Mesoporous Silica as an Alternative Vehicle to Overcome Solubility Limitations.

Toxicological studies are a part of the drug development process and the preclinical stages, for which suitable vehicles ensuring easy and safe administration are crucial. However, poor aqueous solubility of drugs complicates vehicle screening for oral administration since non-aqueous solvents are often not tolerable. In the case of the anti-infective corallopyronin A, currently undergoing preclinical investigation for filarial nematode and bacterial infections, commonly used vehicles such as polyethylene glycol 200, aqueous solutions combined with cosolvents or solubilizers, or aqueous suspension have failed due to insufficient tolerability, solubility, or the generation of a non-homogeneous suspension. To this end, the aim of the study was to establish an alternative approach which offers suitable tolerability, dissolution, and ease of handling. Thus, a corallopyronin A-mesoporous silica formulation was successfully processed and tested in a seven-day toxicology study focused on Beagle dogs, including a toxicokinetic investigation on day one. Sufficient tolerability was confirmed by the vehicle control group. The vehicle enabled high-dose levels resulting in a low-, middle-, and high-dose of 150, 450, and 750 mg/kg. Overall, it was possible to achieve high plasma concentrations and exposures, leading to a valuable outcome of the toxicology study and establishing mesoporous silica as a valuable contender for challenging drug candidates.

Matching by OS Prognostic Score to Construct External Controls in Lung Cancer Clinical Trials.

External controls (eControls) leverage historical data to create non-randomized control arms. The lack of randomization can result in confounding between the experimental and eControl cohorts. To balance potentially confounding variables between the cohorts, one of the proposed methods is to match on prognostic scores. Still, the performance of prognostic scores to construct eControls in oncology has not been analyzed yet. Using an electronic health record-derived de-identified database, we constructed eControls using one of three methods: ROPRO, a state-of-the-art prognostic score, or either a propensity score composed of five (5Vars) or 27 covariates (ROPROvars). We compared the performance of these methods in estimating the overall survival (OS) hazard ratio (HR) of 11 recent advanced non-small cell lung cancer. The ROPRO eControls had a lower OS HR error (median absolute deviation (MAD), 0.072, confidence interval (CI): 0.036-0.185), than the 5Vars (MAD 0.081, CI: 0.025-0.283) and ROPROvars eControls (MAD 0.087, CI: 0.054-0.383). Notably, the OS HR errors for all methods were even lower in the phase III studies. Moreover, the ROPRO eControl cohorts included, on average, more patients than the 5Vars (6.54%) and ROPROvars cohorts (11.7%). The eControls matched with the prognostic score reproduced the controls more reliably than propensity scores composed of the underlying variables. Additionally, prognostic scores could allow eControls to be built on many prognostic variables without a significant increase in the variability of the propensity score, which would decrease the number of matched patients.

Reproducible image-based profiling with Pycytominer.

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.

Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

PURPOSE: Overall survival (OS) is the primary end point in phase III oncology trials. Given low success rates, surrogate end points, such as progression-free survival or objective response rate, are used in early go/no-go decision making. Here, we investigate whether early trends of OS prognostic biomarkers, such as the ROPRO and DeepROPRO, can also be used for this purpose. METHODS: Using real-world data, we emulated a series of 12 advanced non-small-cell lung cancer (aNSCLC) clinical trials, originally conducted by six different sponsors and evaluated four different mechanisms, in a total of 19,920 individuals. We evaluated early trends (until 6 months) of the OS biomarker alongside early OS within the joint model (JM) framework. Study-level estimates of early OS and ROPRO trends were correlated against the actual final OS hazard ratios (HRs). RESULTS: We observed a strong correlation between the JM estimates and final OS HR at 3 months (adjusted R2 = 0.88) and at 6 months (adjusted R2 = 0.85). In the leave-one-out analysis, there was a low overall prediction error of the OS HR at both 3 months (root-mean-square error [RMSE] = 0.11) and 6 months (RMSE = 0.12). In addition, at 3 months, the absolute prediction error of the OS HR was lower than 0.05 for three trials. CONCLUSION: We describe a pipeline to predict trial OS HRs using emulated aNSCLC studies and their early OS and OS biomarker trends. The method has the potential to accelerate and improve decision making in drug development.

Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models.

Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass metabolism. The objective of the present study was to assess the predictability of human pharmacokinetics by using biphasic dissolution results obtained with the previously established BiPHa+ assay and PBPK tools. For six commercial drug products, formulated by different enabling technologies, the respective organic partitioning profiles were processed with two PBPK in silico modeling tools, namely PK-Sim and GastroPlus®, similar to extended-release dissolution profiles. Thus, a mechanistic dissolution/precipitation model of the assessed drug products was not required. The developed elimination/distribution models were used to simulate the pharmacokinetics of the evaluated drug products and compared with available human data. In essence, an in vitro to in vivo extrapolation (IVIVE) was successfully developed. Organic partitioning profiles obtained from the BiPHa+ dissolution analysis enabled highly accurate predictions of the pharmacokinetic behavior of the investigated drug products. In addition, PBPK models of (pro-)drugs with pronounced first-pass metabolism enabled adjustment of the solely passive diffusion predicting organic partitioning profiles, and increased prediction accuracy further.

Risky behavior during stair descent for young adults: Differences in men versus women.

Injuries commonly occur on stairs, with high injury rates in young adults, especially young women. High injury rates could result from physiological and/or behavioral differences; this study focuses on behaviors. The purposes of this observational study were (1) to quantify young adult behaviors during stair descent and (2) to identify differences in stair descent behavior for young adult men versus women. Young adult pedestrians (N = 2,400, 1,470 men and 930 women) were videotaped during descent of two indoor campus staircases, a short staircase (2 steps) and a long staircase (17 steps). Behaviors during stair descent were coded by experimenters. Risky behaviors observed on the short staircase included: No one used the handrail, 16.1% used an electronic device, and 16.4% had in-person conversations. On the long staircase: 64.8% of pedestrians did not use the handrail, 11.9% used an electronic device, and 14.5% had in-person conversations. Risky behaviors observed more in women included: less likely to use the handrail (long staircase), more likely to carry an item in their hands (both staircases), more likely to engage in conversation (both staircases), and more likely to wear sandals or heels (both staircases) (p≤0.05). Protective behaviors observed more in women included: less likely to skip steps (both staircases), and more likely to look at treads during transition steps (long staircase) (p≤0.05). The number of co-occurring risky behaviors was higher in women: 1.9 vs 2.3, for men vs women, respectively (p<0.001). Five pedestrians lost balance but did not fall; four of these pedestrians lost balance on the top step and all five had their gaze diverted from the steps at the time balance was lost. The observed behaviors may be related to the high injury rate of stair-related falls in young adults, and young women specifically.

Predicting compound activity from phenotypic profiles and chemical structures.

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources-chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)-to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6-10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.

Identification of de novo variants in nonsyndromic cleft lip with/without cleft palate patients with low polygenic risk scores.

BACKGROUND: Nonsyndromic cleft lip with/without cleft palate (nsCL/P) is a congenital malformation of multifactorial etiology. Research has identified >40 genome-wide significant risk loci, which explain less than 40% of nsCL/P heritability. Studies show that some of the hidden heritability is explained by rare penetrant variants. METHODS: To identify new candidate genes, we searched for highly penetrant de novo variants (DNVs) in 50 nsCL/P patient/parent-trios with a low polygenic risk for the phenotype (discovery). We prioritized DNV-carrying candidate genes from the discovery for resequencing in independent cohorts of 1010 nsCL/P patients of diverse ethnicities and 1574 population-matched controls (replication). Segregation analyses and rare variant association in the replication cohort, in combination with additional data (genome-wide association data, expression, protein-protein-interactions), were used for final prioritization. CONCLUSION: In the discovery step, 60 DNVs were identified in 60 genes, including a variant in the established nsCL/P risk gene CDH1. Re-sequencing of 32 prioritized genes led to the identification of 373 rare, likely pathogenic variants. Finally, MDN1 and PAXIP1 were prioritized as top candidates. Our findings demonstrate that DNV detection, including polygenic risk score analysis, is a powerful tool for identifying nsCL/P candidate genes, which can also be applied to other multifactorial congenital malformations.

PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback.

We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re-)training problem accompanying deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on time series. Automatic pattern retrieval methods often suffer from inefficient training data, a lack of ground truth labels, and a discrepancy between the similarity perceived by the algorithm and required by the user or the task. Our proposal is based on the query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption to converge to users' similarity notion. We demonstrate PSEUDo's performance in terms of accuracy, speed, steerability, and usability through quantitative benchmarks with representative time series retrieval methods and a case study. We find that PSEUDo detects patterns in high-dimensional time series efficiently, improves the result with relevance feedback through feature selection, and allows an understandable as well as user-friendly retrieval process.

