Throughput Screening Research Articles

Use of stable isotope labeled (SIL) antibodies in cassette dosing to improve pharmacokinetics screening efficiency of ADCs with novel cytotoxic payloads

Stable isotope labelling by amino acids in cell culture (SILAC) is an established technique used in quantitative mass spectrometry (MS)-based proteomics. SILAC is also used to generate stable isotope labelled (SIL) antibodies for internal standards (IS) used in LC-MS/MS bioassays to improve quantitative robustness. Total antibody (TAb) is measured to evaluate pharmacokinetics (PK) of antibody drug conjugate (ADC) candidates measured by either ligand binding (LBA) or LC-MS/MS. Herein, we describe an application of SILAC, where multiple SIL combinations of an antibody are used for cassette dosing and PK evaluation. Our preclinical studies demonstrate SILAC-labelled ADC therapeutics did not alter antibody PK. Furthermore, with cassette dosing SIL antibodies exhibited comparable exposure to discretely administered unlabelled test articles in rats. In addition, SIL antibodies were conjugated to cytotoxic payloads to create SIL ADCs and cassette dosed in a cynomolgus monkey PK study and SIL ADCs yielded comparable PK results to discrete dosed unlabelled ADCs. In conclusion, SIL antibodies used with a cassette dosing strategy increases PK screening throughput of ADC candidates in preclinical species. Additionally, cassette dosing strategy further facilitates the responsible use of laboratory animals to achieve the three-Rs (Replacement, Reduction, and Refinement).

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

Strategies for Increasing the Throughput of Genetic Screening: Lessons Learned from the COVID-19 Pandemic within a University Community.

Amidst the COVID-19 pandemic, the Polytechnic University of Setúbal (IPS) used its expertise in molecular genetics to establish a COVID-19 laboratory, addressing the demand for community-wide testing. Following standard protocols, the IPS COVID Lab received national accreditation in October 2020 and was registered in February 2021. With the emergence of new SARS-CoV-2 variants and safety concerns for students and staff, the lab was further challenged to develop rapid and sensitive diagnostic technologies. Methodologies such as sample-pooling extraction and multiplex protocols were developed to enhance testing efficiency without compromising accuracy. Through Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) analysis, the effectiveness of sample pooling was validated, proving to be a clear success in COVID-19 screening. Regarding multiplex analysis, the IPS COVID Lab developed an in-house protocol, achieving a sensitivity comparable to that of standard methods while reducing operational time and reagent consumption. This approach, requiring only two wells of a PCR plate (instead of three for samples), presents a more efficient alternative for future testing scenarios, increasing its throughput and testing capacity while upholding accuracy standards. The lessons learned during the SARS-CoV-2 pandemic provide added value for future pandemic situations.

Bioinformatic Prediction and High Throughput In Vivo Screening to Identify Cis-Regulatory Elements for the Development of Algal Synthetic Promoters.

Algae biotechnology holds immense promise for revolutionizing the bioeconomy through the sustainable and scalable production of various bioproducts. However, their development has been hindered by the lack of advanced genetic tools. This study introduces a synthetic biology approach to develop such tools, focusing on the construction and testing of synthetic promoters. By analyzing conserved DNA motifs within the promoter regions of highly expressed genes across six different algal species, we identified cis-regulatory elements (CREs) associated with high transcriptional activity. Combining the algorithms POWRS, STREME, and PhyloGibbs, we predicted 1511 CREs and inserted them into a minimal synthetic promoter sequence in 1, 2, or 3 copies, resulting in 4533 distinct synthetic promoters. These promoters were evaluated in vivo for their capacity to drive the expression of a transgene in a high-throughput manner through next-generation sequencing post antibiotic selection and fluorescence-activated cell sorting. To validate our approach, we sequenced hundreds of transgenic lines showing high levels of GFP expression. Further, we individually tested 14 identified promoters, revealing substantial increases in GFP expression─up to nine times higher than the baseline synthetic promoter, with five matching or even surpassing the performance of the native AR1 promoter. As a result of this study, we identified a catalog of CREs that can now be used to build superior synthetic algal promoters. More importantly, here we present a validated pipeline to generate building blocks for innovative synthetic genetic tools applicable to any algal species with a sequenced genome and transcriptome data set.

