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Review ArticleReview Article
Open Access

Organotypic and Microphysiological Human Tissue Models for Drug Discovery and Development—Current State-of-the-Art and Future Perspectives

Sonia Youhanna, Aurino M. Kemas, Lena Preiss, Yitian Zhou, Joanne X. Shen, Selgin D. Cakal, Francesco S. Paqualini, Sravan K. Goparaju, Reza Zandi Shafagh, Johan Ulrik Lind, Carl M. Sellgren and Volker M. Lauschke
Gunnar Schulte, ASSOCIATE EDITOR
Pharmacological Reviews January 2022, 74 (1) 141-206; DOI: https://doi.org/10.1124/pharmrev.120.000238
Sonia Youhanna
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Aurino M. Kemas
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Lena Preiss
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Yitian Zhou
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Joanne X. Shen
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Selgin D. Cakal
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Francesco S. Paqualini
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Sravan K. Goparaju
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Reza Zandi Shafagh
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Johan Ulrik Lind
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Carl M. Sellgren
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Volker M. Lauschke
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (S.Y., A.M.K., L.P., Y.Z., J.X.S., S.K.G., R.Z.S., C.M.S., V.M.L.); Department of Drug Metabolism and Pharmacokinetics (DMPK), Merck KGaA, Darmstadt, Germany (L.P.); Department of Health Technology, Technical University of Denmark, Lyngby, Denmark (S.D.C., J.U.L.); Synthetic Physiology Laboratory, Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy (F.S.P.); Division of Micro- and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden (Z.S.); and Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany (V.M.L.)
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Gunnar Schulte
Roles: ASSOCIATE EDITOR
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    Fig. 1

    Overview of commonly used organotypic and microphysiological human liver models. (A) In the sandwich culture paradigm, conventional hepatocyte monolayers are overlaid with an additional layer of ECM. (B) Extravidin peroxidase staining shows canalicular expression of BSEP, BCRP, and MRP2 after 5 days of sandwich culture. BCRP and BSEP expression remained relatively stable over 8 days in different donors. (C) In spheroid systems using ultra–low attachment surfaces, hepatocytes (purple) with or without nonparenchymal liver cells (blue) aggregate to form spheroidal aggregates. (D) PHH spheroids retain the proteomic signature of the human liver, whereas cells from the same donors in 2D culture rapidly deteriorate. Furthermore, spheroids exhibit a stable metabolic configuration as shown by untargeted Orbitrap high-resolution mass spectrometry. (E) Schematic depiction of a chip design for microphysiological liver models in which hepatocytes (purple) and nonparenchymal liver cells (blue) are cocultured on a membrane that separates two adjacent flow channels. Note that these systems are methodologically diverse, and only one exemplary layout is shown for clarity. (F) Relative expression of markers related to liver function (left panel) and metabolic activity (right panel) remained mostly stable over 28 days in a microphysiological liver-on-a-chip. (G) In MPCCs, liver cells (purple) are spatially constrained in collagen-coated islands surrounded by stabilizing mouse fibroblasts (green). (H) In MPCCs using only hepatocytes, albumin production, urea synthesis, and CYP3A4, expression remains stable or increases during 2 weeks of culture. Coculture with human stellate cells (MPCC-HSC) causes a decrease in functional parameters. Figure modified with permission from (Bell et al., 2016; Lauschke et al., 2016a; Li et al., 2009; Maschmeyer et al., 2015; Davidson et al., 2017).

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    Fig. 2

    Organotypic liver models can faithfully predict human drug hepatotoxicity. (A) Representative image of a screen of 300 drugs and chemicals in which hepatotoxicity is assessed using high content imaging of markers for ROS, mitochondrial membrane potential (MMP) and intracellular glutathione (GSH) levels. Cells were treated for 24 hours at 100-fold therapeutic cmax. Nimesulide caused a significant increase in ROS, whereas telithromycin, nefazodone, and perhexiline caused a decrease in MMP and GSH. (B) In MPCCs based on hepatocyte-like cells derived from induced pluripotent stem cells, treatment with the four hepatotoxins acetaminophen, diclofenac, tolcapone, and troglitazone dose-dependently reduced albumin and urea secretion after 8 days repeated exposure, indicative of hepatocyte toxicity. In contrast, no decrease was detected for the nontoxic compounds aspirin, dextromethorphan, propranolol, and rosiglitazone. (C) Summary result of the hepatotoxicity test of 123 drugs (70 hepatotoxic, 53 nonhepatotoxic) in primary human hepatocyte spheroids. The model detected dose-dependent toxicity and, at 20-fold cmax concentration, successfully detected 69% of all hepatotoxic compounds without any false positive hits. Error bars indicate S.D. **, ***, and **** indicate P < 0.01, P < 0.001, and P < 0.0001, respectively. (D) Hepatotoxicity of acetaminophen and methotrexate (MTX) was evaluated using a perfused liver chip system. Increased ROS levels (magenta) were detected at acetaminophen concentrations of 10 mM, whereas MTX-induced toxicity manifested as intracellular lipid droplets (yellow) and αSMA (green). Figure modified with permission from (Jang et al., 2019a; Berger et al., 2015; Vorrink et al., 2018; Xu et al., 2008).

