Net present value approaches for drug discovery
 Andreas M Svennebring^{1}Email author and
 Jarl ES Wikberg^{1}
DOI: 10.1186/219318012140
© Svennebring and Wikberg; licensee Springer. 2013
Received: 6 February 2013
Accepted: 21 March 2013
Published: 1 April 2013
Abstract
Three dedicated approaches to the calculation of the riskadjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the postdiscovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.
Keywords
Biotechnology Drug development Drug discovery Investment under uncertainty Life science NPV Riskadjusted net present value rNPVBackground
where C _{ n } is the n ^{th} cash flow of N in total, R _{ 0 } and R _{ n } is the estimated probability of obtaining the entire series of cash flows from the initiation of the project and from the n ^{th} cash flow, respectively, r is the discount rate and t(n) the time of the n ^{th} cash flow (Stewart 2002; Stewart et al. 2001). Presentations and demonstrations of the implementation of the rNPV calculation in drug development projects found in literature generally start only right at the initiation of the phase I clinical trial, in the discovery and development pipeline. The complexity of the drug discovery, the high costs for producing suitable data, and the limited access to such information for actors outside organizations undertaking discovery activities, it may seem difficult to model the cost of the drug discovery process. In projects requiring major economical investments such as the construction of a building, bridge, oil platform or the clinical development of a drug, the time required for each development step is uncertain. While a clinical phase III trial usually takes 3–5 years, the average of 4 years may be a good estimate for the rNPV approach. However, in drug discovery endeavors, the time from initiation of the project to the generation of the first candidate drug ready for clinical development may vary from a few years and up without any distinct upper limit or guarantee that a compound ever will be found.
Nevertheless, in view of the high risk involved and the constant decline in productivity among the industry, the financial aspects of drug discovery endeavors, rNPV extensions tailored for the evaluation of drug discovery projects are urgently needed. Herein, a few approaches valid under different assumptions are suggested and discussed.
Model framework
New model suggestions
Instead of dividing the discovery phase in parts, the process is seen as a black box generating compounds ready for clinical development with a certain probability. The planned time cause of the discovery process is divided in periods of constant length, which is set on a pragmatic basis, and the project continuous for N time periods. The chance of finding a compound ready for clinical development is either constant (p) or different (p _{ n }) between the different time periods.
where C _{ n } denotes the cash flow to discovery activities at the n ^{th} interval.
In many drug discovery projects, the synthesis and early ADMET (absorbtion, distribution, metabolism, elimination and toxicology) investigations are done by contract research organizations (CROs). When a compound suited for clinical developments has been found the entire focus may swiftly shift to the clinical phase. Our first model is based on the assumption that a drug discovery project is run until a suitable candidate for clinical development has been found, at which time point the discovery activities cease and the clinical development is initiated. The project goes on for a finite period of time of totally N periods.
In our third model, we assume that more than one compound may be selected for clinical development. We also assume that the project continues even in the event that a candidate compound is found. If a large number of compounds are prepared and the probability that one of the compounds will be suitable as a drug is p, the chance of finding two compounds during the same time period is p ^{2}, and the chance of finding three is p ^{3}. The probability of finding m compounds for further development at one time interval is thus p ^{ m }. The model is designed particularly for application in projects where the probability of finding compounds for development is high enough to make it probable to find more than one suitable compound for development during the same time period, and where it is judged meaningful to initiate the clinical development with multiple compounds.
respectively.
We wish to demonstrate the utility of the model from a few simple calculations in which rough estimates of the parameters have been deduced from publicly available data. It has been estimated that the discovery phase account for approximately 30% of the total discovery and development cost of a drug, which may be around $1b making $300 m a rough estimate of the outgoing cashflow necessary for the discovery of a drug including failing compounds (DiMasi et al. 2003). Of the drugs entering into phase I trials, approximately 5% later reach approval by regulatory authorities (Arrowsmith 2012). The cost per compound entering clinical development is thus $15 m and P = 1/$15 m approximating 7 10^{8} compounds/$.
The probability of finding a drug in year 1–5 according to model 1 and 3
Cashflow to discovery/year (million $)  

