Stability of Protein Pharmaceuticals: Recent Advances.

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Data-driven dynamical coarse-graining for condensed matter systems.

Simulations of condensed matter systems often focus on the dynamics of a few distinguished components but require integrating the full system. A prime example is a molecular dynamics simulation of a (macro)molecule in a solution, where the molecule(s) and the solvent dynamics need to be integrated, rendering the simulations computationally costly and often unfeasible for physically/biologically relevant time scales. Standard coarse graining approaches can reproduce equilibrium distributions and structural features but do not properly include the dynamics. In this work, we develop a general data-driven coarse-graining methodology inspired by the Mori-Zwanzig formalism, which shows that macroscopic systems with a large number of degrees of freedom can be described by a few relevant variables and additional noise and memory terms. Our coarse-graining method consists of numerical integrators for the distinguished components, where the noise and interaction terms with other system components are substituted by a random variable sampled from a data-driven model. The model is parameterized using data from multiple short-time full-system simulations, and then, it is used to run long-time simulations. Applying our methodology to three systems-a distinguished particle under a harmonic and a bistable potential and a dimer with two metastable configurations-the resulting coarse-grained models are capable of reproducing not only the equilibrium distributions but also the dynamic behavior due to temporal correlations and memory effects. Remarkably, our method even reproduces the transition dynamics between metastable states, which is challenging to capture correctly. Our approach is not constrained to specific dynamics and can be extended to systems beyond Langevin dynamics, and, in principle, even to non-equilibrium dynamics.

The Storage and In-Use Stability of mRNA Vaccines and Therapeutics: Not A Cold Case.

The remarkable impact of mRNA vaccines on mitigating disease and improving public health has been amply demonstrated during the COVID-19 pandemic. Many new mRNA-based vaccine and therapeutic candidates are in development, yet the current reality of their stability limitations requires their frozen storage. Numerous challenges remain to improve formulated mRNA stability and enable refrigerator storage, and this review provides an update on developments to tackle this multi-faceted stability challenge. We describe the chemistry underlying mRNA degradation during storage and highlight how lipid nanoparticle (LNP) formulations are a double-edged sword: while LNPs protect mRNA against enzymatic degradation, interactions with and between LNP excipients introduce additional risks for mRNA degradation. We also discuss strategies to improve mRNA stability both as a drug substance (DS) and a drug product (DP) including the (1) design of the mRNA molecule (nucleotide selection, primary and secondary structures), (2) physical state of the mRNA-LNP complexes, (3) formulation composition and purity of the components, and (4) DS and DP manufacturing processes. Finally, we summarize analytical control strategies to monitor and assure the stability of mRNA-based candidates, and advocate for an integrated analytical and formulation development approach to further improve their storage, transport, and in-use stability profiles.

Applying lessons learned from nanomedicines to understand rare hypersensitivity reactions to mRNA-based SARS-CoV-2 vaccines.

After over a billion of vaccinations with messenger RNA-lipid nanoparticle (mRNA-LNP) based SARS-CoV-2 vaccines, anaphylaxis and other manifestations of hypersensitivity can be considered as very rare adverse events. Although current recommendations include avoiding a second dose in those with first-dose anaphylaxis, the underlying mechanisms are unknown; therefore, the risk of a future reaction cannot be predicted. Given how important new mRNA constructs will be to address the emergence of new viral variants and viruses, there is an urgent need for clinical approaches that would allow a safe repeated immunization of high-risk individuals and for reliable predictive tools of adverse reactions to mRNA vaccines. In many aspects, anaphylaxis symptoms experienced by the affected vaccine recipients resemble those of infusion reactions to nanomedicines. Here we share lessons learned over a decade of nanomedicine research and discuss the current knowledge about several factors that individually or collectively contribute to infusion reactions to nanomedicines. We aim to use this knowledge to inform the SARS-CoV-2 lipid-nanoparticle-based mRNA vaccine field.

Ongoing Challenges to Develop High Concentration Monoclonal Antibody-based Formulations for Subcutaneous Administration: Quo Vadis?

