Predicting efficacy assessment of combined treatment of radiotherapy and nivolumab for NSCLC patients through virtual clinical trials using QSP modeling.

Non-Small Cell Lung Cancer (NSCLC) remains one of the main causes of cancer death worldwide. In the urge of finding an effective approach to treat cancer, enormous therapeutic targets and treatment combinations are explored in clinical studies, which are not only costly, suffer from a shortage of participants, but also unable to explore all prospective therapeutic solutions. Within the evolving therapeutic landscape, the combined use of radiotherapy (RT) and checkpoint inhibitors (ICIs) emerged as a promising avenue. Exploiting the power of quantitative system pharmacology (QSP), we undertook a study to anticipate the therapeutic outcomes of these interventions, aiming to address the limitations of clinical trials. After enhancing a pre-existing QSP platform and accurately replicating clinical data outcomes, we conducted an in-depth study, examining different treatment protocols with nivolumab and RT, both as monotherapy and in combination, by assessing their efficacy through clinical endpoints, namely time to progression (TTP) and duration of response (DOR). As result, the synergy of combined protocols showcased enhanced TTP and extended DOR, suggesting dual advantages of extended response and slowed disease progression with certain combined regimens. Through the lens of QSP modeling, our findings highlight the potential to fine-tune combination therapies for NSCLC, thereby providing pivotal insights for tailoring patient-centric therapeutic interventions.

Dynamical behavior-based approach for the evaluation of treatment efficacy: The case of immuno-oncology.

Sophistication of mathematical models in the pharmacological context reflects the progress being made in understanding physiological, pharmacological, and disease relationships. This progress has illustrated once more the need for advanced quantitative tools able to efficiently extract information from these models. While dynamical systems theory has a long history in the analysis of systems biology models, as emphasized under the dynamical disease concept by Mackey and Glass [Science 197, 287-289 (1977)], its adoption in pharmacometrics is only at the beginning [Chae, Transl. Clin. Pharmacol. 28, 109 (2020)]. Using a quantitative systems pharmacology model of tumor immune dynamics as a case study [Kosinsky et al., J. Immunother. Cancer 6, 17 (2018)], we here adopt a dynamical systems analysis to describe, in an exhaustive way, six different statuses that refer to the response of the system to therapy, in the presence or absence of a tumor-free attractor. To evaluate the therapy success, we introduce the concept of TBA, related to the Time to enter the tumor-free Basin of Attraction, and corresponding to the earliest time at which the therapy can be stopped without jeopardizing its efficacy. TBA can determine the optimal time to stop drug administration and consequently quantify the reduction in drug exposure.

Population pharmacokinetic analysis of entrectinib in pediatric and adult patients with advanced/metastatic solid tumors: support of new drug application submission.

PURPOSE: Entrectinib (ROZLYTREK®) is a CNS-active, potent, and selective inhibitor of ROS1, TRK A/B/C, and ALK kinase activity. It was recently approved for the treatment of ROS1-positive non-small cell lung cancer and NTRK gene fusion-positive solid tumors. The main objective of this analysis was to characterize the pharmacokinetics (PK) of entrectinib and its main active metabolite, M5. METHODS: A total of 276 cancer patients receiving oral entrectinib were included in the analysis. A model-based population approach was used to characterize the PK profiles of both entities using NONMEM® 7.4. A joint model captures the PK of both entrectinib and M5. The effects of pH modifiers, formulation, weight, age, and sex on model parameters were assessed. Model performance was evaluated using visual predictive checks (VPCs). RESULTS: The absorption of entrectinib was best described using a sequential zero- and first-order absorption model and the disposition with one-compartment model for each entity with linear elimination. Moderate-to-high between-patient variability was estimated in model parameters (from 30.8% for the apparent clearance of entrectinib to 122% for the first-order absorption rate constant). Theory-based allometric scaling using body weight on clearances and volumes and a 28% lower relative bioavailability of the F1 formulation in pediatric patients were retained in the model. The VPC confirmed the good predictive performance of the PopPK model. CONCLUSIONS: A robust population PK model was built and qualified for entrectinib and M5, describing linear PK for both entities. This model was used to support the ROZLYTREK® new drug application.

