A Semi-Mechanistic Integrated Pharmacokinetic/Pharmacodynamic Model of the Testosterone Effects of the Gonadotropin-Releasing Hormone Agonist Leuprolide in Prostate Cancer Patients

Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

The use of in silico trials is expected to play an increasingly important role in the development and regulatory evaluation of new medical products. Among the advantages that in silico approaches offer, is that they permit testing of drug candidates and new medical devices using virtual patients or computational emulations of preclinical experiments, allowing to refine, reduce or even replace time-consuming and costly benchtop/in vitro/ex vivo experiments as well as the involvement of animals and humans in in vivo studies. To facilitate and widen the adoption of in silico trials, InSilicoTrials Technologies has developed a cloud-based platform, hosting healthcare simulation tools for different bench, preclinical and clinical evaluations, and for diverse disease areas. This paper discusses four use cases of in silico trials performed using the InSilicoTrials.com platform. The first application illustrates how in silico approaches can improve the early preclinical assessment of drug-induced cardiotoxicity risks. The second use case is a virtual reproduction of a bench test for the safety assessment of transcatheter heart valve substitutes. The third and fourth use cases are examples of virtual patients generation to evaluate treatment effects in multiple sclerosis and prostate cancer patients, respectively.

CHST7 Methylation Status Related to the Proliferation and Differentiation of Pituitary Adenomas

Pituitary adenomas (PAs) are the second most common primary brain tumor and may develop from any of the cell lineages responsible for producing the different pituitary hormones. DNA methylation is one of the essential epigenetic mechanisms in cancers, including PAs. In this study, we measured the expression profile and promoter methylation status of carbohydrate sulfotransferase 7 (CHST7) in patients with PA; then, we investigated the effect of the CHST7 methylation status on the proliferation and differentiation of PAs. The volcano map and Metascape results showed that the levels of CHST7 were related to the lineages’ differentiation and the cell adhesion of PAs, and patients with low CHST7 had greater chances of having an SF-1 lineage (p = 0.002) and optic chiasm compression (p = 0.007). Reactome pathway analysis revealed that most of the DEGs involved in the regulation of TP53 regulated the transcription of cell cycle genes (HSA-6791312 and HSA6804116) in patients with high CHST7. Correlation analysis showed that CHST7 was significantly correlated with the eIF2/ATF4 pathway and mitochondrion-related genes. The AUC of ROC showed that CHST7 (0.288; 95% CI: 0.187–0.388) was superior to SF-1 (0.555; 95% CI: 0.440–0.671) and inferior to FSHB (0.804; 95% CI: 0.704–0.903) in forecasting the SF-1 lineage (p < 0.001). The SF-1 lineage showed a higher methylation frequency for CHST7 than the Pit-1 and TBX19 lineages (p = 0.009). Furthermore, as the key molecule of the hypothalamic–pituitary–gonadal axis, inhibin βE (INHBE) was positively correlated with the levels of CHST7 (r = 0.685, p < 0.001). In summary, CHST7 is a novel pituitary gland specific protein in SF-1 lineage adenomas with a potential role in gonadotroph cell proliferation and lineage differentiation in PAs.

Mathematical model of hormone sensitive prostate cancer treatment using leuprolide: A small step towards personalization

In this paper we present a new version of a mathematical model of Elishmereni et al. describing androgen deprivation therapy (ADT) for hormone sensitive prostate cancer patients (HSPC). We first focus on the detail description of the model, and then we present mathematical analysis of the proposed model, starting from the simplified model without resistance and ending on the full model with two resistance mechanisms present. We make a step towards personalization proposing an underlying tumor growth law base on a cohort of patients from Mayo hospital. We conclude that the model is able to reflect reality, that is in clinical scenarios the level of testosterone in HSPC patients inevitably rises leading to the failure of ADT.

