tooluniverse-drug-regulatory

# Drug Regulatory Research **Regulatory status depends on jurisdiction.** FDA approval does not equal EMA approval — check the specific market the user is asking about. Generic availability depends on BOTH patent expiry AND regulatory approval — a patent may have expired but no ANDA may yet be filed or approved. Exclusivity codes (NCE, ODE, PED) can block generics even after patent expiry; always check `FDA_OrangeBook_get_exclusivity` before concluding a generic can enter. A 505(b)(2) NDA is not a generic — it requires its own clinical data and gets its own exclusivity period. **LOOK UP DON'T GUESS**: never assume NDA numbers, exclusivity dates, or ATC codes — always call FDAGSRS, Orange Book, and RxClass tools to retrieve current data; regulatory status changes with new approvals and expirations. Regulatory intelligence for drugs: identify FDA substances, classify drugs by therapeutic category, check approval and generic status, retrieve label sections, and find clinical trials. ## When to Use - "What is the FDA regulatory status of semaglutide?" - "Is there a generic for Humira?" - "What ATC class does metformin belong to?" - "Get adverse reactions from the ibuprofen drug label" - "When does the patent for Eliquis expire?" - "List all drugs in the ACE inhibitor class" - "Find clinical trials for a biosimilar of adalimumab" ## NOT for (use other skills instead) - Drug-drug interactions -> Use `tooluniverse-drug-drug-interaction` - Pharmacogenomics / dosing by genotype -> Use `tooluniverse-pharmacogenomics` - Drug mechanism of action / target binding -> Use `tooluniverse-drug-mechanism-research` - Drug repurposing / new indications -> Use `tooluniverse-drug-repurposing` --- ## Workflow Overview ``` Input (drug name / brand name / UNII) | v Phase 1: Substance Identification -- FDAGSRS_search_substances, FDAGSRS_get_substance | v Phase 2: Drug Classification -- RxClass_get_drug_classes, RxClass_find_classes | v Phase 3: Approval & Generic Status -- FDA_OrangeBook_search_drug, FDA_OrangeBook_check_generic_availability | v Phase 4: Patent & Exclusivity -- FDA_OrangeBook_get_patent_info, FDA_OrangeBook_get_exclusivity | v Phase 5: Label Parsing -- DailyMed_parse_adverse_reactions, DailyMed_parse_dosing, etc. | v Phase 6: Clinical Trials -- search_clinical_trials | v Phase 7: Pharmacovigilance -- FAERS_count_reactions_by_drug_event (param: medicinalproduct) | v Phase 8: Literature & Approval -- PubMed_search_articles, OpenFDA_get_approval_history, RxNorm_get_drug_names ``` > **Supplementary tools** (not in core phases but useful): > - `OpenFDA_get_approval_history` — full FDA submission/approval history (requires `operation` param) > - `FAERS_count_reactions_by_drug_event` — top adverse events by report count (param: `medicinalproduct`, ALL CAPS) > - `RxNorm_get_drug_names` — resolve drug to RXCUI and brand names > - `drugbank_vocab_search` — DrugBank ID, CAS, UNII lookup > - `PubMed_search_articles` — regulatory and clinical literature --- ## Phase 1: Substance Identification (FDAGSRS) **FDAGSRS_search_substances**: `query` (string REQUIRED -- drug name, UNII, InChIKey, or formula), `substance_class` (string, optional: "chemical"/"protein"/"nucleic acid"/"polymer"/"mixture"), `limit` (int, 1-50, default 10). Returns `{status, data: {substances: [{unii, name, substance_class, status, cross_references: [{type, value}]}]}}`. - `cross_references` contains DrugBank IDs, WHO-ATC codes, CAS numbers, CFR citations. - Use to get the official UNII identifier before calling `FDAGSRS_get_substance`. **FDAGSRS_get_substance**: `unii` (string REQUIRED, 10-char FDA UNII code). Returns complete substance record including all synonyms, names, structure, and cross-references. - Provides definitive list of all registered names (INN, USAN, brand, chemical). **FDAGSRS_get_structure**: `unii` (string REQUIRED). Returns `{status, data: {smiles, formula, inchikey, molfile, molecular_weight, stereochemistry, optical_activity}}`. - Only works for chemical substances; returns error for biologics, mixtures, polymers. ```python # Full substance lookup workflow search = tu.tools.FDAGSRS_search_substances(query="semaglutide") unii = search["data"]["substances"][0]["unii"] full = tu.tools.FDAGSRS_get_substance(unii=unii) ``` --- ## Phase 2: Drug Classification (RxClass) **RxClass_get_drug_classes**: `drug_name` (string, drug name), `rxcui` (string, RxNorm RXCUI -- alternative to drug_name), `rela_source` (string, optional: "ATC"/"FDASPL"/"MESH"/"VA"), `limit` (int, default 20). Returns `{status, data: {classes: [{class_id, class_name, class_type, rela}]}}`. - Returns ALL classification systems unless `rela_source` filters to one. - `class_type` values: "ATC1-4", "EPC" (FDA Established Pharmacologic Class), "MoA", "VA", "MESH". - Use to find a drug's ATC code, pharmacological class, mechanism of action label. **RxClass_find_classes**: `query` (string REQUIRED, keyword e.g., "beta blocker"), `class_type` (string, optional: "ATC1-4"/"EPC"/"MoA"), `limit` (int, default 20). Returns matching drug classes with class IDs. - Use when you need to find a class ID before calling `RxClass_get_class_members`. **RxClass_get_class_members**: `class_id` (string REQUIRED, e.g., "M01AE"), `rela_source` (string, optional: "ATC"/"FDASPL"), `ttys` (string, optional: "IN" for ingredients), `limit` (int, default 50). Returns all drug ingredients in the class with RXCUIs and names. - `ttys="IN"` restricts to active ingredient-level entries (recommended). ```python # Find all proton pump inhibitors classes = tu.tools.RxClass_find_classes(query="proton pump inhibitor", class_type="EPC") class_id = classes["data"]["classes"][0]["class_id"] members = tu.tools.RxClass_get_class_members(class_id=class_id, ttys="IN") ``` --- ## Phase 3: Approval & Generic Status (FDA Orange Book / Purple Book) **Orange Book covers small-molecule drugs; biologics and biosimilars (e.g. "is there a biosimilar of adalimumab") are a different FDA list entirely** — use `FDAPurpleBook_search_products` for those instead of Orange Book, which won't have them. **FDA_OrangeBook_search_drug**: `brand_name` (string), `generic_name` (string), `application_number` (string), `limit` (int, default 10). Returns `{status, data: {products: [{brand_name, generic_name, dosage_form, strength, te_code, application_number, approval_date}]}}`. - Use brand name (UPPERCASE) or generic name to find NDA/ANDA numbers and approval info. - `te_code`: Therapeutic Equivalence code (e.g., "AB" = therapeutically equivalent). **FDA_OrangeBook_check_generic_availability**: `brand_name` (string), `generic_name` (string). Returns `{status, data: {reference_listed_drug, generics_available: bool, generics_count, generic_products: [...]}}`. - Primary tool for "is there a generic?" questions. **FDA_OrangeBook_get_te_code**: No special params beyond `brand_name`/`application_number`. Returns therapeutic equivalence codes for substitutability assessment. **FDA_OrangeBook_get_approval_history**: `application_number` (string, e.g., "NDA020402"). Returns chronological approval history including supplemental approvals and label changes. ```python # Check generic availability result = tu.tools.FDA_OrangeBook_check_generic_availability(brand_name="LIPITOR") # result["data"]["generics_available"] -> True # result["data"]["generics_count"] -> N ``` --- ## Phase 4: Patent & Exclusivity **FDA_OrangeBook_get_patent_info**: `application_number` (string), `brand_name` (string). Returns patent information. Note: Full patent numbers and expiration dates require Orange Book data files. **FDA_OrangeBook_get_exclusivity**: `application_number` (string), `brand_name` (string). Returns `{status, data: {exclusivities: [{exclusivity_code, exclusivity_date, description}]}}`. - `exclusivity_code` values: "NCE" (New Chemical Entity, 5 years), "ODE" (Orphan Drug, 7 years), "PED" (Pediatric, 6 months), "NP" (New Product), "M" (new formulation). --- ## Phase 5: Label Parsing (DailyMed) All DailyMed parse tools accept either `setid` (SPL Set ID UUID) OR `drug_name` (auto-lookup). Using `drug_name` is recommended when the setid is unknown. **DailyMed_parse_adverse_reactions**: `setid` or `drug_name`. Returns structured adverse reaction table with frequencies and severity. **DailyMed_parse_dosing**: `setid` or `drug_name`. Returns dosage and administration section (doses, schedules, renal/hepatic adjustments). **DailyMed_parse_contraindications**: `setid` or `drug_name`. Returns contraindications section. **DailyMed_parse_drug_interactions**: `setid` or `drug_name`. Returns drug-drug interaction section with clinical management