Although many subcutaneously (s.c.) delivered, high-concentration antibody formulations (HCAF) have received regulatory approval and are widely used commercially, formulation scientists are still presented with many ongoing challenges during HCAF development with new mAb and mAb-based candidates. Depending on the specific physicochemical and biological properties of a particular mAb-based molecule, such challenges vary from pharmaceutical attributes e.g., stability, viscosity, manufacturability, to clinical performance e.g., bioavailability, immunogenicity, and finally to patient experience e.g., preference for s.c. vs. intravenous delivery and/or preferred interactions with health-care professionals. This commentary focuses on one key formulation obstacle encountered during HCAF development: how to maximize the dose of the drug? We examine methodologies for increasing the protein concentration, increasing the volume delivered, or combining both approaches together. We discuss commonly encountered hurdles, i.e., physical protein instability and solution volume limitations, and we provide recommendations to formulation scientists to facilitate their development of s.c. administered HCAF with new mAb-based product candidates.

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model.

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.

mRNA-lipid nanoparticle COVID-19 vaccines: Structure and stability.

A drawback of the current mRNA-lipid nanoparticle (LNP) COVID-19 vaccines is that they have to be stored at (ultra)low temperatures. Understanding the root cause of the instability of these vaccines may help to rationally improve mRNA-LNP product stability and thereby ease the temperature conditions for storage. In this review we discuss proposed structures of mRNA-LNPs, factors that impact mRNA-LNP stability and strategies to optimize mRNA-LNP product stability. Analysis of mRNA-LNP structures reveals that mRNA, the ionizable cationic lipid and water are present in the LNP core. The neutral helper lipids are mainly positioned in the outer, encapsulating, wall. mRNA hydrolysis is the determining factor for mRNA-LNP instability. It is currently unclear how water in the LNP core interacts with the mRNA and to what extent the degradation prone sites of mRNA are protected through a coat of ionizable cationic lipids. To improve the stability of mRNA-LNP vaccines, optimization of the mRNA nucleotide composition should be prioritized. Secondly, a better understanding of the milieu the mRNA is exposed to in the core of LNPs may help to rationalize adjustments to the LNP structure to preserve mRNA integrity. Moreover, drying techniques, such as lyophilization, are promising options still to be explored.

Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model.

In this study, we investigate uncertainties in a large eddy simulation of the atmosphere, employing modern uncertainty quantification methods that have hardly been used yet in this context. When analysing the uncertainty of model results, one can distinguish between uncertainty related to physical parameters whose values are not exactly known, and uncertainty related to modelling choices such as the selection of numerical discretization methods, of the spatial domain size and resolution, and the use of different model formulations. While the former kind is commonly studied e.g. with forward uncertainty propagation, we explore the use of such techniques to also assess the latter kind. From a climate modelling perspective, uncertainties in the convective response and cloud formation are of particular interest, since these affect the cloud-climate feedback, one of the dominant sources of uncertainty in current climate models. Therefore we analyse the DALES model in the RICO case, a well-studied convection benchmark. We use the VECMA toolkit for uncertainty propagation, assessing uncertainties stemming from physical parameters as well as from modelling choices. We find substantial uncertainties due to small random initial state perturbations, and that the choice of advection scheme is the most influential of the modelling choices we assessed. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Addressing the Cold Reality of mRNA Vaccine Stability.

As mRNA vaccines became the frontrunners in late-stage clinical trials to fight the COVID-19 pandemic, challenges surrounding their formulation and stability became readily apparent. In this commentary, we first describe company proposals, based on available public information, for the (frozen) storage of mRNA vaccine drug products across the vaccine supply chain. We then review the literature on the pharmaceutical stability of mRNA vaccine candidates, including attempts to improve their stability, analytical techniques to monitor their stability, and regulatory guidelines covering product characterization and storage stability. We conclude that systematic approaches to identify the key physicochemical degradation mechanism(s) of formulated mRNA vaccine candidates are currently lacking. Rational design of optimally stabilized mRNA vaccine formulations during storage, transport, and administration at refrigerated or ambient temperatures should thus have top priority in the pharmaceutical development community. In addition to evidence of human immunogenicity against multiple viral pathogens, including compelling efficacy results against COVID-19, another key strength of the mRNA vaccine approach is that it is readily adaptable to rapidly address future outbreaks of new emerging infectious diseases. Consequently, we should not wait for the next pandemic to address and solve the challenges associated with the stability and storage of formulated mRNA vaccines.

The Science is There: Key Considerations for Stabilizing Viral Vector-Based Covid-19 Vaccines.

