Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data.

Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp3, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models.

The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound properties. Although these models can be deployed as early as the stage of molecule design, they have a limited applicability domain─if the structures of interest differ substantially from the chemical space on which the model was trained, a reliable prediction will not be possible. Image-informed ligand-based models partly solve this shortcoming by focusing on the phenotype of a cell caused by small molecules, rather than on their structure. While this enables chemical diversity expansion, it limits the application to compounds physically available and imaged. Here, we employ an active learning approach to capitalize on both of these methods' strengths and boost the model performance of a mitochondrial toxicity assay (Glu/Gal). Specifically, we used a phenotypic Cell Painting screen to build a chemistry-independent model and adopted the results as the main factor in selecting compounds for experimental testing. With the additional Glu/Gal annotation for selected compounds we were able to dramatically improve the chemistry-informed ligand-based model with respect to the increased recognition of compounds from a 10% broader chemical space.

Moving beyond Binary Predictions of Human Drug-Induced Liver Injury (DILI) toward Contrasting Relative Risk Potential.

The hepatic risk matrix (HRM) was developed and used to differentiate lead clinical and back-up drug candidates against competitor/marketed drugs within the same pharmaceutical class for their potential to cause human drug-induced liver injury (DILI). The hybrid HRM scoring system blends physicochemical properties (Rule of Two Model: dose and lipophilicity or Partition Model: dose, ionization state, lipophilicity, and fractional carbon bond saturation) with common toxicity mechanisms (cytotoxicity, mitochondrial dysfunction, and bile salt export pump (BSEP) inhibition) that promote DILI. HRM scores are based on bracketed safety margins (<1, 1-10, 10-100, and >100× clinical Cmax,total). On the basis of well-established clinical safety experience of marketed/withdrawn drug candidates, the background analysis consists of 200 drugs from the Liver Toxicity Knowledge Base annotated as Most-DILI- (79), Less-DILI- (56), No-DILI- (47), and Ambiguous-DILI-concern (18) drugs. Scores were generated for over 21 internal and 7 external drug candidates discontinued for unacceptable incidence/magnitude of liver transaminase elevations during clinical trials or withdrawn for liver injury severity. Both hybrid scoring systems identified 70-80% Most-DILI-concern drugs, but more importantly, stratified successful/unsuccessful drug candidates for liver safety (incidence/severity of transaminase elevations and approved drug labels). Incorporating other mechanisms (reactive metabolite and cytotoxic metabolite generation and hepatic efflux transport inhibition, other than BSEP) to the HRM had minimal beneficial impact in DILI prediction/stratification. As is, the hybrid scoring system was positioned for portfolio assessments to contrast DILI risk potential of small molecule drug candidates in early clinical development. This stratified approach for DILI prediction aided decisions regarding drug candidate progression, follow-up mechanistic work, back-up selection, clinical dose selection, and due diligence assessments in favor of compounds with less implied clinical hepatotoxicity risk.

Evaluation of in Vitro Mitochondrial Toxicity Assays and Physicochemical Properties for Prediction of Organ Toxicity Using 228 Pharmaceutical Drugs.

Mitochondrial toxicity has been shown to contribute to a variety of organ toxicities such as liver, cardiac, and kidney. In the past decades, two high-throughput applicable screening assays (isolated rat liver mitochondria; glucose-galactose grown HepG2 cells) to assess mitochondrial toxicity have been deployed in many pharmaceutical companies, and numerous publications have demonstrated its usefulness for mechanistic investigations. However, only two publications have demonstrated the utility of these screens as a predictor of human drug-induced liver injury. In the present study, we screened 73 hepatotoxicants, 46 cardiotoxicants, 49 nephrotoxicants, and 60 compounds not known to cause human organ toxicity for their effects on mitochondrial function(s) in the assays mentioned above. Predictive performance was evaluated using specificity and sensitivity of the assays for predicting organ toxicity. Our results show that the predictive performance of the mitochondrial assays are superior for hepatotoxicity as compared to cardiotoxicity and nephrotoxicity (sensitivity 63% vs 33% and 28% with similar specificity of 93%), when the analysis was done at 100* Cmax (drug concentration in human plasma level). We further explored the association of mitochondrial toxicity with physicochemical properties such as calculated log partition coefficient (cLogP), topological polar surface area, ionization status, and molecular weight of the drugs and found that cLogP was most significantly associated mitochondrial toxicity. Since these assays are amenable to higher throughput, we recommend that chemists use these assays to perform structure activity relationship early in the drug discovery process, when chemical matter is abundant. This assures that compounds that lack the propensity to cause mitochondrial dysfunction (and associated organ toxicity) will move forward into animals and humans.

Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays.

Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC50 values obtained from the chip platform were correlated with rat LD50 values, human C max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC50 and C max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective.

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.

Toxicology Strategies for Drug Discovery: Present and Future.

Attrition due to nonclinical safety represents a major issue for the productivity of pharmaceutical research and development (R&D) organizations, especially during the compound optimization stages of drug discovery and the early stages of clinical development. Focusing on decreasing nonclinical safety-related attrition is not a new concept, and various approaches have been experimented with over the last two decades. Front-loading testing funnels in Discovery with in vitro toxicity assays designed to rapidly identify unfavorable molecules was the approach adopted by most pharmaceutical R&D organizations a few years ago. However, this approach has also a non-negligible opportunity cost. Hence, significant refinements to the "fail early, fail often" paradigm have been proposed recently to reflect the complexity of accurately categorizing compounds with early data points without taking into account other important contextual aspects, in particular efficacious systemic and tissue exposures. This review provides an overview of toxicology approaches and models that can be used in pharmaceutical Discovery at the series/lead identification and lead optimization stages to guide and inform chemistry efforts, as well as a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Development of a cell viability assay to assess drug metabolite structure-toxicity relationships.

Many adverse drug reactions are caused by the cytochrome P450 (CYP)-dependent activation of drugs into reactive metabolites. In order to reduce attrition due to metabolism-induced toxicity and to improve the safety of drug candidates, we developed a simple cell viability assay by combining a bioactivation system (human CYP3A4, CYP2D6 and CYP2C9) with Hep3B cells. We screened a series of drugs to explore structural motifs that may be responsible for CYP450-dependent activation caused by reactive metabolite formation, which highlighted specific liabilities regarding certain phenols and anilines.

Human drug-induced liver injury severity is highly associated with dual inhibition of liver mitochondrial function and bile salt export pump.

UNLABELLED: Drug-induced liver injury (DILI) accounts for 20-40% of all instances of clinical hepatic failure and is a common reason for withdrawal of an approved drug or discontinuation of a potentially new drug from clinical/nonclinical development. Numerous individual risk factors contribute to the susceptibility to human DILI and its severity that are either compound- and/or patient-specific. Compound-specific primary mechanisms linked to DILI include: cytotoxicity, reactive metabolite formation, inhibition of bile salt export pump (BSEP), and mitochondrial dysfunction. Since BSEP is an energy-dependent protein responsible for the efflux of bile acids from hepatocytes, it was hypothesized that humans exposed to drugs that impair both mitochondrial energetics and BSEP functional activity are more sensitive to more severe manifestations of DILI than drugs that only have a single liability factor. As annotated in the United States National Center for Toxicological Research Liver Toxicity Knowledge Base (NCTR-LTKB), the inhibitory properties of 24 Most-DILI-, 28 Less-DILI-, and 20 No-DILI-concern drugs were investigated. Drug potency for inhibiting BSEP or mitochondrial activity was generally correlated across human DILI concern categories. However, drugs with dual potency as mitochondrial and BSEP inhibitors were highly associated with more severe human DILI, more restrictive product safety labeling related to liver injury, and appear more sensitive to the drug exposure (Cmax) where more restrictive labeling occurs. CONCLUSION: These data affirm that severe manifestations of human DILI are multifactorial, highly associated with combinations of drug potency specifically related to known mechanisms of DILI (like mitochondrial and BSEP inhibition), and, along with patient-specific factors, lead to differences in the severity and exposure thresholds associated with clinical DILI.

