Rapid Identification Of Combinatory Osteogenic Cocktails For Mesenchymal Stem Cells

Objectives: Bone morphogenic protein 2 (BMP-2) is a potent osteogenic factor. However, relatively high dose concentrations are required to achieve its therapeutic effect, resulting in the healthcare cost and side effect issues that may prevent its clinical use in dentistry. Drug combinations are often more effective than single drug administration, although the identification of such drug combinations is extremely challenging due to the large number of possible combinations. Recently, the feedback system control (FSC) technique using an engineering scheme has been shown useful as a tool for effectively searching for the optimal drug combinations, rather than screening all the possible combinations by applying the traditional high-throughput screening. The objective of this study was to apply FSC to identify potent osteogenic drug combinations including BMP-2 at lower concentrations. Methods: FSC was performed as follows: Murine mesnchymal stem cells (D1 cells) were stimulated by different drug combinations which all included BMP-2 and six osteogenic co-factors at ten possible concentrations. With FSC, the level of alkaline phosphatase (ALP) was measured at day 3 as an indicator of the potency of each drug combination. The results were fed into a stochastic search algorithm, evolutional algorithm, to derive modified concentrations. A total of 9 iterations examined a total of 180 drug combinations. Results: After the 5th iteration, candidate drug combinations started to emerge, which induced the ALP secretion approximately 2.5 times greater than BMP-2 alone, despite the concentration of BMP-2 in those cocktails reduced to 1/8. Conclusion: The result indicates that FSC successfully identified candidate drug combinations that enhanced the ALP secretion, out of a possible 10⁷ different combinations. FSC may serve as a useful tool for identifying potent osteogenic combinatory drugs for bone regenerative medicine.