Pan-viral efficacy profile of ribavirin: quantitative potency and safety landscape across virus families

Pan-viral efficacy profile of ribavirin: quantitative potency and safety landscape across virus families Fredmoore Orosco1,2 1 Virology and Vaccine Research Program, Industrial Technology Development Institute, Department of Science and Technology, Taguig, Philippines 2 The UPLB Graduate School, University of the Philippines Los Baños, Laguna, Philippines Corresponding author: Fredmoore Orosco (orosco.fredmoor[email protected]) Received 22 May 2025♦ Accepted 10 October 2025♦ Published 14 November 2025 Citation: Orosco F (2025) Pan-viral efficacy profile of ribavirin: quantitative potency and safety landscape across virus families. Pharmacia 72: 1–11. https://doi.org/10.3897/pharmacia.72.e159854 Abstract This study quantifies ribavirin’s pan-viral efficacy across seven virus families, compares it with favipiravir and remdesivir, and evaluates viral trait predictors alongside clinical concordance. In vitro analyses showed the highest potency against Flaviviridae and Paramyxoviridae, whereas Coronaviridae and Astroviridae were less susceptible, consistent with proofreading polymerases. Compared with its peers, remdesivir was most potent in coronavirus and filovirus models, while favipiravir exhibited the widest therapeutic window in orthomyxovirus assays. Family-wise means and bootstrap trait modeling identified the presence of a proofreading polymerase as the strongest positive predictor of log EC50, with negative-sense genomes also associated with reduced susceptibility. Clinical evidence aligned with laboratory findings, demonstrating robust benefits in hepatitis C and Lassa fever, with mixed effects in respiratory infections. Overall, the results provide an actionable benchmark for broad-spectrum drug selection and prioritization in preparedness and outbreak response. Keywords antivirals, cytotoxicity, ribavirin, selectivity index, trait analysis Introduction Pandemic preparedness and the ongoing challenge of emerging infectious diseases have highlighted the importance of broad-spectrum antivirals as foundational tools in modern medical therapeutics (Thomas et al. 2012; Loustaud-Ratti et al. 2016). Unlike pathogen-specific therapies or vaccines, which may require months or years to develop and distribute, broad-spectrum antiviral agents can offer immediate, flexible response options against novel or re-emerging viral threats. Among these, ribavirin has stood out for decades as one of the most extensively deployed and investigated compounds, demonstrating inhibitory activity against a wide variety of both RNA and DNA viruses (Graci and Cameron 2006; Thomas et al. 2012; Loustaud-Ratti et al. 2016). Initially introduced in the 1970s as a synthetic guanosine analog, ribavirin has earned clinical approval or emergency use authorization for several high-consequence viral infections. Its applications encompass the treatment of chronic hepatitis C virus (HCV) infection (Lawitz et al. 2013; Zeuzem et al. 2014), Lassa fever (McCormick et al. 1986; Bausch et al. 2010), and respiratory syncytial virus (RSV) infections (Rodriguez et al. 1987). Copyright Orosco F. This is an open access article distributed under the terms of the Creative Commons Attribution License (CCBY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Pharmacia 72: 1–11 DOI 10.3897/pharmacia.72.e159854 Research Article Orosco F: Pan-viral efficacy profile of ribavirin2 More recently, ribavirin has also been utilized in the management of severe coronavirus outbreaks, including SARS and COVID-19, sometimes as part of combination regimens or compassionate use protocols (Koren et al. 2003; Tong et al. 2020). The breadth of these indications attests to the compound’s broad-spectrum potential but also underscores the complex interplay of viral, host, and pharmacological factors that can influence clinical outcomes. Despite ribavirin’s longstanding clinical presence, the full spectrum of its antiviral mechanisms remains a subject of ongoing research and debate. Multiple modes of action have been described, including inhibition of inosine monophosphate dehydrogenase (IMPDH), which leads to depletion of intracellular guanosine pools; direct interference with viral RNA-dependent RNA polymerase; the induction of lethal mutagenesis through increased replication errors; and modulation of host immune responses (Crotty et al. 2001; Tam et al. 2001; Leyssen et al. 2005; Graci and Cameron 2006). Notably, the dominant mechanism appears to vary among viral families. In cell culture systems for flaviviruses and paramyxoviruses, IMPDH inhibition is frequently observed as the key pathway (Leyssen et al. 2005), while in coronaviruses, the presence of a proofreading exoribonuclease can mitigate the mutagenic effects of ribavirin, reducing its efficacy (Smith et al. 2013; Agostini et al. 2018; Ferron et al. 2018). This mechanistic diversity is reflected in the marked variability of ribavirin’s potency across different viruses, as well as among strains within a given family (Graci and Cameron 2006; Thomas et al. 2012; Loustaud-Ratti et al. 2016). Comparative studies involving other nucleoside analogs, such as favipiravir and remdesivir, have further clarified ribavirin’s position within the antiviral pharmacopeia (Furuta et al. 2013; Sheahan et al. 2017; Xu et al. 2021; Radoshitzky et al. 2023). While ribavirin demonstrates moderate activity in vitro against a number of