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)

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

drug_name

. Returns structured adverse reaction table with frequencies and severity.

DailyMed_parse_dosing:

setid

drug_name

. Returns dosage and administration section (doses, schedules, renal/hepatic adjustments).

DailyMed_parse_contraindications:

setid

drug_name

. Returns contraindications section.

DailyMed_parse_drug_interactions:

setid

drug_name

. Returns drug-drug interaction section with clinical management guidance.

DailyMed_parse_clinical_pharmacology:

setid

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

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?

tooluniverse-drug-regulatory

NPX Install

Tags

SKILL.md Content

Drug Regulatory Research

When to Use

NOT for (use other skills instead)

Workflow Overview

Phase 1: Substance Identification (FDAGSRS)

Phase 2: Drug Classification (RxClass)

Phase 3: Approval & Generic Status (FDA Orange Book)

Phase 4: Patent & Exclusivity

Phase 5: Label Parsing (DailyMed)

Phase 6: Clinical Trials

Example Workflows

Workflow 1: Full Regulatory Profile for a Drug

Workflow 2: List All Drugs in a Therapeutic Class

Workflow 3: Drug Label Review

Common Mistakes

Reasoning Framework

Interpretation Guidance

Synthesis Questions