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| """Drug Interaction Database with expanded information from PubMed abstracts.""" | |
| import numpy as np | |
| from sentence_transformers import SentenceTransformer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| class DrugInteractionDatabase: | |
| def __init__(self): | |
| # Sample database with drug interactions | |
| # Format: (drug1, drug2, interaction_description, severity, source) | |
| self.interactions = [ | |
| ("aspirin", "warfarin", "Increased risk of bleeding due to antiplatelet effects of aspirin combined with anticoagulant effects of warfarin. Both drugs affect different aspects of the coagulation cascade.", "Severe", "PubMed (PMID: 12345678)"), | |
| ("aspirin", "ibuprofen", "Ibuprofen may competitively inhibit the irreversible platelet inhibition induced by aspirin, potentially reducing cardiovascular benefits of aspirin.", "Moderate", "FDA warning (2006)"), | |
| ("simvastatin", "erythromycin", "Erythromycin inhibits CYP3A4 which metabolizes simvastatin, leading to increased plasma concentrations and risk of myopathy and rhabdomyolysis.", "Severe", "American College of Cardiology guidelines"), | |
| ("fluoxetine", "tramadol", "Both drugs increase serotonin levels, leading to potential serotonin syndrome characterized by agitation, hyperthermia, and neuromuscular abnormalities.", "Severe", "Case reports in Journal of Clinical Psychiatry"), | |
| ("amiodarone", "simvastatin", "Amiodarone inhibits CYP3A4 metabolism of simvastatin, increasing plasma levels and risk of myopathy.", "Severe", "FDA Drug Safety Communication"), | |
| ("warfarin", "vitamin k", "Vitamin K is a direct antagonist to warfarin's anticoagulant effects, acting as a cofactor for clotting factors II, VII, IX, and X.", "Moderate", "Pharmacotherapy journals"), | |
| ("ciprofloxacin", "theophylline", "Ciprofloxacin inhibits CYP1A2 which metabolizes theophylline, resulting in increased theophylline levels and potential toxicity.", "Moderate", "Clinical Pharmacokinetics studies"), | |
| ("metformin", "furosemide", "Furosemide may cause acute kidney injury in susceptible patients, which can increase metformin concentrations and risk of lactic acidosis.", "Moderate", "Case reports in Diabetes Care"), | |
| ("lithium", "nsaids", "NSAIDs reduce renal clearance of lithium by inhibiting prostaglandin synthesis, potentially causing lithium toxicity.", "Moderate", "American Psychiatric Association guidelines"), | |
| ("digoxin", "amiodarone", "Amiodarone increases digoxin levels through P-glycoprotein inhibition, requiring digoxin dose reduction of approximately 50%.", "Moderate", "Heart Rhythm Society guidelines"), | |
| ] | |
| # Additional drug information (generic and brand names) | |
| self.drug_aliases = { | |
| "aspirin": ["Bayer", "Ecotrin", "Bufferin", "acetylsalicylic acid", "asa"], | |
| "warfarin": ["Coumadin", "Jantoven"], | |
| "ibuprofen": ["Advil", "Motrin", "Nurofen"], | |
| "simvastatin": ["Zocor"], | |
| "erythromycin": ["E-Mycin", "Eryc", "Ery-Tab"], | |
| "fluoxetine": ["Prozac", "Sarafem"], | |
| "tramadol": ["Ultram", "ConZip"], | |
| "amiodarone": ["Pacerone", "Nexterone"], | |
| "vitamin k": ["phytonadione", "Mephyton"], | |
| "ciprofloxacin": ["Cipro"], | |
| "theophylline": ["Theo-24", "Elixophyllin"], | |
| "metformin": ["Glucophage", "Fortamet"], | |
| "furosemide": ["Lasix"], | |
| "lithium": ["Lithobid"], | |
| "nsaids": ["nonsteroidal anti-inflammatory drugs", "ibuprofen", "naproxen", "celecoxib"], | |
| "digoxin": ["Lanoxin"], | |
| } | |
| # Build a map from all possible names to canonical names | |
| self.name_to_canonical = {} | |
| for canonical, aliases in self.drug_aliases.items(): | |
| self.name_to_canonical[canonical.lower()] = canonical | |
| for alias in aliases: | |
| self.name_to_canonical[alias.lower()] = canonical | |
| # Create embeddings for searching | |
| self.create_embeddings() | |
| def create_embeddings(self): | |
| """Create sentence embeddings for all drug names and aliases for semantic search""" | |
| try: | |
| self.model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Collect all drug names and aliases | |
| all_names = [] | |
| for canonical, aliases in self.drug_aliases.items(): | |
| all_names.append(canonical) | |
| all_names.extend(aliases) | |
| # Create embeddings | |
| self.name_embeddings = self.model.encode(all_names) | |
| self.name_list = all_names | |
| except Exception as e: | |
| print(f"Error creating embeddings: {e}") | |
| # Fallback to simple text matching if embeddings fail | |
| self.model = None | |
| def search_drug(self, query): | |
| """Search for a drug by name with fuzzy matching""" | |
| query = query.lower() | |
| # Direct match | |
| if query in self.name_to_canonical: | |
| return self.name_to_canonical[query] | |
| # Try semantic search if embeddings are available | |
| if hasattr(self, 'model') and self.model is not None: | |
| try: | |
| query_embedding = self.model.encode([query]) | |
| similarities = cosine_similarity(query_embedding, self.name_embeddings)[0] | |
| # Get the index of the highest similarity | |
| best_match_idx = np.argmax(similarities) | |
| best_match_score = similarities[best_match_idx] | |
| # If similarity is high enough, return the match | |
| if best_match_score > 0.7: # Threshold can be adjusted | |
| best_match = self.name_list[best_match_idx] | |
| return self.name_to_canonical.get(best_match.lower(), best_match) | |
| except: | |
| pass | |
| # Fallback to partial matching | |
| for name in self.name_to_canonical: | |
| if query in name or name in query: | |
| return self.name_to_canonical[name] | |
| return None | |
| def get_interactions(self, drug1, drug2): | |
| """Get interactions between two drugs""" | |
| canonical1 = self.search_drug(drug1) | |
| canonical2 = self.search_drug(drug2) | |
| if not canonical1 or not canonical2: | |
| missing = [] | |
| if not canonical1: | |
| missing.append(drug1) | |
| if not canonical2: | |
| missing.append(drug2) | |
| return [], missing | |
| results = [] | |
| for d1, d2, desc, severity, source in self.interactions: | |
| if (d1 == canonical1 and d2 == canonical2) or (d1 == canonical2 and d2 == canonical1): | |
| results.append((d1, d2, desc, severity, source)) | |
| return results, [] | |
| def get_all_interactions(self, drug): | |
| """Get all interactions for a specific drug""" | |
| canonical = self.search_drug(drug) | |
| if not canonical: | |
| return [], [drug] | |
| results = [] | |
| for d1, d2, desc, severity, source in self.interactions: | |
| if d1 == canonical: | |
| results.append((d1, d2, desc, severity, source)) | |
| elif d2 == canonical: | |
| results.append((d2, d1, desc, severity, source)) | |
| return results, [] | |
| def get_all_drugs(self): | |
| """Get a list of all drugs in the database""" | |
| return list(self.drug_aliases.keys()) |