From Technology to Therapy: FDA unlocks new opportunities & challenges for platform therapeutic companies
How new FDA guidelines are unlocking market opportunities for platform technology companies.
The FDA has released a new set of guidelines that may encourage platform therapeutic companies to pursue new drug targets and indications.
Platform therapeutic companies can be divided into two primary categories; those who actively pursue novel targets and those who prioritize licensing their platform technology often opting for well characterized targets.
According to the FDA, platform companies utilize a well understood and reproducible technology that includes a molecular structure, mechanism of action, delivery method, vector, nucleic acid sequence or any combination of the aforementioned.
The technology must:
Be essential to the structure or function of a drug.
Can be adapted for, or used by more than one drug sharing similar structural elements.
Facilitate the development of more than one drug through a standardized process.
A significant value proposition for platform companies lies not only the success of a single program but how many drug programs and indications it can facilitate, thus the desire to be a “designated platform technology”.
To be considered a “designated platform technology” the platform must:
Be used by a drug approved under section 505 of the FD&C Act or section 351 of the PHS Act.
Have preliminary evidence that demonstrates the platform technology can be used by more than one drug without adverse effects on quality, manufacturing or safety.
Data indicates that the technology has a reasonable likelihood to bring significant efficiencies to the drug development, manufacturing or review processes.
Historically, the process for reusing platform technologies in new applications has been long and inefficient. However, new guidelines aim to operationalize this process.
Guidance for Industry : Platform Technology Designation Program for Drug Development
What has changed?
If a platform has an approved ANDA, NDA or BLA companies can request designation of that platform technology to enable leveraging of the technology in new or future applications.
What are the benefits?
Shorter and more efficient timelines
Engaging early with the FDA enables team to receive timely advice.
FDA prioritization
If there is significant public health benefit the FDA may prioritize additional engagements leveraging the designated platform technology.
Have an arsenal of data for subsequent programs
No need to repeat work that was already done, evidence that was used to support the efficacy of a platform technology for a previous submission can be used for subsequent applications. This includes batch, stability and non-clinical safety data.
What is required?
Submissions require the following
Description of the platform technology and how it meets FDA standards.
The approved ANDA, NDA or BLA for the technology.
Identification of shared structural elements between drug products and how the element facilitates the use of the platform technology.
Scientific support for the use of the platform technology across multiple drugs and how this would not affect safety, quality or manufacturing.
Risk assessment to evaluate differences between previous and proposed drug product.
Information to justify why the platform technology would bring significant efficiencies to the drug development , manufacturing or review process.
Read the full document here.
The guidelines are currently in review and the FDA is accepting comments until July 29th.
The road to platform designation became a little easier, now what?
Companies that were traditionally focused solely on licensing their technology might start looking towards developing their own target pipelines. Companies that already have a target pipeline may expand their approach to additional indications and targets.
These processes involve complex decision making and requires a deep understanding of disease biology, patient population, and market needs.
Target selection and indication prioritization challenges
Target selection and indication prioritization are inherently difficult. These challenges become heightened for platform companies who have dedicated the majority of their resources to developing their technology.
Some of these challenges include
Complexity of Disease Biology
Understanding the intricate mechanisms of diseases and identifying relevant molecular targets is a significant challenge. Diseases often involve multiple pathways and interactions, making it difficult to pinpoint the most effective targets for therapeutic intervention.
Multi-Modal Data Integration
Integrating multi-omic public and private data to identify actionable insights requires countless hours of data wrangling and database maintenance.
Cross functional Collaboration
Computational and Translational teams need a medium to share hypothesis, data and collaborate in real time.
Designing the right experiments to evaluate a target
Identifying the in vivo and in vitro experiments that will best evaluate a target is notoriously complex.
Having enough evidence to support a target or indication
Combing through thousands of data points to find enough high conviction evidence to support a target often seems like a never ending task.
The BioBox Data Intelligence Platform: A Solution
The BioBox data intelligence platform can help platform therapeutic companies overcome these challenges. Here's how:
Build a GPS to understand the complexity of disease biology: The backbone of target selection is understanding the biology of the disease. Through a fully customizable knowledge graph teams can map out the important biological concepts and relationships that the care about in the context of a disease. Enabling them to identify serendipitous relationships and novel connections. We break down why this is important here.
Seamless Data Integration: With our API and SDK we make multi-omic data integration as easy as possible so you can leave the data wrangling and Table.Joins behind.
Curate custom target and indication prioritization reports: Teams can leverage the knowledge graph to curate custom target and indication prioritization reports. Prioritize data points on the basis of scientific criteria that matters to the team. Check out our case study on how we enable a research team to develop a target prioritization system for renal cell carcinoma here
Searchable and accessible data in one place. Keep track of all of the data supporting a program or a target in a single unified resource that is accessible by all team members. Search for specific relationships and data points across the graph.
Develop an arsenal of targets. It’s no secret that not all targets make it through the validation phase. After substantial wet lab interrogation many targets get cut from the pipeline. Instead of having to start from ground zero, stockpile prospective targets and have backups for your backups.
Scaleable Resource. The data intelligence platform is designed to grow and scale with your team and data. As new data is streamed into the platform reports and analyses automatically update.
If you would like to learn how we enable therapeutic research teams to save time and resources while creating a collaborative ecosystem for target prioritization book a demo with us here.
About BioBox
BioBox is a data intelligence platform for drug discovery. Rapidly build a custom data graph and deploy graph ML models for target prioritization, indication selection, and MOA analysis. Teams using BioBox make faster and better data driven decisions to de-risk drug programs and get assets into clinical studies quicker.