Screening Campaign Research Articles

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.

To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting. In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard. A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%). Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.

Practical guidelines for the use of gradient boosting for molecular property prediction

Decision tree ensembles are among the most robust, high-performing and computationally efficient machine learning approaches for quantitative structure–activity relationship (QSAR) modeling. Among them, gradient boosting has recently garnered particular attention, for its performance in data science competitions, virtual screening campaigns, and bioactivity prediction. However, different variants of gradient boosting exist, the most popular being XGBoost, LightGBM and CatBoost. Our study provides the first comprehensive comparison of these approaches for QSAR. To this end, we trained 157,590 gradient boosting models, which were evaluated on 16 datasets and 94 endpoints, comprising 1.4 million compounds in total. Our results show that XGBoost generally achieves the best predictive performance, while LightGBM requires the least training time, especially for larger datasets. In terms of feature importance, the models surprisingly rank molecular features differently, reflecting differences in regularization techniques and decision tree structures. Thus, expert knowledge must always be employed when evaluating data-driven explanations of bioactivity. Furthermore, our results show that the relevance of each hyperparameter varies greatly across datasets and that it is crucial to optimize as many hyperparameters as possible to maximize the predictive performance. In conclusion, our study provides the first set of guidelines for cheminformatics practitioners to effectively train, optimize and evaluate gradient boosting models for virtual screening and QSAR applications.Graphical abstract

Exploring rigid-backbone protein docking in biologics discovery: a test using the DARPin scaffold.

Accurate protein-protein docking remains challenging, especially for artificial biologics not coevolved naturally against their protein targets, like antibodies and other engineered scaffolds. We previously developed ProPOSE, an exhaustive docker with full atomistic details, which delivers cutting-edge performance by allowing side-chain rearrangements upon docking. However, extensive protein backbone flexibility limits its practical applicability as indicated by unbound docking tests. To explore the usefulness of ProPOSE on systems with limited backbone flexibility, here we tested the engineered scaffold DARPin, which is characterized by its relatively rigid protein backbone. A prospective screening campaign was undertaken, in which sequence-diversified DARPins were docked and ranked against a directed epitope on the target protein BCL-W. In this proof-of-concept study, only a relatively small set of 2,213 diverse DARPin interfaces were selected for docking from the huge theoretical library from mutating 18 amino-acid positions. A computational selection protocol was then applied for enrichment of binders based on normalized computed binding scores and frequency of binding modes against the predefined epitope. The top-ranked 18 designed DARPin interfaces were selected for experimental validation. Three designs exhibited binding affinities to BCL-W in the nanomolar range comparable to control interfaces adopted from known DARPin binders. This result is encouraging for future screening and engineering campaigns of DARPins and possibly other similarly rigid scaffolds against targeted protein epitopes. Method limitations are discussed and directions for future refinements are proposed.

Molecular Docking Assessment of Cathinones as 5-HT2AR Ligands: Developing of Predictive Structure-Based Bioactive Conformations and Three-Dimensional Structure-Activity Relationships Models for Future Recognition of Abuse Drugs.

Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure-activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects.

A split-luciferase lysate-based approach to identify small-molecule modulators of phosphatase subunit interactions

An emerging strategy for the therapeutic targeting of protein phosphatases involves the use of compounds that interfere with the binding of regulatory subunits or substrates. However, high-throughput screening strategies for such interfering molecules are scarce. Here, we report on the conversion of the NanoBiT split-luciferase system into a robust assay for the quantification of phosphatase subunit and substrate interactions in cell lysates. The assay is suitable to screen small-molecule libraries for interfering compounds. We designed and validated split-luciferase sensors for a broad range of PP1 and PP2A holoenzymes, including sensors that selectively report on weak interaction sites. To facilitate efficient hit triaging in large-scale screening campaigns, deselection procedures were developed to eliminate assay-interfering molecules with high fidelity. As a proof-of-principle, we successfully applied the split-luciferase screening tool to identify small-molecule disruptors of the interaction between the C-terminus of PP1β and the ankyrin-repeat domain of the myosin-phosphatase targeting subunit MYPT1.

