards a negative mean, providing a more accurate view of the risks involved in pharmaceutical R&D.

Monte Carlo (MC) simulation: The MC calculation is usually repeated hundreds of times, using different input values for each parameter. The rNPV (in US$K) is plotted against the probability for each rNPV value.

Given the importance of preclinical data for the outcome of all subsequent clinical trials, only a plausible evaluation of the quality, robustness and integrity of all pre-clinical studies, ideally via a third-party assessment, will complete any Due Diligence process and should therefore be a critical and valuable part of the decision-making procedure for modern portfolio management.