Predictive mechanistic model for separation of monoclonal antibody, fab fragment, and aggregate species on multimodal chromatography.

The use of antibody fragments for crystallization and structure determinations.

Species distribution models (SDMs) have been widely used in ecology to understand how species relate to environmental variation. Most SDMs are correlative, and they lack explicit reference to the underlying processes, and therefore, the reliability of their predictions might be questionable. Mechanistic models that incorporate components that relate to underlying processes, such as trophic interactions or dispersal, have been less utilized due to their case‐specificity and difficulties related to their parametrization, which typically requires significantly more data than the parametrization of correlative models. We compare correlative and mechanistic species distribution models in prediction tasks under different scenarios. We define a mechanistic agent‐based models of resource‐consumer dynamics to generate data with known processes and parameter values. We fit correlative and mechanistic models to these data to study under which conditions mechanistic models might give more accurate predictions and how robust they are to possible model misspecification. The mechanistic models provided better extrapolation predictions than the correlative model in a simulated setting when the model used for fitting the data matched the data‐generating model. The mechanistic model predictions were sensitive to the correctness of the model, and the quality of them dropped significantly even under slight model misspecification. In real data analyses, the correlative models consistently outperformed the mechanistic models that were not tailored for the specific situations. Mechanistic species distribution models may provide a significant advantage in prediction compared to more commonly used correlative models when predicting new environmental conditions. However, this requires that the model is carefully tailored for the specific system because the predictions from the mechanistic models are sensitive to model misspecification.

An accelerated approach for mechanistic model based prediction of linear gradient elution ion-exchange chromatography of proteins

Standardized method for mechanistic modeling of multimodal anion exchange chromatography in flow through operation

The external domains of Ig superfamily members are involved in multiple binding interactions, both homophilic and heterophilic, that initiate molecular events leading to the execution of diverse cell functions. Human carcinoembryonic antigen (CEA), an Ig superfamily cell surface glycoprotein used widely as a clinical tumor marker, undergoes homophilic interactions that mediate intercellular adhesion. Recent evidence supports the view that deregulated overexpression of CEA has an instrumental role in tumorigenesis through the inhibition of cell differentiation and the disruption of tissue architecture. The CEA-mediated block of the myogenic differentiation of rat L6 myoblasts depends on homophilic binding of its external domains. We show here that L6 transfectant cells expressing CEA can "trans-block" the myogenesis of juxtaposed differentiation-competent L6 transfectant cells expressing a deletion mutant of CEA (DeltaNCEA). This result implies the efficacy of antiparallel CEA-CEA interactions between cells in the differentiation block. In addition, DeltaNCEA can acquire differentiation blocking activity by cross-linking with specific anti-CEA antibodies, thus implying the efficacy of parallel CEA-CEA interactions on the same cell surface. The myogenic differentiation blocking activity of CEA was demonstrated by site-directed mutations to involve three subdomains of the amino-terminal domain, shown previously to be critical for its intercellular adhesion function. Monovalent Fab fragments of monoclonal antibodies binding to the region bridging subdomains 1 and 2 could both inhibit intercellular adhesion and release the myogenic differentiation block. Amino acid substitutions Q80A, Q80R, and D82N in subdomain 3, QNDTG, however, were found to completely ablate the differentiation blocking activity of CEA but had no effect on intercellular adhesion activity. A cyclized peptide representing this subdomain was the most effective at releasing the differentiation block.

The selection and application of mathematical models to work tasks is challenging. Previously, we developed and evaluated a semi-empirical two-zone model that predicts time-weighted average (TWA) concentrations (Ctwa) of dust emitted during the sanding of drywall joint compound. Here, we fit the emission rate and random air speed variables of a mechanistic two-zone model to testing event data and apply and evaluate the model using data from two field studies. We found that the fitted random air speed values and emission rate were sensitive to (i) the size of the near-field and (ii) the objective function used for fitting, but this did not substantially impact predicted dust Ctwa. The mechanistic model predictions were lower than the semi-empirical model predictions and measured respirable dust Ctwa at Site A but were within an acceptable range. At Site B, a 10.5 m3 room, the mechanistic model did not capture the observed difference between PBZ and area Ctwa. The model predicted uniform mixing and predicted dust Ctwa up to an order of magnitude greater than was measured. We suggest that applications of the mechanistic model be limited to contexts where the near-field volume is very small relative to the far-field volume. [Supplementary materials are available for this article. Go to the publisher's online edition of the Journal of Occupational and Environmental Hygiene for the following free supplemental resource: a PDF file containing tables giving data for fitted emission rates and random air speeds for testing events with measured airflow rates for both near- and far-field zones and near-field zone only, and figures showing fitted random air speed varies with near-filed geometry when fitted with objective functions 1 and 2.]