Towards comprehensive analysis of large crystallographic fragment screening campaigns

Towards comprehensive analysis of large crystallographic fragment screening campaigns

An in vitro assay to investigate venom neurotoxin activity on muscle-type nicotinic acetylcholine receptor activation and for the discovery of toxin-inhibitory molecules

Snakebite envenoming is a neglected tropical disease that causes over 100,000 deaths annually. Envenomings result in variable pathologies, but systemic neurotoxicity is among the most serious and is currently only treated with difficult to access and variably efficacious commercial antivenoms. Venom-induced neurotoxicity is often caused by α-neurotoxins antagonising the muscle-type nicotinic acetylcholine receptor (nAChR), a ligand-gated ion channel. Discovery of therapeutics targeting α-neurotoxins is hampered by relying on binding assays that do not reveal restoration of receptor activity or more costly and/or lower throughput electrophysiology-based approaches. Here, we report the validation of a screening assay for nAChR activation using immortalised TE671 cells expressing the γ-subunit containing muscle-type nAChR and a fluorescent dye that reports changes in cell membrane potential. Assay validation using traditional nAChR agonists and antagonists, which either activate or block ion fluxes, was consistent with previous studies. We then characterised antagonism of the nAChR by a variety of elapid snake venoms that cause muscle paralysis in snakebite victims, before defining the toxin-inhibiting activities of commercial antivenoms, and new types of snakebite therapeutic candidates, namely monoclonal antibodies, decoy receptors, and small molecules. Our findings show robust evidence of assay uniformity across 96-well plates and highlight the amenability of this approach for the future discovery of new snakebite therapeutics via screening campaigns. The described assay therefore represents a useful first-step approach for identifying α-neurotoxins and their inhibitors in the context of snakebite envenoming, and it should provide wider value for studying modulators of nAChR activity from other sources.

Discovery of novel methionine adenosyltransferase 2A (MAT2A) allosteric inhibitors by structure-based virtual screening

Methionine adenosyltransferase 2A (MAT2A) has been indicated as a drug target for oncology indications. Clinical trials with MAT2A inhibitors are currently on-going. Here, a structure-based virtual screening campaign was performed on the commercially available chemical space which yielded two novel MAT2A-inhibitor chemical series. The binding modes of the compounds were confirmed with X-ray crystallography. Both series have acceptable physicochemical properties and show nanomolar activity in the biochemical MAT2A inhibition assay and single-digit micromolar activity in the proliferation assay (MTAP -/- cell line). The identified compounds and the relating structural data could be helpful in related drug discovery projects.

Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders.

E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries such as Asinex for the rational design of E3 ligase binders. The scripts and data associated with this study can be found on GitHub at https://github.com/Fraunhofer-ITMP/E3_binder_Model.

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Comparative Performance of High-Throughput Methods for Protein pKa Predictions.

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pKa prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pKa shifts from the pKa database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pKa units and a correlation coefficient (R2) of 0.45 to experimental pKa shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pKa prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

APPLICATION OF EXCHABODY TECHNOLOGY FOR PAIRING VHHS TO ACHIEVE SYNERGISTIC EFFECTS

Abstract Objective Single-domain antibodies, such as VHH and nanobody, have shown potential for use in therapy and diagnostics. One application of VHHs is the tethering of two fragments to different epitopes on the same target, which is difficult to achieve with conventional antibodies. Synergistic heterologous VHH dimers have higher affinity, better specificity, and broad applications in developing high-affinity monoclonal antibodies, bispecific antibodies, ADCs, and CAR-Ts. However, finding the best pair of VHHs for these applications requires combinational screening, which is traditionally a time-consuming and costly process. The objective of this study is to develop a technology that can quickly screen and pair two synergistic VHHs without the need to express tandem VHH dimers. Methods The researchers developed proprietary tags and specific dockers that, when stabilized on a solid station, can capture any VHHs that the dockers recognize and pull them together to form a non-covalent dimer. This platform is called ExchaBody technology, and the VHH dimers formed this way are ExchaBodies. The researchers used this technology to conduct a bi-epitope screening campaign, where VHHs were first expressed as monomers with tags and then binned and grouped into different categories. VHHs were then paired with all reasonable combinations using ExchaBody technology, and these ExchaBodies were evaluated for their combined activities. Results ExchaBody technology was able to link any two VHHs together within one hour, and the resulting ExchaBodies had bivalent or bifunctional VHH activities. The bi-epitope VHH screening campaign, which would have taken months to complete using traditional methods, was finished within two weeks using ExchaBody technology, saving time and cost. The researchers were able to construct two lead molecules, a bi-specific VHH-Fc fusion protein and a tri-valent VHH molecule, using ExchaBody technology. These lead molecules were found to be superior to their counterparts on the market based on affinity and functional assays. Conclusion ExchaBody technology is a bispecific VHH screening and pairing platform that can quickly and cost-effectively create non-covalent, bispecific VHHs (ExchaBodies) without the need to express them. ExchaBodies possess the binding and cellular activities of a covalently linked, bispecific, tandem VHH dimer. This technology has broad applications in developing high-affinity monoclonal antibodies, bispecific antibodies, ADCs, and CAR-Ts.

Versatile Product Detection via Coupled Assays for Ultrahigh-Throughput Screening of Carbohydrate-Active Enzymes in Microfluidic Droplets.

