Mechanism of action, potency and efficacy: considerations for cell therapies.

One of the most challenging aspects of developing advanced cell therapy products (CTPs) is defining the mechanism of action (MOA), potency and efficacy of the product. This perspective examines these concepts and presents helpful ways to think about them through the lens of metrology. A logical framework for thinking about MOA, potency and efficacy is presented that is consistent with the existing regulatory guidelines, but also accommodates what has been learned from the 27 US FDA-approved CTPs. Available information regarding MOA, potency and efficacy for the 27 FDA-approved CTPs is reviewed to provide background and perspective. Potency process and efficacy process charts are introduced to clarify and illustrate the relationships between six key concepts: MOA, potency, potency test, efficacy, efficacy endpoint and efficacy endpoint test. Careful consideration of the meaning of these terms makes it easier to discuss the challenges of correlating potency test results with clinical outcomes and to understand how the relationships between the concepts can be misunderstood during development and clinical trials. Examples of how a product can be "potent but not efficacious" or "not potent but efficacious" are presented. Two example applications of the framework compare how MOA is assessed in cell cultures, animal models and human clinical trials and reveals the challenge of establishing MOA in humans. Lastly, important considerations for the development of potency tests for a CTP are discussed. These perspectives can help product developers set appropriate expectations for understanding a product's MOA and potency, avoid unrealistic assumptions and improve communication among team members during the development of CTPs.

The importance of peer imitation on smoking initiation over time: a dynamical systems approach.

A recent Institute of Medicine Report calls for explicit modeling of smoking initiation, cessation and addiction processes. We introduce a model of smoking initiation that explicitly teases out the percentage of initiation due to social pressures, which we call "peer-imitation," and the percentage due to other factors, such as media ads, family smoking, and psychological factors, which we call "self-initiation." We propose a dynamic non-linear behavioral contagion model of smoking initiation and employ data from the National Survey on Drug Use and Health to estimate the relative contributions of imitation and self-initiation to the overall smoking initiation process. Although the percent of total smoking due to peer imitation has been trending downward over time, it remains higher than the percent due to self-initiation. We note unexpected changes for the 2007 cohort, and we discuss possible implications for intervention and for the spread of e-cigarettes.

Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images.

BACKGROUND: Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds. RESULTS: We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision. CONCLUSIONS: The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells.

BACKGROUND: The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. METHODS: We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. RESULTS: The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. CONCLUSIONS: The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Modeling bacterial colonization and infection routes in health care settings: analytic and numerical approaches.

Health-care associated infections are a major problem in our society, accounting for tens of thousands of patient deaths and millions of dollars in wasted health care expenditures each year. Many of these infections are caused by bacteria that are transmitted from patient to patient either through direct contact or via the hands or clothing of health care workers. Because of the complexity of bacterial transmission routes in health care settings, computational approaches are essential, though often analytically intractable. Here we describe the construction and detailed analysis of a model for bacterial transmission in health care settings. Our model includes both colonization and disease stages for patients and health care workers, as well as an isolation ward and both patient-patient and patient-HCW-patient transmission pathways. We explicitly derive the basic reproductive ratio for this complex model, a nine-term expression that contains all nine ways with which a new colonization can occur. Using key parameters found in the medical literature, we use our model to gain insight into the relative importance of various bacterial transmission pathways within health care facilities, and to identify which forms of interventions are likely to prove most effective in hospitals and long-term care settings. We show that analytical and numerical approaches can complement each other as we seek to untangle the complex web of interactions that occur within a health care facility.

Three-dimensional, label-free cell viability measurements in tissue engineering scaffolds using optical coherence tomography.