viruses, its therapeutic application is often limited by the attainable plasma drug concentrations and the selectivity index, which defines the margin between antiviral efficacy and cytotoxicity. Hemolytic anemia, a well-recognized dose-limiting toxicity, remains a key concern in clinical management, particularly during prolonged therapy (De Franceschi et al. 2000; Xu et al. 2021). This risk–benefit scenario must be carefully balanced, especially when alternative broad-spectrum agents are available. Recent advances in viral genomics and molecular virology have brought increasing attention to the influence of viral traits on drug susceptibility. Features such as genome polarity, replication site, the presence or absence of viral proofreading mechanisms, and genome segmentation are now recognized as important determinants of antiviral response (De Franceschi et al. 2000; Smith et al. 2013; Ferron et al. 2018). The systematic analysis of these traits may provide a foundation for rational drug development and facilitate the prediction of antiviral efficacy in both established and emerging pathogens (Minskaia et al. 2006; Bouvet et al. 2012). While a substantial literature exists describing the in vitro effects, mechanisms, and clinical experience with ribavirin, no comprehensive synthesis has yet quantified its pan-viral efficacy in a way that integrates potency, selectivity, viral determinants, and therapeutic outcomes across the major human viral pathogens. Such an analysis is necessary to benchmark ribavirin’s true position among current and future broad-spectrum antivirals, to identify the most relevant viral predictors of response, and to inform public health strategies for outbreak management and pandemic response. The present work addresses this gap by assembling a harmonized dataset of in vitro potency and selectivity data for ribavirin across seven viral families, with direct benchmarking against favipiravir and remdesivir. The analysis further investigates the viral trait determinants of susceptibility and synthesizes clinical outcome data to contextualize laboratory findings. Thus, this study aims to provide an up-to-date, quantitative profile of ribavirin’s strengths and limitations, with implications for both mechanistic understanding and translational antiviral strategy. Materials and methods Literature screening and data extraction A systematic review of the published literature was conducted to identify original studies assessing the antiviral activity of ribavirin, as well as comparator data for favipiravir and remdesivir, against major human virus families. Bibliographic databases searched included PubMed, Scopus, and Web of Science, supplemented by reference mining from relevant reviews and prior meta-analyses. The search encompassed articles published up to March 2024, without language restrictions at the initial stage. The search strategy combined the terms “ribavirin,” “broad-spectrum antiviral,” and the names of targeted virus families or genera. Studies were considered eligible if they reported original, experimentally determined in vitro potency data (EC₅₀ or IC₅₀), cytotoxicity (CC₅₀), or selectivity index (SI), or if they presented clinical outcomes following ribavirin administration in human viral infections. Inclusion required the presence of at least one quantitative EC₅₀ or equivalent measure for a virus–drug pair. Exclusion criteria comprised the absence of experimental or clinical outcome data, lack of relevant quantitative measures, duplicate or overlapping publications, and non-English articles at the full-text review stage if no translation was available. The literature screening proceeded through multiple phases. Initial retrieval produced 48 records. After deduplication, 42 unique titles and abstracts were screened for relevance. Full-text assessment was performed for 25 articles, with 22 studies meeting all eligibility criteria and included for data extraction. Extraction of data from in vitro studies included virus species, viral family, assay platform, host cell or animal model, EC₅₀ or IC₅₀ value, CC₅₀, SI, assay conditions (temperature, MOI, incubation time), number of replicates, and study reference. For clinical studies, data fields encompassed virus species, study design, patient population, primary clinical Pharmacia 72: 1–11 3 endpoint, effect size (e.g., risk ratio, odds ratio, response rate), confidence intervals or p-values, dosing regimen, and sample size. Discrepancies and ambiguities were addressed by consulting Suppl. materials, cross-referencing with other datasets, or contacting study authors if necessary. In vitro potency and safety data In vitro antiviral potency was characterized by the half-maximal effective concentration (EC₅₀), defined as the concentration of drug required to inhibit 50% of virus replication relative to untreated controls. When reported, the half-maximal inhibitory concentration (IC₅₀) was considered equivalent to EC₅₀ unless otherwise specified (Leyssen et al. 2005; Graci and Cameron 2006; Loustaud-Ratti et al. 2016). Cytotoxicity was measured by the half-maximal cytotoxic concentration (CC₅₀), representing the drug concentration that reduces host cell viability by 50%. The selectivity index (SI), a widely used metric of the therapeutic window, was calculated as the ratio of CC₅₀ to EC₅₀ (SI = CC₅₀/EC₅₀) (Graci and Cameron 2006; Loustaud-Ratti et al. 2016). Assay platforms included cytopathic effect (CPE) reduction assays, plaque