• Open Access

Current status of drug screening and disease modelling in human pluripotent stem cells

Authors

  • Divya Rajamohan,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • Elena Matsa,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • Spandan Kalra,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • James Crutchley,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • Asha Patel,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • Vinoj George,

    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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  • Chris Denning

    Corresponding author
    1. Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
    • Department of Stem Cells, Tissue Engineering & Modelling, Centre for Biomolecular Sciences, University of Nottingham, Nottingham, UK
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Abstract

The emphasis in human pluripotent stem cell (hPSC) technologies has shifted from cell therapy to in vitro disease modelling and drug screening. This review examines why this shift has occurred, and how current technological limitations might be overcome to fully realise the potential of hPSCs. Details are provided for all disease-specific human induced pluripotent stem cell lines spanning a dozen dysfunctional organ systems. Phenotype and pharmacology have been examined in only 17 of 63 lines, primarily those that model neurological and cardiac conditions. Drug screening is most advanced in hPSC-cardiomyocytes. Responses for almost 60 agents include examples of how careful tests in hPSC-cardiomyocytes have improved on existing in vitro assays, and how these cells have been integrated into high throughput imaging and electrophysiology industrial platforms. Such successes will provide an incentive to overcome bottlenecks in hPSC technology such as improving cell maturity and industrial scalability whilst reducing cost.

Abbreviations:

CHO, Chinese hamster ovary; hESC, human embryonic stem cell; hiPSC, human induced pluripotent stem cell; hPSC, human pluripotent stem cell; LQTS, long QT syndrome.

Introduction

When human embryonic stem cells (hESCs) were first isolated from blastocyst stage embryos in 1998 1, many researchers believed that within 10–15 years the technology would be sufficiently advanced to allow cell replacement of tissues damaged by injury, disease or aging. Within the next few years, approximately 1200 hESC lines had been derived (http://www.umassmed.edu/iscr/index.aspx) and it became possible to produce human induced pluripotent stem cells (hiPSCs) by reprogramming somatic cells with just four genetic factors 2, 3. This provided a considerable resource of human pluripotent stem cells (hPSCs) that could be propagated during long-term culture and yet be differentiated to a variety of lineages representative of the three embryonic germ layers 4. Clinically relevant cell types included cardiomyocytes and blood lineages (mesoderm), hepatocytes and pancreatic lineages (endoderm) and neural and dermal lineages (ectoderm).

An unexpected hurdle was that methods to culture and differentiate hPSCs were inefficient and labour-intensive 5. Improvements in cell passaging and commercial provision of defined culture media (e.g. mTeSR 6, Stem Cell Technologies; StemPro, Invitrogen 7) reduced the labour required by individual labs. Nevertheless, even defined media are susceptible to considerable batch to batch variability, probably due to growth factor manufacture inconstancies or degradation of the growth factors during storage. Growth substrate is another source of variability. hPSCs are typically grown on biological substrates such as human or mouse feeder cells, extracted matrices (e.g. Matrigel) or recombinant proteins (e.g. laminin, collagen, fibronectin and vitronectin), all of which are expensive, variable and/or labile 8. Although synthetic substrates that support pluripotency in defined culture media are an exciting development 9, 10, further refinement is needed. For example, hPSCs can be maintained on Corning® Synthemax™ substrates in mTeSR culture medium 10 but a single 6-well plate costs $75 and passaging cells requires manual scraping, which is not amenable to scaled automation. For differentiation, it is now very encouraging that protocols exist to yield purities in excess of 50–70% for many cell types. However, the diversity of methods published for each differentiated cell lineage 11 belies the challenge of successfully reproducing protocols between different hPSC lines and labs.

The use of hPSC-derivatives in cell replacement therapy faces challenges

In addition to the difficulties discussed above, cell transplantation also brings many other hurdles to the fore. These include regulatory and ethical issues, whether cells survive, engraft in the correct location and function after delivery, whether patients can be recruited successfully, and the costs associated with clinical trials. The first to transplant hESC derivatives into humans in 2009 12, Geron Corporation had to convince the Food and Drug Administration (FDA) that their GRNOPC1 neural progenitor cell line was suitable for transplantation into patients with thoracic spinal cord injury with a 22,000 page document detailing the in vitro and preclinical characterisation that had been performed over many years. Although no adverse events were recorded after GRNOPC1 transplantation and the Regulators approved progression to a Phase II trial, spiralling costs led Geron to abandon their entire hESC programme in late 2011. Many researchers viewed this as a major setback for clinical translation of hPSC-based cell replacement therapies. However, Advanced Cell Technology (ACT) recently received FDA approval for clinical trials to treat macular degeneration with hESC-derived retinal pigment epithelium (RPE) cells 13 and these trials will be watched with interest. Nevertheless, it is sobering that after 14 years of research, there is only one active clinical trial using hPSC-derivatives (see clinicaltrials.gov). It is now becoming accepted that a faster route to realising the potential of hPSCs and their differentiated derivatives is through in vitro application, particularly in drug safety assessment and in providing novel models of genetic disease.

Human conditions are not always reflected in animal models because of species differences

Although in vitro disease modelling could theoretically be realised by harvesting primary cells from healthy donors or those carrying a relevant genetic condition, for many cell types this is not a realistic option. For example, harvesting heart tissue on an industrial scale is limited by suitable donors, lack of proliferation of cardiomyocytes, variability in preparation, disease state and cell viability. These problems are particularly pronounced if the cells are sourced from cadavers. Consequently, there is considerable reliance on material derived from animals. Mice are most commonly used for modelling disease because of the relative ease of precisely manipulating the genome by gene targeted homologous recombination 14. However, there are major differences in the gene expression and physiology between animals and humans, from the single cell level up to the whole animal. The beat rate of the mouse heart is approximately ten times faster than the human (500 bpm vs. 60 bpm) but it has an electrocardiogram duration 5–10 times shorter (450 milliseconds vs. 50–100 milliseconds) 15. Increases in heart rate are associated with increased force of contraction in humans but decreased force in mice 16. Whereas repolarisation of the mouse cardiomyocytes is driven primarily by Ito, IK,slow1, IK,slow2, ISS ion channels, this function is provided by the potassium channels, IKr and IKr in human cells 15. There are species differences in the role of the regulatory molecule, phospholamban 15, and expression of structural genes also varies. In humans, expression of alpha and beta myosin heavy chains (α-/β-MHC) locates to the atria and ventricles, respectively 17, but in the mouse αMHC is expressed in both locations 18. The surface marker, SIRPA, is expressed on cardiomyocytes from human but not mouse hPSCs, and so only the human cells can be enriched by fluorescence or magnetic activated cell sorting 19.

Such differences mean that extrapolation from mouse to human can be misleading. In humans, long QT syndrome (LQTS) type 1 and type 2 are caused by mutations that affect function of IKs and IKr, respectively, and can lead to palpitations, syncope (fainting), seizures and sudden cardiac death 20. Since repolarisation of the mouse heart does not rely on these channels, this animal cannot be used to model the conditions. Outside the cardiovascular system, the survival motor neuron 2 gene (SMN2) gene is implicated in development of spinal muscular atrophy in humans, but this gene is not present in mice, flies and worms 21. The gene sequence of α-synuclein found in healthy wildtype mice and rats can confer Parkinson's disease in humans 22. The ontology of organs affected by cystic fibrosis in humans differs markedly from that in mice 23. Such observations have prompted development of novel in vitro human-based systems for studying human genetic disease.

Development of hPSC-based models of human genetic disease is needed

Human pluripotent stem cells have the potential to play a major role in providing models of genetic disease. Early efforts were directed towards using hESCs, and there are about a dozen examples of where cases in which this has been achieved 24. Lines carrying myotonic dystrophy type 1, cystic fibrosis and Huntington disease have been derived by isolating hESCs from pre-implantation genetic diagnosis (PGD) embryos 25. However, PGD screens for only a limited number of genetic conditions, few scientists have access to these facilities and the use of embryos (even those that harbour detrimental genetic lesions) is ethically sensitive in many countries. Alternatively, gene targeting has been used to inactivate genes, such as HPRT1 in male hESCs, to produce an in vitro model of the metabolic disorder Lesch Nyhan syndrome 26. However, while manipulation of the hPSC genome has become more routine in the last few years 27, engineering specific polymorphisms, deletions or amplifications is time consuming, requires a reasonable level of skill, and becomes increasingly challenging proportionate with the number and complexity of modifications required, even when nuclease-based methods are used 28.

