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.

Modification of haloperidol-induced pattern of c-fos expression by serotonin agonists

Acute challenge with clozapine and haloperidol produce different anatomical patterns of c-fos expression in the forebrain. The pharmacological profile of atypical antipsychotics suggests that serotonin might contribute to the unique therapeutic benefits of these drugs. In order to test this possibility, we examined the abilities of 5-HT1A and 5-HT2A/2c agonists to modify the pattern of c-fos expression induced by haloperidol and clozapine. Various groups of rats were pretreated with either saline, DOI, 8-OH-DPAT, and 8-OH-DPAT + DOI 30 min prior to haloperidol or clozapine administration. Rats were killed 90 min after antipsychotic administration. In saline-pretreated rats, haloperidol produced intense Fos-LI in all four striatal quadrants while the effect of clozapine was restricted to the medial part of the striatum. Prior administration of 8-OH-DPAT significantly reduced haloperidol-induced Fos-LI in all four striatal quadrants while DOI and 8-OHDPAT + DOI significantly reduced Fos-LI only in dorso- and ventrolateral quadrants. In the nucleus accumbens, haloperidol induced intense Fos-LI in the core and the shell regions whereas clozapine induced c-fos expression only in the shell. Pretreatment with 8-OHDPAT in haloperidol treated rats reduced Fos-LI in the core region yielding to a c-fos pattern similar to that induced by clozapine. In the prefrontal cortex of saline-pretreated rats, haloperidol produced a moderate c-fos expression compared with the intense expression produced by clozapine. Pretreatment with serotonin agonists before haloperidol brought the number of FOS-positive neurons to the same level as in clozapine treated rats. These results show the ability of 5-HT agonists to transform the typical pattern of c-fos expression induced by haloperidol into a pattern resembling that of clozapine.