RT Journal Article
SR Electronic
T1 Approaches to generating virtual patient cohorts with applications in oncology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.24.493265
DO 10.1101/2022.05.24.493265
A1 Anudeep Surendran
A1 Justin Le Sauteur-Robitaille
A1 Dana Kleimeier
A1 Jana Gevertz
A1 Kathleen Wilkie
A1 Adrianne L. Jenner
A1 Morgan Craig
YR 2022
UL http://biorxiv.org/content/early/2022/05/25/2022.05.24.493265.abstract
AB Virtual clinical trials (VCTs) have gained popularity for their ability to rationalize the drug development process using mathematical and computational modelling, and to provide key insights into the mechanisms regulating patient responses to treatment. In this chapter, we cover approaches for generating virtual cohorts with applications in cancer biology and treatment. VCTs are an effective tool for predicting clinical responses to novel therapeutics and establishing effective treatment strategies. These VCTs allow us to capture inter-individual variability (IIV) which can lead to diversity in patient drug responses. Here we discuss three main methodologies for capturing IIV with a VCT. First, we highlight the use of population pharmacokinetic (PopPK) models, which extrapolate from empirical data population PK parameters that best fits the individual variability seen in drug disposition using non-linear mixed effects models. Next, we show how virtual patients may be sampled from a normal distribution with mean and standard deviation informed from experimental data to estimate parameters in a mechanistic model that regulates drug PKs. Lastly, we show how optimization techniques can be used to calibrate virtual patient parameter values and generate the VCT. Throughout, we compare and contrast these methods to provide a broader view of the generation of virtual patients, and to aid the decision-making process for those looking to leverage virtual clinical trials in their research.Competing Interest StatementThe authors have declared no competing interest.