Integrating Liver-Chip data into pharmaceutical decision-making processes.

INTRODUCTION: Drug-induced liver injury (DILI) is a potentially lethal condition that heavily impacts the pharmaceutical industry, causing approximately 21% of drug withdrawals and 13% of clinical trial failures. Recent evidence suggests that the use of Liver-Chip technology in preclinical safety testing may significantly reduce DILI-related clinical trial failures and withdrawals. However, drug developers and regulators would benefit from guidance on the integration of Liver-Chip data into decision-making processes to facilitate the technology's adoption. AREAS COVERED: This perspective builds on the findings of the performance assessment of the Emulate Liver-Chip in the context of DILI prediction and introduces two new decision-support frameworks: the first uses the Liver-Chip's quantitative output to elucidate DILI severity and enable more nuanced risk analysis; the second integrates Liver-Chip data with standard animal testing results to help assess whether to progress a candidate drug into clinical trials. EXPERT OPINION: There is now strong evidence that Liver-Chip technology could significantly reduce the incidence of DILI in drug development. As this is a patient safety issue, it is imperative that developers and regulators explore the incorporation of the technology. The frameworks presented enable the integration of the Liver-Chip into various stages of preclinical development in support of safety assessment.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Drug development is lengthy and costly, as it relies on laboratory models that fail to predict human reactions to potential drugs. Because of this, toxic drugs sometimes go on to harm humans when they reach clinical trials or once they are in the marketplace. Organ-on-a-Chip technology involves growing cells on small devices to mimic organs of the body, such as the liver. Organ-Chips could potentially help identify toxicities earlier, but there is limited research into how well they predict these effects compared to conventional models. In this study, we analyzed 870 Liver-Chips to determine how well they predict drug-induced liver injury, a common cause of drug failure, and found that Liver-Chips outperformed conventional models. These results suggest that widespread acceptance of Organ-Chips could decrease drug attrition, help minimize harm to patients, and generate billions in revenue for the pharmaceutical industry.

An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology.

Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point-of-care. Sight OLO (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19-parameter, five-part differential CBC, and is designed to address the limitations in current point-of-care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision. Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN-Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct-from-fingerprick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory-based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.

Measuring direct current trans-epithelial electrical resistance in organ-on-a-chip microsystems.

Trans-epithelial electrical resistance (TEER) measurements are widely used as real-time, non-destructive, and label-free measurements of epithelial and endothelial barrier function. TEER measurements are ideal for characterizing tissue barrier function in organs-on-chip studies for drug testing and investigation of human disease models; however, published reports using this technique have reported highly conflicting results even with identical cell lines and experimental setups. The differences are even more dramatic when comparing measurements in conventional Transwell systems with those obtained in microfluidic systems. Our goal in this work was therefore to enhance the fidelity of TEER measurements in microfluidic organs-on-chips, specifically using direct current (DC) measurements of TEER, as this is the most widely used method reported in the literature. Here we present a mathematical model that accounts for differences measured in TEER between microfluidic chips and Transwell systems, which arise from differences in device geometry. The model is validated by comparing TEER measurements obtained in a microfluidic gut-on-a-chip device versus in a Transwell culture system. Moreover, we show that even small gaps in cell coverage (e.g., 0.4%) are sufficient to cause a significant (~80%) drop in TEER. Importantly, these findings demonstrate that TEER measurements obtained in microfluidic systems, such as organs-on-chips, require special consideration, specifically when results are to be compared with measurements obtained from Transwell systems.

Universal computing by DNA origami robots in a living animal.

Biological systems are collections of discrete molecular objects that move around and collide with each other. Cells carry out elaborate processes by precisely controlling these collisions, but developing artificial machines that can interface with and control such interactions remains a significant challenge. DNA is a natural substrate for computing and has been used to implement a diverse set of mathematical problems, logic circuits and robotics. The molecule also interfaces naturally with living systems, and different forms of DNA-based biocomputing have already been demonstrated. Here, we show that DNA origami can be used to fabricate nanoscale robots that are capable of dynamically interacting with each other in a living animal. The interactions generate logical outputs, which are relayed to switch molecular payloads on or off. As a proof of principle, we use the system to create architectures that emulate various logic gates (AND, OR, XOR, NAND, NOT, CNOT and a half adder). Following an ex vivo prototyping phase, we successfully used the DNA origami robots in living cockroaches (Blaberus discoidalis) to control a molecule that targets their cells.

Barcoding cells using cell-surface programmable DNA-binding domains.

We report an approach to barcode cells through cell-surface expression of programmable zinc-finger DNA-binding domains (surface zinc fingers, sZFs). We show that sZFs enable sequence-specific labeling of living cells by dsDNA, and we develop a sequential labeling approach to image more than three cell types in mixed populations using three fluorophores. We demonstrate the versatility of sZFs through applications in which they serve as surrogate reporters, function as selective cell capture reagents and facilitate targeted cellular delivery of viruses.

Submicrometre geometrically encoded fluorescent barcodes self-assembled from DNA.

The identification and differentiation of a large number of distinct molecular species with high temporal and spatial resolution is a major challenge in biomedical science. Fluorescence microscopy is a powerful tool, but its multiplexing ability is limited by the number of spectrally distinguishable fluorophores. Here, we used (deoxy)ribonucleic acid (DNA)-origami technology to construct submicrometre nanorods that act as fluorescent barcodes. We demonstrate that spatial control over the positioning of fluorophores on the surface of a stiff DNA nanorod can produce 216 distinct barcodes that can be decoded unambiguously using epifluorescence or total internal reflection fluorescence microscopy. Barcodes with higher spatial information density were demonstrated via the construction of super-resolution barcodes with features spaced by ∼40 nm. One species of the barcodes was used to tag yeast surface receptors, which suggests their potential applications as in situ imaging probes for diverse biomolecular and cellular entities in their native environments.

How accurate can genetic predictions be?

BACKGROUND: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. RESULTS: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. CONCLUSION: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.