--- name: 'pharmacogenomics-agent' description: 'AI-driven pharmacogenomic analysis for precision dosing and adverse event prediction using multi-omics data.' keywords: - pharmacogenomics - precision-dosing - cpic-guidelines - adverse-events - multi-omics measurable_outcome: 'Provides validated dosing recommendations for >50 drugs with 99% concordance to CPIC guidelines.' allowed-tools: - read_file - run_shell_command --- # Pharmacogenomics Agent The **Pharmacogenomics Agent** integrates AI and multi-omics data to predict individual drug responses, optimize medication dosing, and minimize adverse events. It implements CPIC guidelines while leveraging deep learning for complex polygenic drug response phenotypes. ## When to Use This Skill * When interpreting pharmacogenomic variants (CYP450, HLA, transporters) for drug selection. * To predict drug response using transcriptomic and proteomic biomarkers. * For calculating polygenic risk scores for drug efficacy/toxicity. * When optimizing doses for narrow therapeutic index drugs. * To identify drug-drug-gene interactions. ## Core Capabilities 1. **Variant Interpretation**: Translates star allele genotypes (*1/*2) into metabolizer phenotypes and actionable CPIC recommendations. 2. **Multi-Omics Response Prediction**: Deep learning models (DeepDRA, MOViDA) integrate genomic, transcriptomic, and proteomic features for drug response prediction. 3. **Polygenic Risk Scoring**: Combines effects of thousands of variants to stratify patients beyond single-gene pharmacogenomics. 4. **Adverse Event Prediction**: Identifies genetic risk factors for serious adverse reactions (HLA associations, G6PD deficiency). 5. **Dose Optimization**: AI-guided dosing for warfarin, tacrolimus, fluoropyrimidines, thiopurines, and other PGx-guided drugs. 6. **Drug-Drug-Gene Interactions**: Detects complex interactions where genetic variants modify drug interaction severity. ## CPIC-Guided Genes and Drugs | Gene | Drugs | Clinical Impact | |------|-------|-----------------| | CYP2D6 | Codeine, tamoxifen, antidepressants | Metabolizer status affects efficacy/toxicity | | CYP2C19 | Clopidogrel, PPIs, antidepressants | Loss-of-function affects activation | | CYP2C9/VKORC1 | Warfarin | Dose requirements vary 10-fold | | TPMT/NUDT15 | Thiopurines | Myelosuppression risk | | DPYD | Fluoropyrimidines | Severe/fatal toxicity in deficient patients | | HLA-B*57:01 | Abacavir | Hypersensitivity screening | | HLA-B*15:02 | Carbamazepine | SJS/TEN risk in Asian populations | ## Workflow 1. **Input**: Patient genotype data (VCF, genotyping array), medication list, clinical parameters. 2. **Star Allele Calling**: Translate variants to star alleles using Stargazer or PharmCAT. 3. **Phenotype Assignment**: Determine metabolizer status (PM, IM, NM, UM) for each gene. 4. **Guideline Lookup**: Retrieve CPIC/DPWG recommendations for patient's medications. 5. **Multi-Omics Prediction**: Apply deep learning for complex response phenotypes. 6. **Output**: Drug-specific recommendations, dose adjustments, alternative medications, interaction alerts. ## Example Usage **User**: "Interpret this patient's pharmacogenomic panel and provide recommendations for their current medications." **Agent Action**: ```bash python3 Skills/Precision_Medicine/Pharmacogenomics_Agent/pgx_analyzer.py \ --genotype patient_pgx_panel.vcf \ --medications current_meds.json \ --guidelines cpic_dpwg \ --risk_scores oncology_response \ --output pgx_recommendations.json ``` ## AI Models for Drug Response | Model | Architecture | Application | Performance | |-------|--------------|-------------|-------------| | DeepDRA | Autoencoders | Drug response from transcriptomics | AUC 0.99 | | MOViDA | Multi-omics VAE | Interpretable response prediction | State-of-art | | DrugCell | Graph neural network | Drug synergy prediction | Improved over baselines | | PaccMann | Multimodal attention | Cancer drug sensitivity | Clinical translation | ## Polygenic Drug Response Beyond single-gene PGx, polygenic scores capture: - **Efficacy polygenic scores**: Statin LDL response, antidepressant remission - **Toxicity polygenic scores**: Metformin GI intolerance, opioid dependence risk - **Combined scores**: Integrating PRS with PGx for personalized prediction ## Prerequisites * Python 3.10+ * PharmCAT or Stargazer for star allele calling * CPIC/DPWG guideline databases * Deep learning frameworks (PyTorch) * Optional: Expression data for multi-omics models ## Related Skills * Variant_Interpretation - For general variant classification * Drug_Repurposing - For alternative drug identification * Clinical_Trials - For PGx-guided trial matching ## Implementation Notes **Clinical Integration**: - Returns structured FHIR-compatible recommendations - Supports CDS Hooks for real-time EMR alerts - Audit trail for clinical decision support **Quality Metrics**: - Validated against PharmGKB annotations - Concordance with reference laboratory calls - Regular updates with new CPIC guidelines ## Author AI Group - Biomedical AI Platform --- Source attribution: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Dave AI resource metadata Creator / publisher: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Healthcare & Life Sciences Roles: Clinical Research, Health Informatics, Medical Writer Specialties: API Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b65fe02e2941366e8ba2073d978e6d3dc5a482abe6f262c68643acf893528a69 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.