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.

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.

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'.

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.

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.

Reflections on the Future of Pharmaceutical Public-Private Partnerships: From Input to Impact.

Public Private Partnerships (PPPs) are multiple stakeholder partnerships designed to improve research efficacy. We focus on PPPs in the biomedical/pharmaceutical field, which emerged as a logical result of the open innovation model. Originally, a typical PPP was based on an academic and an industrial pillar, with governmental or other third party funding as an incentive. Over time, other players joined in, often health foundations, patient organizations, and regulatory scientists. This review discusses reasons for initiating a PPP, focusing on precompetitive research. It looks at typical expectations and challenges when starting such an endeavor, the characteristics of PPPs, and approaches to assessing the success of the concept. Finally, four case studies are presented, of PPPs differing in size, geographical spread, and research focus.

The regulator's perspective: How should new therapies and follow-on products for MS be clinically evaluated in the future?

BACKGROUND: Although there is still no cure for multiple sclerosis (MS), the introduction of several innovative drugs with modes of action different from that of the existing drug arsenal and the progress in monitoring disease progression by imaging and using biomarkers are currently causing a knowledge surge. This provides opportunities for improving patient disease management. New therapies are also under development and pose challenges to the regulatory bodies regarding the optimal design of clinical trials with more patient-focused clinical endpoints. Moreover, with the upcoming patent expiry of some of the key first-line MS treatments in Europe, regulatory bodies will also face the challenge of recommending marketing authorisation for generic and abridged versions based on appropriate requirements for demonstrating equality/similarity to the innovator's product. OBJECTIVE: The goal of this article is to improve the understanding of the relevant guidance documents of the European Medicines Agency (EMA) on clinical investigation of medicinal products and to highlight the issues that the agency will need to clarify regarding follow-on products of first-line MS treatments. CONCLUSION: Today, it is clear that close collaboration between patients, healthcare professionals, regulatory bodies and industry is crucial for developing new safe and effective drugs, which satisfy the needs of MS patients.

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.

Controlled release of octreotide and assessment of peptide acylation from poly(D,L-lactide-co-hydroxymethyl glycolide) compared to PLGA microspheres.

PURPOSE: To investigate the in vitro release of octreotide acetate, a somatostatin agonist, from microspheres based on a hydrophilic polyester, poly(D,L-lactide-co-hydroxymethyl glycolide) (PLHMGA). METHODS: Spherical and non-porous octreotide-loaded PLHMGA microspheres (12 to 16 µm) and loading efficiency of 60-70% were prepared by a solvent evaporation. Octreotide release profiles were compared with commercial PLGA formulation (Sandostatin LAR(®)); possible peptide modification with lactic, glycolic and hydroxymethyl glycolic acid units was monitored. RESULTS: PLHMGA microspheres showed burst release (~20%) followed by sustained release for 20-60 days, depending on the hydrophilicity of the polymer. Percentage of released loaded peptide was high (70-90%); > 60% of released peptide was native octreotide. PLGA microspheres did not show peptide release for the first 10 days, after which it was released in a sustained manner over the next 90 days; > 75% of released peptides were acylated adducts. CONCLUSIONS: PLHMGA microspheres are promising controlled systems for peptides with excellent control over release kinetics. Moreover, substantially less peptide modification occurred in PLHMGA than in PLGA microspheres.

Normal forms for reduced stochastic climate models.

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen-Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability.

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.

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.

DNA nuclear targeting sequences for non-viral gene delivery.

PURPOSE: To evaluate if introduction of DNA nuclear Targeting Sequences (DTS; i.e. recognition sequences for endogenous DNA-binding proteins) in plasmid DNA (pDNA) leads to increased transfection efficiency of non-viral gene delivery by virtue of enhanced nuclear import of the pDNA. METHODS: A set of DTS was identified and cloned into EGFP-reporter plasmids controlled by the CMV-promoter. These pDNA constructs were delivered into A431 and HeLa cells using standard electroporation, pEI-based polyfection or lipofection methods. The amount of pDNA delivered into the nucleus was determined by qPCR; transfection efficiency was determined by flow cytometry. RESULTS: Neither of these DTS increased transgene expression. We varied several parameters (mitotic activity, applied dose and delivery strategy), but without effect. Although upregulated transgene expression was observed after stimulation with TNF-α, this effect could be ascribed to non-specific upregulation of transcription rather than enhanced nuclear import. Nuclear copy numbers of plasmids containing or lacking a DTS did not differ significantly after lipofectamine-based transfection in dividing and non-dividing cells. CONCLUSION: No beneficial effects of DTS on gene expression or nuclear uptake were observed in this study.

The therapeutic equivalence of complex drugs.

When the patent of a small molecule drug expires generics may be introduced. They are considered therapeutically equivalent once pharmaceutical equivalence (i.e. identical active substances) and bioequivalence (i.e. comparable pharmacokinetics) have been established in a cross-over volunteer study. However this generic paradigm cannot be applied to complex drugs as biologics and a number of other therapeutic modalities. For copies of biologics the European Medicine Agency and other regulatory agencies have introduced a new regulatory biosimilar pathway which mandates clinical trials to show therapeutic equivalence. However for other complex drugs such as the iron-carbohydrate drugs, low molecular weight heparins (LMWHs), liposomal drugs and the glatiramoids regulatory guidance is still mostly lacking. In this paper we will discuss (therapeutic) experience obtained so far with these different classes of 'complex drugs' and their specifics to provide scientific arguments and criteria for consideration for a regulatory framework for the market authorization for these type of drugs.

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.

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.

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.

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.