Precision medicine is about getting the right drug to the right patient at the right dose. Here at REPROCELL we are developing our Predictive Drug Discovery services by combining clinically relevant fresh tissue assays with clinical, genomic and transcriptomic data to provide a more powerful predictive model for drug development.
Our biorepository and tissue network allow data to be generated from real patient samples to allow for more translational research.
What is Precision Medicine?
Precision medicine can allow you to target your drug to a specific patient cohort by combing clinical, functional, genomic and transcriptomic data. By being able to access various data, drug developers can better understand what patient will benefit and reduce failure in clinical trials. Historically, patients prescribed standard of care treatments do not always respond, with treatments being prescribed on a ‘trial and error’ approach. With precision medicine, the right patient can be prescribed the right drug at the right dose, reducing complications.
Precision medicine services at REPROCELL
In collaboration with the Stratified Medicine Scotland Innovation Centre (SMS-IC) REPROCELL conducted a study that investigated inter-patient response to various standard of care treatments for chronic obstructive pulmonary disease (COPD). Clinical history, as well as pharmacological, genomic and transcriptomic data was collected and used to populate a database that aimed to find associations between patient response and genomics. A full description of the results is currently under review for publication and this can be accessed here.
Achieve patient stratification earlier
The importance of patient stratification is evidenced in Figure 1. The average reduction in TNFα can be seen on the left, but when individual responses are investigated on the right, it becomes clear where problems may arise during the prescribing of these medications in the clinic.
Our current precision medicine focus covers the area of Inflammatory Bowel Disease. The aim of this project is to populate a database powered by machine learning that will find relationships between real patient clinical data, pharmacology data, and gene sequencing. By generating an interface that is easy to use, predictions can be made which will ultimately result in identifying patients which will serve as the target for clinical trials.