药物相互作用预测¶

# === 基于 CYP 代谢通路的 DDI 风险评估 ===
import pandas as pd                    # 数据处理
from itertools import combinations     # 组合计算

# 1. 定义药物的 CYP 代谢关系
drug_cyp_profile = {
    "氯吡格雷": {
        "substrate": ["CYP2C19", "CYP3A4"],     # 被这些酶代谢
        "inhibitor": [],                          # 抑制哪些酶
        "inducer": [],                            # 诱导哪些酶
        "prodrug": True,                          # 是前药
    },
    "奥美拉唑": {
        "substrate": ["CYP2C19", "CYP3A4"],
        "inhibitor": ["CYP2C19"],                 # 抑制CYP2C19！
        "inducer": [],
        "prodrug": False,
    },
    "利福平": {
        "substrate": [],
        "inhibitor": [],
        "inducer": ["CYP3A4", "CYP2C19", "CYP2C9"],  # 强诱导剂
        "prodrug": False,
    },
    "华法林": {
        "substrate": ["CYP2C9", "CYP3A4"],
        "inhibitor": [],
        "inducer": [],
        "prodrug": False,
    },
    "他莫昔芬": {
        "substrate": ["CYP2D6", "CYP3A4"],
        "inhibitor": [],
        "inducer": [],
        "prodrug": True,                          # 前药，需要CYP2D6激活
    },
    "帕罗西汀": {
        "substrate": ["CYP2D6"],
        "inhibitor": ["CYP2D6"],                  # 强效CYP2D6抑制剂
        "inducer": [],
        "prodrug": False,
    },
}

def predict_ddi(drug_a, drug_b, profiles):
    """预测两种药物的相互作用风险"""
    profile_a = profiles.get(drug_a)
    profile_b = profiles.get(drug_b)
    if not profile_a or not profile_b:
        return []

    warnings = []                      # 预警列表

    # 检查 A 是否抑制 B 的代谢酶
    for enzyme in profile_a["inhibitor"]:
        if enzyme in profile_b["substrate"]:
            if profile_b["prodrug"]:
                warnings.append(
                    f"⚠ {drug_a} 抑制 {enzyme}，可能降低 {drug_b}（前药）的疗效"
                )
            else:
                warnings.append(
                    f"⚠ {drug_a} 抑制 {enzyme}，可能增加 {drug_b} 血浓度（中毒风险）"
                )

    # 检查 A 是否诱导 B 的代谢酶
    for enzyme in profile_a["inducer"]:
        if enzyme in profile_b["substrate"]:
            if profile_b["prodrug"]:
                warnings.append(
                    f"⚠ {drug_a} 诱导 {enzyme}，可能增加 {drug_b}（前药）的活性代谢物"
                )
            else:
                warnings.append(
                    f"⚠ {drug_a} 诱导 {enzyme}，可能降低 {drug_b} 疗效"
                )

    # 反向检查（B 对 A 的影响）
    for enzyme in profile_b["inhibitor"]:
        if enzyme in profile_a["substrate"]:
            if profile_a["prodrug"]:
                warnings.append(
                    f"⚠ {drug_b} 抑制 {enzyme}，可能降低 {drug_a}（前药）的疗效"
                )
            else:
                warnings.append(
                    f"⚠ {drug_b} 抑制 {enzyme}，可能增加 {drug_a} 血浓度"
                )

    for enzyme in profile_b["inducer"]:
        if enzyme in profile_a["substrate"]:
            if profile_a["prodrug"]:
                warnings.append(
                    f"⚠ {drug_b} 诱导 {enzyme}，可能增加 {drug_a}（前药）的活化"
                )
            else:
                warnings.append(
                    f"⚠ {drug_b} 诱导 {enzyme}，可能降低 {drug_a} 疗效"
                )

    return warnings

# 2. 批量检查药物组合
print("=== 药物相互作用风险评估 ===\n")
drugs = list(drug_cyp_profile.keys())
for drug_a, drug_b in combinations(drugs, 2):  # 所有两两组合
    warnings = predict_ddi(drug_a, drug_b, drug_cyp_profile)
    if warnings:
        print(f"【{drug_a} + {drug_b}】")
        for w in warnings:
            print(f"  {w}")
        print()

# === DrugBank DDI 数据查询 ===
import requests                        # HTTP请求

def query_drugbank_ddi(drug_name, api_key=None):
    """查询 DrugBank API 获取已知 DDI（需要 API 密钥）"""
    # DrugBank API（需要订阅）
    # 替代方案：使用 DrugBank 下载的 XML 离线查询
    url = f"https://api.drugbank.com/v1/drugs"
    headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
    params = {"q": drug_name}          # 搜索参数

    # 注意：完整 API 需要商业许可
    # 学术用途建议下载 XML 文件离线解析
    print(f"查询 {drug_name} 的药物相互作用...")
    print("提示：使用 DrugBank XML 文件进行离线查询更高效")

# === PGx + DDI 综合分析 ===
def integrated_pgx_ddi(patient_genotype, medications, profiles):
    """综合考虑患者基因型和用药的风险评估"""
    print("=" * 60)
    print("  综合药物基因组 + 相互作用风险评估")
    print("=" * 60)

    # 1. PGx 风险（基因型影响）
    print("\n【1. 药物基因组风险】")
    for gene, phenotype in patient_genotype.items():
        print(f"  {gene}: {phenotype}")

    # 2. DDI 风险（药物间互作）
    print("\n【2. 药物相互作用风险】")
    for drug_a, drug_b in combinations(medications, 2):
        warnings = predict_ddi(drug_a, drug_b, profiles)
        if warnings:
            for w in warnings:
                print(f"  {w}")

    # 3. PGx + DDI 叠加风险
    print("\n【3. 叠加风险提示】")
    # 例：CYP2D6 PM + CYP2D6 抑制剂 = 极高风险
    if patient_genotype.get("CYP2D6") == "PM":
        for drug in medications:
            profile = profiles.get(drug, {})
            if "CYP2D6" in profile.get("inhibitor", []):
                print(f"  ★ 高危：患者CYP2D6 PM + {drug}(CYP2D6抑制剂) → 无意义")
            if "CYP2D6" in profile.get("substrate", []):
                if profile.get("prodrug"):
                    print(f"  ★ 高危：患者CYP2D6 PM + {drug}(前药) → 无法激活")

# 使用示例
# integrated_pgx_ddi(
#     {"CYP2D6": "PM", "CYP2C19": "IM"},
#     ["他莫昔芬", "帕罗西汀", "氯吡格雷"],
#     drug_cyp_profile
# )