guidance. **DailyMed_parse_clinical_pharmacology**: `setid` or `drug_name`. Returns PK/PD data (Cmax, AUC, half-life, protein binding, metabolism pathway). **DailyMed_search_spls**: `drug_name` (string), returns SPL Set IDs for that drug. Use to find `setid` when needed explicitly. ```python # Parse adverse reactions for apixaban ae = tu.tools.DailyMed_parse_adverse_reactions(drug_name="apixaban") ``` --- ## Phase 6: Clinical Trials **search_clinical_trials**: `condition` (string), `intervention` (string), `query_term` (string), `pageSize` (int, alias: `max_results`/`limit`), `overall_status` (array, alias: `status`). Returns `{status, data: {studies: [{NCT ID, brief_title, brief_summary, overall_status, phase}], total_count}}`. - Use `intervention` for drug name, `condition` for disease. - Filter `overall_status=["RECRUITING"]` for active enrollment. - `total_count` may be None even when results exist; check `len(studies) > 0`. ```python # Find recruiting trials for a biosimilar trials = tu.tools.search_clinical_trials( intervention="adalimumab biosimilar", overall_status=["RECRUITING"], pageSize=10 ) ``` --- ## Example Workflows ### Workflow 1: Full Regulatory Profile for a Drug ``` 1. FDAGSRS_search_substances(query="apixaban") -> UNII, substance class, ATC/DrugBank cross-refs 2. RxClass_get_drug_classes(drug_name="apixaban", rela_source="ATC") -> ATC code B01AF02 (direct factor Xa inhibitor) 3. FDA_OrangeBook_search_drug(brand_name="ELIQUIS") -> NDA206518, approval date, TE code 4. FDA_OrangeBook_check_generic_availability(brand_name="ELIQUIS") -> Generic availability status 5. FDA_OrangeBook_get_exclusivity(brand_name="ELIQUIS") -> Exclusivity codes and expiration dates 6. DailyMed_parse_adverse_reactions(drug_name="apixaban") -> Bleeding rates and other AEs from label ``` ### Workflow 2: List All Drugs in a Therapeutic Class ``` 1. RxClass_find_classes(query="ACE inhibitor", class_type="EPC") -> class_id for "Angiotensin-Converting Enzyme Inhibitor" 2. RxClass_get_class_members(class_id=<id>, ttys="IN") -> All ACE inhibitors (enalapril, lisinopril, ramipril, etc.) 3. For each drug: RxClass_get_drug_classes(drug_name=drug) -> Confirm ATC code and additional classifications ``` ### Workflow 3: Drug Label Review ``` 1. DailyMed_parse_adverse_reactions(drug_name="metformin") -> AE frequencies (GI: lactic acidosis, nausea, diarrhea) 2. DailyMed_parse_contraindications(drug_name="metformin") -> eGFR thresholds, renal impairment contraindications 3. DailyMed_parse_drug_interactions(drug_name="metformin") -> Iodinated contrast, carbonic anhydrase inhibitor interactions 4. DailyMed_parse_clinical_pharmacology(drug_name="metformin") -> Half-life, renal clearance, bioavailability ``` --- ## Common Mistakes - Orange Book `brand_name` must be UPPERCASE (e.g., `"LIPITOR"`) - `FDAGSRS_get_substance` requires UNII, not drug name — call `FDAGSRS_search_substances` first - `FDAGSRS_get_structure` only works for chemical substances, not biologics - `RxClass_get_class_members`: pass `ttys="IN"` to restrict to active ingredients - `search_clinical_trials` `overall_status` must be an array: `["RECRUITING"]` --- ## Reasoning Framework ### Interpretation Guidance **Approval pathways**: A 505(b)(1) NDA is a full new drug application with complete safety/efficacy data from the sponsor. A 505(b)(2) NDA relies partly on published literature or FDA findings for an already-approved drug (common for reformulations, new routes). An ANDA (Abbreviated NDA) is the generic pathway requiring only bioequivalence to the reference listed drug. **Orange Book patent and exclusivity**: NCE (New Chemical Entity) exclusivity gives 5 years of data protection. ODE (Orphan Drug Exclusivity) gives 7 years. PED (Pediatric) adds 6 months to existing patents/exclusivity. A TE code of "AB" means the generic is therapeutically equivalent and substitutable. No TE code or "BX" means substitutability is not established. **DailyMed label sections**: The "Adverse Reactions" section distinguishes clinical trial rates (controlled) from post-marketing reports (uncontrolled, signal-only). "Contraindications" are absolute; "Warnings and Precautions" are conditional risks. "Clinical Pharmacology" provides PK parameters (Cmax, AUC, half-life) essential for drug interaction and dosing assessment. ### Synthesis Questions