Therapeutic Reasoning Chain

Therapeutic Reasoning Chain

Multi-step clinical reasoning that chains ToolUniverse tool calls with pharmacological logic. Inspired by TxAgent's approach of reasoning across 211 biomedical tools rather than making isolated lookups.

When to Use

• Patient-specific treatment questions: "65岁糖尿病患者新确诊 NSCLC，推荐方案"
• Drug interaction analysis: "二甲双胍和帕博利珠单抗有相互作用吗"
• Personalized therapy: "EGFR L858R 突变的 NSCLC 怎么治"
• Comparative therapy: "奥西替尼 vs 吉非替尼 在 EGFR+ NSCLC 的比较"
• Contraindication check: "肝功能不全患者能用索拉非尼吗"

NOT for (use other skills):

• General disease overview → use clinical-query recipe
• Drug target validation → use tooluniverse-drug-target-validation
• Pure literature search → use literature-review recipe

Reasoning Chain Architecture

Every therapeutic query follows a structured chain. Each step produces evidence that feeds the next.

Step 1: PARSE      → Extract patient profile and clinical question
Step 2: PROFILE    → Gather drug/target background via ToolUniverse
Step 3: INTERACT   → Check drug-drug and drug-disease interactions
Step 4: GUIDELINE  → Match against current clinical guidelines
Step 5: PERSONALIZE → Adjust for patient-specific factors
Step 6: SYNTHESIZE → Produce ranked recommendations with evidence levels

Step 1: PARSE — Extract Patient Profile

From the user query, extract structured parameters:

{
  "patient": {
    "age": 65,
    "sex": "male",
    "comorbidities": ["type 2 diabetes mellitus"],
    "current_medications": ["metformin 500mg BID"],
    "allergies": [],
    "organ_function": {"renal": "unknown", "hepatic": "unknown"},
    "genomic_markers": {}
  },
  "diagnosis": {
    "primary": "non-small cell lung cancer",
    "stage": "unknown",
    "histology": "unknown",
    "biomarkers": {}
  },
  "question_type": "treatment_recommendation"
}

If critical information is missing (e.g., cancer stage, biomarker status), note it as a limitation but proceed with available data. Do NOT ask the user to fill every field — provide the best answer with what you have, and note what additional information would refine the recommendation.

Step 2: PROFILE — Drug/Target Background

Query ToolUniverse for relevant drug and target information:

bash: python3 << 'PYEOF'
from tooluniverse import ToolUniverse
import json

tu = ToolUniverse()
tu.load_tools()

# Drug profile
drug_info = tu.run({
    "name": "DrugBank_search_drug",
    "arguments": {"query": "metformin", "limit": 3}
})
print("=== DrugBank: metformin ===")
print(json.dumps(drug_info, indent=2, ensure_ascii=False)[:2000])

# Disease targets
targets = tu.run({
    "name": "OpenTargets_search_disease",
    "arguments": {"query": "non-small cell lung cancer", "limit": 5}
})
print("\n=== OpenTargets: NSCLC ===")
print(json.dumps(targets, indent=2, ensure_ascii=False)[:2000])
PYEOF

If ToolUniverse is not installed, fall back to curl API calls:

bash: echo "=== DrugBank (via web) ===" && \
curl -s "https://go.drugbank.com/unearth/q?searcher=drugs&query=metformin" 2>/dev/null && \
echo -e "\n=== OpenTargets ===" && \
curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query":"{ search(queryString:\"non-small cell lung cancer\", entityNames:[\"disease\"]) { total hits { id name } } }"}'

Step 3: INTERACT — Drug Interaction Analysis

For each candidate treatment, check interactions with existing medications:

bash: python3 << 'PYEOF'
from tooluniverse import ToolUniverse
import json

tu = ToolUniverse()
tu.load_tools()

# Check interactions between metformin and candidate drugs
candidates = ["pembrolizumab", "osimertinib", "carboplatin", "pemetrexed"]

for drug in candidates:
    result = tu.run({
        "name": "DrugBank_get_interactions",
        "arguments": {"drug_name": drug, "limit": 20}
    })
    interactions = [i for i in result.get("data", [])
                    if "metformin" in json.dumps(i).lower()]
    if interactions:
        print(f"⚠️  {drug} ↔ metformin: {json.dumps(interactions, ensure_ascii=False)[:500]}")
    else:
        print(f"✅ {drug} ↔ metformin: No known interaction")
PYEOF

Interaction severity classification:

• Contraindicated: Do not co-administer. Find alternative.
• Major: Use with extreme caution. Document risk-benefit.
• Moderate: Monitor closely. Adjust doses if needed.
• Minor: Generally safe. Standard monitoring.

