A model-based approach to the in vitro evaluation of anticancer activity

Do we really understand how drug eluted from stents modulates arterial healing?

The advent of drug-eluting stents (DES) has revolutionised the treatment of coronary artery disease. These devices, coated with anti-proliferative drugs, are deployed into stenosed or occluded vessels, compressing the plaque to restore natural blood flow, whilst simultaneously combating the evolution of restenotic tissue. Since the development of the first stent, extensive research has investigated how further advancements in stent technology can improve patient outcome. Mathematical and computational modelling has featured heavily, with models focussing on structural mechanics, computational fluid dynamics, drug elution kinetics and subsequent binding within the arterial wall; often considered separately. Smooth Muscle Cell (SMC) proliferation and neointimal growth are key features of the healing process following stent deployment. However, models which depict the action of drug on these processes are lacking. In this article, we start by reviewing current models of cell growth, which predominantly emanate from cancer research, and available published data on SMC proliferation, before presenting a series of mathematical models of varying complexity to detail the action of drug on SMC growth in vitro. Our results highlight that, at least for Sodium Salicylate and Paclitaxel, the current state-of-the-art nonlinear saturable binding model is incapable of capturing the proliferative response of SMCs across a range of drug doses and exposure times. Our findings potentially have important implications on the interpretation of current computational models and their future use to optimise and control drug release from DES and drug-coated balloons.

Evaluation of the influence of green extraction solvents on the cytotoxic activities of Crinum (Amaryllidaeae) alkaloid extracts using in-vitro-in-silico approach

ETHNOPHARMACOLOGICAL RELEVANCE

The traditional use of Amaryllidaceae plants to treat many disease have been known for a very long period of time. The chemical analysis of these plants has yielded a diversity of alkaloids with analgesic, anticholinergic, antitumor and antiviral activities. Crinum bulbispermum (Burm.f.) Milne-Redh. & Schweick in particular has been used by Zulu, Sotho and Tswana people to treat tumors as a form of chemotherapy, while in Madagascar, Crinum powellii Baker Handb. was used in the treatment of abscesses and tumors. Many of the alkaloids spawned by genus Crinum will surely take part in the production of anticancer drugs but their further clinical development is restricted by their limited commercial availability. An emerging area of research is the establishment of green extraction techniques of different targeted compounds.

AIM OF THE STUDY

Our comparative study has investigated the possibility of getting improved biological responses by changing extraction solvent to a better and greener one. This study aimed to assess the cytotoxic activity of Crinum powellii and Crinum bulbispermum bulbs, when extracted by different green solvents.

MATERIALS AND METHODS

The green solvents Genapol X-80 (a surfactant-aided extraction), DES-3 (Choline chloride: fructose 5:2) mixture (a natural deep eutectic solvent) and purified distilled water were used for extraction of the bulbs. Extracts were tested against two cell lines HEPG-2 and HCT 116, with doxorubicin as a positive reference. Molecular docking studies were carried out to illustrate binding orientations of the alkaloids in the active site of several molecular targets for treatment of hepatic and colorectal cancer.

RESULTS

DES aided extraction showed highest cytotoxicity against the two cell lines, followed by surfactant aided extracts and finally aqueous extracts. There is an obvious relationship between alkaloidal content and antiproliferative potency of extracts. Multivariate statistical analyses were performed to aid the prediction of the alkaloids responsible for the activity. The alkaloid crinine showed high correlation coefficient value against HCT colon cancer cell line in the orthogonal projection to latent structures (OPLS) model, suggesting that it could operate with a selective mode of action on this cell line. In addition, the alkaloid lycorine had almost no correlation to anti-proliferative activity against HCT colon cancer cells. Molecular docking studies confirmed the same conclusions.

CONCLUSION

Herein, it was demonstrated that natural deep eutectic solvents (NADES) components and surfactant solutions could be chosen to enhance biological activity of extracts prepared.

Current mathematical models for cancer drug discovery

INTRODUCTION

Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

Correlating Drug-Target Kinetics and In vivo Pharmacodynamics: Long Residence Time Inhibitors of the FabI Enoyl-ACP Reductase

Drug-target kinetics enable time-dependent changes in target engagement to be quantified as a function of drug concentration. When coupled to drug pharmacokinetics (PK), drug-target kinetics can thus be used to predict in vivo pharmacodynamics (PD). Previously we described a mechanistic PK/PD model that successfully predicted the antibacterial activity of an LpxC inhibitor in a model of Pseudomonas aeruginosa infection. In the present work we demonstrate that the same approach can be used to predict the in vivo activity of an enoyl-ACP reductase (FabI) inhibitor in a model of methicillin-resistant Staphylococcus aureus (MRSA) infection. This is significant because the LpxC inhibitors are cidal, whereas the FabI inhibitors are static. In addition P. aeruginosa is a Gram-negative organism whereas MRSA is Gram-positive. Thus this study supports the general applicability of our modeling approach across antibacterial space.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

INTRODUCTION

Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed.

