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What if the results of an inexpensive genome-wide genetic test could be used to target treatment for any common disorder?

Biomarkers of treatment response can be used as companion diagnostics for precision medicine, but they are complicated to design and most miss important aspects of the patient’s biological variation. Our platform harnesses pharmagenic enrichment scores (PES) to integrate an individual’s genetics with systems biology and pharmacology to provide a powerful tool for determining if they will respond more readily to almost any drug. The PolygenRx platform can be implemented as a universal companion diagnostic as it is applicable to any disease with sufficient genetic data and corresponding pharmacological agents with known targets or mode of action. 
 

Applications

This platform would be ideally suited to the clinical trial process in a range of scenarios. For example, the development pipeline of a novel compound, a new trial for a previously failed or abandoned compound, or a repurposing application. Our pharmagenic enrichment score approach could be used to stratify key trial outcomes by each individual’s score, and thus, provide a mechanism to assess whether a companion diagnostic to accompany this compound is warranted. This could not only rescue drugs that would otherwise fail due to response heterogeneity, but also more accurately identify suitable target populations.

This platform is applicable to practically all common disorders, and even rare disorders where common variant modifier data exists. A small selection of examples include:

Neurodegenerative Disorders

Cancer

Autoimmune
Disorders

Kidney Disorders

Asthma and COPD

Musculoskeletal
Disorders

Psychiatric Illness

Heart Disease

Diabetes and
Metabolic Syndrome

How it works

Obtaining genetic data from a patient and genetic data related to the disease

DNA extracted from blood or saliva can be used to obtain DNA sequence information for millions of markers throughout the genome via a rapid and cost-effective genotyping method known as a microarray, or through low-coverage whole genome sequencing. We can even use data from an individual’s own direct to consumer genetics profile.

We can then compare this data with high-quality information related to how these same genetic markers relate to the risk of disease using publicly available genome-wide association study (GWAS, https://en.wikipedia.org/wiki/Genome-wide_association_study) results. These data are typically gathered from population studies with hundreds of thousands of participants, providing the most rigorous information as to how genetics impacts the risk of developing a disorder. These data are available for practically all common human diseases, providing power and versatility to our platform.

Generating the pharmagenic enrichment score

We combine the genetic data collected from patients and determine how those markers impact the clinically actionable components of disease risk using our platforms unique statistical genetics approach. The key feature of our methodology is that we can direct this information selectively to the action of drugs, rather than non-selectively for the disease as a whole. As a result, a continuous score (the pharmagenic enrichment score) is generated using genetic risk for the disorder specifically amongst genes involved with the pharmacology of the drug in question.

Identifying patients most likely to respond to proposed treatment

The pharmagenic enrichment score is scaled relative to an external genetic reference to determine which individuals have a high score. The magnitude of the scaled score is used to prioritise individuals that would gain the most benefit from a given treatment for the condition.

What makes us different?

We do not focus on a single marker of treatment

Competitor companion diagnostics are usually orientated around a single indicator of treatment response, which ignores the biological reality that many factors are involved. We incorporate multiple factors related to the therapeutic target of the drugs, whilst still allowing this complex biological data to be simple to use and clinically interpretable.

We use the most rigorous genetic data available

Our platform leverages genetic factors that are well understood and fixed at birth (common germline variation). In contrast, dynamic and variable state factors used in precision medicine are complicated and subject to rapid changes in response to a host of different stimuli.

Our platform is not limited to a single disease or drug

Currently available companion diagnostic platforms are restricted to a single disease or disease area. We exploit the power of genetics and state-of-the-art statistical approaches to make a universal platform applicable to nearly all common diseases and even rare disorders when sufficient data exists about genetic risk factors across the genome.

Our platform can support population specific data

Existing companion diagnostics cannot take differences between populations into account. Our platform can seamlessly utilise genetic studies from specific populations to maximise the effectiveness of this approach and ensure its equitable use.

Our universal platform is interpretable and scalable

Our approach combines known biological mechanisms with genetics to generate readily interpretable continuous scores. This is enhanced by the scaling step such that each score and its biological significance is relative to a representative population.

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