In contrast, hiPSC technology is readily accessible, and has the potential to revolutionise in vitro disease modelling (Table 1; Fig. 1). It is relatively straightforward for scientists to establish collaborations with clinicians who care for patients with a particular genetic condition, and the ethical frameworks for informed patient consent are commonplace within most universities and industrial settings. Many commercial providers of stem cell reagents now offer complete off-the-shelf kits to progress from patient sample to reasonably well characterised hiPSC lines. Consequently, less than 5 years after the first report of reprogramming somatic cells 3, 63 hiPSC models have been produced for 43 diseases affecting the heart, smooth muscle, skeletal muscle, immune system, skin, central nervous system, blood and eye, as well as imprinting, metabolic and multi-organ disorders (Table 1). It can be expected that the number of hiPSC lines available will rise exponentially over the next few years.

Table 1. Disease-specific human induced pluripotent stem cells: characterisation and use in drug screening
CategoryDisorderGeneMethodPhenotype characterisation assaysDrug treatmentEffectRef.
  1. O, OCT4; S, SOX2; K, KLF4; C, C-MYC; N, NANOG; L, LIN28; hiPSCs, human induced pluripotency stem cells; SMCs, smooth muscle cells; KD, knock-down; FPD, field potential duration; APD, action potential duration; BR, beat rate; EADs, early after-depolarisations; DADs, delayed after-depolarisations; N/S, not specified; N/A, not available.

  2. Grey areas indicate where drug treatment has been tested.

CardiacLong QT-syndrome type 1 (LQT1)KCNQ1OSKC retrovirusProlonged APD in atrial and ventricular cardiomyocytesIsoprenaline (100 nM), propranolol (200 nM)↑ BR, caused EADs

81

Corrected EADs
Long QT-syndrome type 2 (LQT2)KCNH2OSNL lentivirusProlonged FPD and APD in atrial and ventricular cardiomyocytes, reduction in Ikr currentIsoprenaline (100 nM)↓ BR, caused EADs

34

Nadolol (10 µM), propranolol (200 nM)Corrected EADs
E4031 (1 µM)↑ FPD/APD, caused EADs
Nicorandil (20 µM)↓ FPD/APD, corrected EADs
PD-118057 (3 µM)↓ FPD/APD 
OSK retrovirusE4031 (500 nM), Cisapride (N/S)↑ FPD/APD, caused arrhythmogenesis

38

Nifedipine (1 mM),↓ FPD/APD, corrected EADs
Pinacidil (1 mM) 
Ranolazine (15–50 mM)Reduced arrhythmogenesis
OSKC retrovirusAsymptomatic carrier with LQT2 family history used to diagnose LQT2 as hiPSC-cardiomyocytes showed prolonged FPD/APDSotalol (0.8–19.4 µM), E4031 (1 µM)↑ FPD/APD

82

Erythromycin (1.5–16 µM), cisapride (40–330 nM)None
Catecholaminergic polymorphic ventricular tachycardia type 1 (CPVT1)RYR2]OSKC retrovirusElevated diastolic Ca(2+) concentrations, reduced SR Ca(2+) content, increased susceptibility to DADs and arrhythmias after catecholaminergic stimulationIsoprenaline (1 µM)↑ BR, caused DADs

83

Forskolin (5 µM), 8-Br-cAMP (100 µM)↑ Cytosolic cAMP and abolished Ca(2+)-release events after repolarisation
N/ADantrolene (N/A)Restored normal Ca(2+) spark properties and prevented arrhythmogenesis

35

Timothy syndrome (TS)CACNA1COSKC retrovirusIrregular cardiac myocyte contraction, excess Ca(2+) influx, prolonged APD, irregular electrical activity, abnormal calcium transientsRoscovitine (33.3 µM)↑ Ca(V)1.2 voltage-dependent inactivation, restored electrical and Ca(2+) signalling properties

36

OSKC retrovirusAbnormal expression of tyrosine hydroxylase and increased production of norepinephrine and dopamine in neuronsRoscovitine (N/S)Reversed abnormal phenotype

37

LEOPARD syndrome (includes Noonan syndrome)PTPN11, RAF1, SHOC2OSKC retrovirusIncreased sarcomeric organisation and preferential localisation of NFATC4 in the nucleus, which correlate with potential hypertrophic state. Study of molecular insights into disease mechanismNoneNone

33

Smooth muscleHutchinson-Gilford progeria syndrome (HGPS)LMNAOSKC retrovirusPremature senescence in smooth muscle cells. DNAPKcs identified as progerin target, therefore uncovering disease pathogenesisLentiviral anti-progerinshRNAPhenotype correction

84

OSKC retrovirusDNA damage, nuclear abnormalities and calponin-staining inclusion bodies in MSCs, smooth muscle cells and fibroblastsNoneNone

85

Skeletal muscleDuchene muscular dystrophy (DMD)DystrophinOSKC retrovirusGenotypingNoneNone

86

OSNL lentivirusGenotypingNoneNone

87

OSK retrovirusGene-corrected hiPSCs generated using a human artificial chromosomes with complete genomic dystrophin sequenceNoneNone

88

Becker muscular dystrophy (BMD)DystrophinOSKC retrovirusGenotypingNoneNone

86

ImmuneAdenosine deaminase deficiency-associated severe combined immunodeficiency (ADA-SCID)ADAOSKC retrovirusGenotypingNoneNone

86

Multiple-sclerosis (MS)MHCOSKC retrovirusDifferentiation to oligodendrocytes, astrocytes and functional neuronsNoneNone

89

ImprintingAngelman syndromeUBE3AOSKCL retrovirusUBE3A paternalimprinting re-established during hiPSC neuronal differentiationNoneNone

90

Pradder-WilliOSKC retrovirusUBE3A maternal imprinting maintained in hiPSCs, reduced expression of disease-associated RNA HBII-85/SNORD11NoneNone

91

SkinRecessive dystrophic epidermolysisbullosa (RDEB)COL7A1OSKC retrovirusGene-corrected RDEB hiPSCs expressed Col7 and differentiated to skinNoneNone

92

NeurologicalSpinal muscular atrophy (SMA)SMN1OSKC retrovirusReduced differentiation to motoneurons, abnormal neurite outgrowth. Genetic correction of phenotype by ectopic SMN over-expressionNoneNone

93

OSNL lentiviralDeficits in motor neurons, lack of nuclear gemsValproic acid (1 mM),↑ Number of nuclear gems and SMN protein expression

21

tobramycin (320 mM)
Familial dysautonomia (FD)IKBKAPOSKC lentivirusNeurogenic differentiation and migration defects, decreased expression of peripheral neurogenesis and neuronal differentiation markersKinetin (N/S)↓ Mutant IKBKAP splice variant, ↑ wild-type transcript, ↑ neuronal differentiation and neuronal marker expression

94

Epigallocatechin, gallate (N/S), tocotrienol (N/S)None
Rett syndrome (RTT)MECP2OSKC retrovirusGenotyping and differentiation to neuronsNoneNone

95

OSKC retrovirusReduced synapses and dendritic spine density, smaller soma size, altered calcium signalling and electrophysiological defects in neurons, altered neuronal network signallingIGF1 (0.01 nM)↑ Glutamatergic synapses

32

Gentamicin (100 nM)Enabled expression of full length MeCP2 protein
Gabazine (N/S)↑ Ca(2+) transients
CDKL5N/SGenotyping and differentiation to neuronsNoneNone

96

Schizophrenia (SCZD)DISC1OSNLKC + SV40L EpisomalGenotyping and differentiation to neuronsNoneNone

97

N/SOSKCL tet-inducible lentivirusReduced neuronal connectivity, soma outgrowths and PSD95 dendritic protein, altered gene expression profiles implicating Notch signalling, cell adhesion and Slit-Robo-mediated axon guidance in disease pathogenesisLoxapine (N/S)Improved neuronal connectivity and gene expression profiles

29

Clozapine, olanzapine, risperidone, thioridazine (N/S)None
Alzheimer's disease (AD)PS1, PS2OSNLK retrovirusIncreased amyloid Aβ42 secretion in neuronsCompound E (γ-secretase inhibitor XXI; 10–100 nM)↓ Aβ42 and Aβb40 production

30

OSK retrovirusCompound W (selective Aβ42-lowering agent; 10–100 µM)↓ Aβ42:Aβ40 ratio

98

Early onset Alzheimer's disease (AD) in Down syndrome patientsAPP over-expression due to Trisomy 21N/SDifferentiation to cortical neurons secreting pathogenic hyperphosphorylated tau protein and Aβ42, which formed insoluble amyloid aggregatesγ-Secretase inhibitor (N/S)↓ Aβ42 and Aβb40 production

31

Parkinson's disease (PD)PINK1OSKC retrovirusGenotypingNoneNone

86

OSK Cre-excisable lentivirusGenotyping and differentiation to dopaminergic neuronsNoneNone

