Silico Predictions Research Articles

Medicinal plants are traditionally used in folk medicine. Still, there is a misconception about the safety/efficacy of natural treatments, which results in few studies on the toxic, genotoxic, and mutagenic potential of plants. Therefore, this work investigates the toxicological and mutagenic potential of an ethanolic extract and fractions of Calea phyllolepis leaves using Caenorhabditis elegans and Salmonella typhimurium assays. Through the results obtained, it was verified that only the hexane fraction induced toxicity in C. elegans, affecting the survival and development of nematodes. In addition, this fraction was mutagenic through the S. typhimurium assay only in the presence of metabolization. A previous study pointed out that the major compound identified in the hexane fraction, 6-epi-β-verbesinol coumarate, was responsible for the cytotoxic effects. Probably due to the fragility of the carbon-oxygen bond of the 6-epi-β-verbesinol coumarate, this substance undergoes degradation, generating verbesinol (sesquiterpene) and coumaric acid (phenolic acid) in the body in vivo as active metabolites. Based on the in silico predictions for 6-epi-β-verbesinol coumarate metabolism and toxicity, 21 metabolites and 7 potentially mutagenic products were identified, belonging to three classes: epoxides (three metabolites), α,β-unsaturated carbonylated compounds (one metabolite), and simple aldehydes.

The emergence of beta-lactamase producing multidrug-resistant (MDR) gram-negative bacteria presents a significant challenge to effective treatment of infections. This study focuses on the isolation, amplification, and molecular characterization of β-lactamase genes from clinical strains of Escherichia coli and Klebsiella pneumoniae. Seven new partial gene sequences, including novel variants of blaOXA and blaNDM, were identified after screening 108 clinical samples and submitted to NCBI GenBank. In silico analysis revealed considerable diversity and distribution of these resistance genes among different strains of bacteria. Gene structure predictions using GENSCAN showed that blaOXA genes typically contain single exons with moderate GC content, whereas blaNDM genes feature longer exons with higher GC content. Multiple sequence alignment showed that NDM and OXA β-lactamases were highly similar, with only slight differences in a few amino acids. The study also analyzed the physico-chemical properties, functional domains, and phosphorylation patterns of the β-lactamase proteins. Secondary structure prediction indicated a dominance of beta sheets, contributing to protein stability, while tertiary modeling provided insights into their 3D structure. Overall, these findings provide critical insights into the genetic diversity and potential mechanisms of β-lactamase-mediated resistance, offering valuable information for the development of novel therapeutic strategies and surveillance programs.

Withanolides are a class of naturally occurring C-28 ergostane steroidal lactones with an abundance of biological activities, and their members are promising candidates for antineoplastic drug development. The ADMET properties of withanolides are still largely unknown, and in silico predictions can play a crucial role highlighting these characteristics for drug development, shortening time and resources spent on the development of a drug lead. In this work, ADMET properties of promising antitumoral withanolides were assessed. Each chemical structure was submitted to the prediction tools: SwissADME, pkCSM-pharmacokinetics, admetSAR v2.0, and Molinspiration Cheminformatics. The results indicate a good gastrointestinal absorption rate, inability to cross the blood-brain barrier, CYP3A4 metabolization, without inhibition of other P450 cytochromes, high interaction with nuclear receptors, and a low toxicity. It was also predicted for the inhibition of pharmacokinetics transporters and some ecotoxicity. This demonstrates a viability for oral drug development, with low probabilities of side effects.

Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous environment, show the maximum ion transmission for an analyte through the DMS instrument as a function of the separation voltage (SV) and compensation voltage (CV) conditions. To date, there exists no fast, general prediction tool for the dispersion behavior of ions. Here, we demonstrate a machine learning (ML) model that achieves generalized dispersion prediction using an in silico feature addition pipeline. We employ a data set containing 1141 dispersion curve measurements of anions and cations recorded in pure N2 environments and in N2 environments doped with 1.5% methanol (MeOH). Our feature addition pipeline can compute 1591 RDKit and Mordred descriptors using only SMILES codes, which are then normalized to sampled molecular distributions (n = 100 000) using cumulative density functions (CDFs). This tool can be thought of as a "learned" feature fingerprint generation pipeline, which could be applied to almost any molecular (bio)cheminformatics tasks. Our best performing model, which for the first time considers solvent-modified environments, has a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, a significant improvement over the previous state-of-the-art work. We use explainability techniques (e.g., SHAP analysis) to show that this feature addition pipeline is a semideterministic process for feature sets, and we discuss "best practices" to understand feature sets and maximize model performance. We expect that this tool could be used for prescreening to accelerate or even automate the use of DMS in complex analytical workflows (e.g., 2D LC×DMS separation) and perform automated identification of transmission windows and increase the "self-driving" potential of the instrument. We make our models available as a free and accessible tool at https://github.com/HopkinsLaboratory/DispersionCurveGUI.

Spastic Paraplegia Type 78 (SPG78) is a rare form of hereditary spastic paraplegia (HSP), mainly characterized by late-onset lower-limb spasticity, muscle weakness, and in some cases cerebellar dysfunction and cognitive impairment. Understanding its genetic background is essential to distinguish it from other autosomal recessive types of HSP. A pathogenic variant screening in a Spanish HSP patient was carried out by whole-exome sequencing, followed by a software filtering process and validation of candidate variants by Sanger sequencing. The pathogenicity of the selected variants was evaluated by In Silico predictions and a segregation analysis including the proband and 16 family members. Two variants in the ATP13A2 gene, predicted to have damaging effects by In Silico analyses, were considered potentially pathogenic: NM022089.4:c.649G>A (rs199961048), previously associated with SPG78 but with uncertain clinical significance, and NM_022089.4:c.2097delC, an unreported variant. The segregation analysis revealed that both variants were present in compound heterozygosity in the proband and two affected siblings, while unaffected relatives carried only one or none of the variants. These findings suggest that the two variants are pathogenic when occurring in compound heterozygosity and, therefore, should be included in the genetic spectrum of SPG78 diagnosis.

Ivosidenib (ISB) is an anticancer drug used to treat acute myeloid leukemia and cholangiocarcinoma. In order to determine and describe the degradation products (DPs) of ivosidenib, the International Conference on Harmonization (ICH) guidelines (Q1A, R2) suggest conducting stress degradation studies by subjecting the drug to acidic, alkaline, neutral hydrolysis, thermal, photolytic, and oxidative conditions. These studies are crucial for understanding the stability of the drug and ensuring its safe use. Liquid chromatography (LC) and LC-MS/MS are essential for identifying and characterizing these DPs. Ivosidenib is sensitive to degradation under acidic, alkaline, photolytic, and oxidative conditions at room temperature but is stable under neutral hydrolysis and thermal conditions. The separation of ISB and its four DPs was achieved using a Waters Acquity UPLC BEH C-18 column, with gradient elution comprising 0.1% trifluoroacetic acid (TFA) as mobile phase-A and acetonitrile as mobile phase-B. The detection was carried out at a wavelength of 215 nm with a flow rate of 0.3 mL/min and a 5 μL injection volume. The LOQ was 0.05%, and the LOD was 0.02% of the nominal concentration. Four DPs were identified, with DP-I predominant under acidic and alkaline conditions and DP-II under basic and oxidative conditions. Oxidative and photolytic conditions produced DP-III and DP-IV respectively. A novel HRMS-MS/TOF method was developed to identify and characterize these DPs, with each analyzed in an ESI-positive mode. Finally, Glide software was used to predict docking scores, while TOPKAT software was used to evaluate the in silico toxicity of ISB and its DPs. The in silico findings revealed that DP-III is the most active and is the least toxic.