Application of MPBT Assay for Multiplex Determination of Infectious Titers and for Selection of the Optimal Formulation for the Trivalent Novel Oral Poliovirus Vaccine.

Recently, a multiplex PCR-based titration (MPBT) assay was developed for simultaneous determination of infectious titers of all three Sabin strains of the oral poliovirus vaccine (OPV) to replace the conventional CCID50 assay, which is both time-consuming and laborious. The MPBT assay was shown to be reproducible, robust and sensitive. The conventional and MPBT assays showed similar results and sensitivity. The MPBT assay can be completed in two to three days, instead of ten days for the conventional assay. To prevent attenuated vaccine strains of poliovirus from reversion to virulence, a novel, genetically stable OPV (nOPV) was developed by modifying the genomes of conventional Sabin strains used in OPV. In this work, we evaluated the MPBT assay as a rapid screening tool to support trivalent nOPV (tnOPV) formulation development by simultaneous titration of the three nOPV strains to confirm stability as needed, for the selection of the lead tnOPV formulation candidate. We first assessed the ability of the MPBT assay to discriminate a 0.5 log10 titer difference by titrating the two tnOPV samples (undiluted and threefold-diluted) on the same plate. Once the assay was shown to be discriminating, we then tested different formulations of tnOPV drug products (DPs) that were subjected to different exposure times at 37 °C (untreated group and treated groups: 2 and 7 days at 37 °C), and to three freeze and thaw (FT) cycles. Final confirmation of the down selected formulation candidates was achieved by performing the conventional CCID50 assay, comparing the stability of untreated and treated groups and FT stability testing on the top three candidates. The results showed that the MPBT assay generates similar titers as the conventional assay. By testing two trivalent samples in the same plate, the assay can differentiate a 0.5 log10 difference between the titers of the tested nOPV samples. Also, the assay was able to detect the gradual degradation of nOPV viruses with different formulation compositions and under different time/temperature conditions and freeze/thaw cycles. We found that there were three tnOPV formulations which met the stability criteria of less than 0.5 log10 loss after 2 days' exposure to 37 ℃ and after three FT cycles, maintaining the potency of all three serotypes in these formulations. The ability of the MPBT assay to titrate two tnOPV lots (six viruses) in the same plate makes it cheaper and gives it a higher throughput for rapid screening. The assay detected the gradual degradation of the tnOPV and was successful in the selection of optimal formulations for the tnOPV. The results demonstrated that the MPBT method can be used as a stability indicating assay to assess the thermal stability of the nOPV. It can be used for rapid virus titer determination during the vaccine manufacturing process, and in clinical trials. The MPBT assay can be automated and applied for other viruses, including those with no cytopathic effect.

Comparison of MYOCYTER and MUSCLEMOTION in the analysis of Induced Pluripotent Stem Cell Derived Cardiomyocyte Contractility

Patient-specific induced pluripotent stem cells (iPSCs) differentiated into cardiomyocytes (CMs) offer tremendous opportunities for cardiac disease modeling and advancements in precision medicine. There is a need for technologies that allow noninvasive and precise measurements of CM function, such as contractility, to increase throughput for drug screening and testing efforts. Hypoplastic Left Heart Syndrome (HLHS) is a rare and complex congenital heart defect characterized by hypoplasia of the left ventricle, proximal aorta, as well as stenosis or atresia of the mitral and/or aortic valves. Previously it was demonstrated that iPSC-CMs from an HLHS patient with a genetic variant in the alpha myosin heavy chain ( MYH6 gene) exhibited an impaired cardiomyocyte phenotype (dysmorphic sarcomere structure, compensatory expression of MYH7, and altered contractility). Gene editing using CRISPR/Cas9 rescued the phenotype, demonstrating the utility of MYH6 iPSC-CMs as a useful disease model for HLHS. Critical parameters of CM function include contraction and relaxation, assessing systolic and diastolic dysfunction, respectively, in iPSC-CMs. The aim of this study is to compare features from two open-source macros for ImageJ: MYOCYTER and MUSCLEMOTION. MYOCYTER improves upon previous plugins available by allowing for users to select parameters. Both methods allow measurement of movement from high-speed movies. MYOCYTER enables the user to select an optimal threshold, size, and cell count to minimize disturbances such as cellular fragments, background noise, and floating bubbles, to maximize precision during analysis. MUSCLEMOTION allows rapid and easy measurement of movement from high- speed videos and can measure and compare relative or absolute parameters. This study will determine whether a systolic or diastolic dysfunction phenotype is observed under different magnification settings and evaluate the effect of dosing with pharmaceutical compounds. Finally, benefits and drawbacks of both macros will be investigated. Children's Research Institute. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract 242: Development of organoids-bacteria co-cultures for medium-to-high throughput screening

