Comparison of Hemodynamics in Jet Ventilation vs. Intermittent Apnea for Airway Stenosis Surgery.

OBJECTIVE: The objective of this study is to assess the impact of two different ventilation techniques, jet ventilation and apneic anesthesia with intermittent ventilation (AAIV), on patient hemodynamics and operative time during endoscopic laryngotracheal stenosis surgery. METHODS: Retrospective chart review of patients who underwent airway dilation for laryngotracheal stenosis by a single surgeon at a single institution from October 1, 2000 through January 2, 2020. Logistic regression, Mann-Whitney U tests and chi square analysis were used to determine statistical significance. RESULTS: A total of 157 patients, 43 (27.4%) male and 114 (72.6%) female, and 605 total encounters were included for analysis. There were no significant differences in hemodynamic outcomes when comparing the AAIV and jet ventilation groups. Specifically, there was no significant difference in either peak end-tidal CO2 or nadir O2 saturation between the AAIV and jet ventilation groups (p = 0.4016) and (p = 0.1357), respectively. The patients in the AAIV group had a significantly higher median BMI 32.93 (27.40-39.40) compared with 28.80 (24.1-32.65) (p = 0.0001). Although not necessarily clinically significant, patients with higher BMI had lower median O2 nadirs (97.8%) than non-obese patients (99.2%) (p < 0.0001). The median total procedure time was equivalent when comparing the two ventilation techniques. CONCLUSION: AAIV is a safe method of ventilation for patients undergoing endoscopic laryngotracheal stenosis surgery with no significant differences in patient hemodynamics or procedure time when compared with jet ventilation. AAIV was the preferred method of ventilation for obese patients undergoing endoscopic laryngotracheal stenosis surgery. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1343-1348, 2024.

Anti-tumor effects of cryptotanshinone (C19H20O3) in human osteosarcoma cell lines.

Osteosarcoma is the most prevalent malignant bone tumor and occurs most commonly in the adolescent and young adult population. Despite the recent advances in surgeries and chemotherapy, the overall survival in patients with resectable metastases is around 20%. This challenge in osteosarcoma is often attributed to the drastic differences in the tumorigenic profiles and mutations among patients. With diverse mutations and multiple oncogenes, it is necessary to identify the therapies that can attack various mutations and simultaneously have minor side-effects. In this paper, we constructed the osteosarcoma pathway from literature and modeled it using ordinary differential equations. We then simulated this network for every possible gene mutation and their combinations and ranked different drug combinations based on their efficacy to drive a mutated osteosarcoma network towards cell death. Our theoretical results predict that drug combinations with Cryptotanshinone (C19H20O3), a traditional Chinese herb derivative, have the best overall performance. Specifically, Cryptotanshinone in combination with Temsirolimus inhibit the JAK/STAT, MAPK/ERK, and PI3K/Akt/mTOR pathways and induce cell death in tumor cells. We corroborated our theoretical predictions using wet-lab experiments on SaOS2, 143B, G292, and HU03N1 human osteosarcoma cell lines, thereby demonstrating the potency of Cryptotanshinone in fighting osteosarcoma.

In Silico Modeling of the Induction of Apoptosis by Cryptotanshinone in Osteosarcoma Cell Lines.

Osteosarcoma (OS) is the most common primary malignant bone tumor of both children and pet canines. Its characteristic genomic instability and complexity coupled with the dearth of knowledge about its etiology has made improvement in the current treatment difficult. We use the existing literature about the biological pathways active in OS and combine it with the current research involving natural compounds to identify new targets and design more effective drug therapies. The key components of these pathways are modeled as a Boolean network with multiple inputs and multiple outputs. The combinatorial circuit is employed to theoretically predict the efficacies of various drugs in combination with Cryptotanshinone. We show that the action of the herbal drug, Cryptotanshinone on OS cell lines induces apoptosis by increasing sensitivity to TNF-related apoptosis-inducing ligand (TRAIL) through its multi-pronged action on STAT3, DRP1 and DR5. The Boolean framework is used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines.