Exploratory synthesis of solids is essential for the advancement of materials science but is also highly time- and resource-intensive. Here, we demonstrate an efficient strategy to explore solid-state synthesis of quaternary cesium chlorides in the search space of CsnAIBCl6 (n = 2 or 3, A = Li, Na or K, and B = d or p-block metal), where the target compositions are selected from a pool of candidates based on computationally predicted stabilities and availability of viable precursor powders. Synthesizability of the targets is assessed by observing the evolution of starting phases upon heating under in situ synchrotron X-ray diffraction. Laboratory synthesis is attempted for promising targets, and resulting materials are characterized by powder X-ray and neutron diffraction and subsequent Rietveld refinement. We focus on how computational predictions can be bridged to experimental characterizations in exploratory synthesis and report on successful and failed synthesis attempts for compounds of type Cs2AIBIIICl6, revealing underexplored variants including new polymorphs of Cs2LiCrCl6 and Cs2LiRuCl6, and a new compound Cs2LiIrCl6.

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Glutamate dehydrogenase (GDH) interconverts glutamate to a-ketoglutarate and ammonia, interconnecting amino acid and carbohydrate metabolism. In humans, two functional GDH genes, GLUD1 and GLUD2, encode for hGDH1 and hGDH2, respectively. GLUD2 evolved from retrotransposition of the GLUD1 gene in the common ancestor of modern apes. These two isoenzymes are involved in the pathophysiology of human metabolic, neoplastic, and neurodegenerative disorders. The 3D structures of hGDH1 and hGDH2 have been experimentally determined; however, no information is available about the path of GDH2 structure changes during primate evolution. Here, we compare the structures predicted by the AlphaFold Colab method for the GDH2 enzyme of modern apes and their extinct primate ancestors. Also, we analyze the individual effect of amino acid substitutions emerging during primate evolution. Our most important finding is that the predicted structure of GDH2 in the common ancestor of apes was the steppingstone for the structural evolution of primate GDH2s. Two changes with a strong functional impact occurring at the first evolutionary step, Arg443Ser and Gly456Ala, had a destabilizing and stabilizing effect, respectively, making this step the most important one. Subsequently, GDH2 underwent additional modifications that fine-tuned its enzymatic properties to adapt to the functional needs of modern-day primate tissues.

The limited information in existing mass spectral libraries hinders an accurate understanding of the composition, behavior, and toxicity of organic pollutants. In this study, a total of 350 polycyclic aromatic compounds (PACs) in 9 categories were successfully identified in fine particulate matter by gas chromatography high resolution mass spectrometry. Using mass spectra and retention indexes predicted by in silico tools as complementary information, the scope of chemical identification was efficiently expanded by 27%. In addition, quantitative structure-activity relationship models provided toxicity data for over 70% of PACs, facilitating a comprehensive health risk assessment. On the basis of extensive identification, the cumulative noncarcinogenic risk of PACs warranted attention. Meanwhile, the carcinogenic risk of 53 individual analogues was noteworthy. These findings suggest that there is a pressing need for an updated list of priority PACs for routine monitoring and toxicological research since legacy polycyclic aromatic hydrocarbons (PAHs) contributed modestly to the overall abundance (18%) and carcinogenic risk (8%). A toxicological priority index approach was applied for relative chemical ranking considering the environmental occurrence, fate, toxicity, and analytical availability. A list of 39 priority analogues was compiled, which predominantly consisted of high-molecular-weight PAHs and alkyl derivatives. These priority PACs further enhanced source interpretation, and the highest carcinogenic risk was attributed to coal combustion.

