Pharmaceutical organizations invest significant time and resources into developing new drugs. During the early research phase, scientists and researchers have to review multiple sources and databases of biomedical literature, which is time-consuming, can introduce research bias, and is not always comprehensive.1 They can also incur significant costs in developing drugs against targets that ultimately fail. Causaly Cloud is an AI-powered solution that accelerates life sciences research, reduces biomedical literature review time, and helps generate hypotheses for novel drug targets. Causaly helps improve target identification and prioritization accuracy and, in turn, reduces drug development costs.
Causaly Cloud is an AI-powered search solution that uses natural language processing (NLP) to read, analyze, and establish connections in biomedical research and data to accelerate preclinical drug discovery.2 The core use cases are in disease pathophysiology, target3 identification and prioritization, target safety assessment, and biomarker discovery.
Causaly commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) that enterprises may realize by deploying Causaly Cloud.4 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Causaly Cloud on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed five representatives with experience using Causaly Cloud. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a multinational pharmaceutical company. It has headquarters in North America and Europe and generates revenues of $25 billion per year.
Prior to using Causaly Cloud, the interviewees’ organizations faced time-consuming processes of reviewing significant volumes of biomedical literature to understand target biology and search for relevant insights. They also lost time due to the need to access multiple tools and data sources during their research. Due to scientists being limited in the number of articles they can physically read, using traditional search methods resulted in neglecting important information or introducing bias that aligns with existing views. Interviewees said this decreased confidence that their organizations’ research efforts were as comprehensive as they could be.
Interviewees reported that after the investment in Causaly Cloud, their organizations experienced several important benefits including increased research efficiency, accelerated target hypothesis-building, reduced research bias, and improved interdisciplinary collaboration. Key results from the investment include early research time savings and improved accuracy of target identification and prioritization.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Early-research time savings. Prior to using Causaly Cloud, the composite organization spent a significant amount of time on public and independent databases of biomedical literature to carry out research on potential drug targets. By leveraging Causaly Cloud during the preclinical research stage, the composite’s scientists save an average of 50% of their research time when reviewing literature and prioritizing and validating targets. This leads to $18.2 million in savings over three years for the composite organization.
- Saved resources by deprioritizing unviable targets. With Causaly Cloud, the composite makes important improvements to the accuracy of target identification and prioritization, which leads to a rejection of unviable targets at an earlier stage. Causaly Cloud enables the composite to correctly deprioritize a total of three targets per year, which equals savings of $22.4 million over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Ability to delegate work to less-senior roles. Causaly Cloud facilitates the research process, which provides the ability to delegate research workloads to more-junior employees. This enables the composite’s senior employees to focus on value-added tasks while retaining high-quality research results.
- Increased project viability. With Causaly Cloud, the composite organization’s research project timelines are compressed. This allows the organization to take on and progress through more research projects.
- Increased customer retention. The deputy chief medical officer and head of consulting at a life sciences consulting organization said implementing Causaly Cloud enabled their company to improve the quality of its deliverables and that its customer retention rate significantly increases from 60% to 80%.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Implementation, onboarding, and license costs. These costs include initial internal advocacy for Causaly Cloud implementation, initial training for users, and annual license and support fees. For the composite organization, these costs amount to $7.9 million over three years.
- Ongoing training. The composite opts to receive ongoing training from Causaly Cloud to review new product features and receive a refresher on advanced features. This costs the composite $784,000 over three years.
The representative interviews and financial analysis found that a composite organization experiences benefits of $40.55 million over three years versus costs of $8.73 million, adding up to a net present value (NPV) of $31.82 million and an ROI of 365%.
KEY STATISTICS
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Return on investment (ROI):
365%
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Benefits PV:
$40.55M
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Net present value (NPV):
$31.82M
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Payback:
<6 months
Benefits (Three-Year)
Early-research Saved resources by deprioritizing unviable targetsTEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Causaly Cloud.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that Causaly Cloud can have on an organization.
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Due Diligence
Interviewed Causaly stakeholders and Forrester analysts to gather data relative to Causaly Cloud.
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Interviews
Interviewed five representatives at organizations using Causaly Cloud to obtain data about costs, benefits, and risks.
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Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
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Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
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Case Study
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Disclosures
Readers should be aware of the following:
This study is commissioned by Causaly and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Causaly Cloud.
