# 1 化合物基本性质
# 化合物物理化学性质
compound_params <- list(
MW = 581.29, # 分子量 (g/mol)
logP = 1.59, # 油水分配系数
pKa_base = 8.8, # 碱基pKa
Permeability = c(A2B = 0.45e-6, B2A = 14.0e-6, ER = 31.2) # 渗透性 (cm/s)
)
# 生理介质溶解度 (mg/mL)
solubility <- data.frame(
pH = c(1.4, 2.5, 4.0, 5.0, 6.5, 7.4, 6.5, 5.0, 1.4),
medium = c(rep("Buffer", 6), rep("FaSSIF", 1), rep("FeSSIF", 1), rep("FaSSGF", 1)),
value = c(0.08557, 0.06794, 0.07188, 0.07629, 0.08165, 0.07515, 0.06638, 0.06084, 0.07234)
)
# 2 物种特异性参数
# 固有清除率(μL/min/mg protein)
clint_data <- data.frame(
Species = c("Human", "Monkey", "Dog", "Rat", "Mouse"),
Clint_MMS = c(45.55, 168.92, 15.85, 13.34, 27.98)
)
# 血浆蛋白结合参数
protein_binding <- data.frame(
Species = c("Human", "Dog", "Rat", "Mouse"),
Rbp = c(0.954, 1.034, 1.576, 0.975),
fup = c(0.0818, 0.0981, 0.1560, 0.0873) # 游离药物分数
)
# 动物PK参数
pk_params <- data.frame(
Species = rep(c("Mouse", "Rat", "Dog"), each = 4),
Parameter = rep(c("Vd", "Cl", "T1/2", "F"), 3),
Value = c(0.213, 0.116, 2.08, 15.94, 5.01, 1.67, 2.82, 3.24, 51.4, 19.4, 1.99, 29.48),
Unit = rep(c("L", "L/h", "h", "%"), 3),
BW = rep(c(0.025, 0.22, 11), each = 4) # 体重(kg)
)
# 3.1 组织体积与血流速
# 基于[[180]]获取标准生理参数
physio_params_human <- data.frame(
Tissue = c("Liver", "Muscle", "Adipose", "Brain", "Kidney", "Heart", "Lung", "Spleen", "Intestine"),
Volume_fraction = c(0.026, 0.40, 0.19, 0.02, 0.004, 0.004, 0.01, 0.002, 0.03),
Blood_flow_fraction = c(0.25, 0.17, 0.05, 0.15, 0.19, 0.04, 1.00, 0.02, 0.15)
)
# 按体重缩放其他物种参数
species_weights <- c(Monkey = 5, Dog = 11, Rat = 0.22, Mouse = 0.025) # kg
# 3.2 组织脂质组成
# Rodgers-Rowland方法组织组成数据(基于[[207]][[252]][[258]]
tissue_composition <- list(
Liver = list(phospholipid = 0.569, neutral_lipid = 0.042, water = 0.71, protein = 0.24),
Muscle = list(phospholipid = 0.023, neutral_lipid = 0.012, water = 0.76, protein = 0.20),
Adipose = list(phospholipid = 0.002, neutral_lipid = 0.85, water = 0.15, protein = 0.02),
Brain = list(phospholipid = 0.55, neutral_lipid = 0.30, water = 0.15, protein = 0.0)
)
# 3.3 MPPGL值(微粒体蛋白/克肝脏)
# 基于[[226]]和[[347]]
mppgl_values <- c(
Human = 40.0, # Barter et al. 2007推荐的标准化值
Monkey = 40.0, # 使用人类值替代
Dog = 45.0, # 文献报告范围[[226]]
Rat = 40.0, # Barter et al. 2007
Mouse = 35.0 # [[226]]
)
# 4. 核心模型构建
# 4.1 Kp值计算(Rodgers-Rowland方法)
calculate_Kp <- function(logP, pKa, fup, tissue_comp, is.base = TRUE) {
# 电离状态计算
ion_factor <- ifelse(is.base, 10^(pKa - 7.4)/(1 + 10^(pKa - 7.4)), 1)
# Rodgers-Rowland方程
Kp <- (fup * tissue_comp$water +
ion_factor * (0.3 * tissue_comp$phospholipid * 10^logP) +
(1 - fup) * (0.5 * tissue_comp$neutral_lipid * 10^logP)) /
(fup + (1 - fup) * 0.5)
return(Kp)
}
# 计算人体主要组织Kp值
Kp_human <- sapply(names(tissue_composition), function(tissue) {
calculate_Kp(
logP = compound_params$logP,
pKa = compound_params$pKa_base,
