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A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I–II AMEERA‐1‐2 dose escalation and expansion cohorts. A semi‐mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure‐driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression‐free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA‐3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I–II data. This provides a good modeling and simulation tool to inform early development decisions. [ABSTRACT FROM AUTHOR]

Background: Phosphoinositide 3-kinase (PI3K) β is the dominant isoform for PI3K activity in many phosphatase and tensin homolog (PTEN)-deficient tumor models. This was a first-in-human study to determine the maximum tolerated dose, safety, pharmacokinetics (PK), pharmacodynamics, and preliminary activity of SAR260301, a potent PI3Kβ-selective inhibitor (clinicaltrials.gov identifier NCT01673737).Methods: Successive cohorts of patients with advanced solid tumors received increasing doses of oral SAR260301 according to a Bayesian escalation with an overdose-control process based on the occurrence of dose-limiting toxicity in the first 28-day cycle. Adverse events, tumor response, PK, and the effect of food on PK were evaluated. Target engagement was assessed in platelets. Physiologically-based PK modeling was used for exposure predictions.Results: Twenty-one patients received treatment at doses ranging from 100 mg once daily to 440 mg/m2 twice daily. Dose-limiting toxicities included 1 episode of grade 3 pneumonitis (400 mg twice daily) and 1 grade 3 γ-glutamyltransferase increase (600 mg twice daily). The maximum tolerated dose was not reached. The most frequently occurring treatment-related adverse events were nausea, vomiting, and diarrhea (14% each). Pharmacologically active concentrations were reached, but SAR260301 was rapidly cleared, and exposures associated with antitumor activity in preclinical models were not maintained at the highest dose tested. Food further decreased SAR260301 exposure.Conclusions: SAR260301 had an acceptable safety profile, but exposure sufficient to inhibit the PI3K pathway was unachievable because of rapid clearance, and clinical development was terminated. These results demonstrate the importance of PK and pharmacodynamic assessments in early drug development. Cancer 2018;124:315-24. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

Aim Applying physiologically-based pharmacokinetic (PBPK) modelling in paediatric cancer drug development is still challenging. We aimed to demonstrate how PBPK modelling can be applied to optimize dose and sampling times for a paediatric pharmacokinetic (PK) bridging study in oncology and to compare with the allometric scaling population PK (AS-popPK) approach, using docetaxel as an example. Methods A PBPK model for docetaxel was first developed for adult cancer patients using Simcyp® and subsequently used to predict its PK profiles in children by accounting for age-dependent physiological differences. Dose (mg m-2) requirements for children aged 0-18 years were calculated to achieve targeted exposure in adults. Simulated data were then analyzed using population PK modelling with MONOLIX® in order to perform design optimization with the population Fisher information matrix (PFIM). In parallel, the AS-popPK approach was performed for the comparison. Results The PBPK model developed for docetaxel adequately predicted its PK profiles in both adult and paediatric cancer patients (predicted clearance and volume of distribution within 1.5 fold of observed data). The revised dose of docetaxel for a child over 1.5 years old was higher than the adult dose. Considering clinical constraints, the optimal design contained two groups of 15 patients, having three or four sampling times and had good predicted relative standard errors (RSE<30%) for almost all parameters. The AS-popPK approach performed reasonably well but could not predict for very young children. Conclusion This research shows the clinical utility of PBPK modelling in combination with population PK modelling and optimal design to support paediatric oncology development. [ABSTRACT FROM AUTHOR]