99

OSKC retrovirusDopaminergic neurons with impaired Parkin recruitment to mitochondria, increased mitochondrial copy number, upregulation of PGC-1α. Phenotype correction with PINK1 over-expressionNoneNone

100

LRRK2OSK retrovirusDopaminergic neurons with morphological alterations, reduced neurite numbers, neurite arborisation and increased autophagicvacuolationNoneNone

101

Idiopathic
Fragile-X syndrome (FXS)FMR1OSKC retrovirushiPSC aberrant neuronal differentiation directly related to epigenetic modification of FMR1 and loss of FMR protein expressionNoneNone

102

Friedreich ataxia (FRDA)FXNOSKC retrovirusDifferentiation to peripheral neurons and cardiomyocytesNoneNone

103

Huntington's disease (HD)HuntingtinOSKC retrovirusGenotypingNoneNone

86

OSKC retrovirusDifferentiation to neurons with elevated caspase activityNoneNone

104

Olivopontocerebellar atrophy (OPCA)SCA7OSKCDifferentiation to neural cellsNoneNone

105

Autism spectrum disorders (ASDs)MultifactorialN/ADifferentiation to GABAergic neuronsNoneNone

106

Amyotrophic lateral sclerosis (ALS)SOD1OSKC retrovirusGenotyping, differentiation to motor neurons and gliaNoneNone

107

MetabolicGaucher disease type III (GBA)GBAOSKC retrovirusGenotypingNoneNone

86

Lesch-Nyhan syndromeHPRT1OSKC retrovirusGenotypingNoneNone

86

Juvenileonset type 1 diabetesmellitus (T1D)MultifactorialOSKC retrovirusGenotypingNoneNone

86

OSK retrovirusDifferentiation to insulin-producing cellsNoneNone

108

Type 2 diabetes (T2D)MultifactorialOSKC retrovirusDifferentiation to insulin-producing islet-like progenyNoneNone

109

Alpha1-antitrypsin deficiency (A1ATD)A1ATOSKC retrovirusDifferentiation to hepatocytes with endoplasmic reticulum aggregates of misfolded α1-antitrypsinNoneNone

110

Familial hypercholesterolemia (FH)LDLRDifferentiation to hepatocytes with deficient LDL receptor-mediated cholesterol uptakeNoneNone
Glycogen storage disease type 1a (GSD1a)G6PCDifferentiation to hepatocytes with elevated lipid and glycogen accumulationNoneNone
HaematologicalSickle cell anaemiaβ-Globin alleles (β(s)/β(s)OSKC Cre- excisable lentivirusGenetically corrected hiPSCs generated using zinc finger nuclease homologous recombinationNoneNone

111

OSKC piggyBac transposonsHeterozygous β(s)/β(A) gene correction in hiPSCs generated using zinc finger nuclease homologous recombinationNoneNone

112

Fanconi anaemia (FA)MultifactorialOSKC retrovirusGenetic correction of patient fibroblasts by lentiviral overexpression of FANCA or FANCD2 proteins, generation of hiPSCs and differentiation to phenotypically normal myeloid and erythroid hematopoietic progenitorsNoneNone

113

OSKC retrovirus or multi-cistroniclentivirusFA pathway complementation enables reprogramming of somatic cell to hiPSCs capable of hematopoietic differentiationNoneNone

114

Acquired myeloproliferativedisordes (MPDs)JAK2-V617F somatic mutation in blood cellsOSKC retrovirusDifferentiation to CD34(+)CD45(+) hematopoietic progenitors with enhanced erythropoiesis and gene expression profiles similar to primary CD34(+) cells from the patientNoneNone

115

b-Thalassaemia major (Cooley's anaemia)ß-globinOSKC retrovirusGenotypingNoneNone

116

Genetic correction of mutation by homologous recombination followed by implantation of hematopoietic progenitors into SCID mice to improve haemoglobin productionNoneNone

117

EyeRetinitis pigmentosa (RP)RP1, RP9, PRPH2, RHOOSKC retrovirusRod photoreceptor cells recapitulated diseased phenotype of in vitro degenerationα-Tocopherol (100 µM)↑ Rhodopsin+ cells

118

Ascorbic acid (200 µM)No effect
β-Carotene (1.6 µM)No effect
Gyrate atrophy (GA)OATOSNLKC + SV40L EpisomalGene-corrected hiPSCs generatedNoneNone

119

Age-related cataractMultifactorialOSK lentivirushiPSCs differentiated to lens progenitor-like cells expressing lens-specific markersNoneNone

120

Multi-organDown syndrome (DS)Trisomy 21OSKC retrovirusGenotypingNoneNone

86

Shwachman-Bodian-Diamond syndrome (SBDS)SBDSOSKC retrovirusGenotypingNoneNone

86

Dyskeratosiscongenita (DC)DKC1, TERCOSKC retrovirusDisease model use to discovered novel mechanisms of telomerase regulationNoneNone

121

Figure 1.

Current status and emerging technologies in disease modelling and drug screening for hiPSC-based models of human genetic disease. hiPCS-based models of human disease affecting the heart, smooth muscle, skeletal muscle, skin, central nervous system (CNS), liver, blood and eye have been generated. However, only those affecting the heart, CNS and eye have been used to evaluate the effects of drug treatment. Emerging technologies for scale-up, automation and high throughput analysis will enable use of hiPSC-disease models for drug discovery and safety evaluation in an industrial setting. Green and blue arrows show processes amenable to scale-up and automation, or high-content imaging and electrophysiology analysis.

Nevertheless, it is noteworthy that, with the exception of the eye disorder retinitis pigmentosa, only hiPSCs models affecting the heart and central nervous system have been used to evaluate effects of drug treatment in detail (Table 1; Fig. 1). This highlights several critical factors that are often overlooked in hiPSC technology: How will the phenotype of the disease be quantified in vitro? How will benefits of different methods of therapeutic intervention be evaluated? If a disease phenotype is present, how does it relate to the patient's condition? Is the therapy tested in vitro relevant to the patient, and is there potential for clinical translation? As shown in Table 1, the level of genetic and/or pharmacological characterisation in the majority (46/63) of hiPSC models is limited, and the answers to these questions are outstanding.

Phenotype assessment in hiPSC-derived neurons and cardiomyocytes

Most progress has been made in phenotyping and evaluating drugs in hiPSC-based models of neurological and cardiac conditions (Table 1). Motor-, cortical- and dopaminergic-neurons from hiPSC harbouring mutations associated with neurodegenerative (e.g. Alzheimer's, Parkinson's and Huntington's diseases, schizophrenia) and neurodevelopmental disorders (e.g. Rett syndrome, spinal muscular atrophy, familial dysautonomia) have been successfully generated. Quantitative phenotyping of these cells has indicated severe defects in growth, migration and function compared to healthy controls. They therefore provide platforms for drug validation (Table 1). For example, the known anti-psychotic drug, loxapine, has been shown to improve neuronal connectivity in schizophrenia models 29, while compound E, a tobacco-derived γ-secretase inhibitor, decreased secretion of pathogenic Aβ42 in Alzheimer's models 30, 31. Rett syndrome models have also been used for validation of experimental drugs such as gabazine, a GABAA receptor antagonist 32.

Genetic disorders that affect the structure, ion channel composition and functionality in the heart also provide a quantifiable phenotypic readout. One of the consequences of the multi-system disorder of LEOPARD syndrome is cardiac hypertrophy, which has been partially phenocopied using hiPSC-cardiomyocytes 33. The techniques of patch clamping and multi-electrode array (MEA) have proved valuable in interrogating electrophysiology from single or multi-cell clusters of cardiomyocytes, respectively 34. Alterations in calcium handling can be visualised using realtime microscopy in the presence of calcium sensitive dyes 35. Data from hiPSC lines carrying mutations that cause LQTS and catecholaminergic polymorphic ventricular tachycardia (CPVT) are starting to produce evidence that patient-relevant phenotypes and drug response can be recreated in vitro. In the case of LQTS2, caused by mutations in the IKr channel, hiPSC-derived cardiomyocytes developed arrhythmias when exposed to isoprenaline, a stressor used clinically to precipitate and diagnose the condition 34. This effect could be reversed by applying the patient's own medication, nadolol, a β-blocker. Dantrolene and roscovitin, drugs known to be beneficial in moderating calcium flux, stabilised ion flux in hiPSC models of the calcium channel disorders, CPVT and Timothy syndrome (linked to LQT type 8), respectively 35–37.

