The first 15 virtual patients have been modeled by partners from the University of Catania based on the data received from the CRO that manages the clinical trial in India.
These virtual patients have been also used for the initial tuning phase of the UISS-TB in silico platform which has already performed very well in terms of predictivity.
UISS-TB is a computational platform based on a sophisticated model capable of predicting the progression and dynamics of the tuberculosis disease treated with specific therapeutic approaches. The platform is customized on real data from patients recruited in the phase IIb clinical trial officially launched last October at AIIMS, in India.
Later on, the computational model will expand the data from real patients with data from virtual (simulated) patients, through advanced modeling approaches and adaptive Bayesian methodologies. Thanks to the contribution of virtual patients, it will be possible to predict the effects of long-term combined therapy and to promptly detect any adverse or low-efficacy effects, thus suggesting appropriate corrective maneuvers.
The use of UISS-TB computational platform to predict the human immune system response to tuberculosis represents a pioneering approach in the field of drug development and clinical research in general, as for the first time ever a computational model is applied in the clinical trial pipeline.
The tangible benefits of such approach will translate into a reduction and optimization of time and cost of the clinical trial. Finally, when the phase IIb clinical trial will be over and if it proves the effectiveness of the proposed therapy, the computational approach will provide strong validation bases for the phase III, the final phase before placing on the market.