Abstract Introduction Pediatric cancers present significant challenges in terms of diagnosis and treatment, and the anaplastic lymphoma kinase (ALK) protein has emerged as a crucial molecular target in these malignancies. ALK, a receptor tyrosine kinase, plays a vital role in normal cellular processes, but genetic alterations and aberrant activation of the ALK gene have been implicated in various pediatric cancer types. While genetic alterations have been well studied, the precise molecular mechanisms underlying the pathogenicity of the ALK protein in pediatric cancers remain poorly understood. Objective In this study, the primary objective is to uncover the molecular mechanisms associated with the effects of deleterious single-nucleotide polymorphisms (SNPs) on the structure and functionality of the ALK protein. Material and Methods Several known point mutations of the ALK protein were taken for the in silico predictions such as PolyPhen-2, SIFT, PANTHER, PredictSNP, etc., residue conservation analysis using Consurf server, molecular docking (AutoDock), and molecular dynamics simulation studies (GROMACS). Results The computation predictions found that the studied variants are deleterious in different tools. The residue conservation analysis reveals all the variants are located in highly conserved regions. The molecular docking study of wild-type and mutant structures with the crizotinib drug molecule found the variants were modulating the binding cavity and had a strong impact on the binding affinity. The binding energy of the wild-type is –5.896 kcal/mol, whereas the mutants have –9.988 kcal/mol. The specific amino acid Ala1200 of wild-type was found to interact with crizotinib, and Asp1203 residue was found to interact predominantly in the mutant structures. Conclusion The simulation study differentiates the variants in terms of structural stability and residue fluctuation. Among the studied variants, R1275Q, F1245V, and F1174L had strong deleterious effects, structural changes, and pathogenicity based on the in silico predictions. By elucidating the functional consequences of deleterious mutations within the ALK gene, this research may uncover novel therapeutic targets and personalized medicine approaches for the management of pediatric cancers. Ultimately, gaining insights into the molecular mechanisms of the ALK protein's role in driving response and resistance will contribute to improving patient outcomes and advancing our understanding of this complex disease.

EV-A71 is a common viral pathogen that causes hand, foot and mouth disease. It is a single-stranded RNA virus that has a low fidelity RNA polymerase and, as a result, spontaneous mutations frequently occur in the EV-A71 genome. The mutations within the genome give rise to quasispecies within the viral population that could be further defined by haplotypes. In vitro virulence of EV-A71 was shown by plaque size in Rhabdomyosarcoma (RD) cells, which was substantiated by in vitro characterizations of growth, RNA replication, binding, attachment and host cell internalization. Viruses could exhibit different host cell adaptations in different cell lines during viral passaging. The EV-A71/WT (derived from EV-A71 subgenotype B4) was shown to comprise six haplotypes through next-generation sequencing, where only EV-A71/Hap2 was found to be cultivable in RD cells, while EV-A71/Hap4 was the only cultivable haplotype in Vero cells. The EV-A71/WT produced plaques of four different sizes (small, medium, big, huge) in RD cells, while only two plaque variants (small, medium) were present in Vero cells. The small plaque variant isolated from RD cells displayed lower RNA replication rates, slower in vitro growth kinetics, higher TCID50 and lower attachment, binding and entry ability when compared against EV-A71/WT due to the mutation at 3D-S228P that disrupted the active site of the RNA polymerase, resulting in low replication and growth of the variant.

In this study, we report a newly synthesized Schiff base molecule named (E)-N-(2-chloropyridin-3-yl)-1-(5-nitro-2-(piperidin-1-yl)phenyl)methanimine. We also report its structural, chemical, surface, and electronic properties, potential targets, drug-likeness, ADME and toxicity profile, and docking studies for the main protease (Mpro) of SARS-CoV-2. The scope of this study includes the topological and electronic properties, intermolecular interactions, physicochemical and pharmacokinetic properties, metabolic pathways, toxicity endpoints, blood–brain barrier (BBB) permeability, and intestinal absorption activities. We performed the above analyses using bioinformatics/chemoinformatics tools and computational techniques. The topic crystal/compound (TC) contains two crystallographically independent molecules in the asymmetric unit (Z′ = 2). TC is open to attack by electrophilic and nucleophilic species and is a soft, chemically reactive, kinetically unstable material. There are no deviations from the known drug-likeness rules. BBB penetration and GI absorption of TC are possible. The docking values of the complex Mpro/TC and Mpro/native ligand N3 were calculated to be −8.10 and −7.11 kcal/mol, respectively. Therefore, we can say that TC is a potential Mpro inhibitor and can be investigated for further laboratory studies.

