Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling

A crucial aspect of pharmaceutical development is the demonstration of long-term stability of the drug product. Biopharmaceuticals, such as proteins or peptides in liquid formulation, are typically administered via parental routes and should be stable over the shelf life, which generally includes a storing period (e.g., two years at 5 °C) and optionally an in-use period (e.g., 28 days at 30 °C). Herein, we present a case study where chemical degradation of SAR441255, a therapeutic peptide, in different formulations in combination with primary packaging materials was analyzed under accelerated conditions to derive long-term stability predictions for the recommended storing conditions (two years at 5 °C plus 28 days at 30 °C) using advanced kinetic modeling. These predictions served as a crucial decision parameter for the entry into clinical development. Comparison with analytical data measured under long-term conditions during the subsequent development phase demonstrated a high prediction accuracy. These predictions provided stability insights within weeks that would otherwise take years using measurements under long-term stability conditions only. To our knowledge, such in silico studies on stability predictions of a therapeutic peptide using accelerated chemical degradation data and advanced kinetic modeling with comparisons to subsequently measured real-life long-term stability data have not been described in literature before

Microwell Plate-Based Dynamic Light Scattering as a High-Throughput Characterization Tool in Biopharmaceutical Development

High-throughput light scattering instruments are widely used in screening of biopharmaceutical formulations and can be easily incorporated into processes by utilizing multi-well plate formats. High-throughput plate readers are helpful tools to assess the aggregation tendency and colloidal stability of biological drug candidates based on the diffusion self-interaction parameter (kD). However, plate readers evoke issues about the precision and variability of determined data. In this article, we report about the statistical evaluation of intra- and inter-plate variability (384-well plates) for the kD analysis of protein and peptide solutions. ANOVA revealed no significant differences between the runs. In conclusion, the reliability and precision of kD was dependent on the plate position of the sample replicates and kD value. Positive kD values (57.0 mL/g, coefficients of variation (CV) 8.9%) showed a lower variability compared to negative kD values (−14.8 mL/g, CV 13.4%). The variability of kD was not reduced using more data points (120 vs. 30). A kD analysis exclusively based on center wells showed a lower CV (kD analysis within the early formulation development, screening up to 20 formulations consuming less than 50 mg of active pharmaceutical ingredient (API)

Microwell Plate-Based Dynamic Light Scattering as a High-Throughput Characterization Tool in Biopharmaceutical Development

High-throughput light scattering instruments are widely used in screening of biopharmaceutical formulations and can be easily incorporated into processes by utilizing multi-well plate formats. High-throughput plate readers are helpful tools to assess the aggregation tendency and colloidal stability of biological drug candidates based on the diffusion self-interaction parameter (kD). However, plate readers evoke issues about the precision and variability of determined data. In this article, we report about the statistical evaluation of intra- and inter-plate variability (384-well plates) for the kD analysis of protein and peptide solutions. ANOVA revealed no significant differences between the runs. In conclusion, the reliability and precision of kD was dependent on the plate position of the sample replicates and kD value. Positive kD values (57.0 mL/g, coefficients of variation (CV) 8.9%) showed a lower variability compared to negative kD values (−14.8 mL/g, CV 13.4%). The variability of kD was not reduced using more data points (120 vs. 30). A kD analysis exclusively based on center wells showed a lower CV (<2%) compared to edge wells (5–12%) or a combination of edge and center wells (2–5%). We present plate designs for kD analysis within the early formulation development, screening up to 20 formulations consuming less than 50 mg of active pharmaceutical ingredient (API)

A Conserved Hydrophobic Moiety and Helix–Helix Interactions Drive the Self-Assembly of the Incretin Analog Exendin-4

Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers

Efficient Approximation of Ligand Rotational and Translational Entropy Changes upon Binding for Use in MM-PBSA Calculations

