Emerging Applications and Regulatory Strategies for Advanced Medicines Manufacturing - Towards the Development of a Platform Approach.

The last decade has seen Advanced Medicines Manufacturing (AMM) progress from isolated product developments to the creation of industry-academic centres of excellence, regulatory innovation progressing leading to new standards, and product commercialisation across multiple product formats. This paper examines these developments focusing on successful applications and strategies presented at the 2023 Symposium of the International Consortium for Advanced Medicines Manufacturing (ICAMM). Despite these exemplar applications, there remain significant challenges to the sector-wide adoption of AMM technologies. Drawing on Symposium delegate expert responses to open-ended questions, our coding-based thematic analysis suggest three primary enablers drive successful adoption of AMM technologies at scale, namely: the ability to leverage pre-competitive collaborations to challenge-based problem solving; information and knowledge sharing through centres of excellence; and the development of AMM specific regulatory standards. Further analysis of expert responses identified the emergence of a 'Platform creation' approach to AMM innovation; characterised by: i) New collaboration modes; ii) Exploration of common product-process platforms for new dosage forms and therapy areas; iii) Development of modular equipment assets that enable scale-out, and offer more decentralized or distributed manufacturing models; iv) Standards based on product-process platform archetypes; v) Implementation strategies where platform-thinking and AMM technologies can significantly reduce timelines between discovery, approval and GMP readiness. We provide a definition of the Platform creation concept for AMM and discuss the requirements for its systematic development.

Computational Modeling for the Oxidation Reactions of the Cysteine Residues with the Superoxide and the Organic Radical Species.

The current computational study analyzes the oxidation reactions of the superoxide and hydroxyl radicals with cysteine residues due to their importance as natural targets to neutralize the harmful reactive oxygen species. Due to the high reactivity of the hydroxyl radicals with the surrounding environment, we also studied the oxidation reactions of organic radicals with cysteine. In addition, we explored the different reaction pathways between cysteine and the superoxide radicals in both anionic and protonated forms. All calculations were performed at the integrated quantum mechanical/molecular mechanical level in an explicit water box under periodic boundary conditions. Higher energy barriers were observed for the organic radicals than the hydroxyl radical, where the chemical nature of the organic radical and the branching pattern are the main factors contributing to the Gibbs energy barriers. The superoxide radical oxidation pathway exhibits a more complex nature due to the complicated interplay of various factors such as the underlying reaction mechanism, the involved oxidizing agent, the kinetic accessibility of the oxidation reaction, and the thermodynamics favorability of those oxidation reactions. We also examined the effect of the solvent-assisted hydrogen atom transfer on the different reaction barriers, which was found to be kinetically unfavorable.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions.

Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes.Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein-protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain.

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein interactions among many antibodies. Molecular simulation is a promising method to study protein-protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867-2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies.

Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low- and high-viscosity mAbs.

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation.

Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.

Computational Modeling of the Disulfide Cross-Linking Reaction.

Disulfide cross-linking is one of the fundamental covalent bonds that exist prevalently in many biological molecules that is involved in versatile functional activities such as antibody stability, viral assembly, and protein folding. Additionally, it is a crucial factor in various industrial applications. Therefore, a fundamental understanding of its reaction mechanism would help gain insight into its different functional activities. Computational simulation of the disulfide cross-linking reaction with hydrogen peroxide (H2O2) was performed at the integrated quantum mechanical/molecular mechanical (QM/MM) level of theory in a water box under periodic boundary conditions. A benchmarking study for the barrier height of the disulfide formation step was performed on a model system between methanethiol and methane sulfenic acid to determine, for the QM system, the best-fit density functional theory (DFT) functional/basis set combination that produces comparable results to a higher-level theory of the coupled-cluster method. Computational results show that the disulfide cross-linking reaction with H2O2 reagent can proceed through a one-step or a two-step pathway for the high pKa cysteines or two different pathways for the low pKa cysteines to ultimately produce the sulfenic acid/sulfenate intermediate complex. Subsequently, those intermediates react with another neutral/anionic cysteine residue to form the cysteine product. In addition, the solvent-assisted proton-exchange/proton-transfer effects were examined on the energetic barriers for the different transition states, and the molecular contributions of the chemically involved water molecules were studied in detail.

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity.

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Machine Learning Models of Antibody-Excipient Preferential Interactions for Use in Computational Formulation Design.