Causaly reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Causaly provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Stefanie Vollmer
Jan Sythoff
Ana Botelho
Drivers leading to the Causaly Cloud investment
Interviews
| Role | Industry | Revenue | Drug Development Stage |
|---|---|---|---|
| Head of digital biology | Cosmetics | $40B | Early research and discovery and development |
| Director | Pharmaceutical | $30B | Early research and discovery |
| Independent consultant | Pharmaceutical, biotech | $30B | Early research and discovery |
| Senior director of medical value and strategy | Pharmaceutical | $12B | Early research and development |
| Deputy chief medical officer and head of consulting | Life sciences | $12M | Clinical development and strategic market positioning |
Key Challenges
Literature review is an ongoing, iterative process for scientists in the early research stage of drug discovery. Interviewees said that prior to implementing Causaly Cloud, employees at their organizations faced a time-consuming process of reading biomedical literature to understand target biology and search for relevant insights. This was typically carried out via searching public and independent databases (e.g., PubMed) for potential targets.
The interviewees noted how their organizations struggled with common challenges, including:
- Time-consuming literature review and research. Each interviewee stated that literature reviews are time-consuming and that traditional search engines access databases of references, life sciences journals, and biomedical sources and find thousands of papers per area of interest that early research scientists need to read. The interviewees also mentioned there is a risk that scientists lose time reviewing less pertinent research and even following the wrong research directions because traditional literature searches do not consistently surface the most relevant or up-to-date results.
- Inefficiencies accessing multiple tools and data sources. Interviewees said that during preclinical research, scientists tend to access a variety of biomedical data sources to complement their research. Before their organizations used Causaly Cloud, researchers had to log into different tools and databases to extract relevant data and collate it into a report or spreadsheet. The head of digital biology at a cosmetics organization stated: “[Previously,] we were using different databases and different tools. You can imagine that it is was time-consuming.”
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Research results that are not comprehensive. Interviewees said traditional searches via publicly available search engines only look through available abstracts, do not search through full-text articles, and can neglect important information as a result. For instance, they said adverse effects are predominantly reported within the bodies of articles and they can easily be missed when relying only on abstracts.
The director at a pharmaceutical organization said: “[Traditional searches] are time-consuming, and the search results do not always reflect the full evidence. The results are quite static.” An independent consultant for pharmaceutical and biotech organizations echoed this: “To [carry out research] through traditional means is slow, and you get a lot of results. You then hope that the tools as well as your knowledge of how to use them doesn’t eliminate topics or data sources of interest.”
Consequently, researchers reported they were not confident their research efforts were comprehensive.
- Introduction of bias into research. Due to growing volumes of scientific literature and constrained timeframes, interviewees said significant bias can emerge during the literature review process during which scientists must limit the number of articles to read and potentially opt for research that aligns with their existing views.
Solution Requirements/Investment Objectives
The interviewees’ organizations searched for a solution that could:
- Increase research efficiency. Several interviewees said the gaining ability to save time while maintaining research quality was the top driver for their organization’s Causaly Cloud investment. They said Causaly Cloud surfaces only the most relevant papers while exposing scientists to a larger breadth of results that are not reported in abstracts. With an increasing volume of publications, this makes target discovery more challenging over time. Interviewees said Causaly Cloud enables research scientists to manage the exponential growth of biomedical literature while rapidly identifying potential targets that are worth investigating further. They also said Causaly Cloud provides an integrated one-stop solution that shows results from different databases (e.g., PubMed, ClinicalTrials.gov, patents, genomewide association studies catalogs), which enables additional time savings.
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Accelerate target hypotheses building. Interviewees said their organizations wanted a solution that would allow them to build hypotheses on potential drug targets more easily and quickly and that Causaly Cloud provides relevant information while filtering out “noise” in literature. They explained they can find hidden connections across papers through Causaly’s Multi-Hop and Delta Analysis features that enable novel hypothesis generation from published literature and that this allows their scientists to gather available information much more quickly and strengthen their understanding of disease pathophysiology and the relationships between potential targets and diseases.5
The director at a pharmaceutical organization added: “Understanding relationships is quite useful for scientists, and it helps to improve decision-making. … If your hypothesis is validated with the help of the dendrogram, it helps you to feel empowered about your deep dive. You build confidence much [more quickly].”
- Help avoid research bias. Interviewees said that because it is very challenging for their organizations’ researchers to physically read all information, it was difficult to avoid inherent research bias in a reasonable timeframe. They said Causaly Cloud helps scientists avoid research bias because it machine-reads all available scientific literature, surfaces the relevant insights, and provides evidence for and against a hypothesis in one search. This helps avoid confirmation bias when choosing papers to read.
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Provide visualization capabilities. Interviewees said their organizations were looking to provide their scientists with an easier way to visualize evidence (e.g., the relationship between a gene and a protein and the direction of the relationship) and that with Causaly Cloud, scientists gained the ability to toggle between views to show a list of targets or a timeline (e.g., to show established insights vs. novel insights).