n  0,5  1  3  
Model 1  1  0,0350  0,0700  0,2100 
2  0,0338  0,0651  0,1659  
3  0,0326  0,0605  0,1311  
4  0,0315  0,0563  0,1035  
5  0,0304  0,0524  0,0818  
Model 3  All  0,0376  0,0809  0,3365 
In model 1 with a cashflow of $0.5 m/year, the chance of finding a compound is 3.5% and falling slightly over the coming years due to the chance that a compound has been found during the previous years and the discovery work will be discontinued. The decrease becomes higher with increasing annual allocation and with a cashflow of $3 m/year, it is after four years half of the probability of the first year. While using model 3, the probability weighted amount of compounds found with an annual investment of $0.5 m is only slightly higher than what was found for model 1, an increase that is due to the fact that in model 1 only one compound can be found, but more than one in model 3. However, the probability of finding two compounds suitable for development with this limited budget is very low. With increasing annual allocations, the probability of finding a second compound starts to affect the probability rate considerably.
Results and discussion
Mathematical models are fabrications designed to capture the most essential aspects of reality. It is therefore imperative to acknowledge all imperfections to the models and to the degree it is possible to account for them in the process of setting the parameters (i.e. parameter estimates) fed to the model or to craft the models. The probability of finding a compound fit for development is likely to change from the beginning of a project and onwards due to experience, and a considerable lag time must be expected between cash flows to the project and the probability that a compound fit for development will be generated as a result of the investment.
When the models presented herein are used practically, it must be considered that the resource allocation between early and late activities in the discovery pipeline differ between projects, and in particular between newly founded and mature projects. Assume the life cycle from foundation to termination of a drug discovery enterprise based on only one single project, a socalled pure play company. In the newly founded enterprise, not so many compounds have qualified for preclinical investigations yet. In many cases, the entire outgoing cash flow goes to compound synthesis, i.e. discovery and optimization of leads. Maybe the lead optimization process has not even yet started. The probability of obtaining a compound fit for clinical development must be practically null under these circumstances. However, while the project or company matures, an increasing fraction of the research spending goes to preclinical activities. In this stage of maturity, the prospect of generating compounds fit for clinical development becomes considerable. When the drug discovery pipeline is wellpopulated with compounds, all four phases are cost drivers and the probability of generating a candidate drug is decent.
In view of the above discussion it is possible to utilize management accounting information to adjust the probability rates in the models so that in every time interval they reflect the probability of finding a compound fit for clinical development as accurately as possible. For an investor, without access to this information, it may be possible to use official reports for the same purpose. If a project is just about to start, it might be rational to assume that no compounds will be generated during the first years, and that a certain starting time with a startup cost should apply. It might also be meaningful to add a delay to the probability time series so that the cash flows during one time period reflect the probability of generating a candidate drug during a later period. The knowledge that a relatively large amount of the cash flows will go to late preclinical development might give reason to increase the calculated p _{ i }s.
The question of how many compounds that are generated for clinical phase I trials is in reality not as straightforward as the models presented herein suggest. In drug discovery projects, usually a large number of highly reminiscent structures are synthesized, from which usually a number of compounds can be identified as good candidates for further development. However, only one or maybe two of these are generally chosen for clinical development. If a compound fails in clinical phase I, which is usually due to toxicity or other adverse effects, it is likely that other compounds with just minor changes to the structure will share the same problems. It may therefore be misjudged to look at each individual compound that could be considered for clinical development as a hit in the models presented. Rather, a structurally more distant molecule might be meaningful to take to the clinic. In fact, there exist mathematical methods to compute the chemical similarities of compounds, based on which it would be possible to implement filters to judge if a compound is truly ’novel’ in the sense of bringing it into clinical development, compared with previously failed compounds.
The models should therefore be regarded in instrumentalistic terms rather than as a reflection of how reality works. How many compounds that are considered fit for clinical development is dependent on how strict criteria that we apply in the selection process. With very liberal criteria, the probability of finding suitable compounds must be set high, but the lower success rate in the clinical development must be reflected in the parameters for probability in the clinical development.
At last we would like to mention that the practices for economic evaluation of biotechnology pure play enterprises is currently poorly developed. In a report from the management consulting firm McKinsey from 2000 based on interviews with 44 CEOs and business developers from representative pharmaceutical and biotechnology companies found that one third admitted not to employ any economically valid evaluation method. Among these, 21% used simple cost plus approaches and 12% simply made a guess (Moscho et al. 2000). This study argued that a reliable economic valuation is necessary in order to persuade financiators to invest in the enterprice and partners to form partnership agreements. Further on, it is potentially deleterious for all parties when deals are based on unrealistic expectations. It is also noteworthy that the pharmaceutical industry is in a crisis with declining productivity and increasing costs, which the major companies seek to counteract by in licensing only late projects in clinical development, shifting the major risk to small enterprises and startups, while on the other hand venture capital has become increasingly reluctant to fund the early projects. In an overall perspective these developments may not be costeffective for the sake of public, for which there is still a very large unmet need for effective treatments of a large number of severe and disabling diseases. The development of methods for the rational evaluation of drug discovery is thus essential for a wealthy drug discovery sector to develop, to which end we hope that our proposed approaches will contribute.
Conclusions
We have suggested three dedicated extensions to the net present value calculation for drug discovery projects. The process of setting parameters for the models and their overall utility has been discussed. We propose that the models shall be considered in the evaluation of early drug discovery endeavors for the future, and that their practical implementation for the purpose is a highly desired task to study.
Abbreviations
 ADMET:

Absorbtion, distribution, metabolism, elimination and toxicology
 CROs:

Contract research organizations
 rNPV :

Riskadjusted net present value
 SARs:

Structureactivity relationships.
Declarations
Authors’ Affiliations
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.