Preferential interactions of formulation excipients govern their impact on the stability properties of proteins in solution. The ability to predict these interactions without the need to perform experiments would enable formulation design to begin early in the development of a new antibody therapeutic. With that in mind, we developed a feature set to numerically describe local regions of an antibody's surface for use in machine learning applications. Then, we used these features to train machine learning models for local antibody-excipient preferential interactions for the excipients sorbitol, sucrose, trehalose, proline, arginine·HCl, and NaCl. Our models had accuracies of up to about 85%. We also used linear (elastic net) models to quantify the contribution of antibody surface features to the preferential interaction coefficients, finding that the carbohydrates and proline tend to have similar important features, while the interactions of arginine·HCl and NaCl are governed by charge features. We present several case studies demonstrating how these machine learning models could be used to predict experimental aggregation and viscosity behavior in solution. Finally, we propose an approach to computational formulation design wherein a panel of excipients may be considered while designing an antibody sequence.

Why We Need Continuous Pharmaceutical Manufacturing and How to Make It Happen.

We make the case for why continuous pharmaceutical manufacturing is essential, what the barriers are, and how to overcome them. To overcome them, government action is needed in terms of tax incentives or regulatory incentives that affect time.

Molecular Computations of Preferential Interaction Coefficients of IgG1 Monoclonal Antibodies with Sorbitol, Sucrose, and Trehalose and the Impact of These Excipients on Aggregation and Viscosity.

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol because of their larger size and their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol. Two of the antibodies had high viscosity in sodium acetate buffer, and for these, we found that sucrose and trehalose tended to have opposite effects on viscosity. The data presented here provide further insight into the mechanisms of interactions of these three carbohydrate excipients with the antibody surface and thus their impact on excipient stabilization of antibody formulations.

On computing the solubility of molecular systems subject to constraints using the extended Einstein crystal method.

A method to compute solubilities for molecular systems using atomistic simulations, based on an extension of the Einstein crystal method, has recently been presented [Li et al., J. Chem. Phys. 146, 214110 (2017)]. This methodology is particularly appealing to compute solubilities in cases of practical importance including, but not limited to, solutions where the solute is sparingly soluble and molecules of importance for the pharmaceutical industry, which are often characterized by strong polar interactions and slow relaxation time scales. The mathematical derivation of this methodology hinges on a factorization of the partition function which is not necessarily applicable in the case of a system subject to holonomic molecular constraints. We show here that, although the mathematical procedure to derive it is slightly different, essentially the same mathematical relation for calculating the solubility can be safely applied for computing the solubility of systems subject to constraints, which are the majority of the systems used for practical molecular simulations.

Achieving continuous manufacturing in lyophilization: Technologies and approaches.

This paper provides an organic overview of the most interesting continuous freeze-drying concepts that have been proposed over the years. Attention has mainly been focused on the field of pharmaceuticals, but some background has also been given on the food industry. This work aims at providing a solid starting point for future research on continuous manufacturing for the freeze-drying of pharmaceuticals.

Understanding the Role of Preferential Exclusion of Sugars and Polyols from Native State IgG1 Monoclonal Antibodies and its Effect on Aggregation and Reversible Self-Association.

PURPOSE: To investigate differences in the preferential exclusion of trehalose, sucrose, sorbitol and mannitol from the surface of three IgG1 monoclonal antibodies (mAbs) and understand its effect on the aggregation and reversible self-association of mAbs at high-concentrations. METHODS: Preferential exclusion was measured using vapor pressure osmometry. Effect of excipient addition on accelerated aggregation kinetics was quantified using size exclusion chromatography and on reversible self-association was quantified using dynamic light scattering. RESULTS: The doubling of excipient concentration in the 0 to 0.5 m range resulted in a doubling of the mAb transfer free energy for all excipients and antibodies tested in this study. Solution pH and choice of buffering agent did not significantly affect the magnitude of preferential exclusion. We find that aggregation suppression for trehalose, sucrose and sorbitol (but not mannitol) correlates with the magnitude of their preferential exclusion from the native state of the three IgG1 mAbs. We also find that addition of sugars and polyols reduced the tendency for reversible self-association in two mAbs that had weakly repulsive or neutral self-interactions in the presence of buffer alone. CONCLUSIONS: The magnitude of preferential exclusion for trehalose, sucrose and sorbitol correlates well with their partial molar volumes in solution. Mannitol is excluded to a greater extent than that expected from its partial molar volume, suggesting specific interactions of mannitol that might be different than the other sugars and polyols tested in this study. Local interactions play a role in the effect of excipient addition on the reversible self-association of mAbs. These results provide further insights into the stabilization of high-concentration mAb formulations by sugars and polyols.

