A batch screening technique for the calculation of chromatographic separability.

This paper employs a high-throughput parallel batch (microtiter plate) adsorption screen with sequential salt step increases to rapidly generate protein elution profiles for multiple resins at different pHs using a protein library. The chromatographic set used in this work includes single mode, multimodal anion-exchange (MMA), and multimodal cation-exchange (MMC) resins. The protein library consists of proteins with isoelectric points ranging from 5.1 to 11.4 with varying hydrophobicities as determined by their retention on hydrophobic interaction chromatography. The batch sequential experiments are carried out using one protein at a time with a wide set of resins at multiple pH conditions, thus enabling simple microtiter plate detection. A mathematical formulation is then used to determine the first moment of the distributions from each chromatogram (sequential step elution) generated in the parallel batch experiments. Batch data first moments (expressed in salt concentration) are then compared to results obtained from column linear salt gradient elution, and the techniques are shown to be consistent. In addition, first moment data are used to calculate one-resin separability scores, which are a measure of a resin's ability, at a specified pH, to separate the entire set of proteins in the library from one another. Again, the results from the batch and column experiments are shown to be comparable. The first moment data sets were then employed to calculate the two-resin separability scores, which are a measure of the ability of two resins to synergistically separate the entire set of proteins in the library. Importantly, these results based on the two-resin separability performances derived from the batch and column experiments were again shown to be consistent. This approach for rapidly screening large numbers of chromatographic resins and mobile phase conditions for their elution behavior may prove useful for enabling the rapid discovery of new chromatographic ligands and resins.

Disability and Recurrent Stroke Among Participants in Stroke Prevention Trials.

Importance: Stroke secondary prevention trials have disproportionately enrolled participants with mild or no disability. The impact of this bias remains unclear. Objective: To investigate the association between poststroke disability and the rate of recurrent stroke during long-term follow up. Design, Setting, and Participants: This cohort study is a post hoc analysis of the Prevention Regimen For Effectively Avoiding Second Strokes (PRoFESS) and Insulin Resistance Intervention After Stroke (IRIS) secondary prevention clinical trial datasets. PRoFESS enrolled patients from 2003 to 2008, and IRIS enrolled patients from 2005 to 2015. Data were analyzed from September 23, 2023, to May 16, 2024. Exposure: The exposure was poststroke functional status at study baseline, defined as modified Rankin Scale (mRS; range, 0-5; higher score indicates more disability) score of 0 vs 1 to 2 vs 3 or greater. Main Outcomes and Measures: The primary outcome was recurrent stroke. The secondary outcome was major cardiovascular events (MACE), defined as recurrent stroke, myocardial infarction, new or worsening heart failure, or vascular death. Results: A total of 20 183 PRoFESS participants (mean [SD] age, 66.1 [8.5] years; 12 931 [64.1%] male) and 3265 IRIS participants (mean [SD] age, 62.7 [10.6] years; 2151 [65.9%] male) were included. The median (IQR) follow-up was 2.4 (1.9-3.0) years in PRoFESS and 4.7 (3.2-5.0) years in IRIS. In PRoFESS, the recurrent stroke rate was 7.2%, among patients with an mRS of 0, 8.7% among patients with an mRS of 1 or 2, and 10.6% among patients with an mRS of 3 or greater (χ22 = 27.1; P < .001); in IRIS the recurrent stroke rate was 6.4% among patients with an mRS of 0, 9.0% among patients with an mRS of 1 or 2, and 11.7% among patients with an mRS of 3 or greater (χ22 = 11.1; P < .001). The MACE rate was 10.1% among patients with an mRS of 0, 12.2% among patients with an mRS of 1 or 2, and 17.2% among patients with an mRS of 3 or greater (χ22 = 103.4; P < .001) in PRoFESS and 10.9% among patients with an mRS of 0, 13.3% among patients with an mRS of 1 or 2, and 15.3% among patients with an mRS of 3 or greater (χ22 = 5.8; P = .06) in IRIS. Compared with patients with an mRS of 0, patients with an mRS of 3 or greater had increased hazard for recurrent stroke in PRoFESS (hazard ratio [HR], 1.63; 95% CI, 1.38-1.92; P < .001) and in IRIS (HR, 1.91; 95% CI, 1.28-2.86; P = .002). There was also increased hazard for MACE in PRoFESS (HR, 1.90; 95% CI, 1.66-2.18; P < .001) and in IRIS (HR, 1.45; 95% CI, 1.03-2.03; P = .03). Conclusions and Relevance: This cohort study found that higher baseline poststroke disability was associated with increased rates of recurrent stroke and MACE. Including more patients with greater baseline disability in stroke prevention trials may improve the statistical power and generalizability of these studies.