fup = protein_binding$fup[protein_binding$Species == "Human"],
tissue_comp = tissue_composition[[tissue]],
is.base = TRUE
)
})
# 4.2 清除率外推(IVIVE)
# 固有清除率标化
calculate_hepatic_clearance <- function(species) {
# 获取生理参数
bw <- ifelse(species == "Human", 70, species_weights[species])
liver_fraction <- physio_params_human$Volume_fraction[physio_params_human$Tissue == "Liver"]
liver_mass <- bw * liver_fraction * 1000 # 克
# 肝血流速(Qh) - 基于[[240]]
Qh <- switch(species,
Human = 1.24 * bw,
Monkey = 2.7 * bw,
Dog = 4.2 * bw,
Rat = 4.2 * bw,
Mouse = 5.4 * bw)
# 微粒体蛋白总量
microsomal_protein_total <- liver_mass * mppgl_values[species]
# 固有清除率
Clint <- clint_data$Clint_MMS[clint_data$Species == species] # μL/min/mg
Clint_mL_min <- Clint / 1000 # mL/min/mg
# 体外→体内转换
fup_val <- protein_binding$fup[protein_binding$Species == species]
# 肝清除率Well-stirred模型
CLint <- Clint_mL_min * microsomal_protein_total * 60 / 1000 # L/h
CLh <- (Qh * fup_val * CLint) / (Qh + fup_val * CLint)
return(list(CLint = CLint, CLh = CLh))
}
# 4.3 吸收模型(Ka估算)
calculate_Ka <- function() {
# 肠腔溶解度取FaSSIF和FeSSIF平均值
sol_intestine <- mean(c(
solubility$value[solubility$medium == "FaSSIF"],
solubility$value[solubility$medium == "FeSSIF"]
))
# Caco-2渗透性→人空肠Peff转换(基于Web Page 提供的经验关系)
Peff_human <- 10^(-4) * (0.3 * compound_params$Permeability["A2B"] + 0.05) # cm/s
# 扩散限制吸收
D <- 1e-5 # 扩散系数(cm²/s)
h <- 30e-4 # 未搅拌水层厚度(cm)
# 吸收速率常数Ka(1/h)
Ka <- (2 * Peff_human * 3600) / (h + (D / Peff_human)) # 转换为h⁻¹
return(Ka)
}
# 5. 完整PBPK模型实现
# 加载必要库
library(deSolve)
# PBPK模型方程
pbpk_model <- function(time, state, parameters) {
with(as.list(c(state, parameters)), {
# 中心室浓度(血浆)
C_plasma <- A_central / V_central
# 组织室浓度计算(血流限制)
dA_liver <- Q_liver * (C_plasma - C_liver/Kp_liver)
dA_muscle <- Q_muscle * (C_plasma - C_muscle/Kp_muscle)
dA_adipose <- Q_adipose * (C_plasma - C_adipose/Kp_adipose)
dA_brain <- Q_brain * (C_plasma - C_brain/Kp_brain)
dA_other <- sum(Q_other) * (C_plasma - C_other/Kp_other)
# 中央室(血浆+瞬时平衡组织)
dA_central <- - (Q_total * C_plasma) +
Ka * A_GI + # 吸收项
(dA_liver + dA_muscle + dA_adipose + dA_brain + dA_other) -
CL_total * C_plasma # 清除项
# 肠道吸收
dA_GI <- -Ka * A_GI
# 返回微分值
return(list(c(dA_GI, dA_central, dA_liver, dA_muscle, dA_adipose, dA_brain, dA_other)))
})
}
# 参数初始化与模拟
run_pbpk_simulation <- function(species = "Human", dose = 10, route = "oral") {
# 生理参数
bw <- ifelse(species == "Human", 70, species_weights[species])
tissue_fractions <- physio_params_human$Volume_fraction
names(tissue_fractions) <- physio_params_human$Tissue
# 血流比例
flow_fractions <- physio_params_human$Blood_flow_fraction
names(flow_fractions) <- physio_params_human$Tissue
# 心输出量(CO, L/h) - 基于[[240]]
CO <- switch(species,
Human = 70 * 60 * 1.24, # L/h
Monkey = 5 * 60 * 1.5, # 估算值
Dog = 11 * 60 * 1.2,