Preclinical PK/PD model for the combinatorial use of dexamethasone and sulpiride in the treatment of breast cancer

Previous studies show that dopamine D₂-like receptor (D2DR) antagonist sulpiride (SUL) enhances the antitumor efficacy of dexamethasone (DEX) in drug-resistant breast cancer involving cancer stem-like cells (CSCs). In this study, we investigated the pharmacokinetic (PK) properties of SUL in nude mice and developed a semi-mechanism PK/PD model to quantitatively characterize the synergistic effect of DEX and SUL in preclinical breast cancer xenografts. After nude mice received oral administration of a single dose of SUL (50 mg/kg, ig), plasma concentrations were assessed using LC-MS/MS. A two-compartment model with double first-order absorption rate was developed to describe the PK profiles of SUL. The pharmacodynamic (PD) study was conducted in nude mice bearing human breast cancer MCF-7/Adr xenografts, which received oral administration of DEX (1, 8 mg·kg⁻¹·d⁻¹) or SUL (25, 50 mg·kg⁻¹·d⁻¹) alone or in various combination. Tumor volumes were measured every other day. The PK model of SUL as well as that of DEX with a time-dependent clearance were integrated into the final PK/PD model both using Hill’s function, where DEX exerted its antitumor efficacy by inhibiting the proliferation of tumor cells, and SUL enhanced DEX responses by decreasing the sensitivity parameter EC₅₀. The PK/PD model was evaluated and subjected external validation. Finally, simulations were performed to predict the antitumor efficacy of DEX combined with SUL under various dose regimens, where changing dosing frequency of SUL had little effect, while the antitumor efficacy was predicted to be improved when DEX was given more frequently. The established PK/PD model in this study quantitatively characterizes the antitumor efficacy of the DEX combined with SUL as well as their synergism, and the simulations could provide reference for dose optimization of the combination in future studies.

Pharmacokinetic-Pharmacodynamic Model for the Testosterone-Suppressive Effect of Leuprolide in Normal and Prostate Cancer Rats

This study developed the pharmacokinetic (PK)–pharmacodynamic (PD) model of the testosterone-suppressive effect of leuprolide for evaluation of the sustained release (SR) depot and leuprolide solution in rats with or without prostate cancer. Six groups of rats were divided by the routes of administration (intravenous and subcutaneous injection) and kinds of formulation (vehicle, leuprolide solution, and SR depot). The PK profile after subcutaneous injection of leuprolide solution could be well-described by the one-compartment model. The absorption rate constant, the total body clearance, and the volume of distribution were estimated at 16.67 h⁻¹, 514.46 mL/h, and 487.40 mL. Using PK parameters in the solution-administered group, the PK model for the SR depot was developed. The PK–PD model was constructed by describing the testosterone-suppressive effect of leuprolide using the feedback turnover model. The response of testosterone after administration of each formulation was well described using this PK–PD model for the estimation of PD parameters (EC₅₀, E_max, h) and systemic parameters (k_in, k_out, k_{f on}, k_{f off}). The developed PK–PD model containing an inhibitory feedback system could successfully describe the testosterone-suppressive effect of leuprolide in the type of formulation. The PK–PD model developed would be useful for evaluating the formulation of similar drugs whose effect is regulated through the feedback mechanism.

Dosage Optimization of Nemolizumab Using Population Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Modeling and Simulation

Nemolizumab is a humanized anti-interleukin-31 receptor A monoclonal antibody for treating atopic dermatitis, and it especially improves pruritus. The objective of the simulation study was to optimize the dose regimen using a flat dose. The serum nemolizumab concentration and pruritus visual analog scale as an efficacy end point were modeled using the population analysis approach in 299 patients with atopic dermatitis who received placebo or doses between 0.1 and 3 mg/kg as a single dose once every 4 weeks or 2 mg/kg once every 8 weeks. A 1-compartment model with first-order absorption was employed as the pharmacokinetic model. An indirect turnover model with an inhibition component was employed as the main part of the pharmacokinetic-pharmacodynamic model. The models well described the observations. Therefore, simulations with several dose regimens were performed to optimize the dose regimen including a flat dose. The simulated area under the concentration-time curve at a steady state around 75 mg in the every-4-week regimen corresponds to that associated with the dose range of 0.5 to 2 mg/kg in the 4-week regimen. The simulated pruritus visual analog scale also showed a similar tendency. These simulation results support dose optimization during the clinical development program of nemolizumab.