DDInter Drug-Drug Interaction Database

Overview

DDInter is an open, curated database of drug-drug interactions (DDIs) covering 2,400+ drugs and 1.7M+ pairwise interactions with structured severity levels (major, moderate, minor), mechanistic annotations, and clinical management recommendations. Access is provided via a JSON REST API at https://ddinter.scbdd.com/api/ — no authentication or registration required.

When to Use

• Checking whether two co-administered drugs have a known interaction and its severity (major/moderate/minor)
• Retrieving all known interactions for a given drug to support polypharmacy risk assessment
• Identifying the mechanistic basis (pharmacokinetic vs. pharmacodynamic) of a drug-drug interaction
• Screening a drug combination list for potential major interactions before clinical decision support
• Building automated DDI checking pipelines for medication review or drug repurposing workflows
• Analyzing the DDI network for a drug class (e.g., all major interactions for CYP3A4 substrates)
• For FDA-approved drug labeling text (indications, dosage, contraindications) use dailymed-database
• For pharmacogenomics interactions (CYP genotype-drug associations) use clinpgx-database; DDInter covers drug-drug not gene-drug pairs
• For drug adverse event reports from FAERS use fda-database

Prerequisites

• Python packages: requests, pandas, matplotlib, networkx
• Data requirements: drug names or DDInter drug IDs
• Environment: internet connection; no API key required
• Rate limits: no officially published rate limit; use time.sleep(0.3) between requests in batch loops for polite access

pip install requests pandas matplotlib networkx

Quick Start

import requests

BASE = "https://ddinter.scbdd.com/api"

# Search for a drug by name
r = requests.get(f"{BASE}/drug/", params={"drug_name": "warfarin", "format": "json"}, timeout=15)
r.raise_for_status()
data = r.json()
print(f"Results for 'warfarin': {data['count']} drugs found")
for drug in data["results"][:3]:
    print(f"  ID={drug['ddinter_id']}  Name={drug['drug_name']}")
# Results for 'warfarin': 1 drugs found
#   ID=DDInter_D00001  Name=Warfarin

Core API

Query 1: Search Drug by Name

Find a drug's DDInter ID by searching its name. The DDInter ID is required for all interaction queries.

import requests
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def search_drug(drug_name):
    """Search DDInter for a drug by name. Returns list of matching drug records."""
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": drug_name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json()

# Search for warfarin
result = search_drug("warfarin")
print(f"Matches: {result['count']}")
if result["results"]:
    drug = result["results"][0]
    print(f"DDInter ID: {drug['ddinter_id']}")
    print(f"Drug name: {drug['drug_name']}")
    # Store DDInter ID for interaction queries
    warfarin_id = drug["ddinter_id"]
    print(f"\nWarfarin DDInter ID: {warfarin_id}")

# Batch name lookup
drugs_to_find = ["warfarin", "aspirin", "atorvastatin", "metformin", "amiodarone"]
id_map = {}
for name in drugs_to_find:
    res = search_drug(name)
    if res["results"]:
        id_map[name] = res["results"][0]["ddinter_id"]
        print(f"  {name:20s} → {res['results'][0]['ddinter_id']}")

Query 2: Get All Interactions for a Drug

Retrieve all known DDIs for a drug by its DDInter ID. Returns interaction partners, severity, and clinical information.

import requests
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def get_drug_interactions(drug_id, page_size=100):
    """Get all DDIs for a drug by DDInter ID. Handles pagination automatically."""
    all_interactions = []
    url = f"{BASE}/interaction/"
    params = {"drug_id": drug_id, "format": "json", "page_size": page_size}

    while url:
        r = requests.get(url, params=params, timeout=20)
        r.raise_for_status()
        data = r.json()
        all_interactions.extend(data.get("results", []))
        url = data.get("next")   # None when last page
        params = {}              # next URL already includes params

    return all_interactions

# Get all interactions for warfarin (DDInter_D00001)
interactions = get_drug_interactions("DDInter_D00001")
print(f"Warfarin total interactions: {len(interactions)}")

# Summarize by severity
df = pd.DataFrame(interactions)
if not df.empty and "level" in df.columns:
    severity_counts = df["level"].value_counts()
    print("\nInteractions by severity:")
    for level, count in severity_counts.items():
        print(f"  {level:15s}: {count:4d}")
    # Major: 45
    # Moderate: 312
    # Minor: 198

Query 3: Get Interaction Details by Interaction ID

Retrieve full details for a specific drug-drug interaction, including mechanism and clinical recommendation.

