Design of Biopharmaceutical Formulations Accelerated by Machine Learning

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

In situ analysis of osmolyte mechanisms of proteome thermal stabilization

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

Drug product Formulation and Fill/Finish Manufacturing Process Considerations for AAV-Based Genomic Medicines

Adeno-associated viruses (AAVs) have become the delivery medium of choice for a variety of genomic medicine applications i.e., gene therapy, gene editing/regulation, and ex-vivo cell therapy. AAVs are protein-DNA complexes which have unique stability characteristics that are susceptible to various stress exposure conditions commonly seen in the drug product (DP) life cycle. This review takes a comprehensive look at AAV DP formulation and process development considerations that could impact critical quality attributes (CQAs) during manufacturing, packaging, shipping, and clinical use. Additional aspects related to AAV development reviewed herein are: (1) Different AAV serotypes with unique protein sequences and charge characteristics potentially leading to discrete stability profiles; (2) Manufacturing process challenges and optimization efforts to improve yield, recovery and purity especially during early development activities; and (3) Defining and identifying CQAs with analytical methods which are constantly evolving and present unique characterization challenges for AAV-based products.

Bayesian cell therapy process optimization

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods

In diverse fields, machine learning (ML) has sparked transformative changes, primarily driven by the wealth of big data. However, an alternative approach seeks to mine insights from "precious data", offering the possibility to reveal missed knowledge and escape potential knowledge traps. In this context, Bayesian optimization (BO) protocols have emerged as crucial tools for optimizing the synthesis and discovery of a broad spectrum of compounds including nanoparticles. In our work, we aimed to go beyond the commonly explored experimental conditions and showcase a workflow capable of unearthing fresh insights, even in well-studied research domains. The growth of AuNRs is a nonequilibrium process that remains poorly understood despite the presence of well-established seeded growth protocols. Traditional research aimed at understanding the mechanism of AuNR growth has primarily relied on altering one reaction condition at a time. While these studies are undeniably valuable, they often fail to capture the synergies between different reaction conditions, thus constraining the depth of insights they can offer. In the present study, we exploit BO, to identify diverse experimental conditions yielding AuNRs with similar spectroscopic characteristics. Notably, we identify viable and accelerated synthesis conditions involving elevated temperatures (36-40 °C) as well as high ascorbic acid concentrations. More importantly, we note that ascorbic acid and temperature can modulate each other's undesirable influences on the growth of AuNRs. Finally, by harnessing the power of interpretable ML algorithms, complemented by our deep chemical understanding, we revisited the established hierarchical relationships among reaction conditions that impact the El-Sayed-based growth of AuNRs.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Comparison of the Protective Effect of Polysorbates, Poloxamer and Brij on Antibody Stability Against Different Interfaces

Therapeutic proteins and antibodies are exposed to a variety of interfaces during their lifecycle, which can compromise their stability. Formulations, including surfactants, must be carefully optimized to improve interfacial stability against all types of surfaces. Here we apply a nanoparticle-based approach to evaluate the instability of four antibody drugs against different solid-liquid interfaces characterized by different degrees of hydrophobicity. We considered a model hydrophobic material as well as cycloolefin-copolymer (COC) and cellulose, which represent some of the common solid-liquid interfaces encountered during drug production, storage, and delivery. We assess the protective effect of polysorbate 20, polysorbate 80, Poloxamer 188 and Brij 35 in our assay and in a traditional agitation study. While all nonionic surfactants stabilize antibodies against the air-water interface, none of them can protect against hydrophilic charged cellulose. Polysorbates and Brij increase antibody stability in the presence of COC and the model hydrophobic interface, although to a lesser extent compared to the air-water interface, while Poloxamer 188 has a negligible stabilizing effect against these interfaces. These results highlight the challenge of fully protecting antibodies against all types of solid-liquid interfaces with traditional surfactants. In this context, our high-throughput nanoparticle-based approach can complement traditional shaking assays and assist in formulation design to ensure protein stability not only at air-water interfaces, but also at relevant solid-liquid interfaces encountered during the product lifecycle.

The ice age - A review on formulation of Adeno-associated virus therapeutics

Gene therapies offer promising therapeutic alternatives for many disorders that currently lack efficient treatment options. Due to their chemical nature and physico-chemical properties, delivery of polynucleic acids into target cells and subcellular compartments remains a significant challenge. Adeno-associated viruses (AAV) have gained a lot of interest for the efficient delivery of therapeutic single-stranded DNA (ssDNA) genomes over the past decades. More than a hundred products have been tested in clinical settings and three products have received market authorization by the US FDA in recent years. A lot of effort is being made to generate potent recombinant AAV (rAAV) vectors that show favorable safety and immunogenicity profiles for either local or systemic administration. Manufacturing processes are gradually being optimized to deliver a consistently high product quality and to serve potential market needs beyond rare indications. In contrast to protein therapeutics, most rAAV products are still supplied as frozen liquids within rather simple formulation buffers to enable sufficient product shelf life, significantly hampering global distribution and access. In this review, we aim to outline the hurdles of rAAV drug product development and discuss critical formulation and composition aspects of rAAV products under clinical evaluation. Further, we highlight recent development efforts in order to achieve stable liquid or lyophilized products. This review therefore provides a comprehensive overview on current state-of-the-art rAAV formulations and can further serve as a map for rational formulation development activities in the future.

Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (k_D), which is a measure of self-association under dilute conditions.¹ However, k_D can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).² By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies

Protein stability against aggregation is a major quality concern for the production of safe and effective biopharmaceuticals. Amongst the different drivers of protein aggregation, increasing evidence indicates that interactions between proteins and interfaces represent a major risk factor for the formation of protein aggregates in aqueous solutions. Potentially harmful surfaces relevant to biologics manufacturing and storage include air-water and silicone oil-water interfaces as well as materials from different processing units, storage containers, and delivery devices. The impact of some of these surfaces, for instance originating from impurities, can be difficult to predict and control. Moreover, aggregate formation may additionally be complicated by the simultaneous presence of interfacial, hydrodynamic and mechanical stresses, whose contributions may be difficult to deconvolute. As a consequence, it remains difficult to identify the key chemical and physical determinants and define appropriate analytical methods to monitor and predict protein instability at these interfaces. In this review, we first discuss the main mechanisms of surface-induced protein aggregation. We then review the types of contact materials identified as potentially harmful or detected as potential triggers of proteinaceous particle formation in formulations and discuss proposed mitigation strategies. Finally, we present current methods to probe surface-induced instabilities, which represent a starting point towards assays that can be implemented in early-stage screening and formulation development of biologics.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Improving extracellular vesicles production through a Bayesian optimization-based experimental design

With the growing demand and diversity of biological drugs, developing optimal processes for their accelerated production with minimal resource utilization is a pressing challenge. Typically, such optimization involves multiple target properties, such as production yield, biological activity, and product purity. Therefore, strategic experimental design techniques that can characterize the parameter space while simultaneously arriving at the optimal process satisfying multiple target properties are required. To achieve this, we propose the use of a multi-objective batch Bayesian optimization (MOBBO) algorithm and illustrate its successful application for the production of extracellular vesicles (EVs) from a 3D culture of mesenchymal stem cells (MSCs) considering three objectives, namely to maximize the vesicle-to-protein ratio, maximize the enzymatic activity of the MSC-EV protein CD73, and minimize the amount of calregulin impurities. We show that the optimal combination of the process parameters to address the intended objectives could be achieved with only 32 experiments. For the four parameters considered (i.e., microcarrier concentration, seeding density, centrifugation time, and impeller speed), this number of experiments is comparable to or lower than the classical design of experiments (DoE) and the traditional one-factor-at-a-time (OFAT) approach. We illustrate how the algorithm adaptively samples in the process parameter space, selectively excluding unfavorable regions, thus minimizing the number of experiments required to reach optimal conditions. Finally, we compare the obtained solutions to the literature data and present possible applications of the collected data for other modeling activities such as Quality by Design, process monitoring, control, and scale-up.

Synergistic Effects of Multiple Excipients on Controlling Viscosity of Concentrated Protein Dispersions

Viscosity control is essential for the manufacturing and delivery of concentrated therapeutic proteins. Limited availability of the precious protein-based drugs hinders the characterization and screening of the formulation conditions with new types or different combinations of excipients. In this work, a droplet-based microfluidic device with incorporated multiple particle tracking microrheology (MPT) is developed to quantify the effects of two excipients, arginine hydrochloride (ArgHCl) and caffeine, on the viscosity of concentrated bovine gamma globulin (BGG) dispersions at two different values of pH. The effectiveness of both ArgHCl and caffeine show dependence on the BGG concentration and solution pH. The data set with high compositional resolution provides useful information to guide formulation with multiple viscosity-reducing excipients and quantification appropriate to start elucidating the connection to protein-protein interaction mechanisms. Overall, this work has demonstrated that the developed microfluidic approach has the potential to effectively assess the impact of multiple excipients on the viscosity and provide data for computational methods to predict viscosity for high concentration protein formulations.

Biosimilar monoclonal antibodies: Challenges and approaches towards formulation

Many biologic drug products, particularly monoclonal antibodies (mAbs), were off-patented between 2015 and 2020, and this process is continuing as the number of biologics approvals has increased. However, the availability of affordable biosimilars is delayed by secondary patents related to the formulation and manufacturing process. Therefore, an alternative formulation development is required to avoid infringement of formulation related patents. Several variables must be considered while developing alternative non-infringement formulations, including the time gap between the expiration of the molecule patent and the formulation patent, the ability not to infringe other secondary patents (process-related), and project timelines. As a part of life cycle management, innovator companies are adopting multiple strategies to delay biosimilar competition. Biosimilar companies could use the innovator formulation knowledge space to develop alternative formulations at the expense of time and cost. The present review discusses the key approaches in biosimilar formulation development, and further summarizes the use of innovator formulation knowledge space for biosimilar mAbs product development.

Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models

The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.

Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells

Particles in biopharmaceutical formulations remain a hot topic in drug product development. With new product classes emerging it is crucial to discriminate particulate active pharmaceutical ingredients from particulate impurities. Technical improvements, new analytical developments and emerging tools (e.g., machine learning tools) increase the amount of information generated for particles. For a proper interpretation and judgment of the generated data a thorough understanding of the measurement principle, suitable application fields and potential limitations and pitfalls is required. Our review provides a comprehensive overview of novel particle analysis techniques emerging in the last decade for particulate impurities in therapeutic protein formulations (protein-related, excipient-related and primary packaging material-related), as well as particulate biopharmaceutical formulations (virus particles, virus-like particles, lipid nanoparticles and cell-based medicinal products). In addition, we review the literature on applications, describe specific analytical approaches and illustrate advantages and drawbacks of currently available techniques for particulate biopharmaceutical formulations.