Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding.

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

Home Care of Tracheostomized Chronically Critically Ill Patients: A Study of Caregivers' Burden and Comparison with the Burden of Palliative Care Patients in India.

Introduction: Chronically critically ill (CCI) patients often have high costs of care and poor outcomes. Disease management programs offering home care may reduce costs but need buy-in from informal caregivers. An understanding of caregiver burden in this population is lacking. We aimed to study the caregiver burden, its change over time, and factors affecting it, in post-ICU tracheostomized patients. We compared the caregiver burden among CCI carers to that of palliative caregivers. Materials and methods: Informal caregivers of thirty chronically critically ill tracheostomized patients (CGcci) were administered the Caregivers Burden Scale (CBS) tool at discharge, 2 and 4 weeks after discharge. A one-point assessment of burden was made in 30 caregivers of patients enrolled in Pain and Palliative care clinic (CGpc). Linear mixed models for repeated measures were used to analyze score of CGcci over time and compared to the burden in physical, psychologic, economic, time, and social domains between groups. Results: All 60 caregivers were young (33-35 years), predominantly male, and children of the patients. Both CGcci and CGpc had moderate burden score of 60.5 (14.7) vs 61.5 (13), respectively. Physical burden (11.5 vs 8) was greater in CGcci (p = 0.001) compared to psychologic domain (10 vs 12.5) in CGpc (p = 0.01). Burden score over all domains in CGcci decreased rapidly from 67.5 (8) to 55 (16.5) (p = 0.001) in the first month after discharge. Conclusion: Burden of care among caregivers of tracheostomized chronically critically ill patients is comparable to those of palliative caregivers and reduces significantly with time. CTRI: 2020/11/029443 (registered on: 27/11/2020). How to cite this article: Hansda U, Tripathy S, Sahoo AK, Panda I, Shetty AP, Mitra JK, et al. Home Care of Tracheostomized Chronically Critically Ill Patients: A Study of Caregivers' Burden and Comparison with the Burden of Palliative Care Patients in India. Indian J Crit Care Med 2022;26(5):579-583.

Combination of high throughput and structural screening to assess protein stability - A screening perspective.

High throughput screening for measuring the stability of industrially relevant proteins and their variants is necessary for quality assessment in the development process. Advances in automation, measurement time and sample consumption for many techniques allow rapid measurements with minimal amount of protein. However, many methods include automated data analysis, potentially neglecting important aspects of the protein's behavior in certain conditions. In this study we implement small angle X-ray scattering (SAXS), typically not used to assess protein behavior in industrial screening, in a high throughput screening workflow to address problems of contradicting results and reproducibility among different high throughput methods. As a case study we use the lipases of Thermomyces lanuginosus and Rhizomucor miehei, widely used industrial biocatalysts. We show that even the initial analysis of the SAXS data without performing any time-consuming modelling provide valuable information on interparticle interactions. We conclude that recent advances in automation and data processing, have enabled SAXS to be used more widely as a tool to gain in-depth knowledge highly useful for protein formulation development. This is especially relevant in light of increasing accessibility to SAXS due to the commercial availability of benchtop instruments.

Self-Interactions of Two Monoclonal Antibodies: Small-Angle X-ray Scattering, Light Scattering, and Coarse-Grained Modeling.

Using light scattering (LS), small-angle X-ray scattering (SAXS), and coarse-grained Monte Carlo (MC) simulations, we studied the self-interactions of two monoclonal antibodies (mAbs), PPI03 and PPI13. With LS measurements, we obtained the osmotic second virial coefficient, B22, and the molecular weight, Mw, of the two mAbs, while with SAXS measurements, we studied the mAbs' self-interaction behavior in the high protein concentration regime up to 125 g/L. Through SAXS-derived coarse-grained representations of the mAbs, we performed MC simulations with either a one-protein or a two-protein model to predict B22. By comparing simulation and experimental results, we validated our models and obtained insights into the mAbs' self-interaction properties, highlighting the role of both ion binding and charged patches on the mAb surfaces. Our models provide useful information about mAbs' self-interaction properties and can assist the screening of conditions driving to colloidal stability.

Albumin-neprilysin fusion protein: understanding stability using small angle X-ray scattering and molecular dynamic simulations.

Fusion technology is widely used in protein-drug development to increase activity, stability, and bioavailability of protein therapeutics. Fusion proteins, like any other type of biopharmaceuticals, need to remain stable during production and storage. Due to the high complexity and additional intramolecular interactions, it is not possible to predict the behavior of fusion proteins based on the behavior the individual proteins. Therefore, understanding the stability of fusion proteins on the molecular level is crucial for the development of biopharmaceuticals. The current study on the albumin-neprilysin (HSA-NEP) fusion protein uses a combination of thermal and chemical unfolding with small angle X-ray scattering and molecular dynamics simulations to show a correlation between decreasing stability and increasing repulsive interactions, which is unusual for most biopharmaceuticals. It is also seen that HSA-NEP is not fully flexible: it is present in both compact and extended conformations. Additionally, the volume fraction of each conformation changes with pH. Finally, the presence of NaCl and arginine increases stability at pH 6.5, but decreases stability at pH 5.0.

Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2.

Characterization of a protein's conformational stability is a key step in the development of biotherapeutics, where protein unfolding leads to adverse properties, such as aggregation and loss of efficacy. Isothermal chemical denaturation (ICD) can be applied to determine chemical stability, aiming to identify the optimal solvent conditions, in terms of pH, salt concentration, and added excipients. For seven monoclonal antibodies, this study investigates the observed intrinsic protein fluorescence emission spectra as a function of denaturant concentration. Protein formulations are screened in two experimental series. We show how the peak shapes of folded and unfolded proteins are preserved under added salt (0-140 mM NaCl) and added excipients concentrations, as typically found in biotherapeutic formulations and that only minor effects in tryptophan fluorescence peak tailing are observed over a large pH range (5.5-9.0). The data of seven mAbs, where GuHCl was a suitable denaturant, are modeled using PARAFAC2. PARAFAC2, a linear decomposition method, is well suited for the data and yields robust, valid, and automated models that allow for the detection of erroneous measurements. Analysis of the errors show correlation with the well-based experimental setup, and differences in observed errors between the two experimental series. We additionally show a correction method for these outliers based on PARAFAC2 model scores, such that full transition curves can be retrieved, increasing the accuracy of any subsequent analysis.

Characterization of Native Reversible Self-Association of a Monoclonal Antibody Mediated by Fab-Fab Interaction.

The native reversible self-association of monoclonal antibodies has been associated with high viscosity, liquid-liquid, and liquid-solid phase separation. We investigated the native reversible self-association of an IgG1, which exerts this association even at low protein concentrations, in detail to gain further understanding of this phenomenon by extensive characterization of the association as a function of multiple factors, namely pH, temperature, salt concentration, and protein concentration. The nature of the self-association of the full-length IgG1 as well as the corresponding Fab and Fc fragment was studied by viz. size exclusion chromatography combined with multiangle light scattering, batch dynamic and static light scattering, analytical ultracentrifugation, small angle X-ray scattering, asymmetric flow field flow fractionation coupled with multiangle light scattering, and intrinsic fluorescence. We rationalized the self-association as a combination of hydrophobic and electrostatic interactions driven by the Fab fragments. Finally, we investigated the long-term stability of the IgG1 molecule.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

Novel non-linear curve fitting to resolve protein unfolding transitions in intrinsic fluorescence differential scanning fluorimetry.

In biotherapeutic protein research, an estimation of the studied protein's thermal stability is one of the important steps that determine developability as a function of solvent conditions. Differential Scanning Fluorimetry (DSF) can be applied to measure thermal stability. Label-free DSF measures amino acid fluorescence as a function of temperature, where conformational changes induce observable peak deformation, yielding apparent melting temperatures. The estimation of the stability parameters can be hindered in the case of multidomain, multimeric or aggregating proteins when multiple transitions partially coincide. These overlapping protein unfolding transitions are hard to evaluate by the conventional methodology, as peak maxima are shifted by convolution. We show how non-linear curve fitting of intrinsic fluorescence DSF can deconvolute highly overlapping transitions in formulation screening in a semi-automated process. The proposed methodology relies on synchronous, constrained fits of the fluorescence intensity, ratio and their derivatives, by combining linear baselines with generalized logistic transition functions. The proposed algorithm is applied to data from three proteins; a single transition, a double separated transition and a double overlapping transition. Extracted thermal stability parameters; apparent melting temperatures Tm,1, Tm,2 and melting onset temperature Tonset are obtained and compared with reference software analysis. The fits show R2 = 0.94 for single and R2 = 0.88 for separated transitions. Obtaining values and trends for Tonset in a well-described and automated way, will aid protein scientist to better evaluate the thermal stability of proteins.

Application of interpretable artificial neural networks to early monoclonal antibodies development.

The development of a new protein drug typically starts with the design, expression and biophysical characterization of many different protein constructs. The initially high number of constructs is radically reduced to a few candidates that exhibit the desired biological and physicochemical properties. This process of protein expression and characterization to find the most promising molecules is both expensive and time-consuming. Consequently, many companies adopt and implement philosophies, e.g. platforms for protein expression and formulation, computational approaches, machine learning, to save resources and facilitate protein drug development. Inspired by this, we propose the use of interpretable artificial neuronal networks (ANNs) to predict biophysical properties of therapeutic monoclonal antibodies i.e. melting temperature Tm, aggregation onset temperature Tagg, interaction parameter kD as a function of pH and salt concentration from the amino acid composition. Our ANNs were trained with typical early-stage screening datasets achieving high prediction accuracy. By only using the amino acid composition, we could keep the ANNs simple which allows for high general applicability, robustness and interpretability. Finally, we propose a novel "knowledge transfer" approach, which can be readily applied due to the simple algorithm design, to understand how our ANNs come to their conclusions.