Rat = 0.22 * 60 * 1.5,
Mouse = 0.025 * 60 * 1.6)
# 计算组织体积(L)
V_tissues <- sapply(names(tissue_fractions), function(t) bw * tissue_fractions[t])
# 计算组织血流(L/h)
Q_tissues <- sapply(names(flow_fractions), function(t) CO * flow_fractions[t])
# Kp值
if(species == "Human") {
Kp_values <- Kp_human
} else {
# 使用与人类相同的组织组成
Kp_values <- sapply(names(tissue_composition), function(t) {
calculate_Kp(
compound_params$logP,
compound_params$pKa_base,
protein_binding$fup[protein_binding$Species == species],
tissue_composition[[t]],
TRUE
)
})
}
# 清除率
clearance <- calculate_hepatic_clearance(species)
# 吸收参数
Ka_val <- if(route == "oral") calculate_Ka() else 0
# 初始状态
state <- c(
A_GI = if(route == "oral") dose * 1e6 else 0, # μg
A_central = 0,
A_liver = 0,
A_muscle = 0,
A_adipose = 0,
A_brain = 0,
A_other = 0
)
# 参数列表
parameters <- list(
Ka = Ka_val,
V_central = tissue_fractions["Plasma"] * bw, # 血浆体积
Kp_liver = Kp_values["Liver"],
Kp_muscle = Kp_values["Muscle"],
Kp_adipose = Kp_values["Adipose"],
Kp_brain = Kp_values["Brain"],
Kp_other = mean(Kp_values), # 其他组织平均Kp
Q_total = CO,
Q_liver = Q_tissues["Liver"],
Q_muscle = Q_tissues["Muscle"],
Q_adipose = Q_tissues["Adipose"],
Q_brain = Q_tissues["Brain"],
Q_other = CO - sum(Q_tissues[c("Liver","Muscle","Adipose","Brain")]),
CL_total = clearance$CLh,
V_liver = V_tissues["Liver"],
V_muscle = V_tissues["Muscle"],
V_adipose = V_tissues["Adipose"],
V_brain = V_tissues["Brain"],
V_other = sum(V_tissues) - sum(V_tissues[c("Plasma","Liver","Muscle","Adipose","Brain")])
)
# 时间点
times <- seq(0, 24, by = 0.1)
# ODE求解
out <- ode(y = state, times = times, func = pbpk_model, parms = parameters)
# 结果处理
results <- data.frame(out)
results$C_plasma <- results$A_central / parameters$V_central # μg/mL
return(list(
results = results,
parameters = parameters,
AUC = trapz(results$time, results$C_plasma),
Cmax = max(results$C_plasma),
Tmax = results$time[which.max(results$C_plasma)]
))
}
# 6. 模型验证与结果外推
# 6.1 动物模型验证
# 大鼠验证示例
rat_iv <- run_pbpk_simulation(species = "Rat", dose = 10, route = "iv")
rat_oral <- run_pbpk_simulation(species = "Rat", dose = 10, route = "oral")
# 计算预测PK参数
predicted_CL_rat <- rat_iv$parameters$CL_total
predicted_Vd_rat <- sum(rat_iv$parameters$V_central,
rat_iv$parameters$V_liver * rat_iv$parameters$Kp_liver,
rat_iv$parameters$V_muscle * rat_iv$parameters$Kp_muscle,
rat_iv$parameters$V_adipose * rat_iv$parameters$Kp_adipose,
rat_iv$parameters$V_brain * rat_iv$parameters$Kp_brain,
rat_iv$parameters$V_other * rat_iv$parameters$Kp_other)
predicted_F_rat <- (rat_oral$AUC / rat_iv$AUC) * (dose_iv/dose_oral)
# 与实际PK参数比较
comparison_rat <- data.frame(
Parameter = c("CL", "Vd", "F"),
Actual = c(1.67, 5.01, 3.24/100),
Predicted = c(predicted_CL_rat, predicted_Vd_rat, predicted_F_rat)
)
