Rigorous and rapid evidence assessment in digital health with the evidence DEFINED framework.

Dozens of frameworks have been proposed to assess evidence for digital health interventions (DHIs), but existing frameworks may not facilitate DHI evidence reviews that meet the needs of stakeholder organizations including payers, health systems, trade organizations, and others. These organizations may benefit from a DHI assessment framework that is both rigorous and rapid. Here we propose a framework to assess Evidence in Digital health for EFfectiveness of INterventions with Evaluative Depth (Evidence DEFINED). Designed for real-world use, the Evidence DEFINED Quick Start Guide may help streamline DHI assessment. A checklist is provided summarizing high-priority evidence considerations in digital health. Evidence-to-recommendation guidelines are proposed, specifying degrees of adoption that may be appropriate for a range of evidence quality levels. Evidence DEFINED differs from prior frameworks in its inclusion of unique elements designed for rigor and speed. Rigor is increased by addressing three gaps in prior frameworks. First, prior frameworks are not adapted adequately to address evidence considerations that are unique to digital health. Second, prior frameworks do not specify evidence quality criteria requiring increased vigilance for DHIs in the current regulatory context. Third, extant frameworks rarely leverage established, robust methodologies that were developed for non-digital interventions. Speed is achieved in the Evidence DEFINED Framework through screening optimization and deprioritization of steps that may have limited value. The primary goals of Evidence DEFINED are to a) facilitate standardized, rapid, rigorous DHI evidence assessment in organizations and b) guide digital health solutions providers who wish to generate evidence that drives DHI adoption.

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG.

Objective.The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as 'spikes', in people with epilepsy.Approach.A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results.Main results.We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise<300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG.Significance.These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies in Clinical Trials.

Background: The proliferation and increasing maturity of biometric monitoring technologies allow clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of traditional clinical settings. This includes capturing meaningful aspects of health in daily living and a more granular and objective manner compared to traditional tools in clinical settings. Summary: Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures. They are called upon to provide input into trial planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. Key Messages: This paper discusses key considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that investigators may encounter while dealing with data from biometric monitoring technologies.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

BACKGROUND: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. OBJECTIVE: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. METHODS: We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). RESULTS: Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. CONCLUSIONS: We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers.

Background: Light exposure is an important driver and modulator of human physiology, behavior and overall health, including the biological clock, sleep-wake cycles, mood and alertness. Light can also be used as a directed intervention, e.g., in the form of light therapy in seasonal affective disorder (SAD), jetlag prevention and treatment, or to treat circadian disorders. Recently, a system of quantities and units related to the physiological effects of light was standardized by the International Commission on Illumination (CIE S 026/E:2018). At the same time, biometric monitoring technologies (BioMeTs) to capture personalized light exposure were developed. However, because there are currently no standard approaches to evaluate the digital dosimeters, the need to provide a firm framework for the characterization, calibration, and reporting for these digital sensors is urgent. Objective: This article provides such a framework by applying the principles of verification, analytic validation and clinical validation (V3) as a state-of-the-art approach for tools and standards in digital medicine to light dosimetry. Results: This article describes opportunities for the use of digital dosimeters for basic research, for monitoring light exposure, and for measuring adherence in both clinical and non-clinical populations to light-based interventions in clinical trials.

Microcirculatory Function during Endotoxemia-A Functional Citrulline-Arginine-NO Pathway and NOS3 Complex Is Essential to Maintain the Microcirculation.

Competition for the amino acid arginine by endothelial nitric-oxide synthase (NOS3) and (pro-)inflammatory NO-synthase (NOS2) during endotoxemia appears essential in the derangement of the microcirculatory flow. This study investigated the role of NOS2 and NOS3 combined with/without citrulline supplementation on the NO-production and microcirculation during endotoxemia. Wildtype (C57BL6/N background; control; n = 36), Nos2-deficient, (n = 40), Nos3-deficient (n = 39) and Nos2/Nos3-deficient mice (n = 42) received a continuous intravenous LPS infusion alone (200 µg total, 18 h) or combined with L-citrulline (37.5 mg, last 6 h). The intestinal microcirculatory flow was measured by side-stream dark field (SDF)-imaging. The jejunal intracellular NO production was quantified by in vivo NO-spin trapping combined with electron spin-resonance (ESR) spectrometry. Amino-acid concentrations were measured by high-performance liquid chromatography (HPLC). LPS infusion decreased plasma arginine concentration in control and Nos3-/- compared to Nos2-/- mice. Jejunal NO production and the microcirculation were significantly decreased in control and Nos2-/- mice after LPS infusion. No beneficial effects of L-citrulline supplementation on microcirculatory flow were found in Nos3-/- or Nos2-/-/Nos3-/- mice. This study confirms that L-citrulline supplementation enhances de novo arginine synthesis and NO production in mice during endotoxemia with a functional NOS3-enzyme (control and Nos2-/- mice), as this beneficial effect was absent in Nos3-/- or Nos2-/-/Nos3-/- mice.

Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment.

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks'') for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.

EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration.

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience.

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography.

Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

Publisher Correction: Therapies for rare diseases: therapeutic modalities, progress and challenges ahead.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Therapies for rare diseases: therapeutic modalities, progress and challenges ahead.

Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features.

OBJECTIVE: state-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside. METHODS: an artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. The results are mapped into a physiologic state space for near real-time patient-state tracking. RESULTS: the framework was validated on cardiac beat-to-beat dynamics processed with the multiscale entropy algorithm, and assessed using PhysioNet databases. We then applied our algorithm to predict 28-day mortality for sepsis patients, and found it had greater prognostic accuracy than standard clinical severity scores. CONCLUSION: we developed a novel framework to classify multiscale features of beat-to-beat dynamics, and performed an initial clinical validation to demonstrate that our approach generates a robust quantification of a patient's state, compatible with real-time bedside implementations. SIGNIFICANCE: the framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of "always-on" diagnostic tools.

Citrulline Supplementation Improves Organ Perfusion and Arginine Availability under Conditions with Enhanced Arginase Activity.

Enhanced arginase-induced arginine consumption is believed to play a key role in the pathogenesis of sickle cell disease-induced end organ failure. Enhancement of arginine availability with L-arginine supplementation exhibited less consistent results; however, L-citrulline, the precursor of L-arginine, may be a promising alternative. In this study, we determined the effects of L-citrulline compared to L-arginine supplementation on arginine-nitric oxide (NO) metabolism, arginine availability and microcirculation in a murine model with acutely-enhanced arginase activity. The effects were measured in six groups of mice (n = 8 each) injected intraperitoneally with sterile saline or arginase (1000 IE/mouse) with or without being separately injected with L-citrulline or L-arginine 1 h prior to assessment of the microcirculation with side stream dark-field (SDF)-imaging or in vivo NO-production with electron spin resonance (ESR) spectroscopy. Arginase injection caused a decrease in plasma and tissue arginine concentrations. L-arginine and L-citrulline supplementation both enhanced plasma and tissue arginine concentrations in arginase-injected mice. However, only the citrulline supplementation increased NO production and improved microcirculatory flow in arginase-injected mice. In conclusion, the present study provides for the first time in vivo experimental evidence that L-citrulline, and not L-arginine supplementation, improves the end organ microcirculation during conditions with acute arginase-induced arginine deficiency by increasing the NO concentration in tissues.

MAPK-activated protein kinase 2-deficiency causes hyperacute tumor necrosis factor-induced inflammatory shock.

BACKGROUND: MAPK-activated protein kinase 2 (MK2) plays a pivotal role in the cell response to (inflammatory) stress. Among others, MK2 is known to be involved in the regulation of cytokine mRNA metabolism and regulation of actin cytoskeleton dynamics. Previously, MK2-deficient mice were shown to be highly resistant to LPS/d-Galactosamine-induced hepatitis. Additionally, research in various disease models has indicated the kinase as an interesting inhibitory drug target for various acute or chronic inflammatory diseases. RESULTS: We show that in striking contrast to the known resistance of MK2-deficient mice to a challenge with LPS/D-Gal, a low dose of tumor necrosis factor (TNF) causes hyperacute mortality via an oxidative stress driven mechanism. We identified in vivo defects in the stress fiber response in endothelial cells, which could have resulted in reduced resistance of the endothelial barrier to deal with exposure to oxidative stress. In addition, MK2-deficient mice were found to be more sensitive to cecal ligation and puncture-induced sepsis. CONCLUSIONS: The capacity of the endothelial barrier to deal with inflammatory and oxidative stress is imperative to allow a regulated immune response and maintain endothelial barrier integrity. Our results indicate that, considering the central role of TNF in pro-inflammatory signaling, therapeutic strategies examining pharmacological inhibition of MK2 should take potentially dangerous side effects at the level of endothelial barrier integrity into account.

A multiscale entropy-based tool for scoring severity of systemic inflammation.

