Predicting multimodal chromatography of therapeutic antibodies using multiscale modeling.

Multimodal chromatography has emerged as a powerful method for the purification of therapeutic antibodies. However, process development of this separation technique remains challenging because of an intricate and molecule-specific interaction towards multimodal ligands, leading to time-consuming and costly experimental optimization. This study presents a multiscale modeling approach to predict the multimodal chromatographic behavior of therapeutic antibodies based on their sequence information. Linear gradient elution (LGE) experiments were performed on an anionic multimodal resin for 59 full-length antibodies, including five different antibody formats at pH 5.0, 6.0, and 7.0 that were used for parameter determination of a linear adsorption model at low loading density conditions. Quantitative structure-property relationship (QSPR) modeling was utilized to correlate the adsorption parameters with up to 1374 global and local physicochemical descriptors calculated from antibody homology models. The final QSPR models employed less than eight descriptors per model and demonstrated high training accuracy (R² > 0.93) and reasonable test set prediction accuracy (Q² > 0.83) for the adsorption parameters. Model evaluation revealed the significance of electrostatic interaction and hydrophobicity in determining the chromatographic behavior of antibodies, as well as the importance of the HFR3 region in antibody binding to the multimodal resin. Chromatographic simulations using the predicted adsorption parameters showed good agreement with the experimental data for the vast majority of antibodies not employed during the model training. The results of this study demonstrate the potential of sequence-based prediction for determining chromatographic behavior in therapeutic antibody purification. This approach leads to more efficient and cost-effective process development, providing a valuable tool for the biopharmaceutical industry.

Risk Factors Associated with Incident Vertebral Fractures in Steroid-treated Males with Duchenne Muscular Dystrophy.

PURPOSE: Prevention of fractures is an unmet need in glucocorticoid (GC)-treated Duchenne muscular dystrophy. This study explored factors associated with incident vertebral fractures (VFs) to inform future fracture prevention efforts. METHODS: VFs were evaluated prospectively at study baseline and 12 months on lateral spine radiographs in participants aged 4 to 25 years with Duchenne muscular dystrophy. Clinical factors were analyzed for their association with the change in Spinal Deformity Index (sum of the Genant-defined VF grades from T4 to L4) between baseline and 12 months. RESULTS: Thirty-eight males were evaluated (mean ± SD age at baseline 11.0 ± 3.6 years; mean ± SD GC duration at baseline 4.1 ± 3.1 years; 74% ambulatory). Nine of 38 participants (24%) had 17 incident VFs, of which 3/17 VFs (18%) were moderate/severe. Participants with 12-month incident VF had lower mean ± SD baseline lumbar spine areal bone mineral density Z-scores (-2.9 ± 1.0 vs -1.9 ± 1.1; P = .049) and lower total body less head areal bone mineral density Z-scores (-3.1 ± 1.2 vs -1.6 ± 1.7; P = .036). Multivariable linear regression showed that at least 1 VF at baseline (P < .001), a higher number of antecedent non-VF (P < .001), and greater bone age delay at baseline (P = .027) were significant predictors of an increase in the Spinal Deformity Index from baseline to 12 months. CONCLUSION: The observation that ≥ 1 prevalent VF and/or non-VF were the strongest predictors of incident VFs at 12 months supports the need for prevention of first fractures in this high-risk setting. Bone age delay, a marker of GC exposure, may assist in the prioritization of patients in efforts to prevent first fractures.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography.

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.

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

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics.

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography.

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies.

Risk factors associated with prevalent vertebral fractures in Duchenne muscular dystrophy.

Patients with Duchenne muscular dystrophy (DMD) have a high fracture burden due to progressive myopathy and steroid-induced osteoporosis. This study in males with DMD showed that markers of systemic glucocorticoid exposure including shorter stature, greater bone age delay, and lower lumbar spine bone mineral density were associated with spine fragility. INTRODUCTION: Fragility  fractures are frequent in DMD. The purpose of this study was to identify clinical factors associated with prevalent vertebral fractures (VF) in boys, teens/young adults with Duchenne muscular dystrophy (DMD). METHODS: This was a cross-sectional study of males aged 4-25 years with DMD. VF were evaluated using the modified Genant semi-quantitative method on T4-L4 lateral spine radiographs. Areal bone mineral density (aBMD) was measured at the lumbar spine (LS) and used to estimate volumetric BMD (vBMD). Clinical factors were analyzed for their association with the Spinal Deformity Index (SDI, the sum of the Genant grades). RESULTS: Sixty participants were enrolled (mean age 11.5 years, range 5.4-19.5). Nineteen participants (32%) had a total of 67 VF; 23/67 VF (34%) were moderate or severe. Participants with VF were shorter (mean height Z-score ± standard deviation: - 3.1 ± 1.4 vs. - 1.8 ± 1.4, p = 0.001), had longer glucocorticoid exposure (mean duration 6.0 ± 3.3 vs. 3.9 ± 3.3 years, p = 0.027), greater bone age (BA) delay (mean BA to chronological age difference - 3.2 ± 3.4 vs. - 1.3 ± 1.2 years, p = 0.035), and lower LSaBMD Z-scores (mean - 3.0 ± 1.0 vs. - 2.2 ± 1.2, p = 0.023). There was no difference in LSvBMD Z-scores. Multivariable Poisson regression showed that every 0.1 mg/kg/day increment in average glucocorticoid daily dose was associated with a 1.4-fold SDI increase (95% confidence interval: 1.1-1.7, p = 0.013). Greater BA delay (p < 0.001), higher weight Z-score (p = 0.004), decreased height Z-score (p = 0.025), and lower LSvBMD Z-score (p = 0.025) were also associated with SDI increase. CONCLUSION: Readily measurable clinical variables were associated with prevalent VF in males with glucocorticoid-treated DMD. These variables may be useful to identify candidates for primary osteoporosis prevention after glucocorticoid initiation.