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    Fig. 3

    Organotypic culture systems for liver disease modeling. (A) Steatosis and insulin resistance can be induced in PHH spheroids by exposure to high insulin (IR) and supplementation with free fatty acids and fructose (“NAFLD”) for 7–14 days. Steatosis was measured as intracellular triglyceride levels, whereas insulin resistance was indicated as elevated expression of key genes involved in gluconeogenesis and de novo lipogenesis and reduced AKT phosphorylation upon insulin stimulation. IS, insulin-sensitive group. Error bars indicate S.E.M. * and **** indicate P < 0.05 and P < 0.0001, respectively. (B) The anti-NASH drug candidates cenicriviroc, elafibranor, and lanifibranor reduce free fatty acid–induced collagen deposition (COL1A1) and αSMA in PHH spheroids cocultured with liver nonparenchymal cells. (C) Exposure to high-fat media (black columns) increased expression of genes associated with fatty liver in a microfluidic liver culture system and reduced activity of CYP3A4 and CYP2C9. Error bars indicate S.D. * indicates P < 0.05. (D) PHH in micropatterned cocultures can be infected with HBV as evidenced by various viral life-circle readouts at 16 days postinfection (dpi). HBV surface antigen (HBsAg) was plotted as mean ± S.E.M. (n = 3). HBV covalently closed circular DNA copies were plotted as average of duplicates ± range. HBV core protein (HBVc) expression was indicated by immunostaining together with isotype-matched control. Figure modified with permission from (Hurrell et al., 2020; Kemas et al., 2021; Kostrzewski et al., 2017; Shlomai et al., 2014).

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    Fig. 4

    Overview of models of the human nephron. (A) Fluorescence images of immortalized human PTECs cocultured with glomerular microvascular endothelial cells showing basolateral expression of Na+/K+ ATPase (1) and apical expression of the glucose transporter SGLT2 (2) and deposition of a laminin-rich basement membrane (3). Furthermore, cells featured α-tubulin positive primary cilia (4). Transmission electron microscopy (5) and scanning electron microscopy (6) images showing dense carpets of microvilli. Scale bars for 1–4 = 10 µm; scale bars for 5–6 = 1 µm. (B) Expression of major renal transporters remains stable in primary human PTEC transwell cultures for at least 7 days. (C) Uptake of FITC-labeled albumin is sensitive to receptor-associated protein (RAP) and cilastatin, inhibitors of megalin/cubulin endocytosis, showing functionality of these endocytic receptors. Error bars indicate S.E.M. ** indicates P < 0.01. (D) Human iPSC-derived podocytes express nephrin and WT1, key markers of a mature phenotype, and lose expression of the intermediate mesoderm marker PAX2. (E) Quantification of changes in protein expression. Differentiated cells are positive for the podocyte markers nephrin, WT1, and podocin, whereas they are negative for the pluripotency marker OCT4 and the progenitor cell markers PAX2 and OSR1. Furthermore, iPSC-derived podocytes are nonproliferative as indicated by a lack of EdU incorporation. (F) Organoids differentiated using vasopressin and aldosterone show expression of the collecting duct markers (AQP2, DBA, and ATP6V1B1) as well as of proximal tubules (LTL), distal tubules (ECAD and SLC12A1), podocytes (WT1 and NPHS1), endothelial cells (CD31), and stroma (MEIS1 and PDGFRβ). Scale bars = 50 µm. (G) Response of collecting duct organoids to cisplatin showed upregulation of the proximal tubule injury marker HAVCR1 in LTL-positive proximal tubule cells and distal tubule injury marker NGAL in E-cadherin–positive distal tubules. Figure modified with permission from (Bajaj et al., 2020; Lin et al., 2019; Uchimura et al., 2020).

