This analysis applies a structured, rule-based HIV-1 drug resistance interpretation framework that infers antiretroviral susceptibility from detected amino-acid mutations. Resistance levels are assigned by mapping observed mutations to curated rules derived from internationally recognized guidelines and expert knowledge bases, reflecting decades of virological, phenotypic, and clinical evidence.

The rule-based engine prioritizes well-characterized major and accessory resistance mutations and assumes largely additive effects of individual mutations. Mutations listed as key mutations are those that directly contributed to the resistance score for a given drug.

Resistance is quantified using a normalized 0–100 scoring system, where higher scores indicate greater predicted loss of drug activity. Scores are translated into categorical resistance levels as follows:

• Susceptible (S): score < 15 — full or near-full drug activity expected
• Low-Level Resistance (LLR): score 15 to < 50 — minor reduction in susceptibility, clinical activity usually preserved
• Intermediate Resistance (IR): score 50 to < 85 — reduced susceptibility, clinical response may be compromised
• High-Level Resistance (HR): score ≥ 85 — marked loss of activity, drug unlikely to be effective

Key mutations are a curated subset of well-validated amino-acid substitutions that have a direct, interpretable, and clinically meaningful impact on drug susceptibility, and are explicitly used to trigger resistance rules. While this approach provides transparent and clinically interpretable resistance estimates, it does not explicitly model complex mutation–mutation interactions or nonlinear resistance effects. Rare, emerging, or previously uncharacterized mutation patterns may therefore be incompletely captured.

Results should be interpreted in conjunction with clinical history and treatment exposure. For advanced genotype–phenotype inference, probabilistic resistance estimates, and modeling of nonlinear mutation interactions, the PhenoGenX machine-learning pipeline offers a complementary analysis.