BACKGROUND: Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer. METHODS: Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 µM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans. RESULTS: Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure. CONCLUSIONS: CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

Targeting oncogenic mutations in colorectal cancer using cryptotanshinone.

Colorectal cancer (CRC) is one of the most prevalent types of cancer in the world and ranks second in cancer deaths in the US. Despite the recent improvements in screening and treatment, the number of deaths associated with CRC is still very significant. The complexities involved in CRC therapy stem from multiple oncogenic mutations and crosstalk between abnormal pathways. This calls for using advanced molecular genetics to understand the underlying pathway interactions responsible for this cancer. In this paper, we construct the CRC pathway from the literature and using an existing public dataset on healthy vs tumor colon cells, we identify the genes and pathways that are mutated and are possibly responsible for the disease progression. We then introduce drugs in the CRC pathway, and using a boolean modeling technique, we deduce the drug combinations that produce maximum cell death. Our theoretical simulations demonstrate the effectiveness of Cryptotanshinone, a traditional Chinese herb derivative, achieved by targeting critical oncogenic mutations and enhancing cell death. Finally, we validate our theoretical results using wet lab experiments on HT29 and HCT116 human colorectal carcinoma cell lines.

In Silico Design and Experimental Validation of Combination Therapy for Pancreatic Cancer.

The number of deaths associated with Pancreatic Cancer has been on the rise in the United States making it an especially dreaded disease. The overall prognosis for pancreatic cancer patients continues to be grim because of the complexity of the disease at the molecular level involving the potential activation/inactivation of several diverse signaling pathways. In this paper, we first model the aberrant signaling in pancreatic cancer using a multi-fault Boolean Network. Thereafter, we theoretically evaluate the efficacy of different drug combinations by simulating this boolean network with drugs at the relevant intervention points and arrive at the most effective drug(s) to achieve cell death. The simulation results indicate that drug combinations containing Cryptotanshinone, a traditional Chinese herb derivative, result in considerably enhanced cell death. These in silico results are validated using wet lab experiments we carried out on Human Pancreatic Cancer (HPAC) cell lines.

A Gaussian Mixture-Model Exploiting Pathway Knowledge for Dissecting Cancer Heterogeneity.

In this work, we develop a systematic approach for applying pathway knowledge to a multivariate Gaussian mixture model for dissecting a heterogeneous cancer tissue. The downstream transcription factors are selected as observables from available partial pathway knowledge in such a way that the subpopulations produce some differential behavior in response to the drugs selected in the upstream. For each subpopulation, each unique (drug, observable) pair is considered as a unique dimension of a multivariate Gaussian distribution. Expectation-maximization (EM) algorithm with hill-climbing is then used to rank the most probable estimates of the mixture composition based on the log-likelihood value. A major contribution of this work is to examine the efficacy of the EM based approach in estimating the composition of experimental mixture sets from cell-by-cell measurements collected on a dynamic cell imaging platform. Towards this end, we apply the algorithm on hourly data collected for two different mixture compositions of A2058, HCT116, and SW480 cell lines for three scenarios: untreated, Lapatinib-treated, and Temsirolimus-treated. Additionally, we show how this methodology can provide a basis for comparing the killing rate of different drugs for a heterogeneous cancer tissue. This obviously has important implications for designing efficient drugs for treating heterogeneous malignant tumors.

Cryptotanshinone Induces Cell Death in Lung Cancer by Targeting Aberrant Feedback Loops.

Signaling pathways oversee highly efficient cellular mechanisms such as growth, division, and death. These processes are controlled by robust negative feedback loops that inhibit receptor-mediated growth factor pathways. Specifically, the ERK, the AKT, and the S6K feedback loops attenuate signaling via growth factor receptors and other kinase receptors to regulate cell growth. Irregularity in any of these supervised processes can lead to uncontrolled cell proliferation and possibly Cancer. These irregularities primarily occur as mutated genes, and an exhaustive search of the perfect drug combination by performing experiments can be both costly and complex. Hence, in this paper, we model the Lung Cancer pathway as a Modified Boolean Network that incorporates feedback. By simulating this network, we theoretically predict the drug combinations that achieve the desired goal for the majority of mutations. Our theoretical analysis identifies Cryptotanshinone, a traditional Chinese herb derivative, as a potent drug component in the fight against cancer. We validated these theoretical results using multiple wet lab experiments carried out on H2073 and SW900 lung cancer cell lines.