A major uncertainty in binding free energy estimates for protein–ligand complexes by methods such as MM-PB­(GB)­SA or docking scores results from neglecting or approximating changes in the configurational entropies (Δ<i>S</i>_config.) of the solutes. In MM/PB­(GB)­SA-type calculations, Δ<i>S</i>_config. has usually been estimated in the rigid rotor, harmonic oscillator approximation. Here, we present the development of a computationally efficient method (termed BEERT) to approximate Δ<i>S</i>_config. in terms of the reduction in translational and rotational freedom of the ligand upon protein–ligand binding (Δ<i>S</i>_R/T), starting from the flexible molecule approach. We test the method successfully in binding affinity computations in connection with MM-PBSA effective energies describing changes in gas-phase interactions and solvation free energies. Compared to related work by Ruvinsky and co-workers, clustering bound ligand poses based on interactions with the protein rather than structural similarity of the poses, and an appropriate averaging over single entropies associated with an individual well of the energy landscape of the protein–ligand complex, were found to be crucial. Employing three data sets of protein–ligand complexes of pharmacologically relevant targets for validation, with up to 20, in part related ligands per data set, spanning binding free energies over a range of ≤7 kcal mol^–1, reliable and predictive linear models to estimate binding affinities are obtained in all three cases (<i>R</i>² = 0.54–0.72, <i>p</i> < 0.001, root mean squared error <i>S</i> = 0.78–1.44 kcal mol^–1; <i>q</i>² = 0.34–0.67, <i>p</i> < 0.05, root mean squared error <i>s</i>_PRESS = 1.07–1.36 kcal mol^–1). These models are markedly improved compared to considering MM-PBSA effective energies alone, scoring functions, and combinations with Δ<i>S</i>_config. estimates based on the number of rotatable bonds, rigid rotor, harmonic oscillator approximation, or interaction entropy method. As a limitation, our method currently requires a target-specific training data set to identify appropriate scaling coefficients for the MM-PBSA effective energies and BEERT Δ<i>S</i>_R/T. Still, our results suggest that the approach is a valuable, computationally more efficient complement to existing rigorous methods for estimating changes in binding free energy across structurally (weakly) related series of ligands binding to one target

Rigidity Theory-Based Approximation of Vibrational Entropy Changes upon Binding to Biomolecules

We introduce a computationally efficient approximation of vibrational entropy changes (Δ<i>S</i>_vib) upon binding to biomolecules based on rigidity theory. From constraint network representations of the binding partners, Δ<i>S</i>_vib is estimated from changes in the number of low frequency (“spongy”) modes with respect to changes in the networks’ coordination number. Compared to Δ<i>S</i>_vib computed by normal-mode analysis (NMA), our approach yields significant and good to fair correlations for data sets of protein–protein and protein–ligand complexes. Our approach could be a valuable alternative to NMA-based Δ<i>S</i>_vib computation in end-point (free) energy methods

Hot Spots and Transient Pockets: Predicting the Determinants of Small-Molecule Binding to a Protein–Protein Interface

Protein–protein interfaces are considered difficult targets for small-molecule protein–protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein–protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein–protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs

Dual Glucagon-like Peptide 1 (GLP-1)/Glucagon Receptor Agonists Specifically Optimized for Multidose Formulations

Novel peptidic dual agonists of the glucagon-like peptide 1 (GLP-1) and glucagon receptor are reported to have enhanced efficacy over pure GLP-1 receptor agonists with regard to treatment of obesity and diabetes. We describe novel exendin-4 based dual agonists designed with an activity ratio favoring the GLP-1 versus the glucagon receptor. As result of an iterative optimization procedure that included molecular modeling, structural biological studies (X-ray, NMR), peptide design and synthesis, experimental activity, and solubility profiling, a candidate molecule was identified. Novel SAR points are reported that allowed us to fine-tune the desired receptor activity ratio and increased solubility in the presence of antimicrobial preservatives, findings that can be of general applicability for any peptide discovery project. The peptide was evaluated in chronic <i>in vivo</i> studies in obese diabetic monkeys as translational model for the human situation and demonstrated favorable blood glucose and body weight lowering effects

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software