Toxicity assessments of nonsteroidal anti-inflammatory drugs in isolated mitochondria, rat hepatocytes, and zebrafish show good concordance across chemical classes.

To reduce costly late-stage compound attrition, there has been an increased focus on assessing compounds in in vitro assays that predict attributes of human safety liabilities, before preclinical in vivo studies are done. Relevant questions when choosing a panel of assays for predicting toxicity are (a) whether there is general concordance in the data among the assays, and (b) whether, in a retrospective analysis, the rank order of toxicity of compounds in the assays correlates with the known safety profile of the drugs in humans. The aim of our study was to answer these questions using nonsteroidal anti-inflammatory drugs (NSAIDs) as a test set since NSAIDs are generally associated with gastrointestinal injury, hepatotoxicity, and/or cardiovascular risk, with mitochondrial impairment and endoplasmic reticulum stress being possible contributing factors. Eleven NSAIDs, flufenamic acid, tolfenamic acid, mefenamic acid, diclofenac, meloxicam, sudoxicam, piroxicam, diflunisal, acetylsalicylic acid, nimesulide, and sulindac (and its two metabolites, sulindac sulfide and sulindac sulfone), were tested for their effects on (a) the respiration of rat liver mitochondria, (b) a panel of mechanistic endpoints in rat hepatocytes, and (c) the viability and organ morphology of zebrafish. We show good concordance for distinguishing among/between NSAID chemical classes in the observations among the three approaches. Furthermore, the assays were complementary and able to correctly identify "toxic" and "non-toxic" drugs in accordance with their human safety profile, with emphasis on hepatic and gastrointestinal safety. We recommend implementing our multi-assay approach in the drug discovery process to reduce compound attrition.

Assessment of drug-induced mitochondrial dysfunction via altered cellular respiration and acidification measured in a 96-well platform.

High-throughput applicable screens for identifying drug-induced mitochondrial impairment are necessary in the pharmaceutical industry. Hence, we evaluated the XF96 Extracellular Flux Analyzer, a 96-well platform that measures changes in the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of cells. The sensitivity of the platform was bench-marked with known modulators of oxidative phosphorylation and glycolysis. Sixteen therapeutic agents were screened in HepG2 cells for mitochondrial effects. Four of these compounds, thiazolidinediones, were also tested in primary feline cardiomyocytes for cell-type specific effects. We show that the XF96 platform is a robust, sensitive system for analyzing drug-induced mitochondrial impairment in whole cells. We identified changes in cellular respiration and acidification upon addition of therapeutic agents reported to have a mitochondrial effect. Furthermore, we show that respiration and acidification changes upon addition of the thiazoldinediones were cell-type specific, with the rank order of mitochondrial impairment in whole cells being in accord with the known adverse effects of these drugs.

Cyclosporine A and palmitic acid treatment synergistically induce cytotoxicity in HepG2 cells.