Bayesian Inference Identifies Combination Therapeutic Targets in Breast Cancer.

OBJECTIVE: Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway. METHODS: We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis. RESULTS: Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer. CONCLUSION: Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer. SIGNIFICANCE: The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.

An in-silico study examining the induction of apoptosis by Cryptotanshinone in metastatic melanoma cell lines.

BACKGROUND: Metastatic melanoma is an aggressive form of skin cancer that evades various anti-cancer treatments including surgery, radio-,immuno- and chemo-therapy. TRAIL-induced apoptosis is a desirable method to treat melanoma since, unlike other treatments, it does not harm non-cancerous cells. The pro-inflammatory response to melanoma by nF κB and STAT3 pathways makes the cancer cells resist TRAIL-induced apoptosis. We show that due to to its dual action on DR5, a death receptor for TRAIL and on STAT3, Cryptotanshinone can be used to increase sensitivity to TRAIL. METHODS: The development of chemoresistance and invasive properties in melanoma cells involves several biological pathways. The key components of these pathways are represented as a Boolean network with multiple inputs and multiple outputs. RESULTS: The possible mutations in genes that can lead to cancer are captured by faults in the combinatorial circuit and the model is used to theoretically predict the effectiveness of Cryptotanshinone for inducing apoptosis in melanoma cell lines. This prediction is experimentally validated by showing that Cryptotanshinone can cause enhanced cell death in A375 melanoma cells. CONCLUSION: The results presented in this paper facilitate a better understanding of melanoma drug resistance. Furthermore, this framework can be used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Phenotype Classification Using Moment Features of Single-Cell Data.

Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116. The networks consist of hidden binary regulatory networks with Gaussian observations. The steady-state distributions of both the original and mutated networks are found, and data are drawn from these for moment-based classification using the mean, variance, skewness, and mixed moments. For the real data, we only observe 1 gene at a time, so that only the mean, variance, and skewness are considered, the analysis being done for 2 genes, EGFR and ERRB2. For the synthetic data, classification improves as we move from just the mean to mean, variance, and skewness and then to these plus the mixed moments. Comparisons are done with 3, 4, or 5 features, using feature selection. Sample size effects are considered. For the real data, we only consider mean, variance, and skewness, with results improving when the higher order moments are used as features.

A Bayesian approach to determine the composition of heterogeneous cancer tissue.

BACKGROUND: Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. METHODS: In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. RESULTS: The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). CONCLUSION: The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.

Assessing Combinational Drug Efficacy in Cancer Cells by Using Image-based Dynamic Response Analysis.

The landscape of translational research has been shifting toward drug combination therapies. Pairing of drugs allows for more types of drug interaction with cells. In order to accurately and comprehensively assess combinational drug efficacy, analytical methods capable of recognizing these alternative reactions will be required to prioritize those drug candidates having better chances of delivering appreciable therapeutic benefits. Traditional efficacy measures are primarily based on the "extent" of drug inhibition, which is the percentage of cells being killed after drug exposure. Here, we introduce a second dimension of evaluation criterion, speed of killing, based on a live cell imaging assay. This dynamic response trajectory approach takes advantage of both "extent" and "speed" information and uncovers synergisms that would otherwise be missed, while also generating hypotheses regarding important mechanistic modes of drug action.

Evaluating gene set enrichment analysis via a hybrid data model.

Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on the ranking performance of seven representative GSA methods. We overcome the limitation on the availability of real data sets by creating hybrid data models from existing large data sets. To build the data model, we pick a master gene from the data set to form the ground truth and artificially generate the phenotype labels. Multiple hybrid data models can be constructed from one data set and multiple data sets of smaller sizes can be generated by resampling the original data set. This approach enables us to generate a large batch of data sets to check the ranking performance of GSA methods. Our simulation study reveals that for the proposed data model, the Q2 type GSA methods have in general better performance than other GSA methods and the global test has the most robust results. The properties of a data set play a critical role in the performance. For the data sets with highly connected genes, all GSA methods suffer significantly in performance.

