Dominic P.
Williams
*a,
Rebecca
Shipley
b,
Marianne J.
Ellis
c,
Steve
Webb
d,
John
Ward
e,
Iain
Gardner
f and
Stuart
Creton
g
aMRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Sherrington Building, Ashton St., Liverpool, L69 3GE, UK. E-mail: dom@liv.ac.uk; Fax: +44 (0)151 794 5540; Tel: +44 (0)151 794 5791
bDepartment of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
cDepartment of Chemical Engineering, University of Bath, Claverton Down, Bath, BA2 7AY, UK
dDepartment of Mathematics and Statistics, University of Strathclyde, Livingstone Tower, 26 Richmond Street, Glasgow, G1 1XH, UK
eSchool of Mathematical Sciences, Loughborough University, Loughborough, LE11 3TU, UK
fSimcyp Limited, Blades Enterprise Centre, John Street, Sheffield S2 4SU, UK
gNC3Rs Gibbs Building, 215 Euston Road, London, NW1 2BE, UK
First published on 28th August 2012
The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. The scientific basis of drug safety is very complex and currently remains poorly understood, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ∼21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current in vitro test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of (1) the physiological gap between cells currently used and human hepatocytes existing in their native state, (2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, (3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, (4) lack of consideration of systemic effects. Reproduction of centrilobular and periportal hepatocyte phenotypes in in vitro culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen and hormone gradients. Application of bioartificial liver (BAL) technology can encompass in vitro predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the in vivo scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of in vitro experiments, and will enable both refinement and reduction of in vivo animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct in vitro and in vivo research, and can be used as a framework for other areas of drug safety science.
The organ that is most frequently affected by CRM-mediated ADRs is the liver. Drug-induced liver failure (DILI) poses a major clinical issue and has become the leading cause of acute liver failure and transplantation in Western countries.7,8 DILI accounts for more than half the cases of acute liver failure in the United States. DILI also represents a major challenge for industry and regulatory authorities: it is a leading cause for termination of further substance development in preclinical and clinical phases, and it is also the most common single adverse drug reaction leading to refusal of market approval. However, in many instances a drug's hepatotoxic potential is only recognized post-marketing and DILI has therefore also been a significant reason for withdrawing drugs from the market. Acetaminophen (paracetamol; APAP) is responsible for 80% of drug-associated cases of liver failure.8 APAP-induced hepatotoxicity is generally predictable from our understanding of its metabolism; however, many other drugs cause idiosyncratic DILI, which, although rare and unpredictable, can cause significant morbidity and mortality. Studies with model compounds and drugs — such as APAP — have helped to define the roles that chemical stress and drug bioactivation have in the various biological outcomes that may be triggered by CRMs. These include effects on transcription factors and/or signalling protein-adaptation (cell defence), apoptosis, necrosis, inflammation and activation of the innate and adaptive immune systems.9
Many different classes of drugs licensed for clinical use are known to cause DILI in man, and the cumulative DILI burden on professional health services coupled with patient well-being and/or mortality is high. Adverse drug reactions affecting the liver may be classified in two main types:
• Dose-dependent DILI which can be replicated readily in various animal species and is evident during preclinical safety testing.
• Human-specific DILI, which cannot be predicted in animal models or animal systems, and include idiosyncratic drug reactions (drug- or individual (Human Leukocyte Antigen (HLA) restriction)-specific) are unpredictable, occur only in certain susceptible patients and have a complex dose-dependent relationship.
Over 1000 drugs and herbal products have been associated with idiosyncratic hepatotoxicity,10–12 which is responsible for more than 10% of all cases of acute liver failure in the US. The incidence of idiosyncratic DILI caused by some drugs can be high as 1 in 100 patients (e.g. chlorpromazine), but more typically lower at 1 in 10000 patients (e.g. flucloxacillin). Idiosyncratic DILI is of major concern because it is not predictable from pre-clinical safety assessment studies due to a lack of predictive models, and consequently is rarely evident until late clinical trials or after regulatory approval.
Despite extensive work over the past 20 years, no new non-clinical tests and/or clinical biomarkers have been forthcoming for DILI that have been universally accepted by drug-developers, clinicians and regulators.13In vivo safety testing in pre-clinical species ensures that drugs, which enter clinical trials, do not cause reproducible and dose-dependent liver injury. However such test systems have failed to predict some serious cases of DILI that actually occurred in man, in particular those of an idiosyncratic nature. An improved understanding of mechanisms that underlie DILI in man is therefore required to enable design of drugs that have minimal potential to cause this adverse reaction in the patient population.
There are a number of different mechanisms through which drugs and their metabolites have been shown to cause ADRs. These include mitochondrial injury, transporter interactions, innate and adaptive immune system activation, direct cytotoxicity and phospholipid/lysosome liabilities. It is beyond the scope of this review to discuss novel in vitro and mathematical models for each of these toxicity mechanisms. The development of more sensitive in vitro models that replicate the in vivo environment allows re-investigation of drugs that have caused serious injury in man or adverse signals in experimental animals. Additionally, closer recapitulation of in vivo conditions would naturally encompass wider mechanistic coverage. These biological signals may have been overlooked in previous investigations due to a lack of appropriate technology or information. More effective in vitro systems would allow pragmatic compound ranking during drug discovery, aiding in vitro/in vivo translation with species/dose selection and translation to man. Therefore, we will focus on novel models exploring metabolic liabilities of drugs and chemicals. In particular, the design of in vitro systems that more closely mimic the in vivo environment, especially with respect to metabolism and bioactivation, and how these systems can be mathematically modelled.
The concept that small organic molecules can undergo bioactivation to electrophiles and free radicals, and elicit toxicity by covalent modification of cellular macromolecules, has its basis in chemical carcinogenicity and the pioneering work of the Millers14–16 (who studied the hepatotoxic effects of p-dimethylaminoazobenzene in the rat and found that aminoazo dyes become tightly bound to the protein constituents of liver tissue). The application of such concepts to human drug-induced hepatotoxicity was established through the studies of Brodie, Gillette, and Mitchell on the covalent binding to hepatic proteins of toxic doses (samples were obtained from overdose patients) of the widely used analgesic APAP. This concept is now well established, and there is a wealth of evidence to suggest that drug metabolism to a Chemically Reactive Metabolite (CRM; bioactivation) is the initiating step in a number of direct and immune mediated toxicities.5,17 A common mechanism for the detoxification of CRM occurs via glutathione (GSH) conjugation.18,19 The identification of drug metabolite-GSH adducts in a preclinical setting is sometimes treated as a hazard signal as it is indicative of CRM formation; conversely it could be argued that a GSH adduct is an indication of an effective detoxification system. Idiosyncratic ADRs are not reproducible in preclinical species, and so, recently there has been an increased focus on the development of surrogate endpoints of toxicity (biomarkers; either biological or chemical), animal models and improved understanding of structure-activity relationships of drugs. A list of chemical moieties frequently associated with drug bioactivation and subsequent toxicity (structural alerts) has been compiled.17 One approach to improving a drug's safety profile would be to avoid incorporating these structural alerts altogether; however, these moieties often are key to the pharmacology of the drug. In addition, the presence of a structural alert does not automatically result in a toxic compound; not all drugs that contain a structural alert are bioactivated and not all bioactivated drugs are toxic.17 The structure-activity relationships are, therefore, not straight forward.