Genetic Variation and Stroke Recovery: The STRONG Study.

BACKGROUND: Genetic association studies can reveal biology and treatment targets but have received limited attention for stroke recovery. STRONG (Stroke, Stress, Rehabilitation, and Genetics) was a prospective, longitudinal (1-year), genetic study in adults with stroke at 28 US stroke centers. The primary aim was to examine the association that candidate genetic variants have with (1) motor/functional outcomes and (2) stress-related outcomes. METHODS: For motor/functional end points, 3 candidate gene variants (ApoE ε4, BDNF [brain-derived neurotrophic factor], and a dopamine polygenic score) were analyzed for associations with change in grip strength (3 months-baseline), function (3-month Stroke Impact Scale-Activities of Daily Living), mood (3-month Patient Health Questionnaire-8), and cognition (12-month telephone-Montreal Cognitive Assessment). For stress-related outcomes, 7 variants (serotonin transporter gene-linked promoter region, ACE [angiotensin-converting enzyme], oxytocin receptor, FKBP5 [FKBP prolyl isomerase 5], FAAH [fatty acid amide hydrolase], BDNF, and COMT [catechol-O-methyltransferase]) were assessed for associations with posttraumatic stress disorder ([PTSD]; PTSD Primary Care Scale) and depression (Patient Health Questionnaire-8) at 6 and 12 months; stress-related genes were examined as a function of poststroke stress level. Statistical models (linear, negative binomial, or Poisson regression) were based on response variable distribution; all included stroke severity, age, sex, and ancestry as covariates. Stroke subtype was explored secondarily. Data were Holm-Bonferroni corrected. A secondary replication analysis tested whether the rs1842681 polymorphism (identified in the GISCOME study [Genetics of Ischaemic Stroke Functional Outcome]) was related to 3-month modified Rankin Scale score in STRONG. RESULTS: The 763 enrollees were 63.1±14.9 (mean±SD) years of age, with a median initial National Institutes of Health Stroke Scale score of 4 (interquartile range, 2-9); outcome data were available in n=515 at 3 months, n=500 at 6 months, and n=489 at 12 months. At 1 year poststroke, the rs6265 (BDNF) variant was associated with poorer cognition (0.9-point lower telephone-Montreal Cognitive Assessment score, P=1×10-5). For stress-related outcomes, rs4291 (ACE) and rs324420 (FAAH) were risk factors linking increased poststroke stress with higher 1-year depression and PTSD symptoms (P<0.05), while rs4680 (COMT) linked poststroke stress with lower 1-year depression and PTSD. Findings were unchanged when considering stroke subtype. STRONG replicated GISCOME: rs1842681 was associated with lower 3-month modified Rankin Scale score (P=3.2×10-5). CONCLUSIONS: This study identified genetic associations with cognitive function, depression, and PTSD 1 year poststroke. Genetic susceptibility to PTSD and depressive symptoms varied according to the amount of poststroke stress, underscoring the critical role of lived experiences in recovery. Together, the results suggest that genetic association studies provide insights into the biology of stroke recovery in humans.

High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.

A single-center, assessor-blinded, randomized controlled clinical trial to test the safety and efficacy of a novel brain-computer interface controlled functional electrical stimulation (BCI-FES) intervention for gait rehabilitation in the chronic stroke population.

BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. TRIAL REGISTRATION: The trial was registered as "BCI-FES Therapy for Stroke Rehabilitation" on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.

Telerehabilitation Following Stroke.