Dynamical systems analysis as an additional tool to inform treatment outcomes: The case study of a quantitative systems pharmacology model of immuno-oncology.

Quantitative systems pharmacology (QSP) proved to be a powerful tool to elucidate the underlying pathophysiological complexity that is intensified by the biological variability and overlapped by the level of sophistication of drug dosing regimens. Therapies combining immunotherapy with more traditional therapeutic approaches, including chemotherapy and radiation, are increasingly being used. These combinations are purposed to amplify the immune response against the tumor cells and modulate the suppressive tumor microenvironment (TME). In order to get the best performance from these combinatorial approaches and derive rational regimen strategies, a better understanding of the interaction of the tumor with the host immune system is needed. The objective of the current work is to provide new insights into the dynamics of immune-mediated TME and immune-oncology treatment. As a case study, we will use a recent QSP model by Kosinsky et al. [J. Immunother. Cancer 6, 17 (2018)] that aimed to reproduce the dynamics of interaction between tumor and immune system upon administration of radiation therapy and immunotherapy. Adopting a dynamical systems approach, we here investigate the qualitative behavior of the representative components of this QSP model around its key parameters. The ability of T cells to infiltrate tumor tissue, originally identified as responsible for individual therapeutic inter-variability [Y. Kosinsky et al., J. Immunother. Cancer 6, 17 (2018)], is shown here to be a saddle-node bifurcation point for which the dynamical system oscillates between two states: tumor-free or maximum tumor volume. By performing a bifurcation analysis of the physiological system, we identified equilibrium points and assessed their nature. We then used the traditional concept of basin of attraction to assess the performance of therapy. We showed that considering the therapy as input to the dynamical system translates into the changes of the trajectory shapes of the solutions when approaching equilibrium points and thus providing information on the issue of therapy.

Modelling the dose-response relationship: the fair share of pharmacokinetic and pharmacodynamic information.

AIMS: The aim of this paper is to investigate the role of drug concentration samplings in the modelling of the dose-response relationship. METHODS: Using an initial PK/PD model, a reference dataset was simulated. PK and PD samples were extracted to create reduced datasets. PK/PD and K-PD models were fitted to theses reduced datasets. Post hoc estimates from both types of models were compared to the initial PK/PD model and performance was assessed. RESULTS: K-PD models were largely biased when the drug has a nonlinear elimination. PK/PD models with 1 PK and 2 PD samples were superior to K-PD models with 3 PD samples. PK/PD models with 1 or 2 PK samples and 3 PD samples proved to be superior to K-PD models with 4 PD samples. CONCLUSIONS: K-PD models should not be used when the drug has nonlinear elimination. K-PD models should not replace PK/PD modelling but are an alternative approach if the PD information is large enough.

Modeling Testosterone Circadian Rhythm in Hypogonadal Males: Effect of Age and Circannual Variations.

The objective of this study was to characterize the baseline circadian rhythm of testosterone levels in hypogonadal men. A total of 859 baseline profiles of testosterone from hypogonadal men were included in this analysis. The circadian rhythm of the testosterone was described by a stretched cosine function. Model parameters were estimated using NONMEM(®) 7.3. The effect of different covariates on the testosterone levels was investigated. Model evaluation was performed using non-parametric bootstrap and predictive checks. A stretched cosine function deeply improved the data goodness of fit compared to the standard trigonometric function (p < 0.001; ΔOFV = -204). The effect of the age and the semester, defined as winter and spring versus summer and fall, were significantly associated with the baseline levels of testosterone (p < 0.001, ΔOFV = -15.6, and p < 0.001, ΔOFV = -47.0). Model evaluation procedures such as diagnostic plots, visual predictive check, and non-parametric bootstrap evidenced that the proposed stretched cosine function was able to model the time course of the diurnal testosterone levels in hypogonadal males with accuracy and precision. The circadian rhythm of the testosterone levels was better predicted by the proposed stretched cosine function than a standard cosine function. Testosterone levels decreased by 5.74 ng/dL (2.4%) every 10 years and were 19.3 ng/dL (8.1%) higher during winter and spring compared to summer and fall.

Population pharmacokinetic and pharmacodynamic analysis of tesamorelin in HIV-infected patients and healthy subjects.

