Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation

CC Chemokine Family Members' Modulation as a Novel Approach for Treating Central Nervous System and Peripheral Nervous System Injury-A Review of Clinical and Experimental Findings

Despite significant progress in modern medicine and pharmacology, damage to the nervous system with various etiologies still poses a challenge to doctors and scientists. Injuries lead to neuroimmunological changes in the central nervous system (CNS), which may result in both secondary damage and the development of tactile and thermal hypersensitivity. In our review, based on the analysis of many experimental and clinical studies, we indicate that the mechanisms occurring both at the level of the brain after direct damage and at the level of the spinal cord after peripheral nerve damage have a common immunological basis. This suggests that there are opportunities for similar pharmacological therapeutic interventions in the damage of various etiologies. Experimental data indicate that after CNS/PNS damage, the levels of 16 among the 28 CC-family chemokines, i.e., CCL1, CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL11, CCL12, CCL17, CCL19, CCL20, CCL21, and CCL22, increase in the brain and/or spinal cord and have strong proinflammatory and/or pronociceptive effects. According to the available literature data, further investigation is still needed for understanding the role of the remaining chemokines, especially six of them which were found in humans but not in mice/rats, i.e., CCL13, CCL14, CCL15, CCL16, CCL18, and CCL23. Over the past several years, the results of studies in which available pharmacological tools were used indicated that blocking individual receptors, e.g., CCR1 (J113863 and BX513), CCR2 (RS504393, CCX872, INCB3344, and AZ889), CCR3 (SB328437), CCR4 (C021 and AZD-2098), and CCR5 (maraviroc, AZD-5672, and TAK-220), has beneficial effects after damage to both the CNS and PNS. Recently, experimental data have proved that blockades exerted by double antagonists CCR1/3 (UCB 35625) and CCR2/5 (cenicriviroc) have very good anti-inflammatory and antinociceptive effects. In addition, both single (J113863, RS504393, SB328437, C021, and maraviroc) and dual (cenicriviroc) chemokine receptor antagonists enhanced the analgesic effect of opioid drugs. This review will display the evidence that a multidirectional strategy based on the modulation of neuronal-glial-immune interactions can significantly improve the health of patients after CNS and PNS damage by changing the activity of chemokines belonging to the CC family. Moreover, in the case of pain, the combined administration of such antagonists with opioid drugs could reduce therapeutic doses and minimize the risk of complications.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Targeting Members of the Chemokine Family as a Novel Approach to Treating Neuropathic Pain

Neuropathic pain is a debilitating condition that affects millions of people worldwide. Numerous studies indicate that this type of pain is a chronic condition with a complex mechanism that tends to worsen over time, leading to a significant deterioration in patients' quality of life and issues like depression, disability, and disturbed sleep. Presently used analgesics are not effective enough in neuropathy treatment and may cause many side effects due to the high doses needed. In recent years, many researchers have pointed to the important role of chemokines not only in the development and maintenance of neuropathy but also in the effectiveness of analgesic drugs. Currently, approximately 50 chemokines are known to act through 20 different seven-transmembrane G-protein-coupled receptors located on the surface of neuronal, glial, and immune cells. Data from recent years clearly indicate that more chemokines than initially thought (CCL1/2/3/5/7/8/9/11, CXCL3/9/10/12/13/14/17; XCL1, CX3CL1) have pronociceptive properties; therefore, blocking their action by using neutralizing antibodies, inhibiting their synthesis, or blocking their receptors brings neuropathic pain relief. Several of them (CCL1/2/3/7/9/XCL1) have been shown to be able to reduce opioid drug effectiveness in neuropathy, and neutralizing antibodies against them can restore morphine and/or buprenorphine analgesia. The latest research provides irrefutable evidence that chemokine receptors are promising targets for pharmacotherapy; chemokine receptor antagonists can relieve pain of different etiologies, and most of them are able to enhance opioid analgesia, for example, the blockade of CCR1 (J113863), CCR2 (RS504393), CCR3 (SB328437), CCR4 (C021), CCR5 (maraviroc/AZD5672/TAK-220), CXCR2 (NVPCXCR220/SB225002), CXCR3 (NBI-74330/AMG487), CXCR4 (AMD3100/AMD3465), and XCR1 (vMIP-II). Recent research has shown that multitarget antagonists of chemokine receptors, such as CCR2/5 (cenicriviroc), CXCR1/2 (reparixin), and CCR2/CCR5/CCR8 (RAP-103), are also very effective painkillers. A multidirectional strategy based on the modulation of neuronal-glial-immune interactions by changing the activity of the chemokine family can significantly improve the quality of life of patients suffering from neuropathic pain. However, members of the chemokine family are still underestimated pharmacological targets for pain treatment. In this article, we review the literature and provide new insights into the role of chemokines and their receptors in neuropathic pain.