It has been estimated that a 10% improvement in predicting failure before the initiation of expensive and time-consuming clinical trials could save upwards of $100 million in the costs associated with drug development.20 The need for better predictive models for DILI is clear and obvious. DILI is widely regarded both during clinical development and post-approval as a leading cause of:
• Drug attrition due to preclinical toxicity
• Drug attrition due to toxicity in man in late clinical trials
• Drug withdrawal post-licensing
• Cautionary and restrictive labelling
• Failed regulatory drug registration
• Serious illness in man
Therefore, it constitutes a major safety concern for drug development that severely impacts the profit margins of pharmaceutical companies through increased costs, longer development times and reduced market capitalization. Elimination of drug candidates likely to cause hepatotoxicity at early stages of drug discovery could significantly decrease the rate of attrition and cut the cost of drug development. In the context of prediction of DILI there are numerous issues to solve. Presently, there is a lack of sufficiently organized data to make an informed conclusion on the predictivity of non-clinical studies for identifying the risk of significant hepatotoxicity in clinical trials and in the post-marketing population. Additionally, there is limited predictive value of animal studies; some compounds have progressed into human trials and have then caused severe human toxicities, and no patterns existed in the animal studies that signalled these events. Currently, retrospective studies are being used to shed light on potential mechanisms that underlie toxicity. Ximelagatran, a first-generation thrombin inhibitor, was developed as an alternative to warfarin for individuals requiring anticoagulation therapy. However, up to 15% of patients treated with this drug experienced elevations in serum ALT levels, which were severe and life threatening in some cases.21,22 An association between the HLA class II allele HLA-DRB1*07 and elevations in serum ALT levels was observed in 74 ximelagatran treated patients, compared with 130 controls who were also given ximelagatran but who did not develop DILI; these findings were replicated in an additional dataset.21,22 Neither preclinical nor clinical studies of ximelagatran had suggested an immune-mediated mechanism underlying the increase in serum ALT levels associated with the use of this agent. However, studies in guinea pigs had suggested the possibility of skin sensitization in response to ximelagatran and some individuals exposed to ximelagatran during the manufacturing process developed a skin rash. Additionally, ximelagatran and its metabolites were shown to selectively inhibit the binding of peptides to HLA-DRB1*0701, further supporting a role for immune dysregulation in the pathogenesis of this instance of DILI.21,22 This exemplifies the fact that human-specific and idiosyncratic hepatotoxicity cannot be detected by conventional animal toxicity studies, with there being no suitable preclinical models combined with a poorly understood pathogenesis. It is likely to arise from complex interactions among genetic, non-genetic host susceptibility, and environmental factors. Additionally, interspecies differences in bioavailability, distribution and metabolism may explain a number of false positives and false negatives.
A much better understanding of the molecular mechanism(s) of liver toxicity in man, as well as the underlying reasons why non-clinical studies fail to prevent compounds which produce serious human hepatotoxicity from proceeding in the clinic, could result in the development of a more predictive non-clinical testing strategy. This is a critical prerequisite for any improvement in the detection of hepatotoxicity during the pre-clinical phase of drug development. Experimental approaches have focused on the development of various in vitro assays that can be used to assess in vivo effects. In vitro studies on liver cells have been developed to reduce or replace animal experiments. However, most of the in vitro tests in use are based on cell lines, which do not necessarily represent normal cell physiology.
The current in vitro test systems are poorly predictive of toxicological potential for a number of reasons. (1) The physiological gap between the cells that are currently used and human hepatocytes as they exist in their native state; (2) the lack of physiological integration with other cells and systems within the liver, that are required to amplify the initial toxicological lesion into overt toxicity, and; (3) there is no way to assess how low level cell damage induced by a drug may, in certain circumstances, lead to overt DILI in only a small minority of patients (i.e. idiosyncratic hepatotoxins). New approaches that improve upon conventional processes of risk assessment and safety evaluation are currently sought. Numerous promising new technologies and approaches have been described or are being developed which replicate many of the key biological processes implicated in reproducible and idiosyncratic/human-specific DILI. These range from simple cell systems to complex in vivo models, and may have the potential to enhance prediction and risk assessment of DILI in man if used during drug discovery and/or pre-clinical development. The choice of system or model to use depends upon the biology under investigation, for example in tissue engineering, it is widely accepted that 3D tissue culture models behave very differently to 2D models.
Clearly, gathering all toxicological information utilizing animal testing would be costly and carries a significant ethical responsibility. It is thought that a thorough gap-analysis investigation of the available toxicological data will highlight areas of data-deficiency, and rather than conducting further animal testing, this will enhance the development and validation of emerging and novel techniques, technologies and platforms, expanding the chemical and biological knowledge base and allowing improved prediction of safety. Ultimately this will enable platforms to be defined to enhance knowledge depth, as opposed to purely being used for testing. The two main principles underlying this approach are (1) that similar chemicals should elicit similar downstream toxicological profiles and (2) both unbiased and case-study approaches are required to address this issue.26–29 Therefore, chemicals that have well characterised pharmacokinetic, metabolic and toxicological profiles can be placed into specific categories, either based upon similarity profiles across both chemistry and nature of the downstream biological perturbation elicited. Subsequently, chemicals with less data available (e.g. only in vitro data) can be assessed for hazard to man using read-across processes to fill data deficits in the toxicology allowing enhanced prediction of safety.27,30 This approach has been shown to be of use to predict a range of chemicals and toxicological endpoints, such as skin and respiratory sensitization, mutagenicity and carcinogenicity, reproductive toxicity, toxicity of petroleum derivatives, nickel compounds, repeat dose toxicity and acute fish toxicity, excellently reviewed by the Cronin lab.24,31–34 However, in order to address these data deficient areas we need more innovative in vitro and in silico approaches that take into account the mechanistic basis underlying the perturbation of normal biological processes by drugs and chemicals – in man.
