Computer software, 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. 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.
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 (Absorbtion, Distribution, Metabolism and Excretion) 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.