From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.

INTRODUCTION: Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA. AREAS COVERED: We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies. EXPERT OPINION: As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P â€‹+ â€‹S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P â€‹+ â€‹S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Animal Models of Osteoarthritis Part 1-Preclinical Small Animal Models: Challenges and Opportunities for Drug Development.

Osteoarthritis (OA) is the most common form of arthritis and a major source of pain and disability in the adult population. There is a significant unmet medical need for the development of effective pharmacological therapies for the treatment of OA. In addition to spontaneously occurring animal models of OA, many experimental animal models have been developed to provide insights into mechanisms of pathogenesis and progression. Many of these animal models are also being used in the drug development pipeline. Here, we provide an overview of commonly used and emerging preclinical small animal models of OA and highlight the strengths and limitations of small animal models in the context of translational drug development. There is limited information in the published literature regarding the technical reliability of these small animal models and their ability to accurately predict clinical drug development outcomes. The cost and complexity of the available models however is an important consideration for pharmaceutical companies, biotechnology startups, and contract research organizations wishing to incorporate preclinical models in target validation, discovery, and development pipelines. Further considerations relevant to industry include timelines, methods of induction, the key issue of reproducibility, and appropriate outcome measures needed to objectively assess outcomes of experimental therapeutics. Preclinical small animal models are indispensable tools that will shine some light on the pathogenesis of OA and its molecular endotypes in the context of drug development. This paper will focus on small animal models used in preclinical OA research. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.

OBJECTIVES: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. METHODS: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. RESULTS: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). CONCLUSION: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. TRIAL REGISTRATION: ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.

Osteoarthritis endotype discovery via clustering of biochemical marker data.

OBJECTIVES: Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning. METHOD: Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. RESULTS: Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. CONCLUSIONS: This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future. TRIAL REGISTRATION NUMBER: NCT03883568.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping.

OBJECTIVES: Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component. METHODS: Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component. RESULTS: OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. CONCLUSIONS: OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.Trial registration numberThe study is registered under clinicaltrials.gov nr: NCT03883568.

Development of a highly sensitive chemiluminescence immunoassay for quantification of aggrecanase-generated ARGS aggrecan fragments in serum.

Objective: Cartilage degradation is a hallmark of osteoarthritis (OA). Aggrecan, a major proteoglycan of articular cartilage extracellular matrix (ECM), is degraded by ADAMTS-5 resulting in the release of ARGS-G2 fragments to synovial fluid and circulation. The aim was to quantify ARGS-G2 in the serum of OA patients using the huARGS immunoassay. Methods: The immunoassay was produced under GMP conditions and the technical performance was assessed. The biological relevance of the immunoassay was assessed in the conditioned media from a bovine full-depth cartilage explant (BEX) model. The diurnal and inter-day variations of ARGS levels were evaluated in OA patients' serum. Post-hoc analysis of huARGS was conducted in a sub-cohort of a phase III OA trial testing the safety and efficacy of oral salmon calcitonin. Results: Technical performance: huARGS demonstrated good technical performance. Biological relevance: ARGS release was induced by inflamatory facotrs stimulation compared to the vehicle group, reaching a peak at day 3 and gradually decreasing to base level at day 12. The ARGS release was suppressed by the addition of the ADAMTS-4/-5 activation inhibitor. Biological variation: No significant diurnal or inter-day effect was found. Phase III clinical trial: The participants in the lowest group (Q1) of baseline huARGS levels were more likely to progress radiographically than the highest group (Q4): OR 3.38[0.81-14.02]. Conclusions: The huARGS shows good technical performance and low biological variation. It has the potential to aid drug development in various stages, both as a PD biomarker and identifying progressors who might be likely to respond to an OA drug.

Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers.

PURPOSE: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development. PARTICIPANTS: APPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression. FINDINGS TO DATE: Selection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: p<0.001, for selected versus excluded patients, respectively. Based on the selection model, this cohort has a predicted higher chance of progression. FUTURE PLANS: Patients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy. TRIAL REGISTRATION NUMBER: NCT03883568.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.

Microfluidic droplet liquid reactors for active pharmaceutical ingredient crystallization by diffusion controlled solvent extraction.

