The development of therapeutic proteins can be hindered by poor decision-making strategies in the early stage

In this study we address two major issues related to the current development process of therapeutic proteins and their characterization. First, due to limited samples amounts, the selection of lead molecules in the early stages is often based on the results from a limited physicochemical characterization. The latter can be based on measurements of only 2-3 parameters, e.g. protein melting temperature, protein aggregation temperature, and is usually performed in only one buffer, e.g. PBS. The hypothesis we present is that such approach can lead to the rejection of lead candidates that can still be manufacturable and can move on to clinical trials. The second matter we address are the often-reported correlations between protein physicochemical parameters in the literature. We propose that such correlations can be found only in a small sample population, e.g. one protein in different solution conditions or different proteins from the same class. However, we expect that such correlations would not be valid in a large population, including various protein structures and solution conditions. In order to address the above-mentioned issues, we created the PIPPI consortium (http://www.pippi.kemi.dtu.dk) and applied systematic approach to map the physicochemical properties of a wide range of proteins and extensively study their stability as a function of the solution conditions. We show that promising therapeutic protein lead candidate can appear as non-manufacturable when only limited physicochemical characterization is performed, e.g. a few methods are used and only a few solution conditions are tested. Therefore, the rejection rate during early-stage development can be improved by more thorough physicochemical characterization. Moreover, only weak linear correlations between biophysical properties of proteins are observed in a large populations. This suggests that the often-reported correlations between parameters describing the protein stability are not representative of a global population. Understanding the connections between various physiochemical parameters would require a systematic database which is currently in development by the PIPPI consortium

The Effect of Point Mutations on the Biophysical Properties of an Antimicrobial Peptide:Development of a Screening Protocol for Peptide Stability Screening

Therapeutic peptides and proteins show enormous potential in the pharmaceutical market, but high costs in discovery and development are limiting factors so far. Single or multiple point mutations are commonly introduced in protein drugs to increase their binding affinity or selectivity. They can also induce adverse properties, which might be overlooked in a functional screen, such as a decreased colloidal or thermal stability, leading to problems in later stages of the development. In this study, we address the effect of point mutations on the stability of the 4.4 kDa antimicrobial peptide plectasin, as a case study. We combined a systematic high-throughput biophysical screen of the peptide thermal and colloidal stability using dynamic light scattering and differential scanning calorimetry with structure-based methods including small-angle X-ray scattering, analytical ultracentrifugation, and nuclear magnetic resonance spectroscopy. Additionally, we applied molecular dynamics simulations to link obtained protein stability parameters to the protein’s molecular structure. Despite their predicted structural similarities, all four plectasin variants showed substantially different behavior in solution. We observed an increasing propensity of plectasin to aggregate at a higher pH, and the introduced mutations influenced the type of aggregation. Our strategy for systematically assessing the stability and aggregation of protein drugs is generally applicable and is of particular relevance, given the increasing number of protein drugs in development

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

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

Formulation, Delivery and Stability of Bone Morphogenetic Proteins for Effective Bone Regeneration

Drug Delivery Technolog