Abstract HUB patient derived organoids, (HUB-OrganoidsTM or PDOs) are self-organized epithelial cell structures with near-physiological features, extensively used to model aspects of cancer initiation and progression. Microinjection of colibactin-producing pks+ E. coli into the lumen of PDOs resulted in the appearance of two co-occurring mutational signatures identified in a subset of colorectal cancer (CRC) patients, demonstrating that pks+ E. coli plays a causative role in CRC development. The scalability of PDO bacteria microinjection is, however, limited and represent a bottleneck in the screening of preventive therapies for these patients. Here we develop a bacteria-PDO co-culture system (PDO-fragment exposure model), alternative to PDO microinjection, that is compatible with medium-to-high throughput screening methods. We validated the genotoxicity of colibactin-producing bacteria and showed the potential of the PDO fragment exposure model for the screening of drugs targeting colibactin-dependent genotoxicity. Citation Format: Eider Valle-Encinas, Mayke Doorn, Farzin Pourfarzad, Lani San Mateo, Prashanth Gokare, David Pocalyko, Sylvia F. Boj, Carla S. Verissimo. Development of organoids-bacteria co-cultures for medium-to-high throughput screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 242.

Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Abstract The battle against many cancers and infectious diseases has long been hindered due to the complexity of finding potent and effective drug combinations. With each new drug considered, the number of combinations to potentially test increases exponentially, posing substantial challenges in screening throughput. These challenges further intensify when accounting for the number of doses of each drug that need to be tested in a drug combination matrix (known as matrix density). There is a pressing need to screen large amounts of combinations at sufficient density to discover new therapies for diseases like cancer, but this has traditionally been out of reach. However, the recent widespread adoption of acoustic liquid handling robots has shown promise to overcome these obstacles by allowing for intricate drug screening template designs which were previously not possible to make. Despite these advances, the throughput achieved by these technologies has been limited due to lack of broadly accessible protocols and analytical tools for drug combination screening. We present Combocat, an end-to-end platform that allows for substantial increases in throughput of drug combination screens by combining experimental protocols that can be deployed for acoustic liquid handlers, machine learning algorithms for data imputation, and software that allows for in-depth analysis of results. We first generated a reference dataset of over 250,000 unique drug combination measurements in multiple cancer cell lines. The combination data were collected in a dense format (10×10 combination matrices) using a novel drug-drug template and achieved a dramatic increase in throughput compared to conventional methods. We then used this dataset to build a computational model which allowed us to accurately estimate drug combination effects using sparse measurements and imputing non-measured values with machine learning. The sparse measurements are collected in 1536-well microplates and substantially boost the throughput capabilities of drug-drug screens. As proof-of-concept, we used our method to screen a preclinical model of neuroblastoma with 9,045 drug combinations. This represents 10% the scale of the largest drug combination studies ever reported, achieved using a fraction of the resources, and in dense formats. We validated our findings by re-screening top hits using the fully-measured, non-imputed method and demonstrate the accuracy of our platform. The Combocat platform’s documentation and codebase is open-source, and we also make a GUI available for interactive exploration of screening results. By integrating advanced experimental and computational methods, we provide a generalizable pipeline that will expedite synergy screens and the drug combination discovery process for many diseases. Citation Format: William C. Wright, Paul Geeleher. An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4936.