Fig. 1 A diagram illustrating the functional units of an hepatic acinus within liver tissue. An enlarged view of an hepatic acinus containing the portal triad – hepatic arteriole (1), portal vein (2), bile duct (3), central vein (4) and the direction of bile (green arrow) and blood (red arrow) flow. Inset. A simplified look at how the hepatic acini individually contribute to the drainage system within liver tissue, which is the unit supplied and drained by terminal branches of portal triad vessels. Nucleus (N), Smooth endoplasmic reticulum (SER), Bile salt export pump (BSEP), Mitochondrion (M). |
Bioreactors that have been used to date as in vitro liver models are summarised in Table 1; while they all have advantages and disadvantages, it is clear that hollow fibre membrane bioreactors (HFB) most closely represent the ideal design (see Fig. 2 for the setup of a HFB). Only membrane bioreactors separate hepatocytes from the main media supply (although the inner cells in spheroids are also removed from the flow and will receive media by diffusion) and thus enable the in vivo fluid shear environment to be replicated. In fact, shear can be applied independently, if desired, by side ports. These side ports can be used to supply different media types as well as apply shear, if desired and can be used to remove ‘bile’. Further, by controlling the media flowrates and pressures, the precise flow environment can be prescribed to mimic the nutrient and chemical delivery environment in vivo.
Fig. 2 Sketch of a hollow fibre membrane bioreactor. The hollow fibre membrane acts as the blood vessel through which media is added, with cells cultured on or around the outside of the fibres. Ports on the sides of the module allow addition or removal of cells, media, and other components. |
Bioreactor design | Is there direct contact of cells and media? | Can a second feed be supplied to the cells? | Can shear experienced by the cells be controlled separately to the media stream? | Can zonation be induced? | Can ‘bile’ be collected separately to exit media? | Example reference for bioreactor design |
---|---|---|---|---|---|---|
Ideal design | No | Yes | Yes | Yes | Yes | |
Hollow fibre membrane | No, except* | Yes | Yes | Yes | Yes | 125–127*. See also41 main focus is BALs128 |
Rectangular slide (flat bed) | Yes | No | No | Yes | No | 40 |
Macro-fabricated array bioreactor (flat bed) | Yes | No | No | Yes | No | 43,44 |
Spheroids in a stirred tank | Yes | No | No | To an extent through the spheroid | No | 46 |
Rotating HFB with cell-cytodex spheroids | No | No | Yes | To an extent through the spheroid | No | 47 |
Flat sheet membrane | No | Yes | Yes | Yes | Yes | 129–133 |
Lab-on-a-chip bioreactors ‘liver chip’ (flat bed) | Yes | No | No | Yes | No | 42 |
A major consideration for a liver bioreactor is the replication of zonation. This was first achieved using a flat plate configuration;40 subsequent designs include the macro-fabricated array bioreactor41 (flat bed), and the lab-on-a-chip bioreactors ‘liver chip’ (flat bed) mentioned in the review by Lerapetritou and co-workers.42 The microfabricated flat plate bioreactors by Park and co-workers43,44 could also induce zonation, and do so linearly so that the waste is removed with the media outlet. Hollow fibre bioreactors build on this and their most significant benefit is the fact that they can be operated to replicate both zonation,45 and the sinusoid physiology. The hollow fibres, which are porous tubular membranes, act as pseudovascularisation. In the liver scenario each fibre acts as a sinusoid; given that the fibre walls are porous, media can flow across them to the cells, but importantly the cells are protected from the flow of the media in the lumen. This means the media supply rate can be altered without risk of shear damage to the cell population. It could be said that cells in spheroids, such as those recently reported46,47 will experience zonation, as far as they will experience a concentration gradient as media diffuses to the centre of the constructs, and waste diffuses back out. This does not however replicate the linearity of the sinusoid.
Hollow fibre bioreactors are not without their own challenges. Achieving homogeneous seeding is more complex than for a flat surface. Further, there are more components to deal with than flat sheet/bed bioreactors so the user needs additional training (and although experience shows that, with training, fabricating and operating them is possible by a wide range of users).
Hollow fibre bioreactors have a large surface area to volume ratio thereby making them space-efficient and more media efficient, both of which are important from an economic perspective particularly on a larger-scale beyond the lab bench, and as such they are used for a number of industrial applications. Hollow fibre bioreactor fluid dynamics and mass transfer are well characterised due to their use in a range of fields including waste water treatment, cheese making and hemodialysis, as well as mammalian cell culture including for bioartificial livers (BAL) which Wang 201041 covers in a comprehensive review. A good introduction to hollow fibre membrane bioreactors can be found in Coulson and Richardson's Chemical Engineering Volume 248 and elsewhere for more detailed design information.49
The structure of a HFB is demonstrated in Fig. 2 and 3; it consists of a bundle of fibres housed in a bioreactor. Each fibre consists of a central cylindrical lumen surrounded by a porous membrane, then extra-capillary space (ECS). Cells can be seeded on the outer surface of the fibres (as relevant for hepatic considerations), or in a matrix surrounding the fibres, and media is driven through the fibre lumen under an applied pressure gradient. Given that the membrane is porous, nutrients, waste products, proteins and chemicals can permeate across it, to or from the cells. Further, exit ports in the ECS space may be opened to promote a fluid flow through the cellular space – this may be used to enhance nutrient delivery to the cell population, or to impose controlled fluid shear forces to the cells if required by the population under consideration. Mathematical models for mass transport in these systems are broadly divided into whether these ports are held shut (diffusion-limited) or open (convection-enhanced), according to the fluid flow regime that each scenario promotes. The general modelling frameworks take the form of convection–diffusion–reaction equations, where convection is applicable to the regions of the bioreactor with fluid flow, and the reaction component is used to, for example, describe uptake/production of nutrients/waste products by the cell population. Fluid flow is generally described using either Navier–Stokes or Darcy equations, depending on whether the flow is free (e.g. in the lumen), or through a porous medium (e.g. the membrane).
