Planning clinically relevant biomarker validation studies using the "number needed to treat" concept.

PURPOSE: Despite an explosion of translational research to exploit biomarkers in diagnosis, prediction and prognosis, the impact of biomarkers on clinical practice has been limited. The elusiveness of clinical utility may partly originate when validation studies are planned, from a failure to articulate precisely how the biomarker, if successful, will improve clinical decision-making for patients. Clarifying what performance would suffice if the test is to improve medical care makes it possible to design meaningful validation studies. But methods for tackling this part of validation study design are undeveloped, because it demands uncomfortable judgments about the relative values of good and bad outcomes resulting from a medical decision. METHODS: An unconventional use of "number needed to treat" (NNT) can structure communication for the trial design team, to elicit purely value-based outcome tradeoffs, conveyed as the endpoints of an NNT "discomfort range". The study biostatistician can convert the endpoints into desired predictive values, providing criteria for designing a prospective validation study. Next, a novel "contra-Bayes" theorem converts those predictive values into target sensitivity and specificity criteria, to guide design of a retrospective validation study. Several examples demonstrate the approach. CONCLUSION: In practice, NNT-guided dialogues have contributed to validation study planning by tying it closely to specific patient-oriented translational goals. The ultimate payoff comes when the report of the completed study includes motivation in the form of a biomarker test framework directly reflecting the clinical decision challenge to be solved. Then readers will understand better what the biomarker test has to offer patients.

Estrogen represses gene expression through reconfiguring chromatin structures.

Estrogen regulates over a thousand genes, with an equal number of them being induced or repressed. The distinct mechanisms underlying these dual transcriptional effects remain largely unknown. We derived comprehensive views of the transcription machineries assembled at estrogen-responsive genes through integrating multiple types of genomic data. In the absence of estrogen, the majority of genes formed higher-order chromatin structures, including DNA loops tethered to protein complexes involving RNA polymerase II (Pol II), estrogen receptor alpha (ERα) and ERα-pioneer factors. Genes to be 'repressed' by estrogen showed active transcription at promoters and throughout the gene bodies; genes to be 'induced' exhibited active transcription initiation at promoters, but with transcription paused in gene bodies. In the presence of estrogen, the majority of estrogen-induced genes retained the original higher-order chromatin structures, whereas most estrogen-repressed genes underwent a chromatin reconfiguration. For estrogen-induced genes, estrogen enhances transcription elongation, potentially through recruitment of co-activators or release of co-repressors with unique roles in elongation. For estrogen-repressed genes, estrogen treatment leads to chromatin structure reconfiguration, thereby disrupting the originally transcription-efficient chromatin structures. Our in silico studies have shown that estrogen regulates gene expression, at least in part, through modifying previously assembled higher-order complexes, rather than by facilitating de novo assembly of machineries.

A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration.

BACKGROUND: In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: "molecular identification" (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices. RESULTS: We describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events. CONCLUSIONS: The paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors.

Genetic variability in DNA repair and cell cycle control pathway genes and risk of smoking-related lung cancer.

DNA repair and cell cycle control play an important role in the repair of DNA damage caused by cigarette smoking. Given this role, functionally relevant single nucleotide polymorphisms (SNPs) in genes in these pathways may well affect the risk of smoking-related lung cancer. We examined the relationship between 240 SNPs in DNA repair and cell cycle control pathway genes and lung cancer risk in a case-control study of white current and ex-cigarette smokers (722 cases and 929 controls). Additive, dominant, and recessive genetic models were evaluated for each SNP. A genetic risk summary score was also constructed. Odds ratios (OR) for lung cancer risk and 95% confidence intervals (95% CI) were estimated using logistic regression models. Thirty-eight SNPs were associated with lung cancer risk in our study population at P < 0.05. The strongest associations were observed for rs2074508 in GTF2H4 (P(additive) = 0.003), rs10500298 in LIG1 (P(recessive) = 2.7 × 10(-4)), rs747658 and rs3219073 in PARP1 (rs747658: P(additive) = 5.8 × 10(-5); rs3219073: P(additive) = 4.6 × 10(-5)), and rs1799782 and rs3213255 in XRCC1 (rs1799782: P(dominant) = 0.006; rs3213255: P(recessive) = 0.004). Compared to individuals with first quartile (lowest) risk summary scores, individuals with third and fourth quartile summary score results were at increased risk for lung cancer (OR: 2.21, 95% CI: 1.66-2.95 and OR: 3.44, 95% CI: 2.58-4.59, respectively; P(trend) < 0.0001). Our data suggests that variation in DNA repair and cell cycle control pathway genes is associated with smoking-related lung cancer risk. Additionally, combining genotype information for SNPs in these pathways may assist in classifying current and ex-cigarette smokers according to lung cancer risk.