The director at a pharmaceutical organization said one of the main reasons their company invested in Causaly Cloud was for its visualization capabilities: “With Causaly Cloud, you can see the impact as a number or as an arrow. It just brings out the information that scientists really need without having to read the full article from the start.”
The senior director of medical value and strategy at a pharmaceutical organization echoed this: “I like the bird’s-eye point of view and that it allows me to zoom in on the least or most amount of evidence for a particular concept … and [that] it allows me to do that early on.”
- Be intuitive and user-friendly. Interviewees said their organizations wanted a solution that is intuitive and has a user-friendly interface. The director at a pharmaceutical organization told Forrester: “Causaly Cloud came into play because the user interface [is] friendly and the adaptation [is] quite fast for the research scientists. … With Causaly Cloud, we have a tool here where we can do quite advanced NLP search, relationship extractions, and directionalities. That would be something a computational biologist would do, but here we felt our scientists could have a chance to do it themselves.”
- Allow for dissemination of findings. Interviewees said their organizations wanted a solution that would make it easy to communicate search results and that Causaly Cloud Workspaces save time and foster interdisciplinary research and collaboration.6 The director at a pharmaceutical organization said: “We have had tools that work in similar ways as Causaly Cloud. However, it was much easier to communicate the search results through Causaly Cloud [through the Workspaces feature].”
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the five interviewees, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization is a multinational pharmaceutical company with headquarters in North America and Europe, and it generates revenues of $25 billion each year. Its preclinical research and development (R&D) spending amounts to $1.2 billion per year. It has an employee base of 10,000 with a total of 2,250 preclinical research employees. On average, the composite screens 3,000 potential targets per year in the preclinical target discovery and prioritization stage, and it creates 300 formal target proposals for assessment by R&D leaders to select 50 successful target proposals per year.
Deployment characteristics. The composite deploys Causaly Cloud for disease pathophysiology, target identification and prioritization, target safety assessments, and biomarker discovery. It has 250 Causaly Cloud licenses with an average active user rate of 70%. The composite also has additional preclinical research employees with profiles similar to existing license holders who could benefit from Causaly Cloud access in the future.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Early-research time savings | $6,258,125 | $7,509,750 | $8,344,167 | $22,112,042 | $18,164,705 |
| Btr | Saved resources by deprioritizing unviable targets | $9,000,000 | $9,000,000 | $9,000,000 | $27,000,000 | $22,381,668 |
| Total benefits (risk-adjusted) | $15,258,125 | $16,509,750 | $17,344,167 | $49,112,042 | $40,546,373 |
Early-Research Time Savings
Evidence and data. Prior to using Causaly Cloud, the interviewees’ organizations mainly relied on public and independent databases of biomedical literature to carry out research on potential drug targets. But interviewees said Causaly Cloud surfaces only the most relevant papers for their organizations’ research questions and allows them to identify and explore more novel hypotheses than traditional methods. By leveraging Causaly Cloud during the preclinical research stage, scientists at the interviewee’s organizations saved an average of 50% of their research time.
The interviewees shared the following:
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Identifying genes and proteins as potential targets requires a comprehensive literature review, and preclinical research scientists spend hours each week reading and reviewing papers.7 Interviewees stressed that in the face of the rapidly increasing volume of literature produced each year, it’s important to leverage innovative resources to accelerate the discovery of novel drug targets.
The director at a pharmaceutical organization told Forrester: “We use Causaly Cloud to speed up the acquisition of knowledge. … Causaly Cloud reduces the number of papers that scientists tend to read at the early stage of research. [It] helps them to go from 200 down to 50 papers by focusing on the most relevant ones.”
Overall, the interviewees reported research time reductions between 30% and 75% depending on the step of the research workflow. The independent consultant in the pharmaceutical and biotech industries stated they saw even more time savings: “We did an ROI analysis with the [Causaly Cloud] pilot super users and we registered a reduction [of initial research time] from three weeks to a few days.”
- The head of digital biology at a cosmetics organization said their company uses Causaly Cloud as the first step of its discovery journey to quickly extract relevant information from literature and data. In terms of usability, the director of at a pharmaceutical organization explained: “Causaly Cloud came into play [for my company] because the user interface [is] very friendly, and the adaptation was quite fast for the users. Causaly Cloud fits very well into our user-empowerment anchor.”
- As a result of reductions to research time, research scientists were able to increase their research capacities and take on ad hoc research requests. The senior director in medical value and strategy at a pharmaceutical organization explained: “With Causaly Cloud, we have been able to prioritize our work. That means I’m able to do more than just one project and be engaged in ad hoc requests. Causaly Cloud doubles or triples my [research] capacity.”