Effects of Elaboration and Instructor Feedback on Retention of Clinical Reasoning Competence Among Undergraduate Medical Students: A Randomized Crossover Trial.

This randomized crossover trial examines whether elaboration on common errors in patient treatment, combined with individualized mailed feedback, improves medium-term retention of clinical reasoning competence.

Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method.

A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10-8. Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci.

In vitro neutrophil migration is associated with inhaled corticosteroid treatment and serum cytokines in pediatric asthma.

Background: Different asthma phenotypes are driven by molecular endotypes. A Th1-high phenotype is linked to severe, therapy-refractory asthma, subclinical infections and neutrophil inflammation. Previously, we found neutrophil granulocytes (NGs) from asthmatics exhibit decreased chemotaxis towards leukotriene B4 (LTB4), a chemoattractant involved in inflammation response. We hypothesized that this pattern is driven by asthma in general and aggravated in a Th1-high phenotype. Methods: NGs from asthmatic nd healthy children were stimulated with 10 nM LTB4/100 nM N-formylmethionine-leucyl-phenylalanine and neutrophil migration was documented following our prior SiMA (simplified migration assay) workflow, capturing morphologic and dynamic parameters from single-cell tracking in the images. Demographic, clinical and serum cytokine data were determined in the ALLIANCE cohort. Results: A reduced chemotactic response towards LTB4 was confirmed in asthmatic donors regardless of inhaled corticosteroid (ICS) treatment. By contrast, only NGs from ICS-treated asthmatic children migrate similarly to controls with the exception of Th1-high donors, whose NGs presented a reduced and less directed migration towards the chemokines. ICS-treated and Th1-high asthmatic donors present an altered surface receptor profile, which partly correlates with migration. Conclusions: Neutrophil migration in vitro may be affected by ICS-therapy or a Th1-high phenotype. This may be explained by alteration of receptor expression and could be used as a tool to monitor asthma treatment.

In Vitro-In Vivo Relationship in Mini-Scale-Enabling Formulations of Corallopyronin A.

In vivo studies in mice provide a valuable model to test novel active pharmaceutical ingredients due to their low material need and the fact that mice are frequently used as a species for early efficacy models. However, preclinical in vitro evaluations of formulation principles in mice are still lacking. The development of novel in vitro and in silico models supported the preclinical formulation evaluation for the anti-infective corallopyronin A (CorA). To this end, CorA and solubility-enhanced amorphous solid dispersion formulations, comprising povidone or copovidone, were evaluated regarding biorelevant solubilities and dissolution in mouse-specific media. As an acidic compound, CorA and CorA-ASD formulations showed decreased solubilities in mice when compared with human-specific media. In biorelevant biphasic dissolution experiments CorA-povidone showed a three-fold higher fraction partitioned into the organic phase of the biphasic dissolution, when compared with CorA-copovidone. Bioavailabilities determined by pharmacokinetic studies in BALB/c mice correlated with the biphasic dissolution prediction and resulted in a Level C in vitro-in vivo correlation. In vitro cell experiments excluded intestinal efflux by P-glycoprotein or breast cancer resistance protein. By incorporating in vitro results into a physiologically based pharmacokinetic model, the plasma concentrations of CorA-ASD formulations were predicted and identified dissolution as the limiting factor for bioavailability.

Corallopyronin A: antimicrobial discovery to preclinical development.

