A novel in vitro high-content imaging assay for the prediction of drug-induced lung toxicity.

The development of inhaled drugs for respiratory diseases is frequently impacted by lung pathology in non-clinical safety studies. To enable design of novel candidate drugs with the right safety profile, predictive in vitro lung toxicity assays are required that can be applied during drug discovery for early hazard identification and mitigation. Here, we describe a novel high-content imaging-based screening assay that allows for quantification of the tight junction protein occludin in A549 cells, as a model for lung epithelial barrier integrity. We assessed a set of compounds with a known lung safety profile, defined by clinical safety or non-clinical in vivo toxicology data, and were able to correctly identify 9 of 10 compounds with a respiratory safety risk and 9 of 9 compounds without a respiratory safety risk (90% sensitivity, 100% specificity). The assay was sensitive at relevant compound concentrations to influence medicinal chemistry optimization programs and, with an accessible cell model in a 96-well plate format, short protocol and application of automated imaging analysis algorithms, this assay can be readily integrated in routine discovery safety screening to identify and mitigate respiratory toxicity early during drug discovery. Interestingly, when we applied physiologically-based pharmacokinetic (PBPK) modelling to predict epithelial lining fluid exposures of the respiratory tract after inhalation, we found a robust correlation between in vitro occludin assay data and lung pathology in vivo, suggesting the assay can inform translational risk assessment for inhaled small molecules.

Use of precision cut lung slices as a translational model for the study of lung biology.

Animal models remain invaluable for study of respiratory diseases, however, translation of data generated in genetically homogeneous animals housed in a clean and well-controlled environment does not necessarily provide insight to the human disease situation. In vitro human systems such as air liquid interface (ALI) cultures and organ-on-a-chip models have attempted to bridge the divide between animal models and human patients. However, although 3D in nature, these models struggle to recreate the architecture and complex cellularity of the airways and parenchyma, and therefore cannot mimic the complex cell-cell interactions in the lung. To address this issue, lung slices have emerged as a useful ex vivo tool for studying the respiratory responses to inflammatory stimuli, infection, and novel drug compounds. This review covers the practicality of precision cut lung slice (PCLS) generation and benefits of this ex vivo culture system in modeling human lung biology and disease pathogenesis.

A novel multi-parametric high content screening assay in ciPTEC-OAT1 to predict drug-induced nephrotoxicity during drug discovery.

Drug-induced nephrotoxicity is a major concern in the clinic and hampers the use of available treatments as well as the development of innovative medicines. It is typically discovered late during drug development, which reflects a lack of in vitro nephrotoxicity assays available that can be employed readily in early drug discovery, to identify and hence steer away from the risk. Here, we report the development of a high content screening assay in ciPTEC-OAT1, a proximal tubular cell line that expresses several relevant renal transporters, using five fluorescent dyes to quantify cell health parameters. We used a validation set of 62 drugs, tested across a relevant concentration range compared to their exposure in humans, to develop a model that integrates multi-parametric data and drug exposure information, which identified most proximal tubular toxic drugs tested (sensitivity 75%) without any false positives (specificity 100%). Due to the relatively high throughput (straight-forward assay protocol, 96-well format, cost-effective) the assay is compatible with the needs in the early drug discovery setting to enable identification, quantification and subsequent mitigation of the risk for nephrotoxicity.

Advances in Predictive Toxicology for Discovery Safety through High Content Screening.

High content screening enables parallel acquisition of multiple molecular and cellular readouts. In particular the predictive toxicology field has progressed from the advances in high content screening, as more refined end points that report on cellular health can be studied in combination, at the single cell level, and in relatively high throughput. Here, we discuss how high content screening has become an essential tool for Discovery Safety, the discipline that integrates safety and toxicology in the drug discovery process to identify and mitigate safety concerns with the aim to design drug candidates with a superior safety profile. In addition to customized mechanistic assays to evaluate target safety, routine screening assays can be applied to identify risk factors for frequently occurring organ toxicities. We discuss the current state of high content screening assays for hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, and genotoxicity, including recent developments and current advances.

Validation of a high throughput flow cytometric in vitro micronucleus assay including assessment of metabolic activation in TK6 cells.