reduction assays, RT-qPCR quantification of viral RNA, luciferaseor fluorescence-based reporter assays, and replicon-based systems, depending on the virus and laboratory protocol. Host cell models varied according to virus family, encompassing Vero, Huh-7, HeLa, MDCK, Caco-2, and RD cells, among others. All EC₅₀ and CC₅₀ values were standardized to micromolar (µM) units. Where results were reported as “greater than” (>) or “less than” (<) a threshold value, these were flagged as censored observations and retained at the reported boundary for visualization purposes but excluded from calculation of medians and interquartile ranges. Records lacking numeric or complete EC₅₀ or CC₅₀ data were excluded from potency and selectivity analyses. For each drug (ribavirin, favipiravir, and remdesivir), a drug– virus–family matrix was constructed to summarize the availability of potency and safety data across the seven major virus families included in this study. Viral trait annotation Virus species were annotated for genomic and replication traits expected to influence antiviral susceptibility. Traits included genome type (positive-sense single-stranded RNA [+ssRNA], negative-sense single-stranded RNA [–ssRNA]), site of replication (cytoplasm or nucleus), presence of a proofreading polymerase (e.g., ExoN in coronaviruses), envelope status (enveloped or non-enveloped), and genome segmentation (monopartite or segmented) (Smith et al. 2013; te Velthuis 2014; Ferron et al. 2018). Annotation sources included primary literature, virus taxonomy databases, and established reviews on viral replication and structure (Strauss and Strauss 1994; Koonin et al. 2015). Trait variables were encoded as follows: genome type as binary categorical (+ssRNA vs –ssRNA), replication site as binary categorical (cytoplasm vs nucleus), proofreading polymerase as binary (yes/no), envelope status as binary (yes/no), and genome segmentation as numeric (number of segments, standardized to zero mean and unit variance for regression). Where ambiguity existed, majority consensus among recent reviews was used. Quantitative analyses Potency and selectivity metrics for each drug–virus–family combination were calculated as described above. Breadth of coverage was defined as the number of virus families with at least one EC₅₀ value reported for a given drug. Family-wise summary statistics (median EC₅₀, interquartile range, mean, and standard error) were computed for each virus family, with only numeric (non-censored) EC₅₀ values included. Radar plots were constructed to visualize and compare each drug’s spectrum in terms of breadth, median potency, and median selectivity index. For visual comparability, each axis was normalized to its global maximum, and polygons for each drug were overlaid with 40% fill transparency. Trait-based predictors of ribavirin potency were evaluated using ridge regression. The response variable was log-transformed EC₅₀ (log₁₀[EC₅₀, µM]), and the five annotated viral traits served as predictors. Predictors were standardized and, where appropriate, encoded using one-hot or binary variables. Ridge regression models were fit with a regularization parameter alpha = 1.0. To assess coefficient stability and confidence intervals, a nonparametric bootstrap was applied with 1,000 resamplings of the dataset. Point estimates correspond to model coefficients from the full dataset, with 95% confidence intervals derived from the 2.5th and 97.5th percentiles of the bootstrap distribution. Scatterplots were generated to visualize the relationship between EC₅₀ and CC₅₀ for all assays, plotted on log₁₀– log₁₀ axes. Points were colored according to SI (capped at the 95th percentile to avoid saturation), and marker shapes were used to differentiate ribavirin, favipiravir, and remdesivir. Diagonal dashed lines (SI = 1 and SI = 10) were included for reference. Clinical outcomes data Clinical studies reporting outcomes of ribavirin therapy in human infections were extracted for primary efficacy endpoints (such as sustained virologic response, mortality, or disease progression), effect size estimates (risk ratio, odds ratio, response rate), associated confidence intervals, p-values, dosing regimens, and sample size. Where effect size was reported as a percent or absolute difference, transformation to a risk-ratio scale was performed for consistency. Odds ratios and hazard ratios were converted to risk ratios using established formulas when required. In cases where only a p-value was available, the 95% confidence interval was approximated using the log-risk ratio and the z-score for the reported significance level. Studies lacking sufficient detail for effect size harmonization were excluded from quantitative synthesis. Orosco F: Pan-viral efficacy profile of ribavirin4 Statistical and visualization methods All data cleaning, analysis, and visualization were performed using Python (pandas, numpy, matplotlib, seaborn, and scikit-learn libraries) and R (tidyverse and ggplot2 packages), as appropriate. Log transformation was applied to EC₅₀, CC₅₀, and SI variables to reduce skew and facilitate visualization. Groupwise statistics and confidence intervals were calculated using standard nonparametric approaches. Main-text figures and tables present the primary dataset, summary statistics, trait analysis, and clinical outcomes. Extended datasets (including favipiravir and remdesivir raw data, virus trait tables, and supplementary family-wise summaries) are provided