AREAS COVERED

This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology.

EXPERT OPINION

PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.

Pharmacodynamic modeling of combined chemotherapeutic effects predicts synergistic activity of gemcitabine and trabectedin in pancreatic cancer cells

PURPOSE

This study investigates the combined effects of gemcitabine and trabectedin (ecteinascidin 743) in two pancreatic cancer cell lines and proposes a pharmacodynamic (PD) model to quantify their pharmacological interactions.

METHODS

Effects of gemcitabine and trabectedin upon the pancreatic cancer cell lines MiaPaCa-2 and BxPC-3 were investigated using cell proliferation assays. Cells were exposed to a range of concentrations of the two drugs, alone and in combination. Viable cell numbers were obtained daily over 5 days. A model incorporating nonlinear cytotoxicity, transit compartments, and an interaction parameter ψ was used to quantify the effects of the individual drugs and combinations.

RESULTS

Simultaneous fitting of temporal cell growth profiles for all drug concentrations provided reasonable cytotoxicity parameter estimates (the cell killing rate constant K max and the sensitivity constant KC50) for each drug. The interaction parameter ψ was estimated as 0.806 for MiaPaCa-2 and 0.843 for BxPC-3 cells, suggesting that the two drugs exert modestly synergistic effects.

CONCLUSIONS

The proposed PD model enables quantification of the temporal profiles of drug combinations over a range of concentrations with drug-specific parameters. Based upon these in vitro studies, trabectedin may have augmented benefit in combination with gemcitabine. The PD model may have general relevance for the study of other cytotoxic drug combinations.

Mathematical modeling of growth and death dynamics of mouse embryonic stem cells irradiated with γ-rays

Following ionizing radiation, mouse embryonic stem cells (mESCs) undergo both apoptosis and block at G2/M phase of the cell cycle. The dynamics of cell growth and the transition through the apoptotic phases cannot be directly inferred from experimental data, limiting the understanding of the biological response to the treatment. Here, we propose a semi-mechanistic mathematical model, defined by five compartments, able to describe the time curves of untreated and γ-rays irradiated mESCs and to extract the information therein embedded. To this end, mESCs were irradiated with 2 or 5 Gy γ-rays, collected over a period of 48 h and, at each time point, analyzed for apoptosis by using the Annexin V assay. When compared to unirradiated mESCs, the model estimates an additional 0.2 probability to undergo apoptosis for the 5 Gy-treated cells, and only a 0.07 (not statistically significantly different from zero) when a 2 Gy-irradiation dose is administered. Moreover, the model allows us to estimate the duration of the overall apoptotic process and also the time length of its early, intermediate, and late apoptotic phase.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Optimised protocol design for the screening of analgesic compounds in neuropathic pain

We have previously shown how screening experiments for neuropathic pain can be optimised taking into account parameter and model uncertainty. Here we demonstrate how optimised protocols can be used to screen and rank candidate molecules. The concept is illustrated by pregabalin as a new chemical entity and gabapentin as a reference compound. ED-optimality was applied to a logistic regression model describing the relationship between drug exposure and response to evoked pain in the complete Freund's adjuvant (CFA) model in rats. Design variables for optimisation of the experimental protocol included dose levels and sampling times. Prior information from the reference compound was used in conjunction with relative in vitro potency as priors. Results from simulated scenarios were then combined with fitting of experimental data to estimate precision and bias of model parameters for the empirical and optimised designs. The pharmacokinetics of pregabalin was described by a two-compartment model. The expected value of EC(50) of pregabalin was 637.5 ng ml(-1). Model-based analysis of the data yielded median (range) of EC(50) values of 1,125 (898-2412) ng ml(-1) for the empirical protocol and 755 (189-756) ng ml(-1) for the optimised design. In contrast to current practice, optimal design entails different sampling schedule across dose levels. ED-optimised designs should become standard practice in the screening of candidate molecules. It ensures lower bias when estimating the drug potency, facilitating accurate ranking and selection of compounds for further development.