Covering: August 1984 up to January 2022Worldwide, increasing morbidity and mortality due to antibiotic-resistant microbial infections has been observed. Therefore, better prevention and control of infectious diseases, as well as appropriate use of approved antibacterial drugs are crucial. There is also an urgent need for the continuous development and supply of novel antibiotics. Thus, identifying new antibiotics and their further development is once again a priority of natural product research. The antibiotic corallopyronin A was discovered in the 1980s in the culture broth of the Myxobacterium Corallococcus coralloides and serves, in the context of this review, as a show case for the development of a naturally occurring antibiotic compound. The review demonstrates how a hard to obtain, barely water soluble and unstable compound such as corallopyronin A can be developed making use of sophisticated production and formulation approaches. Corallopyronin A is a bacterial DNA-dependent RNA polymerase inhibitor with a new target site and one of the few representatives of this class currently in preclinical development. Efficacy against Gram-positive and Gram-negative pathogens, e.g., Chlamydia trachomatis, Orientia tsutsugamushi, Staphylococcus aureus, and Wolbachia has been demonstrated. Due to its highly effective in vivo depletion of Wolbachia, which are essential endobacteria of most filarial nematode species, and its robust macrofilaricidal efficacy, corallopyronin A was selected as a preclinical candidate for the treatment of human filarial infections. This review highlights the discovery and production optimization approaches for corallopyronin A, as well as, recent preclinical efficacy results demonstrating a robust macrofilaricidal effect of the anti-Wolbachia candidate, and the solid formulation strategy which enhances the stability as well as the bioavailability of corallopyronin A.

Using item response theory to appraise key feature examinations for clinical reasoning.

BACKGROUND: Validation of examinations is usually based on classical test theory. In this study, we analysed a key feature examination according to item response theory and compared the results with those of a classical test theory approach. METHODS: Over the course of five years, 805 fourth-year undergraduate students took a key feature examination on general medicine consisting of 30 items. Analyses were run according to a classical test theory approach as well as using item response theory. Classical test theory analyses are reported as item difficulty, discriminatory power, and Cronbach's alpha while item response theory analyses are presented as item characteristics curves, item information curves and a test information function. RESULTS: According to classical test theory findings, the examination was labelled as easy. Analyses according to item response theory more specifically indicated that the examination was most suited to identify struggling students. Furthermore, the analysis allowed for adapting the examination to specific ability ranges by removing items, as well as comparing multiple samples with varying ability ranges. CONCLUSIONS: Item response theory analyses revealed results not yielded by classical test theory. Thus, both approaches should be routinely combined to increase the information yield of examination data.

Use of digital teaching resources and predictors of medical student performance during the pandemic: A prospective study.

BACKGROUND: The coronavirus pandemic has led to increased use of digital teaching formats in medical education. A number of studies have assessed student satisfaction with these resources. However, there is a lack of studies investigating changes in student performance following the switch from contact to virtual teaching. Specifically, there are no studies linking student use of digital resources to learning outcome and examining predictors of failure. METHODS: Student performance before (winter term 2019/20: contact teaching) and during (summer term 2020: no contact teaching) the pandemic was compared prospectively in a cohort of 162 medical students enrolled in the clinical phase of a five-year undergraduate curriculum. Use of and performance in various digital resources (case-based teaching in a modified flipped classroom approach; formative key feature examinations of clinical reasoning; daily multiple choice quizzes) was recorded in summer 2020. Student scores in summative examinations were compared to examination scores in the previous term. Associations between student characteristics, resource use and summative examination results were used to identify predictors of performance. RESULTS: Not all students made complete use of the digital learning resources provided. Timely completion of tasks was associated with superior performance compared to delayed completion. Female students scored significantly fewer points in formative key feature examinations and digital quizzes. Overall, higher rankings within the student cohort (according to summative exams) in winter term 2019/20 as well as male gender predicted summative exam performance in summer 2020. Scores achieved in the first formative key feature examination predicted summative end-of-module exam scores. CONCLUSIONS: The association between timely completion of tasks as well as early performance in a module and summative exams might help to identify students at risk and offering help early on. The unexpected gender difference requires further study to determine whether the shift to a digital-only curriculum disadvantages female students.

Pharmacokinetics and Pharmacodynamics (PK/PD) of Corallopyronin A against Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a World Health Organization's high priority pathogen organism, with an estimated > 100,000 deaths worldwide in 2019. Thus, there is an unmet medical need for novel and resistance-breaking anti-infectives. The natural product Co-rallopyronin A (CorA), currently in preclinical development for filariasis, is efficacious against MRSA in vitro. In this study, we evaluated the pharmacokinetics of CorA after dosing in mice. Furthermore, we determined compound concentrations in target compartments, such as lung, kidney and thigh tissue, using LC-MS/MS. Based on the pharmacokinetic results, we evaluated the pharmacodynamic profile of CorA using the standard neutropenic thigh and lung infection models. We demonstrate that CorA is effective in both standard pharmacodynamic models. In addition to reaching effective levels in the lung and muscle, CorA was detected at high levels in the thigh bone. The data presented herein encourage the further exploration of the additional CorA indications treatment of MRSA- and methicillin-sensitive S. aureus- (MSSA) related infections.