In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Simple SummaryDairy cows are susceptible to a range of welfare factors, which lead to worsening health problems and shorten their productive life span. The health and welfare status of dairy cows could be improved if unwanted abnormalities and risk factors are detected in a timely manner, i.e., before diseases start to occur. Therefore, in addition to veterinary monitoring, quantitative parameters are necessary to predict the risks of early culling of cows. In the study of the age dynamics of culling rate in dairy cow populations, it was found that the average productive life span can be predicted by registration of the reciprocal relative disposal rate (culling for sum of reasons + death). This indicator represents the viability index, which has a maximal value at the first lactation and decreases in subsequent lactations with an inverse exponential trend. According to available scientific information, the structural prerequisites for this index are laid down during prenatal development and in the early periods of postnatal life; therefore, it is necessary to create a system of continuous monitoring of the physiological status of mothers and young animals.Animal welfare includes health but also concerns the need for natural factors that contribute to the increase in viability. Therefore, quantitative parameters are necessary to predict the risks of early culling of cows. In the study of the age dynamics of the disposal rate (culling for sum of reasons + death) in dairy cow populations, it was found that the average productive life span can be predicted by the value of the reciprocal culling/death rate (reciprocal value of Gompertz function) at the first lactation. This means that this potential of viability is formed during the developmental periods preceding the onset of lactation activity. Therefore, taking into account current data in the field of developmental biology, it can be assumed that the structural prerequisites for viability potential are laid down during prenatal development and in the early periods of postnatal life. To prevent unfavorable deviations in these processes due to negative welfare effects, it is advisable to monitor the physiological status of mothers and young animals using biosensors and Big Data systems.

Bg_9562 is a potential broad-spectrum antifungal effector protein derived from the bacteria Burkholderia gladioli strain NGJ1 and is effective against Rhizoctonia solani, the causal agent of sheath blight in rice. In the present study, in vitro antifungal assays showed that Bg_9562 was efficient at 35 °C and 45 °C and ineffective either at high acidic pH (3.0) or alkaline pH (9.5) conditions. Compatibility studies between the native bioagents Trichoderma asperellum TAIK1 and Bacillus subtilis BIK3 indicated that Bg_9562 was compatible with the bioagents. A field study using foliar spray of the Bg_9562 protein indicated the need of formulating the protein before its application. In silico analysis predicted that Bg_9562 possess 111 amino acid residues (46 hydrophobic residues, 12 positive and 8 negative residues) with the high aliphatic index of 89.92, attributing to its thermostability with a half-life of 30 h. Bg_9562 (C491H813N137O166S5) possessed a protein binding potential of 1.27 kcal/mol with a better possibility of interacting and perturbing the membrane, the main target for antimicrobial proteins. The secondary structure revealed the predominance of random coils in its structure, and the best 3D model of Bg_9562 was predicted using an ab initio method with Robetta and AlphaFold 2. The predicted binding ligands were nucleic acids and zinc with confidence scores of 0.07 and 0.05, respectively. The N-terminal region (1–14 residues) and C-terminal region (101 to 111) of Bg_9562 residues were predicted to be disordered regions. Stability and binding properties of the protein from the above studies would help to encapsulate Bg_9562 using a suitable carrier to maintain efficiency and improve delivery against Rhizoctonia solani in the most challenging rice ecosphere.

Early-diverging fungi (EDF) are ubiquitous and versatile. Their diversity is reflected in their genome sizes and complexity. For instance, multiple protein families have been reported to expand or disappear either in particular genomes or even whole lineages. The most commonly mentioned are CAZymes (carbohydrate-active enzymes), peptidases and transporters that serve multiple biological roles connected to, e.g., metabolism and nutrients intake. In order to study the link between ecology and its genomic underpinnings in a more comprehensive manner, we carried out a systematic in silico survey of protein family expansions and losses among EDF with diverse lifestyles. We found that 86 protein families are represented differently according to EDF ecological features (assessed by median count differences). Among these there are 19 families of proteases, 43 CAZymes and 24 transporters. Some of these protein families have been recognized before as serine and metallopeptidases, cellulases and other nutrition-related enzymes. Other clearly pronounced differences refer to cell wall remodelling and glycosylation. We hypothesize that these protein families altogether define the preliminary fungal adaptasome. However, our findings need experimental validation. Many of the protein families have never been characterized in fungi and are discussed in the light of fungal ecology for the first time.

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.