Bioxcel’s popularity grew exponentially following the recent news
that Alexion Pharmaceuticals is using their platform “Orphan Drug Suite” to assist the orphan drug company in developing and/or discovering new treatments.
In this exclusive interview with Rare Disease Report (RDR), Krishnan Nandabalan, PhD, President, CSO and Co-Founder, BioXcel Corporation
talks about this exciting new software program and its potential value to companies/institutions interested in orphan drug discovery and development.
RDR: What is Orphan Drug Suite?
Orphan Disease Suite, part of our Big Data Innovation Lab, is a first-in-class platform that enables the discovery and development of new product candidates to treat rare and ultra-rare diseases. The Suite characterizes more than 9,000 orphan diseases based on need for transformative care ( ie, Life saving, remove the need for organ transplantation or delays, delays need for surgery, reduces or eliminates hospitalization, significant reduction in morbidity leading to substantial increase in quality of life), and utilizes this analysis and meta-data to identify therapeutic candidates including RNA, gene and cell therapies as well as antibody and small molecule therapies.
In addition to supporting the discovery of new therapeutic candidates, PharmGPS Orphan Disease Suite offers robust predictive capabilities, enabling drug developers to forecast the potential success of a pipeline candidate based on criteria such as clinical performance; potential for regulatory approvals; commercialization potential and attractiveness to potential partners/licensors/acquirers.
Why did the company focus rare diseases?
There are more than 9,000 diseases that currently meet the criteria for orphan designation. The majority of these diseases have no FDA-approved treatments, creating a significant unmet medical need and a large market opportunity for pharma and biotech companies.
How is orphan drug suite different from other programs?
The Orphan Disease Suite of PharmGPS is a unique analytics platform that combines Big Data approaches with traditional data mining and processing. Scientific and clinical data that pertain to any specific disease – for example, a liver ailment, is analyzed in the context of the unmet need of that specific disease resulting in the development of safe and efficacious therapeutic solutions that will create a transformative drug for that disease. PharmGPS is unique in that it aggregates and analyzes any and data types to achieve this aim. The different data sources include epidemiological, scientific, clinical, genomic, electronic medical records, and patient reported data among many others.
How was the program developed and how is it maintained/updated?
The orphan disease suite of PharmGPS was developed as a result of working with pharmaceutical companies interested in developing drugs for orphan diseases. The methodology used in PharmGPS was developed iteratively over several programs aimed at discovering new drugs for orphan diseases, which were later validated through rigorous experimentation. The platform consists of raw data accessed from any and every relevant public source which is updated on a daily basis. BioXcel is equipped to do this with its multi-disciplinary team of more than 70 analysts coming from scientific, medical, commercial and IT-Big Data backgrounds. The infrastructure is maintained in the cloud so that it is accessible universally.
Can you provide an example of how the program can be used to:
Find an old unused treatment for a rare disease?
An old unused treatment can be repurposed or reformulated in one of three ways:
Discovering new applications for existing drugs by utilizing novel discoveries in the field
Developing different formulations for an old drug that results in repurposing
Creating new combinations of two or more previously used drugs that result in repurposing.
With a pool of over 18,000 active pharmaceutical ingredients—including those in development or simply “parked’, the platform is able to assess for and prioritize the ingredients that will most likely have a ‘transformative’ effect for a given disease indication based on a defined set or requirements.
Develop a better understanding of the pathophysiology of a rare disease based on data from more common ailments?
The methodology used to understand Rare Disease pathophysiology is twofold: understanding the medical discipline that describes conditions observed during the disease state and biological processes underlying the condition. This information is typically generated using natural language processing, pathway mining and interaction mapping, to identify the severity of the disease based on the clinical symptoms (We have over 1500 symptoms mapped to 9000 diseases