Fig. 3 A bundle of HFB demonstrating the principle of Krogh cylinders. One fibre surrounded by an annulus of ECS can be used to describe the entire fibre bundle when interstitial spaces between the fibre are ignored. The length of an individual fibre is denoted L; the radius of the lumen is denoted Rl, whilst the depth of the membrane and ECS spaces are denoted Rm, Re, respectively. |
Traditional modelling approaches for HFB have been motivated by understanding mass transport of solutes (e.g. oxygen, glucose) and proteins in a single fibre unit, called the Krogh cylinder, which is assumed to be representative of the whole reactor. The concept of a Krogh cylinder was originally introduced to describe diffusion of oxygen from capillaries in human tissues,50 and is now a common approach in efforts to model HFBs.51–55 The model consists of a single fibre surrounded by an annulus of ECS; by assuming the each fibre is identical and that the fibres are arranged in a regular hexagonal pattern, analysis of transport in this single fibre unit is considered representative of that throughout the bundle.
One of the earliest examples of the Krogh cylinder approach is Apelblat et al. 1974,56 where the authors use coupled Navier–Stokes equations (lumen) and Darcy's law (membrane and ECS) to describe fluid transport. In Kelsey et al. (1990),57 Darcy's law was replaced with Navier–Stokes’ equations for a setup with no cells in the ECS. Bruining (1989)58 presented a general description of the hydrodynamics in hollow fibre devices. The scope of his analysis included different modes of operation (e.g. ECS ports open or shut) corresponding to various applications of HFBs. Starting from mass and momentum balance equations, Bruining obtained expressions for the hydrostatic pressure and bypass (fraction of fluid passing through the ECS). However, Bruining's analysis provides no information on local velocity profiles, which are essential to understand, control and predict the transport of nutrients or waste products by advection. A review of Krogh cylinder models for mass transport in HFBs for cell culture is presented in Brotherton and Chau (1996).51 Two further Krogh cylinder approaches modelled the ECS as a porous medium to mimic a densely packed cell population.55,59 Abdullah and Das (2007)59 consider the impact of multi-solute interactions by using the Maxwell–Stefan equations to describe diffusion. Numerical solutions of the full equations using finite-element methods are used to investigate the dependence of the nutrient concentration profiles on parameters such as the cell density, fluid flow rate and depth of the cell layer.
Various approaches by the current authors have focused on the development of equations and data to inform bioreactor design and operation. One example focused on water transport throughout a single module unit in the absence of cells;60 this was integrated with experimental data to determine the permeability of poly(vinyl alcohol) (PVA)–PLGA membranes to pure water transport and to define an operating equation that relates the input conditions (lumen flow rate, lumen exit pressure and bioreactor geometry) to the ratio of flow rates exiting either the lumen and ECS. An extension to this overlaid a Poiseuille flow model for fluid transport in the lumen with oxygen transport throughout the bioreactor; this took the form of an advection–diffusion, diffusion and diffusion–reaction equations in the lumen, membrane and ECS, respectively. The system of equations was reduced by exploiting the small aspect ratio of a fibre to give simplified relationships that describe the relationship between the oxygen concentration throughout the ECS, and geometrical (e.g. lumen length, ECS depth) and operational (e.g. flow rates and pressures) parameters that can be controlled by the user.61 This work was extended to account for more complex fluid flow environments in the membrane and ECS,62 as are encountered if the ECS ports are opened. Similar approaches have been tailored specifically to the use of HFB for BAL applications,45 and to explore zonation.63 Here the mathematical models described above are used to understand how parameters such as the fluid flow rate and lumen length can be exploited to setup controlled spatial gradients in oxygen concentration down the lumen length, thus mimicking zonation in a hepatic sinusoid.
Crucially, the mathematical modelling frameworks described above provide spatial information on the cellular environment that is costly to achieve experimentally, are cell-type specific, and provide relationships that enable a user to tune the bioreactor geometry and operation to the application under consideration. The further development of these frameworks provides the potential for sophisticated in vitro liver models that represent more accurately the physiological and toxicological scenario in vivo, by coupling spatial features such as zonation to hepatocyte function. With respect to predictive toxicology, hepatocyte function, in this case, is more concerned with the enzymes responsible for phase I and II drug metabolism and drug transporters, rather than the enzymes catalyzing more physiological processes such as ammonia and carbohydrate metabolism. Consequently, to build a stable mathematical modelling framework which is applicable to predictive toxicology, we require chemically, metabolically and toxicologically well characterised paradigm compounds with which to begin populating the framework. One of the most thoroughly characterised molecules in this respect is APAP. It is now pertinent to briefly review how APAP causes liver injury through metabolism and bioactivation, and how emerging in vitro models can help closely reflect the in vivo situation both physiologically and toxicologically.
The use of APAP and its associated metabolism, bioactivation and toxicity is a crucially important benchmark which can be used to cross-evaluate novel in vitro systems. The extensive APAP metabolism/toxicity literature allows for a thorough comparison of novel in vitro systems, and the extent to which they improve upon traditional cell cultures methods or re-capitulate the in vivo environment. Comprehensive multi-parametric comparisons with respect to the involvement of phase I and II drug metabolism (CYP2E1, CYP3A4, glucuronidation and sulfation), drug transporters,64–66 detoxification processes (transcription factors e.g. Nrf2), adaptive/regenerative and overt toxic responses during APAP exposure can be compared both quantitatively and semi-quantitatively. These in-depth ‘functional’ assessments are far more important and provide real-life data that can be used for in vivo comparison, than simple mRNA or protein determinations of whether a enzyme is present or whether a fluorescent probe substrate can be handled.
At therapeutic doses, APAP is metabolized predominantly by the phase II metabolic pathways of glucuronidation and sulfation. There is a species- and strain-dependent proportion of APAP which is metabolized by the phase I cytochrome P450 metabolic pathway to the reactive metabolite NAPQI, which is subsequently detoxified by conjugation with GSH.67,68 In higher dose situations, the sulfation pathway becomes saturated with depletion of the sulphate pool, the co-factors of glucuronidation become depleted, and hence a larger portion of APAP is metabolized through the phase I pathway. GSH levels are limited and once depleted below a critical level NAPQI is free to react with cellular macromolecules. After formation of significant quantities of NAPQI, the pathways culminating in cellular injury have been extensively investigated, but their contributions to the actual pathogenesis are more tentative.38,67–69 Based on the weight of evidence, NAPQI binds to various proteins and disrupts their function, leading to altered cellular function. However, there are likely to be other direct or indirect effects of NAPQI leading to cell death, such as alteration of cellular redox status or disruption of signalling pathways. Despite the multitude of cellular pathways that have been shown to play a role in APAP-induced hepatotoxicity, disruption of mitochondrial function is one of the key outcomes.38,69–71 After covalent binding and GSH depletion occur, APAP induces the mitochondrial permeability transition, which allows the leakage of mitochondrial constituents into the cytosol. After activation of the MPT, mitochondria swell, lose membrane potential, and exhibit decreased oxidative phosphorylation and ATP depletion. Finally, depending upon the species, strain and fed/fasting status there may be a window where some hepatic apoptosis occurs, which rapidly degenerates into necrosis.72–74
In order to assess the relative merits of different bioreactor formats, it is necessary to utilise paradigm compounds that have very well characterised in vitro pharmacokinetics and toxicity profiles, such as APAP.