A novel method for crystallization utilizing solvent/antisolvent extraction in microfluidic droplet liquid reactors has been developed for rapid and low-cost screening of crystal polymorphism (i.e. molecular crystallographic arrangement or internal structure) and habit (i.e. crystallographic shape or external structure). The method involves a ternary solvent system consisting of a dispersed phase of two miscible fluids, one in which the active pharmaceutical ingredient (API) is soluble (solvent) and one in which the API is insoluble (antisolvent). The solvent/antisolvent dispersed phase is immiscible with a third continuous phase. Crystallization of an API, GSK1, was controlled within droplets by altering the rate of solvent extraction from droplets into the continuous phase, thereby decreasing API solubility. Crystal size, habit, and population per droplet were directly impacted by the solvent's rate of extraction. Single crystals were grown in individual droplets by slow extraction of solvent into the surrounding continuous phase, which occurs when crystal growth gradually reduces API concentration such that it is maintained within the metastable zone throughout extraction. Rapid extraction of solvent from droplets results in API concentration significantly exceeding its metastable limit, producing a greater number of crystal nuclei compared to slow extraction conditions. When holding constant solubilized API mass per droplet, crystal sizes were larger for slow extraction rates (l = 96.3, w = 16.6 µm) compared to fast extraction rates (l = 48.8, w = 9.5 µm) as a result of crystal growth occurring on fewer crystal nuclei per droplet. Crystal habit can be controlled by adjusting the solvent extraction rate and consequently the saturation, where minimal saturation resulted in a rhombohedral habit and comparatively higher saturation resulted in an acicular habit with a higher aspect ratio. Antisolvents were tested using two hydrophobic APIs demonstrating the method's capability for rapidly identifying favorable crystal morphologies for downstream manufacturability using miniscule amounts of API.

Molecular taxonomy of osteoarthritis for patient stratification, disease management and drug development: biochemical markers associated with emerging clinical phenotypes and molecular endotypes.

PURPOSE OF REVIEW: This review focuses on the molecular taxonomy of osteoarthritis from the perspective of molecular biomarkers. We discuss how wet biochemical markers may be used to understand disease pathogenesis and progression and define molecular endotypes of osteoarthritis and how these correspond to clinical phenotypes. RECENT FINDINGS: Emerging evidence suggests that osteoarthritis is a heterogeneous and multifaceted disease with multiple causes, molecular endotypes and corresponding clinical phenotypes. Biomarkers may be employed as tools for patient stratification in clinical trials, enhanced disease management in the primary care centres of the future and for directing more rational and targeted osteoarthritis drug development. Proximal molecular biomarkers (e.g synovial fluid) are more likely to distinguish between molecular endotypes because there is less interference from systemic sources of biomarker noise, including comorbidities. SUMMARY: In this review, we have focused on the molecular biomarkers of four distinct osteoarthritis subtypes including inflammatory, subchondral bone remodelling, metabolic syndrome and senescent age-related endotypes, which have corresponding phenotypes. Progress in the field of osteoarthritis endotype and phenotype research requires a better understanding of molecular biomarkers that may be used in conjunction with imaging, pain and functional assessments for the design of more effective, stratified and individualized osteoarthritis treatments.

Determinants of versican-V1 proteoglycan processing by the metalloproteinase ADAMTS5.

