Towards a FAIR Return On Investment Methodology - V1.0

FAIR return on investment (ROI) tool methodology V1.0 © 2024 by Pistoia Alliance is licensed under CC BY 4.0

Version: 1.0 Date: 2024-03

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Executive summary

The document presents a first step towards a  FAIR Return on Investment (ROI) methodology, starting with financial modeling of pharmaceutical development projects. This work in progress is addressed to stakeholders in the life sciences industry involved in organizational efficiency, data management, investors and analysts interested in understanding the financial implications and ROI associated with FAIR data implementation.

The material is derived from work executed by the Vlerick Management School students and supported by Ontoforce in 2019. It provides a systematic starting point to calculate the baseline ROI of R&D and quantify the value of FAIR implementation investments through variations of the baseline. 

The document presents an ROI model that combines a Decision-Tree approach and a Discounted Cash Flow model, focusing on enhancing R&D pipeline productivity through the ‘pharmaceutical value equation’, which balances R&D efficiency and effectiveness. The model is designed to balance sophistication and simplicity, with flexible input parameters allowing customization based on industry averages or project-specific data. Financial, R&D, and market assumptions are incorporated, ensuring adaptability to diverse scenarios.

In terms of core assumptions to FAIR benefits, the model currently quantifies the impact of implementing FAIR data principles mainly by accounting for  : efficiency gains, time reduction, and cost savings. For example, it assumes reduced search time for data and estimates the associated cost reductions due to increased efficiency.

In terms of outcome metrics, Return on Investment (ROI) is defined as the increase in Net Present Value resulting from FAIR implementation actions, minus the associated costs. The 'Wins per Day" metric calculates the value of savings per day, emphasizing the impact on project value over time.

The document provides a systematic approach to calculate ROI and quantify the value of investments.  Acknowledging the need for continuous refinement and updates to reflect evolving industry dynamics, the recommendations for further work include updating key references, updating and simplifying the calculator tool, connecting ROI methods to other FAIR resources such as the Fair Maturity Matrix, and illustrating the ROI tool usage in the frame of FAIR implementation use-cases.

1. Introduction 

How can the implementation of FAIR data principles improve the efficacy and efficiency of life-science organizations, especially in pharmaceutical product development?  This is a core question for leadership of pharmaceutical companies and organizations in their ecosystem. In order to address it, another question looms in the background: is it worth it? Return on investment (ROI) tools are commonly used  to assess the business cases of projects and operational transformations. However, these models are internal, proprietary core assets, their outcomes depend on parameters and assumptions that are often not openly shared. How can we then assess the ROI of  FAIR data implementation in ways that can be understood, shared and quantified -at least at a high abstraction level- by different stakeholders? It would be beneficial to have an openly accessible ROI model that is as simple but as realistic as possible to provide a relevant, shared methodology to assess the ROI of FAIR implementation projects. The purpose of this document is to introduce such a method . 

This document is largely derived from work done in 2019 at Ontoforce and the Vlerick Business School and provides a foundation for ROI considerations in pharmaceutical companies.  The discussion and outlook section provide indications on how this methodology could be revisited and updated moving forward to take into account important recent developments. 

2. Methodology - FAIR ROI

The FAIR ROI model is based on a Decision-Tree perspective and a Discounted Cash Flow model. The outcomes are placed in the context that adopting FAIR data principles hopes to affect the R&D pipeline productivity by understanding the 'pharmaceutical value equation.' Each of these items are detailed further below.

2.1 Pharmaceutical value equation method 

Increasing R&D productivity continues to be the pharmaceutical industry’s primary challenge. R&D productivity can be simply defined as the relationship between the value (medical and commercial) created by a new medicine and the investment required to generate that medicine requirements (Paul et al., 2010). According to this paper, R&D productivity can be separated into two dimensions (Figure 1): 

• R&D efficiency: represents the ability of an R&D system to translate inputs (e.g., ideas, investments, effort) into defined outputs, generally over a specified period of time. Or, simply stated, inputs lead to outputs. 

• R&D effectiveness: the ability of the R&D system to produce outputs with certain intended and desired qualities. Or, simply stated, outputs lead to outcomes. 

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In order to come up with a productivity relationship, Paul et al. (2010) developed a 'pharmaceutical value equation' (Figure 2). This includes the key elements that determine both the efficiency and effectiveness of the drug discovery and development process for any given pipeline, according to them. 

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Figure 2. Pharmaceutical Value Equation. Source: Paul et al., 2010

Whereby: 

• P = R&D productivity

• WIP = work-in-process

• p(TS) = probability of technical success

• V = value

• CT = cycle time

• C = cost

Hence, in order to improve R&D productivity, one should try to increase WIP, p(TS), and V without substantially increasing CT or C.