Genotoxicity is an unacceptable property for new drug candidates and we employ three screening assays during the drug discovery process to identify genotoxicity early and optimize chemical series. One of these methods is the flow cytometric in vitro micronucleus assay for which protocol optimizations have been described recently. Here, we report further validation of the assay in TK6 cells including assessment of metabolic activation. We first optimized assay conditions to allow for testing with and without metabolic activation in parallel in a 96-well plate format. Then, we tested a set of 48 compounds carefully selected to contain known in vivo genotoxins, nongenotoxins and drugs. Avoidance of irrelevant positives, a known issue with mammalian cell-based genotoxicity assays, is important to prevent early deselection of potentially promising compounds. Therefore, we enriched the validation set with compounds that were previously reported to produce irrelevant positive results in mammalian cell-based genotoxicity assays. The resulting dataset was used to set the relevant cut-off values for scoring a compound positive or negative, such that we obtained an optimal balance of high sensitivity (88%) and high specificity (87%). Finally, we tested an additional set of 16 drugs to further probe assay performance and 14 of them were classified correctly. To our knowledge, the present study is the most comprehensive validation of the in vitro flow cytometric micronucleus assay and the first to report parallel assessment with metabolic activation in reasonable throughput. The assay allows for rapidly screening novel compounds for genotoxicity and is therefore well-suited for use in early drug discovery projects. Environ.

High-content analysis/screening for predictive toxicology: application to hepatotoxicity and genotoxicity.

High-content imaging/analysis has emerged as a powerful tool for predictive toxicology as it can be used for identifying and mitigating potential safety risks during drug discovery. By careful selection of end-points, some cellular assays can show better predictivity than routine animal toxicity testing for certain adverse events. Here, we present the perhaps most utilized high-content screening assays for predictive toxicology in the pharmaceutical industry. Multi-parametric imaging of cell health in simple and cost-effective model systems can be used to predict human hepatotoxicity and elucidate mechanisms of toxicity, and imaging of bile salt transport inhibition in sandwich-cultured hepatocytes can be used to predict cholestasis-inducing compounds. Imaging of micronuclei formation in simple cell models can be used to detect genotoxic potential and elucidate anuegenic or clastogenic mode of actions. The hope is that application of these relatively predictive assays during drug discovery will reduce toxicity and safety-related attrition of drug development programmes at later stages.

Exploratory toxicology as an integrated part of drug discovery. Part II: Screening strategies.

In an effort to reduce toxicity-related attrition, different strategies have been implemented throughout the pharmaceutical industry. Previously (in Part I), we have outlined our 'integrated toxicology' strategy, which aims to provide timely go/no-go decisions (fail early) but also to show a direction to the drug discovery teams (showing what will not fail). In this review (Part II of the series) we describe our compound testing strategies with respect to cardiovascular safety, hepatotoxicity, genotoxicity, immunotoxicity and exploratory in vivo toxicity. We discuss the in vitro, ex vivo and in vivo assays and models we employ to assess safety risks and optimize compound series during the drug discovery process, including their predictivity and the decisions they generate.

Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how.

Toxicity and clinical safety have major impact on drug development success. Moving toxicological studies into earlier phases of the R&D chain prevents drug candidates with a safety risk from entering clinical development. However, to identify candidates without such risk, safety has to be designed actively. Therefore, we argue that toxicology should be fully integrated into the discovery process. We describe our strategy, including safety assessment of novel targets, selection of chemical series without inherent liabilities, designing out risk factors and profiling of candidates, and we discuss considerations regarding what to screen for. We aim to provide timely go/no-go decisions (fail early) and direction to the discovery teams, by steering away from safety risk (showing what will not fail).

A high content screening assay to predict human drug-induced liver injury during drug discovery.

INTRODUCTION: Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. METHODS: We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. RESULTS DISCUSSION: We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit <2 parameters within that 100-fold TI. In a zone classification model, based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe human exposure levels.

Characterization and optimization of a red-shifted fluorescence polarization ADP detection assay.