Proteolysis of the Glu(441)-Ala(442) bond in the glycosaminoglycan (GAG) ß domain of the versican-V1 variant by a disintegrin-like and metalloproteinase domain with thrombospondin type 1 motif (ADAMTS) proteases is required for proper embryo morphogenesis. However, the processing mechanism and the possibility of additional ADAMTS-cleaved processing sites are unknown. We demonstrate here that if Glu(441) is mutated, ADAMTS5 cleaves inefficiently at a proximate upstream site but normally does not cleave elsewhere within the GAGß domain. Chondroitin sulfate (CS) modification of versican is a prerequisite for cleavage at the Glu(441)-Ala(442) site, as demonstrated by reduced processing of CS-deficient or chondroitinase ABC-treated versican-V1. Site-directed mutagenesis identified the N-terminal CS attachment sites Ser(507) and Ser(525) as essential for processing of the Glu(441)-Ala(442) bond by ADAMTS5. A construct including only these two GAG chains, but not downstream GAG attachment sites, was cleaved efficiently. Therefore, CS chain attachment to Ser(507) and Ser(525) is necessary and sufficient for versican proteolysis by ADAMTS5. Mutagenesis of Glu(441) and an antibody to a peptide spanning Thr(432)-Gly(445) (i.e. containing the scissile bond) reduced versican-V1 processing. ADAMTS5 lacking the C-terminal ancillary domain did not cleave versican, and an ADAMTS5 ancillary domain construct bound versican-V1 via the CS chains. We conclude that docking of ADAMTS5 with two N-terminal GAG chains of versican-V1 via its ancillary domain is required for versican processing at Glu(441)-Ala(442). V1 proteolysis by ADAMTS1 demonstrated a similar requirement for the N-terminal GAG chains and Glu(441). Therefore, versican cleavage can be inhibited substantially by mutation of Glu(441), Ser(507), and Ser(525) or by an antibody to the region of the scissile bond.

An orally active TRPV4 channel blocker prevents and resolves pulmonary edema induced by heart failure.

Pulmonary edema resulting from high pulmonary venous pressure (PVP) is a major cause of morbidity and mortality in heart failure (HF) patients, but current treatment options demonstrate substantial limitations. Recent evidence from rodent lungs suggests that PVP-induced edema is driven by activation of pulmonary capillary endothelial transient receptor potential vanilloid 4 (TRPV4) channels. To examine the therapeutic potential of this mechanism, we evaluated TRPV4 expression in human congestive HF lungs and developed small-molecule TRPV4 channel blockers for testing in animal models of HF. TRPV4 immunolabeling of human lung sections demonstrated expression of TRPV4 in the pulmonary vasculature that was enhanced in sections from HF patients compared to controls. GSK2193874 was identified as a selective, orally active TRPV4 blocker that inhibits Ca(2+) influx through recombinant TRPV4 channels and native endothelial TRPV4 currents. In isolated rodent and canine lungs, TRPV4 blockade prevented the increased vascular permeability and resultant pulmonary edema associated with elevated PVP. Furthermore, in both acute and chronic HF models, GSK2193874 pretreatment inhibited the formation of pulmonary edema and enhanced arterial oxygenation. Finally, GSK2193874 treatment resolved pulmonary edema already established by myocardial infarction in mice. These findings identify a crucial role for TRPV4 in the formation of HF-induced pulmonary edema and suggest that TRPV4 blockade is a potential therapeutic strategy for HF patients.

Discovery of highly potent and selective small molecule ADAMTS-5 inhibitors that inhibit human cartilage degradation via encoded library technology (ELT).

The metalloprotease ADAMTS-5 is considered a potential target for the treatment of osteoarthritis. To identify selective inhibitors of ADAMTS-5, we employed encoded library technology (ELT), which enables affinity selection of small molecule binders from complex mixtures by DNA tagging. Selection of ADAMTS-5 against a four-billion member ELT library led to a novel inhibitor scaffold not containing a classical zinc-binding functionality. One exemplar, (R)-N-((1-(4-(but-3-en-1-ylamino)-6-(((2-(thiophen-2-yl)thiazol-4-yl)methyl)amino)-1,3,5-triazin-2-yl)pyrrolidin-2-yl)methyl)-4-propylbenzenesulfonamide (8), inhibited ADAMTS-5 with IC(50) = 30 nM, showing >50-fold selectivity against ADAMTS-4 and >1000-fold selectivity against ADAMTS-1, ADAMTS-13, MMP-13, and TACE. Extensive SAR studies showed that potency and physicochemical properties of the scaffold could be further improved. Furthermore, in a human osteoarthritis cartilage explant study, compounds 8 and 15f inhibited aggrecanase-mediated (374)ARGS neoepitope release from aggrecan and glycosaminoglycan in response to IL-1ß/OSM stimulation. This study provides the first small molecule evidence for the critical role of ADAMTS-5 in human cartilage degradation.

CD44 differentially activates mouse NK T cells and conventional T cells.

