Early-stage Discovery Research Articles

Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals.

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40.

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure.

Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications.

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC's rigorous evaluation methods with Discovery's engineering capability can facilitate a streamlined development process for bsAb molecules.

Rapid and Definitive Identification of Cyclic Peptide Soft Spots by Isotope-Labeled Reductive Dimethylation and Mass Spectrometry Fragmentation.

Cyclic peptides are an emerging therapeutic modality over the past few decades. To identify drug candidates with sufficient proteolytic stability for oral administration, it is critical to pinpoint the amide bond hydrolysis sites, or soft spots, to better understand their metabolism and provide guidance on further structure optimization. However, the unambiguous characterization of cyclic peptide soft spots remains a significant challenge during early stage discovery studies, as amide bond hydrolysis forms a linearized isobaric sequence with the addition of a water molecule, regardless of the amide hydrolysis location. In this study, an innovative strategy was developed to enable the rapid and definitive identification of cyclic peptide soft spots by isotope-labeled reductive dimethylation and mass spectrometry fragmentation. The dimethylated immonium ion with enhanced MS signal at a distinctive m/z in MS/MS fragmentation spectra reveals the N-terminal amino acid on a linearized peptide sequence definitively and, thus, significantly simplifies the soft spot identification workflow. This approach has been evaluated to demonstrate the potential of isotope-labeled dimethylation to be a powerful analytical tool in cyclic peptide drug discovery and development.

Characterizing the Distribution of a Stimulator of Interferon Genes Agonist and Its Metabolites in Mouse Liver by Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry.

A STING (stimulator of interferon genes) agonist GSK3996915 under investigation in early discovery for hepatitis B was orally dosed to a mouse model for understanding the parent drug distribution in liver, the target organ. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) was used to quantify the distribution of GSK3996915 in liver collected from mice administered a single oral dose at 90 mg/kg. GSK3996915 was detected with a zonal distribution localized in the portal triad and highly concentrated in the main bile ducts, indicating clearance through biliary excretion. High spatial resolution imaging showed the distribution of the parent drug localized to the cellular populations in the sinusoids, including the Kupffer cells. Additionally, a series of drug-related metabolites were observed to be localized in the central zones of the liver. These results exemplify the potential of utilizing MALDI IMS for measuring not only quantitative drug distribution and target exposure but also drug metabolism and elimination in a single suite of experiments. SIGNIFICANCE STATEMENT: An integrated imaging approach utilizing matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) complemented with immunohistochemistry (IHC) and histology was used to address the question of target exposure at the cellular level. Localized quantification of the parent drug in the target organ and identification of potential metabolites in the context of tissue histology were also achieved in one experimental suite to support characterization of pharmacokinetic properties of the drug in the early discovery stage.

An overview of DNA methylation markers for early detection of gastric cancer: current status, challenges, and prospects.

Background: Gastric cancer (GC) is one of the most common malignancies, with a low 5-year survival rate. However, if diagnosed at an early stage, it can be cured by endoscopic treatment and has a good prognosis. While gastrointestinal X-ray and upper endoscopy are used as national GC screening methods in some GC high-risk countries, such as Japan and Korea, their radiation exposure, invasiveness, and high cost suggest that they are not the optimal tools for early detection of GC in many countries. Therefore, a cost-effective, and highly accurate method for GC early detection is urgently needed in clinical settings. DNA methylation plays a key role in cancer progression and metastasis and has been demonstrated as a promising marker for cancer early detection. Aims and methods: This review provides a comprehensive overview of the current status of DNA methylation markers associated with GC, the assays developed for GC early detection, challenges in methylation marker discovery and application, and the future prospects of utilizing methylation markers for early detection of GC. Through our analysis, we found that the currently reported DNA methylation markers related to GC are mainly in the early discovery stage. Most of them have only been evaluated in tissue samples. The majority of non-invasive assays developed based on blood lack standardized sampling protocols, pre-analytical procedures, and multicenter validation, and they exhibit insufficient sensitivity for early-stage GC detection. Meanwhile, the reported GC DNA methylation markers are generally considered pan-cancer markers. Conclusion: Therefore, future endeavors should focus on identifying additional methylation markers specific to GC and establishing non-invasive diagnostic assays that rely on these markers. These assays should undergo multicenter, large-scale prospective validation in diverse populations.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association.

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (kD), which is a measure of self-association under dilute conditions.1 However, kD can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).2 By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Microsecond-long simulation reveals the molecular mechanism for the dual inhibition of falcipain-2 and falcipain-3 by antimalarial lead compounds.

