SGLT2 inhibition improves PI3Kα inhibitor-induced hyperglycemia: findings from preclinical animal models and from patients in the BYLieve and SOLAR-1 trials.

PURPOSE: Alpelisib plus fulvestrant demonstrated a significant progression-free survival benefit versus fulvestrant in patients with PIK3CA-mutated HR+ /HER2- advanced breast cancer (ABC) (SOLAR-1). Hyperglycemia, an on-target adverse effect of PI3Kα inhibition, can lead to dose modifications, potentially impacting alpelisib efficacy. We report data from preclinical models and two clinical trials (SOLAR-1 and BYLieve) on Sodium glucose cotransporter 2 inhibitor (SGLT2i) use to improve PI3Kα inhibitor-associated hyperglycemia. METHODS: Healthy Brown Norway (BN), mild diabetic Zucker diabetic fatty (ZDF), and Rat1-myr-p110α/HBRX3077 tumor-bearing nude rats treated with alpelisib were analyzed for glucose and insulin control with metformin and dapagliflozin (SGLT2i) and alpelisib efficacy. Hyperglycemia adverse events (AEs) were compared between patients receiving SGLT2i with alpelisib (n = 19) and a propensity score-matched cohort not receiving SGLT2i (n = 74) in both trials. RESULTS: Dapagliflozin and metformin in BN and ZDF rats treated with alpelisib normalized blood glucose and reduced insulin levels. No signs of ketosis or drug-drug interaction were observed when metformin and dapagliflozin was administered with alpelisib. Alpelisib antitumor efficacy was maintained when used with dapagliflozin in tumor-bearing rats. Compared with a matched set of patients without SGLT2i, patients receiving SGLT2i had 4.9 and 6.4 times lower rates of grade ≥ 3 hyperglycemia AEs and hyperglycemia AEs resulting in alpelisib dose adjustments, interruptions, or withdrawals, respectively, and a relative reduction in risk of experiencing these AEs (70.6% and 35.7%). CONCLUSION: These data suggest adding an SGLT2i can effectively manage hyperglycemia, resulting in fewer alpelisib dose modifications and discontinuations in patients with PIK3CA-mutated HR+ /HER2- ABC (SOLAR-1: NCT02437318; BYLieve: NCT03056755).

Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models.

One of the objectives of oncology phase I dose-escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model-based designs are becoming mainstream in oncology first-in-human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose-toxicity models to estimate MTD. We extend traditional dose-toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure-toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose-escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule-based and model-based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model-based designs were better than rule-based methods and that exposure-toxicity model-based methods have the potential to valuably complement dose-toxicity model-based methods. Exposure-toxicity model-based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose-escalation studies.