Comparing, evaluating and combining statistical species distribution models and CLIMEX to forecast the distributions of emerging crop pests.

BACKGROUND: Forecasting the spread of emerging pests is widely requested by pest management agencies in order to prioritise and target efforts. Two widely used approaches are statistical Species Distribution Models (SDMs) and CLIMEX, which uses ecophysiological parameters. Each have strengths and weaknesses. SDMs can incorporate almost any environmental condition and their accuracy can be formally evaluated to inform managers. However, accuracy is affected by data availability and can be limited for emerging pests, and SDMs usually predict year-round distributions, not seasonal outbreaks. CLIMEX can formally incorporate expert ecophysiological knowledge and predicts seasonal outbreaks. However, the methods for formal evaluation are limited and rarely applied. We argue that both approaches can be informative and complementary, but we need tools to integrate and evaluate their accuracy. Here we develop such an approach, and test it by forecasting the potential global range of the tomato pest Tuta absoluta. RESULTS: The accuracy of previously developed CLIMEX and new statistical SDMs were comparable on average, but the best statistical SDM techniques and environmental data substantially outperformed CLIMEX. The ensembled approach changes expectations of T. absoluta's spread. The pest's environmental tolerances and potential range in Africa, the Arabian Peninsula, Central Asia and Australia will be larger than previous estimates. CONCLUSION: We recommend that CLIMEX be considered one of a suite of SDM techniques and thus evaluated formally. CLIMEX and statistical SDMs should be compared and ensembled if possible. We provide code that can be used to do so when employing the biomod suite of SDM techniques. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Institutional and policy bottlenecks to IPM.

This paper summarises institutional and policy bottlenecks to IPM in Africa. Agricultural policy in Africa generally prioritises production and productivity above environmental sustainability, so the use of synthetic pesticides for controlling pests is encouraged. Funding for research in IPM is limited, and extension systems struggle to provide the level of farmer support that adoption of IPM often needs. Improved research and extension policies could facilitate uptake of IPM. Public and private food-safety standards can incentivise adoption, but currently this is mainly in production for export. Pesticide and other input regulatory systems unintentionally constrain adoption of IPM, through expensive registration procedures, weak compliance monitoring and limited regional harmonisation. IPM must be seen as a key element of food-system transformation.

Fall armyworm from a maize multi-peril pest risk perspective.

We assembled 3,175 geo-tagged occurrences of fall armyworm worldwide and used that data in conjunction with information about the physiological requirements of the pest to spatially assess its global climate suitability. Our analysis indicates that almost the entire African maize crop is grown in areas with climates that support seasonal infestations of the insect, while almost 92% of the maize area supports year-round growth of the pest. In contrast, rich-country maize production largely occurs in temperate areas where only 2.3% of the area may allow the pest to survive year-round, although still subject to worrisome seasonal risks. This means the African maize crop is especially susceptible to damaging infestation from fall armyworm, on par with the risk exposure to this pest faced by maize producers throughout Latin America. We show that the maize grown in Africa is also especially vulnerable to infestations from a host of other crop pests. Our multi-peril pest risk study reveals that over 95% of the African maize area deemed climate suitable for fall armyworm, can also support year-round survival of at least three or more pests. The spatial concurrence of climatically suitable locations for these pests raises the production risk for farmers well above the risks posed from fall armyworm alone. Starkly, over half (52.5%) of the African maize area deemed suitable for fall armyworm is also at risk from a further nine pests, while over a third (38.1%) of the area is susceptible to an additional 10 pests. This constitutes an exceptionally risky production environment for African maize producers, with substantive and complex implications for developing and implementing crop breeding, biological, chemical and other crop management strategies to help mitigate these multi-peril risks.

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models.

Invasive species have historically been a problem derived from global trade and transport. To aid in the control and management of these species, species distribution models (SDMs) have been used to help predict possible areas of expansion. Our focal organism, the African Armyworm (AAW), has historically been known as an important pest species in Africa, occurring at high larval densities and causing outbreaks that can cause enormous economic damage to staple crops. The goal of this study is to map the AAW's present and potential distribution in three future scenarios for the region, and the potential global distribution if the species were to invade other territories, using 40 years of data on more than 700 larval outbreak reports from Kenya and Tanzania. The present distribution in East Africa coincides with its previously known distribution, as well as other areas of grassland and cropland, which are the host plants for this species. The different future climatic scenarios show broadly similar potential distributions in East Africa to the present day. The predicted global distribution shows areas where the AAW has already been reported, but also shows many potential areas in the Americas where, if transported, environmental conditions are suitable for AAW to thrive and where it could become an invasive species.

Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

BACKGROUND: Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit. RESULTS: We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed. CONCLUSIONS: The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging.

Proteomic analysis of stage I endometrial cancer tissue: identification of proteins associated with oxidative processes and inflammation.

OBJECTIVE: The present study aimed to identify differentially expressed proteins employing a high resolution mass spectrometry (MS)-based proteomic analysis of endometrial cancer cells harvested using laser microdissection. METHODS: A differential MS-based proteomic analysis was conducted from discrete epithelial cell populations gathered by laser microdissection from 91 pathologically reviewed stage I endometrial cancer tissue samples (79 endometrioid and 12 serous) and 10 samples of normal endometrium from postmenopausal women. Hierarchical cluster analysis of protein abundance levels derived from a spectral count analysis revealed a number of proteins whose expression levels were common as well as unique to both histologic types. An independent set of endometrial cancer specimens from 394 patients were used to externally validate the differential expression of select proteins. RESULTS: 209 differentially expressed proteins were identified in a comparison of stage I endometrial cancers and normal post-menopausal endometrium controls (Q<0.005). A number of differentially abundant proteins in stage I endometrial cancer were identified and independently validated by western blot and tissue microarray analyses. Multiple proteins identified with elevated abundance in stage I endometrial cancer are functionally associated with inflammation (annexins) and oxidative processes (peroxiredoxins). PRDX1 and ANXA2 were both confirmed as being overexpressed in stage I cancer compared to normal endometrium by independent TMA (Q=0.008 and Q=0.00002 respectively). CONCLUSIONS: These data provide the basis for further investigation of previously unrecognized novel pathways involved in early stage endometrial carcinogenesis and provide possible targets for prevention strategies that are inclusive of both endometrioid and serous histologic subtypes.

Assessing the Potential of Inoculative Field Releases of Telenomus remus to Control Spodoptera frugiperda in Ghana.

In response to the threat caused by the fall armyworm to African maize farmers, we conducted a series of field release studies with the egg parasitoid Telenomus remus in Ghana. Three releases of ≈15,000 individuals each were conducted in maize plots of 0.5 ha each in the major and minor rainy seasons of 2020, and compared to no-release control plots as well as to farmer-managed plots with chemical pest control. No egg mass parasitism was observed directly before the first field release. Egg mass parasitism reached 33% in the T. remus release plot in the major rainy season, while 72-100% of egg masses were parasitized in the minor rainy season, during which pest densities were much lower. However, no significant difference in egg mass parasitism was found among the T. remus release plots, the no-release control plots and the farmer-managed plots. Similarly, no significant decrease in larval numbers or plant damage was found in the T. remus release fields compared to the no-release plots, while lower leaf and tassel damage was observed in farmer-managed plots. Larval parasitism due to other parasitoids reached 18-42% in the major rainy season but was significantly lower in the minor rainy season, with no significant differences among treatments. We did not observe significant differences in cob damage or yield among the three treatments. However, the lack of any significant differences between the release and no-release plots, which may be attributed to parasitoid dispersal during the five weeks of observation, would require further studies to confirm. Interestingly, a single application of Emamectin benzoate did not significantly affect the parasitism rates of T. remus and, thus, merits further investigation in the context of developing IPM strategies against FAW.

Regional and seasonal activity predictions for fall armyworm in Australia.

Since 2016, the fall armyworm (FAW), Spodoptera frugiperda, has undergone a significant range expansion from its native range in the Americas, to continental Africa, Asia, and in February 2020, mainland Australia. The large dispersal potential of FAW adults, wide host range of immature feeding stages, and unique environmental conditions in its invasive range creates large uncertainties in the expected impact on Australian plant production industries. Here, using a spatial model of population growth and spread potential informed by existing biological and climatic data, we simulate seasonal population activity potential of FAW, with a focus on Australia's grain production regions. Our results show that, in Australia, the large spread potential of FAW will allow it to exploit temporarily favourable conditions for population growth across highly variable climatic conditions. It is estimated that FAW populations would be present in a wide range of grain growing regions at certain times of year, but importantly, the expected seasonal activity will vary markedly between regions and years depending on climatic conditions. The window of activity for FAW will be longer for growing regions further north, with some regions possessing conditions conducive to year-round population survival. Seasonal migrations from this permanent range into southern regions, where large areas of annual grain crops are grown annually, are predicted to commence from October, i.e. spring, with populations subsequently building up into summer. The early stage of the FAW incursion into Australia means our predictions of seasonal activity potential will need to be refined as more Australian-specific information is accumulated. This study has contributed to our early understanding of FAW movement and population dynamics in Australia. Importantly, the models established here provide a useful framework that will be available to other countries should FAW invade in the future. To increase the robustness of our model, field sampling to identify conditions under which population growth occurs, and the location of source populations for migration events is required. This will enable accurate forecasting and early warning to farmers, which should improve pest monitoring and control programs of FAW.

Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry.

Targeted glycoproteomics represents an attractive approach for conducting peripheral blood based cancer biomarker discovery due to the well-known altered pattern of protein glycosylation in cancer and the reduced complexity of the resultant glycoproteome. Here we report its application to a set of pooled nonsmall cell lung cancer (NSCLC) case sera (9 adenocarcinoma and 6 squamous cell carcinoma pools from 54 patients) and matched controls pools, including 8 clinical control pools with computed tomography detected nodules but being nonmalignant as determined by biopsy from 54 patients, and 8 matched healthy control pools from 106 cancer-free subjects. The goal of the study is to discover biomarkers that may enable improved early detection and diagnosis of lung cancer. Immunoaffinity subtraction was used to first deplete the topmost abundant serum proteins; the remaining serum proteins were then subjected to hydrazide chemistry based glycoprotein capture and enrichment. Hydrazide resin in situ trypsin digestion was used to release nonglycosylated peptides. Formerly N-linked glycosylated peptides were released by peptide-N-glycosidase F (PNGase F) treatment and were subsequently analyzed by liquid chromatography (LC)-tandem mass spectrometry (MS/MS). A MATLAB based in-house tool was developed to facilitate retention time alignment across different LC-MS/MS runs, determination of precursor ion m/z values and elution profiles, and the integration of mass chromatograms based on determined parameters for identified peptides. A total of 38 glycopeptides from 22 different proteins were significantly differentially abundant across the case/control pools (P < 0.01, Student's t test) and their abundances led to a near complete separation of case and control pools based on hierarchical clustering. The differential abundances of three of these candidate proteins were verified by commercially available ELISAs applied in the pools. Strong positive correlations between glycopeptide mass chromatograms and ELISA-measured protein abundance was observed for all of the selected glycoproteins.

Failsafe automation of Phase II clinical trial interim monitoring for stopping rules.

BACKGROUND: In Phase II clinical trials in cancer, preventing the treatment of patients on a study when current data demonstrate that the treatment is insufficiently active or too toxic has obvious benefits, both in protecting patients and in reducing sponsor costs. Considerable efforts have gone into experimental designs for Phase II clinical trials with flexible sample size, usually implemented by early stopping rules. The intended benefits will not ensue, however, if the design is not followed. Despite the best intentions, failures can occur for many reasons. PURPOSE: The main goal is to develop an automated system for interim monitoring, as a backup system supplementing the protocol team, to ensure that patients are protected. A secondary goal is to stimulate timely recording of patient assessments. METHODS: We developed key concepts and performance needs, then designed, implemented, and deployed a software solution embedded in the clinical trials database system. RESULTS: The system has been in place since October 2007. One clinical trial tripped the automated monitor, resulting in e-mails that initiated statistician/investigator review in timely fashion. LIMITATIONS: Several essential contributing activities still require human intervention, institutional policy decisions, and institutional commitment of resources. CONCLUSIONS: We believe that implementing the concepts presented here will provide greater assurance that interim monitoring plans are followed and that patients are protected from inadequate response or excessive toxicity. This approach may also facilitate wider acceptance and quicker implementation of new interim monitoring algorithms.

Adaptive Bayesian design for phase I dose-finding trials using a joint model of response and toxicity.

We present a new adaptive Bayesian method for dose-finding in phase I clinical trials based on both response and toxicity. Although clinical responses are usually rare in phase I cancer trials, molecularly targeted therapy may make clinical responses more likely. In addition, biological responses may be common. Thus responses may be frequent enough to help decide how aggressive a phase I escalation should be. The model assumes that response and toxicity events happen depending on respective dose thresholds for the individual, assuming that the thresholds jointly follow a bivariate log-normal distribution or a mixture. The design utilizes prior information about the population threshold distribution as well as accumulated data. The next dose is assigned to maximize a patient-oriented expected utility integrated over the current posterior distribution. The design is evaluated through simulation with population parameters equaling estimates from early Gleevec trials. This exercise provides evidence for the value of the use of the proposed design for future clinical trials.

