Rare diseases (RDs) are health conditions that affect a small number of people as compared to other more common diseases in the general population. In the US, it is defined as a condition affecting fewer than 7 people in 10,000 people and in the UK, fewer than 5. Eighty percent of RDs are genetic in origin and disproportionately affect children. India does not have a definition or epidemiology data for RDs, but applying these statistics, we would have about 72 to 96 million affected in India, which is a fairly significant number.
RDs are difficult to diagnose correctly for many reasons. Besides a lack of understanding of their pathophysiology, RDs are very complex and heterogeneous. Approximately half of the individuals suspected of RDs are undiagnosed, while those who have received a diagnosis wait on average 5– 6 years. Many cases have been reported with diagnostic delays of several decades. Poor clinical outcomes result from delayed or inaccurate diagnosis including unnecessary medical procedures and a psychological burden on patients and families. Many global RD organizations are systematically connecting their patient registries and even leveraging animal data to improve research and patient outcomes by bridging data gaps.
In a clinical context, Phenotype is often referred to as deviation from normal morphology, physiology or behavior. Deep phenotyping can be defined as the comprehensive analysis of phenotypic abnormalities with a precise description of the individual components of the phenotype.
A key responsibility of a clinician investigating RDs is to describe phenotypes of the patients in addition to those of their family members. This data plays a key role in devising differential diagnosis strategies and interpreting molecular findings.
Traditional methods of describing disease phenotypes are often imprecise and fail to capture the diverse manifestations of a complex genetic disease. For instance, “myopathic electromyography” can be used instead of describing the reasons for the diagnosis which can include reduced duration and reduced amplitude of the action potentials, increased spontaneous activity with fibrillations, positive sharp waves, or a reduced number of motor units in the muscle.
While the benefits of deep phenotyping are well known to the genetic community, reaping these benefits requires a computational approach.
Experienced genetic experts and companies in India are aware of disparate analytical tools across the web. These tools are frequently used for specific functions such as generating a list of implicated diseases or prioritizing variants based on observed phenotypes. However, it is not practical or efficient to apply this approach to every patient. Unfortunately, a lot of value remains unrealized for the entire RD community as this data is not captured, stored and utilized systematically.
One innovative global company in the segment is Boston-based FDNA, which out-licensed a technology that could analyze dysmorphic features and recognizable patterns of human malformations from facial photos. FDNA recently closed $27.5 million in their series B and entered into a series of strategic collaborations with diagnostic and orphan drug companies targeting developed countries. It certainly indicates that such global companies would first focus on higher-value markets like the US and Europe than nascent ones like India. Doesn’t it seem important to have homegrown initiatives uniquely addressing our needs?
Over the last few years in the Indian clinical genetics market, I observed significant room for technological improvements with substantial benefits. However, it was not surprising to see no Indian company in this niche space possibly due to factors such as lack of awareness, small market size, no national RD policy and no Indian orphan drug company.
In early 2018, I founded Genetico with some very talented co-founders (US & India) who are passionate about developing software-based products to accelerate research and improve clinical diagnostics. We have worked at various global research centers, bioinformatics start-ups, software development companies and a $200 million venture capital fund.
One of our products aims to address the above-mentioned challenges and is planned for MVP release in Q3 2019 for a closed group of early adopters with the following functionality:
- Pedigree Editor: Graphical interface to store and manage the clinically relevant history of the patient’s family members.
- Deep phenotyping: Gather phenotypic information of a patient in standardized, globally interoperable nomenclatures.
- Diagnostic assistance: Ranked list of implicated diseases based on computational analysis of observed phenotypes.
- Variant analysis: Basic variant analysis capabilities. For advanced capabilities, please register for our bioinformatics product.
- Data security and privacy: Compliant as per global standards.
- Stealth: Hold tight for this! More to come, especially for collaborators!
If you are actively involved in diagnosing, treating or managing RD patients or have existing data for diagnosed or undiagnosed RD cases, we would like to request your participation in our early access program. As early adopters, you would represent the voice of our community and be the first ones to offer some unique services to your patients.
In addition to the product described above, we are actively recruiting early adopters for our bioinformatics product that can analyze raw genomic data with interpretation and tertiary analysis capabilities (such as genome-wide association studies). We aspire to develop a comprehensive, integrative ‘multi-omics’ platform for research and clinical purposes.
If you are an investor with prior investment(s) in genomics (or related) space and/or strong ties with global molecular diagnostic companies or orphan drug development companies, I would like to request an opportunity to connect with you. Please register to learn more about our early access programs and products.