The latest world malaria report revealed that human deaths caused by malaria are currently on the rise and presently stood at over 627,000 per year. In addition, more than 240 million people have the infection at any given time. These figures make malaria the topmost infectious disease and reiterate the need for continuous efforts for the development of novel chemotherapies. Malaria is an infectious disease caused majorly by the protozoan intracellular parasite Plasmodium falciparum and transmitted by mosquitoes. Reports abound on the central role of falcipains (cysteine protease enzymes) in the catabolism of hemoglobin for furnishing the plasmodium cells with amino acids that they require for development and survival in the hosts. Even though falcipains (FPs) have been validated as drug target molecules for the development of new antimalarial drugs, none of its inhibitory compounds have advanced beyond the early discovery stage. Therefore, there are renewed efforts to expand the collection of falcipain inhibitors. As a result, an interesting finding reported the discovery of a quinolinyl oxamide derivative (QOD) and an indole carboxamide derivative (ICD), with each compound demonstrating good potencies against the two essential FP subtypes 2 (FP-2) and 3 (FP-3). In this study, we utilized microsecond-scale molecular dynamics simulation computational method to investigate the interactions between FP-2 and FP-3 with the quinolinyl oxamide derivative and indole carboxamide derivative. The results revealed that quinolinyl oxamide derivative and indole carboxamide derivative bound tightly at the active site of both enzymes. Interestingly, despite belonging to different chemical scaffolds, they are coordinated by almost identical amino acid residues via extensive hydrogen bond interactions in both FP-2 and FP-3. Our report provided molecular insights into the interactions between FP-2 and FP-3 with quinolinyl oxamide derivative and indole carboxamide derivative, which we hope will pave the way towards the design of more potent and druglike inhibitors of these enzymes and will pave the way for their development to new antimalarial drugs.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BackgroundMonoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.ResultsHere, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance (mathrm{AUC}=0.840) with an ensemble learning of the top 3 best models.ConclusionThe prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics.

Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate's developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.

Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers.

A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.

Environmental Management/Marketing Research: Bibliographic Analysis

This study identifies, synthesizes and evaluates existing environmental research marketing and management with the ultimate goal of revealing trends in this area. In particular, we focus on: the methodological aspects of empirical research are the design, scope, and methodology, the field of study covered and the specific issues raised in all regions. Related articles are also identified electronically. Manual bibliographic search method. A total of 530 articles from 119 scientists were identified in magazines published from 1969 to 2008. Each article is analyzed with 6 pieces of content: key aspects: author profile, manuscript characteristics, study design, scope research, research methodology and field of study. Overall, this research facility is facing serious problems. Transformation moves from the early stages of identification and discovery to more advanced stages, an advanced stage characterized by greater maturity and rigidity. This is indicated by: trends towards inter-author, intercultural and interdisciplinary collaboration. The length of the manuscript and the number of references increased over time. Grow up maturity of study design with increasing emphasis on formalization and causality structure; various countries, industries, Products and companies of varying status, size, and geographic focus. The tendency to use stochastic sampling designs to achieve high response rates, ensuring large sample sizes, Apply advanced statistical analysis. Wide variety and deep coverage examining the topic.

Environmental Management/Marketing Research: Bibliographic Analysis

This study identifies, synthesizes and evaluates existing environmental research marketing and management with the ultimate goal of revealing trends in this area. In particular, we focus on: the methodological aspects of empirical research are the design, scope, and methodology, the field of study covered and the specific issues raised in all regions. Related articles are also identified electronically. Manual bibliographic search method. A total of 530 articles from 119 scientists were identified in magazines published from 1969 to 2008. Each article is analyzed with 6 pieces of content: key aspects: author profile, manuscript characteristics, study design, scope research, research methodology and field of study. Overall, this research facility is facing serious problems. Transformation moves from the early stages of identification and discovery to more advanced stages, an advanced stage characterized by greater maturity and rigidity. This is indicated by: trends towards inter-author, intercultural and interdisciplinary collaboration. The length of the manuscript and the number of references increased over time. Grow up maturity of study design with increasing emphasis on formalization and causality structure; various countries, industries, Products and companies of varying status, size, and geographic focus. The tendency to use stochastic sampling designs to achieve high response rates, ensuring large sample sizes, Apply advanced statistical analysis. Wide variety and deep coverage examining the topic.

Estimation of the Viscosity of an Antibody Solution from the Diffusion Interaction Parameter.

Understanding a monoclonal antibody's (MAb) physicochemical properties early in drug discovery is important for determining developability. Viscosity is important because antibodies with high viscosity have limited administration routes. Predicting the viscosity of highly concentrated MAb solutions is therefore essential for assessing developability. Here, we measured the viscosity and diffusion interaction coefficient (kDiff) of 3 MAbs under 15 different formulation conditions (pH and salt) and evaluated correlations between parameters. We also used a computational approach to identify the key factors underlying differences in concentration-dependent curves for viscosity among the MAbs and formulation conditions. Results showed that viscosity increased exponentially at high concentrations, and that this concentration-dependency could be predicted from kDiff. Attempts to set viscosity criterion for use by subcutaneous (SC) and intramuscular (IM) administration suggested that solutions with kDiff greater than -20 mL/g may be candidates. Computational analysis suggested that the presence of a large negative charge in the complementarity determining region (CDR) is a major factor underlying the difference in concentration-dependency among the three MAbs under different formulation conditions. Because it is possible to predict the administration form of antibody solutions, determination of kDiff at the early discovery stage may be essential for effective antibody development.

Accelerating GPCR Drug Discovery With Conformation-Stabilizing VHHs.