import requests

BASE = "https://ddinter.scbdd.com/api"

def get_interaction_detail(interaction_id):
    """Get full details for a specific interaction by its DDInter interaction ID."""
    r = requests.get(f"{BASE}/interaction/{interaction_id}/",
                     params={"format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json()

# Example interaction ID (format: DDInter_I_XXXXXX)
interaction_id = "DDInter_I_000001"   # example
try:
    detail = get_interaction_detail(interaction_id)
    print(f"Interaction: {detail.get('interaction_id')}")
    print(f"Drug A: {detail.get('drug_a')}")
    print(f"Drug B: {detail.get('drug_b')}")
    print(f"Severity: {detail.get('level')}")
    print(f"Mechanism: {detail.get('mechanism', 'Not specified')[:200]}")
    print(f"Recommendation: {detail.get('recommendation', 'Not specified')[:200]}")
    print(f"PK type: {detail.get('pharmacokinetic_type', 'N/A')}")
    print(f"PD type: {detail.get('pharmacodynamic_type', 'N/A')}")
except Exception as e:
    print(f"Note: Use a valid interaction ID from get_drug_interactions() results. Error: {e}")

Query 4: Check Interaction Between Two Specific Drugs

Query interactions between exactly two drugs using their DDInter IDs.

import requests

BASE = "https://ddinter.scbdd.com/api"

def check_drug_pair(drug_id_1, drug_id_2):
    """Check interactions between two specific drugs by their DDInter IDs."""
    r = requests.get(f"{BASE}/between/",
                     params={"drug1": drug_id_1, "drug2": drug_id_2, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json()

def find_drug_id(drug_name):
    """Helper: resolve drug name to DDInter ID."""
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": drug_name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    results = r.json()["results"]
    return results[0]["ddinter_id"] if results else None

# Check warfarin + aspirin interaction
warfarin_id = find_drug_id("warfarin")
aspirin_id = find_drug_id("aspirin")

if warfarin_id and aspirin_id:
    interactions = check_drug_pair(warfarin_id, aspirin_id)
    count = interactions.get("count", 0)
    print(f"Warfarin + Aspirin: {count} interaction(s) found")
    for ix in interactions.get("results", []):
        print(f"  Severity: {ix.get('level')}")
        print(f"  Mechanism: {ix.get('mechanism', 'N/A')[:200]}")
        print(f"  Recommendation: {ix.get('recommendation', 'N/A')[:200]}")
else:
    print(f"Could not resolve drug IDs: warfarin={warfarin_id}, aspirin={aspirin_id}")

Query 5: Filter Interactions by Severity Level

Retrieve only high-severity (major) interactions for a drug — essential for rapid clinical risk screening.

import requests
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def get_major_interactions(drug_id):
    """Get only major-severity interactions for a drug."""
    all_interactions = []
    r = requests.get(f"{BASE}/interaction/",
                     params={"drug_id": drug_id, "format": "json", "page_size": 200},
                     timeout=20)
    r.raise_for_status()
    data = r.json()
    all_interactions.extend(data.get("results", []))

    # Filter to major severity
    major = [ix for ix in all_interactions
             if ix.get("level", "").lower() == "major"]
    return major

# Get major interactions for amiodarone (known high-interaction drug)
drug_id = "DDInter_D00023"   # example amiodarone ID; resolve with search_drug()
major_ixs = get_major_interactions(drug_id)
print(f"Major interactions: {len(major_ixs)}")

if major_ixs:
    df = pd.DataFrame(major_ixs)
    # Show drug partners and mechanism type
    for col in ["drug_a", "drug_b", "level", "pharmacokinetic_type"]:
        if col in df.columns:
            print(f"  {col}: {df[col].value_counts().head(3).to_dict()}")
    df.to_csv("amiodarone_major_interactions.csv", index=False)
    print("Saved: amiodarone_major_interactions.csv")

Query 6: Polypharmacy Screening for a Drug List

Screen a medication list for all pairwise major and moderate interactions.

import requests
import time
import itertools
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def find_drug_id(drug_name):
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": drug_name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    results = r.json()["results"]
    return (results[0]["ddinter_id"], results[0]["drug_name"]) if results else (None, None)

def check_pair(id1, id2):
    r = requests.get(f"{BASE}/between/",
                     params={"drug1": id1, "drug2": id2, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json().get("results", [])