Abstract 2054: A sensitive and specific human primary stem cell-based in vitro assay for predicting the gastrointestinal toxicity risk of therapeutic agents

Abstract Gastrointestinal (GI) toxicities are the most common drug-induced adverse events in human clinical trials. Drugs that cause GI toxicities often affect the proliferative stem and progenitor cell populations responsible for maintaining the cellular composition, self-renewing capacity, and barrier function of the intestinal epithelium. This disruption leads to impaired GI barrier integrity, culminating in a broad range of clinical symptoms, including diarrhea, inflammation, and increased risk of infection. The impact of GI toxicity is substantial and can cause dose-limiting clinical side-effects that may affect patient compliance and dosing adherence. Common nonclinical animal models, including rats, mice and dogs are often poor predictors of human GI toxicity and safety outcomes. Furthermore, in vivo animal testing poses limited screening throughput, which is particularly important in fields such as oncology, where matrices of double or triple drug combinations represent the standard of care. Additionally, existing cell-based model systems, such as Caco-2 cells, are of limited predictive value as they are tumor-derived and do not reprise native epithelium. Thus, there is a need for the development of assay systems that more accurately recapitulate human biology, have higher throughput testing capabilities, and ultimately improve clinical predictability. To this end, we have developed an assay to assess GI toxicity potential using the RepliGut Planar platform, which consists of a primary human stem cell-derived epithelium in a 96-well Transwell format. Quantitative dose-response curves were generated on proliferative and/or differentiated GI cultures for various test articles utilizing endpoints that assess GI proliferative capacity, cell viability, and epithelial barrier formation and maintenance. An initial set of marketed drugs (including diverse anti-proliferative, cytotoxic, or anti-inflammatory mechanisms) with known clinical incidence of diarrhea were over a 5-log dose range to enable modeling of the Toxic Concentration 50% (TC50). When benchmarked against the known human plasma concentrations (Cmax), this model was able to accurately predict clinical outcomes for diarrheagenic-drugs, as well as non-toxic negative controls, within 30-fold of the clinical Cmax. Overall, this model system provides straightforward and informative screening for GI toxicity that integrates sensitive and robust endpoints, enables screening to support early nonclinical development, and uniquely allows for mechanistic insights. Citation Format: James Levi, Reganne Lorichon, Lauren Boone, Maureen Bunger, Elizabeth Boazak, William R. Thelin. A sensitive and specific human primary stem cell-based in vitro assay for predicting the gastrointestinal toxicity risk of therapeutic agents [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2054.

Evaluating Reception Center Models for Radiation Response Screening Capacity and Throughput Predictions.

Introduction: The current fleet of nuclear reactors in the United States is mandated to provide evidence that surrounding jurisdictions can screen their populations should an incident occur. Capacity can be measured as throughput in reception centers used for screening. Due to the significant staffing and resources required to exercise screening capacity, most jurisdictions typically perform smaller exercises and use models to estimate their overall throughput. Objective: To evaluate the applicability and realism of current throughput models and practices. Methods: Throughput capacity for radiation screening is estimated with a mathematical model derived by the Federal Emergency Management Agency (FEMA). The Centers for Disease Control and Prevention developed a discrete event simulation model as a tool, SimPLER, to evaluate capacity and make throughput predictions. Model estimates will be compared and evaluated using timing data collected at a large-scale exercise. Results: The FEMA model estimated a throughput 41.2% higher than the actual radiation screening throughput, while the SimPLER model provided identical values. The FEMA and SimPLER models' predicted throughputs were 50% and 3.8%, respectively, higher than total exercise throughput. Applying each model to the throughput projections for a 12-hour shift, the FEMA model estimates ranged from 665 to 6,646 people and the SimPLER model yielded an estimated throughput of 1,809 people with a standard deviation of 74.6. Conclusion: Discrete event simulation models, such as SimPLER, may provide more realistic and accurate predictions of radiation screening and throughput capacity of reception centers than mathematical models such as the FEMA model.

Accelerate the design of new superhard carbon allotropes in Pca21 space group: High-throughput screening and machine learning strategies

As an efficient calculation and screening method, high-throughput can discover and optimize new materials, shorten the development cycle and cost of new materials. However, using high throughput for material screening and computation, a large amount of computing resources and storage space are indispensable. To accelerate the design of novel superhard carbon materials, we combined machine learning methods with high-throughput computing to construct three machine learning models: support vector machine regression, random forests, and artificial neural networks, and mined data from existing material databases to select 1276 structures as datasets for the model to predict the volume modulus and shear modulus. Through comparative analysis, the optimal model was selected to predict the bulk modulus and shear modulus of the structures obtained by high-throughput calculations, and the prediction results of the model were verified by density functional theory (DFT) calculations, and 8 superhard carbon allotropes in the Pca21 space group were eventually found.

