Growth‐rate model predicts in vivo tumor response from in vitro data

Explaining in-vitro to in-vivo efficacy correlations in oncology pre-clinical development via a semi-mechanistic mathematical model

In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound's peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound's peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.

Pharmacodynamics of Cyclin D1 Degradation in Ovarian Cancer Xenografts with Repeated Oral SHetA2 Dosing

SHetA2 is a promising, orally active small molecule with anticancer properties that target heat shock proteins. In this study, we aimed to investigate the pharmacodynamic (PD) effects of SHetA2 using preclinical in vitro and in vivo models of ovarian cancer and establish a physiologically based pharmacokinetic (PBPK)/PD model to describe their relationships with SHetA2 concentrations in mice. We found that daily oral administration of 60 mg/kg SHetA2 for 7 days resulted in consistent plasma PK and tissue distribution, achieving tumor drug concentrations required for growth inhibition in ovarian cancer cell lines. SHetA2 effectively induced cyclin D1 degradation in cancer cells in a dose-dependent manner, with up to 70% reduction observed and an IC50 of 4~5 µM. We identified cyclin D1 as a potential PD marker for SHetA2, based on a well-correlated time profile with SHetA2 PK. Additionally, we examined circulating levels of ccK18 as a non-invasive PD marker for SHetA2-induced apoptotic activity and found it unsuitable due to high variability. Using a PBPK/PD model, we depicted SHetA2 levels and their promoting effects on cyclin D1 degradation in tumors following multiple oral doses. The model suggested that twice-daily dosing regimens would be effective for sustained reduction in cyclin D1 protein. Our study provides valuable insights into the PK/PD of SHetA2, facilitating future clinical trial designs and dosing schedules.

Metabolomics profiling and chemoresistance mechanisms in ovarian cancer cell lines: Implications for targeting glutathione pathway

Ovarian cancer presents a significant challenge due to its high rate of chemoresistance, which complicates the effectiveness of drug-response therapy. This study provides a comprehensive metabolomic analysis of ovarian cancer cell lines OVCAR-3 and SK-OV-3, characterizing their distinct metabolic landscapes. Metabolomics coupled with chemometric analysis enabled us to discriminate between the metabolic profiles of these two cell lines. The OVCAR-3 cells, which are sensitive to doxorubicin (DOX), exhibited a preference for biosynthetic pathways associated with cell proliferation. Conversely, DOX-resistant SK-OV-3 cells favored fatty acid oxidation for energy maintenance. Notably, a marked difference in glutathione (GSH) metabolism was observed between these cell lines. Our investigations further revealed that GSH depletion led to a profound change in drug sensitivity, inducing a shift from a cytostatic to a cytotoxic response. The results derived from this comprehensive metabolomic analysis offer potential targets for novel therapeutic strategies to overcome drug resistance. Our study suggests that targeting the GSH pathway could potentially enhance chemotherapy's efficacy in treating ovarian cancer.

Three-Dimensional Cell Cultures: The Bridge between In Vitro and In Vivo Models

Although historically, the traditional bidimensional in vitro cell system has been widely used in research, providing much fundamental information regarding cellular functions and signaling pathways as well as nuclear activities, the simplicity of this system does not fully reflect the heterogeneity and complexity of the in vivo systems. From this arises the need to use animals for experimental research and in vivo testing. Nevertheless, animal use in experimentation presents various aspects of complexity, such as ethical issues, which led Russell and Burch in 1959 to formulate the 3R (Replacement, Reduction, and Refinement) principle, underlying the urgent need to introduce non-animal-based methods in research. Considering this, three-dimensional (3D) models emerged in the scientific community as a bridge between in vitro and in vivo models, allowing for the achievement of cell differentiation and complexity while avoiding the use of animals in experimental research. The purpose of this review is to provide a general overview of the most common methods to establish 3D cell culture and to discuss their promising applications. Three-dimensional cell cultures have been employed as models to study both organ physiology and diseases; moreover, they represent a valuable tool for studying many aspects of cancer. Finally, the possibility of using 3D models for drug screening and regenerative medicine paves the way for the development of new therapeutic opportunities for many diseases.

p53 controls choice between apoptotic and non-apoptotic death following DNA damage

DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to DNA damage. In contrast to the conventional model, which suggests that p53-deficient cells should be resistant to DNA damage-induced cell death, we find that p53-deficient cells die at high rates following DNA damage, but exclusively using non-apoptotic mechanisms. Our experimental data and computational modeling reveal that non-apoptotic death in p53-deficient cells has not been observed due to use of assays that are either insensitive to cell death, or that specifically score apoptotic cells. Using functional genetic screening - with an analysis that enables computational inference of the drug-induced death rate - we find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death, called MPT-driven necrosis. Cells deficient for p53 have high basal respiration, which primes MPT-driven necrosis. Finally, using metabolite profiling, we identified mitochondrial activity-dependent metabolic vulnerabilities that can be targeted to potentiate the lethality of DNA damage specifically in p53-deficient cells. Our findings reveal how the dual functions of p53 in regulating mitochondrial activity and the DNA damage response combine to facilitate the choice between apoptotic and non-apoptotic death.

Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study

BACKGROUND AND PURPOSE

Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity.

EXPERIMENTAL APPROACH

We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contribution of these factors to response heterogeneity are compared.

KEY RESULTS

The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Sensitivity to drug strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib.

CONCLUSION AND IMPLICATIONS

The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.