- The interviewees noted further time savings by leveraging Causaly Cloud for target validation, which involves confirming that identified targets are suitable to be pursued for therapeutic benefit. The director at a pharmaceutical organization explained: “Approximately once a month, there is a meeting and a deep dive around [target] hypotheses-building. [With Causaly Cloud,] result-findings were quicker. When scientists need to do follow-up searches [for validation] after meetings, they have the results ready in three days versus previously six to eight days. The evidence rankings and the directionality especially help in the discussions.”
- Causaly Cloud enabled scientists to disseminate research findings between researchers and across research departments, which led to improvements in interdisciplinary research exchange. The head of digital biology at a cosmetics organization said: “It’s easy to communicate search results through Causaly Cloud Workspaces. It’s particularly valuable [for my organization] to be able to share research queries between users. It increases collaboration, leading to a reduction in time to research.” In terms of interdisciplinary collaboration, the interviewee added: “[Causaly Cloud Workspaces] enable you to share information between scientists from different disciplines. A clinician won’t have the same view on some results as a biologist or a chemist. This [interdisciplinary] exchange makes the research process more effective.”
- The interviewees shared that Causaly Cloud helped their organizations avoid bias that could have otherwise been introduced to research. The director at a pharmaceutical organization stated, “Bias in research is factual.”
- Interviewees said it’s impossible for a researcher to read all available papers on a topic of interest in a short timeframe but because Causaly Cloud machine reads all available scientific literature, there’s confidence that a thorough analysis was conducted. The independent consultant in the pharmaceutical and biotech industries said: “[With Causaly Cloud,] you can see all relevant research results and filter from there. The platform provides a complete view of the data out of the gate, which helps to remove user bias.”
Modeling and assumptions. For the composite organization, Forrester assumes:
- The composite organization has a total of 250 Causaly Cloud users with an average active user rate of 70% over three years. The number of active users gradually increases from a total of 150 scientists in Year 1 to 180 in Year 2 and to 200 in Year 3.
- The composite screens a total of 3,000 targets per year to come up with 300 formal target proposals.
- Prior to using Causaly Cloud, the composite’s research scientists spent an average of 2.5 weeks to screen and validate a target, and that included time for a comprehensive literature review. Additionally, research scientists spent an average of one week to validate a target hypothesis. Each early-discovery scientist took research responsibility for 10 to 12 targets per year, which is equal to 25 to 30 weeks per year. In total, early-discovery researchers spent 31 weeks per year on carrying out literature reviews, target prioritization, and hypotheses validation.
- With Causaly Cloud, the composite reduces its research time to review relevant literature and prioritize and validate targets by 50%. Consequently, each early-discovery scientist spends 16 weeks per year in total to prioritize and validate 10 to 12 targets.
- The average fully loaded annual salary of a research scientist is $190,000.
- Forrester applied a productivity recapture rate is 80% based on the assumption that the composite will use only a portion of the newly available time for activities that drive value for the organization.
Risks. The value of this benefit can vary across organizations due to differences in:
- Active user rates, which is typically driven by the research cadence of scientists and balances laboratory work with literature-based work.
- Skepticism among scientists about the potential of AI solutions.
- The potential degree of remaining research bias.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $18.2 million.
Early-Research Time Savings
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Research scientists who study disease pathophysiology, target prioritization, and validation | Interviews | 150 | 180 | 200 | |
| A2 | Time spent per research scientist to carry out literature review, target prioritization and hypotheses validation with traditional research methods (weeks) | Interviews | 31 | 31 | 31 | |
| A3 | Time spent per research scientist to carry out literature review, target prioritization, and hypotheses validation with Causaly Cloud (weeks) | Interviews | 16 | 16 | 16 | |
| A4 | Total time saved by deploying Causaly Cloud in the early research drug development phase (weeks) | A2-A4 | 16 | 16 | 16 | |
| A5 | Percent of time captured while working on additional research requests | TEI standard | 80% | 80% | 80% | |
| A6 | Fully burdened annual salary of a research scientist | TEI standard | $190,000 | $190,000 | $190,000 | |
| At | Early-research time savings | A1*A4*A5*(A6/ | $7,362,500 | $8,835,000 | $9,816,667 | |
| Risk adjustment | ↓15% | |||||
| Atr | Early-research time savings (risk-adjusted) | $6,258,125 | $7,509,750 | $8,344,167 | ||
| Three-year total: $22,112,042 | Three-year present value: $18,164,705 | |||||
Saved Resources By Deprioritizing Unviable Targets
Evidence and data. Interviewees identified this benefit as the most impactful. They said by leveraging Causaly Cloud, their organizations improved the accuracy of target identification and prioritization, which led to rejecting unworthy targets earlier in the drug-development process.