Allen et al. (2005)35 employed a flat-plate bioreactor to impose physiologic gradients over phenotypically stable hepatocytes and evaluate spatial variations of CYP expression and toxicity. Perfusion of APAP resulted in a shift in the dose response such that 20 mM was completely toxic in bioreactor cultures as compared to 40 mM in static cultures. Also, perfusion cultures at 15 mM demonstrated a toxicity pattern similar to the centrilobular localization seen in vivo. This in vitro zonal toxicity provides insight into the deleterious effects of APAP and the factors that contribute to spatial toxicity which would not be observed in conventional culture models. Toxic effects in this study are likely due to the depletion of glutathione, which provides protective inactivation of NAPQI. Though centrilobular localization of APAP toxicity in vivo has been attributed to local expression of CYP isoenzymes 2E1 and 3A, reduced oxygen availability in centrilobular regions may also contribute by depleting ATP and glutathione, or increasing damage by reactive species. A combination of these factors likely resulted in the regional toxicity observed in reactor cultures under dynamic oxygen gradients. Demonstration of zonal toxicity in vitro allows decoupling of the effects of CYP bioactivation and glutathione levels on acute APAP toxicity.35
Hepatocyte sandwich perfusion culture can further improve long-term liver specific functions in vitro, due to the improved transport of oxygen and nutrients to the cell surface and effective waste removal from cellular local environment. Perfusion bioreactors have been developed based on conventional sandwich culture which has a hepatocyte monolayer overlaid by a collagen gel layer 24 h after seeding.77 However, the perfusion flow would introduce the hepatocyte culture to the effect of fluid-induced shear stress not typically encountered by the cells in natural liver where hepatocytes are shielded by a layer of sinusoidal endothelial cells from the direct shear of the blood flow. High shear stress in perfusion culture could be detrimental to hepatocyte viability and in vitro functions. In addition, the integrity of the top collagen layer in direct contact with media flow may be compromised by the long-term perfusion that leads to the degeneration of sandwich matrix and, subsequently, the variation in mass transport of drug access during drug testing.77
Xia et al. (2009)77 have developed a laminar-flow perfusion bioreactor for immediate-overlay sandwich culture that minimizes shear stress and preserves the mass transport consistency. The cultured hepatocytes exhibited restored cell polarity, biliary excretion and maintenance of metabolic functions for two weeks. Liver specific functions of hepatocytes, such as the phase I drug metabolic function (e.g. EROD activity) are reported to be maintained for up to 15 days. The perfusion culture exhibited a higher sensitivity to APAP-induced toxicity than the static culture on both day 7 and day 14, attributable to the well-maintained drug metabolic functions of the sandwich perfusion culture. Approximately 60% of cell death was observed in the perfusion culture treated with 25 mM of APAP for 24 h. Furthermore, the cell viability in the perfusion culture after APAP treatment on day 7 and day 14 was similar. In contrast, static culture treated with APAP on day 7 and day 14 produced highly variable cell viability results.
More recently, Prot et al. (2011),78 have attempted to combine enzymatic functionality with proteomic and transcriptomic assessment of their flat-bed, HepG2-populated bioreactor. Classical studies demonstrating the hepatotoxic effect of APAP performed in vitro show that cytotoxicity is observed for a concentration of APAP ranging up to 5 to 20 mM with interspecies differences. In this case, APAP led to an EC50 at a 1 mM concentration for 72 h of contact only in the microfluidic biochip configuration.78 This result is in accordance with the toxic plasma level observed in humans and which ranges between 1 and 2 mM. They did not take into account the protein binding to APAP. Although APAP has a weak affinity for plasma proteins (<20%), the protein content between blood and culture are different. Consequently, the protein binding quantification will be necessary to fully confirm that the microfluidic biochip condition is a more physiological situation.78
This is a particularly encouraging report, as HepG2 cells in particular are not renowned for their metabolic capability.79 Other hepatic cell lines have been reported to have greater metabolic functionality, however, this is only when treated with proprietary dimethyl sulphoxide-containing ‘induction-media’.79 The recreation of more physiological environment for hepatic cell lines, including flow, oxygen levels, a 3D scaffold, the presence of endothelial cells appears to allow much improved functionality over static cultures. However, the relative importance of each of these parameters should be investigated in such a way as to define whether it is crucial for enhancing metabolism in chemical risk assessment in vitro.
This process will be time consuming and costly to conduct using experiments alone. In the next Section we discuss the use of mathematical models to inform cellular pharmacokinetics, using APAP as an example case. Compared to the mathematical models for transport processes within a bioreactor, here we focus on the pharmacokinetic processes within an individual cell. Such models enable insight to be gained into the relative importance of the range of metabolic processes occurring, can be used to test hypothesis in silico, and also can inform experimental work, thus providing a useful tool to complement existing experimental techniques.
Studying dose response models has been found to be useful in determining safe and hazardous levels and dosages for drugs. A dose-response curve is a simple graph relating the concentration of a compound to a given response (e.g. physiological, biochemical etc.). Analysis of dose response curves is typically performed using regression methods; the curves are typically fitted to the Hill equation (also known as the four parameter logistic fit). Over the years, a large number of studies have used Hill equations for the determination of drug potency, but there are a number issues related with this method. Prinz84 points out that Hill coefficients may give insight into the degree of binding cooperativity but cannot distinguish between competitive, non-competitive or ortho-, iso-, or allosteric mechanisms. The Hill function is clearly a simplification and it is likely that cell responses to toxic chemical compounds may not follow a simple logistic curve. Variation in the curve shape, however, has been explored by Levasseur.85
Levasseur85 has fit a number of models to data for seven anticancer agents against both parental and drug-resistant cell lines with iteratively reweighted non-linear regression. The key focus of this work was to model simultaneously both exposure time and drug concentration, rather than the typical approach at the time of this study which was to focus on concentration effect curves (see for example,86). The models used in their fitting take the form of either single, double or triple hill concentration effect curves, to represent either single cell population sensitivity or two or three populations of cells with different sensitivities and heterogeneities of drug responses. These types of models are useful for quantitative assessment, for example in assessing a growth-inhibitory effect of agents as a function of concentration and exposure time, but they can offer limited insight into underlying physicochemical mechanisms as the Hill parameters in this case have no physical meaning.