in the Suppl. materials. Figure legends and supplementary information describe exact plotting parameters, normalization procedures, and additional data sources where relevant. Results Literature screening and dataset composition A systematic literature review was performed to identify published studies examining the antiviral effects of ribavirin, as well as favipiravir and remdesivir, across a wide spectrum of viruses. The search strategy retrieved 48 unique records, which were reduced to 42 after deduplication. Abstract and title screening led to the selection of 40 studies for further consideration. A full-text review was completed for 25 studies, ultimately resulting in the inclusion of 22 that met all eligibility criteria and contained extractable quantitative data for either in vitro potency, cytotoxicity, selectivity index, or clinical efficacy. The flow of study selection is summarized narratively and corresponds to the standard approach for transparent data synthesis. These 22 studies encompassed a broad diversity of experimental settings, viral taxa, and assay platforms. In total, 41 unique in vitro antiviral assays were included, representing seven major virus families of human relevance: Flaviviridae, Paramyxoviridae, Coronaviridae, Filoviridae, Orthomyxoviridae, Astroviridae, and Picornaviridae. Each family was represented by at least one viral species with standardized EC₅₀ and CC₅₀ data, enabling quantitative cross-family comparisons. In addition to ribavirin, favipiravir and remdesivir were included as comparator drugs wherever sufficient data could be obtained. Datasets for favipiravir and remdesivir, along with additional detailed results by family, are provided in Suppl. material 1: tables S1, S2. The composition of the dataset allowed for robust analysis of drug–virus interactions. For most virus families, multiple independent assays using distinct platforms or host cell lines were available, improving the reliability of potency and selectivity estimates. The studies contributing clinical outcome data included randomized controlled trials, open-label studies, and retrospective cohort analyses, with a total of five studies spanning Flaviviridae, Arenaviridae, Paramyxoviridae, and Coronaviridae. In vitro pan-viral potency of ribavirin Table 1 summarizes the in vitro potency and selectivity profiles of ribavirin across all included assays. Potency, as measured by the half-maximal effective concentration (EC₅₀), ranged widely between virus families and individual assays. Flaviviridae and Paramyxoviridae consistently showed the lowest median EC₅₀ values, with several replicon-based hepatitis C virus assays reporting EC₅₀ values below 1 µM and the majority of CPE and RT-qPCR-based assays yielding values below 50 µM. Conversely, assays targeting SARS-CoV-2, West Nile virus, and human astrovirus often returned EC₅₀ values exceeding 100 µM or were right-censored due to lack of inhibition at the highest tested concentration. Table 1. In vitro potency and selectivity of ribavirin against 22 assay systems spanning seven virus families. The table lists virus species, assay platform, host cell, EC₅₀, CC₅₀, selectivity index (SI = CC₅₀/EC₅₀), experimental conditions, and quality-control flags. Virus species Virus family Assay platform Host cell / model EC50 or IC50 (µM) CC50 (µM) Selectivity index Original units (mean ± SD) Temperature (°C) MOI Duration (h) Yellow Fever Virus 17D Flaviviridae RT-qPCR Vero 40.9 >300 >7.3 12.3 ± 5.6 µg ml–1 37 0.1 120 Yellow Fever Virus 17D Flaviviridae CCID50 Vero 161.7 >300 >1.9 48.5 ± 41.3 µg ml–1 37 0.1 120 Dengue Virus type 2 Flaviviridae CPE reduction Vero 40.9 >300 >7.3 μg ml–1 37 Not available 96 West Nile Virus Flaviviridae CPE reduction Vero 168.0 >300 >1.8 μg ml–1 37 Not available 72 Human Parainfluenza Virus 3 Paramyxoviridae RT-qPCR Vero 31.3 >300 >9.6 9.4 ± 6.1 µg ml–1 37 0.1 120 Human Parainfluenza Virus 3 Paramyxoviridae CCID50 Vero 57.3 >300 >5.2 17.2 ± 6.9 µg ml–1 37 0.1 120 Respiratory Syncytial Virus Paramyxoviridae CPE reduction HeLa 12.5 >100 >8.0 3.74 ± 0.87 µg ml–1 37 Not available 120 Hepatitis C Virus 1b Flaviviridae Replicon Huh-7-lunet 0.089 >20 >225 μM 37 Not available 72 Hepatitis C Virus 2a Flaviviridae Replicon Huh-7-lunet 0.072 17 236 μM 37 Not available 72 SARS-CoV-2 Coronaviridae CPE reduction Vero E6 109.5 >300 >2.7 μM 37 0.01 72 Human Astrovirus VA1 Astroviridae RT-qPCR Caco-2 230 >500 >2.2 μM 37 Not available 48 Pharmacia 72: 1–11 5 The selectivity index (SI), calculated as CC₅₀/EC₅₀, provided further insight into the therapeutic window of ribavirin across different viral systems. The highest SI values were observed in assays with low EC₅₀ and high CC₅₀ (often exceeding 1,000 µM in mammalian cell lines), while lower SI values reflected either increased cytotoxicity or diminished antiviral activity. Suppl. material 1: table S4 details the family-wise means and standard errors for log EC₅₀, as well as the complete SI distributions for each virus group. Fig. 1 presents the distribution of log₁₀ EC₅₀ values for ribavirin in a family-wise manner using violin plots. An overlay of pooled family means and standard errors highlights central tendencies and the degree of dispersion within each family. In Flaviviridae and Paramyxoviridae, the density of points is tightly clustered, reinforcing the observation of consistent, potent activity across multiple viral species within these families. Notably, Flaviviridae assays—primarily those involving