Pharmacometrics Golems: Exposure‐Response Models in Oncology

Abstract

Pharmacometric models are prone to different types of bias, which can confound the analysis and challenge the credibility of causal inference. This holds particularly true for exposureresponse analysis with time-to-event end points. With ever wider use of pharmacometric models and increased recognition of confounding factors, advanced methods addressing these biases are being developed and increasingly utilized. Herein, we provide a perspective highlighting the limitations introduced by the biases and considerations for future applications.

Model-informed approach for risk management of bleeding toxicities for bintrafusp alfa, a bifunctional fusion protein targeting TGF-β and PD-L1

“…Further investigations are needed to evaluate effectiveness of this approach (including longitudinal modeling), based on safety data generated after its implementation. In addition, longitudinal modeling approaches (including those accounting for the immortal time bias [ 36 ]) may be considered to evaluate the exposure-safety relationship for re-occurrence of bleeding AEs. This example highlights that pragmatic dosing modification strategies may be considered to improve the benefit-risk profile for therapeutic proteins.…”

Toward Project Optimus for Oncology Precision Medicine: Multi‐Dimensional Dose Optimization Enabled by Quantitative Clinical Pharmacology

“…Clinical Pharmacology and Therapeutics ( CPT ) has been a home for research articles and reviews illustrating contemporary integrative approaches to inform dose selection of oncology therapeutics, including molecularly targeted small molecules, 5–7 immunotherapies, 8–11 antibody‐drug conjugates, 12–14 and cell therapies 15–17 . Several examples have catalyzed active scientific discussion contributing to growing appreciation of the biological complexity, population variability, and analytical methodology that demand careful consideration for robust dose optimization in oncology drug development 18–24 …”

“…[15][16][17] Several examples have catalyzed active scientific discussion contributing to growing appreciation of the biological complexity, population variability, and analytical methodology that demand careful consideration for robust dose optimization in oncology drug development. [18][19][20][21][22][23][24] In the current issue of CPT, Combes et al 25 illustrate the pivotal role of quantitative clinical pharmacology in optimizing dose selection for a molecularly targeted precision medicine in oncology through their study on asciminib, an allosteric inhibitor of BCR-ABL1 in chronic myeloid leukemia -chronic phase (CML-CP). Asciminib is active against wild-type BCR-ABL1 and several mutant forms of the kinase, including the T315I mutation, albeit with lower potency for T315I mutant BCR-ABL1 as established in cell proliferation assays in vitro, and in preclinical in vivo xenograft models using patient-derived CML cell lines.…”

Quantitative Clinical Pharmacology of CAR T‐Cell Therapy

“…This will require continued evaluation of the translational fidelity of systems pharmacology models as such models should in principle help explain and extend inference from clinically observed exposure‐response relationships to optimize CAR‐T precision therapy. Additional challenges with pharmacometric analyses of exposure‐response relationships for CAR‐T therapies include the need to carefully consider the impact of immortal time bias 18 when dealing with efficacy end points like duration of response or overall survival. Although less of a concern when using early “landmark” measures of cellular expansion as the exposure metric (e.g., AUC over 28 days following infusion) for relationships to response rates, these pharmacostatistical considerations are important if the question being asked involves the relationship of CAR‐T persistence to response duration or survival outcomes in time‐to‐event analyses.…”