Some previous cellular pharmacodynamic models have included cellular pharmacokinetics, for example see.87–90 The87,88 models are discussed below. The model by Lankelma et al.89 incorporates two intracellular components with cell kill from the anticancer agent doxorubicin taken to be a function of compartmental concentration. In Lobo and Balthasar,90 delays are used to implicitly account for cellular uptake and binding for methotrexate.
A pharmacokinetic–pharmacodynamic model has been developed by Kareh et al.87 for the cellular pharmacology of paclitaxel with a critical dependency on the cell cycle – in particular cell cycle checkpoints. Here, cell death is assumed to occur when a threshold level of a lethal compound is exceeded once the cell is at a particular cell-cycle checkpoint. An assumed lognormal probability function for cell survival fraction accounts for cell population heterogeneity. The asynchrony of cell-cycle stages within the cell population is simplified by defining cell damage as the average over all the cells in terms of a maximum value of the concentration of the lethal compound. Experimental observations relating extracellular and intracellular concentrations of paclitaxel in human breast MCF cell monolayers91 has been used to determine the cellular pharmacokinetics for paclitaxel. This assumes equilibrium conditions with depletion, protein binding etc. ignored. Multiple damage mechanisms are taken into account corresponding to damage incurring at G1 and G2M phases of the cell cycle. Additive, where the total damage is represented as an exponent weighted sum, and independent damage models are compared. The cellular pharmacokinetics are based on experimental uptake rates92 but, rather than model the detailed pharmacokinetics, pharmacological terms are chosen to represent how the intracellular concentration of the lethal compound depends on the extracellular concentration. These terms are additionally assumed to decay exponentially over time to represent DNA repair timescales.
Another model for paclitaxel is that by Kuh.91 This model was based on a deterministic pharmacokinetic system to describe paclitaxel uptake, binding and efflux from cells. The key aim of the model was to predict the intracellular concentration of the drug. Ordinary differential equations were used to describe the changes over time of the concentrations of extra- and intracellular paclitaxel, total (free plus bound) drug in media and cells, and free drug in media and cells. There was an explicit consideration of cell volume, which was taken to be an exponentially decaying/increasing function of time due to cell cytotoxicity/cell proliferation – which of these that occurs depends on different drug concentrations. Extracellular drug binding was assumed to occur via Michaelis–Menten kinetics with a constant binding rate. Similar saturating binding kinetics are assumed to also occur intracellularly but, in this case, the binding rate was assumed to increase linearly with time. This was based on experimental observations showing a linear enhancement of tublin concentration over time with paclitaxel treatment. An additional linear intracellular binding term was also included to take into account non-saturable binding. The resulting mass balance equations are solved numerically and a parameter sensitivity analysis is performed to explore effects on intracellular drug accumulation.
In the work of Rougier et al.,83 the area under the time concentration curve (AUC) of amikacine serum concentrations was used to calculate the probability of occurrence of nephrotoxicity. The AUC is a commonly measured pharmacokinetic metric used to estimate exposure effects to a drug (e.g. see ref. 89 which notes that cell uptake and cytotoxicity of doxorubicin depends on the shape of the AUC). However, not all studies support the idea that cell kill is just a function of the AUC (see e.g.85,93) but note that many of these studies do not account for transport of drug into the cells which is suggested be an important factor.88 El-Kareh investigates the effectiveness of the AUC-dependent cytotoxicology measure to test anti-tumour effects to doxorubicin in ref. 88. The model is similar to that of87 but the principle focus was on the cellular uptake and cytotoxicity of the drug. Cell cycle effects were also neglected, a Hill-type dependency of cell survival on drug induced damage was assumed, cell damage was taken to be an additive sum of extracellular and intracellular effects, and the dependence of cell kill was on peak values of concentration rather than on the AUC. El-Kareh88 reports an overall better fit to in vitro cytotoxicity data than compared to previous AUC-dependent models.
The use of the Hill function, however, is limited in that it fails to account for underlying physicochemical reactions. Beyond single or positive cooperative binding interactions the Hill equation is somewhat of a compromise and, given that often in pharmacology, binding events are not restricted to single drug-receptor interactions but are instead more likely to be aggregated measures of drug effects on a large number of receptors,81 the Hill equation parameters have very little physical meaning. We propose that a mechanistic model that describes the physiochemical interaction at the receptor level and incorporates explicitly drug transport across the cell membrane and includes extra- and intracellular drug kinetics will best facilitate the quantitative assessment of drug metabolism. Cellular uptake data (see for example94 for data on doxorubicin) shows that intracellular drug levels often take on the order of hours to equilibrate with those extracellularly, suggesting that it is important to keep track of the non-equilibrium transient kinetics of uptake and cytotoxicity and therefore we propose that a non-equilibrium model is often most appropriate. We will now describe an example of a modelling framework that takes into account these factors and we use APAP as an example compound.
An important step in model development is to establish the mechanisms that are most likely to be the most important in governing the process. To this end, a very useful starting point is to put together a pathway diagram of the process, which can be clearly understood by both the modellers and experimentalists. Once such a pathway is in place then it is usually straightforward to write down a system of equations describing the evolution of the components by, for example, using mass action laws (Fig. 4 shows the pathway diagram used in the model described below). In our preliminary model, we assume that all the components of the APAP metabolic pathway are well mixed in the cell, or at least well mixed in the local site of the cell at which these processes are taking place; this assumption means that we can avoid the added complication of accounting explicitly the spatial variations of the components. This means that our main variables only change in time and the evolution of them can be described in terms of ordinary differential equations.
Fig. 4 Diagram summarising the paracetamol metabolism pathway in hepatocytes. The italicised capital letters indicate the model variables and the subscripted “k”s are the rate constants for the particular path. |
We assume that APAP (concentration P) is introduced into a cell with initial concentration P*0; this will of course vary with dosage and also will be dependent on the location of the cell along the hepatic sinusoid (we note that parameters that depend on location will be denoted with a *). From Fig. 4 we observe that APAP can be converted to excretory products, via sulphation and glucuronidation, or react with P450 to form NAPQI (concentration N). In our first approximation, it is assumed that P450 and the co-factors involved in glucuronidation are present at such high concentrations as to be little effected by the metabolism of the drug, i.e. their concentrations remain approximately constant throughout. For co-factors involved in sulphation, the story is expected to be different, in that their concentrations do change during this process; consequently, we need to track the concentration of these compound(s) (concentration S). We will assume also that NAPQI can spontaneously breakdown, releasing APAP back into the system. Combining all these assumptions we write
The concentration of P450 is dependent on location in a liver lobule, hence the * reaction rate constant k*450.
