Model-based prediction of effective target exposure for MEN1611 in combination with trastuzumab in HER2-positive advanced or metastatic breast cancer patients

Translational PK-PD/TD modeling of antitumor effects and peripheral neuropathy in gemcitabine and nab-paclitaxel chemotherapy from xenograft mice to patients for optimal dose and schedule

PURPOSE

Gemcitabine and nab-paclitaxel (GnP) treatment, the standard first-line chemotherapy for unresectable pancreatic cancer, often causes peripheral neuropathy (PN). To develop alternative dosing strategies to avoid severe PN, understanding the relationship between pharmacokinetics (PK) and pharmacodynamics/toxicodynamics (PD/TD) is necessary. We established a PK-PD/TD model of GnP treatment to develop an optimal dose schedule.

METHODS

A mouse xenograft model of human pancreatic cancer was generated to measure drug concentrations in the plasma and tumor, antitumor effects, and PN after GnP treatment. The Simeoni tumor growth inhibition model with tumor concentrations and empirical indirect response models were used for the PD and TD models, respectively. Clinical outcomes were predicted with reported population estimates of PK parameters in cancer patients.

RESULTS

The PK-PD/TD model simultaneously described the observed tumor volume and paw withdrawal frequency in the von Frey test. For the standard GnP regimen, the model predicted clinical overall response (75.1%), which was overestimated compared to that in a recent phase II study (42.1%) but lower than the observed disease control rate (96.5%). Model simulation showed that dose reduction to less than 40% GnP dose was not effective; a change of dose schedule from every week for 3 weeks to every 2 weeks was a more favorable approach than dose reduction to 60% every week.

CONCLUSION

The PK-PD/TD model-based translational approach provides a guide for optimal dose determination to avoid severe PN while maintaining antitumor effects during GnP chemotherapy. Further research is needed to enhance its applicability and potential for combination chemotherapy regimens.

Targeting the PI3K/AKT signaling pathway in anticancer research: a recent update on inhibitor design and clinical trials (2020-2023)

INTRODUCTION

Recent years have witnessed great achievements in drug design and development targeting the phosphatidylinositol 3-kinase/protein kinase-B (PI3K/AKT) signaling pathway, a pathway central to cell growth and proliferation. The nearest neighbor protein-protein interaction networks for PI3K and AKT show the interplays between these target proteins which can be harnessed for drug discovery. In this review, we discuss the drug design and clinical development of inhibitors of PI3K/AKT in the past three years. We review in detail the structures, selectivity, efficacy, and combination therapy of 35 inhibitors targeting these proteins, classified based on the target proteins. Approaches to overcoming drug resistance and to minimizing toxicities are discussed. Future research directions for developing combinational therapy and PROTACs of PI3K and AKT inhibitors are also discussed.

AREA COVERED

This review covers clinical trial reports and patent literature on inhibitors of PI3K and AKT published between 2020 and 2023.

EXPERT OPINION

To address drug resistance and drug toxicity of inhibitors of PI3K and AKT, it is highly desirable to design and develop subtype-selective PI3K inhibitors or subtype-selective AKT1 inhibitors to minimize toxicity or to develop allosteric drugs that can form covalent bonds. The development of PROTACs of PI3Kα or AKT helps to reduce off-target toxicities.

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory

Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.

A PKPD Case Study: Achieving Clinically Relevant Exposures of AZD5991 in Oncology Mouse Models

Capturing human equivalent drug exposures preclinically is a key challenge in the translational process. Motivated by the need to recapitulate the pharmacokinetic (PK) profile of the clinical stage Mcl-1 inhibitor AZD5991 in mice, we describe the methodology used to develop a refined mathematical model relating clinically relevant concentration profiles to efficacy. Administration routes were explored to achieve target exposures matching the clinical exposure of AZD5991. Intravenous infusion using vascular access button (VAB) technology was found to best reproduce clinical target exposures of AZD5991 in mice. Exposure-efficacy relationships were evaluated, demonstrating that dissimilar PK profiles result in differences in target engagement and efficacy outcomes. Thus, these data underscore the importance of accurately ascribing key PK metrics in the translational process to enable clinically meaningful predictions of efficacy.