### Abstract

- Top of page
- Abstract
- Methods
- Results and Discussion
- Conclusion
- Acknowledgments
- References

Traditional Chinese Medicine has become an important resource for searching the effective drug combinations in multicomponent drug designs. In this article, we investigate the methodology on how to efficiently optimize the combination of several active components from traditional Chinese formula. A new method based upon lattice experimental design and multivariate regression was applied to model the quantitative composition-activity relationship (QCAR) in this study. As a result, multi-objective optimization was achieved by Derringer function using extensive search algorithm. This newly proposed QCAR-based strategy for multicomponent drug design was then successfully applied on search optimal combination of three components from Chinese medicinal formula *Shenmai*. The result validated the effectiveness of the presented method for multicomponent drug design.

Tremendous progress has been made in the pharmaceutical industry and many therapeutic agents have been developed to target specific proteins over the past two decades. Nonetheless, single agent therapy has been proven ineffective to treat cancer, diabetes and other complex diseases because of the diversities of cellular functions and interactions among the pathways, which may limit the performance of single-target-drug paradigm (1). Meanwhile, some researchers found that a combination of multiple compounds, in many cases, can achieve more effectiveness than using a single compound (2,3). Thus, the production of effective combinatorial mixtures to manipulate multiple disease targets has gained increasing attentions in the research community of drug discovery and development.

A few different design strategies for multiple components drug have been reported in literature. For example, high-throughput screening method has been used for identifying effective combinations of clinically used compounds (4). Systematic screening of about 120 000 different two-component combinations only resulted in dozens of confirmed pairwise combinations. It attempted to use computational method to reduce the size of sample sets as well as the labor- and time-consuming biologic assays. Wong *et al.* (5) proposed a stochastic search algorithm to choose only tens of screens out of a large number of possible combinations. Alternatively, others suggested that drug regimens that contain several active components, such as traditional Chinese medicine (TCM) and other herbal medicine, can be important natural sources of drug candidates or multicomponent drugs (6,7). In fact, those natural mixtures from herbs and herbal extracts have been widely applied for the treatment of various diseases for hundreds of years until the monumental discoveries of the sulfonamides and penicillin. It looks like it is the right time to design multicomponent drugs from natural products by integrated use of past medical experiences and modern techniques.

Developing an optimum composition of several active components from TCM for the maximum therapeutic efficacy with minimum side-effects is a critical step in the discovery of multicomponent drug from natural sources, which generally involves two steps. In the first step, the active components of TCM are screened and identified. The primary goal in this step is to validate the efficacy of multiple ingredients and narrow the testing parametric space in the subsequent mixture design. Once the active components critical to the therapeutical effects are found, the second step of combination optimization is to find the optimum proportion of each component for single or multiple goals. If only two active components are combined, the optimal proportion can be easily screened by a set of experiments. However, when a combination consisted of three or more components needed to be optimized, the large parametric space imposes a major challenge to experimental design and data analysis. The application of statistical experimental design methods and mathematical models may greatly contribute to the effective searches for the potent drug combinations.

Computational approaches, which generate structure- or ligand-based models to predict bioactivity, have been found to be valuable in the optimization and development of potent drug candidates (8). Many studies have shown that mathematical and computational approaches, such as graph/diagram analysis (9), pharmacophore modeling (10), structural bioinformatics (11), molecular docking and packing (12), Mote Carlo simulated annealing approach (13), bio-macromolecular internal collective motion simulation (14), quantitative structure-activity relationship (QSAR) (15), protein subcellular location prediction (16,17), identification of enzymes, proteins, proteases and their types (18,19), and cellular automaton analysis (20), can timely provide very useful information and insights into both basic research and drug design and hence are widely welcomed by science community. But the structural information of individual components is rarely related to the activity of multicomponent mixture. The key optimization task for multicomponent drug design that has to be solved is to determine the proper proportion of each component in the optimal combination. In our earlier study, we had proposed computational approaches to modeling the quantitative composition-activity relationship (QCAR) (21,22), and screening optimal proportion of two active components out of six components from a Chinese medicinal formula (23). The approach developed a relational model between biologic potency of a combination and its chemical composition (i.e. proportion and dose of individual component). However, such model still needs to be improved because of the large number of tested samples used as training set. The present study attempts to develop a new computational approach for more efficiently conducting multicomponent drug design.

The purpose of this article is to experimentally implement a QCAR-based scheme for multicomponent drug design from TCM. A simple lattice design is applied to obtain a small set of combinations. Multi-objective optimization of three components combination from a traditional Chinese formula was achieved. The approach proposed in this study effectively identified potential drug combinations toward the goal of integrative therapy.