Mechanistic, translational, quantitative pain assessment tools in profiling of pain patients and for development of new analgesic compounds

• Human experimental pain models can use specific pain mechanism and specific drug actions.
• Pain biomarkers can indicate pain-mechanisms involved and how analgesics modulate pain.
• Pain biomarkers can assess pain from different tissues.
• Multi-modal, multi-tissue, multi-mechanism pain assessment approaches can better individualise treatment and improve analgesic drug development.

BackgroundMechanistic, translational, human experimental pain assessment technologies (pain biomarkers) can be used for: (1) profiling the responsiveness of various pain mechanisms and pathways in healthy volunteers and pain patients, and (2) profiling the effect of new or existing analgesic drugs or pain management procedures. Translational models, which may link mechanisms in animals to humans, are important to understand pain mechanisms involved in pain patients and as tools for drug development. This is urgently needed as many drugs which are effective in animal models fail to be efficient in patients as neither the mechanisms involved in patients nor the drugs’ mechanistic actions are known.AimThe aim of the present topical review is to provide the basis for how to use mechanistic human experimental pain assessment tools (pain biomarkers) in the development of new analgesics and to characterise and diagnose pain patients. The future aim will be to develop such approaches into individualised pain management regimes.MethodExperimental pain biomarkers can tease out mechanistically which pain pathways and mechanisms are modulated in a given patient, and how a given compound modulates them. In addition, pain biomarkers may be used to assess pain from different structures (skin, muscle and viscera) and provoke semi-pathophysiological conditions (e.g. hyperalgesia, allodynia and after-sensation) in healthy volunteers using surrogate pain models.ResultsWith this multi-modal, multi-tissue, multi-mechanism pain assessment regime approach, new opportunities have emerged for profiling pain patients and optimising drug development. In this context these technologies may help to validate targets (proof-of-concept), provide dose–response relationships, predicting which patient population/characteristics will respond to a given treatment (individualised pain management), and hence provide better understanding of the underlying cause for responders versus non-responders to a given treatment.ConclusionIn recent years, pain biomarkers have been substantially developed to have now a role to play in early drug development, providing valuable mechanistic understanding of the drug action and used to characterise/profile pain patients. In drug development phase I safety volunteer studies, pain biomarkers can provide indication of efficacy and later if feasible be included in clinical phase II, III, and IV studies to substantiate mode-of-action.ImplicationsRefining and optimising the drug development process ensures a higher success rate, i.e. not discarding drugs that may be efficient and not push non-efficient drugs too far in the costly development process. Mechanism-based pain bio-markers can help to qualify the development programmes and at the same time help qualifying them by pain profiling (phenotyping) and recognising the right patients for specific trials. The success rate from preclinical data to clinical outcome may be further facilitated by using specific translational pain bio-markers. As human pain biomarkers are getting more and more advanced it could be expected that FDA and EMA in the future will pay more attention to such mechanism-related measures in the approval phase as proof-of-action.