Immunosuppressant cyclosporine A (CsA) treatment can cause severe side effects. Patients taking immunosuppressant after organ transplantation often display hyperlipidemia and obesity. Elevated levels of free fatty acids have been linked to the etiology of metabolic syndromes, nonalcoholic fatty liver and steatohepatitis. The contribution of free fatty acids to CsA-induced toxicity is not known. In this study we explored the effect of palmitic acid on CsA-induced toxicity in HepG2 cells. CsA by itself at therapeutic exposure levels did not induce detectible cytotoxicity in HepG2 cells. Co-treatment of palmitic acid and CsA resulted in a dose dependent increase in cytotoxicity, suggesting that fatty acid could sensitize cells to CsA-induced cytotoxicity at the therapeutic doses of CsA. A synergized induction of caspase-3/7 activity was also observed, indicating that apoptosis may contribute to the cytotoxicity. We demonstrated that CsA reduced cellular oxygen consumption which was further exacerbated by palmitic acid, implicating that impaired mitochondrial respiration might be an underlying mechanism for the enhanced toxicity. Inhibition of c-Jun N-terminal kinase (JNK) attenuated palmitic acid and CsA induced toxicity, suggesting that JNK activation plays an important role in mediating the enhanced palmitic acid/CsA-induced toxicity. Our data suggest that elevated FFA levels, especially saturated FFA such as palmitic acid, may be predisposing factors for CsA toxicity, and patients with underlying diseases that would elevate free fatty acids may be susceptible to CsA-induced toxicity. Furthermore, hyperlipidemia/obesity resulting from immunosuppressive therapy may aggravate CsA-induced toxicity and worsen the outcome in transplant patients.

Mitochondrial bioenergetics and drug-induced toxicity in a panel of mouse embryonic fibroblasts with mitochondrial DNA single nucleotide polymorphisms.

Mitochondrial DNA (mtDNA) variations including single nucleotide polymorphisms (SNPs) have been proposed to be involved in idiosyncratic drug reactions. However, current in vitro and in vivo models lack the genetic diversity seen in the human population. Our hypothesis is that different cell strains with distinct mtDNA SNPs may have different mitochondrial bioenergetic profiles and may therefore vary in their response to drug-induced toxicity. Therefore, we used an in vitro system composed of four strains of mouse embryonic fibroblasts (MEFs) with mtDNA polymorphisms. We sequenced mtDNA from embryonic fibroblasts isolated from four mouse strains, C57BL/6J, MOLF/EiJ, CZECHII/EiJ and PERA/EiJ, with the latter two being sequenced for the first time. The bioenergetic profile of the four strains of MEFs was investigated at both passages 3 and 10. Our results showed that there were clear differences among the four strains of MEFs at both passages, with CZECHII/EiJ having a lower mitochondrial robustness when compared to C57BL/6J, followed by MOLF/EiJ and PERA/EiJ. Seven drugs known to impair mitochondrial function were tested for their effect on the ATP content of the four strains of MEFs in both glucose- and galactose-containing media. Our results showed that there were strain-dependent differences in the response to some of the drugs. We propose that this model is a useful starting point to study compounds that may cause mitochondrial off-target toxicity in early stages of drug development, thus decreasing the number of experimental animals used.

The development of subcutaneous sarcomas in rodents exposed to peroxisome proliferators agonists: hypothetical mechanisms of action and de-risking attitude.

Peroxisome proliferator-activated receptors (PPARs) represent therapeutic targets for the management of type 2 diabetes mellitus and dyslipidemia. Rodent carcinogenicity studies have revealed a link between γ and dual γ/α PPAR agonist treatment and the increased incidence of subcutaneous (SC) liposarcomas/fibrosarcomas or hemangiosarcomas, but very little has been reported for potent and selective PPARα agonists. We present a mode of action framework for the development of SC mesenchymal tumors in rodents given PPAR agonists. (1) Tumor promotion results from pharmacologically mediated recruitment (proliferation and differentiation), thermogenesis and adipogenesis of stromovascular cells, and subsequent generation of oxidative free radicals. (2) Tumor initiation consists of chemotype-driven mitochondrial dysfunction causing uncontrolled oxidative stress and permanent DNA damage. Promotion is characterized by enhanced adipogenesis in the SC adipose tissue, where the baseline PPARγ expression and responsiveness to PPARγ ligands is the highest, and by thermogenesis through expression of the uncoupling protein 1 (UCP-1) and the PPARγ co-activator 1 α (PGC-1α), two factors more highly expressed in brown versus white adipose tissue. Initiation is supported by the demonstration of mitochondrial uncoupling and OXPHOS Complexes dysfunction (Complexes III, IV and V) by compounds associated with increased incidences of sarcomas (muraglitazar and troglitazone), but not others lacking malignant tumor effects (pioglitazone, rosiglitazone).