In the absence of drug, we assume there is a natural turnover of the sulphation co-factors, being produced constitutively at a rate βS and naturally breaks down with decay rate constant δS. Combining this with the removal by sulphation, leads to the equation
As discussed above NAPQI accumulates by the (reversible) reaction of APAP with P450. It is assumed that it can combine with GSH (concentration G) to form harmless products or, less desirable, with proteins-SH to form drug–protein conjugates (concentration T). There are numerous types of proteins-SH that will react with NAPQI and we will, for simplicity, lump all the protein-SH types together. Furthermore, we assume that there is an inexhaustible quantity of them, and describe the overall reaction along the toxin branch as a simple NAPQI decay term. The combination of these assumptions yields
This equation contains the key balance between the success and failure of the hepatocytes dealing with the build-up NAPQI effectively. Initially, GSH will be present in “large quantities”, which means that the breakdown of NAPQI will be predominantly along the branch towards non-toxic products. Mathematically, this says that the quantity kGSHG is much larger than kPSH. However, as G is being consumed and eventually exhausted, then this relationship will switch and the branch towards drug–protein conjugate production will dominate.
As with the sulphation compounds, we assume in the absence of APAP GSH is produced at a constitutive level (at rate β*G) and decays naturally (with decay rate constant δG), so that the concentration of GSH in healthy liver cells will be β*G/δG. We note that GSH levels do vary in the lobule, which we have assumed to be due to differences in production rate. Combining this with reaction of NAPQI yields
The build up of drug–protein conjugate, T, will reflect on the amount of damage the APAP dose will do to the cell. It is only produced by the reaction between NAPQI and protein-SH and we write
We have not considered any further reactions involving the drug–protein conjugate, and the variable T represents an accumulated total concentration of toxic species, rather than the amount of drug–protein conjugate at a particular time point; we could add a decay term to this equation as a simple way to describe these reactions. In order to complete the model we need to introduce an initial state, say at t = 0; we assume that the hepatocyte starts off as being healthy and is dosed with APAP, and write P(0) = P*0, S(0) = βS/δS, N(0) = 0, G(0) = β*G/δG and T(0) = 0.
This relatively simple model consists of ten reaction rate constants. Parameterisation of models such as these always poses a challenge and an extensive trawl through the experimental literature will be required to find as many suitable estimates as possible. This process will help to identify deficiencies in data and motivate further experimentation. Moreover, the model is simple enough to study using a range of mathematical techniques, in which we may be able to establish a full understanding of how the parameters affect the predicted outcome, for example, deriving a simple formula that predicts the extent of damage given a dose of the drug.
The basic principles of IVIVE include making in vitro measurements of the rate of drug/chemical metabolism, measuring (or predicting) the binding of the drug to plasma proteins and the non-specific binding within the experimental in vitro system and a knowledge of the blood:plasma ratio of the compound. Using physiological scaling factors such as the amount of microsomal protein or number of cells per gram tissue together with the weight of the organ the in vitro metabolism data can be scaled up to give an estimate of the intrinsic clearance of the drug/chemical in the whole organ. By incorporating this information into a model accounting for blood flow through the organ the clearance of the drug in the organ can be predicted.101
The second characteristic of the drug/chemical that needs to be understood to use PBPK approaches is the distribution of the drug/chemical into the tissues of the body. Traditionally, this information could only be generated by performing studies in animals where compounds were administered (often by infusion to steady state) then animals were euthanized and plasma and tissues removed and the drug concentrations measured. This allowed calculation of the tissue:plasma ratio in each organ.95 Although many PBPK models have been constructed using this approach (e.g.102) the experimental work needed to construct a PBPK model was laborious, time consuming and required extensive experimentation in animals. Another limitation was that the experiments needed to be performed for each compound of interest and as such was not suited to the rapid evaluation of large numbers of compounds needed in a drug discovery environment. In recent years in silico methods have been developed that allow the tissue:plasma ratios for compounds to be predicted using a knowledge of the compounds physicochemical properties.103–108 This has revolutionised the use of PBPK models within the Pharmaceutical industry as for a given compound routinely measured physicochemical data (log D, pKa,) and in vitro data (plasma protein binding; blood:plasma ratio) can be used to predict the distribution of drugs into tissues. By combining the IVIVE approaches to predict CL and in silico approaches to predict tissue distribution the disposition of a drug/chemical can be simulated. Furthermore, if information on the variability of each of the parameters used in the model is known then it is possible to apply IVIVE-PBPK approaches to predict the exposure (and effects) of drugs and chemicals in populations rather than in an “average” individual.101
As exemplified below this population IVIVE-PBPK approach has been recently applied to understand the toxicological risk posed by some drugs and chemicals and some aspects of the application of population PBPK approaches in human risk assessment have been recently reviewed.109
Although the in vitro assays can give a measure of the hazard posed by individual chemicals, prioritisation of chemicals based only on the toxic concentrations in in vitro assays without considering the impact of bioavailability, clearance, and exposure could over- or under-estimate the potential risk of these chemicals to human health.112
A number of studies have been conducted to try and link IVIVE-PBPK approaches with high throughput toxicity testing results to estimate the exposure/dose that would lead to toxicity in the more susceptible individuals in a simulated human exposure. The information from the population IVIVE-PBPK approach is compared with an estimated of the actual exposure in human populations. By comparing the window between simulated and actual exposure the compounds where toxicity is most likely can be selected for further, more definitive testing.27
This strategy was exemplified in a study by Rotroff et al. (2010).113 In this study metabolic clearance was measured in primary human cryopreserved hepatocytes and plasma protein binding was measured in vitro for 35 Toxcast chemicals. The metabolism and binding data were used as inputs in the Simcyp population-based IVIVE programme.114 Renal clearance was estimated from the glomerular filtration rate and protein binding and absorption was assumed to be 100%. From this the human equivalent dose to produce a steady state in vivo concentration equivalent to the concentration causing 50% of the maximum activity in vitro or the lowest effective concentration values from the Toxcast data could be calculated.113 These doses were then compared with chronic aggregate human oral exposures (where known) to see if biological activity and exposure overlapped. This occurred for only 2 compounds which would not have been at the top of the list based only on in vitro potency criteria.