- Several interviewees stressed the importance of eliminating unworthy targets early in the drug-development process to prioritize targets with the strongest evidence demonstrating a clear role in the disease process. The senior director in medical value and strategy at a pharmaceutical organization explained that rejecting targets early on reduced the chance of pursuing drugs unlikely to succeed in the clinical stage, which saved valuable time and resources. The interviewee noted: “I would take a lot of time searching [and] I would go down rabbit holes, which would take me weeks. [With Causaly Cloud,] I can concentrate on a particular research question and figure it out within a few days. I feel better that I can put that question to rest if I haven’t found anything and move on and dedicate more time to other questions.”
- The head of digital biology at a cosmetics organization explained that Causaly Cloud helped detect previously unknown safety signals, which enabled their team to avoid risk. The interviewee stated: “You can leverage Causaly Cloud to carry out a risk assessment of new ingredients. That prevents us from going too far into the development process, and we can make informed decisions around how to proceed with the target — especially before moving on to clinical trial.” They added that prior to introducing Causaly Cloud, their organization wasted research time on pursuing the wrong cosmetic ingredients. They said: “If any side effects occurred, [we] would stop everything. From research to running a clinical trial, it [would be] around €1.0 million wasted.”
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Interviewees said Causaly Cloud’s visualization capabilities enable research scientists to accelerate insight generation and prioritize research efforts. The director of at a pharmaceutical organization stated: “The value of the dendrogram lies in the filtering.8 I can scan through the results quickly. Due to time constraints, we often must make quick decisions. Causaly Cloud’s dendrogram allows me to get a better picture of the information available, and that's extraordinarily valuable for me, and it really saves me a lot of time and headache.”
The head of digital biology at a cosmetics organization further stated, “Causaly Cloud’s timeline option allows us to filter recent discoveries in the literature, which is crucial when we are carrying out research around novel cosmetic ingredients.”
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Interviewees said Causaly Cloud enables scientists to acquire comprehensive and relevant information. The independent consultant in the pharmaceutical and biotech industries explained: “[With Causaly Cloud,] the search is better [than with traditional research sources], and [it] provides you with relevant information and less noise.”
Interviewees explained that unlike traditional searches, Causaly Cloud reads whole papers instead of only abstracts, which leads to thorough research results and has a direct impact on researchers’ confidence levels. The senior director in medical value and strategy at a pharmaceutical organization noted: “[With traditional research sources,] my confidence level of finding all relevant information was low — 50% or less. With Causaly Cloud, my confidence level goes up to 90% if not more, especially when leveraging the dendrogram.”
The director at a pharmaceutical organization added, “Causaly Cloud has helped me find more potential targets than when I just used [traditional research methods].”
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Several interviewees mentioned that using Multi-Hop for hypothesis generation had an impact on developing innovative research ideas and finding novel insights. The director at a pharmaceutical organization told Forrester: “I’ve used Multi-Hop to find insights I wouldn’t otherwise find. The Multi-Hop analysis and the comparative analysis are unique. [I] especially [value] the graphical directionality. … Causaly Cloud accelerated hypothesis-building as it provides researchers with a better strategy to search. And that again helps them to come up with new, innovative ideas.”
The senior director in medical value and strategy at a pharmaceutical organization stated: “[When using Causaly Cloud,] I sometimes trip over and accidentally find an area where I see a connection with a completely different disease because I recognize a biomarker or … a concept that seems very similar.”
Modeling and assumptions. For the composite organization, Forrester assumes:
- Based on a total of 3,000 targets screened per year, the composite comes up with 300 formal target proposals to be assessed by R&D leaders.
- The average cost of failure per molecule is $6 million, which is calculated in line with data on the probability of advancing a drug candidate by stage provided by Mohs, R.C. and Greig, N.H., 20179 : Based on 3,000 potential targets entering the drug development funnel, 300 formal target proposals are created with the aim of progressing to commercial launch in market. For the composite, the average cost of failure per molecule is calculated by dividing the total cost of failed molecules by drug development stage, by the number of failed targets and compounds. This totals $1.8 billion cost of failure for 290 failed target proposals & compounds, which is in line with the cost (including failure) of bringing a single drug to market.10
- The composite’s percentage of correctly deprioritized targets using Causaly Cloud is 1%, which equals a total of three correctly prioritized targets.
Risks. The value of this benefit can vary across organizations due to differences in:
- The probability of targets successfully entering the next stage of drug development, which may affect the cost of a failed molecule and can vary by modality, therapeutic area, and other measures of drug-discovery complexity.
- Whether or not all users are trained on the Causaly Cloud platform, which may impact their ability to identify relevant literature to deprioritize a target.
Results. To account for these risks, Forrester adjusted this benefit downward by 50%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $22.4 million.