Clearance of aggregates during protein purification is increasingly paramount as protein aggregates represent one of the major impurities in biopharmaceutical products. Aggregates, especially dimer species, represent a significant challenge for purification processing since aggregate separation coupled with high purity protein recovery can be difficult to accomplish. Biochemical characterization of the aggregate species from the hydrophobic interaction and cation exchange chromatography elution peaks revealed two different charged populations, i.e. heterogeneous charged aggregates, which led to further challenges for chromatographic removal. This paper compares multimodal versus conventional cation exchange or hydrophobic chromatography methodologies to remove heterogeneous aggregates. A full, mixed level factorial design of experiment strategy together with high throughput experimentation was employed to rapidly evaluate chromatographic parameters such as pH, conductivity, and loading. A variety of operating conditions were identified for the multimodal chromatography step, which lead to effective removal of two different charged populations of aggregate species. This multimodal chromatography step was incorporated into a monoclonal antibody purification process and successfully implemented at commercial manufacturing scale.

Prevention of passively transferred experimental autoimmune myasthenia gravis by Fab fragments of monoclonal antibodies directed against the main immunogenic region of the acetylcholine receptor

Fab fragments of monoclonal antibodies protect the human acetylcholine receptor against antigenic modulation caused by myasthenic sera

BackgroundPassive acoustic telemetry using coded transmitter tags and stationary receivers is a popular method for tracking movements of aquatic animals. Understanding the performance of these systems is important in array design and in analysis. Close proximity detection interference (CPDI) is a condition where receivers fail to reliably detect tag transmissions. CPDI generally occurs when the tag and receiver are near one another in acoustically reverberant settings. Here we confirm transmission multipaths reflected off the environment arriving at a receiver with sufficient delay relative to the direct signal cause CPDI. We propose a ray-propagation based model to estimate the arrival of energy via multipaths to predict CPDI occurrence, and we show how deeper deployments are particularly susceptible.MethodsA series of experiments were designed to develop and validate our model. Deep (300 m) and shallow (25 m) ranging experiments were conducted using Vemco V13 acoustic tags and VR2-W receivers. Probabilistic modeling of hourly detections was used to estimate the average distance a tag could be detected. A mechanistic model for predicting the arrival time of multipaths was developed using parameters from these experiments to calculate the direct and multipath path lengths. This model was retroactively applied to the previous ranging experiments to validate CPDI observations. Two additional experiments were designed to validate predictions of CPDI with respect to combinations of deployment depth and distance. Playback of recorded tags in a tank environment was used to confirm multipaths arriving after the receiver’s blanking interval cause CPDI effects.ResultsAnalysis of empirical data estimated the average maximum detection radius (AMDR), the farthest distance at which 95% of tag transmissions went undetected by receivers, was between 840 and 846 m for the deep ranging experiment across all factor permutations. From these results, CPDI was estimated within a 276.5 m radius of the receiver. These empirical estimations were consistent with mechanistic model predictions. CPDI affected detection at distances closer than 259–326 m from receivers. AMDR determined from the shallow ranging experiment was between 278 and 290 m with CPDI neither predicted nor observed. Results of validation experiments were consistent with mechanistic model predictions. Finally, we were able to predict detection/nondetection with 95.7% accuracy using the mechanistic model’s criterion when simulating transmissions with and without multipaths.DiscussionClose proximity detection interference results from combinations of depth and distance that produce reflected signals arriving after a receiver’s blanking interval has ended. Deployment scenarios resulting in CPDI can be predicted with the proposed mechanistic model. For deeper deployments, sea-surface reflections can produce CPDI conditions, resulting in transmission rejection, regardless of the reflective properties of the seafloor.

Anti-Langmuir elution behavior of a bispecific monoclonal antibody in cation exchange chromatography: Mechanistic modeling using a pH-dependent Self-Association Steric Mass Action isotherm

In free-form surface machining, it is essential to optimize the feedrate in order to improve the machining efficiency. Conservative constant feedrate values have been mostly used up to now since there was a lack of physical models and optimization tools for the machining processes. The overall goal of this research is the integration of geometric and mechanistic milling models for force prediction and feedrate scheduling in five-axis CNC free-form surface machining. For each tool move, the geometric model calculates the cut geometry, and a mechanistic model is used along with a maximum allowable cutting force to determine a desired feedrate. The results are written into the part NC program with optimized feedrates. When the integrated modeling approach based feedrate scheduling strategy introduced in this paper was used, it was shown that the machining time can be decreased significantly along the tool path.

Titanium alloy Ti-6Al-4V is commonly used in biomedical applications due to its superior properties such as biocompatibility, high strength-to-weight ratio and corrosion resistance. To understand the mechanics of the micro-turning process of these alloys, a mechanistic model has been developed for predicting the cutting forces. A modified Johnson–Cook material model with strain gradient plasticity is used to represent the flow stress of work material. The micro-turning experiments were conducted to verify the cutting forces predicted by mechanistic model. A finite element model is also developed with different shear friction factors and calibrated using experimental results to confirm and interpret the results of mechanistic model. It is inferred that strain rate increases by increasing cutting speed, whereas it decreases with increase in the feed rate due to increase in adiabatic shear band spacing. Since Ti-6Al-4V has low thermal conductivity, when cutting speed increases, there is an increase in the tool-chip interface temperature that leads to decrease in cutting forces. When cutting speed increases, chip morphology changes from discontinuous to continuous, and there is significant deterioration in the surface finish. It is observed that the average cutting force prediction errors for mechanistic and finite element models are 9.69% and 11.45% respectively.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Monoclonal Fab Fragments from Combinatorial Libraries Displayed on the Surface of Phage