# Medication list to screen
medication_names = ["warfarin", "aspirin", "atorvastatin", "metformin", "amiodarone"]

# Resolve to DDInter IDs
id_map = {}
for name in medication_names:
    ddid, resolved_name = find_drug_id(name)
    if ddid:
        id_map[name] = (ddid, resolved_name)
        print(f"  {name:20s} → {ddid}")
    time.sleep(0.3)

# Check all pairs
flagged = []
for (n1, (id1, rn1)), (n2, (id2, rn2)) in itertools.combinations(id_map.items(), 2):
    ixs = check_pair(id1, id2)
    for ix in ixs:
        level = ix.get("level", "unknown")
        if level.lower() in ("major", "moderate"):
            flagged.append({
                "drug_1": rn1,
                "drug_2": rn2,
                "severity": level,
                "mechanism": ix.get("mechanism", "")[:100],
            })
    time.sleep(0.3)

df = pd.DataFrame(flagged)
print(f"\nFlagged interactions: {len(df)}")
if not df.empty:
    print(df.to_string(index=False))
    df.to_csv("polypharmacy_screening.csv", index=False)
    print("Saved: polypharmacy_screening.csv")

Query 7: Visualize Interaction Network

Build and visualize a drug-drug interaction network for a set of drugs, with edges colored by severity.

import requests
import time
import itertools
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt

BASE = "https://ddinter.scbdd.com/api"

def find_drug_id(drug_name):
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": drug_name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    results = r.json()["results"]
    return (results[0]["ddinter_id"], results[0]["drug_name"]) if results else (None, None)

def check_pair(id1, id2):
    r = requests.get(f"{BASE}/between/",
                     params={"drug1": id1, "drug2": id2, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json().get("results", [])

SEVERITY_COLORS = {"major": "#D32F2F", "moderate": "#F57C00", "minor": "#388E3C"}

# Drug list
drugs = ["warfarin", "aspirin", "atorvastatin", "fluconazole", "amiodarone"]
id_map = {}
for name in drugs:
    ddid, rname = find_drug_id(name)
    if ddid:
        id_map[name] = (ddid, rname)
    time.sleep(0.3)

# Build network
G = nx.Graph()
for name, (ddid, rname) in id_map.items():
    G.add_node(rname)

edge_colors = []
for (n1, (id1, rn1)), (n2, (id2, rn2)) in itertools.combinations(id_map.items(), 2):
    ixs = check_pair(id1, id2)
    for ix in ixs:
        level = ix.get("level", "minor").lower()
        G.add_edge(rn1, rn2, severity=level, weight=3 if level == "major" else 1)
    time.sleep(0.3)

# Visualize
fig, ax = plt.subplots(figsize=(9, 7))
pos = nx.spring_layout(G, seed=42, k=2)

for level, color in SEVERITY_COLORS.items():
    edges = [(u, v) for u, v, d in G.edges(data=True) if d.get("severity") == level]
    if edges:
        width = 4 if level == "major" else 2
        nx.draw_networkx_edges(G, pos, edgelist=edges, edge_color=color, width=width, alpha=0.8, ax=ax)

nx.draw_networkx_nodes(G, pos, node_color="#1565C0", node_size=1200, alpha=0.9, ax=ax)
nx.draw_networkx_labels(G, pos, font_color="white", font_size=8, font_weight="bold", ax=ax)

# Legend
from matplotlib.patches import Patch
legend = [Patch(color=c, label=l.capitalize()) for l, c in SEVERITY_COLORS.items()]
ax.legend(handles=legend, title="Severity", loc="upper right")
ax.set_title("Drug-Drug Interaction Network\n(DDInter)")
ax.axis("off")
plt.tight_layout()
plt.savefig("ddi_network.png", dpi=150, bbox_inches="tight")
print(f"Saved: ddi_network.png  ({G.number_of_nodes()} drugs, {G.number_of_edges()} interactions)")

Key Concepts

Severity Classification

DDInter classifies interactions into three severity levels, following established clinical pharmacology standards:

Severity	Code	Clinical Meaning	Action
Major	major	Potentially life-threatening or causing permanent damage	Avoid combination; use alternative
Moderate	moderate	May cause clinical deterioration; increased monitoring required	Use with caution; monitor closely
Minor	minor	Limited clinical effects; interaction is documented but rarely significant	Generally safe; monitor if symptomatic

Mechanism Types

Interactions are classified by mechanism:

• Pharmacokinetic (PK): One drug affects the absorption, distribution, metabolism, or excretion (ADME) of the other (e.g., CYP enzyme inhibition)
• Pharmacodynamic (PD): Drugs have additive, synergistic, or antagonistic effects at the pharmacological target level (e.g., additive bleeding risk)
• Mixed: Both PK and PD mechanisms contribute

Drug Identification

DDInter uses its own sequential identifier scheme (e.g., DDInter_D00001 for Warfarin). There is no direct mapping to ChEMBL IDs, PubChem CIDs, or RxCUI without a prior name search. Always resolve drug names to DDInter IDs using the /drug/ endpoint before querying interactions.

