Role of Pharmacokinetic/ Pharmacodynamic Modeling and Translational Approaches in Glioblastoma

Abstract

Pharmacokinetic- Pharmacodynamic modeling plays a crucial role in accelerating the transition from preclinical studies to clinical trials in glioblastoma by providing valuable insights into the pharmacokinetics and pharmacodynamics of therapeutic agents. Pharmacokinetic- Pharmacodynamic models can optimize dosing regimens by predicting drug concentrations in tumor tissue over time. By combining pharmacokinetic data (such as drug clearance and half-life) with pharmacodynamic data (such as tumor growth inhibition), these models identify the most effective dosing schedules to maximize efficacy and minimize toxicity. One of the approaches with gefitinib in a preclinical tumor model showed that similar therapeutic effects were achieved despite different dosing strategies. Researchers developed a pharmacokinetic- pharmacodynamic model to personalize temozolomide treatment, significantly reducing tumor size and toxicity compared to standard protocols. Overall, precision dosing in oncology is appealing due to its potential to enhance clinical effectiveness and cost-efficiency, requiring collaboration among academia, healthcare, and industry for broader adoption. Pharmacokinetic- Pharmacodynamic models predict clinical responses by quantitatively linking drug exposure to pharmacological effects, estimating therapeutic success by analyzing the relationship between drug concentration and tumor response. In oncology, tumor size is a key biomarker for clinical outcomes, and models of tumor size progression help understand drug effectiveness and predict long-term outcomes. Tumor growth inhibition (TGI) models have been crucial for forecasting clinical trial outcomes and translating preclinical data into clinical insights. Pharmacokinetic- Pharmacodynamic modeling helps identify and validate biomarkers related to glioblastoma treatment by linking biomarker changes with drug exposure, thereby revealing drug action mechanisms and potential prognostic markers. Biomarkers are essential in drug discovery for developing pharmacokinetic- pharmacodynamic models, enhancing model confidence, and supporting translational research. Integrating biomarkers into pharmacokinetic- pharmacodynamic models improves understanding of drug efficacy, with the FDA supporting this approach to reduce drug attrition rates. Pharmacokinetic- Pharmacodynamic models facilitate translating preclinical findings into clinically relevant insights, guiding clinical trial design, dose selection, and patient criteria by extrapolating preclinical data to human populations. Researchers highlighted that glioblastomas' heterogeneity can cause variable drug distribution and responses, which standard analyses might miss, and developed a model to address this variability, integrating tumor pharmacokinetics and pharmacodynamics to predict drug efficacy.