Once Covid-19 vaccines become available, 5-10 billion vaccine doses should be globally distributed, stored and administered. In this commentary, we discuss how this enormous challenge could be addressed for viral vector-based Covid-19 vaccines by learning from the wealth of formulation development experience gained over the years on stability issues related to live attenuated virus vaccines and viral vector vaccines for other diseases. This experience has led -over time- to major improvements on storage temperature, shelf-life and in-use stability requirements. First, we will cover work on 'classical' live attenuated virus vaccines as well as replication competent viral vector vaccines. Subsequently, we address replication deficient viral vector vaccines. Freeze drying and storage at 2-8 °C with a shelf life of years has become the norm. In the case of pandemics with incredibly high and urgent product demands, however, the desire for rapid and convenient distribution chains combined with short end-user storage times require that liquid formulations with shelf lives of months stored at 2-8 °C be considered. In confronting this "perfect storm" of Covid-19 vaccine stability challenges, understanding the many lessons learned from decades of development and manufacturing of live virus-based vaccines is the shortest path for finding promising and rapid solutions.

Formulation of Cell-Based Medicinal Products: A Question of Life or Death?

The formulation of cell-based medicinal products (CBMPs) poses major challenges because of their complexity, heterogeneity, interaction with their environment (e.g., the formulation buffer, interfaces), and susceptibility to degradation. These challenges can be quality, safety, and efficacy related. In this commentary we discuss the current status in formulation strategies of off-the-shelf and non-off-the-shelf (patient-specific) CBMPs and highlight advantages and disadvantages of each strategy. Analytical tools for the characterization and stability assessment of CBMP formulations are addressed as well. Finally, we discuss unmet needs and make some recommendations regarding the formulation of CBMPs.

The impact of uncertainty on predictions of the CovidSim epidemiological code.

Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to contain the rapid spread of the COVID-19 pandemic during March and April 2020; however, CovidSim contains several sources of uncertainty that affect the quality of its predictions: parametric uncertainty, model structure uncertainty and scenario uncertainty. Here we report on parametric sensitivity analysis and uncertainty quantification of the code. From the 940 parameters used as input into CovidSim, we find a subset of 19 to which the code output is most sensitive-imperfect knowledge of these inputs is magnified in the outputs by up to 300%. The model displays substantial bias with respect to observed data, failing to describe validation data well. Quantifying parametric input uncertainty is therefore not sufficient: the effect of model structure and scenario uncertainty must also be properly understood.

Shifting Paradigms Revisited: Biotechnology and the Pharmaceutical Sciences.

In 2003, Crommelin et al. published an article titled: "Shifting paradigms: biopharmaceuticals versus low molecular weight drugs" (https://doi.org/10.1016/S0378-5173(03)00376-4). In the present commentary, 16 years later, we discuss pharmaceutically relevant aspects of the evolution of biologics since then. First, we discuss the increasing repertoire of biologics, in particular, the rapidly growing monoclonal antibody family and the advent of advanced therapy medicinal products. Next, we discuss trends in formulation and characterization as well as summarize our current insights into immunogenicity of biologics. We spend a separate section on new product(ion) paradigms for biologics, such as cell-free production systems, production of advanced therapy medicinal products, and downscaled production approaches. Furthermore, we share our views on issues related to reaching the patient, including routes and techniques of administration, alternative development models for affordable biologics, biosimilars, and handling of biologics. In the concluding section, we outline outstanding issues and make some suggestions for resolving those.

The role of liposomes in clinical nanomedicine development. What now? Now what?

The rapid rise in interest in 'nanomedicines' in the academic world over the last twenty years and the claims of success led to calls for reflection. The main body of text of this Commentary will be on answering the question: 'where to go with nanomedicines'? Research priorities for the future will be outlined based on experience with the most successful nanomedicines family within the broad field of nanomedicine so far: liposomes. An analysis of currently clinically tested, approved and marketed liposome-drug combinations provides these insights.

Identification of Formaldehyde-Induced Modifications in Diphtheria Toxin.

