Amongst many benefits of the Intelligent OMICS technology is the ability to identify a small panel of diagnostic markers with high level of specificity and sensitivity. This enable the development of diagnostic tools which are cost effective and often simple to use. Another major benefit is the ability to identify pathways and drivers of disease progression which can help develop new and targeted therapies.
The Intelligent OMICS technology also offers a significant benefit in being able to rapidly screen large numbers of protein or transcriptomic biomarkers, using non-linear in silico methods, to identify biomarkers (singularly or in panels) that address biomedical, physiological and clinical questions in molecular data. This approach can also identify molecular drivers associated with clinical physiological features, specific to the disease population being analysed. The markers identified by these approaches have been shown to have excellent biological relevance and high sensitivity and specificity both for seen and new unseen populations.
In addition to the technical benefits, the Intelligent OMICS technology offers considerable commercial advantages in terms of cost and time. The Insilco approach allows screening of millions of molecular combinations and interactions without the need for extensive wet lab time and costs, “wet lab work” only being require being for validation of the final small selected panel of markers.
To further improve the efficacy of its technology Intelligent OMICS has developed computational methods that have greatly reduce the computational time required for analysis from weeks to days or even hours enabling it to handle the ever increasing sizes of data sets.
A further benefit, from a commercial viewpoint, is that over 50% of the markers identified are classed as novel biomarkers, not previously associated in relevant literature with the condition under study, leading to the opportunity for the development of patentable IP.
The outcomes from the use of Intelligent OMICS technology include improved Clinical Outcomes and Patient Benefits. These result from new and improved diagnostic tools, patient stratification (personalised medicine) and the potential for new therapies developed as a result of the work initiated by Intelligent OMICS. The scope and range of the outcomes achieved is best illustrated by examining the results of some of the projects undertaken using the Intelligent OMICS technology.
Mycobacterium tuberculosis (Human TB)
A publicly available gene expression dataset (Berry et al.) was used to identify a whole blood transcript signature that discriminates cases with latent TB from those with active. The Intelligent OMICS technology was used to derive an optimal gene transcript signature discriminating between healthy cases, latent TB and active TB in a UK cohort(n=120). Final results included:
- A four Gene Panel discriminating between Latent TB and healthy individuals;
- A diagnostic model with 99% sensitivity and 74% specificity, much better than original results with just a panel of four markers compared with 293 in the original study, differentiating between healthy individuals and those with latent TB
Through our wholly-owned Chinese subsidiary and working with Wuhan Pulmonary Hospital we validated the results in Chinese population and we are now develop a new, low cost diagnostic test.
The Intelligent OMICS ANN tools have been used to analyse both pre and post symptomatic Sepsis patient data. In silico work undertaken by a Innovate UK funded study identified mRNA biomarker panels which clearly differentiate between SIRS/sepsis/severe sepsis in a clinically validated patient cohort. Funding is being sought to validate the panel and incorporate into a Point-Of-Care diagnostic.
The company has just completed a contract with DSTL to conduct combination analysis of microarray and qPCR pre-symptomatic data and has been awarded further contracts to analysis a variety of sepsis and other infectious disease data. We believe this demonstrates the reliability and benefits of the Intelligent OMICS technology.
In collaboration with Syngenta and the University of Nottingham the Intelligent OMICS technology was used to examine the drivers of ripening in tomatoes. Ripening mutants of tomatoes were profiled using expression array technologies. The resultant was mined using the company’s ANN technology and a rank order of biomarkers was identified. The top 500 markers were run through the Network Inference algorithm and this identified three key highly influential Transcription Factors (TF) were identified. When a transgenic plant was created, if these TF were suppressed the tomatoes ripened in third of the time, if these TFs were stimulated the tomatoes did not ripen. A patent has been granted to Syngenta based on these results.
Alzheimer’s Disease (AD)
Alzheimer’s disease is a neurodegenerative disease and widely recognised as the most common form of dementia worldwide. Over the years, research and medical efforts to control the disease by targeting the markers of these phenomena have been almost wholly unsuccessful.
Analysis of multiple publicly available molecular datasets using the Intelligent OMICS technology has already yielded novel results, many of which show potential to be used as markers in prognosis and therapy. Understanding how these molecules interact with one another, will allow us to not only choose the correct target for therapy with minimal system interruption, but also develop personalised treatments to ensure maximum effectiveness.
Additionally, these studies have shown that it is possible to differentiate between AD, MCI and cognitively normal individuals with a high degree of sensitivity and specificity using panels of molecular markers. Based on a small but effective group of markers, patents have been filed for a method that provides an indication between healthy subjects and subjects having Alzheimer’s Diseases
Analysis & Development of an Enzyme that is a possible anti-cancer target
Our client a UK based Biotech company employed Intelligent OMICS to:
Source and format suitable RNA Sequence Data; Quantify the variation and mode of action of the candidate enzyme across different cancers and finally to determine the “Interactome” of the enzyme across selected cancer data sets. RNA sequence data was analysed in order to determine the rank order of Transcripts based on their proportional contribution of splicing to the parent gene. The key molecular drivers were also identified using our Network Inference methodology in a cell line system. Further validation studies were undertaken to validate results by using data in public repositories for ACUTE Myeloid Leukaemia patients.
Intelligent OMICS has been contracted by a UK biotechnology company to analyse time series COPD data in order to predict exacerbations before they occur. The objective is to produce a panel of makers that will provide information for an optimised set of markers that would predict exacerbation.
Using the Intelligent OMICS technology we were able to optimise the panel, reducing the number of individual markers to less than 10, whilst increasing sensitivity and specificity to over 90 percent by the use of the technology’s non-linear functionality.
The results will also help determine the structure of the computer based Decision Support Model, including whether it is a single model or a suite of models.