Understanding smallholders' responses to fall armyworm (Spodoptera frugiperda) invasion: Evidence from five African countries.

Fall armyworm (FAW) is a new invasive pest that is causing devastating effects on maize production and threatening the livelihoods of millions of poor smallholders across sub-Saharan Africa and Asia. Using unique survey data from 2356 maize-growing households in Ghana, Rwanda, Uganda, Zambia and Zimbabwe, we examined how smallholder farmers are fighting this voracious pest. In particular, we assessed the FAW management strategies used by smallholders, socio-economic factors driving the choice of the management options, the complementarities or tradeoffs among the management options, and the (un)safe pesticide use practices of farmers. Results showed that smallholder farm households have adopted a variety of cultural, physical, chemical and local options to mitigate the effects of FAW, but the use of synthetic pesticides remains the most popular option. Results from multivariate probit regressions indicated that the extensive use of synthetic pesticides is driven by household asset wealth, and access to subsidised farm inputs and extension information. We observed that farm households are using a wide range of pesticides, including highly hazardous and banned products. Unfortunately, a majority of the households do not use personal protective equipment while handling the pesticides, resulting in reports of acute pesticide-related illness. Our findings have important implications for policies and interventions aimed at promoting environmentally friendly and sustainable ways of managing invasive pests in smallholder farming systems.

Why don't smallholder farmers in Kenya use more biopesticides?

BACKGROUND: Although Kenya has a relatively high number of registered biopesticide products, little is known about biopesticide use by smallholders. This paper documents farmers' current use and perception of chemical pesticides and biopesticides, their willingness to pay for biopesticides, and the key challenges to biopesticide uptake. RESULTS: A survey found that chemical pesticides are used widely by smallholders despite awareness of the risks to human health and the environment. Almost half of respondents showed awareness of biopesticides, but current use in the survey localities was low (10%). Key reasons for the low use of biopesticides by smallholders in this study are: perceptions of effectiveness, primarily speed of action and spectrum of activity, availability and affordability. Smallholders who used biopesticides cited effectiveness, recommendation by advisory services and perception of safety as key reasons for their choice. Although farmers viewed both pesticides and biopesticides as costly, they invested in the former due to their perceived effectiveness. Average willingness to pay, above current chemical pesticide expenditures per cropping season was 9.6% (US$5.7). Willingness to pay differed significantly between counties, and was higher among farmers with more education or greater awareness of the health risks associated with pesticide use. CONCLUSION: This study confirms the low use of biopesticide products in the survey areas, alongside high use of conventional chemical pesticides. In order to promote greater uptake of biopesticides, addressing farmers' awareness and their perceptions of effectiveness is important, as well as increasing the knowledge of those providing advice and ensuring registered products are available locally at competitive prices. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Parasitoid Complex of Fall Armyworm, Spodoptera frugiperda, in Ghana and Benin.

Keywords: biological control; Chelonus bifoveolatus; Coccygidum luteum; Telenomus remus; Trichogramma; West Africa.

Publisher Correction: Science diplomacy for plant health.

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

Evidence of Leaf Consumption Rate Decrease in Fall Armyworm, Spodoptera frugiperda, Larvae Parasitized by Coccygidium luteum.

Biological control is one of the best options for the sustainable management of the invasive maize pest Spodoptera frugiperda in Africa. However, there is limited knowledge of the efficacy of native natural enemies of S. frugiperda and their potential use in integrated pest management. The endoparasitoid wasp Coccygidium luteum is one of the natural enemies of S. frugiperda in Africa. This study assessed, under laboratory conditions, the effect of C. luteum on the leaf consumption rate of its host. Fifty first instar S. frugiperda larvae were exposed to C. luteum for oviposition and the maize leaf consumption rate of parasitized larvae was assessed and compared to 50 unparasitized larvae from the same cohort. Coccygidium luteum completed a generation, from egg to adult emergence, in 16.7 days. The leaf consumption rate of parasitized S. frugiperda larvae declined gradually compared to unparasitized larvae and the overall consumption reduction by parasitized S. frugiperda larvae was 89%. Our findings show that C. luteum could reduce damage caused by S. frugiperda to maize farms but, prior to its use in biological control programmes, further studies are needed to assess potential parasitism rates in field conditions and develop a cost-effective mass production system.