Drug development is currently an expensive and prolonged process with high attrition rate. The rate of new drug approvals in the U. S. has remained essentially constant since 1950, while the costs of drug development have soared [1
]. Industry analysts estimate that it takes $1 billion to $4 billion in R&D and 10-15 years for every new drug brought to market [1
]. In aggregate, the industrial average rate of attrition measured from first trials in humans to registration seems to be locked at ~85-90% [4
]. The situation in oncology drug development is even worse [3
]. By contrast, the overall clinical success rate for new anticancer agents (~5%) is much lower than other therapeutic areas (e.g. success rate for cardiovascular diseases is ~20%) [8
]. As a result, the American Cancer Society's 2005 statistical report shows that cancer is now the leading cause of death for Americans under age 85 [9
]. One common explanation for the recent shrinking of oncology drug pipelines is that discovery is moving into more complex areas of human health [10
], such as cancer, which is more likely to result from the interaction of several different genes/pathways [12
]. The conundrum confronting the cancer research community is twofold: first, the pharmaceutical industry is facing difficult times owing to low productivity and spiraling cost [4
]; second, on consumers front, patients await better treatments and cancer drugs are an unaffordable luxury for many consumers [14
]. To move ahead, scientists realize that they need some fresh thinking in basic, translational and clinical research [15
] to improve R&D productivity and reduce attrition rates, and such efforts calls for joint collaboration from different disciplines [5
The focus of anticancer drug development in recent years has shifted from cytotoxic drugs to targeted therapy [16
]. The goal of this target-based approach is 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 [21
]. This approach is different from traditional cytotoxic anticancer drugs, where most compounds are targeted against molecules required for the maintenance of structural and genetic integrity of rapidly dividing cells. However, despite advances in understanding of the molecular mechanisms of cancer, the promise of targeted cancer therapy remains largely unfulfilled [8
], with only a few well-known examples, such as imatinib [25
] and trastuzumab [26
], currently approved [27
]. Many promising candidates prove ineffective or toxic owing to a poor understanding of the molecular mechanisms of biological systems they target. Different reasons have been proposed to explain this limited effectiveness of anticancer drug development, including insufficient translational research and lack of adequate preclinical models that recapitulate disease complexity and molecular heterogeneity [8
]. Ideally, preclinical models should validate the target, provide information about the mechanism of action of the drug, and identify pharmacodynamic markers of activity. Once the target and mechanism of action have been identified using in vitro
models, experiments should be undertaken to ensure that inhibition of the target can be achieved at tolerated doses in vivo
and to identify possible biomarkers of response. Improved preclinical evaluation of compounds has the potential to augment the detection of activity and toxicity, and to reduce the high attrition rate.
While the lack of specificity of the traditional cytotoxic anticancer agents allows a relatively straightforward, well-established approach, developing a paradigm to better analyze the efficacy of molecularly targeted agents (MTAs) is substantially more complex [18
]. Many targets are involved in cell signaling pathways, which are most often not linear, but connected and redundant [33
]. Control strategies typically involve a higher multiplicity of inputs and a multiple layer of feedback [34
]. As a result, strategies traditionally applied to the development of cytotoxic drugs may not be appropriate for MTAs [32
]. Current treatment plan and efficacy evaluations are usually designed empirically for MTAs, without adequate knowledge of the optimal dose and the appropriate schedule [32
]. A novel preclinical model combining experimental methods and theoretical analysis is proposed in this study to investigate the mechanism of action and identify pharmacodynamic characteristics of the drug. It is expected that through such preclinical study, valuable suggestions about dosing regimens could be furnished for the in vivo
experimental stage to increase productivity. We consider several challenges for MTA dosing.
Firstly, the optimal dose has usually been defined as the "maximum tolerated dose" (MTD) for conventional cytotoxic anticancer drugs rather than the dose that produces a quantifiable therapeutic effect. 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 [22
]. Such an assumption is sound if the mechanisms of action of the toxic and therapeutic effects are the same, as is often the case with cytotoxic agents. However, most MTAs are expected to be more selective and less toxic than conventional cytotoxic drugs [23
]. As a result, the maximum therapeutic effect may be achieved at a dose, defined as the "biologically effective dose" (BED), which could be substantially lower than the traditionally established MTD as discussed by Johnston [31
]. A hypothetical dose-effect curve is shown in Figure . In addition, the toxic effect may not parallel the therapeutic effect and not be predictive of the therapeutic effect [22
]. Hence, the dosing study for MTAs should be based on both drug efficacy and toxicity considerations. Enhanced efforts to molecularly characterize the drug efficacy for MTAs in preclinical models will be valuable for successfully estimating the BED for clinical trials.
A hypothetical dose-effect curve for targeted therapy.
Secondly, the pharmacodynamics (PD) of drugs have been extensively investigated in vitro
and in vivo
; however, most analyses have reported the relationship of drug exposure to drug effect at a fixed time point. When drug effect is examined at a fixed time point, the drug concentration-effect relationship can be characterized through well established models, such as the Hill equation [35
], also called the sigmoidal Emax
]. However, characterization of the entire time course of drug effect may provide additional information [37
]. For example, it may help to design the optimal schedule for drug administration.
Thirdly, traditional design of the dosing regimen to achieve some desired target goal such as relatively constant serum concentration may not be optimal because MTA targets mostly sit in interacting complex dynamical regulatory networks and such complex target contexts pose significant challenges for assessing mechanisms of action for MTAs [30
]. For example, Shah and co-workers [38
] demonstrate that the BCR-ABL inhibitor dasatinib, which has greater potency and a short half-life, can achieve deep clinical remission in CML patients by achieving transient potent BCR-ABL inhibition, while traditional approved tyrosine kinase inhibitors usually have prolonged half lives resulting in continuous target inhibition. A similar study of whether short pulses of higher dose or persistent dosing with lower doses have the most favorable outcomes has been carried out by Amin and co-workers in the setup of inactivation of HER2-HER3 signaling [39
In sum, it is difficult and expensive to optimize dosing regimens using strictly empirical methods for MTAs. A novel preclinical model combining experimental methods and theoretical analysis is proposed in this study to investigate the mechanisms of action and identify pharmacodynamic characteristic of MTAs. As a first step, the time courses of drug effect for different doses are quantitatively studied on cell line-based platforms using system identification, where a tumor cell's response to investigational drugs through the use of fluorescent reporters is sampled frequently over a time course. A dynamic model is proposed to study the time course of drug efficacy for MTAs and then the experimental data are analyzed by our proposed model using a Kalman filter. Through such preclinical study, valuable suggestions about dosing regimens may be furnished for the in vivo experimental stage to increase productivity.