hepatitis C, dengue, and yellow fever viruses—displayed the most favorable potency profiles, supporting the established clinical role of ribavirin for chronic hepatitis C and severe flaviviral infections (McCormick et al. 1986; Lawitz et al. 2013; Zeuzem et al. 2014). In contrast, Coronaviridae and Astroviridae displayed a broader spread and higher median EC₅₀ values. Individual SARS-CoV-2 and human astrovirus assays demonstrated significant variability, with some data points approaching or exceeding the upper detection threshold, indicating reduced susceptibility. Filoviridae, Orthomyxoviridae, and Picornaviridae showed intermediate potency, often reflecting the influence of both viral and assay-specific factors, such as the choice of host cell, the duration of incubation, and the MOI. This family-wise analysis highlights the nonuniform activity of ribavirin across the virosphere, with a clear gradient from highest potency in Flaviviridae and Paramyxoviridae to lowest in Coronaviridae and Astroviridae. Such variability likely reflects both intrinsic differences in viral biology and the impact of cellular and experimental contexts. Comparative activity of broad-spectrum antivirals Fig. 2 provides a comprehensive heatmap depicting the median EC₅₀ values of ribavirin, favipiravir, and remdesivir for each virus family. Data for favipiravir and remdesivir, derived from both shared and distinct experimental sources, are detailed in Suppl. material 1: tables S1, S2. Potency is encoded by a color scale mapped to log₁₀ EC₅₀, with darker shades indicating higher antiviral activity. The heatmap reveals clear, drug-specific patterns. Ribavirin is most potent against Flaviviridae and Paramyxoviridae, aligning with clinical indications and in vitro findings. Favipiravir exhibits its greatest efficacy against Orthomyxoviridae, especially influenza A and B viruses, with median EC₅₀ values less than 1 µM. This is consistent with favipiravir’s clinical development for influenza and other negative-sense RNA viruses (Furuta et al. 2013). Remdesivir demonstrates superior potency against Coronaviridae and Filoviridae, supporting its recent clinical deployment for COVID-19 and Ebola virus disease (Warren et al. 2016; Sheahan et al. 2017). Blank cells in the heatmap denote virus–drug–family combinations where insufficient or no standardized EC₅₀ data were available. The visual summary provided by the heatmap underscores the concept that no single broad-spectrum antiviral achieves optimal potency across all virus families. Instead, each agent’s efficacy is shaped by both viral biology and its unique mechanism of action, supporting the rationale for tailored, virus family-specific, or combination antiviral strategies. Breadth, potency, and selectivity: an integrated comparison A more holistic comparison of the three drugs is provided in Fig. 3, which displays a radar plot integrating three core performance metrics: family coverage (taxonomic breadth), normalized potency score (1/log EC₅₀), and normalized median selectivity index (log SI). For each axis, values are normalized to the highest observed value among the three drugs, allowing direct shape and size comparisons of their pan-viral performance. Suppl. Figure 1. Family-wise distribution of ribavirin potency (log EC₅₀). Horizontal violin plots show the density of individual log₁₀ EC₅₀ measurements per virus family. Squares with whiskers overlay the pooled mean ± SE, highlighting central tendency alongside experimental spread (n = 22 assays). Figure 2. Median EC₅₀ of three broad-spectrum antivirals across virus families. Heatmap of median EC₅₀ (µM; color-coded on log₁₀ scale, darker = greater potency) for ribavirin, favipiravir, and remdesivir against seven families. Blank gray cells indicate no available data. Orosco F: Pan-viral efficacy profile of ribavirin6 material 1: tables S1, S2, S4 provide the raw and summary statistics underlying each axis. Ribavirin stands out for its pan-viral breadth, with assay data available for all seven families and a generally favorable selectivity profile. Remdesivir, while covering fewer virus families, delivers the highest normalized potency, particularly for coronaviruses and filoviruses. Favipiravir achieves the highest selectivity, attributable to its generally low cytotoxicity, but its overall potency is lower than that of remdesivir. The geometric shapes of the polygons emphasize inherent trade-offs—for example, a wide breadth is not always accompanied by the highest potency or selectivity. This multidimensional view reveals the practical limitations and advantages of each agent in the context of pandemic and emerging pathogen preparedness. Viral trait determinants of ribavirin potency To elucidate the viral determinants influencing ribavirin’s efficacy, regularized regression analysis was performed using trait data from Suppl. material 1: table S3. Fig. 4 presents the ridge regression coefficients and 95% confidence intervals for five core viral traits: genome type, replication site, proofreading polymerase, envelope status, and genome segmentation. The most prominent finding is the significant positive association between the presence of a proofreading polymerase and elevated EC₅₀ (reduced susceptibility). This supports existing mechanistic data showing that coronaviruses, which encode a viral exonuclease, are less susceptible to nucleoside analog mutagenesis due to their enhanced polymerase fidelity (Smith et al. 2013; Agostini