Stroke remains a major cause of disability. Intensive rehabilitation therapy can improve outcomes, but most patients receive limited doses. Telehealth methods can overcome obstacles to delivering intensive therapy and thereby address this unmet need. A specific example is reviewed in detail, focused on a telerehabilitation system that targets upper extremity motor deficits after stroke. Strengths of this system include provision of daily therapy associated with very high patient compliance, safety and feasibility in the inpatient or home setting, comparable efficacy to dose-matched therapy provided in-clinic, and a holistic approach that includes assessment, education, prevention, and activity-based therapy.

Mixed-mode size-exclusion silica resin for polishing human antibodies in flow-through mode.

The polishing step in the downstream processing of therapeutic antibodies removes residual impurities from Protein A eluates. Among the various classes of impurities, antibody fragments are especially challenging to remove due to the broad biomolecular diversity generated by a multitude of fragmentation patterns. The current approach to fragment removal relies on ion exchange or mixed-mode adsorbents operated in bind-and-gradient-elution mode. However, fragments that bear strong similarity to the intact product or whose biophysical features deviate from the ensemble average can elude these adsorbents, and the lack of a chromatographic technology enabling robust antibody polishing is recognized as a major gap in downstream bioprocessing. Responding to this challenge, this study introduces size-exclusion mixed-mode (SEMM) silica resins as a novel chromatographic adsorbent for the capture of antibody fragments irrespective of their biomolecular features. The pore diameter of the silica beads features a narrow distribution and is selected to exclude monomeric antibodies, while allowing their fragments to access the pores where they are captured by the mixed-mode ligands. The static and dynamic binding capacity of the adsorbent ranged respectively between 30-45 and 25-33 gs of antibody fragments per liter of resin. Selected SEMM-silica resins also demonstrated the ability to capture antibody aggregates, which adsorb on the outer layer of the beads. Optimization of the SEMM-silica design and operation conditions - namely, pore size (10 nm) and ligand composition (quaternary amine and alkyl chain) as well as the linear velocity (100 cm/h), ionic strength (5.7 mS/cm), and pH (7) of the mobile phase - afforded a significant reduction of both fragments and aggregates, resulting into a final antibody yield up to 80% and monomeric purity above 97%.

Mechanistic model-based characterization of size-exclusion-mixed-mode resins for removal of monoclonal antibody fragments.

Although antibody fragments are a critical impurity to remove from process streams, few platformable purification techniques have been developed to this end. In this work, a novel size-exclusion-mixed-mode (SEMM) resin was characterized with respect to its efficacy in mAb fragment removal. Inverse size-exclusion chromatography showed that the silica-based resin had a narrow pore size distribution and a median pore radius of roughly 6.2 nm. Model-based characterization was carried out with Chromatography Analysis and Design Toolkit (CADET), using the general rate model and the multicomponent Langmuir isotherm. Model parameters were obtained from fitting breakthrough curves, performed at multiple residence times, for a mixture of mAb, aggregates, and an array of fragments (varying in size). Accurate fits were obtained to the frontal chromatographic data across a range of residence times. Model validation was then performed with a scaled-up column, altering residence time and feed composition from the calibration run. Accurate predictions were obtained, thereby illustrating the model's interpolative and extrapolative capabilities. Additionally, the SEMM resin achieved 90% mAb yield, 37% aggregate removal, 29% [Formula: see text] removal, 54% Fab/Fc removal, 100% Fc fragments removal, and a productivity of 72.3 g mAbL×h. Model predictions for these statistics were all within 5%. Simulated batch uptake experiments showed that resin penetration depth was directly related to protein size, with the exception of the aggregate species, and that separation was governed by differential pore diffusion rates. Additional simulations were performed to characterize the dependence of fragment removal on column dimension, load density, and feed composition. Fragment removal was found to be highly dependent on column load density, where optimal purification was achieved below 100 mg protein/mL column. Furthermore, fragment removal was dependent on column volume (constant load mass), but agnostic to whether column length or diameter was changed. Lastly, the dependence on feed composition was shown to be complex. While fragment removal was inversely related to fragment mass fraction in the feed, the extent depended on fragment size. Overall, the results from this study illustrated the efficacy of the SEMM resin in fragment and aggregate removal and elucidated relationships with key operational parameters through model-based characterization.

Genome-Wide Association Studies of 3 Distinct Recovery Phenotypes in Mild Ischemic Stroke.