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    Fig. 5

    Human intestinal 3D models for studies of drug permeability and disease modeling. (A) Schematic depiction of a static transwell culture. Caco-2 cells are cultured as a monolayer on a porous membrane that separates the apical from the basolateral chamber. Measurement of TEER can be measured to evaluate barrier integrity. (B) Cross-sectional view of an integrated perfusion system for oral drug absorption. The drug of interest is first exposed to artificial gastric juice (pH 2) before being neutralized and mixed with artificial intestinal juice containing bile acids in the duodenum. Subsequently, the solution was perfused past a Caco-2 transwell culture to mimic intestinal permeability. Compound molecules that permeated into the basal channel were exposed HepG2 cells to mimic first-pass metabolism before subsequently entering the target compartment, comprising in this case MCF7 breast cancer cells. (C) Schematic depiction of an intestinal microchip comprising a stretchable porous membrane that separates two independently perfusable microchannels, which are lined by vacuum chambers that allow the repeated application of mechanical strain mimicking intestinal peristalsis. (D) Shear stress due to microperfusion (µF) and the application of cyclic mechanical strain (μF+St) increase cell height and polarization of Caco-2 monolayers while maintaining confluency as judged by localization of the tight junction protein occluding. Scale bars = 20 μm. (E) Intestinal organoids are comprised of villus domains containing enterocytes, enteroendocrine cells, and Goblet cells and alternating crypts enriched in LGR5-positive stem cells and Paneth cells. (F) Brightfield images (top row) and schematics (bottom row) of intestinal organoid injections using a customized robotic setup. For visualization purposes fluorescent DsRED-expressing E. coli are injected. Asterisk marks the needle tip. (G) Dissociated human intestinal organoids are seeded on the chip shown in (C and D). Immunofluorescence imaging shows expression of lysozyme (Lyz, Paneth cells), mucin 2 (Muc2, Goblet cells), chromogranin A (ChgA, enteroendocrine cells), and villin (cell apex). Figure modified with permission from (Imura et al., 2012; Kasendra et al., 2018; Kim et al., 2012; Robinson et al., 2019; Roeselers et al., 2013; Williamson et al., 2018).

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    Fig. 6

    Approaches for generating cerebral organoids for disease modeling. Easily accessible somatic cells from patients and controls can be reprogrammed into iPSCs that can be manipulated by genetic engineering to create isogenic lines. iPSCs can then be used to create either whole brain organoids through undirected differentiation or to region-specific organoids through patterned or directed differentiation. Region-specific organoids can moreover be fused to create so-called assembloids to recapitulate connectivity.

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    Fig. 7

    Organotypic platforms to mimic cardiac function and toxicity. (A) Schematic depiction of the most important endpoints in the evaluation of cardiomyocyte function. The arrival of an AP triggers intracellular calcium transients (Ca), which in turn entail contraction and the generation of contractile force (F). (B) Workflow showing the key principles of engineering laminar tissues and their integration within MEAs for electrophysiological studies or their formulation into contractility assays with embedded soft force gauges. Inlets on the right shows effect of terfinadine (Terf) on rate-corrected field potential duration (DcFPD; top) and an example optical micrograph of a deflecting cantilever with the corresponding electrical readout (bottom). Scale bar = 1 mm. (C) Key principles of the 3D EHT technology. Cardiomyocytes (CMs) encapsulated in a hydrogel are stretched and subjected to electrical stimulation, resulting in tissue compaction and maturation. Insert displays cross-section of a highly mature iPSC-derived EHT with wheat germ agglutinin in green and cardiac troponin T in red. Scale bars = 500 µm (i) and 10 µm (ii and iii). Figure modified with permission from (Kujala et al., 2016; Lind et al., 2017b; Ronaldson-Bouchard et al., 2018).

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    Fig. 8

    Engineered human skeletal muscle models. (A). Desmin and MyoD positive primary human myoblasts are generated by encapsulation in a hydrogel comprised of fibrinogen and matrigel matrix and subsequent casting into PDMS molds. (B and C) Differentiated human muscle fibers shorten over the course of 10 days in 3D culture (B = muscle cultures after gel polymerization, C = after 10 days in culture). (D and E) After compaction, the differentiated myofibers are densely packed and show characteristic striation of sarcomeric α-actinin (SAA). Furthermore, myogenin (MyoG)-positive nuclei and acetylcholine receptor (AChR)-positive domains can be seen. (F) Contractile force trace of a myoblast-derived myobundle showing that increased stimulation frequency results in stronger tetanic contraction. (G) Schematic depiction of a human motor unit on-a-chip formed by coculturing iPSC-derived motorneuron spheroids (MNs) and skeletal muscle fibers. Myoblasts were first injected into the right port of the chip. After 14 days of maturation, MN spheroids that were differentiated and maintained separately were injected into the left port, resulting in neural outgrowth, innervation of the differentiated muscle fiber constructs, and formation of neuromuscular junctions. (H) Comparison of motor units generated from control and ALS patient-derived stem cells. Note that the ALS motor unit has thinner neural fiber with less NMJ formed. Tuj1 (green; neuronal marker), F-actin (purple; muscle), and DAPI (blue; nuclei). Scale bars = 100 □m. (I) Optogenetic stimulation of neurons in ALS patient-derived motor units sometimes failed to translate into muscle contractions (red arrows). The number of such missed twitches could be reduced by cotreatment with rapamycin and bosutinib. Blue dashed lines represent light stimulation intervals (i.e., 200 ms). Figure modified with permission from (Chiron et al., 2012; Madden et al., 2015; Osaki et al., 2018).

