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

Abstract

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.

Fig. 1. Schematic of the Emulate Liver-Chip.

This diagram shows primary human hepatocytes (C) that are sandwiched within an extracellular matrix (B) on a porous membrane (D) within the upper parenchymal channel (A), while human liver sinusoidal endothelial cells (G), Kupffer cells (F), and stellate cells (E) are cultured on the opposite side of the membrane in the lower vascular channel (H).

Fig. 2. Economic value model for assessing the financial impact of improved preclinical testing.

Illustrated is the model’s “base case”, which tracks a representative portfolio of candidate drugs as it progresses and erodes through clinical trials, culminating in a single drug approval. The model bases phase-by-phase attrition rates (“attrition during phase”), discovery and preclinical costs, development costs (“cost per candidate”) and cost of capital on Paul et al. to compute a portfolio-wide discounted cashflow. In contrast with prior approaches, the model tracks the underlying causes of clinical trial failure (safety-related, efficacy-related, and other failures) using parameters derived from literature^,,,, a feature that permits us to determine the composition of the drug portfolio in each stage of development in terms of candidates that are safe and effective, safe and ineffective, unsafe and effective, and unsafe and ineffective, as illustrated. Improvements in the predictive validity of preclinical safety testing can be captured through their impact on the makeup of the portfolio entering Phase I clinical trials: better preclinical safety testing reduces the proportion of unsafe drugs that enter the clinic relative to the “base case”; the model permits analyzing the impact of such changes on the discounted cashflow and the portfolio’s profitability. The model is provided in full in Supplementary Data 2 as a formula-driven Microsoft Excel file.

Fig. 3. Recapitulation of human liver structure and function in the Liver-Chip.

Representative phase contrast microscopic images (scale bar represents 10 µm) of hepatocytes in the upper channel of Liver-Chip (a) and non-parenchymal cells in the lower vascular channel (the regular array of circles are the pores in the membrane) (b). Representative immunofluorescence microscopic images showing the phalloidin stained actin cytoskeleton (green) and ATPB containing mitochondria (magenta) (c) and MRP2-containing bile canaliculi (red) (d). CD31-stained liver sinusoidal endothelial cells (green) and desmin-containing stellate cells (magenta) (e), and CD68⁺ Kupffer cells (green) co-localized with desmin-containing stellate cells (magenta) (f). All images in c–f show DAPI-stained nuclei (blue) and the scale bar represents 100 µm with the inset at 5 times higher magnification; albumin (g) and urea (h) levels in the effluent from the upper channels of vehicle-treated Liver-Chips created with cells from 3 different donors (light and dark gray bars represent donor one and two respectively, white bars represent donor three) on days 1, 3, and 7 post-vehicle administration, measured by ELISA. Data are presented as mean ± standard error of the mean (S.E.M.). For each condition (i.e., specific donor and day), the exact number of samples used to derive the statistics is: (i) Albumin: donor 1-day 1 (n = 46), donor 2-day 1 (n = 46), donor 3-day 1 (n = 39), donor 1-day 3 (n = 40), donor 2-day 3 (n = 44), donor 3-day 3 (n = 38), donor 1-day 7 (n = 29), donor 2-day 7 (n = 38), donor 3-day 7 (n = 30); (ii) Urea: donor 1-day 1 (n = 12), donor 2-day 1 (n = 12), donor 3-day 1 (n = 18), donor 1-day 3 (n = 8), donor 2-day 3 (n = 12), donor 3-day 3 (n = 18), donor 1-day 7 (n = 7), donor 2-day 7 (n = 12), donor 3-day 7 (n = 14). Levels of key liver-specific genes in control Liver-Chips as determined by RNA-seq analysis on days 3 (light gray) and 7 (dark gray) post-vehicle administration with donor two (i) and donor three (j). Data are presented as mean Log2 (fold change) ± standard error of the TPM (Transcript Per Million) expression relative to the mean expression of the freshly thawed hepatocytes with n = 4 chips; statistical significance of values between day 3 and 7 was determined using a paired t-test; *p < 0.05, **p < 0.01. For each time point (e.g., day 3 and 7), the sample size used to derive the statistics was n = 3 for donor two and n = 4 for donor three.

Fig. 4. Detection of drug concentration-dependent toxicity and liver injury.

Effect of Cloazpine (closed circles) or olanzapine (open circles) on albumin production (a), ALT release (b), and morphology score (c); Effect of troglitazone (closed circles) or pioglitazone (open circles) on albumin production (d), ALT release (e), and morphology score (f); Effect of trovafloxacin (closed circles) or levofloxacin (open circles) on albumin production (g), ALT release (h) and morphology score (i); Immunofluorescence microscopic images showing concentration-dependent increases in caspase 3/7 staining (green;) indicative of apoptosis after treatment with trovafloxacin at 0,1, 10, and 100 (j) times the unbound human C_max for 7 days; concentration-dependent decrease in TMRM staining (yellow) indicative of mitotoxicity in response to treatment with sitaxsentan at 0,1,10, and 100 (k) times the unbound human C_max for 7 days. Scale bar represents 50 µm.

Fig. 5. Proposed positioning of the Liver-Chip within a typical pharma preclinical workflow.

Typically, pharma utilizes a series of in vitro tests to guide chemical optimization ahead of animal testing. Promising drug candidates then progress to dose-range finding studies ahead of the required studies to enable regulatory approval to enter clinical trial. With the data presented in this investigation, Liver-Chip would be best placed in between the in vitro tests and dose-range finding animal studies. A drug candidate that did not show toxicity in the Liver-Chip, would increase confidence of the scientist that it can pass through animal testing without a liver toxicity flag and proceed into the clinic with a lower likelihood of clinical hepatic signals. A drug candidate that did show toxicity in the Liver-Chip would encourage scientists to stop and think about the relevance of the toxicity to the therapeutic indication and whether there was a potential margin between this finding and the exposure required for clinical efficacy. This would continue to increase the confidence that candidate drugs are entering the phase I clinical trial process with a greater likelihood of approval and may also reduce animal usage by not conducting dose-range finding or regulatory studies.