Human induced pluripotent stem cell-cardiomyocytes are now providing novel routes to test more experimental drugs. The arrhythmias seen in the LQTS2 models were abolished by the potassium channel modulators, nicorandil and pinacidil (K+ATP channel openers) or PD-118057 (IKr channel activator) 34, 38. Encouragingly, it has been shown that hiPSC-cardiomyocytes can replicate relatively subtle differences between patients. hiPSCs were produced from a healthy donor as well as from a mother and daughter, wherein the mother was clinically asymptomatic (no arrhythmias) with a moderately prolonged QT interval and the daughter was symptomatic with an excessively prolonged QT interval (arrhythmias, syncope and seizure episodes). Recording action potential durations from the different hiPSC-cardiomyocytes showed that the clinical profile was reflected in vitro (i.e. action potential longest in the daughter's cells, then the mother's, then the healthy control) and only hiPSC-cardiomyocytes produced from the daughter developed spontaneous arrhythmias 34. Establishing whether such in vitro to in vivo associations hold true for other conditions will be important for hiPSC technologies to become widely accepted.

Assessing the need for humanised cardiotoxicity testing platforms

The ability to quantify functional responses in lineages such as hPSC-cardiomyocytes will likely find use in drug safety assessment. In recent years, high rates of drug attrition and withdrawal from market (because of unexpected cardiotoxicity) have imposed a multi-billion dollar burden on the pharmaceutical industry. More than ten drugs used to treat various non-cardiac conditions (e.g. inflammatory disease, psychosis, bacterial infection, pain) have been withdrawn from market because of unexpected side effects on the heart 39. Side effects can damage the structural integrity and survival of cardiomyocytes, as is the case with the anti-inflammatory drug, Vioxx 39 and many anti-cancer drugs, such as doxorubicin 40. Beat regularity and duration (QT prolongation or shortening) can also be affected, which can lead to polymorphic ventricular tachyarrhythmia, seizures and sudden death. Indeed, in 2010 this was the reason for the US FDA requesting withdrawal of propoxyphene, an opioid pain reliever marketed by Xanodyne Pharmaceuticals 41, and of sibutramine, a weight loss agent marketed by Abbott Laboratories 42. With development costs of each drug averaging $1.5 billion, high profile withdrawals are extremely damaging for the companies involved, as well as for patients taking the medication; the serotonin agonist, cisparide, caused 125 deaths before its use ceased 43.

The use of suboptimal screening and safety assessment platforms underlies the reason for which drugs with potentially lethal side effects are not eliminated from the development pipeline before they reach the clinic. Early in most development pipelines, drugs are tested for channel modulating activity by utilising aneuploid cell lines (e.g. Chinese hamster ovary [CHO] or human embryonic kidney [HEK] cells) engineered to overexpress single ion channels. Such assays bear little relation to the complex multi-channel phenotype of functional cardiomyocytes 44. This issue is illustrated by the in vitro culture responses seen with verapamil, a ‘safe’ drug in routine clinical use for treatment of hypertenstion, angina pectoris and cardiac arrhythmia. In CHO cells forced to overexpress HERG, verapamil blocks the potassium IKr channel, thereby predicting an association with prolonged QT interval 45. In reality, while outward ion flux through IKr channels is blocked in functional cardiomyocytes, verapamil also blocks inward flux through L-type calcium channels (ICa-L), and the overall effect on QT interval is cancelled out 45. Similarly, ranolazine, a drug used to treat angina, blocks opposing sodium INa and potassium IKr channels, with limited effect on QT duration 46.

As discussed earlier, there are substantial differences in gene expression and physiology between species, which can limit the effectiveness of extrapolating toxicity from animals to humans. Indeed, data from non-rodents or rodents are respectively, 63 and 43% predictive of whether a drug will be toxic in humans. Even when data are combined from rodents (mice and rats) and non-rodents (dogs and monkeys), only 71% predictivity is achieved 47. Notably, mice are at least 10× more tolerant to 37% of drugs than humans, while rats and dogs tolerate 4.5–100-fold the concentration of various chemotherapeutic agents as humans (e.g. ThioTEPA, Myleran, Actinomycin-D, Mitomycin C, Mithramycin, Fludarabine) 48. Conversely, potentially valuable drugs might be eliminated during development because of overt toxicity in animals, when in fact they might be completely innocuous in humans. By way of example, chocolate and coffee can cause organ failure and death in dogs. This is because, relative to humans, the methylxanine ingredients, theobromine and caffeine, of these foods are poorly metabolised in dogs, which leads to potentially fatal toxic build up 49.

Despite these inadequacies, regulatory guidelines (e.g. international conference on harmonisation; ICH S7B) require extensive animal use in safety assessment because predictivity of current in vitro assays is insufficient. This has major implications for the number of animals used, and is not in line with the developing 3Rs (replacement, refinement and reduction of animal use) policies of many countries. For example, in the UK in 2008, a total of 475,290 animal procedures were performed to supply the needs of drug safety assessment and toxicity testing 50. New EU regulation for the registration, evaluation, authorisation and restriction of chemicals (termed REACH) will require toxicological testing of 30,000 compounds, and some reports suggest that this will require up to 54 million animals over the next 10 years in Europe alone 50, 51.

These observations lead to the conclusion that any new human-based in vitro assays that improve or complement existing tests would benefit 1. patients through better drug safety; 2. the 3Rs, through reduced animal use; and 3. pharmaceutical companies, through reduced preclinical costs and drug withdrawals.

Progress towards using hPSC-cardiomyocytes in cardiac safety assessment

In the last few years, tremendous progress has been made in improving the efficiency and robustness of cardiac differentiation from hPSCs, thereby providing a renewable source of human cardiomyocytes. The three differentiation strategies employed are formation of (i) three-dimensional aggregates known as embryoid bodies, (ii) two-dimensional monolayers or (iii) co-cultures with an inducer cell line such as END-2; these methods have recently been reviewed 11. The cardiomyocytes display many of the gene expression patterns associated with in vivo development of the heart, including gene expression, ion channel formation, electrophysiological responsiveness and excitation-contraction coupling 52.

These attributes suggest that hPSC-cardiomyocytes could provide a human-based in vitro assay system for drug testing. Indeed, the pharmacological responses of hPSC-cardiomyocytes have been quantified from nearly 60 different compounds and drugs (Table 2). While the range of agents is extensive, most studies have only used one or two concentrations of drug that are at the upper end or exceed clinically relevant doses. Nonetheless, several important points are emerging, as considered below (see also Tables 1 and 2, and references therein).

Table 2. Drug evaluation in hPSC-cardiomyocytes
AGENTMechanism of actionhPSC linesDrug conc. (M)Detection methodObsrved effect on hPSC-CMsRefs
  1. hPSC, human pluripotent stem cells; hESC, human embryonic stem cells; hiPSC, human induced pluripotent stem cells; N/S, not specified; patch, patch clamp electrophysiology; MEA, multi-electrode array; APD, action potential duration; FPD, field potential duration; EADs, early after depolarisations; QTi, QT interval; CM, cardiomyocytes; ETPC, estimated unbound therapeutic plasma concentrations.

2-APBCell permeate IP3R antagonisthIH-I-clone 1&2; hfib2-5 (hiPSC)2 µMLaser confocal Ca2+ imagingSignificant decrease in whole-cell (Ca2+)I transients amplitude and frequency

38

2,3-Butanedione monoximeUncompetitive ATPase inhibitorH1 (hESC)10−3 MMEAArrested contraction

122

AcetylcholineMuscarinic receptor agonistSA002, SA121 (hESC)10−6–10−3 MMicroscopy↓ Beat rate

123

Adrenalineβ1-Adrenoceptor agonistKhES1 (hESC), 201B7 (hiPSC)0.5–50 µMMEA↑ Beat rate

124

SA002, SA121 (hESC)10−9–10−5Microscopy

123

Atenololβ1-Adrenoceptor antagonistSA002, SA121 (hESC)10−8–10−6Microscopy↓ Beat rate, blocked effect of adrenaline 
AmiodaroneK channel blockerKhES1 (hESC), 201B7 (hiPSC)1–100 µMMEA↓ Beat rate

124

AtropineCompetitive Ach inhibitorSA002, SA121 (hESC)10−6MicroscopyBlocked effect of acetylcholine

123

ATX-IIINa,late enhancerSA002 (hESC)<1 µmol/LPatchNo effect on APD and triangulation

61

BaCl2IK1 blockerSA002 (hESC)10 µMPatchNo effect on triangulation or AP prolongation
H1 (hESC)0.5 mMIncreased the slope of diastolic depolarisation

63

Bay K8644Calcium channel enhancerSA002 (hESC)1 µMPatchAPD50 and APD90 increased by 27%; no effect on triangulation

61

hiPSC (iCells, Cellular Dynamics International)10 and 100 nMNo or little stimulation of Ca channel current amplitude. 100 nM, inhibited current. Slowed Ca channel inactivation/activation

