Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm² chip in a device measuring 23 × 16 × 3.5 cm³. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.

This article presents the first flow-control codesign methodology, which seamlessly integrates both flow-layer and control-layer design stages. Experimental results show that the proposed codesign flow achieves notable improvements over the regular design framework with separate design stages.

Flow-based microfluidic biochips are one of the most promising platforms used in biochemical and pharmaceutical laboratories due to their high efficiency and low costs. Inside such a chip, fluids of nanoliter volumes are transported between devices for various operations such as mixing and detection. The transportation channels and corresponding operation devices are controlled by microvalves driven by external pressure sources. Since assigning an independent pressure source to every microvalve would be impractical due to high costs and limited system dimensions, states of microvalves are switched by a control logic using time multiplexing. Existing control logic designs, however, still switch only a single control channel per operation, leading to a low efficiency. In this paper, we present the first automatic synthesis approach for a control logic that is able to switch multiple control channels simultaneously. Moreover, we propose the first fault-aware design in control logic by introducing backup control paths to maintain the correct function even when manufacturing defects occur. The construction of control logic is achieved by a highly efficient framework based on Particle Swarm Optimization, Boolean logic simplification, grid routing, together with mixing multiplexing. Simulation results demonstrate that the proposed multi-channel switching mechanism leads to fewer valve-switching times and lower total logic cost, while realizing fault tolerance for all control channels.

Flow-based microfluidic biochips are attracting increasing attention with successful applications in biochemical experiments, point-of-care diagnosis, etc. Existing works in design automation consider the flow-layer design and control-layer design separately, lacking a global optimization and hence resulting in degraded routability and reliability. This paper presents a novel integrated physical co-design methodology, which seamlessly integrates the flow-layer and control-layer design stages. In the flow-layer design stage, a sequence-pair-based placement method is presented, which allows for an iterative placement refinement based on routing feedbacks. In the control-layer design stage, the minimum cost flow formulation is adopted to further improve the routability. Besides that, effective placement adjustment strategies are proposed to iteratively enhance the solution quality of the overall control-layer design. Experimental results show that compared with the existing work, the proposed design flow obtains an average reduction of 40.44% in flow-channel crossings, 31.95% in total chip area, and 22.02% in total flow-channel length. Moreover, all the valves are successfully routed in the control-layer design stage.

According to the complexity of human physiology and variability among individuals, e.g., genes, environment, lifestyle exposures, etc., personalized medicine aims to synthesize the specific efficacious drug for each individual patient. For synthesizing personalized medicine, customized solutions with specific concentrations are required. Equipped with the advantages in saving costly reagents and rare samples, microfluidic biochips are promising in generating different concentrations for personalized medicine. On the one hand, digital microfluidic biochips require the programming control for driving the movement of the droplets, which suffer from random errors caused by imbalanced droplet splitting. On the other hand, existing flow-based microfluidic biochips can only generate linear concentration gradients, which cause significant waste for synthesizing personalized medicine. To address the above issues, this paper proposes the first artificial neural network (ANN)-based design method for flow-based microfluidic biochips, which accurately generates the customized concentration gradients. According to the required concentration, an initial chip is first selected from the prebuilt database and then fine-tuned by ANN to better match the required concentration. Computational simulation results show that the induced deviations in generated concentrations are generally less than 0.014, which validates the accuracy of the proposed neural network model.