BACKGROUND AND OBJECTIVES: Stroke genetic research has made substantial progress in the past decade. Its recovery application, however, remains behind, in part due to its reliance on the modified Rankin Scale (mRS) score as a measure of poststroke outcome. The mRS does not map well to biological processes because numerous psychosocial factors drive much of what the mRS captures. Second, the mRS contains multiple disparate biological events into a single measure further limiting its use for biological discovery. This led us to investigate the effect of distinct stroke recovery phenotypes on genetic variation associations with Genome-Wide Association Studies (GWASs) by repurposing the NIH Stroke Scale (NIHSS) and its subscores. METHODS: In the Vitamin Intervention for Stroke Prevention cohort, we estimated changes in cognition, motor, and global impairments over 2 years using specific measures. We included genotyped participants with a total NIHSS score greater than zero at randomization and excluded those with recurrent stroke during the trial. A GWAS linear mixed-effects model predicted score changes, with participant as a random effect, and included initial score, age, sex, treatment group, and the first 5 ancestry principal components. RESULTS: In total, 1,270 participants (64% male) were included with a median NIHSS score of 2 (interquartile range [IQR] 1-3) and median age 68 (IQR 59-75) years. At randomization, 20% had cognitive deficits (NIHSS Cog-4 score >0) and 70% had ≥1 motor deficits (impairment score >1). At 2 years, these percentages improved to 7.2% with cognitive deficits and 30% with motor deficits. GWAS identified novel suggestive gene-impairment associations (p < 5e-6) for cognition (CAMK2D, EVX2, LINC0143, PTPRM, SGMS1, and SMAD2), motor (ACBD6, KDM4B, MARK4, PTPRS, ROBO1, and ROBO2), and global (MSR1 and ROBO2) impairments. DISCUSSION: Defining domain-specific stroke recovery phenotypes and using longitudinal clinical trial designs can help detect novel genes associated with chronic recovery. These data support the use of granular endpoints to identify genetic associations related to stroke recovery.

Factors affecting product association as a mechanism of host-cell protein persistence in bioprocessing.

Product association of host-cell proteins (HCPs) to monoclonal antibodies (mAbs) is widely regarded as a mechanism that can enable HCP persistence through multiple purification steps and even into the final drug substance. Discussion of this mechanism often implies that the existence or extent of persistence is directly related to the strength of binding but actual measurements of the binding affinity of such interactions remain sparse. Two separate avenues of investigation of HCP-mAb binding are reported here. One is the measurement of the affinity of binding of individual, commonly persistent Chinese hamster ovary (CHO) HCPs to each of a set of mAbs, and the other uses quantitative proteomic measurements to assess binding of HCPs in a null CHO harvested cell culture fluid (HCCF) to mAbs produced in the same cell line. The individual HCP measurements show that the binding affinities of individual HCPs to different mAbs can vary appreciably but are rarely very high, with only weak pH dependence. The measurements on the null HCCF allow estimation of individual HCP-mAb affinities; these are typically weaker than those seen in affinity measurements on isolated HCPs. Instead, the extent of binding appears correlated with the initial abundance of individual HCPs in the HCCF and the forms of the HCPs in the solution, i.e., whether HCPs are present as free molecules or as parts of large aggregates. Separate protein A chromatography experiments performed by feeding different fractions of a mAb-containing HCCF obtained by size-exclusion chromatography (SEC) showed clear differences in the number and identity of HCPs found in the protein A eluate. These results indicate a significant role for HCP-mAb association in determining HCP persistence through protein A chromatography, presumably through binding of HCP-mAb complexes to the resin. Overall, the results illustrate the importance of considering more fully the biophysical context of HCP-product association in assessing the factors that may affect the phenomenon and determine its implications. Knowledge of the abundances and the forms of individual or aggregated HCPs in HCCF are particularly significant, emphasizing the integration of upstream and downstream bioprocessing and the importance of understanding the collective properties of HCPs in addition to just the biophysical properties of individual HCPs.

Exploring preferred binding domains of IgG1 mAbs to multimodal adsorbents using a combined biophysics and simulation approach.

