Role of pharmacometrics and systems pharmacology in facilitating efficient dose optimization in oncology

• Published in: CPT: pharmacometrics and systems pharmacology Vol. 12; no. 11; pp. 1569 - 1572
• Main Authors: Jayachandran, Priya, Desikan, Rajat, Krishnaswami, Sriram, Hennig, Stefanie
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.11.2023; Wiley
• Subjects: Antigens; Artificial intelligence; Biomarkers; Chemotherapy; Clinical trials; Cytokines; Design; Drug dosages; Drug therapy; Humans; Medical Oncology; Network Pharmacology; Neutropenia; Oncology; Optimization; Pharmacology; Pharmacology, Clinical; Radiation therapy; Regulatory approval; Response rates; Simulation
• ISSN: 2163-8306; 2163-8306
• DOI: 10.1002/psp4.13066

The articles in the themed issue can be broadly classified into three categories: (1) continuation of core/traditional quantitative clinical pharmacology applications (e.g., population pharmacokinetics [PK] and PK/pharmacodynamics [PD]) to characterize dose/exposure-response (ER) relationships enabling dose optimization, (2) newer quantitative modeling and simulation methodologies (e.g., machine learning [ML], quantitative systems pharmacology [QSP], and model-based meta-analyses [MBMA] among others) for informing dose and biomarker selection, and (3) model-informed drug development (MIDD) strategies for rational clinical trial design. Tosca et al. 2 illustrate a translational model-based approach integrating PK and tumor growth inhibition (TGI) data in mice to extrapolate a range of minimum effective concentrations for MEN1611, a compound in clinical development in combination with trastuzumab for patients with breast cancer. Adoptive cell therapies have unique challenges; they are delivered once making the determination of optimal exposure a high priority, and cellular proliferation following drug administration can complicate the understanding of the dose-exposure relationship, which is also highlighted by Mc Laughlin et al. 9 Connarn et al. 8 used ER models for efficacy end points (overall response rate [ORR] and complete response rate) and safety events (cytokine release syndrome [CRS]) to simulate dose–response relationships and demonstrate a positive benefit–risk assessment. Utilization of their routine therapeutic drug monitoring data in a modeling and simulation study showed that the dosing interval for atezolizumab could be extended greatly while still maintaining exposures above the target threshold. 11 Transitioning to newer or non-traditional approaches, we note the work by Gevertz and Kareva 12 who introduce a new algorithm to predict drug synergy—Multi-Objective Optimization of Combination Synergy – Dose Selection (MOOCS-DS) – which decouples the synergies of potency and efficacy and identifies Pareto optimal solutions in a multi-objective synergy space.