Enzyme discovery and directed evolution are the two major contemporary approaches for the improvement of industrial processes by biocatalysis in various fields. Customization of catalysts for improvement of single enzyme reactions or de novo reaction development is often complex and tedious. The success of screening campaigns relies on the fraction of sequence space that can be sampled, whether for evolving a particular enzyme or screening metagenomes. Ultrahigh-throughput screening (uHTS) based on in vitro compartmentalization in water-in-oil emulsion of picoliter droplets generated in microfluidic systems allows screening rates >1 kHz (or >107 per day). Screening for carbohydrate-active enzymes (CAZymes) catalyzing biotechnologically valuable reactions in this format presents an additional challenge because the released carbohydrates are difficult to monitor in high throughput. Activated substrates with large optically active hydrophobic leaving groups provide a generic optical readout, but the molecular recognition properties of sugars will be altered by the incorporation of such fluoro- or chromophores and their typically higher reactivity, as leaving groups with lowered pKa values compared to native substrates make the observation of promiscuous reactions more likely. To overcome these issues, we designed microdroplet assays in which optically inactive carbohydrate products are made visible by specific cascades: the primary reaction of an unlabeled substrate leads to an optical signal downstream. Successfully implementing such assays at the picoliter droplet scale allowed us to detect glucose, xylose, glucuronic acid, and arabinose as final products of complex oligosaccharide degradation by glycoside hydrolases by absorbance measurements. Enabling the use of uHTS for screening CAZyme reactions that have been thus far elusive will chart a route toward faster and easier development of specific and efficient biocatalysts for biovalorization, directing enzyme discovery by challenging catalysts for reaction with natural rather than model substrates.

Pharmacological profiling identifies divergent chemosensitivities of differentiating and maturing iPSC-derived human cortical neuron populations.

Neuronal differentiation and maturation are extended developmental processes. To determine whether neurons at different developmental stages have divergent chemosensitivities, we screened differentiating and maturing neuronal populations using a small compound library comprising FDA-approved and investigational drugs. Using a neurotoxicity assay format, both respective neuronal population-based screening campaigns performed robustly (Z-factors = 0.7-0.8), although the hit rate for the differentiating neurons (2.8%) was slightly higher than for maturing neurons (1.9%). While the majority of hits were toxic to both neuronal populations, these hits predominantly represented promiscuous drugs. Other drugs were selectively neurotoxic, with receptor tyrosine kinase inhibitors disproportionally represented after confirmation. Ponatinib and amuvatinib were neuroinhibitory for differentiating and maturing neurons, respectively. Chemoinformatic analyses confirmed differences in potential drug targets that may be differentially expressed during neuronal development. Subsequent studies demonstrated neuronal expression of AXL, an amuvatinib target, in both neuronal populations. However, functional AXL activity was confirmed only in the maturing neuronal population as determined by AXL phosphorylation in response to GAS6, the cognate ligand of AXL, and concurrent STAT3Y705 phosphorylation. Differentiating neurons were unresponsive to the effects of GAS6 suggesting that the AXL-STAT3 signaling axis was nonfunctional. Amuvatinib treatment of maturing neuronal cultures significantly reduced pAXL levels. These studies indicate that neuronal developmental states may exhibit unique chemosensitivities and that drugs may have different neuro-inhibitory effects depending upon the developmental stage of the neuronal population.

Multicenter Cervical Cancer Screening, using Kayes (Mali) Visual Inspection Methods

Introduction: Cervical cancer is a tissue neoformation due to excessive, abnormal, uncontrolled and autonomic cell proliferation that develops at the expense of the cervix [1]. The aim of this study was to take stock of the multicenter cervical cancer screening campaigns carried out in the Kayes region using IVA visual methods; IVL. Materials and Methods: this was a multicentre descriptive and analytical cross-sectional study. Extending over a period of 12 months from 1 January 2017 to December 2018; on women of childbearing potential who have had sex and agree to participate in screening. Data entry was done on Microsoft Word 10 and analyzed on Microsoft Excel 10, IBM SPSS 20.0 software. The statistical test used was the khi2 test to compare our results which were significant for a probability p< 0.05. Results: our study took place in 4 screening campaigns and involved 1000 women who agreed to undergo cervical cancer screening. The average age was 31.16 years with extremes ranging from 14 to 70 years. Married women accounted for 87.5% compared to 7.1% of single women. Among the women screened married, 409 had a polygamous partner or 46.74%. Among the women screened the multiparous were the most numerous 299/1000 or 29.9% followed by the pauciparous 26.5%. The JPC (squamocolumnar junction) was visible in 986/1000 or 98.6%. The positivity of IVA and IVL was equal to 49/1000 or 4.9%; with suspicion of cancer at 2/1000 or 0.2% each. The biopsy was performed in 5.1% (51/1000) of the women screened. The frequency of precancerous and cancerous lesions of the cervix was 1.9% and 0.5%, respectively. The most common histological type was CIN1 found on 27.45% of biopsy pieces. Twenty-five women received anti-infective treatment (cervicitis); 6 women received a total hysterectomy; 2 cases of conization for CIN1 and 16 women referred to the CHU-GT (Centre Hospitalo-Universitaire GABRIEL TOURE) in Bamako (MALI) for treatment. Conclusion: cervical cancer is a public health ......