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    Fig. 9

    Schematic depiction of bioprinting technology. The cells to be printed are embedded in a biocompatible matrix comprising polymers, nutrients, growth factors, and functional peptides jointly termed bioink. This solution can be deposited with high spatiotemporal resolution using different methodologically distinct modalities. In extrusion-based bioprinting (left), the cell-laden bioinks are deposited as continuous streams, whereas droplet-based bioprinting (middle) partitions the bioink into individual microdroplets. In contrast to both of these methods, laser-assisted bioprinting (right) deposits cells using a nozzle-free approach in which the bioink is locally heated using a UV laser and then drops onto the printing stage.

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    TABLE 1

    Comparison of the sensitivity of organotypic liver models

    Cell ModelCell TypeMono-/CocultureExposure RegimenExposure TimeTC50Reference
    Toxicity Mechanism: Reactive Metabolites
    Acetaminophen (Cmax = 0.136 mM; TC50 in mM)
    SpheroidPHHMonocultureRepeated14 days0.32–0.8(Bell et al., 2018)
    SpheroidPHHMonocultureRepeated14 days0.46(Bell et al., 2020)
    SpheroidPHHCoculture with NPCsRepeated14 days∼0.5(Richert et al., 2016)
    SpheroidPHHCoculture with NPCsRepeated14 days0.57(Proctor et al., 2017)
    SpheroidPHHCoculture with Kupffer cellsRepeated14 days∼0.6(Richert et al., 2016)
    SpheroidPHHMonocultureRepeated14 days0.64(Bell et al., 2017)
    SpheroidPHHCoculture with NPCsRepeated14 days0.75(Messner et al., 2013)
    SpheroidPHHMonocultureRepeated14 days0.88(Hendriks et al., 2016)
    SpheroidPHHMonocultureSingle24 h0.93(Li et al., 2020)
    SpheroidHepaRGMonocultureRepeated14 days1.1(Hendriks et al., 2016)
    SpheroidHepaRGMonocultureSingle24 h∼1.1(Leite et al., 2012)
    SpheroidPHHMonocultureRepeated96 h1.21(Bell et al., 2020)
    SpheroidPHHMonocultureRepeated8 days1.3(Hendriks et al., 2016)
    SpheroidPHHCoculture with NPCsRepeated14 days1.35(Bell et al., 2020)
    SpheroidHepaRGMonocultureRepeated8 days1.8(Hendriks et al., 2016)
    SpheroidPHHCoculture with NPCsRepeated10 days1.7(Foster et al., 2019)
    SpheroidPHHCoculture with Kupffer cellsSingle5 days2.25(Li et al., 2020)
    Perfused liver-chipPHHCoculture with LSECsRepeated10 days2.4(Foster et al., 2019)
    SpheroidPHHCoculture with NPCsRepeated96 h2.46(Bell et al., 2020)
    SpheroidHepaRGMonocultureSingle24 h2.48(Wang et al., 2016)
    SpheroidHepaRGMonocultureSingle24 h2.7(Gunness et al., 2013)
    SpheroidHepaRGMonocultureRepeated6 days2.879(Ramaiahgari et al., 2017)
    SpheroidPHHMonocultureSingle5 days3.28(Li et al., 2020)
    SpheroidHepaRGMonocultureRepeated7 days∼4(Liu et al., 2018a)
    MPCCiPSC-HLCMonocultureRepeated6 days4.48(Ware et al., 2015)
    Perfused liver-chipPHHCoculture with NPCsRepeated14 days5.59(Novik et al., 2017)
    SpheroidHepG2MonocultureRepeated4 days7.21(Gaskell et al., 2016)
    SpheroidHepaRGMonocultureSingle24 h7.62(Wang et al., 2015)
    SpheroidHepG2MonocultureRepeated6 days∼9.4(Ramaiahgari et al., 2014)
    SpheroidhiPSC/hESC-HLCMonocultureSingle24 h>10(Tasnim et al., 2016)
    SpheroidhiPSC-HLCMonocultureSingle24 h>20(Takayama et al., 2013)