125

CaffeineInducer of SR Ca2+ releaseH1, HES2 (hESC)10 mMFura-2/AM↑ Cytosolic Ca

126

hiPSC, H9.2 (hESC)10 mMMEAMinor increase in diastolic [Ca2+]i ratio

127

hIH-I-clone 1&2; hfib2-5 (hiPSC)20 mMLaser confocal Ca2+ imagingIncrease in Ca induced transient amplitude-dose dependent increase

38

CarbamylcholineMuscarinic receptor agonisthFib2-iPS (hiPSC)1 and 10 µMMEADose-dependent ↓ in beat rate

128

H2 (hESC)0.1 mMPatch↓ Beat rate

129

H7 (hESC)10 µMPatchSignificant drop in beat rate

130

H9.2 (hESC)1 µMMEA↓ In beat rate

131

CGP 20712Aβ1-Adrenoceptor antagonistH7 (hESC)0.3 µMPatchReduced beating rate and further increased in conjunction with isoprenaline. No significant effect on relaxation (R50 & R90)

130

Chromanol 293BIKs blockhFib2-iPS (hiPSC)10 and 30 µMMEADose dependent ↑ in cFPD

128

SA002 (hESC)100 µMPatchProlonged APD90; no EAD; no effect on triangulation

61

201B7 (hiPSC)N/SPatchTime and dose dependent AP prolongation

132

CisaprideSerotonin 5HT agonistUTA.00514.LQT2 (hiPSC)40–330 nMMEANo ↑ in arrhythmogenicity

82

LQT2-hiPSC100 nMMEA↑ cFPD, ↑ arrythmogenicity

38

HES2, HES3 (hESC)0.1 nM–1 µMMEA↑ FPD only at higher concentrations

54

SA002 (hESC)0.01–1 µMPatchIncrease in APD90; triangulation increased and 1/11 clusters showed EAD at 1 µmol/L

61

Clenbuterolβ2-Adrenoceptor agonistH1, H7 and H9 (hESC) and H9.1 and H9.2 (clonal)10−7–10−9 MPatchNo response to contractions at day 22 and 39 of differentiations. At day 61 and 72 increase in beating frequency

133

DiltiazemL-type Ca2+ channel blockerH9.2 (hESC)1–10 µMMEA/patchNo effect on conduction or automaticity

134

201B7 (hiPSC)0.01 and 1 µMPatchShortened APD30 and APD90; no affect on APD30-90

132

H1, H7 and H9 (hESC) and H9.1 and H9.2 (clonal)10−7–10−5 MPatchDose dependent ↓ in beating frequency. At 10−7 mol/L frequency was significantly reduced and stopped beating at 10−5 mol/L

133

DigoxinInhibit Na+/K+-ATPasehiPSC (iCells, Cellular Dynamics)0.3–10 µMMEAAt 3 µM, reduced Na+-spike amplitude, shortened FPDcf and increased Ca2+-wave amplitude

135

DomperidoneMultiple channel blockerHES2, HES3 (hESC)0.1 nM–100 µMMEAMinor ↑ in FPD at ETPC unbound (5–19 nM), biphasic dose-dependent ↑ in FPD at higher concentrations

54

E4031IKr blockerUTA.00514.LQT2 (hiPSC)500 nMMEA↑ In arrhythmogenicity (effect greater in diseased lines)

82

hiPSC3–100 nMPatch↑ APD50, ↑ APD90 and AP triangulation

79

LQT2-hiPSC500 nMMEA/patch↑ APD/cFPD, ↑ arrythmogenicity and development of EADs

38

LQT2-hiPSC10−1–10−3 MMEA/patch↑ cFPD/APD (77% ↑ in patient CMs as opposed to 50% in control CMs); EADs in 30% of LQT2-CMs vs. none in controls

34

SA002 (hESC)0.03–1 µMPatchDose-dependent ↑ APD90, ↑ AP triangulation, EADs at high concentrations

136

hESC100 nMPatchProlongation of AP; greater effect on APD90 than APD 50

137

HES2, HES3 (hESC)30–300 nMMEADose dependent ↑ in FPD, ↓ in beat rate at micromolar concentrations, EADs between 1−3 µM in ¾ experiments

54

201B7 (hiPSC)10–100 nMMEA↑ FPD

138

 0.01, 0.1 and 1 µMPatchProlonged APD30, APD90 and APD30-90 in concentration dependent manner; EAD in 2/4 cells

132

hFib2-iPS (hiPSC)500 and 1,000 nMMEADose dependent ↑ in cFPD

128

H1 (hESC)10 µMPatchNon-reversible ↑ APD after 30 seconds. Late stage differentiation depolarised diastolic potential/↑ frequency of spontaneous AP

63

500 nMPatchAP ↑ in both atrial and ventricular like-CMs but APD 90 and APD50 response dependent on subtype

139

ErythromycinIKr blockerUTA.00514.LQT2 (hiPSC)1.5–16 µMMEANo ↑ in arrhythmogenicity

82

FlecainideNa channel blockerKhES1 (hESC), 201B7 (hiPSC)0.1–10 µMMEANo effect on beat rate

124

ForskolinAdenylatecyclase stimulatorH9.2 (hESC)1 µMMEA↑ beat rate

131

SA002, SA121 (hESC)10−12–10−7 MMicroscopyIncrease in beat rate

123

FPL 64176L-type Ca2+ channel activatorhiPSC (iCells, Cellular Dynamics); hESC (Geron)100–1,000 nMPatchVariable ↑ in Ca channel current amplitude. Slowed Ca channel activation, inactivation and tail current kinetics

125

HeptanolGap junction blockerH1 (hESC)0.4 mMMEAuncoupling of cardiomyocytes

122

IBMX (Isobutyl methylxanthine) H9.2 (hESC)10 µMMEA↑ beat rate

131

Phosphodiesterase inhibitorH1, H7 and H9 (hESC) and H9.1 and H9.2 (clonal)patchDose dependent increase in contraction rate

133

ICI 118,551β2-Adrenoceptor antagonistH7 (hESC)50 nMPatchIn presence of ICI, increase in beating rate with isoprenaline reduced. Significant acceleration of relaxation (R90)

130

Isoprenalineβ1/β2-Adrenoceptor agonistUTA.00514.LQT2 (hiPSC)80 nMMEA↑ Chronotropy (both diseased and control lines)

82

H7 (hESC)0.1 µMPatchIncrease in beat rate; R50 and R90, were reduced

130

0.001–10 µM Dose dependent increase in beat rate; EC50 of 12.9 nM 
LQT2 hiPSC10−1–10−3MEA/patch↓ in cFPD, APD, APD50 and APD90 (patient lines significantly more sensitive); EADs in 25% of patient, but none of control CMs

34

IMR90 C1, IMR90 C4 (hiPSC). H1, H9 (hESC)1 µMPatch↓ In APD, ↑ in beat rate

140

HUES7, NOTT1 (hESC)1 µMMEA↑ Beat rate, ↓ FPD

8

H2 (hESC)1 µMPatch↑ Beat rate

129

iPSC, H9.2hESC(hESC)10−9–10−7 MMEAConcentration dependent positive inotropiceffect

127

CBiPSC6.2 (hiPSC)20 µMOptical voltage maps↓ AP, ↑ conduction velocity

141

SA002 (hESC)0.1 µMPatch↑ Beating frequency, ↓ APD; suppresses E4031-induced EADs

136

hFib2-iPS (hiPSC)1 and 10 µMMEADose-dependent ↑ in beat rate

128

LQT1-hiPSC100 nMPatch15% ↑ in APD90/AP, ↑ risk of arrhythmias, EADs

81

LQT2-hiPSC10 µMMEA↑ Chronotropy

38

KhES1 (hESC), 201B7 (hiPSC)0.01–1 µMMEADose-dependent ↑ in beat rate

124

201B7 (hiPSC)200–500 nMMEA↑ Beat rate, ↓ FPD

138

H1 (hESC)1 µMMEA↑ Beating frequency

122

H9.2 (hESC)1 µMMEA↑ Beat rate

131

H1, H7 and H9 (hESC) and H9.1 and H9.2 (clonal)10−5–10−9 MPatchEnhanced the contraction rate in dose dependent manner, at differentiation day 15–20

133

H1, H7, H9, H14 (hESC)1 µmol/LPatchIncrease in magnitude of contraction

139

KetoconazoleCyp34a inhibitorHES2, HES3 (hESC)0.3 nM–30 µMMEANo effect on FPD

54

LacidipineL-type Ca2+ channel blockerH1 (hESC)10 µMPatchReduction in plateau duration and height of AP profile recorded from 40 day old beating cluster

63

LidocaineVoltage-gated Na+ channel inhibitorHES2, HES3 (hESC)0 pM–100 µMMEACessation of beating in the 30–100 µM range