et al. 2018; Ferron et al. 2018). Genome type (–ssRNA vs +ssRNA) also emerges as a meaningful predictor, with negative-sense RNA viruses generally showing higher EC₅₀ values. Envelope status, site of replication, and segmentation contribute marginal or inconsistent effects. Regression analysis not only confirms the biological observations seen in the raw data but also quantitatively demonstrates the importance of trait-based predictors for understanding and anticipating antiviral susceptibility. These insights highlight the role of viral genomics and replication mechanisms in shaping responses to ribavirin and related agents. Potency vs. cytotoxicity landscape Fig. 5 visualizes the therapeutic window of each drug by plotting every assay as a point on a log–log scale, with EC₅₀ on the x-axis and CC₅₀ on the y-axis. Data for all three drugs are sourced from Table 1 and Suppl. material 1: tables S1, S2, and SI is encoded by point color (from red to blue, low to high SI). The majority of ribavirin assays are located in the upper-right quadrant, indicating moderate potency Figure 3. Breadth–potency–selectivity radar for three antiviral drugs. The radar plot compares (i) taxonomic breadth (families covered), (ii) potency score (1/log EC₅₀), and (iii) selectivity (log SI). Axes are normalized 0–1; filled polygons illustrate each drug’s overall performance landscape. Figure 4. Association of viral traits with ribavirin potency (ridge regression coefficients). Horizontal forest plot of ridge regression coefficients predicting log EC₅₀ from viral genome type, replication site, proofreading polymerase, envelope status, and genome segmentation (n = 22 assays; 1,000-sample bootstrap 95% CIs). Positive values indicate higher (worse) EC₅₀. Figure 5. Therapeutic-window scatterplot: potency vs. cytotoxicity with SI color scale. Log–log scatter of EC₅₀ (x) versus CC₅₀ (y) for 41 assays of ribavirin (circle), favipiravir (triangle), and remdesivir (square). Point color encodes SI (CC₅₀/EC₅₀); dashed diagonals mark SI = 1 and SI = 10 reference lines. Pharmacia 72: 1–11 7 (EC₅₀: 1–200 µM) and relatively high cytotoxicity thresholds (CC₅₀: 100–1,000 µM), resulting in an intermediate SI. Favipiravir points cluster toward the top of the plot, reflecting extremely high CC₅₀ values and, thus, a wider therapeutic margin, despite more variable potency. Remdesivir points are found toward the lower left, reflecting outstanding potency in selected assays but also somewhat lower CC₅₀ values. Dashed reference lines for SI = 1 and SI = 10 make it easy to discern which assays fall within a desirable therapeutic range. The scatterplot conveys both the comparative safety and potency profiles of the three drugs and highlights the presence of right-censored points (from “>” EC₅₀ or CC₅₀ values) for certain virus–drug–family combinations, as seen in Suppl. material 1. This integrated perspective informs practical drug selection and dosing strategies in both laboratory and clinical settings. Clinical efficacy across viral infections Table 2 details clinical outcome data for ribavirin across Flaviviridae, Arenaviridae, Paramyxoviridae, and Coronaviridae. Data include study design, sample size, dosing, effect size (risk or odds ratios), and confidence intervals. Studies span both randomized trials and cohort analyses. Fig. 6 presents these results as a forest plot, where each point estimate is displayed as a square (size ∝ sample size), with 95% confidence intervals indicated by horizontal whiskers. Hepatitis C trials (Flaviviridae) demonstrated significant benefit in sustained virologic response, while the largest observed effect was in Lassa fever, with ribavirin dramatically reducing mortality (risk ratio ≈ 0.19) (McCormick et al. 1986). The RSV and SARS-CoV-2 studies also indicated reductions in progression or mortality but with broader confidence intervals due to smaller cohorts or retrospective design (Rodriguez et al. 1987; Tong et al. 2020). These results generally align with the family-level in vitro potency trends, with the greatest clinical benefit observed for indications corresponding to the most susceptible virus families in laboratory assays. Together, the data provide a multidimensional picture of ribavirin’s pan-viral efficacy, revealing both its strengths—especially in Flaviviridae and Arenaviridae— and its limitations for other virus families. Suppl. material 1 supply additional stratified results and analytic details supporting these findings. Discussion This study provides a comprehensive quantitative assessment of ribavirin’s pan-viral efficacy profile, integrating in vitro potency, selectivity, viral trait analysis, and clinical outcomes across seven virus families. The resulting landscape reveals both the enduring utility and the limitations of ribTable 2. Clinical outcomes of ribavirin treatment in human infections. Point estimates (risk ratios or response rates), 95% confidence intervals or p-values, sample sizes, and dosing regimens are reported for five studies covering flaviviral hepatitis C, SARS-CoV-2, respiratory syncytial virus, and Lassa fever. Virus family Virus species Study design Primary endpoint Effect size Sample size P-value or 95% CI Dose regimen Flaviviridae Hepatitis C Virus Randomized, open-label Sustained virologic response at 24 wk 76% 410 p < 0.001 Sofosbuvir 400 mg + weight-based ribavirin daily × 12–24 wk Flaviviridae Hepatitis C Virus Phase 3, open-label SVR at 12 wk 61% 327 95% CI 56–66% Sofosbuvir 