In this work, we employ a recently developed biophysical technique that uses diethylpyrocarbonate (DEPC) covalent labeling and mass spectrometry for the identification of mAb binding patches to two multimodal cation exchange resins at different pH. This approach compares the labeling results obtained in the bound and unbound states to identify residues that are sterically shielded and thus located in the mAb binding domains. The results at pH 6 for one mAb (mAb B) indicated that while the complementarity determining region (CDR) had minimal interactions with both resins, the FC domain was actively involved in binding. In contrast, DEPC/MS data with another mAb (mAb C) indicated that both the CDR and FC domains were actively involved in binding. These results corroborated chromatographic retention data with these two mAbs and their fragments and helped to explain the significantly stronger retention of both the intact mAb C and its Fab fragment. In contrast, labeling results with mAb C at pH 7, indicated that only the CDR played a significant role in resin binding, again corroborating chromatographic data. The binding domains identified from the DEPC/MS experiments were also examined using protein surface hydrophobicity maps obtained using a recently developed sparse sampling molecular dynamics (MD) approach in concert with electrostatic potential maps. These results demonstrate that the DEPC covalent labeling/mass spectrometry technique can provide important information about the domain contributions of multidomain proteins such as monoclonal antibodies when interacting with multimodal resins over a range of pH conditions.

Identification and characterization of CHO host-cell proteins in monoclonal antibody bioprocessing.

Host-cell proteins (HCPs) are the foremost class of process-related impurities to be controlled and removed in downstream processing steps in monoclonal antibody (mAb) manufacturing. However, some HCPs may evade clearance in multiple purification steps and reach the final drug product, potentially threatening drug stability and patient safety. This study extends prior work on HCP characterization and persistence in mAb process streams by using mass spectrometry (MS)-based methods to track HCPs through downstream processing steps for seven mAbs that were generated by five different cell lines. The results show considerable variability in HCP identities in the processing steps but extensive commonality in the identities and quantities of the most abundant HCPs in the harvests for different processes. Analysis of HCP abundance in the harvests shows a likely relationship between abundance and the reproducibility of quantification measurements and suggests that some groups of HCPs may hinder the characterization. Quantitative monitoring of HCPs persisting through purification steps coupled with the findings from the harvest analysis suggest that multiple factors, including HCP abundance and mAb-HCP interactions, can contribute to the persistence of individual HCPs and the identification of groups of common, persistent HCPs in mAb manufacturing.

Making Sense of Vagus Nerve Stimulation for Stroke.

Implantable vagus nerve stimulation, paired with high-dose occupational therapy, has been shown to be effective in improving upper limb function among patients with stroke and received regulatory approval from the US Food and Drug Administration and the Centers for Medicare & Medicaid Services. Combining nonsurgical and surgical approaches of vagus nerve stimulation in recent meta-analyses has resulted in misleading reports on the efficacy of each type of stimulation among patients with stroke. This article aims to clarify the confusion surrounding implantable vagus nerve stimulation as a poststroke treatment option, highlighting the importance of distinguishing between transcutaneous auricular vagus nerve stimulation and implantable vagus nerve stimulation. Recent meta-analyses on vagus nerve stimulation have inappropriately combined studies of fundamentally different interventions, outcome measures, and participant selection, which do not conform to methodological best practices and, hence, cannot be used to deduce the relative efficacy of the different types of vagus nerve stimulation for stroke rehabilitation. Health care providers, patients, and insurers should rely on appropriately designed research to guide well-informed decisions.

Post-Stroke Brain Health Monitoring and Optimization: A Narrative Review.

Significant advancements have been made in recent years in the acute treatment and secondary prevention of stroke. However, a large proportion of stroke survivors will go on to have enduring physical, cognitive, and psychological disabilities from suboptimal post-stroke brain health. Impaired brain health following stroke thus warrants increased attention from clinicians and researchers alike. In this narrative review based on an open timeframe search of the PubMed, Scopus, and Web of Science databases, we define post-stroke brain health and appraise the body of research focused on modifiable vascular, lifestyle, and psychosocial factors for optimizing post-stroke brain health. In addition, we make clinical recommendations for the monitoring and management of post-stroke brain health at major post-stroke transition points centered on four key intertwined domains: cognition, psychosocial health, physical functioning, and global vascular health. Finally, we discuss potential future work in the field of post-stroke brain health, including the use of remote monitoring and interventions, neuromodulation, multi-morbidity interventions, enriched environments, and the need to address inequities in post-stroke brain health. As post-stroke brain health is a relatively new, rapidly evolving, and broad clinical and research field, this narrative review aims to identify and summarize the evidence base to help clinicians and researchers tailor their own approach to integrating post-stroke brain health into their practices.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity.

