Pain is one of the most common and distressing clinical symptoms encountered in both outpatient and inpatient settings. It affects individuals of all age groups and significantly reduces quality of life through disability, emotional distress, sleep disturbances, and loss of productivity. Acute pain commonly arises from trauma, surgery, infection, and inflammation, while chronic pain persists beyond normal healing and is increasingly viewed as a complex disease rather than only a symptom.

Chronic pain is now recognized as a public health issue due to high prevalence and long-term functional impairment. Many chronic pain conditions such as osteoarthritis, neuropathic pain, low back pain, cancer pain, and post-surgical pain are difficult to manage effectively. Existing analgesic drugs such as NSAIDs, acetaminophen, opioids, local anesthetics, antidepressants, and anticonvulsants are widely used but have significant limitations.

NSAIDs are effective for inflammatory pain but can cause gastrointestinal bleeding, renal impairment, and cardiovascular risks. Acetaminophen is safer but may cause hepatotoxicity at higher doses. Opioids are powerful analgesics but are limited by tolerance, dependence, sedation, respiratory depression, and addiction. The opioid crisis has intensified the need for safer alternatives, particularly non-opioid therapies.

Traditional drug discovery takes many years and requires high investment, yet many candidates fail due to toxicity or poor efficacy. Pain pathways are complex, involving multiple receptors, neurotransmitters, ion channels, and inflammatory mediators. Additionally, animal models often fail to translate into human success, making analgesic development more difficult.

Artificial Intelligence (AI) has emerged as a powerful tool to accelerate drug discovery. AI algorithms can analyze large datasets, identify novel targets, predict drug-target interactions, optimize molecular properties, and forecast toxicity risks. AI-based drug repurposing can also identify approved drugs with new analgesic potential.

A tertiary care teaching hospital provides a valuable environment for AI-driven analgesic discovery due to its access to diverse patient populations and rich clinical data. Hospital EHRs contain demographics, diagnoses, pain scores, drug prescriptions, laboratory results, imaging, and outcomes. AI models can identify prescribing patterns, predict response, and highlight patient subgroups at risk for adverse drug reactions.

2.1 Study Design

This was a translational, AI-assisted drug discovery study conducted at a tertiary care teaching hospital. The study combined:

• Retrospective analysis of de-identified EHR data
• AI-driven virtual screening and compound prioritization
• In-silico ADMET filtering and lead selection

2.2 Patient Selection

Adult patients (≥18 years) treated for acute or chronic pain were included.

Inclusion criteria:

• Documented pain diagnosis
• At least one pain score record (NRS/VAS)
• Exposure to at least one analgesic medication

Exclusion criteria:

• Missing baseline or follow-up pain score
• Incomplete medication records

2.3 Clinical Data Variables

Data extracted included:

• Demographics: age, sex, BMI
• Pain phenotype: acute/chronic, nociceptive/neuropathic/mixed
• Drug exposures: NSAIDs, paracetamol, opioids, adjuvants

Outcomes: pain score improvement, adverse reactions, discontinuation

2.4 Outcome Labeling

A clinically meaningful response was defined as:

• ≥2-point reduction in NRS/VAS OR
• ≥30% improvement from baseline within the assessment window

Safety outcomes included documented adverse drug reactions (ADRs).

2.5 AI Modeling

Compounds were represented using molecular fingerprints and physicochemical descriptors. ML models used included:

• Random Forest
• XGBoost
• Support Vector Machine (SVM)
• Neural Network

Validation used scaffold-based or clustered splitting to avoid chemical leakage. Metrics included accuracy, precision, recall, F1-score, and AUROC.

2.6 Virtual Screening and Lead Prioritization

The AI pipeline ranked compounds using:

• ML activity prediction scores
• Docking scores (where applicable)

Drug-likeness and ADMET risk prediction

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

Table 2: Clinical Profile and Pain Characteristics

Table 3: Common Comorbidities in Patients

Table 4: Analgesic Prescription Pattern Observed

Table 5: Pain Score Improvement After Treatment

Table 6: Adverse Drug Reactions (ADR) in Analgesic Use

Table 7: Performance of AI Model for Analgesic Candidate Prediction

Table 8: Confusion Matrix of Best Performing AI Model (XGBoost)

This study highlights the value of integrating AI with clinical pain data to support analgesic drug discovery. Most patients had moderate-to-severe pain, demonstrating a high clinical burden. Paracetamol and NSAIDs remained first-line agents due to safety and availability, while opioid use was comparatively lower, reflecting cautious prescribing practices.

Pain improvement was significant overall, especially in acute pain, but chronic and neuropathic pain showed reduced response. This confirms the difficulty of treating chronic pain, which requires multimodal therapy beyond pharmacological treatment.

The ADR profile confirms limitations of existing drugs. NSAIDs caused GI upset and renal impairment, while opioids caused constipation and sedation. These findings reinforce the need for safer non-opioid analgesics.

AI models demonstrated high performance in predicting active candidates. XGBoost performed best, consistent with its strong handling of structured datasets. AI screening identified both validated targets (COX-2) and emerging targets (Nav1.7, TRPV1). These findings show AI’s potential to accelerate discovery and prioritize candidates with better safety profiles.

AI-driven analgesic drug discovery in a tertiary care teaching hospital is feasible and effective. Clinical findings demonstrate significant pain burden and limitations of existing analgesics. AI models, particularly XGBoost, showed strong predictive ability and successfully shortlisted promising non-opioid drug candidates. Future work should include wet-lab validation and clinical translation of AI-generated leads.

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