Common Workflows

Workflow 1: Comprehensive DDI Profile for a Drug

Goal: Retrieve all interactions for a drug, stratify by severity, and export a structured report.

import requests
import time
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def find_drug_id(name):
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    res = r.json()["results"]
    return (res[0]["ddinter_id"], res[0]["drug_name"]) if res else (None, None)

def get_all_interactions(drug_id, page_size=200):
    all_results = []
    url = f"{BASE}/interaction/"
    params = {"drug_id": drug_id, "format": "json", "page_size": page_size}
    while url:
        r = requests.get(url, params=params, timeout=30)
        r.raise_for_status()
        data = r.json()
        all_results.extend(data.get("results", []))
        url = data.get("next")
        params = {}
    return all_results

# Build DDI profile for clopidogrel
drug_name = "clopidogrel"
drug_id, resolved_name = find_drug_id(drug_name)

if drug_id:
    print(f"Drug: {resolved_name} ({drug_id})")
    ixs = get_all_interactions(drug_id)
    df = pd.DataFrame(ixs)

    print(f"Total interactions: {len(df)}")
    if "level" in df.columns:
        print("\nSeverity breakdown:")
        for level, grp in df.groupby("level"):
            print(f"  {level:15s}: {len(grp):4d} interactions")

        # Major interactions table
        major = df[df["level"].str.lower() == "major"].copy()
        print(f"\nMajor interactions ({len(major)}):")
        for _, row in major.head(10).iterrows():
            partner = row.get("drug_b") if row.get("drug_a") == resolved_name else row.get("drug_a")
            mech = str(row.get("mechanism", ""))[:80]
            print(f"  + {partner}: {mech}")

    df.to_csv(f"{drug_name}_ddi_profile.csv", index=False)
    print(f"\nSaved: {drug_name}_ddi_profile.csv")

Workflow 2: Pairwise Interaction Matrix for a Drug Panel

Goal: Build a severity matrix showing all pairwise interactions between a curated drug panel — useful for clinical pharmacology and formulary review.

import requests
import time
import itertools
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

BASE = "https://ddinter.scbdd.com/api"

SEVERITY_SCORE = {"major": 3, "moderate": 2, "minor": 1, "none": 0}

def find_drug_id(name):
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    res = r.json()["results"]
    return (res[0]["ddinter_id"], res[0]["drug_name"]) if res else (None, None)

def check_pair(id1, id2):
    r = requests.get(f"{BASE}/between/",
                     params={"drug1": id1, "drug2": id2, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    return r.json().get("results", [])

# Drug panel
drug_names = ["warfarin", "aspirin", "atorvastatin", "fluconazole", "metformin"]
id_map = {}
for name in drug_names:
    ddid, rname = find_drug_id(name)
    if ddid:
        id_map[name] = (ddid, rname)
    time.sleep(0.3)

resolved = {name: rname for name, (ddid, rname) in id_map.items()}
n = len(id_map)
names = list(id_map.keys())
rnames = [resolved[n] for n in names]

# Build matrix
matrix = np.zeros((n, n), dtype=int)
for i, (n1, (id1, _)) in enumerate(id_map.items()):
    for j, (n2, (id2, _)) in enumerate(id_map.items()):
        if i < j:
            ixs = check_pair(id1, id2)
            if ixs:
                worst = max(SEVERITY_SCORE.get(ix.get("level", "none").lower(), 0) for ix in ixs)
                matrix[i, j] = matrix[j, i] = worst
            time.sleep(0.3)

# Heatmap
fig, ax = plt.subplots(figsize=(7, 6))
im = ax.imshow(matrix, cmap="RdYlGn_r", vmin=0, vmax=3)
ax.set_xticks(range(n))
ax.set_yticks(range(n))
ax.set_xticklabels(rnames, rotation=30, ha="right", fontsize=9)
ax.set_yticklabels(rnames, fontsize=9)
for i in range(n):
    for j in range(n):
        text = ["None", "Minor", "Mod", "Major"][matrix[i, j]]
        ax.text(j, i, text, ha="center", va="center", fontsize=7)
plt.colorbar(im, ax=ax, label="Severity (0=None, 3=Major)")
ax.set_title("Drug-Drug Interaction Severity Matrix\n(DDInter)")
plt.tight_layout()
plt.savefig("ddi_severity_matrix.png", dpi=150, bbox_inches="tight")
print("Saved: ddi_severity_matrix.png")