ATP depletion and ADP formation are generic detection methods used for the identification of kinase and other ATP-utilizing enzyme inhibitors in high-throughput screening campaigns. However, the most widely used nucleotide detection approaches require high ATP consumption rates or involve the use of coupling enzymes, which can complicate the selection of lead compounds. As an alternative, we have developed the Transcreener (BellBrook Labs, Madison, WI) platform, which relies on the direct immunodetection of nucleotides. Here we describe the development of antibodies with >100-fold selectivity for ADP versus ATP, which enable robust detection of initial velocity rates (Z' > 0.7 at 10% substrate consumption) at ATP concentrations ranging from 0.1 microM to 1,000 microM in a competitive fluorescence polarization (FP) immunoassay. Competitive binding experiments indicate similar affinities for other nucleotide diphosphates, including 2' -deoxy ADP, GDP, and UDP. The antibody-tracer complex and the red-shifted, ratiometric FP signal are stable for at least 24 h at room temperature, providing suitable conditions for high-throughput screening. A method for calculating a kinase ATP Km with this FP immunoassay is also presented. The Transcreener ADP assay provides a simple, generic assay platform for inhibitor screening and selectivity profiling that can be used for any ADP-generating enzyme.

Introduction to systems biology.

The developments in the molecular biosciences have made possible a shift to combined molecular and system-level approaches to biological research under the name of Systems Biology. It integrates many types of molecular knowledge, which can best be achieved by the synergistic use of models and experimental data. Many different types of modeling approaches are useful depending on the amount and quality of the molecular data available and the purpose of the model. Analysis of such models and the structure of molecular networks have led to the discovery of principles of cell functioning overarching single species. Two main approaches of systems biology can be distinguished. Top-down systems biology is a method to characterize cells using system-wide data originating from the Omics in combination with modeling. Those models are often phenomenological but serve to discover new insights into the molecular network under study. Bottom-up systems biology does not start with data but with a detailed model of a molecular network on the basis of its molecular properties. In this approach, molecular networks can be quantitatively studied leading to predictive models that can be applied in drug design and optimization of product formation in bioengineering. In this chapter we introduce analysis of molecular network by use of models, the two approaches to systems biology, and we shall discuss a number of examples of recent successes in systems biology.

Metabolic control analysis to identify optimal drug targets.

This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystem is, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.

Finding the way in the jungle of kinase drug targets.

Kinase inhibitors are developed for the treatment of various diseases. Because multiple factors control disease progression and kinases are part of large nonlinear networks, it is complicated to predict which kinase is the best to target. We substantiate the need for Systems Biology to assist in dealing with this complexity. Then, we discuss some of its contributions to kinase drug discovery with potential implications for the validation of kinases as drug targets and some of its present limitations.

Epidermal growth factor receptor-induced activator protein 1 activity controls density-dependent growth inhibition in normal rat kidney fibroblasts.

Density-dependent growth inhibition secures tissue homeostasis. Dysfunction of the mechanisms, which regulate this type of growth control is a major cause of neoplasia. In confluent normal rat kidney (NRK) fibroblasts, epidermal growth factor (EGF) receptor levels decline, ultimately rendering these cells irresponsive to EGF. Using an activator protein (AP)-1 sensitive reporter construct, we show that AP-1 activity is strongly decreased in density-arrested NRK cells, but is restored after relaxation of densitydependent growth inhibition by removing neighboring cells. EGF could not induce AP-1 activity or S-phase entry in density-arrested cells, but could do so after pretreatment with retinoic acid, which enhances EGF receptor expression. Our results support a model in which the EGF receptor regulates density-dependent growth control in NRK fibroblasts, which is reflected by EGF-induced mitogenic signaling and consequent AP-1 activity.

Oncogenes are to lose control on signaling following mutation: should we aim off target?

Rationalized cancer therapy aims at blocking overactive signaling pathways in cancer cells using kinase inhibitors. Essential for its success is the identification of suitable drug targets. Several recent reports have shown that by using control analysis, one can determine which component of a pathway is in control of its output. However, it has not been analyzed how a mutation in an oncogene affects the extent to which the various components are important. Are the same proteins still important after an oncogene has been activated? In the present study, we show that, upon mutation, oncogenes such as mutant kinases tend to lose part of their control on signaling. On the other hand, some of the nonmutated genes may become more important, when compared to the situation before the mutation. This may imply that, perhaps paradoxically, signaling proteins encoded by nonmutated genes should make better drug targets against cancer.

Cancer: a Systems Biology disease.

Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets? Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.