Multiplexed effector screening for recognition by endogenous resistance genes using positive defense reporters in wheat protoplasts.

Plant resistance (R) and pathogen avirulence (Avr) gene interactions play a vital role in pathogen resistance. Efficient molecular screening tools for crops lack far behind their model organism counterparts, yet they are essential to rapidly identify agriculturally important molecular interactions that trigger host resistance. Here, we have developed a novel wheat protoplast assay that enables efficient screening of Avr/R interactions at scale. Our assay allows access to the extensive gene pool of phenotypically described R genes because it does not require the overexpression of cloned R genes. It is suitable for multiplexed Avr screening, with interactions tested in pools of up to 50 Avr candidates. We identified Avr/R-induced defense genes to create a promoter-luciferase reporter. Then, we combined this with a dual-color ratiometric reporter system that normalizes read-outs accounting for experimental variability and Avr/R-induced cell death. Moreover, we introduced a self-replicative plasmid reducing the amount of plasmid used in the assay. Our assay increases the throughput of Avr candidate screening, accelerating the study of cellular defense signaling and resistance gene identification in wheat. We anticipate that our assay will significantly accelerate Avr identification for many wheat pathogens, leading to improved genome-guided pathogen surveillance and breeding of disease-resistant crops.

Ultrahigh-throughput screening-assisted in vivo directed evolution for enzyme engineering.

Classical directed evolution is a powerful approach for engineering biomolecules with improved or novel functions. However, it traditionally relies on labour- and time-intensive iterative cycles, due in part to the need for multiple molecular biology steps, including DNA transformation, and limited screening throughput. In this study, we present an ultrahigh throughput in vivo continuous directed evolution system with thermosensitive inducible tunability, which is based on error-prone DNA polymerase expression modulated by engineered thermal-responsive repressor cI857, and genomic MutS mutant with temperature-sensitive defect for fixation of mutations in Escherichia coli. We demonstrated the success of the in vivo evolution platform with β-lactamase as a model, with an approximately 600-fold increase in the targeted mutation rate. Furthermore, the platform was combined with ultrahigh-throughput screening methods and employed to evolve α-amylase and the resveratrol biosynthetic pathway. After iterative rounds of enrichment, a mutant with a 48.3% improvement in α-amylase activity was identified via microfluidic droplet screening. In addition, when coupled with an in vivo biosensor in the resveratrol biosynthetic pathway, a variant with 1.7-fold higher resveratrol production was selected by fluorescence-activated cell sorting. In this study, thermal-responsive targeted mutagenesis coupled with ultrahigh-throughput screening was developed for the rapid evolution of enzymes and biosynthetic pathways.

Advances in Droplet-Based Microfluidic High-Throughput Screening of Engineered Strains and Enzymes Based on Ultraviolet, Visible, and Fluorescent Spectroscopy

Genetic engineering and directed evolution are effective methods for addressing the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well-plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields, such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as those involving materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions as well as improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved the high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase as well as the high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

Ultrahigh-Throughput Directed Evolution of Polymer-Degrading Enzymes Using Yeast Display.

Enzymes that degrade synthetic polymers have attracted intense interest for eco-friendly plastic recycling. However, because enzymes did not evolve for the cleavage of abiotic polymers, directed evolution strategies are needed to enhance activity for plastic degradation. Previous directed evolution efforts relied on polymer degradation assays that were limited to screening ∼104 mutants. Here, we report a high-throughput yeast surface display platform to rapidly evaluate >107 enzyme mutants for increased activity in cleaving synthetic polymers. In this platform, individual yeast cells display distinct mutants, and enzyme activity is detected by a change in fluorescence upon the cleavage of a synthetic probe resembling a polymer of interest. Highly active mutants are isolated by fluorescence activated cell sorting and identified through DNA sequencing. To demonstrate this platform, we performed directed evolution of a polyethylene terephthalate (PET)-depolymerizing enzyme, leaf and branch compost cutinase (LCC). We identified activity-boosting mutations that substantially increased the kinetics of degradation of solid PET films. Biochemical assays and molecular dynamics (MD) simulations of the most active variants suggest that the H218Y mutation improves the binding of the enzyme to PET. Overall, this evolution platform increases the screening throughput of polymer-degrading enzymes by 3 orders of magnitude and identifies mutations that enhance kinetics for depolymerizing solid substrates.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems—biosensor-based controllers—for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