400 mg + weight-based ribavirin daily × 24 wk Paramyxoviridae Respiratory Syncytial Virus Randomized, controlled Progression to pneumonia 38% relative risk reduction 40 p = 0.035 Aerosolized ribavirin 6 g/300 ml daily × 3 d Coronaviridae SARS-CoV-2 Retrospective cohort All-cause mortality OR 0.31 115 95% CI 0.09–0.93 Ribavirin 400–600 mg + IFN-α 5 MU twice daily × 10 d Arenaviridae Lassa virus Prospective observational Mortality rate 9% vs 47% 312 p < 0.001 IV ribavirin: 30 mg kg–1 load → 15 mg kg–1 q6h × 4 d → 7.5 mg kg–1 q8h × 6 d Figure 6. Clinical efficacy of ribavirin across viral infections. Forest plot of risk-ratio effect estimates for five human studies. Square size is ∝ √n; horizontal lines show 95% CIs. Values < 1 favor ribavirin. A solid vertical line at RR = 1 denotes the no-effect position. Orosco F: Pan-viral efficacy profile of ribavirin8 avirin as a broad-spectrum antiviral, particularly in comparison to newer agents such as favipiravir and remdesivir. A major finding is the nonuniform distribution of ribavirin’s potency across viral taxa. As detailed in Table 1 and visualized in Fig. 1, the most consistent and potent activity is observed among flaviviruses and paramyxoviruses, with median EC₅₀ values substantially lower than those for coronaviruses, astroviruses, and filoviruses. Selectivity indices reinforce this pattern, indicating a favorable therapeutic window in these most susceptible families, but also highlight assayand virus-dependent variability (Table 1, Suppl. material 1: table S4). The breadth of ribavirin’s activity, encompassing all seven surveyed families, remains unmatched among currently licensed antivirals (Fig. 3). Mechanistic insights from the integrated trait analysis provide a molecular explanation for much of this variability. Ridge regression modeling identifies the presence of a viral proofreading polymerase—most notably the ExoN function of coronaviruses—as the strongest trait predictor of ribavirin resistance (Fig. 4, Suppl. material 1: table S3). This observation is consistent with previous reports that such polymerases can excise misincorporated nucleotides and limit the effectiveness of lethal mutagenesis, a principal mechanism of ribavirin action in susceptible viruses (Smith et al. 2013; Agostini et al. 2018; Ferron et al. 2018). The model also finds that genome type contributes modestly to potency, with negative-sense RNA viruses generally less responsive than positive-sense RNA viruses. Other traits, such as envelope status and genome segmentation, exert less consistent effects, suggesting that additional, unmeasured viral or host factors may also shape susceptibility. When placed alongside favipiravir and remdesivir in direct comparative profiling, unique and overlapping strengths are evident (Figs 2, 3). Favipiravir displays exceptional selectivity and potency against orthomyxoviruses, consistent with its clinical focus in influenza (Furuta et al. 2013). Remdesivir’s greatest strength is its unrivaled potency against coronaviruses and filoviruses—diseases for which it is now approved or authorized for emergency use (Warren et al. 2016; Sheahan et al. 2017). Ribavirin’s advantage lies in its exceptional breadth, encompassing all major virus families considered here, with a moderate but consistent selectivity profile. Importantly, no single agent demonstrated optimal performance across all metrics, highlighting the necessity for context-specific or combination therapy approaches. Clinical outcome data reinforce the laboratory findings while also emphasizing the importance of matching pharmacological profile to disease context. Robust efficacy for ribavirin is established in chronic hepatitis C virus infection and Lassa fever, as shown in Table 2 and Fig. 6, where substantial improvements in sustained virologic response or reductions in mortality have been achieved (McCormick et al. 1986; Lawitz et al. 2013; Zeuzem et al. 2014). Moderate clinical benefit is evident for severe RSV and some coronavirus infections, but these results are more variable and often limited by study size or design (Rodriguez et al. 1987; Tong et al. 2020). The alignment between in vitro potency rankings and clinical outcomes underscores the translational value of laboratory-based profiling while also revealing settings—such as some coronaviruses or astroviruses— where clinical efficacy may be attenuated by trait-based resistance mechanisms or pharmacokinetic constraints. These findings have immediate relevance for pandemic preparedness and rational antiviral deployment. Ribavirin’s unmatched breadth suggests that it remains a valuable first-line or adjunctive agent for emergent or re-emergent viral threats, especially where alternative options are lacking or unavailable. At the same time, the demonstrated limitations in potency for specific virus families call for careful consideration of dosing, toxicity risk, and the potential utility of combination regimens. Pan-viral profiling frameworks, such as that presented here, enable rapid benchmarking of both existing and novel agents against a defined spectrum of pathogens and mechanistic barriers. The family-specific potency hierarchy and selectivity profiles support practical, triage-oriented prescribing. For suspected or confirmed infections due to Flaviviridae or Paramyxoviridae, ribavirin remains a reasonable first-line or adjunctive option when disease severity and timing justify therapy, given lower family-wise EC₅₀ and favorable