OBJECTIVE: Early detection and start of appropriate treatment are highly correlated with survival of sepsis and septic shock, but the currently available predictive tools are not sensitive enough to identify patients at risk. DESIGN: Linear (time and frequency domain) and nonlinear (unifractal and multiscale complexity dynamics) measures of beat-to-beat interval variability were analyzed in two mouse models of inflammatory shock to determine if they are sensitive enough to predict outcome. SETTING: University research laboratory. SUBJECTS: Blood pressure transmitter-implanted female C57BL/6J mice. INTERVENTIONS: IV administration of tumor necrosis factor (n = 11) or lipopolysaccharide (n = 14). MEASUREMENTS AND MAIN RESULTS: Contrary to linear indices of variability, unifractal dynamics, and absolute heart rate or blood pressure, quantification of complex beat-to-beat dynamics using multiscale entropy was able to predict survival outcome starting as early as 40 minutes after induction of inflammatory shock. Based on these results, a new and clinically relevant index of multiscale entropy was developed that scores the key features of a multiscale entropy profile. Contrary to multiscale entropy, multiscale entropy scoring can be followed as a function of time to monitor disease progression with limited loss of information. CONCLUSIONS: Analysis of multiscale complexity of beat-to-beat dynamics at high temporal resolution has potential as a sensitive prognostic tool with translational power that can predict survival outcome in systemic inflammatory conditions such as sepsis and septic shock.

Simultaneous targeting of IL-1 and IL-18 is required for protection against inflammatory and septic shock.

RATIONALE: Sepsis is one of the leading causes of death around the world. The failure of clinical trials to treat sepsis demonstrates that the molecular mechanisms are multiple and are still insufficiently understood. OBJECTIVES: To clarify the long disputed hierarchical contribution of several central inflammatory mediators (IL-1ß, IL-18, caspase [CASP] 7, CASP1, and CASP11) in septic shock and to explore their therapeutic potential. METHODS: LPS- and tumor necrosis factor (TNF)-induced lethal shock, and cecal ligation and puncture (CLP) were performed in genetically or pharmacologically targeted mice. Body temperature and survival were monitored closely, and plasma was analyzed for several markers of cellular disintegration and inflammation. MEASUREMENTS AND MAIN RESULTS: Interestingly, deficiency of both IL-1ß and IL-18 additively prevented LPS-induced mortality. The detrimental role of IL-1ß and IL-18 was confirmed in mice subjected to a lethal dose of TNF, or to a lethal CLP procedure. Although their upstream activator, CASP1, and its amplifier, CASP11, are considered potential therapeutic targets because of their crucial involvement in endotoxin-induced toxicity, CASP11- or CASP1/11-deficient mice were not, or hardly, protected against a lethal TNF or CLP challenge. In line with our results obtained in genetically deficient mice, only the combined neutralization of IL-1 and IL-18, using the IL-1 receptor antagonist anakinra and anti-IL-18 antibodies, conferred complete protection against endotoxin-induced lethality. CONCLUSIONS: Our data point toward the therapeutic potential of neutralizing IL-1 and IL-18 simultaneously in sepsis, rather than inhibiting the upstream inflammatory caspases.

Nitric oxide production by endotoxin preparations in TLR4-deficient mice.

Sepsis and septic shock result from an exacerbated systemic inflammatory reaction to infection. Their incidence is rising, and they have recently become the main cause of death in intensive care units. Septic shock is defined as sepsis accompanied by life-threatening refractory hypotension, for which excessive nitric oxide (NO), produced by inducible NO synthase iNOS, is thought responsible. LPS, a vital outer membrane component of Gram-negative bacteria, mimics most of the septic effects and is widely used as a model for septic shock. TLR4 is the signal-transducing receptor for LPS, evidenced by the resistance of TLR4-deficient C3H/HeJ and C57BL/10ScNJ mice. As expected, we found that TLR4 deficiency precludes LPS-induced cytokine production, independent of the purity of the LPS preparation. However, various conventional LPS preparations induced NO in TLR4-deficient mice to the same level as in control animals, while ultrapure LPS did not, indicating the presence of NO-producing contaminant(s). Nevertheless, despite identical iNOS induction pattern and systemic NO levels, the contaminant does not cause hypotension, hypothermia, or any other sign of morbidity. Using mice deficient for TLR2, TRL3, TLR4, TRL2x4, TLR9, MyD88 or TRIF, we found that the contaminant signals via TLR2 and MyD88. In conclusion, conventional LPS preparations generally used in endotoxic shock research contain TLR2 agonists that induce iNOS and high levels of systemic NO as such, and synergize with LPS towards the production of pro-inflammatory cytokines, morbidity and mortality. Surprisingly, the excessive iNOS-derived systemic NO production induced by impure LPS in TLR4⁻/⁻ is not accompanied by hypotension or morbidity.