    SpheroidHepG2MonocultureSingle24 h>20(Takayama et al., 2013)
    MPCCPHHMonocultureSingle24 h35(Khetani and Bhatia, 2008)
    SpheroidHepG2MonocultureSingle24 h40(Fey and Wrzesinski, 2012)
    Toxicity Mechanism: Inhibition of Mitochondrial Respiration
    Amiodarone (Cmax = 5 μM; TC50 in μM)
    SpheroidPHHMonocultureRepeated28 days1.6(Bell et al., 2016)
    SpheroidPHHMonocultureRepeated14 days6.5(Bell et al., 2016)
    Perfused liver-chipPHHCoculture with NPCsRepeated14 days9(Novik et al., 2017)
    SpheroidPHHMonocultureRepeated14 days12(Bell et al., 2017)
    SpheroidPHHMonocultureRepeated14 days12(Hendriks et al., 2016)
    MPCCPHHMonocultureRepeated14 days14(Khetani et al., 2013)
    SpheroidhiPSC-HLCMonocultureSingle24 h>25(Takayama et al., 2013)
    SpheroidPHHMonocultureSingle5 days26(Li et al., 2020)
    SpheroidPHHCoculture with NPCsRepeated14 days32(Proctor et al., 2017)
    SpheroidHepaRGMonocultureSingle7 days49(Ott et al., 2017)
    SpheroidHepG2MonocultureSingle24 h∼50(Takayama et al., 2013)
    SpheroidPHHCoculture with NPCsRepeated14 days∼60(Richert et al., 2016)
    SpheroidHepaRGMonocultureSingle24 h83(Ott et al., 2017)
    SpheroidHepaRGMonocultureSingle24 h178(Mueller et al., 2014)
    SpheroidHepG2MonocultureSingle24 h>200(Mueller et al., 2014)
    SpheroidHepG2MonocultureSingle24 h260(Fey and Wrzesinski, 2012)
    Toxicity Mechanism: Cholestasis
    Chlorpromazine (Cmax = 0.84 μM; TC50 in μM)
    SpheroidPHHMonocultureSingle5 days4.4(Li et al., 2020)
    SpheroidPHHMonocultureRepeated14 days4.6(Bell et al., 2017)
    SpheroidPHHCoculture with NPCsRepeated14 days∼7(Proctor et al., 2017)
    SpheroidPHHCoculture with only kupffer cellsRepeated14 days∼9(Richert et al., 2016)
    SpheroidPHHCoculture with NPCsRepeated14 days∼10(Richert et al., 2016)
    SpheroidPHHMonocultureRepeated14 days11(Hendriks et al., 2016)
    SpheroidPHHMonocultureRepeated8 days13(Hendriks et al., 2016)
    SpheroidHepaRGMonocultureRepeated14 days13(Hendriks et al., 2016)
    SpheroidHepaRGMonocultureRepeated8 days16(Hendriks et al., 2016)
    Perfused liver-chipPHHCoculture with NPCsRepeated14 days28(Novik et al., 2017)
    SpheroidHepaRGMonocultureRepeated7 days∼40(Liu et al., 2018a)
    SpheroidHepG2MonocultureSingle24 h43(Mueller et al., 2014)
    SpheroidHepaRGMonocultureSingle24 h98(Mueller et al., 2014)
    MPCCPHHMonocultureSingle24 h100(Khetani and Bhatia, 2008)
    SpheroidHepaRGMonocultureSingle24 h120(Wang et al., 2015)
    Toxicity Mechanism: Mitochondrial Depletion
    Fialuridine (Cmax = 0.64 μM; TC50 in μM)
    SpheroidPHHMonocultureRepeated28 days0.1(Bell et al., 2016)
    SpheroidPHHMonocultureRepeated32 days0.28(Hendriks et al., 2019)
    SpheroidPHHMonocultureRepeated14 days0.7(Bell et al., 2016)
    SpheroidPHHMonocultureRepeated14 days∼3(Bell et al., 2018)
    SpheroidPHHMonocultureRepeated7 days7.86(Hendriks et al., 2019)
    SpheroidPHHCoculture with NPCsRepeated10 days77(Foster et al., 2019)
    Perfused liver-chipPHHCoculture with LSECsRepeated10 days84(Foster et al., 2019)
    SpheroidPHHCoculture with NPCsRepeated14 days>100(Richert et al., 2016)
    SpheroidHepG2MonocultureRepeated4 days200(Gaskell et al., 2016)
    • hESC, human embryonic stem cell.