Step 4: GUIDELINE — Clinical Guideline Matching

Search for current treatment guidelines:

bash: echo "=== NCCN/ESMO Guidelines ===" && \
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=10&sort=pub_date&term=(NSCLC+OR+%22non-small+cell+lung+cancer%22)+AND+(guideline+OR+%22clinical+practice%22+OR+NCCN+OR+ESMO)+AND+2024:2026[pdat]" && \
echo -e "\n=== ClinicalTrials.gov ===" && \
curl -s "https://clinicaltrials.gov/api/v2/studies?query.term=NSCLC+first-line&filter.overallStatus=COMPLETED&pageSize=10&sort=LastUpdatePostDate:desc"

Extract first-line, second-line, and third-line recommendations. Note evidence level for each (1A, 1B, 2A, etc.).

Step 5: PERSONALIZE — Patient-Specific Adjustment

Apply patient factors to modify recommendations:

If genomic markers are unknown, recommend testing and provide conditional recommendations:

• "If EGFR+: osimertinib first-line"
• "If EGFR-/ALK-/PD-L1≥50%: pembrolizumab monotherapy"
• "If EGFR-/ALK-/PD-L1<50%: pembrolizumab + chemotherapy"

Step 6: SYNTHESIZE — Ranked Recommendations

Output format:

## 治疗推荐

### 患者画像
- 65岁男性，T2DM（二甲双胍 500mg BID），新确诊 NSCLC（分期/病理/分子标志物待确认）

### 推荐检查
- [ ] EGFR/ALK/ROS1/BRAF 分子检测
- [ ] PD-L1 (22C3) 表达检测
- [ ] 肾功能评估（肌酐清除率）— 影响铂类选择
- [ ] 肝功能评估 — 影响 TKI 选择

### 条件化治疗方案

#### 情景 A: EGFR 突变阳性
| 线数 | 方案 | 证据级别 | 关键试验 | 注意事项 |
|-----|------|---------|---------|---------|
| 一线 | Osimertinib 80mg QD | 1A | FLAURA (HR=0.80) | 与 metformin 无已知相互作用 |
| 二线 | Carboplatin + Pemetrexed | 1A | PROFILE 1014 | 监测肾功能（metformin + carboplatin） |

#### 情景 B: EGFR 野生型，PD-L1 ≥ 50%
| 线数 | 方案 | 证据级别 | 关键试验 | 注意事项 |
|-----|------|---------|---------|---------|
| 一线 | Pembrolizumab 200mg Q3W | 1A | KEYNOTE-024 (HR=0.60) | 监测甲状腺功能和血糖 |

#### 情景 C: EGFR 野生型，PD-L1 < 50%
| 线数 | 方案 | 证据级别 | 关键试验 | 注意事项 |
|-----|------|---------|---------|---------|
| 一线 | Pembrolizumab + Carboplatin + Pemetrexed | 1A | KEYNOTE-189 (HR=0.49) | Carboplatin 优于 cisplatin（肾安全） |

### 药物相互作用摘要
| 候选药 | 与 Metformin 交互 | 严重度 | 建议 |
|--------|-----------------|--------|------|
| Osimertinib | 无已知交互 | — | 可安全联用 |
| Pembrolizumab | 无已知交互 | — | 可安全联用 |
| Carboplatin | 肾功能影响 | Moderate | 监测 CrCl，必要时调 metformin 剂量 |

### 局限性
- 未获得分期和分子检测结果，方案为条件化推荐
- 药物相互作用数据来自 DrugBank，可能不涵盖所有文献报道的交互
- 本分析不替代肿瘤科医生的临床判断

### 数据来源
- DrugBank (accessed 2026-03-18)
- OpenTargets Platform (v24.12)
- ClinicalTrials.gov
- PubMed guidelines search

Evidence Level Classification

Always state evidence level for each recommendation.

Safety Flags

When any of these conditions are detected, prominently flag at the top of the output:

• 🔴 Contraindicated combination: Drug pair has a contraindication
• 🟠 Major interaction: Dose adjustment or enhanced monitoring required
• 🟡 Organ function concern: Impaired organ may affect drug metabolism/clearance
• 🔵 Genomic implication: Pharmacogenomic variant affects drug choice/dose