Integrated process model for the prediction of biopharmaceutical manufacturing chromatography and adjustment steps.

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units.

Transient vs Permanent Congenital Hypothyroidism in Ontario, Canada: Predictive Factors and Scoring System.

CONTEXT: The apparent increased incidence of congenital hypothyroidism (CH) is partly due to increased detection of transient disease. OBJECTIVE: This work aims to identify predictors of transient CH (T-CH) and establish a predictive tool for its earlier differentiation from permanent CH (P-CH). METHODS: A retrospective cohort study was conducted of patients diagnosed with CH from 2006 to 2015 through Newborn Screening Ontario (NSO). RESULTS: Of 469 cases, 360 (76.8%) were diagnosed with P-CH vs 109 (23.2%) with T-CH. Doses of levothyroxine predicting T-CH were less than 3.9 µg/kg at age 6 months, less than 3.0 µg/kg at ages 1 and 2 years, and less than 2.5 µg/kg at age 3 years. Descriptive statistics and multivariable logistic modeling demonstrated several diverging key measures between patients with T-CH vs P-CH, with optimal stratification at age 1 year. Thyroid imaging was the strongest predictor (P < .001). Excluding imaging, significant predictors in the first year of life included thyroxine dose/kg (P < .001-.002), increase in thyrotropin (TSH) above the reference interval during treatment (P = .002), screening TSH (P = .03), and a history of maternal thyroid disease (P = .02). Based on the 1-year model without imaging, a risk score was developed to identify children with T-CH who may benefit from an earlier trial off therapy, to reduce excess medicalization and health care costs. CONCLUSION: A levothyroxine dose of less than 3 µg/kg at ages 1 and 2 years and less than 2.5 µg/kg at age 3 years can be predictive of T-CH. A novel risk score was developed that can be clinically applied to predict the likelihood of a successful trial off therapy for a given patient at age 1 year.

In silico process characterization for biopharmaceutical development following the quality by design concept.

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

Variability in Emergency Management of Pediatric Diabetic Ketoacidosis.

OBJECTIVES: Our aim in this study was to identify pediatric patients presenting in diabetic ketoacidosis (DKA) who received initial treatment inconsistent with current guidelines and then to track associated complications. METHODS: This retrospective chart review examined 47 pediatric patients admitted to our institution with DKA, including those transferred from community hospitals. Primary outcome measures were exposure to treatment-related risk factors for cerebral edema (CE), including administration of an intravenous (IV) insulin bolus, IV bicarbonate, insulin within 1 hour of IV fluid start and non-NPO (nil per os) status. RESULTS: Seventy-five percent of patients were exposed to at least one management practice that deviated from guidelines. Thirty-four percent of patients were exposed to a treatment-related risk factor for CE, with a significantly higher prevalence in those presenting to community centres (52% vs 19%; p=0.02). There were no radiologically confirmed cases of CE. CONCLUSIONS: Despite the presence of multiple evidence-based guidelines, a significant proportion of children---and especially those who present to community centres---are exposed to practices that increase CE risk.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography.

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Cross-scale quality assessment of a mechanistic cation exchange chromatography model.

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications.

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.

Prediction of congenital hypothyroidism based on initial screening thyroid-stimulating-hormone.

BACKGROUND: In thyroid-stimulating-hormone (TSH)-based newborn congenital hypothyroidism (CH) screening programs, the optimal screening-TSH cutoff level is critical to ensuring that true cases of CH are not missed. Screening-TSH results can also be used to predict the likelihood of CH and guide appropriate clinical management. The purpose of this study is to evaluate the predictive value of various screening-TSH levels in predicting a diagnosis of CH in the Ontario Newborn Screening Program (ONSP). METHODS: The initial screening and follow-up data of 444,744 full term infants born in Ontario, Canada from April 1, 2006 to March 31, 2010 were analyzed. Confirmed CH cases were based on local endocrinologists' report and initiation of thyroxine treatment. RESULTS: There were a total of 541 positive screening tests (~1/822 live births) of which 296 were true positives (~1:1,500 live births). Subjects were further subdivided based on screening-TSH and positive predictive values (PPV) were calculated. Twenty four percent in the 17-19.9 mIU/L range were true positives. In the 17-30 mIU/L range, 29 % were true positives with a significantly higher PPV for those sampled after (43 %) rather than before (25 %) 28 h of age (p < 0.02). Seventy three percent of neonates with an initial screening-TSH of ≥ 30 mIU/L and 97 % of those with ≥ 40 mIU/L were later confirmed to have CH. CONCLUSIONS: Infants with modestly elevated screening positive TSH levels between 17 and 19.9 mIU/L have a significant risk (24 %) of having CH. The very high frequency of true positives in term newborns with initial TSH values ≥ 30mIU/L suggests that this group should be referred directly to a pediatric endocrinologist in an effort to expedite further assessment and treatment. Screen positives with a modestly elevated TSH values (17-19.9 mIU/L) need to be examined in more detail with extended follow-up data to determine if they have transient or permanent CH.