The human genome encodes 850 G protein-coupled receptors (GPCRs), half of which are considered potential drug targets. GPCRs transduce extracellular stimuli into a plethora of vital physiological processes. Consequently, GPCRs are an attractive drug target class. This is underlined by the fact that approximately 40% of marketed drugs modulate GPCRs. Intriguingly 60% of non-olfactory GPCRs have no drugs or candidates in clinical development, highlighting the continued potential of GPCRs as drug targets. The discovery of small molecules targeting these GPCRs by conventional high throughput screening (HTS) campaigns is challenging. Although the definition of success varies per company, the success rate of HTS for GPCRs is low compared to other target families (Fujioka and Omori, 2012; Dragovich et al., 2022). Beyond this, GPCR structure determination can be difficult, which often precludes the application of structure-based drug design approaches to arising HTS hits. GPCR structural studies entail the resource-demanding purification of native receptors, which can be challenging as they are inherently unstable when extracted from the lipid matrix. Moreover, GPCRs are flexible molecules that adopt distinct conformations, some of which need to be stabilized if they are to be structurally resolved. The complexity of targeting distinct therapeutically relevant GPCR conformations during the early discovery stages contributes to the high attrition rates for GPCR drug discovery programs. Multiple strategies have been explored in an attempt to stabilize GPCRs in distinct conformations to better understand their pharmacology. This review will focus on the use of camelid-derived immunoglobulin single variable domains (VHHs) that stabilize disease-relevant pharmacological states (termed ConfoBodies by the authors) of GPCRs, as well as GPCR:signal transducer complexes, to accelerate drug discovery. These VHHs are powerful tools for supporting in vitro screening, deconvolution of complex GPCR pharmacology, and structural biology purposes. In order to demonstrate the potential impact of ConfoBodies on translational research, examples are presented of their role in active state screening campaigns and structure-informed rational design to identify de novo chemical space and, subsequently, how such matter can be elaborated into more potent and selective drug candidates with intended pharmacology.

Predicting Approximate Clinically Effective Doses in Oncology Using Preclinical Efficacy and Body Surface Area Conversion: A Retrospective Analysis.

The correlation between efficacious doses in human tumor-xenograft mouse models and the human clinical doses of approved oncology agents was assessed using published preclinical data and recommended clinical doses. For 90 approved small molecule anti-cancer drugs, body surface area (BSA) corrected mouse efficacious doses were strongly predictive of human clinical dose ranges with 85.6% of the predictions falling within three-fold (3×) of the recommended clinical doses and 63.3% within 2×. These results suggest that BSA conversion is a useful tool for estimating human doses of small molecule oncology agents from mouse xenograft models from the early discovery stage. However, the BSA based dose conversion poorly predicts for the intravenous antibody and antibody drug conjugate anti-cancer drugs. For antibody-based drugs, five out of 30 (16.7%) predicted doses were within 3× of the recommended clinical dose. The body weight-based dose projection was modestly predictive with 66.7% of drugs predicted within 3× of the recommended clinical dose. The correlation was slightly better in ADCs (77.7% in 3×). The application and limitations of such simple dose estimation methods in the early discovery stage and in the design of clinical trials are also discussed in this retrospective analysis.

Ahead of the Curve: The Arc Institute Aims to Reshape the Biomedical Research Landscape

Ahead of the Curve: The Arc Institute Aims to Reshape the Biomedical Research Landscape

Pharmacokinetic Developability and Disposition Profiles of Bispecific Antibodies: A Case Study with Two Molecules.

Bispecific antibodies (BsAb) that engage multiple pathways are a promising therapeutic strategy to improve and prolong the efficacy of biologics in complex diseases. In the early stages of discovery, BsAbs often exhibit a broad range of pharmacokinetic (PK) behavior. Optimization of the neonatal Fc receptor (FcRn) interactions and removal of undesirable physiochemical properties have been used to improve the ‘pharmacokinetic developability’ for various monoclonal antibody (mAb) therapeutics, yet there is a sparsity of such information for BsAbs. The present work evaluated the influence of FcRn interactions and inherent physiochemical properties on the PK of two related single chain variable fragment (scFv)-based BsAbs. Despite their close relation, the two BsAbs exhibit disparate PK in cynomolgus monkeys with BsAb-1 having an aberrant clearance of ~2 mL/h/kg and BsAb-2 displaying a an ~10-fold slower clearance (~0.2 mL/h/kg). Evaluation of the physiochemical characteristics of the molecules, including charge, non-specific binding, thermal stability, and hydrophobic properties, as well as FcRn interactions showed some differences. In-depth drug disposition results revealed that a substantial disparity in the complete release from FcRn at a neutral pH is a primary factor contributing to the rapid clearance of the BsAb-1 while other biophysical characteristics were largely comparable between molecules.

Network-Driven Drug Discovery.

We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framework in which network biology is used to bridge between individual molecular entities and the cellular phenotype that emerges when those entities interact in a network. Multiple aspects of early stage discovery are addressed including the data-driven elucidation of biological processes implicated in disease, target identification and validation, phenotypic discovery of active molecules and their mechanism of action, and extraction of genetic target support from human population genetics data. Validation is described via summary of a number of discovery projects and details from a project aimed at COVID-19 disease.