    • View popup
    TABLE 2

    Overview of studies using organotypic liver cultures for hepatic clearance predictions

    Number of Compounds Predicted within 2-Fold of In Vivo Cl
    Culture ParadigmLow-Clearance CompoundsMedium- to High-Clearance CompoundsPrediction MethodResults/ConclusionReference
    PHH in MPCC7/104/7ConventionalMedium- to high-Cl compounds mainly underpredicted(Chan et al., 2013)
    PHH in MPCC9/153/10ConventionalHigh-Cl compounds underpredicted(Da-silva et al., 2018)
    PHH in MPCC5/8—Direct scalingNo clear tendency(Kratochwil et al., 2017)
    PHH in MPCC4/122/3ConventionalClinical drug candidates (low Cl) were better predicted using direct scaling(Kratochwil et al., 2017)
    PHH spheroids2/31/3ConventionalCompounds were underpredicted(Kanebratt et al., 2021)
    PHH in chip0/23/4Direct scalingLow-Cl compounds below LOQ, high Cl underpredicted(Chao et al., 2009)
    • Cl, clearance; LOQ, limit of quantification.

    • View popup
    TABLE 3

    Table with strengths/weaknesses of different models for areas of the nephron

    Nephron PartCell OriginApplicationsProsCons
    Proximal tubulePrimary PTECsDrug transport and nephrotoxicityTEER similar to native tissue, metabolic enzymes, and drug transporters preservedShort-term cultures (up to 2 weeks max), limited access to primary material
    iPSCsDrug transport and nephrotoxicityCan be expanded, compatible with long-term cultureImmature expression of uptake transporters
    Immortalized PTECsDrug transport and nephrotoxicityCan be expanded (30 passages), compatible with long-term cultureImmature expression of uptake transporters
    GlomerulusPrimary podoctyesStudies of podocytopathiesExpress slit diaphragm proteins, patient-derived podocyte cultures are possibleLimited access to primary material
    iPSCsStudy of podocytopathiesLong-term cultures, generation of isogenic linesLoss of foot processes and slit diaphragms in most of models
    Distal tubule and collecting ductiPSCsDrug testing, disease modelExpression of AQP2, response to toxinsMost models show incomplete maturation of distal tubule and collecting duct
    • View popup
    TABLE 4

    Advantages and limitations of current intestinal in vitro culture systems

    Transwell CulturesPerfusion SystemsUssing ChambersOrganoidsExplant Cultures
    Medium dynamicsStaticPerfusedStaticStaticStatic
    Available drug permeability data+++++++—++
    Cellular complexityEnterocytes +/− Goblet cells and M cellsEnterocytes, Goblet cells, enteroendocrine cells, Paneth cellsAll intestinal cell typesEnterocytes, Goblet cells, enteroendocrine cells, Paneth cellsAll intestinal cell types
    Molecular phenotype+++++++++++
    MucusSome, if Goblet cells are addedCan reach physiologic levelsYesSomeYes
    Phenotypic stabilityMultiple weeksMultiple weeksHoursMultiple weeks, allow passaging6–24 h
    Throughput++++++++++
    Ease of use+++++++++
    Costs+++++++++++
    Model versatility+++++++++++
    MicrobiotaNoYesNoYesNo
    PeristalsisNoYesNoNoNo
    Main applicationsDrug absorptionDisease modelingDrug absorptionIntestinal development, regenerative medicineDrug absorption, acute toxicity
    • View popup
    TABLE 5

    Overview of organotypic tissue cultures for cerebral disease modeling

    DiseaseGeneCellsProtocolPhenotypeReference
    Micro-cephalyCDK5RAP2hiPSCsCerebral cortexSmaller neuroepithelial regions, altered spindle orientation of radial glial cells, abundant neuronal outgrowth, smaller organoid size.(Lancaster et al., 2013)
    Seckel syndromeCPAPhiPSCsCerebral cortexExtended G1‐S transition in NPCs from Seckel cell patient iPSCs. Premature NPC differentiation resulting in reduced cell number.(Gabriel et al., 2016)
    Macro-cephalyPTENhESCsCerebral cortexPromoted cell cycle re-entry and delayed neuronal differentiation, resulting in a pronounced expansion of the radial glia and intermediate progenitor cells and increased organoid size.(Li et al., 2017)
    Autism spectrum disorder—hiPSCsDorsal telencephalonAbnormal proliferation of neural progenitor cells and an increased production of inhibitory GABAergic neurons, enhanced FOXG1 expression(Mariani et al., 2015)
    SchizophreniaDISC1hiPSCsForebrainProlongs mitotic length and cell-cycle deficits in radial glial cells(Ye et al., 2017)
    SchizophreniaFGFR1hiPSCs and hESCsCerebral cortexDisorganized migration of proliferating cells and depletion of cortical neurons. Cortical malformation and altered FGFR1 signaling.(Stachowiak et al., 2017)
    Schizophrenia—hiPSCsDorsal forebrainDifferential expression of genes involved in synaptic biology and neurodevelopment. Subdued responses to stimulation and depolarization. Mitochondrial functional defects.(Kathuria et al., 2020)
    Alzheimer disease—hiPSCsCortexGeneration of pathologic amyloid β peptides(Lee et al., 2016)
    Alzheimer diseaseAPPhiPSCsNeocortexβ-amyloid aggregation, hyperphosphorylated τ, endosome abnormalities(Raja et al., 2016)
    Alzheimer disease—hiPSCsTriculture of neurons, astrocytes, and microgliaIncreased Aβ aggregation, phosphorylated τ accumulation, neuroinflammatory activity, and microglia recruitment(Park et al., 2018)
    Parkinson diseaseLRRK2hiPSCsMidbrainIncreased aggregation of α-synuclein and decreased dopaminergic neurite length(Kim et al., 2019)
    Miller-Dieker syndrome—hiPSCsForebrainIncreased apoptosis and horizontal divisions in neural stem cells, mitotic delay in outer radial glia-like cells(Bershteyn et al., 2017)
    Miller-Dieker syndromeLIS1hiPSCsForebrainSmaller organoids with fewer neuroepithelial loops, fewer symmetric ventricular radial glia cells divisions, disrupted cortical niche(Iefremova et al., 2017)
    Rett syndromeMeCP2hiPSCsUndefinedAberrant neurogenesis and neuronal differentiation, increased ventricular area, and decreased radial thickness of organoid(Mellios et al., 2018)
    ZIKA virus infectionZIKVhiPSCsForebrainSuppression of neural progenitor cell proliferation, decreased neuronal layer thickness and organoid size, enlarged lumen/ventricles(Qian et al., 2016)
    ZIKA virus infectionZIKVhESCsCerebral cortexZIKV-mediated TLR3 activation, reduction in organoid volume resembling microcephaly.(Dang et al., 2016)
    ZIKA virus infectionZIKV-NS2AhiPSCsForebrainZIKV-NS2A impairs radial glial cell proliferation and adherens junction formation(Yoon et al., 2017)
    • hESC, human embryonic stem cell.