Consistent Assignment of Risk and Benign Allele at rs2303153 in the CF Modifier Gene SCNN1B in Three Independent F508del-CFTR Homozygous Patient Populations.

CFTR encodes for a chloride and bicarbonate channel expressed at the apical membrane of polarized epithelial cells. Transepithelial sodium transport mediated by the amiloride-sensitive sodium channel ENaC is thought to contribute to the manifestation of CF disease. Thus, ENaC is a therapeutic target in CF and a valid cystic fibrosis modifier gene. We have characterized SCNN1B as a genetic modifier in the three independent patient cohorts of F508del-CFTR homozygotes. We could identify a regulatory element at SCNN1B to the genomic segment rs168748-rs2303153-rs4968000 by fine-mapping (Pbest = 0.0177), consistently observing the risk allele rs2303153-C and the contrasting benign allele rs2303153-G in all three patient cohorts. Furthermore, our results show that expression levels of SCNN1B are associated with rs2303153 genotype in intestinal epithelia (p = 0.003). Our data confirm that the well-established biological role of SCNN1B can be recognized by an association study on informative endophenotypes in the rare disease cystic fibrosis and calls attention to reproducible results in association studies obtained from small, albeit carefully characterized patient populations.

Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study.

Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods. Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database comprised 136K first-line treated cancer patients across multiple cancer types and was split into a 90% training and 10% testing dataset, respectively. The OAK dataset comprised 1,187 patients diagnosed with non-small cell lung cancer. To assess the effect of the covariate number on prognostic performance, we formed three feature sets with 27, 44 and 88 covariates. In terms of methods, we benchmarked ROPRO, a prognostic score based on the Cox model, against eight complex machine-learning models: regularized Cox, Random Survival Forests (RSF), Gradient Boosting (GB), DeepSurv (DS), Autoencoder (AE) and Super Learner (SL). The C-index was used as the performance metric to compare different models. Results: For in-sample testing on the real-world database the resulting C-index [95% CI] values for RSF 0.720 [0.716, 0.725], GB 0.722 [0.718, 0.727], DS 0.721 [0.717, 0.726] and lastly, SL 0.723 [0.718, 0.728] showed significantly better performance as compared to ROPRO 0.701 [0.696, 0.706]. Similar results were derived across all feature sets. However, for the out-of-sample validation on OAK, the stronger performance of the more complex models was not apparent anymore. Consistently, the increase in the number of prognostic covariates did not lead to an increase in model performance. Discussion: The stronger performance of the more complex models did not generalize when applied to an out-of-sample dataset. We hypothesize that future research may benefit by adding multimodal data to exploit advantages of more complex models.

Assessment of significance of conditionally independent GWAS signals.

MOTIVATION: Multiple independently associated SNPs within a linkage disequilibrium region are a common phenomenon. Conditional analysis has been successful in identifying secondary signals. While conditional association tests are limited to specific genomic regions, they are benchmarked with genome-wide scale criterion, a conservative strategy. Within the weighted hypothesis testing framework, we developed a 'quasi-adaptive' method that uses the pairwise correlation (r2) and physical distance (d) from the index association to construct priority functions G =G(r2, d), which assign an SNP-specific α-threshold to each SNP. Family-wise error rate (FWER) and power of the approach were evaluated via simulations based on real GWAS data. We compared a series of different G-functions. RESULTS: Simulations under the null hypothesis on 1,100 primary SNPs confirmed appropriate empirical FWER for all G-functions. A G-function with optimal r2 = 0.3 between index and secondary SNP which down-weighted SNPs at higher distance step-wise-strong and gave more emphasis on d than on r2 had overall best power. It also gave the best results in application to the real datasets. As a proof of concept, 'quasi-adaptive' method was applied to GWAS on free thyroxine (FT4), inflammatory bowel disease (IBD) and human height. Application of the algorithm revealed 5 secondary signals in our example GWAS on FT4, 5 secondary signals in case of the IBD and 19 secondary signals on human height, that would have gone undetected with the established genome-wide threshold (α=5×10-8). AVAILABILITY AND IMPLEMENTATION: https://github.com/sghasemi64/Secondary-Signal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.