In a follow-up study Wetmore et al.29 extended the in vitro measurements to a total of 239 compounds. In addition to hepatocyte stability and plasma protein binding in this study Caco-2 permeability data and blood:plasma ratio data was also generated for some compounds. For 18 (9.9%) chemicals, where data was available, the estimated (from IVIVE-PBPK) dose that would exert a biological effect in the population was equivalent to or less than the highest exposure estimates in the US population. The biological effects perturbed included cell growth kinetic effects, alterations in cytokine and drug metabolising enzyme/transporter expression and CYP inhibition, also affected were prostaglandin E receptor and urokinase-type plasminogen activator, which were both down regulated. Judson et al.,111 have also reported case studies (bisphenol A estrogenicity and for conazole CAR/PXR activation) where the ability of the population IVIVE-PBPK approaches to predict doses that would perturb the biological pathway was compared with the doses derived from animal toxicology studies.
Population IVIVE-PBPK approaches are also being utilised in an EU funded framework 7 project (predict-IV). (http://www.predict-iv.toxi.uni-wuerzburg.de). The overall aim of the project is to develop strategies that improve safety assessments in the late discovery-early development stage of the drug discovery process. To do this a combination of non-animal based in vitro test systems (including high content screening), mechanistic toxicology approaches (including toxicogenomics and metabolomics analysis) together with in silico modelling approaches are being utilised.
As greater application of IVIVE-PBPK approaches to tackle toxicological problems are made there is a great opportunity to use the models to help reduce the needs for in vivo testing of chemicals in animals, and also to make greater use of these approaches to predict the likely toxicity of chemicals in humans. For instance Tonnelier et al.,116 have examined the ability of IVIVE-PBPK approaches to predict human bioaccumulation of environmental chemicals whilst Tan et al.,117 have discussed the challenges with extending these approaches to look at the interaction between and subsequent toxicological outcome when chemical mixtures are dosed rather than individual chemicals.
Although the PBPK models currently used provide useful information further refinements of the models have been suggested including alternative representations of the tissue compartments118 and it is likely that to understand some of the more complex aspects of drug/chemical toxicology more detailed models that can account for differences in zonation across the liver, specific sub-populations of cells that are at risk of toxicity etc will be needed to accurately predict the relationship between tissue concentration and toxicity in susceptible individuals. In addition the challenges that need to be resolved to allow the use of IVIVE and PBPK approaches in formal human health risk assessment have been recently discussed.119 Whilst the combination of high throughput in vitro toxicity data with population IVIVE approaches is showing great promise particularly as a tool to prioritise compounds for further consideration there are some limitations of the high throughput screening approach. These include the fact that most of the cell types used in high throughput assays lack the ability to catalyse chemical biotransformation (particularly at a quantitatively relevant rate) and as such often will only allow information on parent compound (not more or less toxic metabolites) to be made. In addition using only a single cell type as is often done then a number of paracrine signalling pathways may be missing and in order to restore these, a mixture of cell types, appropriate extracellular matrices and three dimensional geometries may be needed. Both of these limitations outline the need for more complex in vitro systems that can be used to generate detailed mechanistic understanding of the toxicological risk of compounds identified in the initial screening approach.
Within the field of toxicology, studies have traditionally been conducted in animals at high doses with default methods used to extrapolate to low level exposures in human populations. However, there is growing interest in developing new approaches that could revolutionise how toxicity testing is conducted. In addition to animal welfare considerations, there are a number of economic, legislative and scientific drivers for this e.g.: (i) in vivo toxicity studies are expensive and time consuming, which limits their practicality in large-scale chemical testing programmes such as REACH; (ii) recent changes to the European Cosmetics Directive prescribe a ban on animal testing and the marketing of products containing ingredients tested on animals; (iii) the animal data can be difficult to interpret in terms of predicting potential effects in humans.
In 2007 the US National Research Council (NRC) published a landmark report on ‘Toxicity Testing in the 21st Century: A Vision and a Strategy’.121,122 The NRC was tasked with considering how toxicity testing could incorporate recent advances in science and technology in order to generate more relevant data for risk assessment, expand the number of chemicals of concern that could be assessed, and reduce the time, money and number of animals involved. The report sees a future in which routine toxicity testing would be conducted in primary human cells, human tissue surrogates, or human cell lines in vitro by evaluating cellular responses in a suite of toxicity pathway assays. These tools would enable risk assessors to focus on predicting exposures that are expected to be without adverse consequences, rather than making predictions on the incidence of specific adverse responses in human populations.
Implementing this Vision will require a major research effort involving scientists from a broad range of disciplines and sectors. Key elements to its realisation include identification and understanding of toxicity pathways, plus the development of systems to understand exposure parameters in vitro and their extrapolation to inform safe in vivo exposure/in use scenarios. A number of research efforts are currently ongoing, with some taking a case study approach with a specific toxicity pathway, and others applying a more unbiased approach seeking to design convenient in vitro systems that would provide information on as much of the biological response landscape as possible.27,123,124
The UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) works closely with scientists in industry, academia and government organisations to identify opportunities to minimise animal use and/or improve welfare, using the 3Rs as a framework to drive innovation. In 2011, the Centre launched CRACK IT (http://www.crackit.org.uk), a new open innovation programme designed to accelerate the application of the 3Rs by fostering and funding multidisciplinary collaborations that broaden the expertise involved. A central component of CRACK IT is a new challenge-led research competition, CRACK IT Challenges. For this competition, the NC3Rs works closely with companies from the pharmaceutical, chemical, agrochemical and consumer product industries to identify global business challenges relating to the use of animals. Challenges are supported by funding from the NC3Rs and in-kind contributions from industry such as data, compounds and equipment.
The 2011 CRACK IT Challenges competition included a Challenge seeking to stimulate further research to explore a pathways approach to safety risk assessment. The research award was made to the authors of the present paper, on the strength of the truly multidisciplinary approach proposed; integrating new tissue engineered liver models based on human cells with mathematical modelling in order to promote liver zonation and explore effects on biological pathways associated with liver toxicity. The various approaches will ultimately be combined with the aim of extrapolating from in vitro to make decisions around safety in humans. This CRACK IT project will, if successful, provide proof of principle that the pathways approach proposed as a means of replacing animals in toxicology studies can be successfully employed. This project is complementary to and collaborative with the established Hamner DILIsim project and the new Innovative Medicines Initiative project “Mechanism-Based Integrated Systems for the Prediction of Drug-Induced Liver Injury” (MIP-DILI).
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