# 狗和小鼠采用相同验证流程...
# 6.2 人体PK参数外推
# 人体IV和PO模拟
human_iv <- run_pbpk_simulation(species = "Human", dose = 100, route = "iv")
human_oral <- run_pbpk_simulation(species = "Human", dose = 100, route = "oral")
# 外推的人体PK参数
human_pk <- list(
Ka = human_oral$parameters$Ka,
Kp = Kp_human,
Vss = sum(human_iv$parameters$V_central,
human_iv$parameters$V_liver * human_iv$parameters$Kp_liver,
human_iv$parameters$V_muscle * human_iv$parameters$Kp_muscle,
human_iv$parameters$V_adipose * human_iv$parameters$Kp_adipose,
human_iv$parameters$V_brain * human_iv$parameters$Kp_brain,
human_iv$parameters$V_other * human_iv$parameters$Kp_other),
CL = human_iv$parameters$CL_total,
t1_2 = log(2) * human_iv$parameters$V_central / human_iv$parameters$CL_total,
F = (human_oral$AUC / human_iv$AUC) * (100/100) # 剂量相同
)
# 7. 结果分析与讨论
# 7.1 关键外推参数
# 输出人体外推参数
cat("人体外推PK参数：\n")
cat(sprintf("Ka = %.2f h⁻¹\n", human_pk$Ka))
cat("组织分配系数(Kp)：\n")
print(human_pk$Kp)
cat(sprintf("\n分布容积(Vss) = %.1f L\n", human_pk$Vss))
cat(sprintf("清除率(CL) = %.2f L/h\n", human_pk$CL))
cat(sprintf("半衰期(t₁/₂) = %.1f h\n", human_pk$t1_2))
cat(sprintf("口服生物利用度(F) = %.1f%%\n", human_pk$F * 100))
#7.3 敏感性分析建议
# 示例：Ka的敏感性分析
ka_values <- seq(0.5, 5, by = 0.5) * human_pk$Ka
sensitivity_auc <- sapply(ka_values, function(ka) {
params <- human_oral$parameters
params$Ka <- ka
out <- ode(y = state, times = times, func = pbpk_model, parms = params)
trapz(out[,1], out[,2]/params$V_central)
})
plot(ka_values, sensitivity_auc, type = "b", xlab = "Ka (h⁻¹)", ylab = "AUC")
> rat_iv <- run_pbpk_simulation(species = "Rat", dose = 10, route = "iv")
错误于eval(substitute(expr), data, enclos = parent.frame()):
找不到对象'C_liver'
Called from: eval(substitute(expr), data, enclos = parent.frame())
Browse[1]> rat_oral <- run_pbpk_simulation(species = "Rat", dose = 10, route = "oral")
收捲时出错: 找不到对象'C_liver'
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
本文记录了作者刷入 CM-4.1.999 版本 ROM 的过程及遇到的问题，包括解决 io 占用高、蓝牙耳机控制键失灵、部分应用权限问题等，并分享了对新系统的初步感受。
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