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Lifetime and Acute Stress Predict Functional Outcomes Following Stroke: Findings From the Longitudinal STRONG Study.

BACKGROUND: Stroke is a sudden-onset, uncontrollable event; stroke-related stress may impede rehabilitation and recovery. Lifetime stress may sensitize patients to experiencing greater stroke-related stress and indirectly affect outcomes. We examine lifetime stress as predictor of poststroke acute stress and examine lifetime and acute stress as predictors of 3- and 12-month functional status. We also compare acute stress and baseline National Institutes of Health Stroke Scale as predictors of poststroke functional status. METHODS: Between 2016 and 2020 the STRONG Study (Stroke, Stress, Rehabilitation, and Genetics) enrolled adults with new radiologically confirmed stroke 2 to 10 days poststroke onset at 28 acute care US hospitals. Participants were interviewed 3 times: acute admission (acute stress; Acute Stress Disorder Interview), 3 months (Fugl-Meyer Upper Extremity motor impairment [Fugl-Meyer Upper Arm Assessment; N=431], modified Rankin Scale [3 months; N=542], Stroke Impact Scale-Activities of Daily Living [3 months; N=511], Lifetime Stress Exposure Inventory), and 12 months (modified Rankin Scale, N=533; Stroke Impact Scale 3.0 Activities of Daily Living; N=485; Telephone Montreal Cognitive Assessment; N=484) poststroke. Structural equation models examined whether acute stress predicted 3- and 12-month functional outcomes, and mediated an association between lifetime stress and outcomes controlling for demographics and initial National Institutes of Health Stroke Scale. Standardized betas are reported. RESULTS: Sample (N=763) was 19 to 95 years old (mean=63; SD=14.9); 448 (58.7%) were male. Acute stress scores ranged from 0 to 14 (mean, 3.52 [95% CI, 3.31-3.73]). Controlling for age, gender, baseline National Institutes of Health Stroke Scale, and race and ethnicity, higher lifetime stress predicted higher acute stress (ß=0.18, P<0.001), which predicted lower 3-month Fugl-Meyer Upper Arm Assessment scores (ß=-0.19, P<0.001), lower Stroke Impact Scale 3.0 Activities of Daily Living scores at 3 months (ß=-0.21, P<0.001) and 12 months (ß=-0.21, P<0.001), higher modified Rankin Scale scores at 3 months (ß=0.23, P<0.001) and 12 months (ß=0.22, P<0.001), and lower 12-month Telephone Montreal Cognitive Assessment scores (ß=-0.20, P<0.001). Acute stress predicted 12-month tMoCA (χ2[1]=5.29, P=0.022) more strongly, 3-month and 12-month modified Rankin Scale and SIS scores as strongly (all Ps>0.18), but Fugl-Meyer scores (χ2[1]=7.01, P=0.008) less strongly than baseline National Institutes of Health Stroke Scale. CONCLUSIONS: Lifetime stress/trauma is associated with more poststroke acute stress, which is associated with greater motor and cognitive impairment and disability 3 and 12 months poststroke. Poststroke interventions for acute stress may help mitigate stroke-related disability.

Continuous precipitation-filtration process for initial capture of a monoclonal antibody product using a four-stage countercurrent hollow fiber membrane washing step.

The significant increase in product titers, coupled with the growing focus on continuous bioprocessing, has renewed interest in using precipitation as a low-cost alternative to Protein A chromatography for the primary capture of monoclonal antibody (mAb) products. In this work, a commercially relevant mAb was purified from clarified cell culture fluid using a tubular flow precipitation reactor with dewatering and washing provided by tangential flow microfiltration. The particle morphology was evaluated using an inline high-resolution optical probe, providing quantitative data on the particle size distribution throughout the precipitation process. Data were obtained in both a lab-built 2-stage countercurrent washing system and a commercial countercurrent contacting skid that provided 4 stages of continuous washing. The processes were operated continuously for 2 h with overall mAb yield of 92 ± 3% and DNA removal of nearly 3 logs in the 4-stage system. The high DNA clearance was achieved by selective redissolution of the mAb using a low pH acetate buffer. Host cell protein clearance was 0.59 ± 0.08 logs, comparable to that based on model predictions. The process mass intensity was slightly better than typical Protein A processes and could be significantly improved by preconcentration of the antibody feed material.