Mcl-1 mediates TWEAK/Fn14-induced non-small cell lung cancer survival and therapeutic response.

UNLABELLED: Insensitivity to standard clinical interventions, including chemotherapy, radiotherapy, and tyrosine kinase inhibitor (TKI) treatment, remains a substantial hindrance towards improving the prognosis of patients with non-small cell lung cancer (NSCLC). The molecular mechanism of therapeutic resistance remains poorly understood. The TNF-like weak inducer of apoptosis (TWEAK)-FGF-inducible 14 (TNFRSF12A/Fn14) signaling axis is known to promote cancer cell survival via NF-κB activation and the upregulation of prosurvival Bcl-2 family members. Here, a role was determined for TWEAK-Fn14 prosurvival signaling in NSCLC through the upregulation of myeloid cell leukemia sequence 1 (MCL1/Mcl-1). Mcl-1 expression significantly correlated with Fn14 expression, advanced NSCLC tumor stage, and poor patient prognosis in human primary NSCLC tumors. TWEAK stimulation of NSCLC cells induced NF-κB-dependent Mcl-1 protein expression and conferred Mcl-1-dependent chemo- and radioresistance. Depletion of Mcl-1 via siRNA or pharmacologic inhibition of Mcl-1, using EU-5148, sensitized TWEAK-treated NSCLC cells to cisplatin- or radiation-mediated inhibition of cell survival. Moreover, EU-5148 inhibited cell survival across a panel of NSCLC cell lines. In contrast, inhibition of Bcl-2/Bcl-xL function had minimal effect on suppressing TWEAK-induced cell survival. Collectively, these results position TWEAK-Fn14 signaling through Mcl-1 as a significant mechanism for NSCLC tumor cell survival and open new therapeutic avenues to abrogate the high mortality rate seen in NSCLC. IMPLICATIONS: The TWEAK-Fn14 signaling axis enhances lung cancer cell survival and therapeutic resistance through Mcl-1, positioning both TWEAK-Fn14 and Mcl-1 as therapeutic opportunities in lung cancer.

Synthetic, spectroscopic, and DFT studies of iron complexes with iminobenzo(semi)quinone ligands: implications for o-aminophenol dioxygenases.

The oxidative C-C bond cleavage of o-aminophenols by nonheme Fe dioxygenases is a critical step in both human metabolism (the kynurenine pathway) and the microbial degradation of nitroaromatic pollutants. The catalytic cycle of o-aminophenol dioxygenases (APDOs) has been proposed to involve formation of an Fe(II)/O2/iminobenzosemiquinone complex, although the presence of a substrate radical has been called into question by studies of related ring-cleaving dioxygenases. Recently, we reported the first synthesis of an iron(II) complex coordinated to an iminobenzosemiquinone (ISQ) ligand, namely, [Fe((Ph2)Tp)((tBu)ISQ)] (2a; where (Ph2)Tp=hydrotris(3,5-diphenylpyrazol-1-yl)borate and (tBu)ISQ is the radical anion derived from 2-amino-4,6-di-tert-butylphenol). In the current manuscript, density functional theory (DFT) calculations and a wide variety of spectroscopic methods (electronic absorption, Mössbauer, magnetic circular dichroism, and resonance Raman) were employed to obtain detailed electronic-structure descriptions of 2a and its one-electron oxidized derivative [3a](+). In addition, we describe the synthesis and characterization of a parallel series of complexes featuring the neutral supporting ligand tris(4,5-diphenyl-1-methylimidazol-2-yl)phosphine ((Ph2)TIP). The isomer shifts of about 0.97 mm s(-1) obtained through Mössbauer experiments confirm that 2a (and its (Ph2)TIP-based analogue [2b](+)) contain Fe(II) centers, and the presence of an ISQ radical was verified by analysis of the absorption spectra in light of time-dependent DFT calculations. The collective spectroscopic data indicate that one-electron oxidation of the Fe(II)-ISQ complexes yields complexes ([3a](+) and [3b](2+)) with electronic configurations between the Fe(III)-ISQ and Fe(II)-IBQ limits (IBQ=iminobenzoquinone), highlighting the ability of o-amidophenolates to access multiple oxidation states. The implications of these results for the mechanism of APDOs and other ring-cleaving dioxygenases are discussed.