In the field of tissue engineering, 3D scaffolds and cells are often combined to yield constructs that are used as therapeutics to repair or restore tissue function in patients. Viable cells are often required to achieve the intended mechanism of action for the therapy, where the live cells may build new tissue or may release factors that induce tissue regeneration. Thus, there is a need to reliably measure cell viability in 3D scaffolds as a quality attribute of a tissue-engineered medical product. Here, we developed a noninvasive, label-free, 3D optical coherence tomography (OCT) method to rapidly (2.5 min) image large sample volumes (1 mm3 ) to assess cell viability and distribution within scaffolds. OCT imaging was assessed using a model scaffold-cell system consisting of a polysaccharide-based hydrogel seeded with human Jurkat cells. Four test systems were used: hydrogel seeded with live cells, hydrogel seeded with heat-shocked or fixed dead cells and hydrogel without any cells. Time series OCT images demonstrated changes in the time-dependent speckle patterns due to refractive index (RI) variations within live cells that were not observed for pure hydrogel samples or hydrogels with dead cells. The changes in speckle patterns were used to generate live-cell contrast by image subtraction. In this way, objects with large changes in RI were binned as live cells. Using this approach, on average, OCT imaging measurements counted 326 ± 52 live cells per 0.288 mm3 for hydrogels that were seeded with 288 live cells (as determined by the acridine orange-propidium iodide cell counting method prior to seeding cells in gels). Considering the substantial uncertainties in fabricating the scaffold-cell constructs, such as the error from pipetting and counting cells, a 13% difference in the live-cell count is reasonable. Additionally, the 3D distribution of live cells was mapped within a hydrogel scaffold to assess the uniformity of their distribution across the volume. Our results demonstrate a real-time, noninvasive method to rapidly assess the spatial distribution of live cells within a 3D scaffold that could be useful for assessing tissue-engineered medical products.

Measuring dimensionality of cell-scaffold contacts of primary human bone marrow stromal cells cultured on electrospun fiber scaffolds.

The properties and structure of the cellular microenvironment can influence cell behavior. Sites of cell adhesion to the extracellular matrix (ECM) initiate intracellular signaling that directs cell functions such as proliferation, differentiation, and apoptosis. Electrospun fibers mimic the fibrous nature of native ECM proteins and cell culture in fibers affects cell shape and dimensionality, which can drive specific functions, such as the osteogenic differentiation of primary human bone marrow stromal cells (hBMSCs), by. In order to probe how scaffolds affect cell shape and behavior, cell-fiber contacts were imaged to assess their shape and dimensionality through a novel approach. Fluorescent polymeric fiber scaffolds were made so that they could be imaged by confocal fluorescence microscopy. Fluorescent polymer films were made as a planar control. hBSMCs were cultured on the fluorescent substrates and the cells and substrates were imaged. Two different image analysis approaches, one having geometrical assumptions and the other having statistical assumptions, were used to analyze the 3D structure of cell-scaffold contacts. The cells cultured in scaffolds contacted the fibers in multiple planes over the surface of the cell, while the cells cultured on films had contacts confined to the bottom surface of the cell. Shape metric analysis indicated that cell-fiber contacts had greater dimensionality and greater 3D character than the cell-film contacts. These results suggest that cell adhesion site-initiated signaling could emanate from multiple planes over the cell surface during culture in fibers, as opposed to emanating only from the cell's basal surface during culture on planar surfaces.

Biglycan regulates bone development and regeneration.

Endochondral bone development and regeneration relies on activation and proliferation of periosteum derived-cells (PDCs). Biglycan (Bgn), a small proteoglycan found in extracellular matrix, is known to be expressed in bone and cartilage, however little is known about its influence during bone development. Here we link biglycan with osteoblast maturation starting during embryonic development that later affects bone integrity and strength. Biglycan gene deletion reduced the inflammatory response after fracture, leading to impaired periosteal expansion and callus formation. Using a novel 3D scaffold with PDCs, we found that biglycan could be important for the cartilage phase preceding bone formation. The absence of biglycan led to accelerated bone development with high levels of osteopontin, which appeared to be detrimental to the structural integrity of the bone. Collectively, our study identifies biglycan as an influencing factor in PDCs activation during bone development and bone regeneration after fracture.

Quantitative, traceable determination of cell viability using absorbance microscopy.

Cell viability, an essential measurement for cell therapy products, lacks traceability. One of the most common cell viability tests is trypan blue dye exclusion where blue-stained cells are counted via brightfield imaging. Typically, live and dead cells are classified based on their pixel intensities which may vary arbitrarily making it difficult to compare results. Herein, a traceable absorbance microscopy method to determine the intracellular uptake of trypan blue is demonstrated. The intensity pixels of the brightfield images are converted to absorbance images which are used to calculate moles of trypan blue per cell. Trypan blue cell viability measurements, where trypan blue content in each cell is quantified, enable traceable live-dead classifications. To implement the absorbance microscopy method, we developed an open-source AbsorbanceQ application that generates quantitative absorbance images. The validation of absorbance microscopy is demonstrated using neutral density filters. Results from four different microscopes demonstrate a mean absolute deviation of 3% from the expected optical density values. When assessing trypan blue-stained Jurkat cells, the difference in intracellular uptake of trypan blue in heat-shock-killed cells using two different microscopes is 3.8%. Cells killed with formaldehyde take up ~50% less trypan blue as compared to the heat-shock-killed cells, suggesting that the killing mechanism affects trypan blue uptake. In a test mixture of approximately 50% live and 50% dead cells, 53% of cells were identified as dead (±6% standard deviation). Finally, to mimic batches of low-viability cells that may be encountered during a cell manufacturing process, viability was assessed for cells that were 1) overgrown in the cell culture incubator for five days or 2) incubated in DPBS at room temperature for five days. Instead of making live-dead classifications using arbitrary intensity values, absorbance imaging yields traceable units of moles that can be compared, which is useful for assuring quality for biomanufacturing processes.

