A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

Abstract

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Keywords: diclofenac; fit-for-purpose models; hepatotoxicity; in vitro liver model; paracetamol; three-dimensional culture; troglitazone.

FIGURE 1

Schematic representation of the mechanisms of hepatotoxicity including examples of associated drugs. Drug biotransformation (phase I and II metabolism) is based on the chemical modification of a parent drug into a metabolite which may become inactive (detoxification), leading to its rapid and innocuous excretion, or reactive (bioactivation), leading to potential toxicity. Specifically, hepatotoxicity may result from direct damage, from failure of repairing mechanisms or from immune-mediated responses, leading to alterations in lipids metabolism, mitochondrial dysfunction, oxidative stress and accumulation of bile, amongst others. Moreover, the saturation of cells stress defense mechanisms may lead to carcinogenic events and promote tissue necrosis or fibrosis, resulting in liver’s functions impairment. For a given hepatotoxic compound different mechanisms of toxicity can be involved. GSH, reduced glutathione; ROS, reactive oxygen species.

FIGURE 2

Summary of the advantages and limitations of commonly used cell sources for in vitro liver models. HLCs, hepatocyte-like cells; hpHep, human primary hepatocytes; NPCs, non-parenchymal cells.

FIGURE 3

Summary of the characteristics of complex 3D in vitro cell culture systems for hepatotoxicity studies. (A) Sandwich cultures; (B) static spheroid cultures; (C) dynamic spheroid cultures; (D) bioreactors; (E) bioprinting; (F) microfluidic platforms. PBPK, physiologically based pharmacokinetic modeling; TD, toxicodynamics; TK, toxicokinetics.

FIGURE 4

Roadmap for assessing drugs hepatotoxicity mechanisms using in vitro models that might be used alone or in combination at different points and on different scales. Tier 1 comprises single-cell systems that report on immediate chemical/biological effects such as cytotoxicity and bioactivation while Tier 2 includes more complex systems containing liver cells in a more physiologic state, enabling assessment of the consequences of acute and chronic drug exposure. Moreover, phenotypic characterization and the pharmacological and toxicological functionality of a system and the ability to identify toxicity mechanisms needs to be considered before undertaking toxicological investigations to ensure that the most appropriate methods are used. Depending on the complexity, each model might be able to represent one or more liver functional endpoints and can be used alone or in combination depending on the hepatotoxicity mechanisms that are intended to study. To integrate findings from different test systems and to dissect the multilevel impact of compounds, bioinformatics and machine learning models may also be useful, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields. SC, stem cells; HLCs, hepatocyte-like cells; MPS, microphysiological system.

FIGURE 5

Data integration from non-clinical assays for prediction of clinical conditions. A shift in paradigm where fit-for-purpose human-based in vitro models, particularly using in vitro 3D systems and causality-inferring bioinformatic approaches, might provide high-quality data for relevant extrapolation of human toxicokinetics and toxicodynamics, ultimately leading to the prediction of human hepatotoxicity mechanisms and molecules’ risk assessment. As a consequence, animal models will then become progressively less used with the increasing complexity and relevance of these strategies. AOP, adverse outcome pathway; AUC, area under the curve; C_max, maximum plasma concentration; IC₅₀, half maximal inhibitory concentration; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; PD, pharmacodynamic; TD, toxicodynamics; TK, toxicokinetics.