During the preclinical phase of drug development, gathering sufficient data is crucial to determine the viability of a new compound for first-in-human trials. However, despite best efforts, failure rates during early clinical phase can be as high as 70%. This is largely due to the challenge of accurately translating animal results to patients.
neuroPhINDr is our answer to the problem of low translational value of preclinical neuroimaging. With neuroPhINDr, we aim to shorten time-to-market, increase confidence in preclinical results and increasing success rate in phase I trials.
neuroPhINDr is a set of highly optimised and standardised protocols for data acquisition, processing and analysis.
It combines multiple modalities of brain imaging and corroborative techniques to create a comprehensive fingerprint of CNS effect of a new compound. neuroPhINDr measures:
All modalities are acquired in each subject during a single session, maximising experimental output
and minimising the requirement for large groups.
Data is then processed using a fully automated cloud based pipeline. A comprehensive
quality assurance report is reviewed by one of our experts before data is cleared for analysis.
Rigorous statistical analysis is also preformed in the cloud, with comprehensive
report automatically generated at the end of the process.
Our experts then review and interpret the results, before a final report
is delivered to our clients.
Through standardisation, optimisation and automation, we slash times to
deliver results from months to a few weeks.
We have developed a drug classification algorithm which uses our multi-modal results to compare a novel compound agains a number of well-establish drugs. With our currently limited database, we managed to achieve accuracy levels higher than 80%.
neuroPhINDr classification allows us to confirm a new compound has the desired therapeutical effect, but can also suggest secondary indications, opening up new potential uses for your therapy.
By comparing your molecule with other successful drugs already validated and in the market, we can help increase your confidence moving into early clinical trials.
We are currently working with our partners in academia to collect an extensive database of validated CNS active drugs in order to further train our AI-powered drug classifier.
We are also in negotiations with our collaborators to get access to clinical data. This will allow us to create modelling tools to correlate preclinical and clinical results.
Finally, we plan to include compounds that failed in clinical trials in order to increase our capability to predict how your novel compound is going to perform going into humans.