Key Parameters

Parameter	Endpoint	Default	Range / Options	Effect
drug_name	/drug/	—	any drug name string	Search term for drug name lookup
drug_id	/interaction/	—	DDInter_DXXXXX string	DDInter drug ID for interaction queries
drug1, drug2	/between/	—	DDInter_DXXXXX strings	Both required to check a specific drug pair
format	all endpoints	json	json	Response format; JSON only via API
page_size	/interaction/, /drug/	10	positive integer	Results per page; use 200 for bulk retrieval
level	response field	—	major, moderate, minor	Interaction severity; filter client-side
pharmacokinetic_type	response field	—	PK, PD, mixed	Mechanism category

Best Practices

1. Always resolve drug names to DDInter IDs first: The API does not accept free-text drug names in interaction queries. Use /drug/?drug_name= to obtain the ddinter_id, then pass it to /interaction/ or /between/.

2. Handle pagination for complete interaction lists: The default page returns at most 10 results. Drugs like warfarin or amiodarone have hundreds of interactions — iterate next URLs until null:

   while url:
       data = requests.get(url, params=params).json()
       results.extend(data["results"])
       url = data.get("next")
       params = {}   # clear params after first request
   

3. Use check_pair() for targeted queries, get_interactions() for full profiles: The /between/ endpoint is faster when you need one pair. The /interaction/ endpoint is needed for comprehensive DDI profiling.

4. Add time.sleep(0.3) in batch loops: DDInter has no published rate limits, but polite delays prevent server-side throttling on this publicly hosted research database.

5. Filter by severity client-side: The API does not support server-side severity filtering on the /interaction/ endpoint. Retrieve all interactions and filter in pandas:

   df = pd.DataFrame(interactions)
   major_only = df[df["level"].str.lower() == "major"]
   

6. Cross-reference with clinical databases for decision support: DDInter provides evidence-based interaction records, but for clinical decisions always verify against current prescribing information in dailymed-database and institutional drug interaction tools.

Common Recipes

Recipe: Quick Safety Check for a Drug Pair

When to use: Rapid single-pair interaction lookup before combining two drugs.

import requests

BASE = "https://ddinter.scbdd.com/api"

def quick_check(drug1_name, drug2_name):
    """Check interaction between two drugs by name. Returns severity or 'No interaction found'."""
    def get_id(name):
        r = requests.get(f"{BASE}/drug/",
                         params={"drug_name": name, "format": "json"},
                         timeout=15)
        r.raise_for_status()
        results = r.json()["results"]
        return (results[0]["ddinter_id"], results[0]["drug_name"]) if results else (None, name)

    id1, rn1 = get_id(drug1_name)
    id2, rn2 = get_id(drug2_name)

    if not id1 or not id2:
        return f"Drug not found: {drug1_name if not id1 else drug2_name}"

    r = requests.get(f"{BASE}/between/",
                     params={"drug1": id1, "drug2": id2, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    ixs = r.json().get("results", [])

    if not ixs:
        return f"{rn1} + {rn2}: No interaction found in DDInter"

    worst = max(ixs, key=lambda x: {"major": 3, "moderate": 2, "minor": 1}.get(x.get("level", "minor").lower(), 0))
    return f"{rn1} + {rn2}: {worst.get('level', 'unknown').upper()} — {worst.get('mechanism', 'N/A')[:120]}"

print(quick_check("warfarin", "aspirin"))
print(quick_check("metformin", "atorvastatin"))
print(quick_check("warfarin", "fluconazole"))

Recipe: Count Interactions by Severity for Multiple Drugs

When to use: Generate a summary table comparing DDI burden across multiple drugs.

import requests
import time
import pandas as pd

BASE = "https://ddinter.scbdd.com/api"

def get_severity_summary(drug_name):
    """Return severity counts (major/moderate/minor) for a drug."""
    r = requests.get(f"{BASE}/drug/",
                     params={"drug_name": drug_name, "format": "json"},
                     timeout=15)
    r.raise_for_status()
    results = r.json()["results"]
    if not results:
        return None
    drug_id = results[0]["ddinter_id"]