A Reproducible Computational Framework for Precision Clinical Pharmacology: Mechanistic PK/PD Modeling, Global Sensitivity Analysis, and Neural Residual Correction

Background: Model informed precision dosing is hindered by fragmented workflows, opaque parameter sourcing, and pharmacometric results that cannot be regenerated from the code said to have produced them. We present a computational framework for pharmacokinetic and pharmacodynamic analysis, using vancomycin as a validation exemplar, in which every numerical result reported here is generated by the accompanying code.Methods: We implement a one compartment population PK model fitted by the Standard Two Stage method with patient level bootstrap; variance based global sensitivity analysis using a Saltelli and Jansen estimator built on SciPy's Sobol sampler; a Sobol guided adaptive prior refinement operator; Sobol based dynamic parameter fixing with an information theoretic error bound; and a deep ensemble neural residual correction layer for approximate predictive uncertainty. PyMC, SALib, and ArviZ are unavailable in the compute environment used here, so the corresponding components are implemented on NumPy, SciPy, and scikit learn instead; this substitution and its consequences are discussed throughout the manuscript.Results: Population clearance is estimated at 4.21 liters per hour (95 percent bootstrap confidence interval 3.84 to 4.66; n equals 119), consistent with the literature benchmark of 4.23 liters per hour. Sensitivity analysis attributes most parameter level AUC variance to population clearance (total order Sobol index 0.950), while a separate analysis over patient level covariates finds that between subject random variability in clearance and volume exceeds the direct effect of estimated glomerular filtration rate (indices of 0.354 and 0.423 versus 0.241). In an ablation study with 30 held out patients, adaptive prior shrinkage and model reduction do not improve point prediction accuracy; their demonstrated value is diagnostic, identifying which parameters drive AUC variability and reducing effective dimensionality accordingly, not predictive. A deep ensemble residual correction gives a modest 1.4 percent reduction in mean absolute error, from 2.645 to 2.609 milligrams per liter, but its raw intervals under cover, achieving 66.7 percent against a 95 percent target. Applying split conformal calibration on top of the same point predictor restores coverage to 96.7 percent while further reducing mean absolute error to 2.573 milligrams per liter, a configuration that performs comparably to an independently fitted FOCE style joint Laplace nonlinear mixed effects model. In a three cohort leave one cohort out comparison with 127 patients, all cohorts generated in house and not retrieved from external repositories, the adaptive prior framework does not outperform a fixed prior comparator, with a paired t test p value of 0.047 favoring the comparator; we identify a specific design artifact that likely contributes to this and use it to motivate a falsifiable follow up. A decision analytic nephrotoxicity simulation shows no distinguishable benefit for the proposed framework; we therefore report no number needed to treat or cost effectiveness estimate, since neither can be derived from an executed cost model here.Conclusions: Several of these findings are modest or null rather than favorable, and we report them alongside a concrete, falsifiable plan for testing whether they reflect the specific experimental conditions used here. This is a methodological proof of concept on synthetic and simulated data only; no clinical inference should be drawn from it.