Diphtheria toxoid is produced by detoxification of diphtheria toxin with formaldehyde. This study was performed to elucidate the chemical nature and location of formaldehyde-induced modifications in diphtheria toxoid. Diphtheria toxin was chemically modified using 4 different reactions with the following reagents: (1) formaldehyde and NaCNBH3, (2) formaldehyde, (3) formaldehyde and NaCNBH3 followed by formaldehyde and glycine, and (4) formaldehyde and glycine. The modifications were studied by SDS-PAGE, primary amino group determination, and liquid chromatography-electrospray mass spectrometry of chymotryptic digests. Reaction 1 resulted in quantitative dimethylation of all lysine residues. Reaction 2 caused intramolecular cross-links, including the NAD+-binding cavity and the receptor-binding site. Moreover, A fragments and B fragments were cross-linked by formaldehyde on part of the diphtheria toxoid molecules. Reaction 3 resulted in formaldehyde-glycine attachments, including in shielded areas of the protein. The detoxification reaction typically used for vaccine preparation (reaction 4) resulted in a combination of intramolecular cross-links and formaldehyde-glycine attachments. Both the NAD+-binding cavity and the receptor-binding site of diphtheria toxin were chemically modified. Although CD4+ T-cell epitopes were affected to some extent, one universal CD4+ T-cell epitope remained almost completely unaltered by the treatment with formaldehyde and glycine.

Rare event simulation for steady-state probabilities via recurrency cycles.

We develop a new algorithm for the estimation of rare event probabilities associated with the steady-state of a Markov stochastic process with continuous state space Rd and discrete time steps (i.e., a discrete-time Rd-valued Markov chain). The algorithm, which we coin Recurrent Multilevel Splitting (RMS), relies on the Markov chain's underlying recurrent structure, in combination with the Multilevel Splitting method. Extensive simulation experiments are performed, including experiments with a nonlinear stochastic model that has some characteristics of complex climate models. The numerical experiments show that RMS can boost the computational efficiency by several orders of magnitude compared to the Monte Carlo method.

Report of the AAPS Guidance Forum on the FDA Draft Guidance for Industry: "Drug Products, Including Biological Products, that Contain Nanomaterials".

To guide developers of innovative and generic drug products that contain nanomaterials, the U.S. Food and Drug Administration issued the draft guidance for industry titled: "Drug Products, Including Biological Products, that Contain Nanomaterials" in December 2017. During the AAPS Guidance Forum on September 11, 2018, participants from industry, academia, and regulatory bodies discussed this draft guidance in an open setting. Two questions raised by the AAPS membership were discussed in more detail: what is the appropriate regulatory pathway for approval of drug products containing nanomaterials, and how to determine critical quality attributes (CQAs) for nanomaterials? During the meeting, clarification was provided on how the new FDA center-led guidance relates to older, specific nanomaterial class, or specific product-related guidances. The lively discussions concluded with some clear observations and recommendations: (I) Important lessons can be learned from how CQAs were determined for, e.g., biologics. (II) Publication of ongoing scientific discussions on strategies and studies determining CQAs of drug products containing nanomaterials will significantly strengthen the science base on this topic. Furthermore, (III) alignment on a global level on how to address new questions regarding nanomedicine development protocols will add to efficient development and approval of these much needed candidate nanomedicines (innovative and generic). Public meetings such as the AAPS Guidance Forum may serve as the place to have these discussions.

Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees.

In this study, we present a novel method for quantifying dependencies in multivariate datasets, based on estimating the Rényi mutual information by minimum spanning trees (MSTs). The extent to which random variables are dependent is an important question, e.g., for uncertainty quantification and sensitivity analysis. The latter is closely related to the question how strongly dependent the output of, e.g., a computer simulation, is on the individual random input variables. To estimate the Rényi mutual information from data, we use a method due to Hero et al. that relies on computing minimum spanning trees (MSTs) of the data and uses the length of the MST in an estimator for the entropy. To reduce the computational cost of constructing the exact MST for large datasets, we explore methods to compute approximations to the exact MST, and find the multilevel approach introduced recently by Zhong et al. (2015) to be the most accurate. Because the MST computation does not require knowledge (or estimation) of the distributions, our methodology is well-suited for situations where only data are available. Furthermore, we show that, in the case where only the ranking of several dependencies is required rather than their exact value, it is not necessary to compute the Rényi divergence, but only an estimator derived from it. The main contributions of this paper are the introduction of this quantifier of dependency, as well as the novel combination of using approximate methods for MSTs with estimating the Rényi mutual information via MSTs. We applied our proposed method to an artificial test case based on the Ishigami function, as well as to a real-world test case involving an El Nino dataset.