Description and Modeling of Relevant Demographic and Laboratory Variables in a Large Oncology Cohort to Generate Virtual Populations

<b>Background/Objectives</b>: Pathophysiological variability in patients with cancer is associated with differences in responses to pharmacotherapy. In this work, we aimed to describe the demographic characteristics and hematological, biochemical, and coagulation variables in a large oncology cohort and to develop, optimize, and provide open access to modeling equations for the estimation of variables potentially relevant in pharmacokinetic modeling. <b>Methods</b>: Using data from 1793 patients with cancer, divided into training (<i>n</i> = 1259) and validation (<i>n</i> = 534) datasets, a modeling network was developed and used to simulate virtual oncology populations. All analyses were conducted in RStudio 4.3.2 Build 494. <b>Results</b>: The simulation network based on sex, age, biogeographic origin/ethnicity, and tumor type (fixed or primary factors) was successfully validated, able to predict age, height, weight, alpha-1-acid glycoprotein, albumin, hemoglobin, C-reactive protein and lactate dehydrogenase serum levels, platelet-lymphocyte and neutrophil-lymphocyte ratios, and hematocrit. This network was then successfully extrapolated to simulate the laboratory variables of eight oncology populations (<i>n</i> = 1200); only East Asians, Sub-Saharan Africans, Europeans, only males, females, patients with an ECOG performance status equal to 2, and only patients with pancreas cancer or ovarian cancer. <b>Conclusions</b>: this network constitutes a valuable tool to predict relevant characteristics/variables of patients with cancer, which may be useful in the evaluation and prediction of pharmacokinetics in virtual oncology populations, as well as for model-based optimization of oncology treatments.