SI (Fig. 1, Table 1; Suppl. material 1: table S4). In Lassa fever, early initiation is associated with substantial mortality reduction (McCormick et al. 1986; Table 2, Fig. 6). For chronic HCV, historical synergy with sofosbuvir is well documented (Lawitz et al. 2013; Zeuzem et al. 2014), although contemporary DAA combinations often obviate ribavirin where available. In RSV, use is best reserved for severe disease in high-risk hosts, typically via aerosolized formulations (Rodriguez et al. 1987; Table 2). In Coronaviridae, trait-based resistance from proofreading exoribonuclease argues against ribavirin monotherapy; agents with superior potency (e.g., remdesivir) or combination regimens (e.g., interferon-α–based) align better with the potency map (Figs 2, 3; Smith et al. 2013; Agostini et al. 2018; Ferron et al. 2018; Tong et al. 2020). A practical pathway is to (i) identify the likely virus family, (ii) consult the family-wise potency and selectivity summaries (Figs 2, 5; Suppl. material 1: tables S1–S4), (iii) prioritize the agent with the most favorable potency–safety balance for that family, (iv) initiate treatment early for rapidly progressive disease, and (v) monitor for hemolytic anemia with hemoglobin-guided dose adjustments or discontinuation (De Franceschi et al. 2000). These steps align bedside decisions with the quantitative landscape and can be embedded into stewardship algorithms and stockpiling policies. Several methodological strengths underlie this study. The dataset integrates in vitro and clinical data from a diverse array of sources, encompassing a wide range of virus species, assay systems, and experimental designs. The inclusion of detailed viral trait annotations allows for mechanistic inference, while the comparative, family-wise analytic structure provides clear visual and statistical context for interpreting drug performance (Figs 1–6, Suppl. material 1: tables S1– S4). The combination of quantitative synthesis, trait-based modeling, and clinical correlation yields a multidimensional profile of each drug’s strengths and weaknesses. Nonetheless, important limitations must be recognized. Heterogeneity in in vitro assay protocols, including Pharmacia 72: 1–11 9 differences in host cell types, MOI, assay duration, and detection thresholds, may influence EC₅₀ and SI estimates, potentially confounding direct comparisons. The clinical dataset, while representative, remains limited in size, scope, and statistical power for certain disease settings and is affected by differences in study design, dosing regimens, and patient populations. Trait annotations are constrained by current knowledge and may not capture all relevant viral or host factors; additional determinants, such as host immune response, viral population diversity, or pharmacodynamic variability, warrant further investigation. Recommendations for future research include the systematic evaluation of additional viral traits, the prospective testing of newer ribavirin analogs and related nucleoside inhibitors, and the design of prospective clinical trials in highneed indications or emerging pathogens. Expanded trait studies, leveraging next-generation sequencing and molecular phenotyping, may enable the identification of more precise predictors of antiviral susceptibility, while advances in vitro models (e.g., organoids, primary cell cultures) could yield results more representative of clinical reality. In summary, ribavirin remains a uniquely broad-spectrum antiviral with proven clinical utility in select disease contexts and a favorable—though not uniform—therapeutic window. Its strengths reside in its pan-viral breadth and established efficacy in certain settings, while its limitations stem from trait-based resistance in viruses such as coronaviruses and astroviruses and from dose-limiting toxicity. The pan-viral profiling approach demonstrated here provides a valuable framework for drug discovery, benchmarking, and rational pandemic response planning, highlighting the need for both breadth and precision in future antiviral development. Acknowledgements The author thanks the S&T Fellows Program for funding this research and the Industrial Technology Development Institute of the Department of Science and Technology for hosting this project. Additional information Conflict of interest The author has declared that no competing interests exist. Ethical statements The authors declared that no clinical trials were used in the present study. The authors declared that no experiments on humans or human tissues were performed for the present study. The authors declared that no informed consent was obtained from the humans, donors or donors’ representatives participating in the study. The authors declared that no experiments on animals were performed for the present study. The authors declared that no commercially available immortalised human and animal cell lines were used in the present study. Use of AI No use of AI was reported. Funding This work was supported by Department of Science and Technology Republic of the Philippines. Author contributions FLO: Conceptualization, methodology, data curation, data analysis, data visualization, interpretation, manuscript writing, revision. Author ORCIDs Fredmoore Orosco https://orcid.org/0000-0002-8861-7923 Data availability All of the data that support the findings of this study are available in the main text or Supplementary Information. 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