54

H1 (hESC)100 µMMEA↓ Conduction rate

122

201B7 (hiPSC)100, 1,000 µMPatchConcentration dependent inhibition of INa

131

MexiletineNa+ channel blockerKhES1 (hESC), 201B7 (hiPSC)0.1–10 µMMEANo effect on beat rate

124

Nadololβ-Adrenoceptor antagonistLQT2 hiPSC10−1–10−3 MPatchAttenuation of isoprenaline-induced arrythmias

34

NicorandilIKATP openerLQT2 hiPSC10−1–10−3 MPatch↓ APD, abolishment of spontaneously occurring EADs

34

NifedipineL-type Ca2+ channel blockerhiPSC3–100 nMPatch↓ APD10, ↓ APD50, ↓ APD90

79

LQT2-hiPSC1 µMMEA/patch↓ cFPD, ↓ APD and ↓ APD90; eliminated EADs and triggered beats

38

HES2, HES3 (hESC)10 nM–1 µMMEADose dependent ↓ in FPD, ↑ in beat rate, but no arrhythmic activity, loss of spontaneous activity between 300 nM and 1 µM

54

H9.2 (hESC)0.1–1 µMMEA/patchNo effect on conduction or automaticity

134

SA002 (hESC)10 nMPatchShortened AP; negated effect of BAY K8644

61

hiPSC (iCells, Cellular Dynamics)0.01–3 µMMEAAccelerated beat rate; shortened FDPcf; reduced Ca wave amplitude; reduction of Na spike amplitude by 20% at 3 µM

135

hiPSC (iCells, Cellular Dynamics); hESC(Geron)6 nM(hESc); 3 nM(hiPSC)PatchInhibit Ca2+ channel currents

125

hIH-I-clone 1&2; hfib2-5 (hiPSC)1 µMLaser confocal Ca2+ imagingElimination of whole cell (Ca2+)I transients; decrease in (Ca2+)I transients amplitude at lower nifedipine concentration

142

OuabainInhibit Na+/K+-ATPasehiPSC (iCells, Cellular Dynamics)0.3–10 µMMEATime and dose dependent-reduced Na+-spike amplitude, shortened FPDcf and increased Ca2+-wave amplitude

135

PD-118057Type 2 IKr channel enhancerLQT2 hiPSC10−1–10−3 MPatch↓ APD

34

Phenoxybenzamineα1-/α2-Adrenoceptor antagonistSA 002 and SA 121 (hESC)10−7–10−5 MMicroscopyReduces beat rate

123

Phenylephrineα1-Adrenoceptor antagonistHES2 (hESC)0.1 mMPatch↑ Beat rate

129

H1, H7 and H9 (hESC) and H9.1 and H9.2 (clonal)10−4–10−8 MPatch↑ Contraction rate in dose dependent manner, at differentiation day 15–20

133

SA 002 and SA 121 (hESC)10−7–10−11 MPatchDose dependent increase in contractile activity

123

PinacidilIKATP openerCBiPSC6.2 (hiPSC)100 µMOptical voltage maps↓ AP, ↑ conduction velocity

141

LQT2-hiPSC1 µMMEA/patch↓ cFPD, ↓ APD and ↓ APD90, eliminated EADs/triggered beats

38

ProcainamideNa+ channel blockerKhES1 (hESC), 201B7 (hiPSC)10–1,000 µMMEANo effect on beat rate

124

Propranololβ-Adrenoceptor antagonistLQT1-hiPSC200 nMPatchAttenuation of catecholamine-induced tachyarrhythmias

81

LQT2 hiPSC10−1–10−3 MMEA/patchAttenuation of isoprenaline-induced arrhythmias

34

KhES1 (hESC), 201B7 (hiPSC)0.3–30 µMMEANo effect on beat rate, blocked effect of isoprenaline

123

QuinidineMultiple ion channel blocker (Ito, IKatp, IKI, IKr, IKs, ICa, INaL)hFib2-iPS (hiPSC)100 µMMEA↑ In cFPD, variable effect on the amplitude of the 1st negative peak of the FP, variable effect on chronotropy

128

HES2, HES3 (hESC)0.1 nM–100 µMMEADose dependent ↑ in FPD and QTi (i.e. prolonged FPD at physiologically relevant plasma concentrations)

54

201B7 (hiPSC)4–50 µMMEA↓ FP amplitude

138

HES2 (hESC)1 µMMEA↑ APD

143

RanolazineMultiple ion channel blocker (IKr, ICa, INaL)LQT2-hiPSC15–50 µMMEA/patchNo change in cFPD/APD, pronounced anti-arrythmic effect

38

RyanodineRyanodine receptor inhibitorH1, HES2 (hESC)10 µMFura-2/MEA↓ Ca current amplitude

126

hIH-I-clone 1&2; hfib2-5 (hiPSC)10 µMLaser confocal Ca2+ imagingSignificant reduction in Ca2+ release. Increasing doses of ryanodine led to increase in % decrease in (Ca2+)I

142

H9.2 (hESC)10 µMFura-2/MEANo effect on contraction

144

hiPSC, H9.2 (hESC)10 µMMEA↓ In contraction in iPSC-CMs, No effect on contractionsinhESC-CMs

127

SertindoleMultiple ion channel blocker (IKr, ICa, INaL)HES2, HES3 (hESC)0.01 nM–100 µMMEANo effect on FPD at ETPC unbound (0.02–1.59 nM), relatively weak ↑ in FPD at higher concentrations

54

SotalolIKr blockerUTA.00514.LQT2hiPSC19 µMMEA↑ In arrhythmogenicity (only in diseased lines)

82

HES2, HES3 (hESC)0.1 nM–100 µMMEADose dependent ↑ in FPD and QTi (i.e. prolong FPD at physiologically relevant plasma concentrations)

54

H1 (hESC)300 µMMEA↑ FP duration; time dependent ↑ of repolarisation phase; no significant change in beating rate

145

SparfloxacinIKr blockerHES2, HES3 (hESC)0.1 nM–100 µMMEANo effect on FPD at ETPC unbound (0.19–1.76 µM), ↑ FPD at higher concentrations

54

Sunitinib malateIKr blockeriCells, Cellular Dynamics1–30 µMMEA↑ cFPD, dose-dependent ↓ in beat rate, arrhythmic beats at 10 µM, with altered amplitude and beat duration at 30 µM

146

TetrodotoxinVoltage-gated Na+ channel inhibitorhiPSC3–30 µMPatchDelay in upstroke, ↓ dV/dtmax

79

hFib2-iPS (hiPSC)10 µMMEA↓ In conduction time

128

H9.2 (hESC)10–100 µMMEA↓ Conduction rate and beat rate, local conduction blocks

134

Miz-hES2 and HSF-6 (hESC)200 nMPatchComplete depletion of action potential

147

TerfenadineMultiple ion channel blocker (IKr, ICa, INaL)HES2, HES3 (hESC)0.1 nM–100 µMMEANo effect on FPD at ETPC unbound (0.1–0.29 nM), ↑ FPD at higher concentrations but ↓ FPD at micromolar concentrations

54

0.01, 0.1 and 1 µMPatchProlonged APD30, APD90 and APD30-90

132

ThapsigarginSERCA2A inhibitorH1, HES2 (hESC)0.1–1 µMFura-2/AM↓ Amplitude of Ca transients

126

H9.2 (hESC)10 nMFura-2/MEANo effect on contraction

144

U73122Phospholipase C inhibitorhIH-I-clone 1&2; hfib2-5 (hiPSC)2 µMConfocal Ca2+ imagingSignificant ↓ in Ca2+ release. Increasing doses of ryanodine led to increase in % decrease in (Ca2+)I

142

VerapamilMultiple ion channel blocker (IKr, ICa)hFib2-iPS (hiPSC)1 and 5 µMMEADose dependent ↓ in cFPD and beating frequency (complete arrest of spontaneous beating frequency at 5 µmol/L

128

  hIH-I-clone 1&2; hfib2-5 (hiPSC)10 µMConfocal Ca2+ imagingDose dependent ↓ in whole cell (Ca2+)I transients amplitude in hIH-I and hfib2-5

142

  KhES1 (hESC), 201B7 (hiPSC)0.1–10 µMMEADose-dependent ↓ in beat rate

124

  HES2, HES3 (hESC)25–81 nMMEAMinor FPD shortening at ETPC unbound (25–81 nM), greater ↓ in FPD at higher concentrations

152

  201B7 (hiPSC)10–1,000 nMMEA↓ FPD

138

   0.01, 0.1 and 1 µMPatchShortening of APD30, APD90; prolongation of APD30-90

132

  HES2 (hESC)5 µMFura-2/patch↓ Beat rate

129

  SA002, SA121 (hESC)10−12–10−9 MMicroscopyReduced or stopped contractile activity

123

VeratridineNa channel modulatorhESC10 mMPatchProlonged AP/increased triangulation; reversible