A Study of Essential Computational Software in Medicinal Chemistry

Today, it is common practice to employ computational software tools for investigating the structure, dynamics, surface characteristics, and thermodynamics of inorganic, biological, and polymeric systems. Computational software tools are a vital part of the guide for drug discovery. It is frequently employed in approaches for rational drug design and structure-based drug design. The process of drug design and discovery is essential in the invention of a new chemical entity. For this process, plenty of computational tools are available globally, Those computational software tools are fast, free, open online excess paid. Pharmaceutical software decreases human efforts, error, and time utilization in a particular task without compromising the quality of work with great accuracy and efficiency. This software is utilized by various institutes globally related to science and medicine. A computer program that transforms an input structure according toa library of medicinal chemical transformation rules before allowing evaluation of the output structures. High throughput screening is now widely accepted as a viable option that CADD supports. The development of top-notch datasets and design libraries that may be optimized for molecular diversity or similarity has resulted from the quest for novel molecular entities. On the other hand, breakthroughs in computing infrastructure and molecular docking methods are allowing screening throughput to increase quickly.

Transient testing of oxide fuels by spark plasma sintering and finite element analysis

In this paper, we report an innovative approach of using spark plasma sintering (SPS) combining with finite element modeling (FEM) to investigate the transient behavior of nuclear fuels. The temperature ramping rates of the SPS with special tooling can be controlled from 5 to 10 °C/s up to 500 °C/s, mimicking prototypical thermal profiles of the loss-of-coolant-accident (LOCA) and reactivity-induced-accident (RIA) events, respectively. The fresh UO2 pellets with micron grain size are synthesized by SPS. Their fracture and fragmentation behaviors are investigated under transient thermal testing, and temperature-gradient and thermal-stress are calculated by FEM. The micron-sized fresh UO2 displays good thermal shock stability and anti-cracking performance under the LOCA thermal test. Under simulated RIA conditions, the UO2 fuels crack due to the stress induced by the large thermal gradient. The unique capability of SPS with controlled temperature ramping enables a cost-effective approach for rapid screening and high throughput evaluation of nuclear fuels under power transients.

Functional imaging-guided cell selection for evolving genetically encoded fluorescent indicators

Genetically encoded fluorescent indicators are powerful tools for tracking cellular dynamic processes. Engineering these indicators requires balancing screening dimensions with screening throughput. Herein, we present a functional imaging-guided photoactivatable cell selection platform, Faculae (functional imaging-activated molecular evolution), for linking microscopic phenotype with the underlying genotype in a pooled mutant library. Faculae is capable of assessing tens of thousands of variants in mammalian cells simultaneously while achieving photoactivation with single-cell resolution in seconds. To demonstrate the feasibility of this approach, we applied Faculae to perform multidimensional directed evolution for far-red genetically encoded calcium indicators (FR-GECIs) with improved brightness (Nier1b) and signal-to-baseline ratio (Nier1s). We anticipate that this image-based pooled screening method will facilitate the development of a wide variety of biomolecular tools.

A high throughput blood-brain barrier model incorporating shear stress with improved predictive power for drug discovery.

The blood-brain barrier is a key structure regulating the health of the brain and access of drugs and pathogens to neural tissue. Shear stress is a key regulator of the blood-brain barrier; however, the commonly used multi-well vitro models of the blood-brain barrier do not incorporate shear stress. In this work, we designed and validated a high-throughput system for simulating the blood-brain barrier that incorporates physiological flow and incorporates an optimized cellular model of the blood-brain barrier. This system can perform assays of blood-brain barrier function with shear stress, with 48 independent assays simultaneously. Using the high throughput assay, we conducted drug screening assays to explore the effects of compounds for opening or closing blood-brain barrier. Our studies revealed that assays with shear stress were more predictive and were able to identify compounds known to modify the blood-brain barrier function while static assays were not. Overall, we demonstrate an optimized, high throughput assay for simulating the blood-brain barrier that incorporates shear stress and is practical for use in drug screening and other high throughput studies of toxicology.