    • View popup
    TABLE 6

    Table with strengths/weaknesses of skeletal muscle models

    Pluripotent Stem CellsPrimary MyoblastsMature Myofiber Cultures
    Maturation methodTransgene- or small molecule-basedTransgene- or small molecule–basedNot applicable
    Phenotype++++++
    Length of differentiation protocol+++Not applicable
    Stability in culture++++++
    Accessibility++++++
    Homogeneity of culture++++++
    Functionality++++++
    Current use in drug testing+++++Not tested yet
    • View popup
    TABLE 7

    Overview of multiorgan-on-a-chip combinations used for pharmacological or toxicological applications

    ApplicationTissuesType of CultureType of CellsFindingsReferences
    ToxicityLiver and heart3DiPSC-derivedMetabolic interaction underlying clomipramine toxicity(Yin et al., 2021)
    ToxicityLiver and heart2D and 3DiPSC-derived and primary cellsMetabolic interaction underlying cyclophosphamide and terfenadine toxicity(Oleaga et al., 2018)
    ToxicityLiver and heart3DiPSC-derived and primary cellsTissue-specific toxicity of acetaminophen and doxorubicin(Zhang et al., 2017)
    ToxicityLiver and kidney2DCell lines and primary cellsMetabolic interaction underlying ifosfamide and verapamil nephrotoxicity(Li et al., 2018c)
    ToxicityLiver and kidney2DCell linesTissue interactions in vitamin D3 bioactivation(Theobald et al., 2019)
    ToxicityLiver and lung3D and ALICell linesLiver cells reduce aflatoxin B1 pulmonary toxicity(Bovard et al., 2018)
    ToxicityLiver and lung3D and ALICell linesLiver cells reduce aflatoxin B1 pulmonary toxicity(Schimek et al., 2020)
    ToxicityLiver, heart, and lung3DiPSC-derived and primary cellsLung is essential in bleomycin-induced cardiotoxicity(Skardal et al., 2017)
    ToxicityLiver, heart, lung, endothelium, brain, and testes3DiPSC-derived, cell lines, and primary cellsMetabolic interaction underlying ifosfamide neurotoxicity(Rajan et al., 2020)
    ToxicityLiver, brain, pancreas, lung, heart, gut, and endometrium2DCell lines and primary cellsTolcapone metabolism and mechanism of action(Wang et al., 2019b)
    ToxicityLiver, cancer, bone marrow3DCell linesHepatic bioactivation of capecitabine and tegafur(LaValley et al., 2021)
    ToxicityLiver and cancer2DCell linesHepatic bioactivation of capecitabine and tegafur(Satoh et al., 2017)
    ToxicityLiver and cancer2D and 3DCell linesHepatic bioactivation and inactivation of ifosfamide and temozolomide, respectively(Ma et al., 2012)
    ToxicityLiver and cancer2DCell linesEffects of hepatic metabolism on luteolin toxicity(Lee et al., 2017b)
    ToxicityLiver and cancer2DCell linesHepatic bioactivation of irinotecan(Shinha et al., 2020)
    ToxicityLiver, intestine, and lung2DCell linesHepatic bioactivation of cyclophosphamide or irinotecan(Kimura et al., 2015)
    ToxicityLiver, lung, kidney, and adipose tissue3DCell linesTissue-specific effects of TGFβ(Zhang et al., 2009)
    ToxicityLiver, heart, lung, endothelium, testis, colon, and brain3DPrimary stem cells and primary cellsComparison of tissue-specific toxicity in coculture(Skardal et al., 2020)
    PKLiver and intestine3DCell lines and primary cellsSystem retains drug absorption of panadol, mannitol, and caffeine(Chen et al., 2018)
    PKLiver and intestine2D and 3DCell lines and primary cellsSystem retains drug permeability similar to monoculture(Esch et al., 2016)
    PKLiver and intestine2DCell linesApigenin mertabolism in both intestine and liver(Choe et al., 2017)
    PKLiver and intestine3DCell lines and primary cellsEstimation of diclofenac and hydrocortisone permeability and clearance(Tsamandouras et al., 2017)
    PKLiver and intestine2DCell linesAbsorption of fatty acid and evaluation of antisteatotic effect of metformin and XL-335(Jeon et al., 2021)
    Gut-brain-axisBrain and intestine2DiPSC-derivedEvaluation of the impact of the intestinal microflora on neurodegeneration(Raimondi et al., 2019)
    Gut-brain-axisLiver, intestine, and brain2D and 3DPrimary stem cells, cell lines, and primary cellsMicrobiome-derived short-chain fatty acids increase the expression of pathology-associated pathways in neurodegenerative disease(Trapecar et al., 2021)
    Metastasis formationBrain and lung2DCell linesNSCLC metastasizing propensity to brain(Liu et al., 2019)
    Glucose metabolismLiver and pancreas3DCell lines and primary cellsRecapitulation of glucose metabolism and homeostasis(Bauer et al., 2017)
    Glucose metabolismLiver, pancreas, and skeletal muscle2DCell linesRecapitulation of glucose metabolism and homeostasis(Lee et al., 2019)
    • ALI, air-liquid interface; NSCLC, non–small cell lung cancer; PK, pharmacokinetics.