Implications of AAV affinity column reuse and vector stability on product quality attributes.

In this work, the implications of AAV9 capsid design and column reuse on AAV9 vector product quality were assessed with POROS CaptureSelect (PCS) AAVX and AAV9 resins using sf9 insect cell-derived model AAV9 vectors with varying viral protein (VP) ratios. Chromatographic experiments with purified drug substance AAV9 model feeds indicated consistent vector elution profiles, independent of adeno-associated virus (AAV) VP ratio, or cycle number. In contrast, the presence of process impurities in the clarified lysate feeds resulted in clear changes in the elution patterns. This included increased aggregate content in the vector eluates over multiple cycles as well as clear differences in the performance of these affinity resin systems. The AAV9-serotype specific PCS AAV9 column, with lower vector elution pH, resulted in higher aggregate content over multiple cycles as compared to the serotype-independent PCS AAVX column. Further, the results with vectors of varying VP ratio indicated that while one vector type eluate displayed higher aggregation in both affinity columns over column reuse, the eluate with the other vector type did not exhibit changes in the aggregation profile. Interestingly, vector aggregates in the affinity eluates also contained double-stranded DNA impurities and histone proteins, with similar trends to the aggregate levels. This behavior upon column reuse indicates that these host cell impurities are likely carried over to subsequent runs due to incomplete clean-in-place (CIP). These results indicate that feed impurities, affinity resin characteristics, elution pH, column CIP, and vector stability can impact the reusability of AAV affinity columns and product quality.

Essential information for neurorecovery clinical trial design: trajectory of global disability in first 90 days post-stroke in patients discharged to acute rehabilitation facilities.

BACKGROUND: Many stroke recovery interventions are most beneficial when started 2-14d post-stroke, a time when patients become eligible for inpatient rehabilitation facilities (IRF) and neuroplasticity is often at its peak. Clinical trials focused on recovery need to expand the time from this plasticity to later outcome timepoints. METHODS: The disability course of patients with acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) enrolled in Field Administration of Stroke Therapy Magnesium (FAST-MAG) Trial with moderate-severe disability (modified Rankin Scale [mRS] 3-5) on post-stroke day4 who were discharged to IRF 2-14d post-stroke were analyzed. RESULTS: Among 1422 patients, 446 (31.4%) were discharged to IRFs, including 23.6% within 2-14d and 7.8% beyond 14d. Patients with mRS 3-5 on day4 discharged to IRFs between 2-14d accounted for 21.7% (226/1041) of AIS patients and 28.9% (110/381) of ICH patients, (p < 0.001). Among these AIS patients, age was 69.8 (± 12.7), initial NIHSS median 8 (IQR 4-12), and day4 mRS = 3 in 16.4%, mRS = 4 in 50.0%, and mRS = 5 in 33.6%. Among these ICH patients, age was 62.4 (± 11.7), initial NIHSS median 9 (IQR 5-13), day 4 mRS = 3 in 9.4%, mRS = 4 in 45.3%, and mRS = 5 in 45.3% (p < 0.01 for AIS vs ICH). Between day4 to day90, mRS improved ≥ 1 levels in 72.6% of AIS patients vs 77.3% of ICH patients, p = 0.3. For AIS, mRS improved from mean 4.17 (± 0.7) to 2.84 (± 1.5); for ICH, mRS improved from mean 4.35 (± 0.7) to 2.75 (± 1.3). Patients discharged to IRF beyond day14 had less improvement on day90 mRS compared with patients discharged between 2-14d. CONCLUSIONS: In this acute stroke cohort, nearly 1 in 4 patients with moderate-severe disability on post-stroke day4 were transferred to IRF within 2-14d post-stroke. ICH patients had nominally greater mean improvement on mRS day90 than AIS patients. This course delineation provides a roadmap for future rehabilitation intervention studies.