Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

TREASURE: Text Mining Algorithm Based on Affinity Analysis and Set Intersection to Find the Action of Tuberculosis Drugs against Other Pathogens

Tuberculosis (TB) is one of the top causes of death in the world. Though TB is known as the world’s most infectious killer, it can be treated with a combination of TB drugs. Some of these drugs can be active against other infective agents, in addition to TB. We propose a framework called TREASURE (Text mining algoRithm basEd on Affinity analysis and Set intersection to find the action of tUberculosis dRugs against other pathogEns), which particularly focuses on the extraction of various drug–pathogen relationships in eight different TB drugs, namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin. More than 1500 research papers from PubMed are collected for each drug. The data collected for this purpose are first preprocessed, and various relation records are generated for each drug using affinity analysis. These records are then filtered based on the maximum co-occurrence value and set intersection property to obtain the required inferences. The inferences produced by this framework can help the medical researchers in finding cures for other bacterial diseases. Additionally, the analysis presented in this model can be utilized by the medical experts in their disease and drug experiments.

Analgesic Efficacy of Nefopam as an Adjuvant in Patient-Controlled Analgesia for Acute Postoperative Pain After Laparoscopic Colorectal Cancer Surgery

Despite rapid advancements in laparoscopic surgical techniques and perioperative management, postoperative pain remains a significant clinical issue. We examined the analgesic efficacy of nefopam as an adjuvant in patient-controlled analgesia (PCA) for acute postoperative pain in patients undergoing laparoscopic colorectal cancer surgery. We retrospectively analyzed the medical records of 120 patients who did or did not receive 80 mg of nefopam as an adjuvant in fentanyl PCA; they were allocated to the nefopam (n = 60) or non-nefopam group (n = 60). The demographic, clinical, and anesthetic data, with data on pain severity and opioid administration at the postoperative anesthesia care unit (PACU) on postoperative days (PODs) 1, 3, and 5, were compared between the groups. The pain score and opioid administration did not differ at the PACU or on PODs 1, 3, or 5. The day of PCA discontinuation, time to pass flatus, length of the hospital stay, and incidence of nausea/vomiting, dizziness, and headache also did not differ between the groups. Fentanyl PCA with 80 mg of nefopam as an adjuvant did not have a superior analgesic effect after laparoscopic colorectal cancer surgery.