[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Spatially defined peptidomimetics for pain relief and for targeted cancer therapy and diagnostics

Cancer is a disease that has plagued scientists for decades, and how to treat cancer and its complications are inevitable topics in current scientific research. Cancer pain is a major factor that reduces the quality of life of patients. Therefore, the development of analgesic agents with minimal adverse side effects, especially with low addiction, has attracted more and more attention. Among them, opioid analgesics are widely used to alleviate cancer pain and improve the quality of life of patients with advanced cancer, such as in the palliative therapy. Although peptide drugs are efficient, selective and safe, they have several unignorable disadvantages such as poor biological stability, rapid excretion, difficulty in penetrate blood brain barrier. In order to solve these problems, peptidomimetics were developed by introducing unnatural/modified amino acids, decorated peptide backbone, conformational restrictions and secondary structure mimics in peptide sequence. Compared with peptides, peptidomimetics have improved biological stability, increased bioavailability, high affinity and selectivity for receptor binding, and decreased adverse side effects. As the second part of this thesis, I explored the opportunity to design peptide-functionalized responsive biomaterials for the detection of cancer cell and the selective delivery of cytotoxic drugs. The conjugation of peptides with biomaterials enhanced the stability of the loaded drugs, improved targeted delivery, decreased side effects, and increased bioavailability. The precise and controllable drug delivery platform has profound application prospects in cancer treatment. Grafting specific peptides sequence on the surface of biomaterials can satisfy different drug delivery demands according to the characteristics of both peptides and biomaterials. For example, the introduction of tumor-targeting peptides can guide biomaterials into tumor lesions, and blood-brain barrier (BBB) shuttle peptides can lead biomaterials to penetrate the BBB, etc.