Chronic pain is a complex disease suffered by about 20% of Europeans. Up to 60% of these patients do not experience adequate pain relief from currently available analgesic combinational therapies and/or suffer confounding adverse effects. Of the many conceivable combinations, only a few have been studied in formal clinical trials. Thus, physicians have to rely on clinical experience when treating chronic pain patients. The vision of the QSPainRelief consortium is that alternative novel drug combinations with improved analgesia and reduced adverse effects can be identified and assessed by mechanism-based Quantitative Systems Pharmacology (QSP) in silico modelling. This is far cheaper and less time-consuming than clinical trials alone.
We will develop an in silico QSPainRelief platform which integrates recently developed 1) physiologically based pharmacokinetic model to quantitate and adequately predict drug pharmacokinetics in human CNS, 2) target-binding kinetic models, 3) cellular signalling models, and 4) a proprietary neural circuit model to quantitate the drug effects on the activity of relevant brain neuronal networks, that also adequately predicts clinical outcome. This platform will include patient characteristics such as age, sex, disease status, and genotypes, and will predict efficacy and tolerability of a wide range of analgesic and other centrally active drug combinations, and rank these. The best combinations will then be validated in a suitable animal model, in two clinical studies in healthy volunteers, as well as in real-world clinical practice.
Quantitative insights and confirmed effective combinational treatments will result in a game-changer by improving the management of pain in individuals and stratified subpopulations of chronic pain patients and greatly reduce the huge burden on health-care providers. It would also increase the understanding of chronic pain in general, and trigger the development of even better combination therapies in the future.