137

ZatebradineIKr blockerSA002 (hESC)0.1, 1 and 10 µMPatchIncreasing concentration caused slowing of beating and changes APD and triangulation. EADs

61

H1 (hESC)10 µMPatch↓ Depolarisation rate and spontaneous rhythm

63

ZD7288If blockerH1 (hESC)NAMEA↓ Beating frequency

122

First, functionality in hPSC-cardiomyocytes has been shown for many of the key ion channels (potassium: IKs, IKr, If, Ito, IK1; sodium: INa; calcium: ICa-L, SERCA2a) and regulator molecules (e.g. receptors: muscarinic, adrenoceptors, acetylcholine, ryanodine) found at the cell membrane or in the sarcoplasmic reticulum. Second, functional responses can be quantified by methods of relevance to the pharmaceutical industry, such as patch clamp electrophysiology and calcium detection. Third, responses can be measured from cardiomyocytes derived from a range of healthy and disease-carrying hPSC lines. Fourth, the complex multi-ion channel phenotype of hPSC-cardiomyocytes provides an advantage over CHO cells forced to overexpress a single channel. Dual channel blocking agents such verapamil (blocks IKr and ICa-L) and ranolazine (blocks IKr and INa) are QT-neutral when clinically relevant doses are applied to hPSC-cardiomyocytes. Fifth, in some cases, hPSC-cardiomyocytes can detect toxic effects at lower doses than is possible in animal systems. We have found that the IKr blocker, risperidone, causes increased field potential duration of hPSC-cardiomyocytes at 0.1 µM 46, but data from GlaxoSmithKline indicate that prolongation occurs in guinea-pig myocytes at 1 µM. Moreover, direct comparison between hPSC-cardiomyocytes and myocytes isolated from dogs or rabbits concluded that the human cells more accurately predicted moxifloxacin-induced cardiotoxicity 53. Finally, a careful study examined drug effects over a 6-log dose-response range that covered the estimated unbound therapeutic plasma concentrations 54. There was good association between clinical and hPSC-cardiomyocyte toxicity for drugs such as quinidine and D,L-sotalol known to prolong QT interval, whereas drugs with a low incidence of arrhythmogenesis (e.g. cisapride, terfenadine, sertindole, sparfloxacin) only caused prolongation of field potential duration at higher doses 54.

Limitations and challenges to overcome in hPSC technology

The emerging data for disease modelling and drug screening are encouraging. However, this is a new field with limitations yet to be overcome. Although hESCs are often considered the gold standard, these cells are derived from spare embryos donated by couples experiencing fertility problems, hence the need for in vitro fertilisation (IVF) treatment. It is known that different methods of embryo culture can alter epigenetic status 55. For hiPSC derivation, delivery of reprogramming factors can be achieved by viral (e.g. retroviruses, lentiviruses, adenoviruses, sendaivirus) or non-viral (episomes, plasmids, miRNA, mRNA and protein) strategies 56. It is notable that virtually all disease models have used the ‘original’ retroviral and lentiviral methods (Table 1) 2, 3, and a potential concern is random integration of the viral genome into the host genome 57. Assessment is further complicated, because it depends on whether the reprogramming factors are contained on single or multiple vectors, and whether small molecule enhancers of hiPSC production were used 56, 58. There is not yet a consensus on the cell type to reprogram 56, although skin and blood cells are preferred because of the ease of patient consent, minimal discomfort to the patient, and accessibility. Each of these variables has the capacity to alter the genotype, epigenome and phenotype of the hiPSCs produced, as well as the subsequently derived differentiated lineages. Therefore, it is difficult to know whether problems reported for hiPSC (e.g. transfer of epigenetic legacy from somatic cells to hiPSC, improper reprogramming/disease modelling [e.g. Fragile X] or genetic instability) 59 are inherent to the technology or are a consequence of the reprogramming method(s) used. Detailed studies to resolve these issues are required, as is a consensus of the best cell type to reprogram and how.

In addition to the careful consideration of how disease presentation will be phenotyped in vitro (discussed earlier), there is also an issue of whether hPSC derivatives mature sufficiently in culture to make them fit for their intended purpose. To date, drug treatment and phenotypic studies in hiPSC-derived neurons have been more successful for neurodevelopmental disorders than late-onset neurodegenerative disorders, likely because of the foetal-like properties of the cells 60. The absence of functional potassium channels (IK1) and shifted activation of sodium channels (INa) indicates an immature status of hPSC-cardiomyocytes, and has raised concerns about their suitability in drug screening 61. Therefore it is encouraging that maturation of hPSC-cardiomyocytes can be facilitated by prolonged time culture 62, 63, transgenic overexpression of calsequestrin 64, formation of 3D aggregates 62, tissue-engineered constructs and mechanical stress 65, 66.

It is unlikely that hiPSC technology will successfully model all disorders. The epigenetic status that underlies some diseases will be erased during somatic cell reprogramming, while for other conditions a suitable phenotype may not be present in an in vitro setting 59. Although several studies have now demonstrated robust association with the phenotypes and drug responses seen in hiPSCs models with known patient pathologies (e.g. LQTS), similar validation is required for a broad range of conditions (Table 1). The timing of some late onset conditions may exceed the lifespan of hiPSC-derivatives in culture, and innovative strategies are required. For example, the dopaminergic neurons differentiated from hiPSCs carrying a mutation in the PINK1 gene (causes Parkinson's disease) only showed altered patterns of survival when additionally treated with a mitochondrial stressor 67. Finally, differentiation of the hiPSC into relevant cell types is necessary. So far, hiPSC modelling has been restricted to about 10 tissue or organ systems (Table 1) and future work will be needed to expand this range.

Industrial scalability of hPSC technologies

For hPSC derivatives to be used for disease modelling and drug screening at an industrial level (Fig. 1), sufficient numbers of cells need to be produced in a cost-effective manner. Undifferentiated hPSCs have been produced using stirred bioreactors in suspension 68 and using fully automated robotic platforms such as the CompacT SelecT, which cultures adherent cells in up to 90 T175 flasks 69. However, the cost of the reagents for hPSC culture is prohibitive because of the reliance of expensive culture media that contain various growth factors. To this end, high throughput screening has sought to identify putative chemicals that maintain pluripotency in the absence of growth factors or that improve cell survival after passage 70–72. Such approaches have identified a series of inhibitors of the Rho kinase pathway and prosurvival compounds such as Y27632 that are now used by many labs during routine hPSC culture. The same degree of success has not been achieved in replacing basic fibroblast growth factor (bFGF), which remains the gold standard for maintaining hPSC pluripotency in many labs.

Similar to the undifferentiated state, scaled production of differentiated lineages has been achieved, but also tends to rely on costly growth factors; in the case of hPSC-cardiomyocytes these typically include bFGF, bone morphogenetic protein (BMP4) and activin A 11. Commercial production of hPSC-cardiomyocytes is now in progress, with GE-healthcare, Cellular Dynamics International and Cellartis/Cellectis charging approximately $2000–3000 per vial of ∼1 million cells. It is encouraging that small molecules that promote cardiac differentiation are being identified from high throughput screens and from rational compound selection (Table 3). Time- and concentration-dependent application of the BMP inhibitor, dorsomorphin, has proved to be highly effective in improving cardiomyocyte differentiation efficiencies 73. In time, it is hoped that such strategies will allow hPSC-cardiomyocytes to be produced to short time scales, in large quantities at low cost. This goal has been achieved for production of >3 × 109 mPSC-cardiomyocytes in stirred bioreactors 74. Elegant work has also shown pipeline conversion of mouse fibroblasts into iPSCs and then into iPSC-cardiomyocytes in a single suspension bioreactor 75; the challenge now is to translate the high efficiency ‘inducible secondary’ iPSC reprogramming into a technology that is compatible with human cells.

Table 3. Agents that influence cardiomyocyte differentiation of human pluripotent stem cells
 AgentCellsWhen addedConc.ObservationsRefs
  1. DMSO, dimethyl sulphoxide; ITS, insulin-transferrin-selenium; IWP, inhibitor of WNT production; DKK1, Dickkopf-related protein 1; EGF, epidermal growth factor; WNT, wingless-int; BMP, bone morphogenetic protein; RA, retinoic acid; FGF, fibroblast growth factor; TGF-beta, transforming growth factor beta; cTnT, cardiac troponin-T; EBs, embryoid bodies; hiPSCs, human induced pluripotent stem cells; hESCs, human embryonic stem cells; aMHC, alpha myosin heavy chain.