Learning contextual gene set interaction networks of cancer with condition specificity.

BACKGROUND: Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. RESULTS: In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. CONCLUSIONS: The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.

Structurally similar estradiol analogs uniquely alter the regulation of intracellular signaling pathways.

Ligand structure can affect the activation of nuclear receptors, such as estrogen receptors (ERs), and their control of signaling pathways for cellular responses including death and differentiation. We hypothesized that distinct biological functions of similar estradiol (E(2)) analogs could be identified by integrating gene expression patterns obtained from human tumor cell lines with receptor binding and functional data for the purpose of developing compounds for treatment of a variety of diseases. We compared the estrogen receptor subtype selectivity and impact on signaling pathways for three distinct, but structurally similar, analogs of E(2). Modifications in the core structure of E(2) led to pronounced changes in subtype selectivity for estrogen receptors, ER-α or ER-ß, along with varying degrees of ER dimerization and activation. While all three E(2) analogs are predominantly ER-ß agonists, the cell growth inhibitory activity commonly associated with this class of compounds was detected for only two of the analogs and might be explained by a ligand-specific pattern of gene transcription. Microarray studies using three different human tumor cell lines demonstrated that the analogs distinctly affect the transcription of genes in signaling pathways for chromosome replication, cell death, and oligodendrocyte progenitor cell differentiation. That the E(2) analogs could lower tumor cell viability and stimulate neuronal differentiation confirmed that gene expression data could accurately distinguish biological activity of the E(2) analogs. The findings reported here confirm that cellular responses can be regulated by making key structural alterations to the core structure of endogenous ER ligands.

Newton, laplace, and the epistemology of systems biology.

For science, theoretical or applied, to significantly advance, researchers must use the most appropriate mathematical methods. A century and a half elapsed between Newton's development of the calculus and Laplace's development of celestial mechanics. One cannot imagine the latter without the former. Today, more than three-quarters of a century has elapsed since the birth of stochastic systems theory. This article provides a perspective on the utilization of systems theory as the proper vehicle for the development of systems biology and its application to complex regulatory diseases such as cancer.

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification.

BACKGROUND: Molecularly targeted agents (MTAs) are increasingly used for cancer treatment, the goal being to improve the efficacy and selectivity of cancer treatment by developing agents that block the growth of cancer cells by interfering with specific targeted molecules needed for carcinogenesis and tumor growth. This approach differs from traditional cytotoxic anticancer drugs. The lack of specificity of cytotoxic drugs allows a relatively straightforward approach in preclinical and clinical studies, where the optimal dose has usually been defined as the "maximum tolerated dose" (MTD). This toxicity-based dosing approach is founded on the assumption that the therapeutic anticancer effect and toxic effects of the drug increase in parallel as the dose is escalated. On the contrary, most MTAs are expected to be more selective and less toxic than cytotoxic drugs. Consequently, the maximum therapeutic effect may be achieved at a "biologically effective dose" (BED) well below the MTD. Hence, dosing study for MTAs should be different from cytotoxic drugs. Enhanced efforts to molecularly characterize the drug efficacy for MTAs in preclinical models will be valuable for successfully designing dosing regimens for clinical trials. RESULTS: A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanism of action and identify pharmacodynamic characteristics of the drug. Instead of fixed time point analysis of the drug exposure to drug effect, the time course of drug effect for different doses is quantitatively studied on cell line-based platforms using system identification, where tumor cells' responses to drugs through the use of fluorescent reporters are sampled over a time course. Results show that drug effect is time-varying and higher dosages induce faster and stronger responses as expected. However, the drug efficacy change along different dosages is not linear; on the contrary, there exist certain thresholds. This kind of preclinical study can provide valuable suggestions about dosing regimens for the in vivo experimental stage to increase productivity.