Using an in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies.

Recent publications have demonstrated that using calculated physiochemical properties can help in the design of compounds that have a decreased risk of significant findings in rodent toxicology studies. In this Letter, we extend this concept and incorporate results from a high throughput cytotoxicity assay to help the drug discovery community select compounds for progression into in vivo studies. The results are presented in an easily interpretable odds ratio so that teams can readily compare compounds and progress potential clinical candidates to the necessary rodent in vivo studies.

Introduction to a manuscript series on the characterization and use of microphysiological systems (MPS) in pharmaceutical safety and ADME applications.

Safety related drug failures continue to be a challenge for pharmaceutical companies despite the numerous complex and lengthy in vitro assays and in vivo studies that make up the typical safety screening funnel. A lack of complete translation of animal data to humans can explain some of those shortcomings. Differences in sensitivity and drug disposition between animals and humans may also play a role. Many gaps exist for potential target tissues of drugs that cannot be adequately modeled in vitro. Microphysiological systems (MPS) may help to better model these target tissues and provide an opportunity to better assess some aspects of human safety prior to clinical studies. There is hope that these systems can supplement current preclinical drug safety and disposition evaluations, filling gaps and enhancing our ability to predict and understand human relevant toxicities. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) MPS Affiliate is a group of pharmaceutical industry scientists who seek to expedite appropriate characterization and incorporation of MPS to potentially improve drug safety assessment and provide safer and more effective medicines to patients. In keeping with this mission, the IQ MPS Affiliate scientists have prepared a series of organotypic manuscripts for several key drug safety and disposition target tissues (lung, liver, kidney, skin, gastrointestinal, cardiovascular, and blood brain barrier/central nervous system). The goal of these manuscripts is to provide key information related to likely initial contexts of use (CoU) and key characterization data needed for incorporation of MPS in pharmaceutical safety screening including a list of characteristic functions, cell types, toxicities, and test agents (representing major mechanisms of toxicity) that can be used by MPS developers. Additional manuscripts focusing on testing biologically based therapeutics and ADME considerations have been prepared as part of this effort. These manuscripts focus on general needs for assessing biologics and ADME endpoints and include similar information to the tissue specific manuscripts where appropriate. The current manuscript is an introduction to several general concepts related to pharmaceutical industry needs with regard to MPS application and other MPS concepts that apply across the organ specific manuscripts.

In vitro analysis of cell metabolism using a long-decay pH-sensitive lanthanide probe and extracellular acidification assay.

Metabolic perturbations play a critical role in a variety of disease states and toxicities. Therefore, knowledge of the interplay between the two main cellular ATP generating pathways, glycolysis and oxidative phosphorylation, is particularly informative when examining such perturbations. Here we describe a new fluorescence-based screening assay for the assessment of glycolytic flux and demonstrate the value of such analysis in assessing the cellular "energy budget." The assay employs a long-decay pH-sensitive lanthanide probe to monitor extracellular acidification (ECA) in standard 96- or 384-well plates on a time-resolved fluorescence plate reader. The simple mix-and-measure procedure and fluorescence lifetime-based pH sensing allow the use of standard adherent cell culture techniques, providing high sample throughput and excellent assay performance. The assay also facilitates multiplexed or parallel analysis with existing oxygen consumption and ATP assays, thereby providing a detailed multiparametric assessment of cell metabolism. Data on cellular CO(2) production can also be obtained by comparing sealed and unsealed samples. The utility of the approach in assessing perturbed cell metabolism is demonstrated using a panel of metabolic effectors with known mechanisms of action. More complex metabolic stimuli, such as G protein-coupled receptor (GPCR) activation and perturbed ion homeostasis, are also examined.