Optimizing Oncology Therapeutics Through Quantitative Translational and Clinical Pharmacology: Challenges and Opportunities K Venkatakrishnan1, LE Friberg2, D Ouellet3, JT Mettetal4, A Stein5, IF Troc oniz6, R Bruno7, 8 9 10 N Mehrotra , J Gobburu and DR Mould Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure–response understanding to optimize the benefit–risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.
INTRODUCTION
Oncology therapeutics have transformed since the turn of this century, with increased understanding of the molecular basis of cancer translating to the discovery, development, and availability of targeted therapies that have brought meaningful benefit to patients with diverse malignancies. Despite progress in cancer research and oncology drugs representing a substantial proportion of new drug approvals,1 success rates of anticancer drug development remain low: 7% from phase I and 20 msec) through sufficiently precise evaluation of electrocardiogram (ECG) effects is the overall goal of clinical risk assessment.92 This is most efficiently accomplished using concentration-QTc modeling. 47
Figure 4 Structure and applications of the mechanism-based myelosuppression model by Friberg et al. (ref. 80). Following dose administration, the drug concentration–time profile affects the proliferative cells in the bone marrow. Due to the maturation steps of nonmitotic (and nonsensitive) cells, the decline in circulating neutrophil counts is delayed relative to dose administration. The concentration–effect relationship may be described by a linear (slope) model or an Emax-model. The model has found diverse applications across the phases of oncology drug development and pharmacotherapy. These include interspecies scaling of myelosuppression to predict human MTD, dose/schedule optimization in single agent and combination settings, design of optimal G-CSF rescue regimens, identification of patient-specific covariates to inform risk management, and ANC-guided dose adaptation.
Inclusion of PK time-matched triplicate ECGs in FIH dose escalation studies followed by a mixed effects model-based analysis of PK-QTc relationships on centrally read ECG data is a powerful approach to quantify drug effects on QTc. A concentration-QTc model-based approach that integrates data from all patients across dose cohorts can maximize the ability to estimate potential effects on QTc even in the setting of high biological variability in a heterogeneous patient population with advanced cancers, associated comorbidities, and electrolyte changes. As the dose escalation study typically comprises a range of dose levels including a dose above the MTD and an MTD expansion cohort, ECGs can be collected over a wide exposure range.91 In most cases, it should be possible to estimate the change from baseline QTc with fitfor-purpose precision to rule out large increases at the clinical dose (i.e., upper end of one-sided 95% confidence interval [CI]
INTRODUCTION
Oncology therapeutics have transformed since the turn of this century, with increased understanding of the molecular basis of cancer translating to the discovery, development, and availability of targeted therapies that have brought meaningful benefit to patients with diverse malignancies. Despite progress in cancer research and oncology drugs representing a substantial proportion of new drug approvals,1 success rates of anticancer drug development remain low: 7% from phase I and 20 msec) through sufficiently precise evaluation of electrocardiogram (ECG) effects is the overall goal of clinical risk assessment.92 This is most efficiently accomplished using concentration-QTc modeling. 47
Figure 4 Structure and applications of the mechanism-based myelosuppression model by Friberg et al. (ref. 80). Following dose administration, the drug concentration–time profile affects the proliferative cells in the bone marrow. Due to the maturation steps of nonmitotic (and nonsensitive) cells, the decline in circulating neutrophil counts is delayed relative to dose administration. The concentration–effect relationship may be described by a linear (slope) model or an Emax-model. The model has found diverse applications across the phases of oncology drug development and pharmacotherapy. These include interspecies scaling of myelosuppression to predict human MTD, dose/schedule optimization in single agent and combination settings, design of optimal G-CSF rescue regimens, identification of patient-specific covariates to inform risk management, and ANC-guided dose adaptation.
Inclusion of PK time-matched triplicate ECGs in FIH dose escalation studies followed by a mixed effects model-based analysis of PK-QTc relationships on centrally read ECG data is a powerful approach to quantify drug effects on QTc. A concentration-QTc model-based approach that integrates data from all patients across dose cohorts can maximize the ability to estimate potential effects on QTc even in the setting of high biological variability in a heterogeneous patient population with advanced cancers, associated comorbidities, and electrolyte changes. As the dose escalation study typically comprises a range of dose levels including a dose above the MTD and an MTD expansion cohort, ECGs can be collected over a wide exposure range.91 In most cases, it should be possible to estimate the change from baseline QTc with fitfor-purpose precision to rule out large increases at the clinical dose (i.e., upper end of one-sided 95% confidence interval [CI]
