Simcyp's drug modelling programme enhanced to better reflect real life
Simcyp, a specialist in the simulation and prediction of clinical drug-drug interactions, has expanded the capabilities of its population-based ADME simulator.
Simcyp, a specialist in the simulation and prediction of clinical drug-drug interactions, has expanded the capabilities of its population-based ADME simulator.
Version 9.0, released today, was developed in consultation with the Simcyp Consortium of global pharmaceutical companies including Pfizer, AstraZeneca and Johnson & Johnson. The Consortium's top priority was the implementation of a model accommodating the effects of influx and efflux transporters, which are increasingly becoming a focus for regulatory agencies, including the US FDA.
The transporter feature further advances the simulator's ability to reflect "real-life" population variability in the processes of drug absorption, distribution, metabolism and excretion (ADME) through the conduct of studies in virtual human populations.
Drug development researchers using Simcyp Version 9.0 now have the capability of modelling the absorption of drugs that are inhaled or applied to the skin. In addition, enhancements to trial design elements within the Simulator provide greater flexibility to assess the potential outcomes of Phase I, II or III clinical trials early in the drug development process. Examining complex and potentially dangerous scenarios and assessing the likelihood of such cases in the safety of a computer allows clinical studies to be optimised, prioritised or even abandoned.
Professor Amin Rostami-Hodjegan, director of scientific r&d at Simcyp, said: "The realisation that there is virtually no end to the number of various studies which would be required to cover all possible permutations of clinical scenarios and patient populations in real life, has encouraged implementation of more modelling and simulation strategies into drug development.
"The new version of the Simcyp Simulator takes us, once again, another step towards the optimal use of routinely generated in vitro data and the integration of relevant prior knowledge to inform drug development processes".