Microfluidic separation of live and dead yeast cells using reservoir-based dielectrophoresis

Separating live and dead cells is critical to the diagnosis of early stage diseases and to the efficacy test of drug screening, etc. This work demonstrates a novel microfluidic approach to dielectrophoretic separation of yeast cells by viability. It exploits the cell dielectrophoresis that is induced by the inherent electric field gradient at the reservoir-microchannel junction to selectively trap dead yeast cells and continuously separate them from live ones right inside the reservoir. This approach is therefore termed reservoir-based dielectrophoresis (rDEP). It has unique advantages as compared to existing dielectrophoretic approaches such as the occupation of zero channel space and the elimination of any mechanical or electrical parts inside microchannels. Such an rDEP cell sorter can be readily integrated with other components into lab-on-a-chip devices for applications to biomedical diagnostics and therapeutics.

Selective concentration of human cancer cells using contactless dielectrophoresis

This work is the first to demonstrate the ability of contactless dielectrophoresis (cDEP) to isolate target cell species from a heterogeneous sample of live cells. Since all cell types have a unique molecular composition, it is expected that their dielectrophoretic (DEP) properties are also unique. cDEP is a technique developed to improve upon traditional and insulator-based DEP devices by replacing embedded metal electrodes with fluid electrode channels positioned alongside desired trapping locations. Through the placement of the fluid electrode channels and the removal of contact between the electrodes and the sample fluid, cDEP mitigates issues associated with sample/electrode contact. MCF10A, MCF7, and MDA-MB-231 human breast cells were used to represent early, intermediate, and late-staged breast cancer, respectively. Trapping frequency responses of each cell type were distinct, with the largest difference between the cells found at 20 and 30 V. MDA-MB-231 cells were successfully isolated from a population containing MCF10A and MCF7 cells at 30 V and 164 kHz. The ability to selectively concentrate cells is the key to development of biological applications using DEP. The isolation of these cells could provide a workbench for clinicians to detect transformed cells at their earliest stage, screen drug therapies prior to patient treatment, increasing the probability of success, and eliminate unsuccessful treatment options.

In vivo imaging of early stage apoptosis by measuring real-time caspase-3/7 activation

In vivo imaging of apoptosis in a preclinical setting in anticancer drug development could provide remarkable advantages in terms of translational medicine. So far, several imaging technologies with different probes have been used to achieve this goal. Here we describe a bioluminescence imaging approach that uses a new formulation of Z-DEVD-aminoluciferin, a caspase 3/7 substrate, to monitor in vivo apoptosis in tumor cells engineered to express luciferase. Upon apoptosis induction, Z-DEVD-aminoluciferin is cleaved by caspase 3/7 releasing aminoluciferin that is now free to react with luciferase generating measurable light. Thus, the activation of caspase 3/7 can be measured by quantifying the bioluminescent signal. Using this approach, we have been able to monitor caspase-3 activation and subsequent apoptosis induction after camptothecin and temozolomide treatment on xenograft mouse models of colon cancer and glioblastoma, respectively. Treated mice showed more than 2-fold induction of Z-DEVD-aminoluciferin luminescent signal when compared to the untreated group. Combining D: -luciferin that measures the total tumor burden, with Z-DEVD-aminoluciferin that assesses apoptosis induction via caspase activation, we confirmed that it is possible to follow non-invasively tumor growth inhibition and induction of apoptosis after treatment in the same animal over time. Moreover, here we have proved that following early apoptosis induction by caspase 3 activation is a good biomarker that accurately predicts tumor growth inhibition by anti-cancer drugs in engineered colon cancer and glioblastoma cell lines and in their respective mouse xenograft models.

Selective isolation of live/dead cells using contactless dielectrophoresis (cDEP)

Contactless dielectrophoresis (cDEP) is a recently developed method of cell manipulation in which the electrodes are physically isolated from the sample. Here we present two microfluidic devices capable of selectively isolating live human leukemia cells from dead cells utilizing their electrical signatures. The effect of different voltages and frequencies on the gradient of the electric field and device performance was investigated numerically and validated experimentally. With these prototype devices we were able to achieve greater than 95% removal efficiency at 0.2-0.5 mm s(-1) with 100% selectivity between live and dead cells. In conjunction with enrichment, cDEP could be integrated with other technologies to yield fully automated lab-on-a-chip systems capable of sensing, sorting, and identifying rare cells.