Circulating tumor cells (CTCs) are rare cells that detach from a primary or metastasis tumor and flow into the bloodstream. Intact and viable tumor cells are needed for genetic characterization of CTCs, new drug development, and other research. Although separation of CTCs using spiral channel with two outlets has been reported, few literature demonstrated simultaneous isolation of different types of CTCs from human blood using cascaded inertial focusing microfluidic channel. Herein, we introduce a cascaded microfluidic device consisting of two spiral channels and one zigzag channel designed with different fluid fields, including lift force, Dean drag force, and centrifugal force. Both red blood cells (RBCs)-lysed human blood spiked with CTCs and 1:50 diluted human whole blood spiked with CTCs were tested on the presented chip. This chip successfully separated RBCs, white blood cells (WBCs), and two different types of tumor cells (human lung cancer cells (A549) and human breast cancer cells (MCF-7)) simultaneously based on their physical properties. A total of 80.75% of A549 and 73.75% of MCF-7 were faithfully separated from human whole blood. Furthermore, CTCs gathered from outlets could propagate and remained intact. The cell viability of A549 and MCF-7 were 95% and 98%, respectively. The entire separating process for CTCs from blood cells could be finished within 20 min. The cascaded microfluidic device introduced in this study serves as a novel platform for simultaneous isolation of multiple types of CTCs from patient blood.

In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.

Effective capture and analysis of a single circulating tumor cell (CTC) is instrumental for early diagnosis and personalized therapy of tumors. However, due to their extremely low abundance and susceptibility to interference from other cells, high-throughput isolation, enrichment, and single-cell-level functional protein analysis of CTCs within one integrated system remains a major challenge. Herein, we present an integrated multifunctional microfluidic system for highly efficient and label-free CTC isolation, CTC enrichment, and single-cell immunoblotting (ieSCI). The ieSCI-chip is a multilayer microfluidic system that combines an inertia force-based cell sorter with a membrane filter for label-free CTC separation and enrichment and a thin layer of a photoactive polyacrylamide gel with microwell arrays at the bottom of the chamber for single-cell immunoblotting. The ieSCI-chip successfully identified a subgroup of apoptosis-negative (Bax-negative) cells, which traditional bulk analysis did not detect, from cisplatin-treated cells. Furthermore, we demonstrated the clinical application of the ieSCI-chip with blood samples from breast cancer patients for personalized CTC epithelial-to-mesenchymal transition (EMT) analysis. The expression level of a tumor cell marker (EpCAM) can be directly determined in isolated CTCs at the single-cell level, and the therapeutic response to anticancer drugs can be simultaneously monitored. Therefore, the ieSCI-chip provides a promising clinical translational tool for clinical drug response monitoring and personalized regimen development.

Strategies for identifying potential drug combinations provide great benefits for the clinical treatment of complex diseases. However, existing approaches primarily focus on optimization of drug combinations and doses without comprehensive joint consideration of drug sequence, which compresses the therapeutic space. Herein, a novel combinatorial drug screening technique is proposed, named innovative design and active learning (IDEAL). This approach integrates innovative design, experimentation, and active learning. IDEAL allows for accurate predictions on the most efficacious drug combination, drug doses, and drug sequence with minimal amount of experimental effort. In this study, IDEAL is applied to lymphoma treatment, which successfully identifies the best drug-dose sequence combination of chemotherapeutics on Raji cells. The IDEAL optimized regimen indicates that both drug dose and sequence are critical to ensure the optimal phenotype efficacy. This research offers an efficient methodology to identify the optimal drug administration setups in combination therapy.