Solubility of paracetamol in ethanol by molecular dynamics using the extended Einstein crystal method and experiments.

Li and co-workers [Li et al., J. Chem. Phys. 146, 214110 (2017)] have recently proposed a methodology to compute the solubility of molecular compounds from first principles, using molecular dynamics simulations. We revise and further explore their methodology that was originally applied to naphthalene in water at low concentration. In particular, we compute the solubility of paracetamol in an ethanol solution at ambient conditions. For the simulations, we used a force field that we previously reparameterized to reproduce certain thermodynamic properties of paracetamol but not explicitly its solubility in ethanol. In addition, we have determined the experimental solubility by performing turbidity measurements using a Crystal16 over a range of temperatures. Our work serves a dual purpose: (i) methodologically, we clarify how to compute, with a relatively straightforward procedure, the solubility of molecular compounds and (ii) applying this procedure, we show that the solubility predicted by our force field (0.085 ± 0.014 in mole ratio) is in good agreement with the experimental value obtained from our experiments and those reported in the literature (average 0.0585 ± 0.004), considering typical deviations for predictions from first principle methods. The good agreement between the experimental and the calculated solubility also suggests that the method used to reparameterize the force field can be used as a general strategy to optimize force fields for simulations in solution.

Kirkwood-Buff-Derived Alcohol Parameters for Aqueous Carbohydrates and Their Application to Preferential Interaction Coefficient Calculations of Proteins.

The CHARMM36 carbohydrate parameter set did not adequately reproduce experimental thermodynamic data of carbohydrate interactions with water or proteins or carbohydrate self-association; thus, a new nonbonded parameter set for carbohydrates was developed. The parameters were developed to reproduce experimental Kirkwood-Buff integral values, defined by the Kirkwood-Buff theory of solutions, and applied to simulations of glycerol, sorbitol, glucose, sucrose, and trehalose. Compared to the CHARMM36 carbohydrate parameters, these new Kirkwood-Buff-based parameters reproduced accurately carbohydrate self-association and the trend of activity coefficient derivative changes with concentration. When using these parameters, preferential interaction coefficients calculated from simulations of these carbohydrates and the proteins lysozyme, bovine serum albumin, α-chymotrypsinogen A, and RNase A agreed well with the experimental data, whereas use of the CHARMM36 parameters indicated preferential inclusion of carbohydrates, in disagreement with the experiment. Thus, calculating preferential interaction coefficients from simulations requires using a force field that accurately reproduces trends in the thermodynamic properties of binary excipient-water solutions, and in particular the trend in the activity coefficient derivative. Finally, the carbohydrate-protein simulations using the new parameters indicated that the carbohydrate size was a major factor in the distribution of different carbohydrates around a protein surface.

General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.

Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal's morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques.

Retracted: Molecular characterization of excipients' preferential interactions with therapeutic monoclonal antibodies.

Retraction: Molecular characterization of excipients' preferential interactions with therapeutic monoclonal antibodies by Jehoon Kim, Mark R. H. Krebs and Bernhardt L. Trout The above article from the Journal of Pharmacy and Pharmacology, first published online on 4 August 2017 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editor-in-Chief, Professor David Jones, and John Wiley & Sons Ltd. The authors discovered that the analysis of simulations was faulty making the data incorrect. Reference Kim J et al. Molecular characterization of excipients' preferential interactions with therapeutic monoclonal antibodies. J Pharm Pharmacol 2017. https://doi.org/10.1111/jphp.12787.

Nucleation of Molecular Crystals Driven by Relative Information Entropy.

Simulating nucleation of molecular crystals is extremely challenging for all but the simplest cases. The challenge lies in formulating effective order parameters that are capable of driving the transition process. In recent years, order parameters based on molecular pair-functions have been successfully used in combination with enhanced sampling techniques to simulate nucleation of simple molecular crystals. However, despite the success of these approaches, we demonstrate that they can fail when applied to more complex cases. In fact, we show that order parameters based on molecular pair-functions, while successful at nucleating benzene, fail for paracetamol. Hence, we introduce a novel approach to formulate order parameters. In our approach, we construct reduced dimensional distributions of relevant quantities on the fly and then quantify the difference between these distributions and selected reference distributions. By computing the distribution of different quantities and by choosing different reference distributions, it is possible to systematically construct an effective set of order parameters. We then show that our new order parameters are capable of driving the nucleation of ordered states and, in particular, the form I crystal of paracetamol.