Applying the Cytocentric Principles to Regenerative Medicine for Reproducibility.

Purpose of Review: Cell and tissue products do not just reflect their present conditions; they are the culmination of all they have encountered over time. Currently, routine cell culture practices subject cell and tissue products to highly variable and non-physiologic conditions. This article defines five cytocentric principles that place the conditions for cells at the core of what we do for better reproducibility in Regenerative Medicine. Recent Findings: There is a rising awareness of the cell environment as a neglected, but critical variable. Recent publications have called for controlling culture conditions for better, more reproducible cell products. Summary: Every industry has basic quality principles for reproducibility. Cytocentric principles focus on the fundamental needs of cells: protection from contamination, physiologic simulation, and full-time conditions for cultures that are optimal, individualized, and dynamic. Here, we outline the physiologic needs, the technologies, the education, and the regulatory support for the cytocentric principles in regenerative medicine.

Modeling the population effects of escape mutations in SARS-CoV-2 to guide vaccination strategies.

SARS-Cov-2 escape mutations (EM) have been detected and are spreading. Vaccines may need adjustment to respond to these or future mutations. We designed a population level model integrating both waning immunity and EM. We also designed a set of criteria for elaborating and fitting this model to cross-neutralization and other data with a goal of minimizing vaccine decision errors. We formulated four related models. These differ regarding which strains can drift to escape immunity in the host when that immunity was elicited by different strains. Across changing waning and escape mutation parameter values, these model variations led to patterns where: 1) EM are rare in the first epidemic, 2) rebound outbreaks after the first outbreak are accelerated by increasing waning and by increasing drifting, 3) the long term endemic level of infection is determined mostly by waning rates with small effects of the drifting parameter, 4) EM caused loss of vaccine effectiveness, and under some conditions: vaccines induced EM that caused higher levels of infection with vaccines than without them. The differences and similarities across the four models suggest paths for developing models specifying the epitopes where EM act. This model provides a base on which to construct epitope specific evolutionary models using new high-throughput assay data from population samples to guide vaccine decisions.

A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations.

We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.

Tissue Engineering Measurands.

A metrological perspective for thinking about the characterization of tissue engineered medical products (TEMPs) may help improve communication between researchers. During the development lifecycle of a TEMP, many product properties are measured over the long path to a product release. The selection of each measurement is designed to establish that the product is safe and efficacious (i.e., successful). However, there is often miscommunication during discussions of product characterization. The miscommunication stems from inherent assumptions that are made about the measurements. A "measurand chart" can help clarify these assumptions to enable a more coherent discussion of the value of each measurement. A measurand is defined as "the quantity or property intended to be measured". Tissue engineering measurands are discussed in terms of three case studies including "cell viability in a scaffold", "potency", and "biocompatibility". Topics including a measurement model, defining tissue engineering measurands and definitional uncertainty, are discussed to further refine thinking about tissue engineering measurands. Awareness of these concepts while discussing product characterization can enhance communication and strategic thinking so that the resulting plan is clear and purposeful.

Comparison of Artificial Intelligence based approaches to cell function prediction.

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels.

The geometry of reaction norms yields insights on classical fitness functions for Great Lakes salmon.

