Computer software, to run partly as a Cloud computing service, enabling patient-specific parameters for complex models of drug-disease interactions to be reconstructed from limited fragments of noise-polluted time-series measurements for individual patients. This means that cutting-edge mathematical control methods can then be deployed in a patient-specific way, to enhance the effectiveness of medication and reduce side-effects. These methods work for complex systems, where the conventional statistical methods employed by Pharma companies have difficulty giving useful results.
In other words, we're using advanced mathematics to achieve personalised medicine for complex diseases, partly by data-mining medical histories in a completely new way. This is achieved without waiting for genetics-based therapies and in a way that cannot be replaced by genetics-based therapies.
Already validated in silico (i.e. using computer simulations), this software is expected to be most relevant for enhancing medication in chronic or degenerative diseases: immediate medical tasks include Type-1 diabetes, haemodynamics and some forms of cancer. The objective is to improve therapeutic outcomes for patients suffering from these diseases.
This technology also has potential relevance in helping to reduce the current Phase II bottleneck in clinical trials, by improving calculation of ADME parameters and generating mathematical strategies for improved drug administration. This will enhance the potential effectiveness and reduce the risk of side-effects of a good drug candidate, and hence improve its success probabilities.
Our flagship application is machine-intelligent Artificial Pancreas software, designed to interrogate medical data to construct personalised diabetes models for people with Type-1 diabetes, and then generate suggested insulin infusion control laws to improve control of blood glucose, steering it to desired levels and keeping it there.
In 2011 Dr Jenny Gunton, of the Garvan Institute of Medical Research and Dr Nigel Greenwood, of NeuroTech Research Pty Ltd, were awarded an Innovative grant by the JDRF in New York, to demonstrate the prototype Neuromathix Artificial Pancreas. Dr Greenwood is a mathematician specialising in artificial intelligence, while Dr Gunton is Head of the Diabetes and Transcription Factors Group at the Garvan. With additional funding provided by the Queensland Government and by the directors of NeuroTech Research Pty Ltd, the team used a mix of simulated and actual human diabetic data to demonstrate the software, with spectacular results (see News).
One of the purposes of this project was to demonstrate that existing medical hardware used for diabetes therapies (insulin pumps and continuous glucose monitors) can have its performance transformed simply by using sophisticated mathematics and high-performance computing.
A dedicated start-up company, Diabetes Neuromathix Pty Ltd, has now been formed to complete the clinical study and trialling of this software and engage in worldwide commercialisation of this intellectual property, to improve therapies for Type-1 and the insulin-using subset of Type-2 diabetes.
Given that the US FDA has a dedicated track for artificial pancreas candidates (due to their urgent need) and the extremely stable, safe design of the Neuromathix Artificial Pancreas, it is anticipated that the first of our tools ('KnowsMe') will reach the market by late 2017, with versions of the Neuromathix Artificial Pancreas software reaching the market in 2017/18 ('ShowsMe') and 2018/19 ('OptimizesMe'), provided sufficient funding is achieved to enable a suitable clinical study and clinical trials.