Summary of: Mettler et al., 2026, Journal of Pharmaceutical Sciences

Navigating ADME challenges isn’t just about the chemistry of a drug, it’s about the biological and engineering system you ask to metabolize it.

In this new publication led by scientists at Novartis and the University of Tübingen, the authors take a deep, engineering level look at the performance of a microphysiological liver on chip model for predicting human hepatic clearance, a central parameter in preclinical drug development.

Navigating ADME challenges isn’t just about the chemistry of a drug, it’s about the biological and engineering system you ask to metabolize it.

In this new publication led by scientists at Novartis and the University of Tübingen, the authors take a deep, engineering level look at the performance of a microphysiological liver on chip model for predicting human hepatic clearance, a central parameter in preclinical drug development.

Their findings highlight both the promise and the boundaries of all current liver on-chip technologies when used for quantitative ADME applications. Importantly, the study reinforces what multiple cross-platform analyses have shown in recent years: systematic underprediction of human hepatic clearance, especially for high turnover compounds is a known, industry-wide challenge across liver on chip systems, not a limitation of any single platform. This is consistent with observations reported across other microphysiological systems, where factors like compound absorption, intracellular drug availability, and flow architecture shape quantitative predictivity.

What this paper shows:

🧫 Sustained hepatocyte performance
Primary human hepatocytes maintain metabolic activity for >30 hours in the chip, enabling linear parent‑drug depletion even for low‑clearance compounds, something traditional hepatocyte suspensions cannot reliably achieve.

🧬 Broad metabolic coverage
The chip metabolized substrates of key enzyme families (CYPs, UGTs, AO, CES), demonstrating multienzyme competence across pathways relevant to drug development.

🔎 Clearance underprediction – consistent with the field
Human hepatic clearance was systematically underpredicted, especially for high‑clearance compounds where in vivo hepatic blood flow becomes rate‑limiting. Some high‑turnover drugs showed >1000‑fold underprediction, a trend also described in other liver‑on‑chip investigations.

📉 Tubing – not chip materials – was a major source of nonspecific binding
Biochip materials recovered >98% of compounds, but perfusion tubing caused substantial losses of both drug and albumin. Something we observed as well in our experiments in the past: https://dynamic42.com/compound-adsorption-by-soft-polymer-biochips/

📈 Scaling addresses model‑level kinetic constraints
A systematic, model‑specific scaling factor rescued predictivity: 79% of predicted clearances fell within 3‑fold of observed human values, particularly for low‑ to moderate‑clearance compounds.

🔬 Co‑culture refinements
Liver sinusoidal endothelial cells improved cell attachment but did not significantly increase metabolic activity or clearance relative to hepatocyte monoculture.

Why this matters

This study is one of the most rigorous evaluations to date of a commercial liver‑chip system for quantitative ADME applications. It demonstrates that liver‑on-chip models:

✔️ excel at prolonged clearance measurements where standard suspensions fall short,
✔️ capture a wide range of metabolic pathways,
✔️ and deliver the strongest performance when paired with mechanistic, model‑based scaling approaches.

At the same time, it reinforces a critical insight shared across recent cross‑platform analyses in the organ‑on‑chip field: system‑level engineering constraints like membrane diffusion, compound equilibration, intracellular drug availability, tubing materials and perfusion architecture strongly shape quantitative clearance predictions across all liver‑chip platforms.

Understanding and accounting for these constraints is essential for turning rich microphysiological data into clinically meaningful PK predictions.

👏 Congratulations to Lina Mettler, Julia Riede, Felix Huth, Christian Lohasz, Peter Loskill, and Birk Poller on advancing microphysiological ADME science!

Paper:  Lina Mettler, Julia Riede, Felix Huth, Christian Lohasz, Peter Loskill, Birk Poller, Navigating ADME profiling challenges in microphysiological systems: Evaluation of a liver-chip model for clearance prediction, Journal of Pharmaceutical Sciences, Volume 115, Issue 5, 2026, 104242, ISSN 0022-3549, https://doi.org/10.1016/j.xphs.2026.104242.

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