Life history theory examines how characteristics of organisms, such as age and size at maturity, may vary through natural selection as evolutionary responses that optimize fitness. Here we ask how predictions of age and size at maturity differ for the three classical fitness functions-intrinsic rate of natural increase r, net reproductive rate R0, and reproductive value Vx-for semelparous species. We show that different choices of fitness functions can lead to very different predictions of species behavior. In one's efforts to understand an organism's behavior and to develop effective conservation and management policies, the choice of fitness function matters. The central ingredient of our approach is the maturation reaction norm (MRN), which describes how optimal age and size at maturation vary with growth rate or mortality rate. We develop a practical geometric construction of MRNs that allows us to include different growth functions (linear growth and nonlinear von Bertalanffy growth in length) and develop two-dimensional MRNs useful for quantifying growth-mortality trade-offs. We relate our approach to Beverton-Holt life history invariants and to the Stearns-Koella categorization of MRNs. We conclude with a detailed discussion of life history parameters for Great Lakes Chinook Salmon and demonstrate that age and size at maturity are consistent with predictions using R0 (but not r or Vx) as the underlying fitness function.

Workshop on the characterization of fiber-based scaffolds: Challenges, progress, and future directions.

A critical component of many tissue-engineered medical products (TEMPs) is the scaffold or biomaterial. The industry's understanding of scaffold properties and their influence on cell behavior has advanced, but our technical capability to reliably characterize scaffolds requires improvement, especially to enable large-scale manufacturing. In response to the key findings from the 2013 ASTM International Workshop of Standards and Measurements for Tissue Engineering Scaffolds, the National Institute of Standards and Technology (NIST), ASTM International, BiofabUSA, and the Standards Coordinating Body (SCB) organized a workshop in 2018 titled, "Characterization of Fiber-Based Scaffolds". The goal was to convene a group of 40 key industry stakeholders to identify major roadblocks in measurements of fiber-based scaffold properties. This report provides an overview of the findings from this collaborative workshop. The four major consensus findings were that (a) there is need for a documentary standard guide that would aid developers in the selection of test methods for characterizing fiber-based scaffolds; (b) there is a need for a strategy to assess the quality of porosity and pore size measurements, which could potentially be ameliorated by the development of a reference material; (b) there are challenges with the lexicon used to describe and assess scaffolds; and (d) the vast array of product applications makes it challenging to identify consensus test methods. As a result of these findings, a working group was formed to develop an ASTM Standard Guide for Characterizing Fiber-Based Constructs that will provide developers guidance on selecting measurements for characterizing fiber-based scaffolds.

Improving patient outcomes with regenerative medicine: How the Regenerative Medicine Manufacturing Society plans to move the needle forward in cell manufacturing, standards, 3D bioprinting, artificial intelligence-enabled automation, education, and training.

The Regenerative Medicine Manufacturing Society (RMMS) is the first and only professional society dedicated toward advancing manufacturing solutions for the field of regenerative medicine. RMMS's vision is to provide greater patient access to regenerative medicine therapies through innovative manufacturing solutions. Our mission is to identify unmet needs and gaps in regenerative medicine manufacturing and catalyze the generation of new ideas and solutions by working with private and public stakeholders. We aim to accomplish our mission through outreach and education programs and securing grants for public-private collaborations in regenerative medicine manufacturing. This perspective will cover four impact areas that the society's leadership team has identified as critical: (a) cell manufacturing and scale-up/out, respectively, for allogeneic and autologous cell therapies, (b) standards for regenerative medicine, (c) 3D bioprinting, and (d) artificial intelligence-enabled automation. In addition to covering these areas and ways in which the society intends to advance the field in a collaborative nature, we will also discuss education and training. Education and training is an area that is critical for communicating the current challenges, developing solutions to accelerate the commercialization of the latest technological advances, and growing the workforce in the rapidly expanding sector of regenerative medicine.

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell-derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning.

Prevalence of antibiotic resistance in drinking water treatment and distribution systems.

The occurrence and spread of antibiotic-resistant bacteria (ARB) are pressing public health problems worldwide, and aquatic ecosystems are a recognized reservoir for ARB. We used culture-dependent methods and quantitative molecular techniques to detect and quantify ARB and antibiotic resistance genes (ARGs) in source waters, drinking water treatment plants, and tap water from several cities in Michigan and Ohio. We found ARGs and heterotrophic ARB in all finished water and tap water tested, although the amounts were small. The quantities of most ARGs were greater in tap water than in finished water and source water. In general, the levels of bacteria were higher in source water than in tap water, and the levels of ARB were higher in tap water than in finished water, indicating that there was regrowth of bacteria in drinking water distribution systems. Elevated resistance to some antibiotics was observed during water treatment and in tap water. Water treatment might increase the antibiotic resistance of surviving bacteria, and water distribution systems may serve as an important reservoir for the spread of antibiotic resistance to opportunistic pathogens.