NK T (NKT) cells are an important component of the innate immune system and recognize the MHC class I-like CD1d molecule. NKT cells possess significant immunoregulatory activity due to their rapid secretion of large quantities of pro- and anti-inflammatory cytokines following CD1d-dependent stimulation. Because the innate immune system is programmed to respond to a multitude of diverse stimuli and must be able to quickly differentiate between pathogenic and endogenous signals, we hypothesized that, apart from stimulation via the TCR (e.g., CD1d-dependent activation), there must be multiple activation pathways that can be triggered through other cell surface receptors on NKT cells. Therefore, we analyzed the ability of CD44, a structurally diverse cell surface receptor expressed on most cells, to stimulate murine NKT cells, compared with conventional T cells. Stimulation of CD44 through Ab cross-linking or binding to its natural ligands hyaluronan and osteopontin induced NKT cells to secrete cytokines, up-regulate activation markers, undergo morphological changes, and resist activation-induced cell death, whereas conventional T cells only exhibited changes in morphology and protection from activation-induced cell death. This CD44-specific stimulation of NKT cells correlated with their ability to bind hyaluronan. Thus, fundamental differences in CD44 function between these lymphocyte subsets suggest an important biological role for CD44 in the innate immune response.

Cytokine-activated natural killer cells exert direct killing of hepatoma cells harboring hepatitis C virus replicons.

Hepatitis C virus (HCV)-specific impairments in host immunity have been described at multiple levels of the innate and adaptive response, which may lead to viral persistence in the majority of infections. Understanding of HCV-associated immune defects could lead to novel therapeutic advances. Natural killer (NK) cells, the major effector cells of the innate immune system, are functionally impaired in chronic HCV infection. It has been suggested that this phenotype is a result of virus-specific defects in antigen-presenting cells (APCs) that regulate NK cell activity, as normal NK function is restored when they are stimulated ex vivo. In this study, we used human NK cell cytotoxicity assays to evaluate the activation-induced effects of NK cells on the HCV replicon-containing hepatic cells. We found that cytokine-activated NK cells were capable of inducing an HCV-associated, perforin/granzyme-dependent lysis of human hepatoma cells and that this required direct cellular contact and was independent of MHC class I expression levels. In contrast, on removal of cytokine stimulation, NK cells failed to exert any direct cytolytic effect on replicon targets. These findings suggest an important underlying mechanism by which NK cells control HCV infection and, with appropriate understanding of HCV-associated immune defects, could lead to novel therapeutic advances.

Synergistic antiviral activity of human interferon combinations in the hepatitis C virus replicon system.

The use of type I interferon (IFN), in combination with ribvirin, to treat chronic hepatitis C virus (HCV) infection has many drawbacks that prevent widespread application, ultimately leading to a significant unmet clinical need. Potential improvements in IFN therapy through targeted delivery, molecular alteration, and combination with other agents are ongoing in an attempt to decrease adverse effects and increase efficacy. In this report, the HCV replicon cell culture system was used to assess potential synergistic antiviral effects of multiple IFN species when administered in combination. Quantitative analysis of HCV replicon RNA by TaqMan (PE Applied Biosystems, Foster City, CA) and qualitative analysis of HCV protein expression were used to measure the antiviral efficacy of individual and combination IFN treatments, and synergistic responses of IFN combinations were determined through statistical analysis of the TaqMan results. We found that when administered simultaneously, type I/II IFN combinations (IFN-alpha2b + IFN-gamma or IFN-beta + IFN-gamma) resulted in dramatic antiviral synergy, whereas a type I/I combination (IFN-alpha2b + IFN-beta) demonstrated a slightly antagonistic profile. The synergistic effect is likely due to differential cell surface receptors and signaling pathways employed by types I and II IFNs. Conversely, all type I IFN species bind the same receptor and signal through similar pathways, possibly accounting for the nearly additive response observed. In support of this hypothesis, IFN treatment resulted in differential induction of Stat1 phosphorylation at Tyr 701. In conclusion, simultaneous type I/II IFN combination treatment may allow an overall decreased effective IFN dose, which may reduce the side effect profiles that hinder current therapy.