2.2 Methods for valuation

2.2.1 Decision-Tree Perspective

Pharmaceutical R&D can be considered as a sequence of several research stages, each defined by 3 major parameters: (project-specific) cycle time (CT), cost (C), and probability of technical success (pTS). Whether or not a project will progress toward the next stage depends on how well the drug performs in each of those stages. When a (lead) molecule does not meet the limited requirements (e.g., too narrow therapeutic window), investment and hence proceedings to further stages must be canceled. 

Hence, pharmaceutical R&D follows a typical decision tree process (Figure 3), offering flexibility at each stage for managers to decide whether to continue or abandon the R&D project. This is further utilized as aforementioned in the literature as the way in which Pharmaceutical companies choose to value their projects (Shockley et al., 2002).

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2.2.2 Discounted-Cash Flow Method

Still considered the golden standard in the field of capital budgeting and valuation, the discounted cash flow methodology (DCF) was used to develop a model that allows the valuation of a pharmaceutical R&D project. Based on the decision tree rationale mentioned in the previous section, a commonly used additional sophistication compared to a conventional DCF model was used by considering the (cumulative) probabilities for a pharmaceutical R&D project in proceeding to the following stages leading to eventual market launch. Key to performing a DCF analysis is the determination of future free cash flows (FCFs), which can be approximated by applying the formula mentioned in Figure 4. The Free Cash Flow (FCF) may be estimated as a function of  Earnings Before Interest and Taxes (EBIT), the tax rate, Depreciation and Amortization (D&A), Capital Expenditures (CAPEX) and net Operating Working Capital Requirement (DWCR)

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This approach allows to calculate the net present value (NPV), depending on which starting position/stage the R&D project resides in the decision tree and, hence, which remaining phase transitions, each with their intrinsic probabilities, need to be completed before obtaining product launch. The latter allows to determine at which stage(s) the impact of applying FAIR data principles is most significant valuation-wise, as well as at which stages one could focus further in the future in improving the impact even more. 

As illustrated by the decision tree (Figure 3), R&D merely involves cash outflows (i.e., costs). The cash inflows are only obtained once the drug receives market approval. Due to the latter, valuation of pharmaceutical R&D projects focusing on challenging therapeutic indications (i.e., Alzheimer's disease; Calcoen et al., 2015) often results in a negative NPV, especially when the latter are still residing in early stages (e.g., Hit-to-Lead), despite the tremendous market potential in the final stages of the decision tree (Brous et al., 2011; Shockley at al., 2003).

3. Model Design and Input Assumptions

3.1 General Model Design

A key objective was to design the model in a balanced way to be sophisticated enough to allow for gaining reasonable estimates regarding Return on Investment (ROI) and Wins per Day, but not overly complex. This was implemented in an excel spreadsheet. (see Figures 5 and 6).

A technique that significantly improves the translation of the model towards non finance professionals involves using relative instead of absolute input parameters for as many assumptions as possible. A simple example can most easily demonstrate the reasoning behind this. For instance, business development managers might be unable to come up with a good estimate for the cost of goods sold (COGS) in absolute dollar amounts. However, they will most likely be able to provide a more accurate estimate for the latter when expressed in a relative amount (e.g., percentage of sales). Merely for illustrative purposes, Figure 7 depicts the relative input parameters implemented with regard to the financial assumptions applied to the model, which will be discussed more elaborately in Section 3.3. 

The strategy to develop a user-friendly model was to integrate a high level of flexibility. The latter was considered crucial, as most, if not all, of the input parameters are very much project-specific. The latter has indeed been confirmed both by literature (Calcoen et al., 2015) and by discussions with industry experts and ONTOFORCE customers. Hence, the aim was to develop the model in such a way so that the input parameters are pre-set with industry average values (based on consolidated literature data), with the option for the latter to be customized with project-specific and/or in-house company data if available, in order to obtain more accurate outcomes. A dropdown box was implemented to make this model's optionality user-friendly, allowing the user to select the desired model input (Figure 8). 

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Figure 8. Dropdown box to select standard (literature) or customized (in-house) values.
Source: ONTOFORCE Model

3.2 R&D Assumptions

As mentioned in Section 3.1, one of the objectives is to obtain accurate industry-average values for each of the parameters implemented in the model, as these will be used as standard input. In scope of R&D assumptions, we focused on the 3 most important parameters that characterize the different R&D stages: CT, C, and pTS (cf. Section 2). In order to obtain the latter, thorough literature research was conducted, focusing on top-tier journals (i.e., A1), along with insights provided by industry experts. After data collection from several resources, the consolidated average value and standard deviation for each of the three R&D parameters and each R&D stage was calculated (Figure 9). The latter also allowed us to calculate the estimated number of high-potential molecules required per stage in order to achieve 1 molecule for product launch (Figure 10). This is within the same range compared to an earlier report (Paul et al., 2010). However, the calculations would imply that a slightly higher number of molecules is required based on the most recent R&D parameter values. This assumption is plausible as several reports can be found in literature describing a decline in R&D productivity despite higher levels of investment (DiMasi et al., 2016; Carter et al., 2016; Scannell et al., 2012).