Small moleculesAscorbic acidhiPSCThroughout differentiation50 µg/mLImproved cardiac differentiation and maturation

148

5′-AzacytidineH9 hESCDay 6–8 of differentiation1 or 10 µMIncreased aMHC expression

133

DMSOHUES7, HUES9EBs in suspension and 24–48 hours postplating0.01%Upregulation of mesoderm markers

149

Retinoic acidH9 hESCPostplating of EBs1 µMActivate ectodermal and mesodermal markers

150

ITShESC, hiPSCDay 0–2 and 4 onwardsInsulin from d2–d4 inhibited cardiac specification

141

Cyclosporin-AhiPSCd8 of END2 co-culture (hiPSC)3 µg/mLNumber of beating colonies increased

151

InhibitorsSB203580 (p38 MAPK inhibitor)H9 hESCDay 4–6 of EB differentiation5–10 µM2.1-fold increase in cardiomyocytes

152

HES2, 3, 4 hESCDay 0 of EB differentiation10 µMOne-time addition increased percentage of beating EBs

153

SB431542 (inhibitor of TGF-β/Nodal/Activin pathway)hESC, hiPSCDay 3–5 of EB differentiation5.4 µMaMHC RNA increased by 70%

73

IWP-4(Wnt inhibitor)HES3, H9, MEL1 hESCDay 3–15 monolayer differentiation5 µMIWP-4 induced expression of cardiac markers

154

IWP-3 (Wnt inhibitor)hESCDay 4–5 on plating of EBs2 µMPromoted cardiogenesis by about 40 times compared to DKK1

155

IWR-1(Wnt inhibitor)hESCDay 4–5 on plating of EBs4 µMMaximal cardiac induction by IWR-1 corresponds from day 4–5 
53AH (analogue of IWR-1)hESCDay 4–5 on plating of EBs1 µMPromoted cardiogenesis by about 40-fold compared to DKK1 
XAV939 (inhibitor of tankyrase)hESCDay 4–5 on plating of EBs2.5 µMPromoted cardiogenesis by about 40-fold compared to DKK1 
DKK1 (Wnt inhibitor)H7, H1 hESCDay 5–11 monolayer differentiation200 ng/mLIncreased cardiomyocyte generation

156

SU5402 (FGF receptor inhibitor)hESC, hiPSC4 or 6 days in culture1 µMSynergy between BMP2, Wnt3a and SU5402 (FGF receptor inhibitor) facilitate precardiac mesoderm

157

Noggin (BMP4 inhibitor)H7 hESCDay 4–5 in differentiation media250 ng/mLTimed inhibition increased cardiac differentiation efficiency

158

Dorsomorphin (BMP inhibitor)hESC, hiPSCDay 3–5 of EB differentiation0.25 µMIn presence of SB431542 and dorsomorphin, cTnT positive cells increased fourfold

73

BMS-189453 (RA receptor antagonist)H7 hESCDay 6–9 in differentiation media1 µMTimed inhibition of RA signalling promotes cardiac differentiation

158

Growth factorsWNT3aHUES1, 7, 8 hESCDay 1–4 of differentiation25 ng/mLWnt3a and BMP4 are prominent cytokines in the posterior primitive streak and direct cells toward mesoderm

159

TGFbeta1H7 hESCPre-differentiation culture0.5 ng/mLUsed in culture and pre-treatment of undifferentiated hPSCs

160

FGF-2hESC, hiPSCDay 0–2 of EB differentiation5 ng/mLCombination of BMP4 and FGF2 was determined to be necessary for efficient cardiac differentiation

141

EGFH9 hESCPostplating of EBs100 ng/mLFactors (EGF,RA,BMP4 and bFGF) activate ectodermal and mesodermal markers

150

Activin-AHES3, H9, MEL1 hESCDay 0–3 of differentiation6 ng/mLCardiomyocyte induction in RPMI/B27 media supplemented with activin A and BMP4

154

BMP4hESC, hiPSCDay 0–2 of EB differentiation25 ng/mLCombination of BMP4 and FGF2 was determined to be necessary for efficient cardiac differentiation

141

H1 hESC4 Days in EB suspension25 ng/mLBMP4 treatment promotes cardiac induction from hESCs

161

BMP2hESC, hiPSC4 or 6 days in culture10 ng/mLSynergy between BMP2, Wnt3a and SU5402 (FGF receptor inhibitor) facilitate precardiac mesoderm

157

Progress towards high throughput analysis

In an industrial setting, drug discovery and safety evaluation relies on high content imaging of many thousands of wells in 96-, 384- and 1,536-well plates (Fig. 1). Various manufacturers offer fully automated platforms 76 such as BD pathway (BD Biosciences), In Cell Analyser 2000 (GE-healthcare), ImageXpress (Molecular Devices), Opera (Perkin Elmer) and Cellomics Arrayscan (ThermoFisher). These deliver a vast array of information on cell physiology and function, including cell number, cell shape/size, proliferation, viability, membrane integrity, phagocytosis, apoptosis, cell migration, cell-cell contacts and organelle health (e.g. numbers, size, shape, activity of nucleus, mitochondria, lysosomes) 77. Fluorescent assays are also used to readout on G-protein coupled receptor (GPCR) activity, calcium handling and transgenic reporter expression 77. As discussed above, such platforms have been used to evaluate molecules that help maintain pluripotency or promote differentiation of hPSCs but they are starting to find use in phenotypic evaluation of differentiated cells. The Cellomics Arrayscan platform was used to evaluate the effect of various modulators of hypertrophy (e.g. angiotensin II, phenylephrine, p38-MAPK) on cell morphology of hPSC-cardiomyocytes by examining 1,000–1,500 cells per well in 96-well plate formats 78. Data have been presented by Cellular Dynamics International on quantification of the cardiotoxic effect of valinomycin, etoposide and rotenone in hPSC-cardiomyocytes using high content imaging of changes in mitochondrial and lysosomal physiology, DNA damage and oxidative stress. At a recent Predictive Toxicology Meeting in London (February 2012), data from GE-healthcare showed how 26 anti-cancer agents changed 19 different cell morphological and functional parameters in hPSC-cardiomyocytes. The analysis was carried out on three replicates, two timepoints and seven doses in a 384-well plate format using the In Cell 2000 platform. This analysis produced graphical profile sets that were associated with high, moderate, low or no drug-induced cellular toxicity.

High throughput electrophysiology provides a route to recording functional readouts from viable cells. The pharmaceutical industry uses PatchXpress, IonWorks and QT-screen to assess the effect of channel modulators on transgenic CHO cells overexpressing IKr potassium channel. Recently, it was demonstrated that high purity hPSC-cardiomyocytes could be adapted to the PatchXpress platform 79. This allowed simultaneous recording from 16 channels and the authors quantified the effect of tetrodotoxin, nifedipine and E4031 on INa, ICa-L and IKr, respectively 79. Further integration of hPSC-derivatives into high throughput platforms will help accelerate the use of these cells by the pharmaceutical industry.

Conclusions and future perspectives

Recent developments have boosted the likelihood of widespread use of hPSC-derivatives in disease modelling and drug development. Reprogramming somatic cells with four genetic factors has allowed rapid derivation of many hiPSC disease models. Differentiation efficiencies have radically improved, while clinical pathologies have been demonstrably replicated in cardiac and neural hiPSC-based models. Such models respond appropriately to pharmacological challenge, particularly for LQTS and potassium or calcium channel blockers. Nevertheless, hPSC technology requires improvements. Standardised methods that stabilise the genotype, epigenome and phenotype of hPSCs and their derivatives are paramount, as are methods to quantify phenotypic responses in lineages other than hPSC-cardiomyocytes and neurons. Current differentiation methods yield heterogeneous populations of immature cells; for cardiomyocytes, this includes ventricular, atrial and pacemaker subtypes 34, but mature ventricular cells are most relevant to drug safety assessment. Although hPSCs and their derivatives are adaptable to high throughput screening, current methods are not cost effective. These are surmountable issues, especially when driven by the needs of the pharmaceutical industry, where industry figures show that 98% of sales are based on products of >5 years old. 110,000 jobs have recently been lost in the US, and patent expiry will cost the industry USD$130 during 2011–2014. Not surprisingly, most major pharmaceutical companies now have in-house stem cell programmes, and collaborate with academic groups or purchase hPSC products from commercial suppliers 39. Just as new bioinformatics approaches are being applied to predict adverse drug interactions 80, so too will hPSC technologies in order to further understand disease and develop new drugs. Estimates indicate that even if an assay improves predictability of toxicity in humans by just 1%, up to $100 million will be saved by the pharmaceutical industry. Therefore, even small, incremental, improvements can be extremely worthwhile pursuing.

Acknowledgements

Financial support is from British Heart Foundation, Medical Research Council, Biotechnology and Biological Sciences Research Council and Engineering and Physical Research Council.

The authors have declared no conflict of interest.

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