Current mathematical models for cancer drug discovery

A translational model-based approach to inform the choice of the dose in phase 1 oncology trials: the case study of erdafitinib

PURPOSE

Erdafitinib (JNJ-42756493, BALVERSA) is a tyrosine kinase inhibitor indicated for the treatment of advanced urothelial carcinoma. In this work, a translational model-based approach to inform the choice of the doses in phase 1 trials is illustrated.

METHODS

A pharmacokinetic (PK) model was developed to describe the time course of erdafitinib plasma concentrations in mice and rats. Data from multiple xenograft studies in mice and rats were analyzed using the Simeoni tumor growth inhibition (TGI) model. The model parameters were used to derive a range of erdafitinib exposures that might inform the choice of the doses in oncology phase 1 trials. Conversion of exposures to doses was based on preliminary PK assessments from the first-in human (FIH) study.

RESULTS

A one-compartment PK disposition model, with linear absorption and dose-dependent clearance, adequately described the PK data in both mice and rats via an allometric scaling approach. The TGI model was able to describe tumor growth dynamics, providing quantitative measurements of erdafitinib antitumor potency in mice and rats. Based on these estimates, ranges of efficacious unbound concentration were identified for erdafitinib in mice (0.642-5.364 μg/L) and rats (0.782-2.565 μg/L). Based on the FIH data, it was possible to transpose exposures into doses and doses of above 4 mg/day provided erdafitinib exposures associated with significant TGI in animals. The findings were in agreement with the results of the FIH trial, in which the first hints of clinical activities were observed at 6 mg.

CONCLUSION

The successful modeling exercise of erdafitinib preclinical data showed how translational PK-PD modeling might be a tool to help to inform the choice of the doses in FIH studies.

Modeling restoration of gefitinib efficacy by co-administration of MET inhibitors in an EGFR inhibitor-resistant NSCLC xenograft model: A tumor-in-host DEB-based approach

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non-small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor-in-host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly-targeted combination therapies. The population DEB-based tumor growth inhibition (TGI) model well-described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib-resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB-TGI model allowed to capture gefitinib anticancer activity enhanced by the co-administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model-based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB-based tumor-in-host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor-in-host DEB-TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.

Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm

BACKGROUND

Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it.

RESULTS

A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them.

CONCLUSION

Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.

Optimization of Cancer Treatment in the Frequency Domain

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

[Effect of methanol-ethyl acetate partitioned fractions from Descurainia sophia on proliferation and apoptosis of human non-small cell lung cancer H1975 cells]

OBJECTIVE

To investigate the effects of methanol-ethyl acetate partitioned fractions from Descurainia sophia (MEDS) on the proliferation and apoptosis of human non-small cell lung cancer H1975 cells.

METHODS

The systemic solvent extraction method was used to preliminary separation of the effective fractions in the methanol extract of Descurainia sophia. The cytotoxicity of each extract (5, 10, 20, 40, and 80 μg/mL) was tested using MTT assay. Colony cloning method was used to assess the effect of different concentrations of methanol-ethyl acetate partitioned fractions from MEDS (5, 10, 20, 40, and 80 μg/ mL) on the proliferation of H1975 cells. Flow cytometric analysis with Annexin V-FITC/PI staining was performed to detect the apoptosis of the cells after treatment with different concentrations of MEDS fractions (10, 20, and 40 μg/mL). Western blotting was used to evaluate the effects of MEDS fractions on the expressions of apoptosis-related proteins Akt, Bax, and Bcl-2. The anti-tumor activity of 100 mg/kg MEDS fractions was tested in a nude mouse model bearing H1975 cell xenografts.

RESULTS

MTT assay and colony forming experiment showed that MEDS fractions significantly inhibited the proliferation of H1975 cells in a dose-and time-dependent manner (P < 0.05). The results of flow cytometry showed that MEDS fractions induced obvious apoptosis of H1975 cells in a concentration-dependent manner (P < 0.05). MEDS fractions also significantly decreased the expressions of Bcl-2 and Akt protein and increased the protein expression of Bax (P < 0.05). In the tumor-bearing nude mouse model, MEDS fractions showed potent anti-tumor effects with a low toxicity to affect the body weight and organs of the mice.

CONCLUSIONS

The methanol-ethyl acetate partitioned fractions from MEDS show potent anti-tumor activity both in vivo and in vitro, suggesting their value as promising therapeutic agents against lung cancer.

A Population Dynamic Energy Budget-Based Tumor Growth Inhibition Model for Etoposide Effects on Wistar Rats

PURPOSE

This work aimed to develop a population PK/PD tumor-in-host model able to describe etoposide effects on both tumor cells and host in Walker-256 tumor-bearing rats.

METHODS

Etoposide was investigated on thirty-eight Wistar rats randomized in five arms: two groups of tumor-free animals receiving either placebo or etoposide (10 mg/kg bolus for 4 days) and three groups of tumor-bearing animals receiving either placebo or etoposide (5 or 10 mg/kg bolus for 8 or 4 days, respectively). To analyze experimental data, a tumor-in-host growth inhibition (TGI) model, based on the Dynamic Energy Budget (DEB) theory, was developed. Total plasma and free-interstitial tumor etoposide concentrations were assessed as driver of tumor kinetics.

RESULTS

The model simultaneously describes tumor and host growths, etoposide antitumor effect as well as cachexia phenomena related to both the tumor and the drug treatment. The schedule-dependent inhibitory effect of etoposide is also well captured when the intratumoral drug concentration is considered as the driver of the tumor kinetics.

CONCLUSIONS

The DEB-based TGI model capabilities, up to now assessed only in mice, are fully confirmed in this study involving rats. Results suggest that well designed experiments combined with a mechanistic modeling approach could be extremely useful to understand drug effects and to describe all the dynamics characterizing in vivo tumor growth studies.