Saved Resources By Deprioritizing Unviable Targets
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Targets to be validated | Composite | 300 | 300 | 300 | |
| B2 | Average failure cost of a molecule | Composite | $6,000,000 | $6,000,000 | $6,000,000 | |
| B3 | Percentage of correctly deprioritized targets using Causaly Cloud | Interviews | 1% | 1% | 1% | |
| B4 | Correctly deprioritized targets using Causaly Cloud | B1*B3 | 3.0 | 3.0 | 3.0 | |
| Bt | Saved resources by deprioritizing unviable targets | B2*B4 | $18,000,000 | $18,000,000 | $18,000,000 | |
| Risk adjustment | ↓50% | |||||
| Btr | Saved resources by deprioritizing unviable targets (risk-adjusted) | $9,000,000 | $9,000,000 | $9,000,000 | ||
| Three-year total: $27,000,000 | Three-year present value: $22,381,668 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
- Ability to delegate work to less-senior roles. Some of the interviewees highlighted the ability to delegate research to less-experienced colleagues because Causaly Cloud facilitates the research process and enables the delivery of high-quality results. The deputy chief medical officer and head of consulting at a life science consultancy noted that senior employees used to spend 50% to 60% of their workloads on research projects and that this time has been reduced to 10% to 20%. They said “,Leveraging Causaly Cloud enabled us to delegate work to junior employees.” Consequently, senior employees gained the ability to focus on more value-add tasks.
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Increased project viability. Causaly Cloud compresses research timelines, enabling teams to take on more time-sensitive research projects that may previously have been challenging. The senior director in medical value and strategy at a pharmaceutical organization detailed: “From a team perspective, it allowed us to successfully respond to requests that seemed a little impossible. We know we have this tool in our toolbox, and it allows us to problem-solve, and I have much more confidence to accept a request that seemed outlandish at times.”
The deputy chief medical officer and head of consulting at a life sciences consultancy said: “Our normal process takes 12 to 14 weeks. Some clients pressure us to get results sooner. By using Causaly Cloud, the ability to compress timeline while keeping quality is very useful.”
This benefit can also potentially increase revenue as more research projects become viable.
- Increased customer retention. Some interviewees said that because their organizations are better able to deliver projects on time and with a high level of quality, follow-up work has increased. The deputy chief medical officer and head of consulting at a life sciences consultancy explained: “[Another benefit is the] improved quality of deliverables. Given we’re a small team and niche, we can maintain quality throughout project, [come] out with a range of positioning offers, [and push] the client to think outside the box. As a consequence, 80% of our clients come back for more work, [which is up] from 60% previously.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Causaly Cloud and later realize additional uses and business opportunities, including:
- Research spillover and increased collaboration. Interviewees said the benefits of Causaly Cloud’s research capabilities spill over to wider teams. Research teams can easily share research, which promotes collaboration and brings it to others, and using workspaces creates a system of record in which findings can be shared across teams and institutional knowledge is preserved. Shorter timeless also increases capacity, which allows research teams to pick up more ad hoc support work requested by colleagues outside their teams. In turn, this brings benefits of Causaly Cloud to the wider organization, which increases its value.
- Increased project management flexibility. Because research projects can be completed in less time, it becomes easier to manage project completion and allocate resources in a more efficient way. And because research can be completed by a wider range of roles and in a shorter time, it increases teams’ capacities and the organization’s overall flexibility.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Ctr | Implementation, onboarding, and license costs | $223,052 | $3,105,000 | $3,105,000 | $3,105,000 | $9,538,052 | $7,944,728 |
| Dtr | Ongoing training | $0 | $315,144 | $315,144 | $315,144 | $945,433 | $783,717 |
| Total costs (risk-adjusted) | $223,052 | $3,420,144 | $3,420,144 | $3,420,144 | $10,483,485 | $8,728,445 |
Implementation, Onboarding, And License Costs
Evidence and data. Each interviewee noted the importance of training and onboarding when their organization implemented Causaly Cloud because it ensured users realize maximum value from the platform. Interviewees said power users also attended additional one-to-one sessions with Causaly’s scientific liaison that were tailored towards their specific research questions. Causaly typically charges annual license and support fees at a rate of 20% of the organization’s total expected benefit.
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Prior to implementing Causaly Cloud, the interviewees spent some time introducing and promoting the solution internally to their organizations. The director at a pharmaceutical organization stated: “Our ambition was to get Causaly Cloud in the hands of drug-discovery scientists. Hence, we spent some time trying to invite ourselves to meetings, visualizing the tool for them, and giving them a proper demo.”
The independent consultant in the pharmaceutical and biotech industries added that to promote Causaly Cloud internally, it was about finding internal advocates for the platform. The interviewee explained, “Specifically scientists who are subject-matter experts need to invest time to understand how to use the solution properly, understand the value, and promote its use.”