Identification of 2-Sulfonyl/Sulfonamide Pyrimidines as Covalent Inhibitors of WRN Using a Multiplexed High-Throughput Screening Assay.

Werner syndrome protein (WRN) is a multifunctional enzyme with helicase, ATPase, and exonuclease activities that are necessary for numerous DNA-related transactions in the human cell. Recent studies identified WRN as a synthetic lethal target in cancers characterized by genomic microsatellite instability resulting from defects in DNA mismatch repair pathways. WRN's helicase activity is essential for the viability of these high microsatellite instability (MSI-H) cancers and thus presents a therapeutic opportunity. To this end, we developed a multiplexed high-throughput screening assay that monitors exonuclease, ATPase, and helicase activities of full-length WRN. This screening campaign led to the discovery of 2-sulfonyl/sulfonamide pyrimidine derivatives as novel covalent inhibitors of WRN helicase activity. The compounds are specific for WRN versus other human RecQ family members and show competitive behavior with ATP. Examination of these novel chemical probes established the sulfonamide NH group as a key driver of compound potency. One of the leading compounds, H3B-960, showed consistent activities in a range of assays (IC50 = 22 nM, KD = 40 nM, KI = 32 nM), and the most potent compound identified, H3B-968, has inhibitory activity IC50 ∼ 10 nM. These kinetic properties trend toward other known covalent druglike molecules. Our work provides a new avenue for screening WRN for inhibitors that may be adaptable to different therapeutic modalities such as targeted protein degradation, as well as a proof of concept for the inhibition of WRN helicase activity by covalent molecules.

A DNA-encoded chemical library based on chiral 4-amino-proline enables stereospecific isozyme-selective protein recognition.

DNA-encoded chemical libraries (DELs) consist of large chemical compound collections individually linked to DNA barcodes, facilitating pooled construction and screening. However, screening campaigns often fail if the molecular arrangement of the building blocks is not conducive to an efficient interaction with a protein target. Here we postulated that the use of rigid, compact and stereo-defined central scaffolds for DEL synthesis may facilitate the discovery of very specific ligands capable of discriminating between closely related protein targets. We synthesized a DEL comprising 3,735,936 members, featuring the four stereoisomers of 4-aminopyrrolidine-2-carboxylic acid as central scaffolds. The library was screened in comparative selections against pharmaceutically relevant targets and their closely related protein isoforms. Hit validation results revealed a strong impact of stereochemistry, with large affinity differences between stereoisomers. We identified potent isozyme-selective ligands against multiple protein targets. Some of these hits, specific to tumour-associated antigens, demonstrated tumour-selective targeting in vitro and in vivo. Collectively, constructing DELs with stereo-defined elements contributed to high library productivity and ligand selectivity.

Mathematical relationships between control group variability and assay quality metrics.

Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z'-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z'-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.

Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy.

A smartphone 12-Lead ECG that enables layman ECG screening is still lacking. We aimed to validate D-Heart ECG device, a smartphone 8/12 Lead electrocardiograph with an image processing algorithm to guide secure electrode placement by non-professional users. One-hundred-fourty-five patients with HCM were enrolled. Two uncovered chest images were acquired using the smartphone camera. An image with virtual electrodes placement by imaging processing algorithm software was compared to the 'gold standard' electrode placement by a doctor. D-Heart 8 and 12-Lead ECG were obtained, immediately followed by 12‑lead ECGs and were assessed by 2 independent observers. Burden of ECG abnormalities was defined by a score based on the sum of 9 criteria, identifying four classes of increasing severity. A total of 87(60%) patients presented a normal/mildly abnormal ECG, whereas 58(40%) had moderate or severe ECG alteration. Eight(6%) patients had ≥1 misplaced electrode. D-Heart 8-Lead and 12‑lead ECGs concordance according to Cohen's weighted kappa test was 0,948 (p<0,001, agreement of 97.93%). Concordance was high for the Romhilt-Estes score (kw=0,912; p<0.01). Concordance between D-Heart 12-Lead ECG and standard 12-Lead ECG was perfect (kw=1). PR and QRS intervals measurements comparison with Bland-Altman method showed good accuracy (95% limit of agreement ±18ms for PR and±9ms for QRS). D-Heart 8/12-Lead ECGs proved accurate, allowing an assessment of ECG abnormalities comparable to the standard 12‑lead ECG in patients with HCM. The image processing algorithm provided accurate electrode placement, standardizing exam quality, potentially opening perspectives for layman ECG screening campaigns.