药品批准状态与监管资料研究｜个人思维图谱小站

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获取原文件

SKILL.md

中文导读

按指定市场查询药品批准、分类、标签和独占期信息，明确 FDA、EMA 等监管范围及查询日期。

输入材料

药物名称、地区与研究问题
当前监管数据访问条件

交付结果

批准与标签资料
地区、日期和来源记录

阅读与使用

本页保存指定版本的原始 Skill 和配套文件，可查看中文导读、原文与参考资料。获取完整包后，按原文准备所需工具与材料。执行需要 ToolUniverse SDK 或 MCP 服务，部分数据库需要独立访问权限。医疗资料供研究、资料整理和专业人员复核，试验资格、用药与诊疗结论需要核实当前来源。

来源仓库为 mims-harvard/ToolUniverse，采用 Apache-2.0 许可。Stars 表示仓库关注度，核验日期为 2026-10-15。分类表示适用的工作环节，实际执行条件以原文为准。

原始 Skill

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)
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  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: [{uuid, unii, name, substanceClass, status, formula, smiles, synonyms, xrefs}], metadata: {query, total, returned, substance_class_filter}} (data is a list).

xrefs (dict) holds the substance's cross-references (DrugBank IDs, ATC codes, CAS numbers ...).
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.

# Full substance lookup workflow
search = tu.tools.FDAGSRS_search_substances(query="semaglutide")
unii = search["data"][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: [{classId, className, classType, rxcui, drugName, tty, rela, relaSource}], metadata} (data is a list).

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 {status, data: [{classId, className, classType}]} (a list; classType shows the system: ATC1-4, EPC, MOA, DISPOS ...).

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 {status, data: [{rxcui, name, tty}]}.

rela_source must match the class system: the default ATC only lists members of ATC codes (e.g. A02BC). For EPC/MoA class IDs such as N0000175525 pass rela_source="DAILYMED" (or "FDASPL"); with the default it returns an empty list.
ttys="IN" restricts to active ingredient-level entries (recommended).

# Find all proton pump inhibitors
classes = tu.tools.RxClass_find_classes(query="proton pump inhibitor", class_type="EPC")
class_id = classes["data"][0]["classId"]  # N0000175525 (EPC class)
members = tu.tools.RxClass_get_class_members(class_id=class_id, rela_source="DAILYMED", 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.

# 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.

# 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.

# 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

A complete drug regulatory report should answer:

What is the current FDA approval status and pathway (NDA vs ANDA vs 505(b)(2))?
Are generic equivalents available, and what is their therapeutic equivalence rating?
When do key patents and exclusivities expire (or have they already)?
What drug class does this belong to (ATC, EPC, MoA), and what are peer drugs in the class?
What are the most clinically significant adverse reactions and contraindications from the label?

药品批准状态与监管资料研究

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