- In terms of effort required to promote Causaly Cloud internally, the director at a pharmaceutical organization told Forrester: “We have spent time to introduce and to promote the solution. This is standard [procedure] that we need to do every time when implementing a new research solution. It’s nothing unique for Causaly Cloud.”
- Each interviewee mentioned that Causaly Cloud is intuitive and highlighted the importance of up-front training to achieve the full potential of the platform. They said the initial training provided by Causaly took between 1 and 3 hours per user across three sessions of increasing complexity. The deputy chief medical officer and head of consulting at a life sciences consultancy reported that scientists particularly benefited from initial face-to-face training sessions, and the director at a pharmaceutical organization explained that some users also received intensive training tailored towards their specific research questions. This typically led to high engagement with the full range of platform features, including Multi-Hop and Delta Analysis.
- The independent consultant in the pharmaceutical and biotech industries told Forrester that organizations may face some resistance in adopting a new product: “Some researchers are hesitant to change and [adopt] new technologies. … And you have some scientists [who] tend to use whatever tools they were using previously.”
- Relatedly, interviewees mentioned the need to find a research scientist who is willing to try out new technologies and new ways of working. The independent consultant in the pharmaceutical and biotech industries explained, “Without that internal stakeholder who is ideally a scientist, you don't have as much gravitas to get other [scientists] to participate in the training.”
Modeling and assumptions. For the composite organization, Forrester assumes:
- The composite requires 24 weeks for initial internal advocacy. This combined effort comes from eight scientists who each spend three weeks introducing and promoting Causaly Cloud.
- The composite has a total of 250 Causaly Cloud users and 5% are power users. Power users frequently interact with the solution (two or three times per week) and fully integrate Causaly Cloud into their workflows.
- 250 users receive a 3-hour up-front training session and introduction to Causaly Cloud.
- Power users undergo 20 hours of one-to-one training sessions with a Causaly scientific liaison.
- The average fully loaded annual salary of a research scientist is $190,000, which equates to an hourly rate of $91.
- The composite pays annual license and support fees of $2.7 million, which totals $8.1 million over three years.
Risks. Risks that could affect the magnitude of this cost include:
- The amount of time it takes the organization to introduce and promote Causaly Cloud internally, which can take longer than what’s modeled for the composite due to entrenched research and workflow structures.
- Whether or not the organization finds the right internal stakeholders to promote the use of Causaly Cloud, which can impact initial adoption.
- Whether or not users need additional training to learn the platform’s full functionality through additional sessions with Causaly’s scientific liaison team.
- The organization’s size and use cases.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $7.9 million.
Implementation, Onboarding, And License Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| C1 | NLP scientists needed to introduce and promote Causaly Cloud within the organization | Interviews | 8 | ||||
| C2 | Time to promote Causaly Cloud within the organization (weeks) | Interviews | 3 | ||||
| C3 | Total time to introduce and promote Causaly Cloud within the organization (weeks) | C1*C2 | 24 | ||||
| C4 | Causaly Cloud users who receive up-front training | Interviews | 250 | ||||
| C5 | Time for initial training (hours) | Interviews | 3 | ||||
| C6 | Causaly Cloud power users who receive tailored and intensive initial training | Interviews | 13 | ||||
| C7 | Time of initial training for power users (hours) | Interviews | 20 | ||||
| C8 | Total time of initial training (weeks) | ((C4*C5)+(C6*C7 ))/40 | 25 | ||||
| C9 | Fully burdened annual salary of a research scientist | TEI standard | $190,000 | ||||
| C10 | Total internal introduction and initial training costs | (C3+C8)*(C9/48) | $193,958 | ||||
| C11 | Causaly value-based model costs | Interviews | $0 | $2,700,000 | $2,700,000 | $2,700,000 | |
| Ct | Implementation, onboarding, and license costs | C10+C11 | $193,958 | $2,700,000 | $2,700,000 | $2,700,000 | |
| Risk adjustment | ↑15% | ||||||
| Ctr | Implementation, onboarding, and license costs (risk-adjusted) | $223,052 | $3,105,000 | $3,105,000 | $3,105,000 | ||
| Three-year total: $9,538,052 | Three-year present value: $7,944,728 | ||||||
Ongoing Training
Evidence and data. Interviewees said their organizations’ research scientists receive ongoing training and support from Causaly’s scientific liaison team. This focused on teaching how to leverage new features and platform updates or providing support for specific research questions.
The interviewees shared the following:
- Causaly conducts quarterly training sessions on all the new product features released during that timeframe. Moreover, interviewees mentioned that users sometimes need a refresher on how to use the more advanced platform options. The independent consultant in the pharmaceutical and biotech industries explained: “There’s some training required to use some of the more advanced tooling. If you don’t use Causaly Cloud regularly enough, you tend to forget the elements of the tool, how to use it properly, and how to get value.”