    • View popup
    TABLE 8

    Advantages and disadvantages of bioprinting methods

    Extrusion-
    Based
    Droplet-
    Based
    Laser-
    Assisted
    Cell viability50%–70%80%–90%95%
    Printing precision++++++
    Printing resolution100–200 µm30–50 µm10 µm
    Throughput++++++
    Cell density108 cells/ml<106 cells/ml<106 cells/ml
    Bioink viscosity30–107 mPa3–12 mPa1–300 mPa
    Single-cell depositionNoInconsistentYes
    Possibility to print
    scaffold-free
    YesYesNo
    CostLowMediumHigh
    Commercial availability++++++
    • View popup
    TABLE 9

    Advantages and disadvantages of commonly used materials for microphysiological devices

    GlassSiliconThermoplasticsThermosetsElastomers
    Material propertiesKnowledge base in biologyHighMediumMediumMediumGood
    Optical transparencyHighNoneMedium-goodMedium-goodGood
    Gas permeabilityVery lowVery lowLowLowMedium-high
    Mechanical robustnessHighGoodGood-highGood-highLow
    Tunable mechanical propertyLowLowMediumMedium-goodLow
    Chemical leachabilityNoneNoneLow-mediumLow-mediumHigh
    Hydrophobic molecule sorptionVery lowVery lowLow-mediumLow-mediumHigh
    Cell attachmentVery highHighMedium-highMedium-highLow-medium
    Manufacturing propertiesCommon fabrication methodsPhotolithographyPhotolithography, E-beam lithographyInjection moldingSoft lithography, reaction injection moldingSoft lithography
    Ease of rapid prototypingVery lowLowLowMedium-highMedium-high
    Cost of prototyping (material and fabrication)Very highHighMediumLow-mediumLow
    Industrial microstructuring capabilityHighHighHighMedium-highLow
    ScalabilityMedium-highMedium-highHighGoodLow
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Pharmacological Reviews: 74 (1)
Pharmacological Reviews
Vol. 74, Issue 1
1 Jan 2022
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Review ArticleReview Article

3D Human Tissue Models

Sonia Youhanna, Aurino M. Kemas, Lena Preiss, Yitian Zhou, Joanne X. Shen, Selgin D. Cakal, Francesco S. Paqualini, Sravan K. Goparaju, Reza Zandi Shafagh, Johan Ulrik Lind, Carl M. Sellgren and Volker M. Lauschke
Pharmacological Reviews January 1, 2022, 74 (1) 141-206; DOI: https://doi.org/10.1124/pharmrev.120.000238

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Review ArticleReview Article

3D Human Tissue Models

Sonia Youhanna, Aurino M. Kemas, Lena Preiss, Yitian Zhou, Joanne X. Shen, Selgin D. Cakal, Francesco S. Paqualini, Sravan K. Goparaju, Reza Zandi Shafagh, Johan Ulrik Lind, Carl M. Sellgren and Volker M. Lauschke
Pharmacological Reviews January 1, 2022, 74 (1) 141-206; DOI: https://doi.org/10.1124/pharmrev.120.000238
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  • Article
    • Abstract
    • I. Introduction
    • II. Liver
    • III. Kidney
    • IV. Intestine
    • V. Brain
    • VI. Heart
    • VII. Skeletal Muscle
    • VIII. Fluidic Integration of Organotypic Tissue Models
    • IX. Bioprinting as a Novel Modality for the Generation of Organotypic Cultures
    • X. Technical Considerations for Culture Devices
    • XI. Conclusions and Perspectives
    • Authorship Contributions
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