The objective of this analysis was to characterize the time course of selected pharmacodynamic (PD) markers of tesamorelin: growth hormone (GH) and insulin-like growth factor (IGF-1) concentrations in HIV-infected patients and healthy volunteers. A total of 41 subjects in Phase I trials receiving subcutaneous daily doses of 1 or 2 mg of tesamorelin during 14 consecutive days were included in this analysis. A previous pharmacokinetic (PK) model of tesamorelin was used as the input function for the PD model of GH. Tesamorelin was hypothesized to stimulate the secretion of GH in an "episodic" manner, i.e., for a finite duration of time. The resulting PK/PD model of GH was used to describe the time course of IGF-1. The effect of age, body weight, body mass index, sex, race, and health status on the model parameters was evaluated. The model was qualified using predictive checks and non-parametric bootstrap. Within the range of the values evaluated no covariates were significantly associated with GH or IGF-1 model parameters. Model evaluation procedures indicated accurate prediction of the selected pharmacodynamic markers. The time course of GH and IGF-1 concentrations following multiple doses of tesamorelin were well predicted by the sequential PK/PD model developed using Phase I data.

Population pharmacokinetic analysis of tesamorelin in HIV-infected patients and healthy subjects.

BACKGROUND AND OBJECTIVES: Tesamorelin is a synthetic analogue of growth hormone-releasing factor (GRF), which increases basal and pulsatile growth hormone (GH) secretion and subsequently increases insulin-like growth factor (IGF)-1. Limited information is available about the pharmacokinetics of this compound. Consequently, the aim of this study was to characterize the population pharmacokinetics of tesamorelin in HIV-infected patients and healthy subjects. METHODS: A total of 38 HIV-infected patients and healthy subjects receiving subcutaneous tesamorelin doses of 1 or 2 mg administered daily during 14 consecutive days were included in the analysis. An open one-compartment model with first- and zero-order absorption and first-order elimination was developed to best describe the data using NONMEM(®) VII. The effect of different covariates on tesamorelin pharmacokinetics was investigated. Model evaluation was performed using predictive checks and non-parametric bootstrap. RESULTS: Plasma clearance and its interindividual variability [% coefficient of variation (CV)] was estimated to be 1,060 L/h (33.6 %). Volume of distribution was calculated to be 200 L (17.7 %). Age, body size measures, race and health status were not related to tesamorelin pharmacokinetic parameters within the range of covariates studied. The fraction of tesamorelin absorbed by a first-order process is 13.1 % higher on day 14 compared with day 1. Predictive checks and non-parametric bootstrap demonstrated that the model is appropriate in describing the time course of tesamorelin plasma concentrations in both HIV-infected patients and healthy subjects. CONCLUSIONS: An open one-compartment model with first and zero order absorption processes and linear elimination is suitable to characterize the pharmacokinetics of tesamorelin. The fraction of tesamorelin absorbed by a first-order process evolves with time. No clinically relevant covariates were identified as predictors of tesamorelin pharmacokinetics.

Pharmacokinetic characterization of baclofen in patients with chronic kidney disease: dose adjustment recommendations.

The pharmacokinetics of baclofen is well delineated in subjects with normal kidney function (KF); however, pharmacokinetics data in patients with chronic kidney disease (CKD) are not and dosage recommendations remain empirical. The effects of CKD on baclofen pharmacokinetics were assessed through a multi-center, open-label, single 5-mg dose, pharmacokinetics study. The KF was measured as the creatinine clearance (CrCL) calculated with the Cockroft-Gault (C-G) equation or as the estimated glomerular filtration rate (eGFR) using subjects' CKD-EPI equation. Subjects were assigned to 1 of 4 groups based on their CrCL (>80 mL/min, 50-80 mL/min; 30-50 mL/min and <30 mL/min). Cmax was not statistically different between the groups, while AUC and T1/2el increased, and CL/F decreased, with increasing severity of CKD. Baclofen's oral clearance and CrCL were statistically significantly correlated, and the trend was the same when classifying subjects either with the CKD-EPI or C-G equations. Linear equations using KF as variable were set to recommend individual dose reduction in CKD patients. Results suggest a mean dose reduction of 1/3, 1/2, and 2/3 in patients with mild, moderate, and severe CKD respectively, in order to achieve baclofen exposure comparable to that observed in healthy subjects.