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药代动力学与药效研究建模

组织 PK/PD 研究模型、参数、暴露与反应分析,保存数据依据、模型假设及评价结果。

开源精选医疗业务31 个文件50,135 Stars浏览次数 0K-Dense-AI/scientific-agent-skills MIT版本 a1043ef
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适用环境:Requires Python 3.12+ with NumPy 2+ and SciPy. No network access and no proprietary software. The estimation tools this skill orients you towards (NONMEM, Monolix, Phoenix, Simcyp, GastroPlus) are licensed separately and are never invoked by these scripts.

输入资料
获准使用的 PK/PD 研究资料 模型目标、参数与分析环境 阅读本节完整说明
交付内容
可复查的研究模型 模型评价与假设说明 阅读本节完整说明
  1. 获取完整包,保留配套文件的目录结构。
  2. 按照原文准备所需工具、依赖与输入资料。
  3. 阅读对应使用步骤与输入输出说明,在自己的环境中验证结果。

使用提示摘自原文,配套文件保留来源版本;实际运行条件以各包说明为准。

正文

获取原文件

references/nca-conventions.md

Define the estimand before integration

Record dose, analyte, matrix, actual times, concentration units, assay/LLOQ and dose history. AUC(0-t), AUC(0-inf) after a single dose, and AUC(0-tau) at steady state are different quantities. The helper integrates from the first retained sample and does not extrapolate to time zero. For an IV bolus this omits early exposure if C0 was not sampled or independently estimated.

Decision Bundled implementation Report
Trapezoids Linear, linear-up/log-down, or log for any positive unequal endpoints Rule and zero/equal-endpoint fallback
BLQ Global zero, half-LLOQ, or missing Location-dependent preprocessing, if applied
Missing values Blank concentration is treated as BLQ Remove truly missing samples before analysis
Lambda_z At least three positive non-BLQ post-Tmax points; negative slope Selected times, count, adjusted R², span in half-lives
Tail Observed and predicted Clast alternatives Which AUCinf drove CL/F and Vz/F
Partial AUC Interpolate only inside sampled bounds Prespecified boundaries and interpolation rule

The log-down AUC segment is dt*(C1-C2)/log(C1/C2). Equal endpoints require the linear limit. AUMC uses integration of t*C, not the trapezoidal rule on transformed log concentrations. Sorting is explicit: NumPy's trapezoid integrates in the supplied order and does not sort. NumPy documentation.

Terminal phase

Automatic selection begins with the last three eligible points and extends backwards only when adjusted R² improves by more than 0.0001. This differs from Phoenix Best Fit, which favors a longer window within its tolerance of the best score. Tmax exclusion here applies even to IV bolus. Use a manual window when scientifically justified; it still needs three declining post-Tmax points. A predicted Clast is evaluated at the last quantifiable time, even if the selected window ends earlier. Phoenix 8.4 guide.

A good regression can reflect absorption-limited decline, distribution or assay artifacts rather than elimination. Plot the fit. Screening flags (20% extrapolated AUC, adjusted R² below 0.8, span below two half-lives) are not universal exclusion criteria. State any exclusion rule prospectively.

Parameter identities and route

  • t_half=log(2)/lambda_z; AUCinf=AUClast+Clast/lambda_z.
  • Single-dose CL=D/AUCinf for IV, CL/F=D/AUCinf for extravascular input.
  • Vz=CL/lambda_z differs from Vss=CL*MRT.
  • IV infusion MRT needs correction for mean input time (duration/2 for constant infusion).
  • Oral MRT contains mean absorption time; it cannot directly determine Vss.
  • At demonstrated steady state, CLss(/F)=D/AUCtau and Cavg=AUCtau/tau.

With --tau, provide exactly one sampled interval including its boundaries. The helper withholds single-dose AUCinf/CL/Vz/Vss and explicitly records the steady-state assumption. It does not test whether the patient or simulated system has actually reached steady state. BLQ substitution and unmeasured early exposure remain sensitivity analyses, not biological measurements.

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