Pain remains one of the leading causes of hospital attendance worldwide. Both acute and chronic pain contribute significantly to disability and poor quality of life. In tertiary care hospitals, pain is frequently associated with surgical procedures, trauma, cancer, and musculoskeletal conditions.

Pain treatment relies on NSAIDs, acetaminophen, opioids, and adjuvant therapies. Despite their wide use, these drugs present major limitations. NSAIDs are associated with GI toxicity, renal impairment, and cardiovascular risk. Opioids, though effective, pose risks of dependence and overdose, and their long-term use is discouraged. Neuropathic pain is especially difficult to treat and often responds poorly to standard analgesics.

The complexity of pain mechanisms makes discovery of new analgesics difficult. Multiple pathways involving prostaglandins, cytokines, neurotransmitters, ion channels, and central nervous system modulation contribute to pain transmission. Traditional drug discovery approaches are slow, costly, and prone to failure.

Artificial Intelligence provides new solutions. AI can analyze hospital clinical datasets, identify patterns in analgesic response, predict adverse effects, and support identification of drug targets. AI-based drug discovery can accelerate screening, optimize molecular structures, and prioritize candidates with improved ADMET profiles.

Tertiary care teaching hospitals are ideal for such integration due to high patient load, advanced diagnostic facilities, and availability of clinical and academic expertise. This research focuses on the clinical pain burden and how AI tools can support the discovery of safer, non-opioid analgesics.

2.1 Study Design

A hospital-based retrospective analysis was performed with an AI-driven predictive module for analgesic discovery.

2.2 Patient Cohort

Adult patients receiving treatment for pain were included.

2.3 Data Collection

Variables included:

• Age, sex, BMI
• Pain type, severity, mechanism
• Diagnosis categories
• Comorbidities
• Medication class and exposure
• Pain outcomes and ADRs

2.4 Statistical Analysis

Pain score changes were analyzed using appropriate tests with significance set at p < 0.05.

2.5 AI Modeling

ML models were trained on known activity datasets and hospital-derived patterns. Model evaluation used accuracy, AUROC, precision, recall, and confusion matrix assessment.

Table 1: Baseline Demographic Characteristics of Study Population (n = 180)

Table 2: Distribution of Pain Type and Pain Mechanism

Table 3: Pain Severity Distribution (Numeric Rating Scale – NRS)

Table 4: Major Diagnoses Among Patients Presenting with Pain

Table 5: Common Comorbidities in Study Population

Table 6: Analgesic Utilization Pattern Observed in Hospital Practice

Table 7: Pain Score Improvement After Analgesic Treatment

Table 8: Adverse Drug Reactions (ADRs) Reported During Analgesic Therapy

This study confirms that pain remains a major clinical burden in tertiary care hospitals. Acute pain cases were more frequent, likely due to post-operative and trauma-related admissions. Chronic pain remains substantial, including cancer pain and neuropathic pain conditions.

Prescription trends showed high use of paracetamol and NSAIDs, reflecting cost-effectiveness and safety in short-term use. Opioids were less used, likely due to regulatory and safety concerns. However, opioid-related ADRs were still present.

Pain improvement was significant overall, but chronic and neuropathic pain showed reduced response. This aligns with clinical evidence that chronic pain requires multimodal management strategies.

ADRs highlight the limitations of current therapy. NSAID GI toxicity and opioid constipation remain common. These safety concerns strengthen the need for alternative non-opioid therapies.

AI models demonstrated strong predictive ability. Identifying targets such as TRPV1 and Nav1.7 is significant because these targets are linked to nociception and pain signaling and offer potential for non-opioid analgesics. AI can help optimize candidate molecules and reduce toxicity risk early in development.

The study demonstrates that real-world hospital pain data can support AI-driven analgesic discovery. Current analgesic use shows effective acute pain control but inadequate chronic and neuropathic pain relief. ADRs remain a challenge. AI models successfully predicted promising analgesic targets and candidate compounds, suggesting a pathway toward safer non-opioid drug development.

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