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Drug discovery research is one of the more high-risk ventures of the twenty-first century.

Millions of U.S. dollars that are invested in materials, employees, and clinical designs can be made or lost by the discovery of a single drug

Given the current state of inflation and its effect on the global economy, this trend grows more worrisome yet. Some biotech market observers see the problem for startups compounding due to investor uncertainty and bank failures (including Silicon Valley Bank).

Leslie Wubbel is a business thinker and coder, with roots in the pharmaceutical industry. He serves as CoBaseKRM Project Manager.
Nathaniel Leies directs marketing at Predictum and contributes to CoBaseKRM.
The authors thank Noel T. South and Guruprasad Udapi for their help researching FDA regulation for this and future articles.

A recent BioPharma Dive article notes that biotech investors are now opting for drug programs nearing or already in human testing before investing in or issuing new funding rounds.  
So it begs the questions: why do IND applications commonly fail? How will drug discovery researchers mitigate the risk of IND failure? 

Exploring the Problem of IND Failure

Chemist Lee Geismar, who was part of the team that reviewed the new drug application for thalidomide in the early 1960s, is shown some years later poring over several volumes of another application.
Even in earlier days, applications were extensive. This 1960s photo shows Chemist, Lee Geismar, new drug applications.

As long as new drugs have come forward for review, applicants have been required to provide extensive proof of efficacy and safety, among other considerations.

Success results from an organization’s ability to conduct organized, well-managed research. It also relies upon establishing traceability between FDA inquiries and high-quality data, definitive analysis, and robust documentation at every stage of the IND application process.

Information on Rejection Causes for Past IND Applications is Not Widely Available

Those looking to uncover reasons why a previous IND application was rejected will find that published information is limited. On the regulatory side, the FDA does not publish information relevant to prior applications to protect intellectual property.  

Scientific literature in this area is also lacking, compared to research regarding clinical trial success rates of New Drug Applications (NDAs) or Biological License applications (BLAs).  

One publication, “Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, 2000-2012,” used statistical text analysis on FDA action letters to identify factors in the denial of IND applications for “therapeutics for new molecular entities (NMEs) submitted to the FDA.”  

The paper defines the following categorial reasons for failure or denial: 

  • Efficacy (pharmacokinetic, pharmacodynamic)
  • Safety (Toxicological Studies)
  • Chemistry, Manufacturing and Controls (CMC)
  • Product Labels (Unable to determine suitable dose for drug labeling)

The following table outlines other major reasons that are defined in the study:

Filtered table of “Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, 2000-2012.”
Above is a filtered table, sourced from “Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, 2000-2012.”

Lower Amounts of Quality Data Is a Current Reality

IND applications also fail on the merit of the data and analyses included in every submission. Many of Predictum’s clients consult with us for our help with this very fact.

Without relevant and high-quality data and analyses that are readily searchable, retrievable, and in a ready-to-use format, IND applications will fail. 

There are three business areas in which we have seen contributing risk factors: 

  • Data acquisition is costly and time-consuming
  • Companies of all sizes struggle to organize the data, analyses, and knowledge that their engineers create during experimentation. They manually create hundreds of disparate excel spreadsheets and cloud drive documents without a unified, structured, and collaborative approach.
  • Companies have difficulty in supporting their IND applications with searchable, retrievable, well-documented, and relevant data at every stage of the IND application process. 

A Knowledge Relationship Management Solution to Support IND Meet CoBaseKRM

Noting the shortfalls of how scientific data is collected, structured, and leveraged, a Knowledge Relationship Management solution presents a powerful method for unifying relevant and complex data for reuse, collaboration, reporting, and decision making.  These concepts sparked the vision for CoBaseKRM. 

What is Knowledge Relationship Management? 

Knowledge Relationship Management (KRM) is a methodology developed by Predictum for capturing research knowledge from your science and engineering teams, storing that knowledge in a structured and accessible way, and making it available to your teams for virtually any future use.  

Supporting IND Applicants: Enter CoBaseKRM

CoBaseKRM is Predictum’s solution to preserving knowledge at every stage of research. 

A collaborative framework for scientists and engineers, CoBaseKRM captures, structures, and interconnects any types of information your staff creates, such as spreadsheet data, analyses, text documents, image files, lab notes, and wiki entries.

The CoBaseKRM platform and its methodology provide invaluable capabilities to address FDA inquiries that may arise during IND applications by quickly helping you furnish relevant, high-quality, documented research knowledge.

CoBaseKRM supports a wide array of other features, including: 

Data Governance

Ensure your data are unified using a standardized format and nomenclature across your project teams. CoBaseKRM helps you create well-defined parameters, units of measurement, and data capture templates to ensure your team members can search for, retrieve, and use data from past research.

A gif in which a user drags and drops boxes that contain global parameters.

A record is created every time you upload knowledge, providing an audit trail. Researchers can use collaboration features to make notes of their experimentation at upload and record the context.

Data Capture at Every Research Step

Build projects and collect data from every step of your research using CoBaseKRM’s Network Map for visually representing the core data elements of projects and experiments. 

Gif of a user adding nodes to the CoBase network map feature in the CoBase user interface.

Network maps are a simple, flexible way to visually represent the process steps, tasks, or phases in your research and experimentation. Robust capabilities enable you to store data, knowledge, documents, and your team members’ thinking for every step of your work. 


Perform fast, high-level searches that support granularity of detail down to individual data points. Search across all of your projects and research knowledge.

Define filters and conditions to search between data sets easily and retrieve relevant results that are fit for purpose. Access original experiments, data sets, data points and all related information.

CoBaseKRM search feature

CoBaseKRM’s search capabilities are fast, right down to the level of data points, helping you search for and retrieve relevant information and address new problems with old data.


Given the causes for IND application failures, can the KRM methodology mitigate Investigational New Drug (IND) application rejection? 

Our assessment is that KRM hosts broad range of robust capabilities for IND applicants. On one hand, our platform helps strengthen your approach to the research you perform without disrupting the organizational culture in which you carry out your research.  

On the other hand, we recognize the essential need for relevant and high-quality data and analyses, which play a crucial role in the success of IND applications, and how they require a unified collaboration and management solution.

Can CoBaseKRM help you with your drug applications? We’d love to chat.

For over 25 years, Predictum has enabled companies to achieve higher levels of productivity, operational improvement and innovation, and realize significant savings in cost, materials, and time. Our team of engineers, data scientists, statisticians, and programmers leverages deep expertise across various industries to provide our clients with unique solutions and services that transform data into insightful discoveries in engineering, science, and research. To get in touch with our team, visit

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