- Interviewees said that when new updates are available, Causaly’s scientific liaison team reaches out to their organization to make it aware of the new features, provide training, and explain the value-add for its specific research. The independent consultant in the pharmaceutical and biotech industries stated, “[Causaly’s scientific liaison] does a good job with customer engagement and making sure that you’re getting value out of the system so that it doesn’t become just another product left unused to its potential.”
- Interviewees told Forrester that Causaly Cloud is not an everyday solution, but that their organizations use it in line with the typical research cadence of drug-discovery scientists who balance lab- and literature-based efforts. The senior director in medical value and strategy at a pharmaceutical organization explained: “Researchers use [Causaly Cloud] constantly until [they find] what they need for a particular project. Then they might not use it as much for a month. So users need time to reacquaint themselves with the tool.” Consequently, some scientists may need additional ad hoc training.
Modeling and assumptions. For the composite organization Forrester assumes:
- A total of 250 Causaly Cloud users receive ongoing training on a quarterly basis. These sessions last 1 to 2 hours per user.
- The average fully loaded annual salary of a research scientist is $190,000, which equates to an hourly rate of $91.
Risks. A risk that could impact the magnitude of this cost is whether the organization requires ongoing training sessions to take place more frequently than once per quarter, which may be caused by a need to answer specific, in-depth questions.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $784,000.
Ongoing Training
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| D1 | Causaly Cloud users who receive ongoing training | Interviews | 0 | 250 | 250 | 250 | |
| D2 | Time for ongoing training and ad hoc training with scientific liaison (hours) | Interviews | 0 | 12 | 12 | 12 | |
| D3 | Fully burdened annual salary of a research scientist | TEI standard | $0 | $190,000 | $190,000 | $190,000 | |
| D4 | Fully burdened hourly salary of a research scientist | D3/2,080 | $0 | $91 | $91 | $91 | |
| Dt | Ongoing training | D1*D2*D4 | $0 | $274,038 | $274,038 | $274,038 | |
| Risk adjustment | ↑15% | ||||||
| Dtr | Ongoing training (risk-adjusted) | $0 | $315,144 | $315,144 | $315,144 | ||
| Three-year total: $945,433 | Three-year present value: $783,717 | ||||||
Consolidated Three-Year Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits-
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Cash Flow Analysis (Risk-Adjusted Estimates)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($223,052) | ($3,420,144) | ($3,420,144) | ($3,420,144) | ($10,483,485) | ($8,728,445) |
| Total benefits | $0 | $15,258,125 | $16,509,750 | $17,344,167 | $49,112,042 | $40,546,373 |
| Net benefits | ($223,052) | $11,837,981 | $13,089,606 | $13,924,022 | $38,628,557 | $31,817,928 |
| ROI | 365% | |||||
| Payback | <6 months |
Appendix A: Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
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Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
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Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
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Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
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Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Appendix B: Endnotes
1 Drug discovery and development typically consists of four phases: basic research (e.g., disease pathophysiology), preclinical development (e.g., target discovery, prioritization, and validation), clinical trials (e.g., phases I, II, and III) and FDA filling/approval. Source: Richard C Mohs, Nigel H Greig, “Drug discovery and development: Role of basic biological research,” Alzheimer’s & dementia (New York, N.Y.) vol. 3,4 651-657, November 11, 2017.
2 Ibid.
3 A target is a protein or pathway involved in a disease process that can be modulated by a drug to achieve a therapeutic effect.
4 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s
technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
5 Multi-Hop is a feature of Causaly Cloud that allows scientists to explore intermediary pathways and mediators between different concepts (e.g., targets and diseases). Delta analysis is a feature that allows scientists to run comparative analyses (e.g., common and unique targets across multiple diseases).
6 Workspaces is a feature of Causaly Cloud that allows scientists to share findings across interdisciplinary teams and departments.
7 A gene is a set of instructions in cells that tell our bodies how to make specific proteins and determine our traits. A protein is a large, complex molecule (e.g., antibodies, enzymes) required for the body’s structure and function.
8 Dendogram is a feature of Causaly Cloud that allows scientists to visualize data as interactive networks, timelines, or Venn diagrams to explore connections.
9 Source: Richard C Mohs, Nigel H Greig, “Drug discovery and development: Role of basic biological research,” Drug discovery and development: Role of basic biological research,” Alzheimer’s & dementia (New York, N.Y.) vol. 3,4 651-657, November 11, 2017.
10 Source: “Deloitte pharma study: Drop-off in returns on R&D investments – sharp decline in peak sales per asset,” Deloitte press release, January 23, 2023.