Abstract 4921: Dynamic and quantitative modeling of drug effect

The current drug development is a very expensive and prolonged process with high attrition rate, especially in the oncology drug development. New tools with dynamic and quantitative modeling and experimental support are needed to accelerate the drug discovery process and increase productivity. The process begins with building a quantitative model of biological regulatory system based on the extensive data-mining from literature and experiments. By incorporating the preclinical PK/PD data relevant to the target system, a new computational model under drug perturbation can be built. Through iterative process of hypotheses generating and experimental testing, such models can be fine-tuned. Consequences of particular perturbations, such as optimal dosage and treatment schedules, optimal drug target, and combination therapy can be simulated in time course using such models. A better understanding of the biological system will be gained and better therapeutic interventions suggestions can be provided. In this study, we use NF-κB pathway as an example study considering NF-κB pathway has become a focal point for intense drug discovery and development efforts. Dynamic drug perturbation is incorporated in the model stem from Hoffmann et al. (science 2002 Nov 8;298) through hybrid system theory to link dose – concentration relationships (PK) and concentration – effect relationships (PD). This approach facilitates the description and prediction of the time course of drug effects resulting from a certain dose regimen. Extensive simulations of the proposed model are performed in MATLAB/SIMULINK. An important observation is that there exists a “sweet spot” for certain dosage and frequency arrangements given the totaled delivered drug. The perturbation effect for different targets is simulated and compared in this model. However, the specificity of some drugs to inhibit several of these components of the NF-κB pathway is one of the concerns. For example, if we block the degradation pathway of IκB by using proteasome inhibitor (such as Bortezomib) to inhibit ubiquitination of IκB by the 26S protesome. The side effects are unacceptable when the drug is effective. Hence, we design combination therapy to induce a acceptable combined effect and using lower doses for each drugs. We observe that some combinations of the drugs with non-overlapping toxicities might provide enormous benefit. While such dynamical modeling approaches are still in their infancy, advancing techniques of the quantitative single live cell measurement of the protein processes will provide us more accurate parameters and help us to test and confirm some observations. This will allow a better understanding of the kinetics and function of mechanisms within regulatory networks. The authors believe that appropriate models can help establishing a mechanistic understanding of the disease and of drug action instead of the traditional “black-box” approach in drug discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4921. doi:10.1158/1538-7445.AM2011-4921